Reviewerly/exHarmony huggingface dataset:
LightDefender: Protecting PIN Input using Ambient Light Sensor

Nowadays, personal identification number (PIN) is one of the most popular methods for identity verification. However, recent researches show that attackers can easily recover victims’ PINs in spite of the large number of combinations PIN provides. Existing protection approaches require alteration of the original interaction between the user and PIN-based authentication systems, or still fail if the attacker can observe and mimic the victim’s input behavior. Considering these limitations, we propose a defense system called LightDefender to protect current PIN-based systems from PIN replay attacks using a single ambient light sensor. Specifically, we protect the PIN input by leveraging the biometrics in the received light intensity that is influenced by input behaviors and biological features. To our best knowledge, our work is the first one to protect PIN input using the light intensity. Different from existing approaches, LightDefender does not change the original interaction methods between the user and PIN-based authentication systems, and the extra hardware cost is low. In addition, by leveraging biological differences (e.g. finger length) among different users, LightDefender still claims high-security protection against strong attackers who can mimic the victim’s input behaviors. Experiments with 10 volunteers show that LightDefender can achieve an average true acceptance rate of 95% for normal users. More importantly, LightDefender can correctly reject two types attackers with an average true rejection rate of at least 93.6% without data of new attackers.
Improving students' motivation and self-confidence in speaking using mingling games

This study aims to improve the students' intrinsic motivation and self-confidence in speaking by using mingling games. This study was carried out in one of the junior high schools in Bengkayang county. It employed a classroom action research method. The study was done in two cycles involving one English teacher as a collaborator and 24 junior high school students as the subject of the research. The data collection techniques were gained through interviews, observation, document, and questionnaire. The instruments used are interview guidelines, field notes, observation checklist, lesson plans, and questionnaire guidelines. Since the research is classroom action research, the research procedure includes four stages: planning, acting, observing and reflecting. Before starting the procedure, a preliminary study was conducted to find the problem. The finding reveals that the implementation of mingling games is successful to improve the students’ motivation and self-confidence in speaking practice. The information gained from this study can be worthwhile considerations for teachers to enhance their knowledge dealing with a teaching strategy in improving the students' intrinsic motivation and self-confidence in speaking. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp
The Impact of Romanian Diacritics on Intent Detection and Slot Filling

This paper aims to provide an empirical analysis of the effect of Romanian diacritics on the performance of two well known frameworks for intent detection and slot filling: the RASA framework and Facebook's Wit.ai API. We use a previously defined home assistant scenario and datasets, which we modify to include diacritics. The results of several alternative processing pipelines in the two frameworks suggest that diacritics introduce a new level on learning complexity, in addition to the ones considered initially - synonymy, missing slot values and class imbalance - and they contribute to augmenting the effect of those complexities on the performance of the learning models. Moreover, the slot filling task seems to be the most affected by this aspect, with a drop in performance as high as ~30% for RASA and more that 40% for Wit.ai.
Analysing COVID-19 News Impact on Social Media Aggregation

The pandemic coronavirus so called COVID-19 outbreak, which was recognized in late 2019, requires special consciousness because of its future epidemics and possible worldwide menace.Besides its clinical approaches and treatments.As Artificial Intelligence (AI) assures a new prototype for healthcare.There are various AI tools that are built upon Machine Learning (ML) algorithms are included to examine the data and decision-making processes.People spend many hours every-day on social media websites to share their views, ideas, opinions, and expressions with others, so in this paper, I have analysed the sentiments on reddit news regarding coronavirus disease(COVID-19) jeopardy, because most of the peoples from various countries are affected by coronavirus that is very condemnatory issue at present days, so analyze the sentiments of various people's opinion for this disease, we are fetching the reddit streaming articles connected to coronavirus using reddit API and scrutinized these articles using machine learning methods and tools as positive, negative and neutral.In this paper, I have run experiments through Python programming on various articles related to pandemic corona virus using reddit API and NLTK library is used for pre-processing of articles and then scrutinizing the articles dataset by using Textblob and it pointers to stimulating outcomes as positive, negative, neutral sentiments over different conceptions.The outcomes show that on reddit related to COVID-19 articles about 50% percentage articles are neutral, 22% articles are positive and about 28 % was negative.
Detecting the Usage of Vulgar Words in Cyberbully Activities from Twitter

