Reviewerly/exHarmony huggingface dataset:
Multi-modal Deep Learning Based Fusion Approach to Detect Illicit Retail Networks from Social Media

Illicit drug trafficking poses a significant threat in today’s society in general and in the younger population in particular. Drug abuse has been known to be correlated with car-accidents, crimes, diseases, deaths and many other negative aspects of society. With the advancement of social media being an open platform to express all kinds of social activities, drug use can be encouraged on this platform to lead other people towards drug abuse. Where people abuse drugs on social media, it is a prominent platform for drug dealing as well. Drug dealers post different types of drug images along with their contact information on social media. Tracking drug dealers among millions of social media users can be very challenging for law enforcement agencies. Therefore, automatic detection of drug dealers (along with the type of drugs they sell and their contact information) is crucial. In this article we have presented a state-of-the-art social media analytic algorithm which does multi modal analysis in order to detect drug-related posts and drug dealers from social media. We propose to detect different types of drugs from social media posts which include: pills, mushrooms, LSD, cannabis, cocaine, syrup, hookah and cigars using our drug type detection model. We also propose to extract drug dealer’s information from social media and create novel AI algorithms to improve understanding of the operations of Illicit Retail Networks (IRN) that will help detect, disrupt and ultimately dismantle these networks. Our approach is generalizable to detect different illicit networks from social media such as human trafficking, illegal gun sales, money laundering and others.
Comparative analysis of JavaScript package managers - yarn and npm

In this article, two leading solutions for managing packages in projects which are using JavaScript technology (yarn and npm) were subjected to a comparative analysis. As part of the implementation, two configuration files were created, one of which represents an empty application created on the basis of an application template based on the Angular framework in version 8. The second file reflects an extensive web application based on the same framework, but with the addiction of over 100 dependencies. The research was focused on the time efficiency of both solutions.
Automatically Selecting Follow-up Questions for Deficient Bug Reports

The availability of quality information in bug reports that are created daily by software users is key to rapidly fixing software faults. Improving incomplete or deficient bug reports, which are numerous in many popular and actively developed open source software projects, can make software maintenance more effective and improve software quality. In this paper, we propose a system that addresses the problem of bug report incompleteness by automatically posing follow-up questions, intended to elicit answers that add value and provide missing information to a bug report. Our system is based on selecting follow-up questions from a large corpus of already posted follow-up questions on GitHub. To estimate the best follow-up question for a specific deficient bug report we combine two metrics based on: 1) the compatibility of a follow-up question to a specific bug report; and 2) the utility the expected answer to the follow-up question would provide to the deficient bug report. Evaluation of our system, based on a manually annotated held-out data set, indicates improved performance over a set of simple and ablation baselines. A survey of software developers confirms the held-out set evaluation result that about half of the selected follow-up questions are considered valid. The survey also indicates that the valid follow-up questions are useful and can provide new information to a bug report most of the time, and are specific to a bug report some of the time.
Automated Classification of Mobile App Reviews Considering User’s Quality Concerns

App review analysis has recently emerged as an active research area in software engineering considering the immensely large user base and potential benefits of automated information extraction.Rehman, Bisma To achieve user satisfaction and to survive in the app market, addressing these requirements is essential.Alam, Khubaib Amjad Therefore, in this research, we formulate this problem as a Multi-label classification problem and propose a classification model.Ko, Kwang Man We have trained multi-label review classifier-convolutional neural network (MLRC-CNN) model on top of pre-trained word embedding (known as word2vec embedding) for review level classification of ten thousand user reviews extracted from fifty Android apps. This is the first study of its kind in mobile app review classification that classifies mobile app reviews, using CNN classifier, considering user’s quality concerns related to an app. Considering the promising results and significance of this model, it can be exploited as a classification model for other user feedback analysis platforms.
TENET: Joint Entity and Relation Linking with Coherence Relaxation

The joint entity and relation linking task aims to connect the noun phrases (resp., relational phrases) extracted from natural language documents to the entities (resp., predicates) in general knowledge bases (KBs). This task benefits numerous downstream systems, such as question answering and KB population. Previous works on entity and relation linking rely on the global coherence assumption, i.e., entities and predicates within the same document are highly correlated with each other. However, this assumption is not always valid in many real-world scenarios. Due to KB incompleteness or data sparsity, sparse coherence among the entities and predicates within the same document is common. Moreover, there may exist isolated entities or predicates that are not related to any other linked concepts. In this paper, we propose TENET, a joint entity and relation linking technique, which relaxes the coherence assumption in an unsupervised manner. Specifically, we formulate the joint entity and relation linking task as a minimum-cost rooted tree cover problem on the knowledge coherence graph constructed based on the document. We then propose effective approximation algorithms with pruning strategies to solve this problem and derive the linking results. Extensive experiments on real-world datasets demonstrate the superior effectiveness and efficiency of our method against the state-of-the-art techniques.
Managing Context during Scholarly Knowledge Synthesis: Process Patterns and System Mechanics

