70 skills found · Page 1 of 3
michpolicht / CuteHMICuteHMI is an open-source HMI (Human Machine Interface) software written in C++ and QML, using Qt libraries as a framework. GitHub repository is a mirror!
aryanveturekar / Health Care Chat BotThrough chatbots one can communicate with text or voice interface and get reply through artificial intelligence. Typically, a chat bot will communicate with a real person. Chat bots are used in applications such as ecommerce customer service, call centres and Internet gaming. Chatbots are programs built to automatically engage with received messages. Chatbots can be programmed to respond the same way each time, to respond differently to messages containing certain keywords and even to use machine learning to adapt their responses to fit the situation. A developing number of hospitals, nursing homes, and even private centres, presently utilize online Chatbots for human services on their sites. These bots connect with potential patients visiting the site, helping them discover specialists, booking their appointments, and getting them access to the correct treatment. In any case, the utilization of artificial intelligence in an industry where individuals’ lives could be in question, still starts misgivings in individuals. It brings up issues about whether the task mentioned above ought to be assigned to human staff. This healthcare chatbot system will help hospitals to provide healthcare support online 24 x 7, it answers deep as well as general questions. It also helps to generate leads and automatically delivers the information of leads to sales. By asking the questions in series it helps patients by guiding what exactly he/she is looking for.
shreyasharma04 / HealthChatbot🤖 HealthCare ChatBot Major -1 (4th year - 7th semester) Health Care Chat-Bot is a Healthcare Domain Chatbot to simulate the predictions of a General Physician. ChatBot can be described as software that can chat with people using artificial intelligence. These software are used to perform tasks such as quickly responding to users, informing them, helping to purchase products and providing better service to customers. We have made a healthcare based chatbot. The three main areas where chatbots can be used are diagnostics, patient engagement outside medical facilities, and mental health. In our major we are working on diagnostic. 📃 Brief A chatbot is an artificially intelligent creature which can converse with humans. This could be text-based, or a spoken conversation. In our project we will be using Python as it is currently the most popular language for creating an AI chatbot. In the middle of AI chatbot, architecture is the Natural Language Processing (NLP) layer. This project aims to build an user-friendly healthcare chatbot which facilitates the job of a healthcare provider and helps improve their performance by interacting with users in a human-like way. Through chatbots one can communicate with text or voice interface and get reply through artificial intelligence Typically, a chat bot will communicate with a real person. Chat bots are used in applications such as E-commerce customer service, Call centres, Internet gaming,etc. Chatbots are programs built to automatically engage with received messages. Chatbots can be programmed to respond the same way each time, to respond differently to messages containing certain keywords and even to use machine learning to adapt their responses to fit the situation. A developing number of hospitals, nursing homes, and even private centres, presently utilize online Chatbots for human services on their sites. These bots connect with potential patients visiting the site, helping them discover specialists, booking their appointments, and getting them access to the correct treatment. In any case, the utilization of artificial intelligence in an industry where individuals’ lives could be in question, still starts misgivings in individuals. It brings up issues about whether the task mentioned above ought to be assigned to human staff. This healthcare chatbot system will help hospitals to provide healthcare support online 24 x 7, it answers deep as well as general questions. It also helps to generate leads and automatically delivers the information of leads to sales. By asking the questions in series it helps patients by guiding what exactly he/she is looking for. 📜 Problem Statement During the pandemic, it is more important than ever to get your regular check-ups and to continue to take prescription medications. The healthier you are, the more likely you are to recover quickly from an illness. In this time patients or health care workers within their practice, providers are deferring elective and preventive visits, such as annual physicals. For some, it is not possible to consult online. In this case, to avoid false information, our project can be of help. 📇 Features Register Screen. Sign-in Screen. Generates database for user login system. Offers you a GUI Based Chatbot for patients for diagnosing. [A pragmatic Approach for Diagnosis] Reccomends an appropriate doctor to you for the following symptom. 📜 Modules Used Our program uses a number of python modules to work properly: tkinter os webbrowser numpy pandas matplotlib 📃 Algorithm We have used Decision tree for our health care based chat bot. Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems. It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome.It usually mimic human thinking ability while making a decision, so it is easy to understand. :suspect: Project Members Anushka Bansal - 500067844 - R164218014 Shreya Sharma - 500068573 - R164218070 Silvi - 500069092 - R164218072 Ishika Agrawal - 500071154 - R164218097
rtrvr-ai / RoverTurn any web interface into an AI agent — for humans and machines. Open-source, DOM-native SDK. Sub-second actions, no screenshots, no VMs. Websites, Chrome extensions, Electron apps, and more.
girishp92 / Human Activity Recognition Using Recurrent Neural Nets RNN LSTM And Tensorflow On SmartphonesThis was my Master's project where i was involved using a dataset from Wireless Sensor Data Mining Lab (WISDM) to build a machine learning model to predict basic human activities using a smartphone accelerometer, Using Tensorflow framework, recurrent neural nets and multiple stacks of Long-short-term memory units(LSTM) for building a deep network. After the model was trained, it was saved and exported to an android application and the predictions were made using the model and the interface to speak out the results using text-to-speech API.
Aryia-Behroziuan / NeuronsAn ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so
slrbl / Human In The Loop Machine Learning Tool TornadoTornado is an open source Human-in-the-loop machine learning tool. It helps you label your dataset on the fly while training your model through a simple web user interface. It supports all data types: structured, text and image.
cxdzyq1110 / Posture Recognition CNNTo help machines learn what we human beings are doing via a camera is important. Once it comes true, machines can make different responses to all kinds of human's postures. But the process is very difficult as well, because usually it is very slow and power-consuming, and requires a very large memory space. Here we focus on real-time posture recognition, and try to make the machine "know" what posture we make. The posture recognition system is consisted of DE10-Nano SoC FPGA Kit, a camera, and an HDMI monitor. SoC FPGA captures video streams from the camera, recognizes human postures with a CNN model, and finally shows the original video and classification result (standing, walking, waving, etc.) via HDMI interface.
