Reviewerly/exHarmony huggingface dataset:
Hate speech in online social media

Social media platforms like Twitter, Gab, Facebook are available in the market to billions 1 of users. These platforms allow users to share their ideas and opinions instantly almost with no cost. These have already been utilized by bad actors in the society to cause damage. Scenarios like the Rohingya genocide in Myanmar, anti-Muslim mob violence in Sri Lanka, and the Pittsburgh synagogue shooting can be linked to these platforms. Recently, hate speech is considered to be one of the major issues poisoning the online social media environment. To keep these platforms healthy there is a need to understand how these hateful content spread, how hateful users behave and finally, what could be an effective way to mitigate hate speech. In this article, we look at the recent advances and issues surrounding hate speech in online social media. We take three different perspectives - analysis &amp; spread, detection , and mitigation.
Evolution of technical debt remediation in Python: A case study on the Apache Software Ecosystem

Abstract In recent years, the evolution of software ecosystems and the detection of technical debt received significant attention by researchers from both industry and academia. While a few studies that analyze various aspects of technical debt evolution already exist, to the best of our knowledge, there is no large‐scale study that focuses on the remediation of technical debt over time in Python projects—that is, one of the most popular programming languages at the moment. In this paper, we analyze the evolution of technical debt in 44 Python open‐source software projects belonging to the Apache Software Foundation. We focus on the type and amount of technical debt that is paid back. The study required the mining of over 60K commits, detailed code analysis on 3.7K system versions, and the analysis of almost 43K fixed issues. The findings show that most of the repayment effort goes into testing, documentation, complexity, and duplication removal. Moreover, more than half of the Python technical debt is short term being repaid in less than 2 months. In particular, the observations that a minority of rules account for the majority of issues fixed and spent effort suggest that addressing those kinds of debt in the future is important for research and practice.
Chosen-Ciphertext Secure Homomorphic Proxy Re-Encryption

Homomorphic Proxy Re-Encryption (HPRE) is an extension of Proxy Re-Encryption (PRE) which combines the advantages of both Homomorphic Encryption (HE) and PRE. A HPRE scheme allows arbitrary evaluations to be performed on ciphertexts under one (the delegator's) public key and, using a re-encryption key, it transforms the resulting ciphertext to a new ciphertext under another (the delegatee's) public key. Prior HPRE schemes are either CPA-secure or CCA-secure but only support partial homomorphic operations. We propose a generic construction of single-hop HPRE scheme which supports fully homomorphic operations. The proposed scheme is proven secure in our new index-based CCA-HPRE model. Our technique is to give a generic transformation that turns any multi-identity identity-based FHE (IBFHE) scheme with key switching into Fully Homomorphic Encryption (FHE) with key switching from which we can obtain the proposed single-hop HPRE scheme. We also present a concrete instantiation of multi-identity IBFHE with key switching from learning with errors (LWE) in the standard model.
A systematic literature review of cyber insurance challenges

This paper presents a systematic literature review of the challenges in cyber insurance. The study uses PRISMA to obtain transparent results. This review summarized sectors which risk a cyber incident and cyber insurance's challenges and its solutions. The articles included in this study were collected from the search results on four digital libraries: ACM, IEEE Xplore, ScienceDirect, and Springer Link, published in 2016-2020. There are 46 out of 313 articles that meet the criteria. The sectors that risk a cyber incident are government, public, financial, professional, and technology. The cyber insurance challenges are divided into three categories based on contractual agreement status, namely before the contractual agreement, in the contractual agreement, and no contractual agreement. Challenges included in the condition before the contractual are organization eligibility and insurance contract design. The difficulties faced if the contractual exists, namely self-reporting, external security audit, and the insurer's refusal to pay the claim. Finally, no contractual agreement leads to a general challenge in cyber insurers, namely cyber insurance awareness and cost-benefit aspects of cybersecurity investment. The proposed solutions to the challenges are unique. However, there are some similarities, namely proposals in the form of frameworks, models, or suggestions. Apart from these three things, the solutions can also be in the form of analysis to get optimal things.
Application and challenges of Blockchain technology for real-time operation in a water distribution system

