78 skills found · Page 1 of 3
opengs / UashieldVoluntary Ukraine security platform to protect us from Russian forces in the Internet
boustrophedon / ExtrasafeMake your code extra-safe by voluntarily dropping privileges
Aastha2104 / Parkinson Disease PredictionIntroduction Parkinson’s Disease is the second most prevalent neurodegenerative disorder after Alzheimer’s, affecting more than 10 million people worldwide. Parkinson’s is characterized primarily by the deterioration of motor and cognitive ability. There is no single test which can be administered for diagnosis. Instead, doctors must perform a careful clinical analysis of the patient’s medical history. Unfortunately, this method of diagnosis is highly inaccurate. A study from the National Institute of Neurological Disorders finds that early diagnosis (having symptoms for 5 years or less) is only 53% accurate. This is not much better than random guessing, but an early diagnosis is critical to effective treatment. Because of these difficulties, I investigate a machine learning approach to accurately diagnose Parkinson’s, using a dataset of various speech features (a non-invasive yet characteristic tool) from the University of Oxford. Why speech features? Speech is very predictive and characteristic of Parkinson’s disease; almost every Parkinson’s patient experiences severe vocal degradation (inability to produce sustained phonations, tremor, hoarseness), so it makes sense to use voice to diagnose the disease. Voice analysis gives the added benefit of being non-invasive, inexpensive, and very easy to extract clinically. Background Parkinson's Disease Parkinson’s is a progressive neurodegenerative condition resulting from the death of the dopamine containing cells of the substantia nigra (which plays an important role in movement). Symptoms include: “frozen” facial features, bradykinesia (slowness of movement), akinesia (impairment of voluntary movement), tremor, and voice impairment. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. Performance Metrics TP = true positive, FP = false positive, TN = true negative, FN = false negative Accuracy: (TP+TN)/(P+N) Matthews Correlation Coefficient: 1=perfect, 0=random, -1=completely inaccurate Algorithms Employed Logistic Regression (LR): Uses the sigmoid logistic equation with weights (coefficient values) and biases (constants) to model the probability of a certain class for binary classification. An output of 1 represents one class, and an output of 0 represents the other. Training the model will learn the optimal weights and biases. Linear Discriminant Analysis (LDA): Assumes that the data is Gaussian and each feature has the same variance. LDA estimates the mean and variance for each class from the training data, and then uses properties of statistics (Bayes theorem , Gaussian distribution, etc) to compute the probability of a particular instance belonging to a given class. The class with the largest probability is the prediction. k Nearest Neighbors (KNN): Makes predictions about the validation set using the entire training set. KNN makes a prediction about a new instance by searching through the entire set to find the k “closest” instances. “Closeness” is determined using a proximity measurement (Euclidean) across all features. The class that the majority of the k closest instances belong to is the class that the model predicts the new instance to be. Decision Tree (DT): Represented by a binary tree, where each root node represents an input variable and a split point, and each leaf node contains an output used to make a prediction. Neural Network (NN): Models the way the human brain makes decisions. Each neuron takes in 1+ inputs, and then uses an activation function to process the input with weights and biases to produce an output. Neurons can be arranged into layers, and multiple layers can form a network to model complex decisions. Training the network involves using the training instances to optimize the weights and biases. Naive Bayes (NB): Simplifies the calculation of probabilities by assuming that all features are independent of one another (a strong but effective assumption). Employs Bayes Theorem to calculate the probabilities that the instance to be predicted is in each class, then finds the class with the highest probability. Gradient Boost (GB): Generally used