NeuralAction / NeuralActionNeural Action is a real-time CNN-based gaze tracking application providing human-machine interface to improve accessibility.
tr1bunal9 / CyberneticCybernetic is a cutting-edge AI-driven platform that enhances human-machine collaboration. It integrates advanced automation, data analytics, and neural interfaces to optimize decision-making and performance. Designed for innovation, Cybernetic bridges the gap between technology and human potential.
rootsaid / Ducky Over WifiHID or Human Interface Devices are Devices that takes input from human and pass it on to the device connected to it. HID devices includes keyboard, joystick, mouse, touch pad, graphic tablet etc. There is a Library available in arduino which enables Digispark to acts as a keyboard and send keystrokes to PC. We are making use of this library to execute a metasploit reverse shell python code in the victim's machine and gain a reverse shell. Here, we will be using an Arduino Board, Arduino MKR1000 to integrate wireless network connectivity and perform HID attacks over WiFi
LMBooth / PybciCreate real-time BCI's with the LSL, PyTorch, SKLearn and TensorFlow packages.
cooelf / OpenIMEOpen Vocabulary Learning for Neural Chinese Pinyin IME (ACL 2020)
manyasrinivas2021 / ARTIFICIAL INTELLIGENCE HEALTHCARE CHATBOT SYSTEM USING PYTHONThrough chatbots one can communicate with text or voice interface and get reply through Artificial intelligence. Typically, a chat bot will communicate with a real person. Chat bots are used in applications such as ecommerce customer service, call centres and Internet gaming. Chatbots are programs built to automatically engage with received messages. Chatbots can be programmed to respond the same way each time, to respond differently to messages containing certain keywords and even to use machine learning to adapt their responses to fit the situation. A developing number of hospitals, nursing homes, and even private centres, presently utilize online Chatbots for human services on their sites. These bots connect with potential patients visiting the site, helping them discover specialists, booking their appointments, and getting them access to the correct treatment. In any case, the utilization of Artificial intelligence in an industry where individuals’ lives could be in question, still starts misgivings in individuals. It brings up issues about whether the task mentioned above ought to be assigned to human staff. This healthcare chatbot system will help hospitals to provide healthcare support online 24 x 7, it answers deep as well as general questions. It also helps to generate leads and automatically delivers the information of leads to sales. By asking the questions in series it helps patients by guiding what exactly he/she is looking for.
KDE / CutehmiOpen-source HMI (Human Machine Interface) software written in C++ and QML
anion0278 / Mediapipe JetsonGoogle's MediaPipe (v0.8.9) and Python Wheel installer for Jetson Nano (JetPack 4.6) compiled for CUDA 10.2
compas-dev / Compas XrCOMPAS XR streamlines extended reality workflows to ease the implementation of human-machine collaborative applications in architectural research and educational environments.
LeadingIndiaAI / Volume Control Using Hand Gestures RecognitionGesture recognition helps computers to understand human body language. This helps to build a more potent link between humans and machines, rather than just the basic text user interfaces or graphical user interfaces (GUIs). In this project for gesture recognition, the human body's motions are read by computer camera. The computer then makes use of this data as input to handle applications. The objective of this project is to develop an interface which will capture human hand gesture dynamically and will control the volume level. For this, Deep Learning techniques such as Yolo model, Inception Net model+LSTM, 3-D CNN+LSTM and Time Distributed CNN+LSTM have been studied to compare the results of hand detection. The results of Yolo model outperform the other three models. The models were trained using Kaggle and 20% of the videos available in 20 billion jester dataset. After the hand detection in captured frames, the next step is to control the system volume depending on direction of hand movement. The hand movement direction is determined by generating and locating the bounding box on the detected hand.
GuanSuns / Simple Human In The Loop ML InterfaceA simple human interface for human-in-the-loop machine learning research, which allows: 1. annote image on webpage, 2. collect human feedback through keyboard input, 3. direct interaction with agents on remote server
