Reviewerly/exHarmony huggingface dataset:
The Channel Between Perception and Cognition Is Perfect: The JND Does Not Exist

In magnitude discrimination tasks (e.g., which ensemble is more numerous?), differences that are more difficult than the ratio specified as the JND (e.g., 8 vs. 9 dots) are sometimes understood to be completely imperceptible. The Just Noticeable Difference (JND) is often defined as the minimum amount of difference between two values required to be perceivable. Importantly, if analog magnitudes, the most common type of representation in visual perception and cognition, are represented as a series of gaussian tuning curves with linearly increasing variability, as is also often assumed, then there should be no ratio other than 1 at which perception transitions qualitatively from “success” to “chance” performance: More boldly, there should be no JND. To test whether subjects can perform above chance at ratios far more difficult than a typical JND, we asked online subjects (N = 207) to complete a series of trials judging which of two ensembles has more dots, where the trials contained increasingly difficult ratios: between 20 vs. 30 dots (ratio 1.5) up to 50 vs. 51 dots (ratio 1.02). We found that subjects were able to successfully discriminate all tested ratios above chance, all ts > 4.6, all ps < .001. That is, we found that people are sensitive to differences even at a ratio of 1.02, which is far below the point typically thought of as a JND. We anticipate that many scientists may already be familiar with these points (e.g., a JND at 75% correct is not a unique datapoint and subjects will be above chance both above and below this JND). But many (perhaps the majority) of scientists have misunderstood the JND to mark a critical boundary below which differences are imperceptible. Our work shows that, given enough trials, any difference in magnitude, no matter how small, will indeed be perceivable.
Accenture at CheckThat! 2021: Interesting claim identification and ranking with contextually sensitive lexical training data augmentation.

This paper discusses the approach used by the Accenture Team for CLEF2021 CheckThat! Lab, Task 1, to identify whether a claim made in social media would be interesting to a wide audience and should be fact-checked. Twitter training and test data were provided in English, Arabic, Spanish, Turkish, and Bulgarian. Claims were to be classified (check-worthy/not check-worthy) and ranked in priority order for the fact-checker. Our method used deep neural network transformer models with contextually sensitive lexical augmentation applied on the supplied training datasets to create additional training samples. This augmentation approach improved the performance for all languages. Overall, our architecture and data augmentation pipeline produced the best submitted system for Arabic, and performance scales according to the quantity of provided training data for English, Spanish, Turkish, and Bulgarian. This paper investigates the deep neural network architectures for each language as well as the provided data to examine why the approach worked so effectively for Arabic, and discusses additional data augmentation measures that should could be useful to this problem.
Mixing and Switching the Codes in Kindergarten Teachers-Students Interaction: A Case Study at Northern Green School

The study of code mixing and code switching has grown in popularity in last decade. The studies of codes on teachers-students interaction, however, were still relatively unexplored. Thus, this study examines code-mixing and code-switching realized in the classroom interaction used by the Kindergarten Teacher in Northern Green School. The study was a qualitative descriptive research using case study. The objective of the study was to analyze the types of the codes and the factors that caused the occurrence of code-mixing and code-switching.  The subjects in the study were 2 teachers and 9 students of first level of the Kindergarten. The data were gained through observation and recording. The results showed that the code-mixing and code-switching used by the teachers in the classroom interaction based on the form in many differences, such as code-mixing in the form of insertion, alternation, and congruent lexicalization. While code-switching is in the form of tag switching, inter-sentential switching and intra-sentential switching. The reason of using code-mixing and code-switching in the learning process was due to the conversational habit and the language background of Indonesia language while the school is as a national school plus using English as a means of communication.
Low-Noise Intensity Amplification of a Bright Entangled Beam

We experimentally demonstrate a low-noise phase-sensitive amplifier (PSA) scheme that is able to amplify bright entangled beams at a high level intensity gain of up to 4.4. Moreover, we demonstrate that the PSA scheme introduces much less uncorrelated extra noise to the entangled state than the phase-insensitive amplifier scheme with the same intensity gain. This PSA scheme has potential applications for quantum communication in continuous variable regimes.
Theoretical bases of introduction of internet technologies in the conditions of digitalization of institutions of higher education

The article reveals the scientific and theoretical foundations, structures, classification and types of Internet technologies in higher education; the features of orientations and attitudes of subjects of university education on the use of Internet technologies in modern conditions are highlighted; studied the audience of Internet users in the universities of Ukraine; revealed the substantive and procedural aspects of the joint pedagogical activity of the teacher and students in the creation and implementation of Internet projects; methodological recommendations were developed for the implementation of Internet resources in the teacher's practical activities.
Emerging Malware Threats