when seeking a model with very high predictive performance. Used to reduce bias and variance (“error”) by combining multiple “weak learners” (not very good models) to create a “strong learner” (high performance model). Involves 3 elements: a loss function (error function) to be optimized, a weak learner (decision tree) to make predictions, and an additive model to add trees to minimize the loss function. Gradient descent is used to minimize error after adding each tree (one by one). Engineering Goal Produce a machine learning model to diagnose Parkinson’s disease given various features of a patient’s speech with at least 90% accuracy and/or a Matthews Correlation Coefficient of at least 0.9. Compare various algorithms and parameters to determine the best model for predicting Parkinson’s. Dataset Description Source: the University of Oxford 195 instances (147 subjects with Parkinson’s, 48 without Parkinson’s) 22 features (elements that are possibly characteristic of Parkinson’s, such as frequency, pitch, amplitude / period of the sound wave) 1 label (1 for Parkinson’s, 0 for no Parkinson’s) Project Pipeline pipeline Summary of Procedure Split the Oxford Parkinson’s Dataset into two parts: one for training, one for validation (evaluate how well the model performs) Train each of the following algorithms with the training set: Logistic Regression, Linear Discriminant Analysis, k Nearest Neighbors, Decision Tree, Neural Network, Naive Bayes, Gradient Boost Evaluate results using the validation set Repeat for the following training set to validation set splits: 80% training / 20% validation, 75% / 25%, and 70% / 30% Repeat for a rescaled version of the dataset (scale all the numbers in the dataset to a range from 0 to 1: this helps to reduce the effect of outliers) Conduct 5 trials and average the results Data a_o a_r m_o m_r Data Analysis In general, the models tended to perform the best (both in terms of accuracy and Matthews Correlation Coefficient) on the rescaled dataset with a 75-25 train-test split. The two highest performing algorithms, k Nearest Neighbors and the Neural Network, both achieved an accuracy of 98%. The NN achieved a MCC of 0.96, while KNN achieved a MCC of 0.94. These figures outperform most existing literature and significantly outperform current methods of diagnosis. Conclusion and Significance These robust results suggest that a machine learning approach can indeed be implemented to significantly improve diagnosis methods of Parkinson’s disease. Given the necessity of early diagnosis for effective treatment, my machine learning models provide a very promising alternative to the current, rather ineffective method of diagnosis. Current methods of early diagnosis are only 53% accurate, while my machine learning model produces 98% accuracy. This 45% increase is critical because an accurate, early diagnosis is needed to effectively treat the disease. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. With an earlier diagnosis, much of this degradation could have been slowed or treated. My results are very significant because Parkinson’s affects over 10 million people worldwide who could benefit greatly from an early, accurate diagnosis. Not only is my machine learning approach more accurate in terms of diagnostic accuracy, it is also more scalable, less expensive, and therefore more accessible to people who might not have access to established medical facilities and professionals. The diagnosis is also much simpler, requiring only a 10-15 second voice recording and producing an immediate diagnosis. Future Research Given more time and resources, I would investigate the following: Create a mobile application which would allow the user to record his/her voice, extract the necessary vocal features, and feed it into my machine learning model to diagnose Parkinson’s. Use larger datasets in conjunction with the University of Oxford dataset. Tune and improve my models even further to achieve even better results. Investigate different structures and types of neural networks. Construct a novel algorithm specifically suited for the prediction of Parkinson’s. Generalize my findings and algorithms for all types of dementia disorders, such as Alzheimer’s. References Bind, Shubham. "A Survey of Machine Learning Based Approaches for Parkinson Disease Prediction." International Journal of Computer Science and Information Technologies 6 (2015): n. pag. International Journal of Computer Science and Information Technologies. 