Nowadays, nearly all people utilize the device which is connected to Internet. People are accustomed to the use information technology devices in their daily life to interact with other people. Currently, many social media platforms such as Facebook, Twitter, Instagram, and YouTube are becoming popular. This study selected Twitter platforms, which is started to gain popularity. By the rapid growth of users signing up for Twitter accounts, at the same time, cybercrime started to bloom each year in social media platforms. Cyberbully is one of the cybercrime practices which had caused a significant impact on the targeted victims. The victims experienced social pressure, which they need to bear each day while the bullies stayed free behind the veil of anonymity. This study aims to identify the common vulgar words used by the cyberbullies on Twitter. Also, this study is subject to produce essential features of Twitter based on the collected tweets. The evaluation in this study includes the occurrences of the vulgar word perpetrated by the cyberbullies from Twitter. This study detected the usage of vulgar words in cyberbully activities on Twitter platform. A list of vulgar words were extracted and evaluated from a corpus of 50 Twitter users who posted a various number of tweets. The vulgar words detection in the tweets enable the tracking process of the cyberbully activities. In the evaluation section, we discussed how the usage of the vulgar words would define the user’s earnestness in doing the cyberbully activities in the Twitter. This study shows there are users with a low number of tweets have a high number of vulgar words occurrences, while other users with high numbers of tweets but less number of vulgar words occurrences. The information collected in this study is expected to assist marking users with a high number of vulgar words occurrences who tend to have high possibilities in doing cyber-bully activities.
Diagnostic performance of artificial intelligence to detect genetic diseases with facial phenotypes

Background: Many genetic diseases are known to have distinctive facial phenotypes, which are highly informative to provide an opportunity for automated detection. However, the diagnostic performance of artificial intelligence to identify genetic diseases with facial phenotypes requires further investigation. The objectives of this systematic review and meta-analysis are to evaluate the diagnostic accuracy of artificial intelligence to identify the genetic diseases with face phenotypes and then find the best algorithm. Methods: The systematic review will be conducted in accordance with the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols” guidelines. The following electronic databases will be searched: PubMed, Web of Science, IEEE, Ovid, Cochrane Library, EMBASE and China National Knowledge Infrastructure. Two reviewers will screen and select the titles and abstracts of the studies retrieved independently during the database searches and perform full-text reviews and extract available data. The main outcome measures include diagnostic accuracy, as defined by accuracy, recall, specificity, and precision. The descriptive forest plot and summary receiver operating characteristic curves will be used to represent the performance of diagnostic tests. Subgroup analysis will be performed for different algorithms aided diagnosis tests. The quality of study characteristics and methodology will be assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Data will be synthesized by RevMan 5.3 and Meta-disc 1.4 software. Results: The findings of this systematic review and meta-analysis will be disseminated in a relevant peer-reviewed journal and academic presentations. Conclusion: To our knowledge, there have not been any systematic review or meta-analysis relating to diagnosis performance of artificial intelligence in identifying the genetic diseases with face phenotypes. The findings would provide evidence to formulate a comprehensive understanding of applications using artificial intelligence in identifying the genetic diseases with face phenotypes and add considerable value in the future of precision medicine. OSF Registration: DOI 10.17605/OSF.IO/P9KUH.
Analysis of Accuracy Improvement in K-Nearest Neighbor using Principal Component Analysis (PCA)

Abstract This research conducted to simplify and eliminate attributes or features that are less relevant without reducing the intent and purpose of the original data using Principal Component Analysis (PCA) to improve the performance (accuracy) of the K-NN (K-Nearest Neighbour) classification method. Modifying the method yields an average accuracy of 88%, where the K value is at K = 3 to K = 9. While the lowest accuracy value generated from K-Nearest Neighbors Conventional with PCA + K-NN classification models for the Pekanbaru Air Quality dataset has an average accuracy of 87% where when the K value is at K = 2, K = 4, K = 6 and K = 8. The highest average error is 0.11% where when the value of K is at K = 2, K = 4, K = 6 and K = 9. While the lowest average error value generated from K-Nearest Neighbors Conventional with PCA + K-NN classification model of the Pekanbaru Air Quality dataset is 0.08% where when the K value was at K = 5.
Internet of Thing Wireless Sensor Entity Authentication Scheme Based on Merkle

IoT is a highly integrated and comprehensive application of the new generation of information network technology. It is an important development direction of the new round of industrial reform and an important driving force for the restructuring of the world industrial pattern. As the perception layer of IoT, the security of wireless sensor network is more important. Based on the study of Merkle tree structure and characteristics as well as the block chain technology, this paper proposes a Merkle based entity authentication scheme to reduce the complexity of traditional schemes, and optimizes the traversal of traditional Merkle tree, which has a good improvement in time complexity.
Web Vulnerability Assessment Tool for Content Management System