marostcs / Konzole09:17:02 Steam Console Client (c) Valve Corporation 09:17:02 -- type 'quit' to exit -- 09:17:02 Loading Steam API...OK. 09:17:02 09:17:03 Connecting anonymously to Steam Public...Logged in OK 09:17:03 Waiting for user info...OK 09:17:04 Success! App '740' already up to date. 09:17:05 @sSteamCmdForcePlatformType windows 09:17:05 "@sSteamCmdForcePlatformType" = "windows" 09:17:05 force_install_dir ../ 09:17:05 app_update 740 09:17:05 quit 09:17:05 Redirecting stderr to 'D:\servers\csgo_297437\steamcmd\logs\stderr.txt' 09:17:05 Params: -game csgo -console -tickrate 128.00 -port 49525 +tv_port 49526 -maxplayers_override 16 -usercon -nowatchdog +sv_pure 0 +sv_lan 0 +ip 89.203.193.220 +game_type 0 +exec server.cfg +game_mode 1 +map de_dust2 +sv_setsteamaccount B74A031F37B9312A5EC65A15FC43AA0C -gamemodes_serverfile gamemodes_server.txt +mapgroup h_custom -condebug -norestart -allowdebug 09:17:06 # 09:17:06 #Console initialized. 09:17:06 #Using breakpad minidump system 740/13776.1219.DC 09:17:06 #Filesystem successfully switched to safe whitelist mode 09:17:06 #Game.dll loaded for "Counter-Strike: Global Offensive" 09:17:06 #CGameEventManager::AddListener: event 'server_pre_shutdown' unknown. 09:17:06 #CGameEventManager::AddListener: event 'game_newmap' unknown. 09:17:06 #CGameEventManager::AddListener: event 'finale_start' unknown. 09:17:06 #CGameEventManager::AddListener: event 'round_start' unknown. 09:17:06 #CGameEventManager::AddListener: event 'round_end' unknown. 09:17:06 #CGameEventManager::AddListener: event 'difficulty_changed' unknown. 09:17:06 #CGameEventManager::AddListener: event 'player_death' unknown. 09:17:06 #CGameEventManager::AddListener: event 'hltv_replay' unknown. 09:17:06 #CGameEventManager::AddListener: event 'player_connect' unknown. 09:17:06 #CGameEventManager::AddListener: event 'player_disconnect' unknown. 09:17:06 #GameTypes: missing mapgroupsSP entry for game type/mode (custom/custom). 09:17:06 #GameTypes: missing mapgroupsSP entry for game type/mode (cooperative/cooperative). 09:17:06 #GameTypes: missing mapgroupsSP entry for game type/mode (cooperative/coopmission). 09:17:06 Seeded random number generator @ 1064343566 ( 0.940 ) 09:17:06 Failed to load gamerulescvars.txt, game rules cvars might not be reported to management tools. 09:17:06 Server is hibernating 09:17:06 No web api auth key specified - workshop downloads will be disabled. 09:17:06 scripts\talker\response_rules.txt(token 3685) : Multiple definitions for criteria 'tlk_cw.regroup' [-1793082848] 09:17:06 scripts\talker\swat.txt(token 1688) : response entry 'radio.sticktogetherswat' with unknown command 'scenes/swat/radiobotregroup02.vcd' 09:17:06 scripts\talker\coopvoice.txt(token 657) : No such response 'guardianroundstartintro' for rule 'guardianroundintro' 09:17:06 Discarded rule guardianroundintro 09:17:06 CResponseSystem: scripts\talker\response_rules.txt (4154 rules, 763 criteria, and 3878 responses) 09:17:06 Plugins: found file "CSay.vdf" 09:17:06 eBot LOADED 09:17:06 Plugins: found file "metamod.vdf" 09:17:06 maxplayers set to 64 09:17:06 Fast Build Temp Cache: 'maps/soundcache/_master.cache' 09:17:07 Elapsed time: 0.00 seconds 09:17:07 ConVarRef cl_embedded_stream_video_playing doesn't point to an existing ConVar 09:17:07 Execing config: valve.rc 09:17:07 Execing config: default.cfg 09:17:07 Unknown command "cl_bobamt_vert" 09:17:07 Unknown command "cl_bobamt_lat" 09:17:07 Unknown command "cl_bob_lower_amt" 09:17:07 Unknown command "cl_viewmodel_shift_left_amt" 09:17:07 Unknown command "cl_viewmodel_shift_right_amt" 09:17:07 Unknown command "cl_teamid_min" 09:17:07 Unknown command "cl_teamid_max" 09:17:07 Unknown command "cl_teamid_overhead" 09:17:07 Unknown command "cl_teamid_overhead_maxdist" 09:17:07 Execing config: joystick.cfg 09:17:07 Execing config: autoexec.cfg 09:17:07 -------------------------------------------------------- 09:17:07 sv_pure set to 0. 09:17:07 -------------------------------------------------------- 09:17:07 Execing config: server.cfg 09:17:07 Unknown command "sv_maxcmdrate" 09:17:07 Unknown command "sv_vote_creation_time" 09:17:07 Writing cfg/banned_user.cfg. 09:17:07 Writing cfg/banned_ip.cfg. 09:17:07 Execing config: banned_user.cfg 09:17:07 Execing config: banned_ip.cfg 09:17:07 Unknown command "allow_spectators" 09:17:07 Setting mapgroup to 'h_custom' 09:17:07 Execing config: modsettings.cfg 09:17:07 NET_CloseAllSockets 09:17:07 NET_GetBindAddresses found 89.203.193.220: 'HP FlexFabric 10Gb 2-port 554FLB Adapter #2' 09:17:07 WARNING: UDP_OpenSocket: unable to bind socket 09:17:07 Network: IP 89.203.193.220 mode