2015. Web. 8 Mar. 2017. Brooks, Megan. "Diagnosing Parkinson's Disease Still Challenging." Medscape Medical News. National Institute of Neurological Disorders, 31 July 2014. Web. 20 Mar. 2017. Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. BioMedical Engineering OnLine 2007, 6:23 (26 June 2007) Hashmi, Sumaiya F. "A Machine Learning Approach to Diagnosis of Parkinson’s Disease."Claremont Colleges Scholarship. Claremont College, 2013. Web. 10 Mar. 2017. Karplus, Abraham. "Machine Learning Algorithms for Cancer Diagnosis." Machine Learning Algorithms for Cancer Diagnosis (n.d.): n. pag. Mar. 2012. Web. 20 Mar. 2017. Little, Max. "Parkinsons Data Set." UCI Machine Learning Repository. University of Oxford, 26 June 2008. Web. 20 Feb. 2017. Ozcift, Akin, and Arif Gulten. "Classifier Ensemble Construction with Rotation Forest to Improve Medical Diagnosis Performance of Machine Learning Algorithms." Computer Methods and Programs in Biomedicine 104.3 (2011): 443-51. Semantic Scholar. 2011. Web. 15 Mar. 2017. "Parkinson’s Disease Dementia." UCI MIND. N.p., 19 Oct. 2015. Web. 17 Feb. 2017. Salvatore, C., A. Cerasa, I. Castiglioni, F. Gallivanone, A. Augimeri, M. Lopez, G. Arabia, M. Morelli, M.c. Gilardi, and A. Quattrone. "Machine Learning on Brain MRI Data for Differential Diagnosis of Parkinson's Disease and Progressive Supranuclear Palsy."Journal of Neuroscience Methods 222 (2014): 230-37. 2014. Web. 18 Mar. 2017. Shahbakhi, Mohammad, Danial Taheri Far, and Ehsan Tahami. "Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine."Journal of Biomedical Science and Engineering 07.04 (2014): 147-56. Scientific Research. July 2014. Web. 2 Mar. 2017. "Speech and Communication." Speech and Communication. Parkinson's Disease Foundation, n.d. Web. 22 Mar. 2017. Sriram, Tarigoppula V. S., M. Venkateswara Rao, G. V. Satya Narayana, and D. S. V. G. K. Kaladhar. "Diagnosis of Parkinson Disease Using Machine Learning and Data Mining Systems from Voice Dataset." SpringerLink. Springer, Cham, 01 Jan. 1970. Web. 17 Mar. 2017.
Bit-Nation / BITNATION ConstitutionDecentralized Borderless Voluntary Nation (DBVN) Liquid Holocracy Constitution
scrtlabs / SafeTracePrivacy preserving voluntary Covid-19 self-reporting platform. Share your location history and status, get alerts you are in high risk areas and identify high risk regions
voluntarily / Vly2The Voluntarily Platform - an open source platform for organising volunteers to do cool things
Voluntarynet / Voluntary.appin-browser webRTC-based secure p2p messaging app platform
hasibullah1811 / Covid 19 Helping Hand Find Help NearbyThis is an app where people can find who needs help in this lock down in their neighbor hood and voluntarily help them.
lo-th / TurbulenceUniversal mechanical linkage for representing the voluntary movements of nature animals
xiyouMc / AppDynamicFramework:fax:For DynamicLoadApk encapsulates a more complete framework for handling, including load Libs directory, each plug-in Service voluntarily injection.. Thanks for DynamicLoadApk.（针对DynamicLoadApk 做的一些上层工作。）
cszmzh / Voluntary Filling System这是一个大学生社团或组织的新生志愿填报管理系统，后台基于SpringBoot+MySQL。
orcwg / Cra AttestationsThis project aims to create a practical framework that helps manufacturers meet their due diligence obligations when using open source software, without adding burden to maintainers. By introducing voluntary security attestations, it seeks to strengthen FOSS security, sustainability, and collaboration with authorities and industry actors.
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virtualabs / VodstokVoluntary Distributed Storage Kit
SOCRadar / TurkeyEarthquakeIntelIncludes voluntary work on all abuses related to earthquakes.
spiralgo / AlgorithmsWe voluntarily solve algorithm problems on this repo.
Alt-Org / AltzoneAltzone is an open-source project focused on creating a mobile multiplayer game using Unity and Photon Quantum. 🎮
oscarrgarciia / HA PVPC UpdatedCustom integration for Home Assistant that retrieves the PVPC (Voluntary Price for Small Consumers) electricity prices in Spain, broken down by hourly periods. Allows using the data in sensors, automations, and energy dashboards in Home Assistant.
