Reviewerly/exHarmony huggingface dataset:
PIM: A PUF-Based Host Tracking Protocol for Privacy Aware Contact Tracing in Crowded Areas

COVID-19 pandemic has spread very fast across the world. This has been a surprise to everyone and shown how unprepared the world was for such a pandemic. Tracking the host carrying the virus has been one of the major issues during this pandemic. Many potential solutions were proposed for tracking the host but the privacy and security of the people being tracked are at stake in this case. This article proposes physical unclonable function (PUF)-based privacy-aware identification module, an Internet-of-Medical-Things (IoMT) architecture to track the host carrying the virus in a crowded area. The privacy and security of the host or the person carrying the IoMT device is given the highest priority and the data being transmitted do not contain any data related to the person carrying the device. A new multikey PUF architecture is also proposed in this article to generate unique identification keys for the proposed protocol. The average time taken to generate the key is 1 s. The keys generated have a uniqueness and reliability of 49.49% and 48.20%. The proposed protocol is independent of a communication, which makes it adaptable across multiple platforms.
Contactless Human Activity Analysis: An Overview of Different Modalities

Human Activity Analysis (HAA) is a prominent research field in this modern era which has enlightened us with the opportunities of monitoring regular activities or the surrounding environment as per our desire. In recent times, Contactless Human Activity Analysis (CHAA) has added a new dimension in this domain as these systems perform without any wearable device or any kind of physical contact with the user. We have analyzed different modalities of CHAA and arranged them into three major categories: RF-based, sound-based, and vision-based modalities. In this chapter, we have presented state-of-the-art modalities, frequently faced challenges with some probable solutions, and currently used applications of CHAA with future directions.
Path planning for mobile robots using Morphological Dilation Voronoi Diagram Roadmap algorithm

This paper presents Morphological Dilation Voronoi Diagram Roadmap (MVDRM) algorithm to address unsafe path computation accompanied by high time and space computation complexity problems of roadmap path planning methods in complex environments for mobile robots. Morphological dilation was employed to inflate the obstacles in the environment of the robot before computing the path to ensure safe path computation for the robots. To reduce time and space complexities in computing the path, a scale factor is introduced to provide sparse and uniform distribution of sample nodes in the environment of the robot for the computation of the roadmap. The purpose of this technique is to reduce the sample nodes and computation time to provide fast-path computation to aid robots to make quick decisions to avoid collision with obstacles. Simulation results using maps of different complexities demonstrate better performance of the proposed method compared to Probabilistic Roadmap (PRM) and conventional VD methods in terms of safe path computation as well as time and space computation complexities. Results indicate that the proposed method is 1.69 and 7.03 times faster than conventional VD and PRM methods, respectively. The proposed method is 88.32 times faster than VD and 1.08 times faster than PRM. The path computation success rate is 96.3% better than the PRM method. MVDRM is a promising roadmap path planning method for computing a safe and quick path for autonomous vehicles.
Unobtrusive patient identification using smart pill-bottle systems

Accurately accounting for medication use is important for the efficacy and safety of patients and family members. Monitoring is also important for medication adherence. This work investigates passive identification of persons taking medication using a sensor-equipped pill-bottle. The bottle is equipped with inertial and switch sensors in both the cap and body, making the added hardware unobtrusive, low-cost, and wireless. Our system uses inertial data to build a patient discrimination model using classification techniques. We evaluated the system using two datasets that we collected from 36 subjects. Our results show that using binary Support Vector Machine (SVM), the system can discriminate one individual among 3 people with > 90% accuracy and recall, and has > 80% accuracy and recall, using a single sensor. Using the same technique, our system has 85% accuracy and 72% recall for discriminating one subject in a population of 16. Identifying the exact person in a set of 3 subjects has an accuracy higher than 91%. We also show that we can infer the correct class for a previously unseen subject in a group of 3 subjects by using one-class SVM, with 75% average overall accuracy and 83% average recall.
Adaptive elitist-ant system for solving combinatorial optimization problems