MP, dedicated No, ports 49525 SV / -1 CL 09:17:07 L 01/15/2021 - 09:17:07: [SM] Error encountered parsing core config file: Line contained too many invalid tokens 09:17:07 CServerGameDLL::ApplyGameSettings game settings payload received: 09:17:07 ::ExecGameTypeCfg { 09:17:07 map { 09:17:07 mapname de_dust2 09:17:07 } 09:17:07 } 09:17:07 ApplyGameSettings: Invalid mapgroup name h_custom 09:17:07 ---- Host_NewGame ---- 09:17:07 Execing config: game.cfg 09:17:07 Switching filesystem to allow files loaded from disk (sv_pure_allow_loose_file_loads = 1) 09:17:08 DISP_VPHYSICS found bad displacement collision face (252.50 1542.13 147.50) (250.00 1543.00 155.00) (250.00 1543.50 155.00) at tri 25 09:17:08 DISP_VPHYSICS entire displacement vdisp_0290 will have no collision, dimensions (6.00 14.00 32.00) from (249.00 1537.00 124.00) to (255.00 1551.00 156.00) 09:17:08 DISP_VPHYSICS found bad displacement collision face (250.13 1539.50 147.50) (249.75 1543.00 155.00) (250.00 1543.00 155.00) at tri 30 09:17:08 DISP_VPHYSICS entire displacement vdisp_0291 will have no collision, dimensions (12.50 7.00 32.00) from (242.00 1537.00 124.00) to (254.50 1544.00 156.00) 09:17:08 DISP_VPHYSICS found bad displacement collision face (-1884.00 704.30 159.97) (-1884.00 703.00 180.00) (-1884.54 704.60 160.25) at tri 6 09:17:08 DISP_VPHYSICS entire displacement vdisp_1842 will have no collision, dimensions (2.54 6.60 82.03) from (-1885.54 699.00 158.97) to (-1883.00 705.60 241.00) 09:17:08 DISP_VPHYSICS found bad displacement collision face (-1884.00 705.40 127.95) (-1884.00 704.30 159.97) (-1884.54 704.60 160.25) at tri 30 09:17:08 DISP_VPHYSICS entire displacement vdisp_1876 will have no collision, dimensions (2.54 8.30 130.25) from (-1885.54 699.00 31.00) to (-1883.00 707.30 161.25) 09:17:11 Host_NewGame on map de_dust2 09:17:11 L 01/15/2021 - 09:17:11: -------- Mapchange to de_dust2 -------- 09:17:11 L 01/15/2021 - 09:17:11: [SM] Failed to load plugin "gloves.smx": Unable to load plugin (no debug string table). 09:17:11 L 01/15/2021 - 09:17:11: [SM] Failed to load plugin "weapons.smx": Unable to load plugin (no debug string table). 09:17:11 CGameEventManager::AddListener: event 'teamplay_win_panel' unknown. 09:17:11 CGameEventManager::AddListener: event 'teamplay_restart_round' unknown. 09:17:11 CGameEventManager::AddListener: event 'arena_win_panel' unknown. 09:17:11 GameTypes: initializing game types interface from GameModes.txt. 09:17:11 GameTypes: merging game types interface from gamemodes_server.txt. 09:17:11 Failed to load gamemodes_server.txt 09:17:11 GameTypes: missing mapgroupsSP entry for game type/mode (custom/custom). 09:17:11 GameTypes: missing mapgroupsSP entry for game type/mode (cooperative/cooperative). 09:17:11 GameTypes: missing mapgroupsSP entry for game type/mode (cooperative/coopmission). 09:17:11 ammo_grenade_limit_default - 1 09:17:11 ammo_grenade_limit_flashbang - 1 09:17:11 ammo_grenade_limit_total - 3 09:17:11 ammo_item_limit_healthshot - 4 09:17:11 bot_allow_grenades - 1 09:17:11 bot_allow_machine_guns - 1 09:17:11 bot_allow_pistols - 1 09:17:11 bot_allow_rifles - 1 09:17:11 bot_allow_rogues - 1 09:17:11 bot_allow_shotguns - 1 09:17:11 bot_allow_snipers - 1 09:17:11 bot_allow_sub_machine_guns - 1 09:17:11 bot_autodifficulty_threshold_high - 5.0 09:17:11 bot_autodifficulty_threshold_low - -2.0 09:17:11 bot_chatter - normal 09:17:11 bot_coop_idle_max_vision_distance - 1400 09:17:11 bot_defer_to_human_goals - 0 09:17:11 bot_defer_to_human_items - 1 09:17:11 bot_difficulty - 1 09:17:11 bot_max_hearing_distance_override - -1 09:17:11 bot_max_visible_smoke_length - 200 09:17:11 bot_max_vision_distance_override - -1 09:17:11 bot_quota - 10 09:17:11 bot_quota_mode - normal 09:17:11 bot_coop_idle_max_vision_distance - 1400 09:17:11 bot_max_vision_distance_override - -1 09:17:11 bot_max_hearing_distance_override - -1 09:17:11 bot_coopmission_dz_engagement_limit - missing cvar specified in bspconvar_whitelist.txt 09:17:11 cash_player_bomb_defused - 300 09:17:11 cash_player_bomb_planted - 300 09:17:11 cash_player_damage_hostage - -30 09:17:11 cash_player_get_killed - 0 09:17:11 cash_player_interact_with_hostage - 150 09:17:11 cash_player_killed_enemy_default - 300 09:17:11 cash_player_killed_enemy_factor - 1 09:17:11 cash_player_killed_hostage - -1000 09:17:11 cash_player_killed_teammate - -300 09:17:11 cash_player_rescued_hostage - 1000 09:17:11 cash_player_respawn_amount - 0 09:17:11 cash_team_elimination_bomb_map - 3250 09:17:11 cash_team_elimination_hostage_map_ct - 2000 09:17:11 cash_team_elimination_hostage_map_t - 1000 09:17:11 cash_team_hostage_alive - 0 09:17:11 