The cyberthreat landscape is constantly shifting as malicious actors find novel ways to take advantage of the security, infrastructure, and social weaknesses that arise. Ingenuity and simply trial-and-error are the driving forces, fueled by the end goal of extracting as much information or valuables as possible with the lowest risk for the attacker. The internet organized crime threat assessment (IOCTA), with an annual report provided by Europol, aims to identify the threat landscape for cybercrimes. The IOCTA report has characterized ransomware as a top priority threat and the most dominant one in cyberspace for three consecutive years. This chapter provides an overview of the current landscape and the driving force behind a large number of documented ransomware attacks: the financial gain. For ransomware and malware, no other means of propagation besides exploiting system vulnerability or using social engineering exists. The chapter explains the jurisdictional and judicial issues ransomware legislation faces.
REVIEW OF ESTIMATION METHODS AND SYSTEMS USED TO PREDICT EARTHQUAKE CASUALTIES

Earthquake casualty estimation is key to earthquake resistance and hazard prevention planning prior to earthquakes and emergency rescue efforts after earthquakes. Scholars in the fields of earthquake engineering, geophysics, sociology, public health etc. have performed multi-perspective studies on this issue. Earthquake casualty assessment models are divided into global/regional models and local models. According to their relevant factors, they are categorized based on the appropriate seismic parameters and building fragility factors, and some researchers additionally consider the influence of secondary disasters and population characteristics. Following an introduction to the importance of earthquake casualty assessment work, traditional casualty estimation methods, and emerging methods combined with artificial intelligence, big data and spatial information technology are summarized in this review. The framework, technical foundation and application of earthquake loss estimation systems are introduced, and future development trends are envisioned.
Leveraging Multi-domain, Heterogeneous Data using Deep Multitask Learning for Hate Speech Detection.

With the exponential rise in user-generated web content on social media, the proliferation of abusive languages towards an individual or a group across the different sections of the internet is also rapidly increasing. It is very challenging for human moderators to identify the offensive contents and filter those out. Deep neural networks have shown promise with reasonable accuracy for hate speech detection and allied applications. However, the classifiers are heavily dependent on the size and quality of the training data. Such a high-quality large data set is not easy to obtain. Moreover, the existing data sets that have emerged in recent times are not created following the same annotation guidelines and are often concerned with different types and sub-types related to hate. To solve this data sparsity problem, and to obtain more global representative features, we propose a Convolution Neural Network (CNN) based multi-task learning models (MTLs)\footnote{code is available at this https URL} to leverage information from multiple sources. Empirical analysis performed on three benchmark datasets shows the efficacy of the proposed approach with the significant improvement in accuracy and F-score to obtain state-of-the-art performance with respect to the existing systems.
Smart Technologies and Social Impact

The global impact of ongoing COVID-19 pandemic on the Internet of Things (IoT) market is expected to increase rapidly. Social distancing methods adopted by people will promote more usage of smart payment technologies to reduce human contact, which will eventually lead to market growth. IoT plays a crucial role in the healthcare sector as new technology is being adapted for the purpose of decreasing human contact and increasing the facility available to people across the globe. The contact tracing apps for coronavirus like BlueTrace, Arogya Setu, Trace Together, Covidwatch, HaMagen, the Corona DataSpende, Covid Symptom tracker, NHS smartphone app help in remote patient monitoring and providing information about the affected people nearby to keep themselves aware and safe. Also, such apps provide information regarding the tracing of contact with an affected person, syndromic mapping, and self-assessment. A lot of industries are affected by COVID-19, such as software solutions, manufacturing, transport, travel, and hospitality. Smart technologies provide a better perspective for health improvement and ease of access but, at the same time, they create a digital divide among people during this crisis time. Smart technologies have a great impact on social behavior. This chapter presents an in-depth survey of the smart technologies for a pandemic and their impact on society.
ROLE OF FORENSIC SCIENCE IN CRIMINAL JUSTICE SYSTEM

Foreign science is a science applied for the law enforcement. It is a multi professional and multi disciplinary functions. It plays an important role and all the principles stand very important in the scene of crime. Depending upon the crime the role of investigation is different scene of crime is different from the mapping it is a place when the incident or crime has occurred. The chapter provides an analysis of forensic evidences from serious crimes homicides, sexual assault, aggravated assault, robberies, and burglaries that occurred. The primary study objectives were to determine forensic evidence is collected, what get analyzed and what the impact of the evidence are outcomes. This study also need of forensic science and principles of forensic science. Fundamentals of forensic science are organized roughly along the timeline of a real case. Forensic science describes the science of associating people, places and things involved in the criminal activities. Death investigation is the most important assignment given to an investigating officer as murder is the most heinous crime.
Sentiment Emotion Scoring for Real Time Tweets