Scholarly knowledge synthesis — the production of a novel conceptual whole such as an effective literature review or theory — is a critical yet consistently challenging subtask of research. We explore how managing the context of knowledge claims being synthesized, such as their production context or methodology, may be a critical under-supported subtask of synthesis in existing tools. Through in situ protocol analyses of researchers doing the work of synthesis, we studied how researchers capture contextual information in their notes and annotations, and how this varies across generic vs. specialized systems for synthesis. Our analysis revealed common process patterns of context capture, and qualitative differences in the nature of support for context capture across generic and specialized systems. Based on these findings, we discuss design implications for systems that aim to better support scholarly synthesis.
Material Identification Using Feature Extraction (Range of IQ) in Wireless System (Lime SDR mini)

Wireless communication and systems are being profusely used these days. In this work, we use a wireless pair (transmitter and receiver) by Lime SDR (Software Defined Radio) mini to identify different kinds of materials: "air", "plastic" and "metal". In our system, the unknown material is placed between the transmitter and the receiver of the Lime SDR mini operating at a frequency of 1250 MHz. The magnitude (I) and phase (Q) of the received signal varies due to the intrinsic characteristics of the material. The received signal is acquired via Lime Suite GUI and further processed in Matlab. Our proposed algorithm utilizes the key feature (fh_nst), which is the sum of the tail of the normalized frequency of the (I/Q data) histogram. The unknown material is detected using the value of the key feature with an accuracy of 98%. This is a novel application of the Lime SDR mini.
Pedagogical Features of the Formation of Pedagogical Ethics in Future Teachers

The article is devoted to the formation of pedagogical ethics in future teachers and the study of the problems of pedagogical characteristics. It is imperative to pay attention to the knowledge of future teachers because mastering the system of competent reading in higher education is their social and professional duty. Therefore, future teachers need to know the rules and requirements of pedagogical ethics. Because mastering the etiquette of a teacher: professional ethics of future teachers — the formation of knowledge, skills and competencies in the ethics of a teacher; guide, regulate and monitor the coach’s relationship with children, colleagues and parents in the educational process; to learn the norms that must be adhered to in ethical relations between the participants in the pedagogical process, to understand the need for their assimilation; serves to increase the spiritual and moral level of future teachers. The theoretical and practical significance of the study lies in the substantiation of the need for optimal coordination of the priorities of professional development, the need for their competent implementation.
Power Analysis of Kuznyechik cipher on Arduino Nano board

Side-channel attacks basically use the features of software or hardware implementations of information security algorithms. An example of global side-channel attacks is an attack based on a power measurements analysis. Therefore, cryptographic algorithm implementations should be tested accordance of robustness features to side-channel attacks, such as power analysis attacks. This paper presents a power analysis traces and practical segmentations of power consumption measurements charts for encryption standard Kuznyechik cipher (GOST R 34.12 - 2015 (n = 128)) on original and modified Arduino Nano boards. Algorithms for differential power analysis of Kuznyechik cipher is developed and applied to experimental charts. Three sets of power consumption charts of various sizes (10, 100 and 1000 charts) were tested. Our experiments led to highlight chart segments of additions with round key, non-linear transformation (S) and linear transformation (L) operations for analyzed implementation of Kuznyechik cipher. The arithmetic mean of power consumption values for S-box encryption operations of Kuznyechik cipher were calculated for all key candidates and set of statistically probable key-bit values were fixed.
Application of Grey Wolf Optimization in Anti-collision Vehicle

Heuristic approaches are recently prevalent. Although they cannot be explained in mathematics, its performances are also impressive. In this paper, comparing with Genetic Algorithm (GA), we make the vehicle avoid obstacles in simulation by applying Grey Wolf Optimizer (GWO) to train the Artificial Neural Network (ANN). This method is unsupervised. Its inputs are the distances between vehicle and obstacle which are collected by rangefinders, while its outputs are the two steering angles that decide which direction the vehicle turns to. The fitness is lifetime of the vehicle. Finally, the experimental results show that the convergence speed of GWO is faster than that of GA.
Efficient Approximate Algorithms for Empirical Entropy and Mutual Information