cash_team_hostage_interaction - 500 09:17:11 cash_team_loser_bonus - 1400 09:17:11 cash_team_loser_bonus_consecutive_rounds - 500 09:17:11 cash_team_planted_bomb_but_defused - 800 09:17:11 cash_team_rescued_hostage - 0 09:17:11 cash_team_survive_guardian_wave - 1000 09:17:11 cash_team_terrorist_win_bomb - 3500 09:17:11 cash_team_win_by_defusing_bomb - 3250 09:17:11 cash_team_win_by_hostage_rescue - 3500 09:17:11 cash_team_win_by_time_running_out_bomb - 3250 09:17:11 cash_team_win_by_time_running_out_hostage - 3250 09:17:11 contributionscore_assist - 1 09:17:11 contributionscore_bomb_defuse_major - 3 09:17:11 contributionscore_bomb_defuse_minor - 1 09:17:11 contributionscore_bomb_exploded - 1 09:17:11 contributionscore_bomb_planted - 2 09:17:11 contributionscore_cash_bundle - 0 09:17:11 contributionscore_crate_break - 0 09:17:11 contributionscore_hostage_kill - -2 09:17:11 contributionscore_hostage_rescue_major - 3 09:17:11 contributionscore_hostage_rescue_minor - 1 09:17:11 contributionscore_kill - 2 09:17:11 contributionscore_kill_factor - 0 09:17:11 contributionscore_objective_kill - 3 09:17:11 contributionscore_suicide - -2 09:17:11 contributionscore_team_kill - -2 09:17:11 ff_damage_reduction_bullets - 0.1 09:17:11 ff_damage_reduction_grenade - 0.25 09:17:11 ff_damage_reduction_grenade_self - 1 09:17:11 ff_damage_reduction_other - 0.25 09:17:11 global_chatter_info - 09:17:11 healthshot_healthboost_damage_multiplier - 1 09:17:11 healthshot_healthboost_speed_multiplier - 1 09:17:11 healthshot_healthboost_time - 0 09:17:11 inferno_child_spawn_max_depth - 4 09:17:11 inferno_max_flames - 16 09:17:11 inferno_max_range - 150 09:17:11 molotov_throw_detonate_time - 2.0 09:17:11 mp_afterroundmoney - 0 09:17:11 mp_anyone_can_pickup_c4 - 0 09:17:11 mp_autokick - 1 09:17:11 mp_autoteambalance - 1 09:17:11 mp_bot_ai_bt - 09:17:11 mp_buy_allow_grenades - 1 09:17:11 mp_buy_allow_guns - 255 09:17:11 mp_buy_anywhere - 0 09:17:11 mp_buy_during_immunity - 0 09:17:11 mp_buytime - 90 09:17:11 mp_c4_cannot_be_defused - 0 09:17:11 mp_c4timer - 40 09:17:11 mp_consecutive_loss_max - 4 09:17:11 mp_coop_force_join_ct - 0 09:17:11 mp_coopmission_bot_difficulty_offset - 0 09:17:11 mp_coopmission_mission_number - 0 09:17:11 mp_coopmission_dz - missing cvar specified in bspconvar_whitelist.txt 09:17:11 mp_ct_default_grenades - 09:17:11 mp_ct_default_melee - weapon_knife 09:17:11 mp_ct_default_primary - 09:17:11 mp_ct_default_secondary - weapon_hkp2000 09:17:11 mp_retake_ct_loadout_default_pistol_round - 1|3;#GameUI_Retake_Card_4v3,0,0,secondary0|1;#GameUI_Retake_Card_FlashOut,0,0,secondary0,grenade2;#GameUI_Retake_Card_HideAndPeek,0,0,secondary0,grenade4 09:17:11 mp_retake_ct_loadout_upgraded_pistol_round - 2|2;#GameUI_Retake_Card_TakeFive,0,0,secondary3|2;#GameUI_Retake_Card_BlindFire,0,0,secondary2,grenade2|2;#GameUI_Retake_Card_OnlyTakesOne,0,0,secondary4|2;#GameUI_Retake_Card_SneakyBeakyLike,0,0,secondary2,grenade4 09:17:11 mp_retake_ct_loadout_light_buy_round - 3|2;#GameUI_Retake_Card_UmpInSmoke,1,1,smg2,grenade4|2;#GameUI_Retake_Card_FunNGun,1,1,smg0,grenade3|2;#GameUI_Retake_Card_Sharpshooter,1,1,rifle2,grenade2|2;#GameUI_Retake_Card_BurstBullpup,1,1,rifle0 09:17:11 mp_retake_ct_loadout_full_buy_round - 4|2;#GameUI_Retake_Card_LightEmUp,1,1,rifle1,grenade2|2;#GameUI_Retake_Card_Kobe,1,1,rifle1,grenade3|1;#GameUI_Retake_Card_1g,1,1,rifle1,grenade0|1;#GameUI_Retake_Card_DisappearingAct,1,1,rifle1,grenade4|1;#GameUI_Retake_Card_EyesOnTarget,1,1,rifle3 09:17:11 mp_retake_ct_loadout_bonus_card_availability - 1,2 09:17:11 mp_retake_ct_loadout_bonus_card - #GameUI_Retake_Card_TheAWPortunity,1,1,rifle4 09:17:11 mp_retake_ct_loadout_enemy_card - #GameUI_Retake_Card_BehindEnemyLines,1,1,rifle1,grenade2 09:17:11 mp_retake_t_loadout_default_pistol_round - 0|3;#GameUI_Retake_Card_4BadGuysLeft,0,0,secondary0|1;#GameUI_Retake_Card_LookAway,0,0,secondary0,grenade2;#GameUI_Retake_Card_WhenThereIsSmoke,0,0,secondary0,grenade4 09:17:11 mp_retake_t_loadout_upgraded_pistol_round - 0|2;#GameUI_Retake_Card_BlindFire,0,0,secondary2,grenade2|2;#GameUI_Retake_Card_QueOta,0,0,secondary4|1;#GameUI_Retake_Card_SmokeScreen,0,0,secondary2,grenade4|1;#GameUI_Retake_Card_TecTecBoom,0,0,secondary3,grenade3 09:17:11 mp_retake_t_loadout_light_buy_round - 0|2;#GameUI_Retake_Card_BackInAFlash,1,1,smg2,grenade2|2;#GameUI_Retake_Card_AllIn,1,1,rifle0|1;#GameUI_Retake_Card_BoomBox,1,1,smg0,grenade3,grenade4|1;#GameUI_Retake_Card_SetThemFree,1,1,rifle2,grenade2 09:17:11 mp_retake_t_loadout_full_buy_round - 0|2;#GameUI_Retake_Card_OlReliable,1,1,rifle1,grenade2|1;#GameUI_Retake_Card_SmokeShow,1,1,rifle1,grenade4|1;#GameUI_Retake_Card_HotShot,1,1,rifle1,grenade0|1;#GameUI_Retake_Card_EyeSpy,1,1,rifle3,grenade3 