Given the wide spread of inaccurate medical advice related to the 2019 coronavirus pandemic (COVID-19), such as fake remedies, treatments and prevention suggestions, misinformation detection has emerged as an open problem of high importance and interest for the NLP community. To combat potential harm of COVID19-related misinformation, we release Covid-HeRA, a dataset for health risk assessment of COVID-19-related social media posts. More specifically, we study the severity of each misinformation story, i.e., how harmful a message believed by the audience can be and what type of signals can be used to discover high malicious fake news and detect refuted claims. We present a detailed analysis, evaluate several simple and advanced classification models, and conclude with our experimental analysis that presents open challenges and future directions.

Drink bleach or do what now? Covid-HeRA: A dataset for risk-informed health decision making in the presence of COVID19 misinformation.

Ransomware: Recent advances, analysis, challenges and future research directions.

As one of the most pervasive applications of machine learning, recommender systems are playing an important role on assisting human decision making. The satisfaction of users and the interests of platforms are closely related to the quality of the generated recommendation results. However, as a highly data-driven system, recommender system could be affected by data or algorithmic bias and thus generate unfair results, which could weaken the reliance of the systems. As a result, it is crucial to address the potential unfairness problems in recommendation settings. Recently, there has been growing attention on fairness considerations in recommender systems with more and more literature on approaches to promote fairness in recommendation. However, the studies are rather fragmented and lack a systematic organization, thus making it difficult to penetrate for new researchers to the domain. This motivates us to provide a systematic survey of existing works on fairness in recommendation. This survey focuses on the foundations for fairness in recommendation literature. It first presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking in order to provide a general overview of fairness research, as well as introduce the more complex situations and challenges that need to be considered when studying fairness in recommender systems. After that, the survey will introduce fairness in recommendation with a focus on the taxonomies of current fairness definitions, the typical techniques for improving fairness, as well as the datasets for fairness studies in recommendation. The survey also talks about the challenges and opportunities in fairness research with the hope of promoting the fair recommendation research area and beyond.

Fairness in Recommendation: A Survey

Studies show lots of advanced research on various data types such as image, speech, and text using deep learning techniques, but nowadays, research on video processing is also an emerging field of computer vision. Several surveys are present on video processing using computer vision deep learning techniques, targeting specific functionality such as anomaly detection, crowd analysis, activity monitoring, etc. However, a combined study is still unexplored. This paper aims to present a Systematic Literature Review (SLR) on video processing using deep learning to investigate the applications, functionalities, techniques, datasets, issues, and challenges by formulating the relevant research questions (RQs). This systematic mapping includes 93 research articles from reputed databases published between 2011 and 2020. We categorize the deep learning technique for video processing as CNN, DNN, and RNN based. We observe significant advancements in video processing between 2017 and 2020, primarily due to the advent of AlexNet, ResNet, and LSTM based deep learning techniques. The prominent fields of video processing research are observed as human action recognition, crowd anomaly detection, and behavior analysis. This SLR is a helpful guide for the researchers to explore the recent literature, available datasets, and existing deep learning techniques for video processing.

/pdf/video-processing-using-deep-learning-techniques-a-systematic-1odomj6rew.pdf

Video Processing Using Deep Learning Techniques: A Systematic Literature Review

Ranking, recommendation, and retrieval systems are widely used in online platforms and other societal systems, including e-commerce, media-streaming, admissions, gig platforms, and hiring. In the recent past, a large “fair ranking” research literature has been developed around making these systems fair to the individuals, providers, or content that are being ranked. Most of this literature defines fairness for a single instance of retrieval, or as a simple additive notion for multiple instances of retrievals over time. This work provides a critical overview of this literature, detailing the often context-specific concerns that such approaches miss: the gap between high ranking placements and true provider utility, spillovers and compounding effects over time, induced strategic incentives, and the effect of statistical uncertainty. We then provide a path forward for a more holistic and impact-oriented fair ranking research agenda, including methodological lessons from other fields and the role of the broader stakeholder community in overcoming data bottlenecks and designing effective regulatory environments.

/pdf/fair-ranking-a-critical-review-challenges-and-future-18wngpop.pdf

Fair ranking: a critical review, challenges, and future directions

Ransomware can produce direct and controllable economic loss making it one of the most prominent threats in cybersecurity. According to the latest statistics, more than half of the malwares reported in Q1 of 2017 are ransomwares, and there is a potential threat of novice cybercriminals accessing ransomware-as-a-service. The concept of public-key based data kidnapping and subsequent extortion was first introduced in 1996. Since then, variants of ransomware emerged with different cryptosystems and larger key sizes; however, the underlying techniques remained the same. There are several works in the literature which propose a generic framework to detect these ransomwares; though, most of them target ransomwares having specific classes of the encryption algorithm. In addition to it, most of these methods either require Operating System (OS) kernel modification or have high detection latency. In this work, we present a generalized two-step unsupervised detection framework: RATAFIA which uses a Deep Neural Network architecture and Fast Fourier Transformation to develop a highly accurate, fast and reliable solution to ransomware detection using minimal tracepoints. The proposed method does not require any OS kernel modification making it adaptable to most of the modern-day system. We also introduce a special detection module for successful identification of benign disk encryption processes having similar characteristics like malicious ransomware programs but having a different intention. We provide a comprehensive study to evaluate the performance of RATAFIA in the presence of standard benchmark programs, disk encryption and regular high computational processes in the light of software security.