Empirical entropy is a classic concept in data mining and the foundation of many other important concepts like mutual information. However, computing the exact empirical entropy/mutual information on large datasets can be expensive. Some recent research work explores sampling techniques on the empirical entropy/mutual information to speed up the top-k and filtering queries. However, their solution still aims to return the exact answers to the queries, resulting in high computational costs.
Multi-view stereo using cross-view depth map completion and row-column depth refinement

Inferring the three-dimensional structure of an object or scene from multi-view images is a hard inverse problem. In this paper, we propose a depth map-based method to reconstruct a 3D model from multiple calibrated images. The proposed method uses global and local optimization in two stages to estimate highly accurate depth maps. The first stage employs Particle Swarm Optimization to explore the search-space extensively and estimate coarse depth maps from downscaled images. A cross-view depth map completion step ensures that good depth estimates are transferred to neighboring views. This helps in removing outliers and fill in missing depth values with reliable estimates. In the second stage, a local optimizer refines the coarse depths by processing each row/column of pixels iteratively to improve photo-consistency and smoothness among neighboring depths. This procedure utilizes distance maps to handle textured and homogeneous regions adaptively. Experimental results on the Middlebury multi-view stereo benchmark demonstrate the effectiveness of our method in producing accurate and complete 3D models with better spatial consistency and level of detail.
Participatory Design for the Anarchive

This paper documents the collaborative design project: “Amagugu Ethu / Our Treasures: Understanding Zulu History and Language with Zulu-Speaking Communities and Their Belongings.” With eight Zulu experts, and the company Museum in a Box [32], we collaborated to create an oral history documentation project that focuses on the belongings at the Iziko South African Museum (SAM). Using the Museum in a Box tool, we created a movable, audio-based exhibit that compiled stories and images of objects from the SAM, for use back in the community in KwaZulu-Natal. This paper documents our shared process and considers the development of this exhibit and documentation project as a kind of participatory anarchive or counter archive [20, 41]. We connect practices and theories in participatory design with those in museum studies and archival studies; and show the productive tensions that exist in an experimental, community-engaged oral history documentation project.
Deep Top-rank Counter Metric for Person Re-identification

In the research field of person re-identification, deep metric learning that guides the efficient and effective embedding learning serves as one of the most fundamental tasks. Recent efforts of the loss function based deep metric learning methods mainly focus on the top rank accuracy optimization by minimizing the distance difference between the correctly matching sample pair and wrongly matched sample pair. However, it is more straightforward to count the occurrences of correct top-rank candidates and maximize the counting results for better top rank accuracy. In this paper, we propose a generalized logistic function based metric with effective practicalness in deep learning, namely the“ <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deep</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">top-rank</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">counter</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">metric</i> ”, to approximately optimize the counted occurrences of the correct top-rank matches. The properties that qualify the proposed metric as a well-suited deep re-identification metric have been discussed and a progressive hard sample mining strategy is also introduced for effective training and performance boosting. The extensive experiments show that the proposed top-rank counter metric outperforms other loss function based deep metrics and achieves the state-of-the-art accuracies.
Using Digital Layers to Evoke Nostalgia in Hybrid Gifting

Gifting has consistently served many economic and social functions, enabling a way to convey sentiment to others. In digital gifting, the absence of physical elements in the exchange can make it difficult to communicate this meaning. Hybrid gifting combines both physical and digital gifting, enabling a paradigm to explore how digital content can be embedded in a meaningful way. Specifically, this PhD focuses on integrating the complex emotion of nostalgia via digital technology into hybrid gift exchanges. Both gifting and nostalgia provide similar social benefits, although the two areas are rarely explored together. The aim is to understand how the two concepts can be combined to create new experiences, as well as exploring the commercial practicalities of nostalgic hybrid gifts.
Pandemic Pivot: Designing a Participatory Simulation to Support Social Distancing and Remote Learning

Participatory simulations usually aim to bring simulations off screen into a shared physical space with people acting as agents in the simulation. In this paper, we describe considerations and design decisions related to creating a participatory simulation for use in learning settings with restrictions imposed due to the COVID-19 pandemic where typical classroom interactions were no longer allowed. We describe how our design decisions might help children both “dive in” and “step out” to understand more about pollinators and the prairie in spite of various restrictions on how exactly they can interact with each other. Our simulation, Buzz About, uses augmented reality in a multi-device setting to allow learners to explore the impacts of a prairie restoration on local beneficial pollinators from the perspective of a bee. We focus on supporting scenarios where the children may be in a shared space with social-distancing requirements or participating remotely, but synchronously.
Distinguishing and Key Recovery Attacks on the Reduced-Round SNOW-V