09:17:11 mp_retake_t_loadout_bonus_card_availability - 1,1,2 09:17:11 mp_retake_t_loadout_bonus_card - #GameUI_Retake_Card_TheAWPortunity,1,1,rifle4 09:17:11 mp_retake_t_loadout_enemy_card - #GameUI_Retake_Card_FindersKeepers,1,1,rifle1,grenade2 09:17:11 mp_retake_max_consecutive_rounds_same_target_site - 2 09:17:11 mp_damage_headshot_only - 0 09:17:11 mp_damage_scale_ct_body - 1.0 09:17:11 mp_damage_scale_ct_head - 1.0 09:17:11 mp_damage_scale_t_body - 1.0 09:17:11 mp_damage_scale_t_head - 1.0 09:17:11 mp_damage_vampiric_amount - 0 09:17:11 mp_death_drop_c4 - 1 09:17:11 mp_death_drop_defuser - 1 09:17:11 mp_death_drop_grenade - 2 09:17:11 mp_death_drop_gun - 1 09:17:11 mp_deathcam_skippable - 1 09:17:11 mp_default_team_winner_no_objective - -1 09:17:11 mp_defuser_allocation - 0 09:17:11 mp_display_kill_assists - 1 09:17:11 mp_dm_bonus_percent - 50 09:17:11 mp_dm_bonus_respawn - 0 09:17:11 mp_dm_bonusweapon_dogtags - 0 09:17:11 mp_dm_dogtag_score - 0 09:17:11 mp_dm_kill_base_score - 10 09:17:11 mp_dm_teammode - 0 09:17:11 mp_dm_teammode_bonus_score - 1 09:17:11 mp_dm_teammode_dogtag_score - 0 09:17:11 mp_dm_teammode_kill_score - 1 09:17:11 mp_dogtag_despawn_on_killer_death - 1 09:17:11 mp_dogtag_despawn_time - 120 09:17:11 mp_dogtag_pickup_rule - 0 09:17:11 mp_drop_grenade_enable - 0 09:17:11 mp_drop_knife_enable - 0 09:17:11 mp_economy_reset_rounds - 0 09:17:11 mp_equipment_reset_rounds - 0 09:17:11 mp_force_assign_teams - 0 09:17:11 mp_force_pick_time - 15 09:17:11 mp_forcecamera - 1 09:17:11 mp_free_armor - 0 09:17:11 mp_freezetime - 6 09:17:11 mp_friendlyfire - 0 09:17:11 mp_ggprogressive_round_restart_delay - 15.0 09:17:11 mp_ggtr_always_upgrade - 0 09:17:11 mp_ggtr_bomb_defuse_bonus - 1 09:17:11 mp_ggtr_bomb_detonation_bonus - 1 09:17:11 mp_ggtr_bomb_pts_for_flash - 4 09:17:11 mp_ggtr_bomb_pts_for_he - 3 09:17:11 mp_ggtr_bomb_pts_for_molotov - 5 09:17:11 mp_ggtr_bomb_pts_for_upgrade - 2.0 09:17:11 mp_ggtr_bomb_respawn_delay - 0.0 09:17:11 mp_ggtr_end_round_kill_bonus - 1 09:17:11 mp_ggtr_halftime_delay - 0.0 09:17:11 mp_ggtr_last_weapon_kill_ends_half - 0 09:17:11 mp_give_player_c4 - 1 09:17:11 mp_global_damage_per_second - 0.0 09:17:11 mp_guardian_bot_money_per_wave - 800 09:17:11 mp_guardian_force_collect_hostages_timeout - 50 09:17:11 mp_guardian_loc_icon - missing cvar specified in bspconvar_whitelist.txt 09:17:11 mp_guardian_loc_string_desc - 09:17:11 mp_guardian_loc_string_hud - #guardian_mission_type_kills 09:17:11 mp_guardian_loc_weapon - 09:17:11 mp_guardian_player_dist_max - 2000 09:17:11 mp_guardian_player_dist_min - 1300 09:17:11 mp_guardian_special_kills_needed - 10 09:17:11 mp_guardian_special_weapon_needed - awp 09:17:11 mp_guardian_target_site - -1 09:17:11 mp_guardian_force_collect_hostages_timeout - 50 09:17:11 mp_guardian_give_random_grenades_to_bots - 1 09:17:11 mp_guardian_ai_bt_difficulty_adjust_wave_interval - 1 09:17:11 mp_guardian_ai_bt_difficulty_max_next_level_bots - 3 09:17:11 mp_guardian_ai_bt_difficulty_cap_beginning_round - 2 09:17:11 mp_guardian_ai_bt_difficulty_initial_value - 2 09:17:11 mp_halftime - 0 09:17:11 mp_halftime_pausetimer - 0 09:17:11 mp_heavyassaultsuit_aimpunch - 1.0 09:17:11 mp_heavyassaultsuit_cooldown - 5 09:17:11 mp_heavyassaultsuit_deploy_timescale - 0.8 09:17:11 mp_heavyassaultsuit_speed - 130 09:17:11 mp_heavybot_damage_reduction_scale - 1.0 09:17:11 mp_hostagepenalty - 10 09:17:11 mp_hostages_max - 2 09:17:11 mp_hostages_spawn_force_positions - 09:17:11 mp_hostages_spawn_same_every_round - 1 09:17:11 mp_items_prohibited - 09:17:11 mp_limitteams - 2 09:17:11 mp_match_can_clinch - 1 09:17:11 mp_match_end_changelevel - 0 09:17:11 mp_max_armor - 2 09:17:11 mp_maxmoney - 16000 09:17:11 mp_maxrounds - 0 09:17:11 mp_molotovusedelay - 15.0 09:17:11 mp_only_cts_rescue_hostages - 1 09:17:11 mp_plant_c4_anywhere - 0 09:17:11 mp_playercashawards - 1 09:17:11 mp_radar_showall - 0 09:17:11 mp_randomspawn - 0 09:17:11 mp_randomspawn_dist - 0 09:17:11 mp_randomspawn_los - 1 09:17:11 mp_respawn_immunitytime - 4.0 09:17:11 mp_respawn_on_death_ct - 0 09:17:11 mp_respawn_on_death_t - 0 09:17:11 mp_respawnwavetime_ct - 10.0 09:17:11 mp_respawnwavetime_t - 10.0 09:17:11 mp_round_restart_delay - 7.0 09:17:11 mp_roundtime - 5 09:17:11 mp_roundtime_defuse - 0 09:17:11 mp_roundtime_hostage - 0 09:17:11 mp_solid_teammates - 1 09:17:11 mp_starting_losses - 0 09:17:11 mp_startmoney - 800 09:17:11 mp_suicide_penalty - 1 09:17:11 mp_t_default_grenades - 09:17:11 mp_t_default_melee - weapon_knife 09:17:11 mp_t_default_primary - 09:17:11 mp_t_default_secondary - weapon_glock 