BlockchainLabs / PebblecoinPebblecoin UPDATE 2015/12/31: Version 0.4.4.1 is now out. The major change is optimizing the daemon to use less RAM. It no longer keeps all the blocks, which are rarely needed, in RAM, and so RAM usage has decreased from around 2 gigabytes, to under 200 megabytes. Mac binaries are also now available. The new wallet is compatible with the old wallet - simply turn off the old wallet, and start the new wallet, and the blockchain will update automatically to use less RAM. Code: Release Notes 0.4.4.1 - (All) Fix blockchain RAM usage, from almost 2 GB to less than 200 MB - Seamless blockchain conversion on first run with new binaries - (Qt) Fix high CPU usage - (Qt) Fix sync indicator (# of total blocks) - (Mac) Mac binaries - Technical Notes: - (All) Blockchain disk-backed storage with sqlite3 and stxxl - (Mac) Fix mac compilation - (All) Update build files & instructions for linux, mac, windows - (All) Remove unused protobuf and OpenSSL dependencies for Qt wallet - (Tests) Fix valgrind errors - (Tests) Use local directory for blockchain instead of default directory - (Tests) Run tests on Windows if using new enough MSVC LINKS: Windows 64-bit: https://www.dropbox.com/s/b4kubwwnb4t7o4w/pebblecoin-all-win32-x64-v0.4.4.1.zip?dl=0 Mac 64-bit: https://www.dropbox.com/s/uoy9z1oxu4x53cv/pebblecoin-all-mac-x64-v0.4.4.1.tar.gz?dl=0 Linux 64-bit: https://www.dropbox.com/s/jq3h3bc29jmndks/pebblecoin-all-linux-x64-v0.4.4.1.tar.gz?dl=0 Exchange: https://poloniex.com/exchange#btc_xpb . Source: https://github.com/xpbcreator/pebblecoin/ CONTACT: xpbcreator@torguard.tg IRC: irc.freenode.net, #pebblecoin UPDATE 2015/06/08: Version 0.4.3.1 is now out. This is a minor, mostly bug-fix release. Work continues on the next major release which will bring us user-created currencies and user-graded contracts. Release notes: Code: Release Notes 0.4.3.1 - RPC calls for DPOS: - getdelegateinfos RPC call - get kimageseqs RPC call - block header contains signing_delegate_id - fix checkpoint rollback bug - fix inability to send coins if voting history was lost UPDATE 2015/05/04: Version 0.4.2.2 is now out. This is a bug-fix/cosmetic release. Release notes: Payment ID support Windows installer Logos updated Improved DPOS tab Sync issues fully fixed Fix rare crash bug Fix min out 0 bug Fix debit display Fix GUI not updating Updated hard-coded seed nodes UPDATE 2015/04/24: The switch-over to DPOS has succeeded without a hitch! DPOS blocks are being signed as we speak, at the far faster pace of 15 seconds per block. This marks the start of a new era for Pebblecoin. UPDATE 2015/04/21: Congratulations to the first registered delegate! This indicates the start of the forking change so everybody please update your daemons if you haven't already. To promote the coin and encourage people to become delegates, we've come up with an incentive scheme. First, we'll send a free 100 XPB to anybody who PMs me their public address, for people to play around with and to start using the coin. Second, once DPOS starts, for the first month of DPOS I'll send an extra 0.5 XPB to the signing delegate for every block they process. This is on top of the usual transaction fees they will receive. This is to encourage more people to become delegates at this important phase of the coin. UPDATE 2015/04/19: All went well on the testnet release, so after a few further minor modifications, we are releasing version 0.4.1.2 to the public. This is a forking change, so please update your clients and servers (links below). At block 83120, sometime on April 21st, registration for DPOS delegates will begin. At block 85300, sometime on April 24th, the network will switch over to DPOS. As with the testnet, to become a delegate and receive block fees for securing the network, just turn on your wallet, register to be a delegate (5 XPB fee), and then leave your wallet on. It will sign the blocks when it is your turn. While Roman works on the next phase of the release - introducing subcurrencies - I will be fixing up some loose ends on the wallet, adding payment ID support, etc. This is truly an exciting time for Pebblecoin. RELEASE NOTES: All clients adjust internal clocks using ntp (client list in src/common/ntp_time.cpp) Added testnet support DPOS registration starts Block 83120 (~April 21st) DPOS phase starts Block 85300 (~April 24th) Default fee bumped to 0.10 XPB Low-free transactions no longer get relayed by default Significantly improved wallet sync Checkpoint at Block 79000 TOTAL CURRENT COINS: Available at this link. BLOCK TARGET TIME: 2 minutes EXPECTED EMISSION: At Block 3600 (End of Day 5): ~78 XPBs At Block 6480 (End of Day 9): ~758 XPBs At Block 9360 (End of Day 13): 6,771.0 XPBs At Block 12240 (End of Day 17): ~61,000 XPBs At Block 15120 (End of Day 21): ~550,000 XPBs, start of regular 300/block emission At Block 21900 (End of Month 1): ~2,600,000 XPBs, 300/block At Block 43800 (End of Month 2): ~9,150,000 XPBs, 300/block At Block 85300 (End of POW phase): ~21,500,300 XPBs. UPDATE: The Pebblecoin Pool is now live! Instructions: Download the linux miner and run it: ./minerd -o