Sentimental analysis is the most interesting topic on social media and utilized in web applications. Sentiment analysis is all about expressing their emotions and opinions on a specific topic or on the actual product within the sort of reviews and tweets. Sentiment analysis can express their emotions within the sort of positive, negative or within the sort of neutral. Some of the benefits of sentiment analysis are we can develop a more insightful, databased marketing strategy. Nothing beats data-based strategy, understand between customers, we can easily find the industry leaders and influencers etc. In our proposed system, the dataset chosen for sentiment classification is recent apple mobile dataset from real time tweets . We examine sentiment classification on Twitter data. In this paper (1) We introduce real time tweets. (2) We explore subjective information on real time tweets which includes opinions, attitudes, emotions, and feelings. It improves accuracy and solves several sentiment assessment challenges.
AN EXPERT SYSTEM FOR DETECTION AND PREVENTION OF SOCIAL ENGINEERING ATTACKS USING MACHINE LEARNING ALGORITHM

Social engineering is the ART OF CONVENCING PEOPLE TO REVEAL THERE INFORMATION .social engineers depend on the fact that people are unaware of there valuable information and not care about there personal information also careless about protecting that information.one of the type of social engineering is insider attack where insider intentionally violating rule of the organization where he is working this sort of attack generally done by third party or terminated employee for there financial gain or revenge or sometime for future competitors reason can be anything like mentioned In social engineering it uses other tactics to get people's attention to achieve sensitive information for a variety of purposes. Theft of sensitive information is a form of social engineering attack. Theft of sensitive information is a criminal act of obtaining personal information by sending fake emails with fake websites and fake weblinks on web pages. The purpose of this study is to develop a system that will find URLs that steal sensitive information from a web page and separate the URL whether it is a valid or illegal URL using a machine learning algorithm. The Prevailing conditions during COVID-19, more number of peoples started moving to Internet medium for their everyday activities. This have increased in the presence of people on the Internet is almost never preceded by education about cyber security and various types of attacks. Social engineering attacks are a group of sophisticated cyber security attack that exploit the innate huma nature to breach securing systems and thus have some of the highest rate of success. The research is to explore into the particulars of how COVID-19 pandemic has set the stage for an increase in social engineering attack, the possible method of identifying the attacks technologies and mitigate them. Key Words: Phishing URL, Legitimate URL, Machine Learning, Logistic Regression, Prediction.
Black Money Monitoring With Secured Cloud Data Storage With Big Data Analysis Using Block Chain

A challenge in such scenarios is that cloud vendors may offer varying and possibly incompatible ways to isolate and interconnect virtual machines located in different cloud networks. Our approach is tenant driven in the sense that the tenant provides its connectivity mechanism. We are implementing Blockchain concept in this project. We implement both Public and Private cloud data storage, Private is for sensitive data storage and public cloud normal data storage. We implement this concept for banking system, to identify overall user behaviour with personal identification. Integration of all his / her transactions like Banking, Land Registrations, Gold Purchase or any cash transactions more than Rs. 20k is accounted and monitored.
The influence of parents socio-economic conditions on student achievement in junior high schools

The research aims to identify, describe, and analyze whether or not the influence of the socio-economic conditions of parents on student achievement in Junior High Schools in Kedaton Peninjauan Raya District. This type of research is quantitative research. Population in this research as many as 464 students. A research sample of around 216 students was established by using the sampling technique proportional random sampling using the formula of slovin 5% of significance. The technique of Data collection by using questionnaires and documentation. Then data analysis using linear regression analysis with SPSS program. The result showed that the socio-economic conditions of parents affect student achievement. This paper is expected to provide directions and solutions for educational institutions to carry out education properly and appropriately by following the environmental conditions of the community in improving the quality of education.
The Strategic Role of Information Technology on Organization Financial Performance: A Case of XYZ Paper Industry Limited, Kenya

