Reviewerly/exHarmony huggingface dataset:
Differentially Private Learning from Label Proportions

Due to IoT and Industry 4.0, more and more data is collected by sensor nodes, which send their data to a central data lake. This approach results in high data traffic and privacy risk, which we want to address in this paper. Therefore we use an existing Learning from Label Proportions (LLP) algorithm, to use the decentralized properties and extend this approach by applying Differential Privacy to the transferred data. This yields to reduced data transfer and increased privacy.
The True Cost of a Charity Data Breach

Abstract Think twice before you send bulk emails, writes Kingsley Hayes, Head of Data Breach at Keller Lenkner UK. Even a single email can cause a significant data breach, which could result in regulatory action, fines and litigation.
SIGNIFICANT FACTORS INFLUENCING THE MODERN IMAGE OF A COLLEGE TEACHER

The image of a teacher of secondary vocational education is considered. The article determines factors that influence the image and determine the existing gap between the requirements of modern production in the conditions of advanced training and the real capabilities of educational organizations. The importance of proficiency of college teachers in soft-competencies, information and communication technologies, and the development of digital literacy is highlighted.
Design of LED grain image recognition and positioning system

Abstract LED grain image recognition and location technology is an important technology in LED grain manufacturing and sorting process. Whether the LED grain can be accurately identified and positioned will greatly affect the production speed and efficiency of LED grain. At present, the research on this technology in China is in its infancy. In this paper, a LED grain image recognition and location system based on rectangle detection algorithm is proposed to solve the problem of LED grain image recognition and location. The system uses image grayscale, binarization, edge extraction, contour detection algorithm to process the image of the target image, and uses the matching algorithm and rectangular detection algorithm to identify the LED grain, although the time is short but the accuracy is not enough. Rectangle detection uses two methods to determine contour minima, the getMin() function and the OpenCV library method, using the getMin() function is more appropriate. The LED grain image recognition and positioning system designed in this paper can realize the recognition of LED grain in a simple environment, and can accurately calculate the deflection Angle of LED grain, reducing the time of grain quality detection.
Voronoi Diagrams Based Digital Tattoo for Multimedia Data Protection

The most important parameter in detecting errors in a message stream is the selection of the generator polynomial. To overcome the problem of generating a small degree generator polynomial, we propose a new approach to second generation watermarking using the Voronoi Diagrams (VD) and high degree standard polynomials with particular mathematical properties like Cyclic Redundancy Check (CRC-32, CRC-16 and CRC-8) to generate the watermark. The latter is inserted into each region of the image after decomposition of VD. Voronoi decomposition is used because it has good recovery performance compared to similar geometric decomposition algorithms. The Harris detector is used to extract the points of interest (FPs) considered to be seeds to create a Voronoi decomposition of the image. The proposed method can be applicable in the case where the detection of alteration is critical and only certain regions of interest need to be retransmitted if they are altered, as in the case of medical images. The security aspect of our proposed method is achieved by using the RSA public key encryption system to encrypt FPs. The experimental results show that the proposed schemes obtain good outcomes in terms of standard evaluation metrics and comparison to the existing scheme, ensuring the suggested scheme’s uniqueness.
Ontology Based Knowledge Visualization for Domestic Violence Cases

Ontology of any domain formally represents the knowledge based on concepts along with its relationship and properties. Legal ontology has a significant contribution in capturing knowledge of legal domain. The legal professionals analyse legal documents to extract knowledge based on the parameters mentioned in legal sections to prepare their draft. This research work mainly contributes in capturing the knowledge of legal section via ontology. The proposed ontology has mapped the entities and their relationship of textual rule related to Criminal major Act on Domestic Violence as stated in Indian Penal Code Section 498A. It mainly focuses on relevant portions on which legal professionals give major emphasis in Domestic Violence cases. Authors have extracted major entities present in Domestic Violence cases using Natural Language Processing techniques like named entity recognition, regex, etc. and generated a parse tree. The entities and relationship generated by the parse tree is further represented as classes, object properties, instances, etc., using Protege. Authors have tried to represent the concepts and their relationship through ontology visualizer i.e. OntoGraf.
Wearable Multi-Sensor Data Fusion Approach for Human Activity Recognition Using Machine Learning Algorithms