09:17:11 mp_tagging_scale - 1.0 09:17:11 mp_taser_recharge_time - -1 09:17:11 mp_teamcashawards - 1 09:17:11 mp_teammates_are_enemies - 0 09:17:11 mp_timelimit - 5 09:17:11 mp_use_respawn_waves - 0 09:17:11 mp_warmup_pausetimer - 0 09:17:11 mp_warmuptime - 30 09:17:11 mp_warmuptime_all_players_connected - 0 09:17:11 mp_weapon_self_inflict_amount - 0 09:17:11 mp_weapons_allow_heavy - -1 09:17:11 mp_weapons_allow_heavyassaultsuit - 0 09:17:11 mp_weapons_allow_map_placed - 0 09:17:11 mp_weapons_allow_pistols - -1 09:17:11 mp_weapons_allow_rifles - -1 09:17:11 mp_weapons_allow_smgs - -1 09:17:11 mp_weapons_allow_typecount - 5 09:17:11 mp_weapons_allow_zeus - 1 09:17:11 mp_weapons_glow_on_ground - 0 09:17:11 mp_weapons_max_gun_purchases_per_weapon_per_match - -1 09:17:11 mp_win_panel_display_time - 3 09:17:11 occlusion_test_async - 0 09:17:11 spec_freeze_panel_extended_time - 0.0 09:17:11 spec_freeze_time - 3.0 09:17:11 spec_replay_bot - 0 09:17:11 spec_replay_enable - 0 09:17:11 spec_replay_leadup_time - 5.3438 09:17:11 sv_accelerate - 5.5 09:17:11 sv_air_pushaway_dist - 0 09:17:11 sv_airaccelerate - 12 09:17:11 sv_allow_votes - 1 09:17:11 sv_alltalk - 0 09:17:11 sv_arms_race_vote_to_restart_disallowed_after - 0 09:17:11 sv_auto_adjust_bot_difficulty - 1 09:17:11 sv_auto_full_alltalk_during_warmup_half_end - 1 09:17:11 sv_autobunnyhopping - 0 09:17:11 sv_autobuyammo - 0 09:17:11 sv_bot_buy_decoy_weight - 1 09:17:11 sv_bot_buy_flash_weight - 1 09:17:11 sv_bot_buy_grenade_chance - 33 09:17:11 sv_bot_buy_hegrenade_weight - 6 09:17:11 sv_bot_buy_molotov_weight - 1 09:17:11 sv_bot_buy_smoke_weight - 1 09:17:11 sv_bots_force_rebuy_every_round - 0 09:17:11 sv_bots_get_easier_each_win - 0 09:17:11 sv_bots_get_harder_after_each_wave - 0 09:17:11 sv_bounce - 0 09:17:11 sv_buy_status_override - -1 09:17:11 sv_deadtalk - 0 09:17:11 sv_disable_immunity_alpha - 0 09:17:11 sv_disable_radar - 0 09:17:11 sv_disable_show_team_select_menu - missing cvar specified in bspconvar_whitelist.txt 09:17:11 sv_duplicate_playernames_ok - 0 09:17:11 sv_enablebunnyhopping - 0 09:17:11 sv_env_entity_makers_enabled - 1 09:17:11 sv_extract_ammo_from_dropped_weapons - 0 09:17:11 sv_falldamage_scale - 1 09:17:11 sv_falldamage_to_below_player_multiplier - 1 09:17:11 sv_falldamage_to_below_player_ratio - 0 09:17:11 sv_force_reflections - 0 09:17:11 sv_friction - 5.2 09:17:11 sv_grassburn - 0 09:17:11 sv_gravity - 800 09:17:11 sv_guardian_extra_equipment_ct - 09:17:11 sv_guardian_extra_equipment_t - 09:17:11 sv_guardian_health_refresh_per_wave - 50 09:17:11 sv_guardian_heavy_all - 0 09:17:11 sv_guardian_heavy_count - 0 09:17:11 sv_guardian_max_wave_for_heavy - 0 09:17:11 sv_guardian_min_wave_for_heavy - 0 09:17:11 sv_guardian_refresh_ammo_for_items_on_waves - 09:17:11 sv_guardian_reset_c4_every_wave - 0 09:17:11 sv_guardian_respawn_health - 50 09:17:11 sv_guardian_spawn_health_ct - 100 09:17:11 sv_guardian_spawn_health_t - 100 09:17:11 sv_health_approach_enabled - 0 09:17:11 sv_health_approach_speed - 10 09:17:11 sv_hegrenade_damage_multiplier - 1 09:17:11 sv_hegrenade_radius_multiplier - 1 09:17:11 sv_hide_roundtime_until_seconds - missing cvar specified in bspconvar_whitelist.txt 09:17:11 sv_highlight_distance - 500 09:17:11 sv_highlight_duration - 3.5 09:17:11 sv_ignoregrenaderadio - 0 09:17:11 sv_infinite_ammo - 0 09:17:11 sv_knife_attack_extend_from_player_aabb - 0 09:17:11 sv_maxspeed - 320 09:17:11 sv_maxvelocity - 3500 09:17:11 sv_occlude_players - 1 09:17:11 sv_outofammo_indicator - 0 09:17:11 sv_show_ragdoll_playernames - missing cvar specified in bspconvar_whitelist.txt 09:17:11 sv_show_team_equipment_force_on - 0 09:17:11 sv_staminajumpcost - .080 09:17:11 sv_staminalandcost - .050 09:17:11 sv_stopspeed - 80 09:17:11 sv_talk_enemy_dead - 0 09:17:11 sv_talk_enemy_living - 0 09:17:11 sv_teamid_overhead_maxdist - 0 09:17:11 sv_teamid_overhead_maxdist_spec - 0 09:17:11 sv_versus_screen_scene_id - 0 09:17:11 sv_vote_to_changelevel_before_match_point - 0 09:17:11 sv_warmup_to_freezetime_delay - 4 09:17:11 sv_water_movespeed_multiplier - 0.8 09:17:11 sv_water_swim_mode - 0 09:17:11 sv_wateraccelerate - 10 09:17:11 sv_waterfriction - 1 09:17:11 sv_weapon_encumbrance_per_item - 0.85 09:17:11 sv_weapon_encumbrance_scale - 0 09:17:11 tv_delay - 10 09:17:11 tv_delay1 - 15 09:17:11 weapon_accuracy_nospread - 0 09:17:11 weapon_air_spread_scale - 1.0 09:17:11 weapon_max_before_cleanup - 0 09:17:11 weapon_recoil_scale - 2.0 09:17:11 weapon_reticle_knife_show - 1 09:17:11 weapon_sound_falloff_multiplier - 1.0 09:17:11 sv_camera_fly_enabled - missing cvar specified in bspconvar_whitelist.txt 09:17:11 Executing dedicated