stratum+tcp://69.60.113.21:3350 -u YOUR_WALLET_ADDRESS -p x UPDATE: The Pebblecoin wallet is now live! There have been thousands of attempts at alternative currencies in the community. Many are 100% copies of existing blockchains with a different name. Some are very slight variations with no significant differences. From recent history it is apparent the only realistic chance for viability of a new currency is one that is innovation and continued support and development. The bitcoin community for good reason has shown interest in currencies that provide privacy of transactions, several currencies such as darkcoin, have become popular based on this desire. The best technology for privacy is cryptonote although for a variety of reasons there hasnt been much development for ease of use, and as a result there has not been significant adoption. Pebblecoin (XPB) is a cryptonote based coin with improvements and changes in some areas, and the promise of development in others. I invite developers to work on this technology with me. There is no premine, any tips or support of any developer including myself will be completely voluntary. These are the following areas which I have determined needs changes/updates: I welcome suggestions, and am interested what else I can try to improve. 1) New Mining algorithm (active) A mining algorithm is either susceptible to ASIC development or to being botnetted, meaning it is either more efficient to have a centralized mining entity (as is the case with bitcoin) or to have an algorithm that requires a real CPU, in which case botnets become very attractive. To my knowledge there does not exist a blockchain that attempts to solve both problems, by having an algorithm that only works on a general purpose computer and is difficult to botnet. Cryptonote coins currently are primarily mined with botnets. Boulderhash is a new mining algorithm requiring 13 GB RAM, nearly eliminating all possible zombie (botnet controlled) computers from mining. Most infected computers in the world do not have 13 GB available, so an algorithm that requires that much RAM severely limits the productivity of a botnet. 13 GB also makes ASICs cost prohibitive, and the current GPUs do not have that much RAM. What's left is general purpose computers as was the original intent of bitcoin's mining process. 2) Distribution of coins (active) It is very common in the launch of a new cryptocurrency the distribution algorithm heavily is weighted towards the very early adopters. Such distribution is designed to give a massive advantage to people who are fully prepared to mine at launch, with a very large difference shortly after sometimes a few days later. If the point of mining is to both secure the network and fairly distribute coins a gradual build up of rewards makes more sense, with no drop off in mining rewards. At a standard block reward of 300, at launch each block will reward 0.3 coins leading up to 3, 30, and finally the standard reward of 300 which will be the standard unchanging reward from that point. It will take approximately 3 weeks for the block reward of 300 to be reached. 3) GUI Software (active) There are no current cryptonote coins that have a downloadable GUI, which makes the user experience much worse than that of bitcoin. It is hard to achieve signficant adoption with a command line interface. The very first update had the exact GUI written for bitcoin fully working with Pebblecoin. The GUI was released on Jan 19, before the full 300 XPB reward was awarded for winning the block. 4) IRC Chat support embedded in Client GUI (active) For user support, and to talk to core developers message boards such as Bitcointalk and reddit are primarily used. I have embedded an IRC client in the GUI and be available at set hours for any kind of support. 5) Address aliasing (to be worked on) Just as a user visiting google does not need to know the ip address, similarly an address should have the ability to have an associated userid. If I ask a friend to send me pebblecoins it would be easier to tell him send it to @myuserid rather than a very long address or scanning a QR code. There should be a way of registering a userid on the blockchain that will permanently translate to a pebblecoin addresss. QT INSTRUCTIONS: Download the package for your respective platform Run the Qt executable. The software will generate a new wallet for you and use a default folder: ~/.pebblecoin on Linux and %appdata%\pebblecoin on Windows. To use an existing wallet, copy the wallet.keys file into the default folder. To use a different data directory and/or wallet file, run the software like so: ./pebblecoin-qt --data-dir <DataDir> --wallet-file <FileName>. To enable mining, run the start_mining_NEEDS_13GB_RAM.bat batch file. Or run the qt wallet with the --enable-boulderhash command line option, or put enable-boulderhash=1 into the config file. It will start mining to the wallet address. To change the number of mining threads (13GB required per thread), do --mining-threads <NumThreads> or edit the batch file. DAEMON + SIMPLEWALLET INSTRUCTIONS: Download the package, run: ./pebblecoind --data-dir pebblecoin_data Once the daemon finished syncing, run the simplewallet: ./simplewallet POOL INSTRUCTIONS: Download the miner binary for your platform. Run the miner using a wallet address gotten from simplewallet or the Qt Wallet: Code: minerd -o stratum+tcp://69.60.113.21:3350 -u YOUR_WALLET_ADDRESS -p x [/li] DEV WALLET (for donations): PByFqCfuDRUPVsNrzrUXnuUdF7LpXsTTZXeq5cdHpJDogbJ8EBXopciN7DmQiGhLEo5ArA7dFqGga2A AhbRaZ2gL8jjp9VmYgk