This work investigates the effects achieved by using an adaptive elite pool over a basic population-based approach, such as, the ant system approach, to different combinatorial optimization problems (COPs). Optimization methods are generally considered a good methodology for solving COPs, while population-based approaches are considered better for exploring the search space and local-based approaches are considered better for exploiting the search space. However, some of these approaches are also hybridized in order to balance between exploiting and exploring the search space. Using an adaptive elite pool offers further balance while also adaptively tuning the importance parameter. Taken together, these three processes may improve and enhance the efficiency of any given search. In this work, three COPs (i.e. CVRP, STSP and 0-1 MKP) are used as test domains. In order to evaluate the effectiveness of using an adaptive elite pool, a comparison is made among the original ant system algorithm, the hybrid Elitist-Ant system algorithm, and other approaches drawn from the literature. Experimental results indicate that using an adaptive elite pool enhances the algorithm and is often able to produce better-quality solutions than other approaches. Where the adaptive elitist-ant system approach obtained 10 out of 14 best known results for CVRP, 20 out of 29 optimal solution results for STSP, 9 out of 11 best known results for 0-1 MKP, and generally better than other individuals approaches in term of better, worse, and similar results.
The Implementation of Artificial Intelligence in Social Media Marketing and Its Impact on Consumer Behavior: Evidence from Bahrain

Businesses and brands have taken advantage of social media to reach a wider market. AI has made it easier for businesses to know what people are talking about, what people want all thanks to AI's analysis on finding the most discussed about topics and summarizing it into trending lists to get a better understanding on consumer behaviors. The aim of this research is to discuss how Artificial Intelligence is used in social media, as well as how can business use this information to market their products. This research will also answer the question Does AI have an impact on consumer behavior? To find help find the results the method used in this survey is a questioner. At the end of this research the finding were they AI has an effect of consumer behavior.
An empirical study on clone consistency prediction based on machine learning

Code Clones have been accepted as a common phenomenon in software, thanks to the increasing demand for rapid production of software. The existence of code clones is recognized by developers in the form of clone group, which includes several pieces of clone fragments that are similar to one another. A change in one of these clone fragments may indicate necessary "consistent changes" are required for the rest of the clones within the same group, which can increase extra maintenance costs. A failure in making such consistent change when it is necessary is commonly known as a "clone consistency-defect", which can adversely impact software maintainability. Predicting the need for "clone consistent changes" after successful clone-creating or clone-changing operations can help developers maintain clone changes effectively, avoid consistency-defects and reduce maintenance cost. In this work, we use several sets of attributes in two scenarios of clone operations (clone-creating and clone-changing), and conduct an empirical study on five different machine-learning methods to assess each of their clone consistency predictability — whether any one of the clone operations will require or be free of clone consistency maintenance in future. We perform our experiments on eight open-source projects. Our study shows that such predictions can be reasonably effective both for clone-creating and changing operating instances. We also investigate the use of five different machine-learning methods for predictions and show that our selected features are effective in predicting the needs of consistency-maintenance across all selected machine-learning methods. The empirical study conducted here demonstrates that the models developed by different machine-learning methods with the specified sets of attributes have the ability to perform clone-consistency prediction.
Word Sense Disambiguation for Marathi Language Using WordNet and the Lesk Approach

Word sense disambiguation (WSD) means finding the correct sense of a word in a specific context. Ambiguity is an open problem of natural language processing and ontology. It is crucial to minimize the ambiguity for the applications: machine translation, information extraction, knowledge acquisition, question answering system and sentiment analysis. Many researchers have worked on automatic WSD for the English language, but the work carried out on Marathi language requires more attention. This paper focuses on word sense disambiguation using WordNet and the Lesk algorithm for Marathi Language. It uses the WordNet for Marathi, developed at IIT Mumbai, which is a very important lexical knowledge base for Marathi. The basic idea is to compare the context of the word in a sentence with the contexts constructed from the WordNet and choosing the correct sense. The algorithm has been implemented and tested on a set of sentences containing ambiguous words, and for majority of the sentences, the correct meaning has been inferred by the synset.
A clustering-based adaptive Neighborhood Retrieval Visualizer

We introduce a novel adaptive version of the Neighborhood Retrieval Visualizer (NeRV). We maintain the advantages of the conventional NeRV method, while proposing an improvement of the data samples' neighborhood width calculation, in the input and output data space. In the standard NeRV, the data samples' neighborhood widths are determined in an arbitrary manner, in this way, inhibiting the possible quality of the resulting data visualization. We propose to compute the widths adaptively, on the basis of the input data scattering. Therefore, we first perform the preliminary input data clustering, next, we calculate the values of the inner-cluster variances, which convey the information on the input data scattering, then, we assign them to each data sample, and finally, we use them as the basis for the data samples' neighborhood widths determination. The results of the experiments conducted on the three different real datasets confirm the effectiveness and usefulness of the proposed approach.
CTI-Twitter: Gathering Cyber Threat Intelligence from Twitter using Integrated Supervised and Unsupervised Learning