server config file 09:17:11 Execing config: server.cfg 09:17:11 Unknown command "sv_maxcmdrate" 09:17:11 Unknown command "sv_vote_creation_time" 09:17:11 Writing cfg/banned_user.cfg. 09:17:11 Writing cfg/banned_ip.cfg. 09:17:11 Execing config: banned_user.cfg 09:17:11 Execing config: banned_ip.cfg 09:17:11 Unknown command "allow_spectators" 09:17:11 Execing config: gamemode_competitive.cfg 09:17:11 Execing config: gamemode_competitive_server.cfg 09:17:11 exec: couldn't exec gamemode_competitive_server.cfg 09:17:11 GameTypes: set convars for game type/mode (classic:0/competitive:1): 09:17:11 exec { 09:17:11 exec gamemode_competitive.cfg 09:17:11 exec_offline gamemode_competitive_offline.cfg 09:17:11 exec gamemode_competitive_server.cfg 09:17:11 } 09:17:11 Set Gravity 800.0 (0.250 tolerance) 09:17:11 CHostage::Precache: missing hostage models for map de_dust2. Adding the default models. 09:17:11 PrecacheScriptSound 'Snowball.Bounce' failed, no such sound script entry 09:17:12 PrecacheScriptSound 'Survival.VO.Taunt4a' failed, no such sound script entry 09:17:13 Failed to load models/weapons/w_knife_ghost_dropped.mdl! 09:17:13 Failed to load models/props/crates/patch_envelope02.mdl! 09:17:13 PrecacheScriptSound 'balkan_epic_blank' failed, no such sound script entry 09:17:13 PrecacheScriptSound 'professional_epic.omw_to_plant_a_04' failed, no such sound script entry 09:17:13 PrecacheScriptSound 'professional_epic.loc_ramp_01' failed, no such sound script entry 09:17:13 PrecacheScriptSound 'professional_epic.loc_back_01' failed, no such sound script entry 09:17:13 PrecacheScriptSound 'professional_epic.loc_platform_01' failed, no such sound script entry 09:17:13 PrecacheScriptSound 'professional_epic.loc_catwalk_03' failed, no such sound script entry 09:17:13 PrecacheScriptSound 'professional_epic.loc_enemy_spawn_01' failed, no such sound script entry 09:17:13 PrecacheScriptSound 'professional_epic.loc_doubledoors_01' failed, no such sound script entry 09:17:13 PrecacheScriptSound 'professional_epic.loc_front_01' failed, no such sound script entry 09:17:13 PrecacheScriptSound 'professional_epic.loc_overpass_03' failed, no such sound script entry 09:17:13 PrecacheScriptSound 'professional_epic.loc_palace_01' failed, no such sound script entry 09:17:13 PrecacheScriptSound 'professional_epic.loc_stairs_01' failed, no such sound script entry 09:17:13 PrecacheScriptSound 'professional_epic.loc_snipers_nest_01' failed, no such sound script entry 09:17:13 PrecacheScriptSound 'professional_epic.loc_connector_01' failed, no such sound script entry 09:17:13 PrecacheScriptSound 'professional_epic.loc_door_01' failed, no such sound script entry 09:17:14 Invalid file size for host.txt 09:17:14 Commentary: Could not find commentary data file 'maps/de_dust2_commentary.txt'. 09:17:14 The Navigation Mesh was built using a different version of this map. 09:17:14 Error parsing BotProfile.db - unknown attribute 'Rank' 09:17:14 Error parsing BotProfile.db - unknown attribute 'Rank' 09:17:14 Error parsing BotProfile.db - unknown attribute 'Rank' 09:17:14 Error parsing BotProfile.db - unknown attribute 'Rank' 09:17:14 Error parsing BotProfile.db - unknown attribute 'Rank' 09:17:14 Error parsing BotProfile.db - unknown attribute 'Rank' 09:17:14 Error parsing BotProfile.db - unknown attribute 'Rank' 09:17:14 Error parsing BotProfile.db - unknown attribute 'Rank' 09:17:14 Created class baseline: 20 classes, 13792 bytes. 09:17:14 Initializing Steam libraries for secure Internet server 09:17:14 Logging into Steam gameserver account with logon token 'B74A031Fxxxxxxxxxxxxxxxxxxxxxxxx' 09:17:14 Initialized low level socket/threading support. 09:17:14 \src\steamnetworkingsockets\clientlib\csteamnetworkingsockets_steam.cpp(138): Assertion Failed: Initted interface twice? 09:17:14 Set SteamNetworkingSockets P2P_STUN_ServerList to '' as per SteamNetworkingSocketsSerialized 09:17:14 SteamDatagramServer_Init succeeded 09:17:14 Execing config: sourcemod/sourcemod.cfg 09:17:14 Execing config: sourcemod\basevotes.cfg 09:17:14 Execing config: sourcemod\funcommands.cfg 09:17:14 Execing config: sourcemod\funvotes.cfg 09:17:14 Connection to Steam servers successful. 09:17:14 Public IP is 89.203.193.220. 09:17:14 Assigned persistent gameserver Steam ID [G:1:3976299]. 09:17:14 Gameserver logged on to Steam, assigned identity steamid:85568392924015723 09:17:14 Set SteamNetworkingSockets P2P_STUN_ServerList to '146.66.155.54:3478' as per SteamNetworkingSocketsSerialized 09:17:15 VAC secure mode is activated. 09:17:15 Received server welcome from GC. 09:17:15 GC Connection established for server version 1219, instance idx 1