Using web application through internet communication has become an important part of our lives.Every day we interact with a large number of custom-built web applications that been implemented using different technologies.Prior to the introduction of web standards, developers faced the problem of browser incompatibility.The highly heterogeneous nature of the web with its different implementation languages, encoding standards, browsers and scripting environments makes it difficult for web application developers to properly secure their applications and stay up-to-date with emerging threats and newly discovered attacks.Web vulnerabilities assessment tools have become a necessity due to the increased security threats posed by hackers and other criminals who are on the lookout for confidential information.In this research, we proposed web assessment tool called SNEAKERZ that detected vulnerability and automatically analyzed exploitable web vulnerabilities and proposed solutions for that vulnerability.Our proposed solutions looked for security loopholes in web applications to prevent hackers from gaining unauthorized access to corporate information and data.
The Influence of Privacy and Security on the Future of IoT

Internet of things (IoT) is a hot topic for study in the last decades.IoT is the internet working of physical devices, vehicles and other objects which consists of an embedded system with sensors, actuators and network connectivity that enable to collect and exchange data.The IoT permits articles to be detected as well as controlled remotely across existing system foundation, making open doors for more coordination of the physical world into PC based frameworks, and result in improved accuracy, efficiency and economic benefit.The IoT is a rapidly increasing and promising technology which becomes more and more present in our everyday lives.Furthermore, the technology is an instance of the more general class of cyber-physical systems, which also encompasses technologies such as smart grids, smart homes and smart cities. Considering the high-rate improvement of IoT advancements, and the critical addition in the quantity of the associated gadgets, complete diagram of the IoT framework points, design, challenges, applications, conventions, and market outline were examined.In order to give an example of IoT security.
A New Hope: Human-Centric Cybersecurity Research Embedded Within Organizations

Humans are and have been the weakest link in the cybersecurity chain (e.g., [1–3]). Not all systems are adequately protected and even for those that are, individuals can still fall prey to cyber-attack attempts (e.g., phishing, malware, ransomware) that occasionally break through, and/or engage in other cyber risky behaviors (e.g., not adequately securing devices) that put even the most secure systems at risk. Such susceptibility can be due to one or a number of factors, including individual differences, environmental factors, maladaptive behaviors, and influence techniques. This is particularly concerning at an organizational level where the costs of a successful cyber-attack can be colossal (e.g., financial, safety, reputational). Cyber criminals’ intent on infiltrating organization accounts/networks to inflict damage, steal data, and/or make financial gains will continue to try and exploit these human vulnerabilities unless we are able to act fast and do something about them. Is there any hope for human resistance? We argue that technological solutions alone rooted in software and hardware will not win this battle. The ‘human’ element of any digital system is as important to its enduring security posture. More research is needed to better understand human cybersecurity vulnerabilities within organizations. This will inform the development of methods (including those rooted in HCI) to decrease cyber risky and enhance cyber safe decisions and behaviors: to fight back, showing how humans, with the right support, can be the best line of cybersecurity defense. In this paper, we assert that in order to achieve the highest positive impactful benefits from such research efforts, more human-centric cybersecurity research needs to be conducted with expert teams embedded within industrial organizations driving forward the research. This cannot be an issue addressed through laboratory-based research alone. Industrial organizations need to move towards more holistic – human- and systems- centric – cybersecurity research and solutions that will create safer and more secure employees and organizations; working in harmony to better defend against cyber-attack attempts. One such example is the Airbus Accelerator in Human-Centric Cyber Security (H2CS), which is discussed as a case study example within the current paper.
Multi-Scan Implementation of the Trajectory Poisson Multi-Bernoulli Mixture Filter

The Poisson multi-Bernoulli mixture (PMBM) and the multi-Bernoulli mixture (MBM) are two multitarget distributions for which closed-form filtering recursions exist. The PMBM has a Poisson birth process, whereas the MBM has a multi-Bernoulli birth process. This paper considers a recently developed formulation of the multitarget tracking problem using a random finite set of trajectories, through which the track continuity is explicitly established. A multiscan trajectory PMBM filter and a multiscan trajectory MBM filter, with the ability to correct past data association decisions to improve current decisions, are presented. In addition, a multiscan trajectory MBM01 filter, in which the existence probabilities of all Bernoulli components are either 0 or 1, is presented. This paper proposes an efficient implementation that performs track-oriented N-scan pruning to limit computational complexity, and uses dual decomposition to solve the involved multiframe assignment problem. The performance of the presented multitarget trackers, applied with an efficient fixed-lag smoothing method, is evaluated in a simulation study.
Phonetic and functional features of pauses, and concurrent gestures, in tourist guides’ speech