yqmark / AgreementPrivacy Policy introduction We understand the importance of personal information to you and will do our utmost to protect your personal information. We are committed to maintaining your trust in us and to abide by the following principles to protect your personal information: the principle of consistency of rights and responsibilities, the principle of purpose , choose the principle of consent, at least the principle of sufficient use, ensure the principle of security, the principle of subject participation, the principle of openness and transparency, and so on. At the same time, we promise that we will take appropriate security measures to protect your personal information according to the industry's mature security solutions. In view of this, we have formulated this "Private Privacy Policy" (hereinafter referred to as "this policy" /This Privacy Policy") and remind you: This policy applies to products or services on this platform. 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This privacy policy section will help you understand the following: How we collect and use your personal information How do we use cookies and similar technologies? How do we share, transfer, and publicly disclose your personal information? How we protect your personal information How do you manage your personal information? How do we deal with the personal information of minors? How your personal information is transferred globally How to update this privacy policy How to contact us 一、How we collect and use your personal information Personal information refers to various information recorded electronically or otherwise that can identify a specific natural person or reflect the activities of a particular natural person, either alone or in combination with other information. We collect and use your information for the purposes described in this policy. 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Our partners are not authorized to use shared personal information for other purposes unrelated to the product or service. Currently, our authorized partners include the following types: (2) Suppliers, service providers and other partners. We send information to suppliers, service providers and other partners who support our business, including providing technical infrastructure services, analyzing how our services are used, measuring the effectiveness of advertising and services, providing customer service, and facilitating payments. Or conduct academic research and investigations. (1) Authorized partners in advertising and analytics services. We will not use your personally identifiable information (information that identifies you, such as your name or email address, which can be used to contact you or identify you) and provide advertising and analytics services, unless you have your permission. Shared by partners. We will provide these partners with information about their advertising coverage and effectiveness, without providing your personally identifiable information, or we may aggregate this information so that it does not identify you personally. For example, we’ll only tell advertisers how effective their ads are when they agree to comply with our advertising guidelines, or how many people see their ads or install apps after seeing ads, or work with them. Partners provide statistical information that does not identify individuals (eg “male, 25-29 years old, in Beijing”) to help them understand their audience or customers. For companies, organizations and individuals with whom we share personal information, we will enter into strict data protection agreements with them to process individuals in accordance with our instructions, this Privacy Policy and any other relevant confidentiality and security measures. information. (2) Transfer We do not transfer your personal information to any company, organization or individual, except: Transfer with the express consent: After obtaining your explicit consent, we will transfer your personal information to other parties; 2, in the case of mergers, acquisitions or bankruptcy liquidation, or other circumstances involving mergers, acquisitions or bankruptcy liquidation, if it involves the transfer of personal information, we will require new companies and organizations that hold your personal information to continue to receive This policy is bound, otherwise we will ask the company, organization and individual to re-seek your consent. (3) Public disclosure We will only publicly disclose your personal information in the following circumstances: We may publicly disclose your personal information by obtaining your explicit consent or based on your active choice; 2, if we