Wearable sensor based human activity recognition (HAR) has a broad range of applications in healthcare, fitness, smart home, and surveillance. In spite of the substantial amount of computational research on HAR, the open challenges in multi-sensor based activity recognition including complex temporal data, identifying discriminative feature vectors from multi-modal data and dimensionality reduction require considerable research attention. Hence, in this paper, using multiresolution time-frequency analysis of received signal strength (RSS) between the wearable sensors, we present a supervised machine learning (ML) based activity recognition framework. The multisensor activity data acquired using the wireless sensor network (WSN) nodes and inertial-sensor embedded in a smartphone are decomposed using the discrete wavelet transform (DWT) and the empirical mode decomposition (EMD) techniques for extracting the prominent feature vector. Using the discriminative statistical features from DWT along with the entropy features from EMD, the four ML classifier models such as support vector machine (SVM), K-nearest neighbour (KNN), ensemble classifier (EC), and decision tree (DT) are trained to classify various human activities. The experimental results assessed using the confusion matrix and parallel coordinate plot (PCP) substantiate that the proposed ML based HAR framework can achieve a maximum classification accuracy of 99.63% and is superior to several state-of-the-art ML techniques.
iLFQA: A Platform for Efficient and Accurate Long-Form Question Answering

We present an efficient and accurate long-form question-answering platform, dubbed iLFQA (i.e., short for intelligent Long-Form Question Answering). The purpose of iLFQA is to function as a platform which accepts unscripted questions and efficiently produces semantically meaningful, explanatory, and accurate long-form responses. iLFQA consists of a number of modules for zero-shot classification, text retrieval, and text generation to generate answers to questions based on an open-domain knowledge base. iLFQA is unique in the question answering space because it is an example of a deployable and efficient long-form question answering system. Question answering systems exist in many forms, but long-form question answering remains relatively unexplored, and to the best of our knowledge none of the existing long-form question answering systems are shown to be sufficiently efficient to be deployable. We have made the source code and implementation details of iLFQA available for the benefit of researchers and practitioners in this field. With this demonstration, we present iLFQA as an open-domain, deployable, and accurate open-source long-form question answering platform.
Smart Solar Data-Logger System

This paper depicts the modeling of hardware and software integration of a smart solar-data logger system (SSDLS). It is principally fabricated for Photo Voltaic (PV) module designers, who can gather essential data from these devices and store data in a cloud network. Solar energy is a dependable renewable energy source that is both ecologically efficacious and capable of alleviating the power shortage. For the prototype designing, a 20-watt PV array is used to convert the sunlight to DC. Arduino UNO is used as a controller of this device. The voltage sensor senses the DC voltage, and the temperature sensor senses the temperature of the PV array. Collected essential data is displayed in LCD as well as stored in the memory unit. The real-time clock is used for showing the exact date and time. These essential data can also find in cloud networks. An external DC power supply is added for biasing the Arduino circuit.
Generalized Net Model of Implementation of Port Knocking on RouterOS

The main goal of the development is to study the information processes taking place in computer networks and network security by modelling them with Generalized networks. This will allow to monitor and analyse the ongoing processes in computer networks the possibilities for building network connections the processes of attack when exchanging information, as well as increasing security in the network. The results obtained are the basis of scientifically based recommendations related to the implementation of computer networks, increasing network security and protection of confidential information transmitted through the social network. The need for network security in today’s increasingly digital and corporate world is now a must. In practice, all companies or organizations are forced to provide Internet services to their users. In the present research we develop Generalized Net [1, 3] models of the network security feature of port knocking, as a security builder for MikroTik communication devices via the RouterOS operating system. The practical application of generalized networks is a direction of research in the field of generalized networks, together with the theoretical studies of the connections between generalized networks and other concepts. Indexed matrices are used to describe the states of the token [2].
Image segmentation application combining moving grid method and level set method