Cyber threat intelligence (CTI) can be gathered from multiple sources, and Twitter is one such open source platform where a large volume and variety of threat data is shared every day. The automated and timely mining of relevant threat knowledge from this data can be crucial for enrichment of existing threat intelligence platforms to proactively defend against cyber attacks. We propose CTI-Twitter: a novel frame-work combining supervised and unsupervised learning models to collect, process, analyze and generate threat specific knowledge from tweets coming from multiple users. CTI-Twitter has multi-fold contributions: i) first collecting tweets through Twitter API, ii) extracting relevant threat tweets from irrelevant ones, and classifying relevant ones into multiple classes of threats iii) then grouping tweets belonging to each class using topic modeling iv) finally performing data enrichment and verification process. We evaluate our proposed model on real-time tweets collected for about four months (in year 2020) using Twitter API. The encouraging results obtained indicate the effectiveness of CTI-Twitter in terms of timeliness and discovery of trending attacks patterns, and vulnerabilities.
Light-Field-Assisted Phase Unwrapping of Fringe Projection Profilometry

A phase unwrapping algorithm assisted by light field without additional time coding is proposed, which can be used in various aspects of optical imaging and metrology. The phase consistency between the light field and the industrial camera is the sticking point to convert the angular resolution of the light field into the order of phase. By virtue of the angle information recorded by the light field, the correct order can be uniquely determined from several candidate orders without confusion in the measurement volume. The mandatory requirements for system fixation and pre-calibration and the connection between the light field and industrial camera are prerequisites for order uniqueness. The mapping relationship established by the phase implies coordinate conversion, lens distortion and other factors, thus providing a high degree of stability for the transmission of phase order information. The achieved results demonstrate that our method is robust and can be taken as an effective tool for means of phase unwrapping.
Behavior Change in Response to Subreddit Bans and External Events

As more people flock to social media to connect with others and form virtual communities, it is important to research how members of these groups interact to understand human behavior on the Web. In response to an increase in hate speech, harassment, and other antisocial behaviors, many social media companies have implemented different content and user moderation policies. On Reddit, for example, communities, i.e., subreddits, are occasionally banned for violating these policies. We study the effect of these regulatory actions as well as when a community experiences a significant external event such as a political election or a market crash. Overall, we find that most subreddit bans prompt a small, but statistically significant, number of active users to leave the platform; the effect of external events varies with the type of event. We conclude with a discussion on the effectiveness of the bans and wider implications for online content moderation.
Noiseless attack and counterfactual security of quantum key distribution

Counterfactual quantum key distribution (QKD) enables two parties to share a secret key using an interaction-free measurement. Here, we point out that the efficiency of counterfactual QKD protocols can be enhanced by including noncounterfactual bits. This inclusion potentially gives rise to the possibility of noiseless attacks, in which Eve can gain knowledge of the key bits without introducing any errors in the quantum channel. We show how this problem can be resolved in a simple way that naturally leads to the idea of ``counterfactual security,'' whereby the noncounterfactual key bits are indicated to be secure by counterfactual detections. This method of enhancing the key rate is shown to be applicable to various existing quantum counterfactual key distribution protocols, increasing their efficiency without weakening their security.
Forensic analysis of Matrix protocol and Riot.im application

Instant messaging (IM) has been around for decades now. Over the last few decades IM has become more and more popular with varied protocols, both open source and closed source. One of the new recent open source ones is the Matrix protocol with the first stable version released in 2019 and the IM application based on this protocol is "Riot.im". In recent years many organizations started using the Matrix protocol to setup and manage their own IM platforms. In addition, the number of users who are using the public Matrix protocol-based servers is also increasing. However, because the Matrix protocol and the Riot.im application are very new, there is a knowledge gap when it comes to investigators in relation to the forensic acquisition and analysis of Riot.im application and the Matrix protocol. Yet, there is very little research in literature on the Matrix protocol forensics. The goal of this paper is to fill this gap by presenting a forensic approach to analyze forensic artifacts of Riot.im and the Matrix protocol.
Skeleton-Based Activity Recognition: Preprocessing and Approaches

Research in Activity Recognition is one of the thriving areas in the field of computer vision. This development comes into existence by introducing the skeleton-based architectures for action recognition and related research areas. By advancing the research into real-time scenarios, practitioners find it fascinating and challenging to work on human action recognition because of the following core aspects—numerous types of distinct actions, variations in the multimodal datasets, feature extraction, and view adaptiveness. Moreover, hand-crafted features and depth sequence models cannot perform efficiently on the multimodal representations. Consequently, recognizing many action classes by extracting some smart and discriminative features is a daunting task. As a result, deep learning models are adapted to work in the field of skeleton-based action recognition. This chapter entails all the fundamental aspects of skeleton-based action recognition, such as—skeleton tracking, representation, preprocessing techniques, feature extraction, and recognition methods. This chapter can be a beginning point for a researcher who wishes to work in action analysis or recognition based on skeleton joint-points.
SuSketch: Surrogate Models of Gameplay as a Design Assistant