determine that you have violated laws and regulations or serious violations of the relevant rules of the platform, or to protect the personal safety of the platform and its affiliates users or the public from infringement, we may be based on laws and regulations or The relevant agreement rules of this platform disclose your personal information, including related violations, and the measures that the platform has taken against you, with your consent. (4) Exceptions for prior authorization of consent when sharing, transferring, and publicly disclosing personal information In the following situations, sharing, transferring, and publicly disclosing your personal information does not require prior authorization from you: Related to national security and national defense security; Related to public safety, public health, and major public interests; 3, related to criminal investigation, prosecution, trial and judgment execution; 4, in order to protect your or other individuals' life, property and other important legal rights but it is difficult to get my consent; Personal information that you disclose to the public on your own; Collect personal information from legally publicly disclosed information, such as legal news reports and government information disclosure. According to the law, sharing, transferring and de-identifying personal information, and ensuring that the data recipient cannot recover and re-identify the personal information subject, does not belong to the external sharing, transfer and public disclosure of personal information. The preservation and processing of the class data will not require additional notice and your consent. How do we protect your personal information? (1) We have taken reasonable and feasible security measures in accordance with the industry's general solutions to protect the security of personal information provided by you, and to prevent unauthorized access, public disclosure, use, modification, damage or loss of personal information. For example, SSL (Secure Socket) when exchanging data (such as credit card information) between your browser and the server Layer) protocol encryption protection; we use encryption technology to improve the security of personal information; we use a trusted protection mechanism to prevent personal information from being maliciously attacked; we will deploy access control mechanisms to ensure that only authorized personnel can access individuals Information; and we will conduct security and privacy protection training courses to enhance employees' awareness of the importance of protecting personal information. (2) We have advanced data security management system around the data life cycle, which enhances the security of the whole system from organizational construction, system design, personnel management, product technology and other aspects. (3) We will take reasonable and feasible measures and try our best to avoid collecting irrelevant personal information. We will only retain your personal information for the period of time required to achieve the purposes stated in this policy, unless the retention period is extended or permitted by law. (4) The Internet is not an absolutely secure environment. We strongly recommend that you do not use personal communication methods that are not recommended by this platform. You can connect and share with each other through our services. When you create communications, transactions, or sharing through our services, you can choose who you want to communicate, trade, or share as a third party who can see your trading content, contact information, exchange information, or share content. If you find that your personal information, especially your account or password, has been leaked, please contact our customer service immediately so that we can take appropriate measures according to your application. Please note that the information you voluntarily share or even share publicly when using our services may involve personal information of you or others or even sensitive personal information, such as when you post a news or choose to upload in public in group chats, circles, etc. A picture containing personal information. Please consider more carefully whether you share or even share information publicly when using our services. Please use complex passwords to help us keep your account secure. We will do our best to protect the security of any information you send us. At the same time, we will report the handling of personal information security incidents in accordance with the requirements of the regulatory authorities. V. How your personal information is transferred globally Personal information collected and generated by us during our operations in the People's Republic of China is stored in China, with the following exceptions: Laws and regulations have clear provisions; 2, get your explicit authorization; 3, you through the Internet for cross-border live broadcast / release dynamics and other personal initiatives. In response to the above, we will ensure that your personal information is adequately protected in accordance with this Privacy Policy.