The demand for water continues to increase day by day and therefore measures must be established to be able to manage water resources more efficiently. Recently, various intelligent systems have been developed with the common goal of ensuring efficient water management and integrating modern technologies such as Internet of Things, Blockchain, Decision Support Systems and Artificial Intelligence. Nowadays, technological advancements allow collecting, processing and analyzing data from water distribution networks to optimize resources and improve decision making for all parties involved. This article presents a complex architecture, in the form of an integrated platform for real-time control and monitoring for residential networks, with secure data management based on Blockchain technology. A Blockchain network based on Hyperledger Fabric was developed in order to evaluate the possibilities of storing the real-time data into a distributed ledger.
Assumption-based argumentation with preferences and goals for patient-centric reasoning with interacting clinical guidelines

A paramount, yet unresolved issue in personalised medicine is that of automated reasoning with clinical guidelines in multimorbidity settings. This entails enabling machines to use computerised generic clinical guideline recommendations and patient-specific information to yield patient-tailored recommendations where interactions arising due to multimorbidities are resolved. This problem is further complicated by patient management desiderata, in particular the need to account for patient-centric goals as well as preferences of various parties involved. We propose to solve this problem of automated reasoning with interacting guideline recommendations in the context of a given patient by means of computational argumentation. In particular, we advance a structured argumentation formalism ABA+G (short for Assumption-Based Argumentation with Preferences (ABA+) and Goals) for integrating and reasoning with information about recommendations, interactions, patient’s state, preferences and prioritised goals. ABA+G combines assumption-based reasoning with preferences and goal-driven selection among reasoning outcomes. Specifically, we assume defeasible applicability of guideline recommendations with the general goal of patient well-being, resolve interactions (conflicts and otherwise undesirable situations) among recommendations based on the state and preferences of the patient, and employ patient-centered goals to suggest interaction-resolving, goal-importance maximising and preference-adhering recommendations. We use a well-established Transition-based Medical Recommendation model for representing guideline recommendations and identifying interactions thereof, and map the components in question, together with the given patient’s state, prioritised goals, and preferences over actions, to ABA+G for automated reasoning. In this, we follow principles of patient management and establish corresponding theoretical properties as well as illustrate our approach in realistic personalised clinical reasoning scenaria.
A Belief Network Reasoning Framework for Fault Localization in Communication Networks

A small fault in a large communication network may cause abrupt and large alarms, making the localization of the root cause of failure a difficult task. Traditionally, fault localization is carried out by an operator who uses alarms in alarm lists; however, fault localization process complexity needs to be addressed using more autonomous and intelligent approaches. Here, we present an overall framework that uses a message propagation mechanism of belief networks to address fault localization problems in communication networks. The proposed framework allows for knowledge storage, inference, and message transmission, and can identify a fault's root cause in an event-driven manner to improve the automation of the fault localization process. Avoiding the computational complexity of traditional Bayesian networks, we perform fault inference in polytrees with a noisy OR-gate model (PTNORgate), which can reduce computational complexity. We also offer a solution to store parameters in a network parameter table, similar to a routing table in communication networks, with the aim of facilitating the development of the algorithm. Case studies and a performance evaluation show that the solution is suitable for fault localization in communication networks in terms of speed and reliability.
Deterministic Wallets in a Quantum World

Most blockchain solutions are susceptible to quantum attackers as they rely on cryptography that is known to be insecure in the presence of quantum adversaries. In this work we advance the study of quantum-resistant blockchain solutions by giving a quantum-resistant construction of a deterministic wallet scheme. Deterministic wallets are frequently used in practice in order to secure funds by storing the sensitive secret key on a so-called cold wallet that is not connected to the Internet. Recently, Das et al. (CCS'19) developed a formal model for the security analysis of deterministic wallets and proposed a generic construction from certain types of signature schemes that exhibit key rerandomization properties. We revisit the proposed classical construction in the presence of quantum adversaries and obtain the following results.
Characterizing Toxicity on Facebook Comments in Brazil

On social media platforms, comments associated with news pieces are usually filled with negativity and toxicity, many times promoting flamed discussions and insults among users. Although designed to encourage conversations and interactions, the high toxicity might end up contributing to create a hostile environment in the online space, which is detrimental to both social media platforms and their users. In this work, we provide a large-scale diagnostic about the toxicity in comments associated with news shared on Facebook. To do that, we collected all posts and comments from relevant pages during a major political event in Brazil, the release of Former President Lula from prison. We then used the Perspective API from Google to measure the toxicity of the comments and posts. Our analysis of the toxicity unveils features that influence toxicity associated with the news, especially in relation to public figures. We hope our findings may affect the design of better content policies able to mitigate the problem.
Improving cybersecurity hygiene through JIT patching