This paper introduces SuSketch, a design tool for first person shooter levels. SuSketch provides the designer with gameplay predictions for two competing players of specific character classes. The interface allows the designer to work side-by-side with an artificially intelligent creator and to receive varied types of feedback such as path information, predicted balance between players in a complete playthrough, or a predicted heatmap of the locations of player deaths. The system also proactively designs alternatives to the level and class pairing, and presents them to the designer as suggestions that improve the predicted balance of the game. SuSketch offers a new way of integrating machine learning into mixed-initiative co-creation tools, as a surrogate of human play trained on a large corpus of artificial playtraces. A user study with 16 game developers indicated that the tool was easy to use, but also highlighted a need to make SuSketch more accessible and more explainable.
Phonetic accommodation in interaction with a virtual language learning tutor: A Wizard-of-Oz study

We present a Wizard-of-Oz experiment examining phonetic accommodation of human interlocutors in the context of human-computer interaction. Forty-two native speakers of German engaged in dynamic spoken interaction with a simulated virtual tutor for learning the German language called Mirabella. Mirabella was controlled by the experimenter and used either natural or hidden Markov model-based synthetic speech to communicate with the participants. In the course of four tasks, the participants' accommodating behavior with respect to wh-question realization and allophonic variation in German was tested. The participants converged to Mirabella with respect to modified wh-question intonation, i.e., rising F0 contour and nuclear pitch accent on the interrogative pronoun, and the allophonic contrast [ɪç] vs. [ɪk] occurring in the word ending 〈-ig〉. They did not accommodate to the allophonic contrast [ɛː] vs. [eː] as a realization of the long vowel 〈-ä-〉. The results did not differ between the experimental groups that communicated with either the natural or the synthetic speech version of Mirabella. Testing the influence of the "Big Five" personality traits on the accommodating behavior revealed a tendency for neuroticism to influence the convergence of question intonation. On the level of individual speakers, we found considerable variation with respect to the degree and direction of accommodation. We conclude that phonetic accommodation on the level of local prosody and segmental pronunciation occurs in users of spoken dialog systems, which could be exploited in the context of computer-assisted language learning.
Differential privacy fuzzy C-means clustering algorithm based on gaussian kernel function

Fuzzy C-means clustering algorithm is one of the typical clustering algorithms in data mining applications. However, due to the sensitive information in the dataset, there is a risk of user privacy being leaked during the clustering process. The fuzzy C-means clustering of differential privacy protection can protect the user’s individual privacy while mining data rules, however, the decline in availability caused by data disturbances is a common problem of these algorithms. Aiming at the problem that the algorithm accuracy is reduced by randomly initializing the membership matrix of fuzzy C-means, in this paper, the maximum distance method is firstly used to determine the initial center point. Then, the gaussian value of the cluster center point is used to calculate the privacy budget allocation ratio. Additionally, Laplace noise is added to complete differential privacy protection. The experimental results demonstrate that the clustering accuracy and effectiveness of the proposed algorithm are higher than baselines under the same privacy protection intensity.
Delineating the successful features of research data management training: a systematic review

Research Data Management (RDM) is an inherently complex area that presents a challenge for institutions to effectively upskill people in RDM and get them to enact RDM best practices. This systematic review aims to provide a reference point for institutions and academic developers to design and deliver effective training for their students and staff to navigate the RDM landscape. After narrowing down from a pool of 1256 articles across 7 databases to 28 articles for analysis, this systematic review provides an overview of available RDM training and identifies three main themes in relation to key features of this RDM training.
Towards Understanding Clustering Problems and Algorithms: An Instance Space Analysis

Various criteria and algorithms can be used for clustering, leading to very distinct outcomes and potential biases towards datasets with certain structures. More generally, the selection of the most effective algorithm to be applied for a given dataset, based on its characteristics, is a problem that has been largely studied in the field of meta-learning. Recent advances in the form of a new methodology known as Instance Space Analysis provide an opportunity to extend such meta-analyses to gain greater visual insights of the relationship between datasets’ characteristics and the performance of different algorithms. The aim of this study is to perform an Instance Space Analysis for the first time for clustering problems and algorithms. As a result, we are able to analyze the impact of the choice of the test instances employed, and the strengths and weaknesses of some popular clustering algorithms, for datasets with different structures.