Intensity inhomogeneity is widely present in real images. When there is intensity inhomogeneity in the image, the curve evolution will be guided by the wrong target edge, which seriously affects the efficiency and accuracy of image segmentation. Aiming at the intensity inhomogeneity distribution in the medical image, this paper proposes an image segmentation algorithm that combines moving grid method and level set method. The grid is encrypted at the uneven intensity level to make the image uniform and applied to the image segmentation. By calculating a monitor function about image intensity gradient, this paper makes grid encryption automatically implemented with the change of image gradient. This makes it more accurate to calculate image segmentation at uneven intensity. In order to improve efficiency, this paper will reduce the grid to 1/4. Then the level set method is applied to achieve computational acceleration. The final results show that the model in this paper has high segmentation accuracy and efficiency for intensity inhomogeneity images.
Stacked multi-modal refining and fusion network for visual entailment

Recently, Visual Entailment is proposed as a new task in the multi-modal field. Its main focus is to reason about entailment relations between a real-world image as a premise and a natural language as a hypothesis. Some papers have proposed models to obtain more accurate entailment relation judgments. However, these models do not consider the semantics of text at both global and local granularity, and the refining of the two modalities is not sufficient. In this paper, a new stacked multi-modal refining and fusion network is proposed. For cross-sniffing and key information activation between global and local features of hypothesis-sentence, a Global & Local Textual Features Fusion block is introduced. Secondly, a Refining and Affine Fusion block is proposed to achieve efficient multi-modal attention and fusion between image and text features. Finally, this paper presents a stacked structured network which embedded an adaptive hypothesis-preserving mechanism to enriching the grounds for semantic implication judgements. The experiments demonstrate that our model has a certain improvement in the accuracy of visual entailment classification compared with some existing methods in this field.
A Front-End Framework Selection Assistance System with Customizable Quantification Indicators Based on Analysis of Repository and Community Data

Front-end frameworks are in increasing demand in web application development. However, it is difficult to compare them manually because of their rapid evolution and big variety. Prior research has revealed several indicators that developers consider important when selecting a framework. In this study, we propose and develop a system that assists developers in the selection process of a front-end framework, which collects data from repository and user community, such as GitHub and other sources, and quantifies a set of indicators. This system is built as a web application, and users can specify the importance of an indicator by adjusting the weight for each indicator. As a result of semi-structured interviews with front-end developers after using our system based on practical scenarios, we found that the proposed system is effective for narrowing down the framework and has practicality.
Digital Learning is the New of Education Face

The existence of the coronavirus in the world today makes education evolve in all aspects.The Ministry of Education and Culture orders online learning as a solution for implementing learning at all levels of education.This paper is a part of a position paper that aims to explain the author's arguments related to the issue of digital learning.This paper seeks to influence the reader that our argument is credible.The author's argument is described in several parts: the current state of education, digital learning; digital learning innovations in several countries; and what essential points must be prepared in digital learning as the new face of education.
Smart Technologies and Gender: A Never-Ending Story

In this chapter, we explore gender issues in/of so-called smart technologies, drawing on analytical tools provided by the field of feminist technoscience. We begin by discussing some major approaches of this area of study, including the work of Cynthia Cockburn, Wendy Faulkner, and Donna Haraway. Our review continues by addressing the issue of gendering of smart technologies and how the gendering has harmful consequences. The we investigate the sources of gender bias, exploring computer science and engineering as a world of and for men. We conclude by describing the gendering of smart technologies as the result of a co-production of the lack of women in the communities developing these technologies and the lack of concern of these communities with the interests and needs of women. This co-production appears to be stabilised to the extent that harmful gendering will continue to characterise smart technologies.
Developing the Real Estate Rental Sector in Third World Countries Using Blockchain Technology

Blockchain technology represents an attractive factor for sectors that are interested in managing and keeping their transactions on a database that records all the movements that take place on each transaction. Therefore, to keep pace with the digital transformation taking place in this field of the real estate sector, this chapter aims to submit a proposal in the Iraqi real estate sector to implement this technology. In addition, the chapter clarified the current mechanism adopted in Iraq in completing real estate rental transactions and the problems that accompany this method. Then, the authors explained the advantages provided by the application of blockchain technology through the smart contract, in addition to eliminating the problems of tax evasion in this sector. Despite the negative points that this technology suffers from, it still represents a safe environment for preserving, managing, and exchanging information.
A roadmap towards determining the universal status of semantic frames