RATAFIA: Ransomware Analysis using Time And Frequency Informed Autoencoders

Twitter has become one of the most sought after places to discuss a wide variety of topics, including medically relevant issues such as cancer This helps spread awareness regarding the various causes, cures and prevention methods of cancer However, no proper analysis has been performed, which discusses the validity of such claims In this work, we aim to tackle the misinformation spread in such platforms We collect and present a dataset regarding tweets which talk specifically about cancer and propose an attention-based deep learning model for automated detection of misinformation along with its spread We then do a comparative analysis of the linguistic variation in the text corresponding to misinformation and truth This analysis helps us gather relevant insights on various social aspects related to misinformed tweets

/pdf/analysing-the-extent-of-misinformation-in-cancer-related-38jbq3jtka.pdf

Analysing the Extent of Misinformation in Cancer Related Tweets.

Twitter has become one of the most sought after places to discuss a wide variety of topics, including medically relevant issues such as cancer. This helps spread awareness regarding the various causes, cures and prevention methods of cancer. However, no proper analysis has been performed, which discusses the validity of such claims. In this work, we aim to tackle the misinformation spread in such platforms. We collect and present a dataset regarding tweets which talk specifically about cancer and propose an attention-based deep learning model for automated detection of misinformation along with its spread. We then do a comparative analysis of the linguistic variation in the text corresponding to misinformation and truth. This analysis helps us gather relevant insights on various social aspects related to misinformed tweets.

Analysing the Extent of Misinformation in Cancer Related Tweets

While large number of cameras have been deployed on campus, governments and restricted areas to protect the safety of residents and their properties, manually searching for the track of a suspicious human or vehicle is painful and prone to errors. An intelligent camera surveillance system is crucial to make the usage of the large-scale camera system efficient and smooth. In this paper, we present our Space-Time Vehicle Tracking system (STVT) which targets to track all vehicles over time and store the result as trajectories of vehicles in a graph database. We are then able to respond to queries such as Where did this suspicious vehicle come from? and Where is it headed to? directly from the stored trajectories. STVT makes use of computer vision techniques including Yolo v2, Dlib's correlation tracker, and adaptive histogram to transform each video stream into a stream of vehicle detection events. A publish-subscribe-based messaging system is built above the camera networks to enable communicating vehicle detection events between cameras. A graph database is used to store the vehicle detections and connect them into trajectories of vehicles. We have a built a prototype of STVT and are currently assessing its effectiveness with a small subset of cameras. Specifically, we process video streams from 7 Georgia Tech's on-campus street cameras with STVT, and identify new challenges and future works in a real-world deployment.

Space-Time Vehicle Tracking at the Edge of the Network

Recommender systems are one of the most widely used services on several online platforms to suggest potential items to the end-users. These services often use different machine learning techniques for which fairness is a concerning factor, especially when the downstream services have the ability to cause social ramifications. Thus, focusing on the non-personalised (global) recommendations in news media platforms (e.g., top-k trending topics on Twitter, top-k news on a news platform, etc.), we discuss on two specific fairness concerns together (traditionally studied separately)---user fairness and organisational fairness. While user fairness captures the idea of representing the choices of all the individual users in the case of global recommendations, organisational fairness tries to ensure politically/ideologically balanced recommendation sets. This makes user fairness a user-side requirement and organisational fairness a platform-side requirement. For user fairness, we test with methods from social choice theory, i.e., various voting rules known to better represent user choices in their results. Even in our application of voting rules to the recommendation setup, we observe high user satisfaction scores. Now for organisational fairness, we propose a bias metric which measures the aggregate ideological bias of a recommended set of items (articles). Analysing the results obtained from voting rule-based recommendation, we find that while the well-known voting rules are better from the user side, they show high bias values and clearly not suitable for organisational requirements of the platforms. Thus, there is a need to build an encompassing mechanism by cohesively bridging ideas of user fairness and organisational fairness. In this abstract paper, we intend to frame the elementary ideas along with the clear motivation behind the requirement of such a mechanism.

Sayan Sinha

Papers

RATAFIA: Ransomware Analysis using Time And Frequency Informed Autoencoders

Analysing the Extent of Misinformation in Cancer Related Tweets.

Analysing the Extent of Misinformation in Cancer Related Tweets

Space-Time Vehicle Tracking at the Edge of the Network

Two-Sided Fairness in Non-Personalised Recommendations.