This study falls into the bigger framework of the CHROME project, addressing the definition and testing of a methodology of collecting, analyzing and modeling multimodal data for the design of virtual agents serving in museums. The paper analyses three tourist guides’ speech, focusing on silent pauses and voiced pauses (filled pauses and segmental prolongations). In this regard, a description of phonetic-acoustic and functional features of pauses and a classification of concomitant gestures have been performed. Results show a) speakers’ idiosyncratic linguistic behaviours; b) a clear distinction between silent pauses, mainly used for grammatical and intentional reasons, and voiced pauses that instead occur as ungrammatical and hesitation devices; c) such a distinction is confirmed by concomitant gestures, they are semantically loaded in silent pauses and semantically empty in voiced pauses.
Neutrosophic Modeling of Talcott Parsons’s Action and Decision-Making Applications for It

The grand theory of action of Parsons has an important place in social theories. Furthermore, there are many uncertainties in the theory of Parsons. Classical math logic is often insufficient to explain these uncertainties. In this study, we explain the grand theory of action of Parsons in neutrosociology for the first time. Thus, we achieve a more effective way of dealing with the uncertainties in the theory of Parsons as in all social theories. We obtain a similarity measure for single-valued neutrosophic numbers. In addition, we show that this measure of similarity satisfies the similarity measure conditions. By making use of this similarity measure, we obtain applications that allow finding the ideal society in the theory of Parsons within the theory of neutrosociology. In addition, we compare the results we obtained with the data in this study with the results of the similarity measures previously defined. Thus, we have checked the appropriateness of the decision-making application that we obtained.
Visual Odometry Implementation and Accuracy Evaluation Based on Real-time Appearance-based Mapping

With the rapid development of artificial intelligence and machine learning technology, various robots serving humans have emerged.The positioning and navigation technology of robots has become a research hotspot.Robots relying on visual odometry (VO) are favored by people for their low price and wide range of applications.In this paper, we focus on the algorithm and implementation of VO based on the feature point method.Firstly, the current mainstream feature detection algorithms are compared and analyzed in terms of real-time performance and time efficiency.The random sample consensus algorithm is used to eliminate the mismatch in the image feature matching process.Secondly, using the Kinect vision sensor and the Turtlebot mobile robot system to build the RGB-D simultaneous localization and mapping (SLAM) experimental platform, the real-time appearance-based mapping (RTAB-MAP)-based VO is realized.Finally, to verify the actual running performance of the VO, a series of motion experiments were performed in an indoor environment.The accuracy of the pose estimation of the VO was evaluated, which provides a useful reference for the development of mobile robot positioning technology.
A Research Review on Indian Sign Language Recognition using Machine Learning

Sign language is the best communication media to interact with the deaf and dumb people. As sign language is completely different from our regular languages, someone has to learn that language and they are called as sign language interpreters. As per the survey of Indian Sign Language Research and Training Center (ISLRTC), there are around 466 million people worldwide with hearing loss and 34 million of these are youth. If we will think about India, only 250 interpreters are available from India, roughly 1 for 20284 deaf and dumb people. So, education of deaf and dumb people is very big question as interpreters, schools/colleges/any learning institutes available for them are very few and not available at every place. So, this problem can be solved by having online educational system for deaf and dumb people. Because it is one of the important purposes of this system that system should be accessed by deaf and dumb people from anywhere or at any place.  Already online systems are available for deaf and dumb people. But problem in existing systems is that, more focus is given on just character recognition, gesture recognition etc.  But I would like to contribute more additional work regarding educational courses (ex. like NPTEL, Coursera, Udemy) for deaf and dumb people instead of just gesture recognition in existing systems. So, more focus is given on Dynamic gesture recognition and Web-based technology for the implantation point of view. As well as vision-based method is chosen for implementation as it is cheaper and more comfortable for user than sensor-based method. As per the Persons with Disability Act 1995, one percent jobs in government and private sectors are reserved for them. But those jobs have conditions like people with disability should have degree certificates from known universities across India. With the help of this application, they can learn as well as can give exams using mobile or laptop camera. If they will clear the exams successfully then certificates will be provided to them. On the basis of those certificates, they can get a job as they will get certified education. This system itself can test the deaf and dumb users in exams as it is using Machine Learning instead of any external help of sign language interpreters. Various machine learning algorithms like Support Vector Machine (SVM) and Artificial Neural Network (ANN), Hidden Markov Model (HMM), Convolutional Neural Network (CNN), Finite State Machine (FSM) are used in existing systems.