Vulnerability patch management remains one of the most complex issues facing modern enterprises; companies struggle to test and deploy new patches across their networks, often leaving myriad attack vectors vulnerable to exploits. This problem is exacerbated by enterprise server applications, which expose tremendous amounts of information about their security postures, greatly expediting attackers' reconnaissance incursions (e.g., knowledge gathering attacks). Unfortunately, current patching processes offer no insights into attacker activities, and prompt attack remediation is hindered by patch compatibility considerations and deployment cycles.
Low-cost Scalable People Tracking System for Human-Robot Collaboration in Industrial Environment

Human-robot collaboration is one of the key elements in the Industry 4.0 revolution, aiming to a close and direct collaboration between robots and human workers to reach higher productivity and improved ergonomics. The first step toward such kind of collaboration in the industrial context is the removal of physical safety barriers usually surrounding standard robotic cells, so that human workers can approach and directly collaborate with robots. Anyway, human safety must be granted avoiding possible collisions with the robot. In this work, we propose the use of a people tracking algorithm to monitor people moving around a robot manipulator and recognize when a person is too close to the robot while performing a task. The system is implemented by a camera network system positioned around the robot workspace, and thoroughly evaluated in different industry-like settings in terms of both tracking accuracy and detection delay.
GAIN: Graph Attention &amp; Interaction Network for Inductive Semi-Supervised Learning Over Large-Scale Graphs

Graph Neural Networks (GNNs) have led to state-of-the-art performance on a variety of machine learning tasks such as recommendation, node classification and link prediction. Graph neural network models generate node embeddings by merging nodes features with the aggregated neighboring nodes information. Most existing GNN models exploit a single type of aggregator (e.g., mean-pooling) to aggregate neighboring nodes information, and then add or concatenate the output of aggregator to the current representation vector of the center node. However, using only a single type of aggregator is difficult to capture the different aspects of neighboring information and the simple addition or concatenation update methods limit the expressive capability of GNNs. Not only that, existing supervised or semi-supervised GNN models are trained based on the loss function of the node label, which leads to the neglect of graph structure information. In this paper, we propose a novel graph neural network architecture, Graph Attention & Interaction Network (GAIN), for inductive learning on graphs. Unlike the previous GNN models that only utilize a single type of aggregation method, we use multiple types of aggregators to gather neighboring information in different aspects and integrate the outputs of these aggregators through the aggregator-level attention mechanism. Furthermore, we design a graph regularized loss to better capture the topological relationship of the nodes in the graph. Additionally, we first present the concept of graph feature interaction and propose a vector-wise explicit feature interaction mechanism to update the node embeddings. We conduct comprehensive experiments on two node-classification benchmarks and a real-world financial news dataset. The experiments demonstrate our GAIN model outperforms current state-of-the-art performances on all the tasks.
GID-Net: Detecting human-object interaction with global and instance dependency

Since detecting and recognizing individual human or object are not adequate to understand the visual world, learning how humans interact with surrounding objects becomes a core technology. However, convolution operations are weak in depicting visual interactions between the instances since they only build blocks that process one local neighborhood at a time. To address this problem, we learn from human perception in observing HOIs to introduce a two-stage trainable reasoning mechanism, referred to as GID block. GID block breaks through the local neighborhoods and captures long-range dependency of pixels both in global-level and instance-level from the scene to help detecting interactions between instances. Furthermore, we conduct a multi-stream network called GID-Net, which is a human-object interaction detection framework consisting of a human branch, an object branch and an interaction branch. Semantic information in global-level and local-level are efficiently reasoned and aggregated in each of the branches. We have compared our proposed GID-Net with existing state-of-the-art methods on two public benchmarks, including V-COCO and HICO-DET. The results have showed that GID-Net outperforms the existing best-performing methods on both the above two benchmarks, validating its efficacy in detecting human-object interactions.
A smartwatch product provides on-body tapping gestures recognition

Smartwatches, which are small and portable, have become dominant devices in the wearable ecosystem. However, due to the limited size of the touch screens, smartwatches typically have a poor interactive experience for users. In our previous work [3, 4], we studied appropriating the human body as a surface to extend the input through tapping-induced vibrations. In this demo, we extend previously published research by presenting a brand-new product: a smartwatch that provides on-body tapping gestures recognition. We design eight tapping gestures for four applications on the new smartwatch: music players, shortcuts, cameras, and phone calls. In 2020, we collaborated with Mad Gaze [1] and launched this smartwatch on a crowdfunding platform. Our smartwatch has exceeded the crowdfunding target amount by 27 times.
MetaRisk: Semi-supervised few-shot operational risk classification in banking industry