The digital revolution has given mankind the ability to treat information with mathematical precision, to transmit it at very high accuracy and to manipulate it at will. Information Technology innovations have the potential for changing the competitive game for any organization. This is because it helps to catalyze efficiency in the provision of better procedures resulting in better production and services. This study aimed at determining the strategic role of Information Technology on organization financial performance, a case of XYZ Paper Industry Limited, Kenya. The objectives were to find out the effect of Information Technology on financial performance of XYZ Paper Industry Limited and to assess the challenges facing the embracement of Information Technology XYZ Paper Industry Limited. The study used descriptive research design and used 51 who were drawn from various departments of XYZ Paper Industry Limited. The study made use of both secondary (literature review) and primary data. Primary data was collected by means of questionnaires and analysed using Statistical Package for the Social Sciences (SPSS). It was found that Information Technology plays a strategic role in the company,which results in improved financial performance of the company. A few challenges that affected the adoption of Information Technology were also highlighted such as budget of the company, government regulations, attitude of the staff, changes of Information Technology platform (obsolescence) among other were found to be challenges Information Technology faces. This research concluded that I.T indeed has a stategic role in the improvement of financial performance.Kewords: Strategic Role of Information Technology, Organization Financial Performance, XYZ Paper Industry Limited, Kenya
Object completion with stochastic completion fields predicts human behavior in recognizing degraded object drawings

Biederman & Cooper (1991) showed that observers were better at classifying degraded line drawings of objects when shown only contour junctions than when shown only middle segments. We here provide an account of these results based on a low-level algorithm for object completion. The human visual system infers geometry not only for visible but also for occluded contours and surfaces. This provides a central theme to figural completion which lays out a computational framework to estimate paths that connect a set of boundary fragments. In our model, we use the Fokker-Planck Equation to extract a set of points with assigned orientations. These points represent the sources and sinks for a stochastic completion field (SCF) algorithm (Williams and Jacobs, 1995). The SCF algorithm produces a distribution of possible completion fields, where each field is a probability density function (PDF) that enables us to score each possible completion path. We tested the algorithm on the Snodgrass and Vanderwart (1980) dataset of 260 manually traced line drawings of objects. The traced objects were separated into two half images; one half with contour segments containing junctions, and the other with segments between junctions. We attempted to complete the half-drawings using the SCF algorithm. The completions replicated the original, intact drawings more faithfully for the half-drawings with junctions than those with middle segments. Our computational results show that contour completion is easier in objects with junctions than with middle segments, which aligns with Biederman & Cooper's behavioural result. Ultimately, the SCF is a method that is potentially useful for predicting which types of incomplete line drawings can be more easily completed by the human visual system. Moreover, SCF may form the computational basis for an image-computable implementation of the Good Continuation Gestalt grouping rule.
Research on the Training of New Engineering Talents in Electronic Information Engineering under the Background of Xiongan New Construction Area Construction

The construction of the Xiongan New Area is a major strategic deployment made by the CPC Central Committee to promote the coordinated development of the Beijing—Tianjin—Hebei region. It is a grand mission of a millennium and a major national event. Higher talents play an important role in social and economic development, and it is of important practical value to cultivate higher talents starting from the needs of social development. The construction of the Xiongan New Area plays an important role in the coordinated development of the Beijing—Tianjin—Hebei region, but the development of the new area needs to pay attention to talent training and application. The development of the Xiongan New Area will inevitably increase the demand for talents. Universities in the Beijing—Tianjin—Hebei region will become an important talent training base for the construction of the new area. Under the background of the Xiongan New Area, economic development challenges the talent training of traditional electronic information engineering majors. Actively layout the talent training in the future technology industry, and reform the talent training mode and practical content. This paper initially explores the training of new engineering talents from three aspects of the teaching system, curriculum teaching standards and training place, to promote the reform and innovation of the electronic information engineering major. The research results can be extended to the electrical engineering specialty.
Novel Authentication Mechanism Using Blockchain

The World Wide Web lacks an overall identity platform. The problem of people creating fake accounts and performing illegal and unethical activities has been evident ever since its origin. Given the ease with which fake accounts can be created on social media platforms, it becomes extremely difficult to find the culprit. Currently, spam and fake account creation is prevented using email and mobile verification. An email or an SMS containing a one-time password is sent to the user’s email address or mobile number.  The OTP is used to verify a user's account and identify. However, there is no proper way in which an email can link to one particular person. One user can have multiple email addresses too and mobile numbers, hence causing the whole system to fail. The ease with which temporary emails are created proves to be another bottleneck in the system. This can be solved using Unique Identification Number verification on an Ethereum based blockchain platform. The user registers on the network using his Aadhaar number, which is then verified by the admin. Once verified, the user can use his account address to sign up, authenticate, and log in to various social media platforms. Unless the user’s account is verified and his social media apps are authenticated, he cannot sign up on a new app, and if his current application is not authenticated, he cannot sign in to that either.