This paper proposes distinguishing and key recovery attacks on the reduced-round versions of the SNOW-V stream cipher. First, we construct a MILP model to search for integral characteristics using the division property, and find the best integral distinguisher in the 3-, 4-, and 5-round versions with time complexities of \(2^{8}\), \(2^{16}\), and \(2^{48}\), respectively. Next, we construct a bit-level MILP model to efficiently search for differential characteristics, and find the best differential characteristics in the 3- and 4-round versions. These characteristics lead to the 3- and 4-round differential distinguishers with time complexities of \(2^{17}\) and \(2^{97}\), respectively. Then, we consider single-bit and dual-bit differential cryptanalysis, which is inspired by the existing study on Salsa and ChaCha. By carefully choosing the IV values and differences, we observe the best bit-wise differential biases with \(2^{-1.733}\) and \(2^{-17.934}\) in the 4- and 5-round versions, respectively. This is feasible to construct a very practical distinguisher with a time complexity of \(2^{4.466}\) for the 4-round version, and a distinguisher with a time complexity of at least \(2^{36.868}\) for the 5-round version. Finally, we improve the existing differential attack based on probabilistic neutral bits, which is also inspired by the existing study on Salsa and ChaCha. As a result, we present the best key recovery attack on the 4-round version with a time complexity of \(2^{153.97}\) and data complexity of \(2^{26.96}\). Consequently, we significantly improve the existing best attacks in the initialization phase by the designers.
Kurdish Spoken Dialect Recognition Using X-Vector Speaker Embedding

This paper presents a dialect recognition system for the Kurdish language using speaker embedding. Two main goals are followed in this research: first, we investigate the availability of dialect information in speaker embedding, then this information is used for spoken dialect recognition in the Kurdish language. Second, we introduce a public dataset for Kurdish spoken dialect recognition named Zar. The Zar dataset comprises 16,385 utterances in 49 h-36 min for five dialects of the Kurdish language (Northern Kurdish, Central Kurdish, Southern Kurdish, Hawrami, and Zazaki). The dialect recognition is done with x-vector speaker embedding which is trained for speaker recognition using Voxceleb1 and Voxceleb2 datasets. After that, the extracted x-vectors are used to train support vector machine (SVM) and decision tree classifiers for dialect recognition. The results are compared with an i-vector system that is trained specifically for Kurdish spoken dialect recognition. In both systems (i-vector and x-vector), the SVM classifier with 87% of precision results in better performance. Our results show that the information preserved in the speaker embedding can be used for automatic dialect recognition.
Synergistic Social Technology: Designing Systems with ‘Needs’ that Encourage and Support Social Interaction

In this paper, we propose a strong concept for interaction design: Synergistic Social Technology (SST). This concept describes systems in which technology is designed with its own ‘need’ for interaction. As a result of responding to these needs, people who use the system may benefit from social interaction with others who use the system. This concept arose through the design, prototyping, and study of a social wearable design that we called Robo-Shoe-Flies. We articulate the core principles of the SST concept. We also describe the Research-through-Design process that inspired its development and associated design-focused observations. This work may inspire those in the IxD or HCI communities focused on the design and development of technology intended to support social interaction, particularly in the sense of encouraging collective, mutually-beneficial action.
Organization of work of education volunteers online

This article explorers the concept of online volunteering, the historical background of its emergence and distribution, the current state of this phenomenon. The essence of the project “Online Education Volunteers”, which is being implemented on the basis of the IKBFU (Kaliningrad, Russia), is revealed. The project is based on the ideas of the cluster approach in education. The stages of project organization are described in detail. The results of a survey of volunteers regarding their attitude to online education are presented. As a result, risks were foreseen that hinder the introduction of online volunteering. The features of testing the program and the organization of reflexive practices based on the results of the project are revealed. As a result, the general goals of online education were identified, contributing to productive interaction and the main areas of training online volunteers. It is concluded that the implementation of the project makes it possible to create an information-rich educational online environment based on the principles of network interaction and social partnership, to implement a cluster approach to build a new educational space, including further joint development of developmental programs, practices and educational relationships with an emphasis on personal development and self-realization of both schoolchildren and students. The tools proposed in the article stimulated the emergence of some pedagogical precedents, which can become the basis for further research and methodological development.