We study the operational risk classification problem, a critical yet challenging problem in the banking industry. In practice, banks build supervised multi-label classification models to identify the pre-defined risks using financial news sources. However, the models are often suboptimal due to the lack of labeled data and diverse combinations of risk types. To address these practical issues, we re-frame multi-label supervised operational risk classification as a semi-supervised few-shot learning problem, named MetaRisk, which can then be effectively learned using the prototypical network. We also propose a weighted scheme to help obtain accurately prototype vectors of multi-risk classes. We evaluate the proposed approach MetaRisk using a real-world operational risk classification dataset, and the results demonstrate that it outperforms a set of standard baselines. Especially, MetaRisk is capable of predicting risk types that are new to the system. We expect our work provides a direct and relevant toolkit that may assist risk officers to predict and intervene risks in the banking industry.
Tracking Emerges by Looking Around Static Scenes, with Neural 3D Mapping

We hypothesize that an agent that can look around in static scenes can learn rich visual representations applicable to 3D object tracking in complex dynamic scenes. We are motivated in this pursuit by the fact that the physical world itself is mostly static, and multiview correspondence labels are relatively cheap to collect in static scenes, e.g., by triangulation. We propose to leverage multiview data of static points in arbitrary scenes (static or dynamic), to learn a neural 3D mapping module which produces features that are correspondable across time. The neural 3D mapper consumes RGB-D data as input, and produces a 3D voxel grid of deep features as output. We train the voxel features to be correspondable across viewpoints, using a contrastive loss, and correspondability across time emerges automatically. At test time, given an RGB-D video with approximate camera poses, and given the 3D box of an object to track, we track the target object by generating a map of each timestep and locating the object's features within each map. In contrast to models that represent video streams in 2D or 2.5D, our model's 3D scene representation is disentangled from projection artifacts, is stable under camera motion, and is robust to partial occlusions. We test the proposed architectures in challenging simulated and real data, and show that our unsupervised 3D object trackers outperform prior unsupervised 2D and 2.5D trackers, and approach the accuracy of supervised trackers. This work demonstrates that 3D object trackers can emerge without tracking labels, through multiview self-supervision on static data.
An automatic clustering method using multi-objective genetic algorithm with gene rearrangement and cluster merging

As an unsupervised approach of machine learning, clustering is an important method to understand and learn structural information from data. However, current adaptive clustering approach based on multi-objective genetic algorithm have two apparent limitations. The first is that prior knowledge, i.e., sample information is needed to get the correct cluster number. The second is that no effective method can be found to select the best clustering solution from the Pareto Optimal Front (POF) generated by a multi-objective optimization. These problems become severer in applications applied on non-category datasets. Therefore, the primary goal of this research is to establish a genetic optimization based multi-objective clustering framework, in which multiple clustering validity indexes (CVIs) can be tested simultaneously to automatically obtain the optimal cluster number without knowing any sample label information in advance. In this effort, we will not only be able to consider clustering measurements such as cluster cohesion and separation, but also take other aspects, such as compactness, connectivity, variation among data elements, into consideration as well. Then, we aim to design a procedure to recommend three best solutions from the POF by using appropriate combination of CVIs without increasing computational cost. This procedure is expected to control the cluster number in a reasonable range and consequently decrease the difficulty in best solution recommendation. Finally, since we have the knowledge that using gene rearrangement in the genetic optimization does not affect partition, we take this advantage to merge clusters effectively and significantly speed the convergence of the algorithm. Our approach can outperform the state-of-the-art counterparts across diverse benchmark datasets in terms of partitioning accuracy and performance, as demonstrated in three experiments conducted on both artificial and typical real-world datasets.
Reinforcement learning based flight controller capable of controlling a quadcopter with four, three and two working motors

In this research, we show how a reinforcement learning based algorithm called Fault-Tolerant Bio-inspired Flight Controller (FT-BFC) is capable of training a single neural network based model to fly a quadcopter with two, three, and four working rotors. Our algorithm can learn a low-level flight controller that directly controls angular velocities of motors to fly a quadcopter when it has four fully functional motors, and also, despite having one or two motor failures (That is, our proposed flight controller is a fault-tolerant controller as well). In the training and running of our controller, we do not use any conventional flight controller, such as a PID or SMC controller. We test our algorithm in a simulation environment, Gazebo simulator, and illustrate our simulation results that backing up our algorithm capabilities. Finally, before concluding our paper, we discuss the implementation of our algorithm in a real quadcopter.