The Berkeley FrameNet project, founded in 1997, organizes the lexicon of English by semantic frames (Fillmore 1982), with valence information derived from attested, manually annotated corpus examples. The resulting FrameNet database contains more than one thousand frames, together with more than twelve thousand lexical unites and close to 200,000 annotated example sentences. FrameNet data have been used to answer a variety of empirical research questions on the mapping from semantics to syntax and they have been employed in a number of NLP tasks such as role labeling and text summarization. Since the early 2000s, several projects have re-used the semantic frames based on English for constructing FrameNets for other languages, most notably Spanish, Japanese, German, and Swedish, among others. While the tools, corpora, and databases differ from each other, the main organizing principle, the semantic frame, used for structuring the lexicon remains similar across all the FrameNets for different languages. The motivation for re-using semantic frames from English for other languages is the idea that frames are universal, similar to Fillmore’s original case roles. However, there has not yet been any empirical investigation into what constitutes “universal” frames or how one can possibly determine the universal status of semantic frames. This paper proposes a systematic method for identifying semantic frames that could be labeled “universal” (based only on data from languages under investigation). We specifically address the question of how semantic frames can be used for contrastive analysis.
AI Methods Used for Real-Time Clean Fraud Detection in Instant Payment

Traditionally, financial institutions rely on rules to prevent the approval of fraudulent payment authorization, and banks could take their time to process a payment transaction. According to protocols, the lifecycle of a transaction might take even days before it’s fully processed. The Central Banks developed instant payment systems to reduce the time it takes to process a payment authorization. These technologies drastically boost the speed at which payments are made and received, which increase the demand for a real-time fraud detection model; in this paper, we will go throw the promising researches related to machine learning and artificial intelligence models to detect fraudulent transactions in real-time, then we will answer the following question, are the proposed methods aligned with the norms and suit to be implemented in the real world? (*) finally, we will go throw critical aspects during the authorization process to classify the proposed solutions based on eligibility to the payment industry and performance.
Efficacy of Transfer Learning Over Semantic Segmentation

Semantic Segmentation is amongst the most difficult and important tasks in the field of computer vision. Used in the field of medical imaging, automated vehicles, geo-sensing, etc., the main idea of semantic segmentation is to link each pixel to a class label. Like other computer vision tasks, it too requires highly powerful and computational resources to produce good results. Also, most of the machine learning algorithms assume that the training, as well as the future data, will be in the same feature space, but, in real-world applications, the assumption does not hold. To resolve this problem, in this paper we have implemented the idea of transfer learning over semantic segmentation to study its efficacy. The paper also proposes some encoder-decoder framework-based models developed using transfer learning that perform at least as good as a fully supervised model. The paper concludes that transfer learning serves as a readily effective solution to enhance supervised learning models when properly carried out.
Faster Final Exponentiation on the KSS18 Curve

The final exponentiation affects the efficiency of pairing computations especially on pairing-friendly curves with high embedding degree. We propose an efficient method for computing the hard part of the final exponentiation on the KSS18 curve at the 192-bit security level. Implementations indicate that the computation of the final exponentiation is 8.74% faster than the previously fastest result.
Localization of Schema.org for Manuscript Description in the Iranian-Islamic Information Context

This study aims to assess the localization of Schema.org for manuscript description in the Iranian-Islamic information context using documentary and qualitative content analysis. The schema.org introduces schemas for different Web content objects so as to generate structured data. Given that the structure of Schema.org is ontological, the inheritance of the manuscript types from the properties of their parent types, as well as the localization and description of the specific properties of the manuscripts in the Iranian-Islamic information context were investigated in order to improve their indexability and semantic visibility in the Web search engines. The proposed properties specific to the manuscript type and the six proposed properties to be added to the “CreativeWork” type are found to be consistent with other schema properties. In turn, these properties lead to the localization of the existing schema for the manuscript type compatibility with the Iranian-Islamic information context. This schema is also applicable to centers with published records on the Web, and if markup with these properties, their indexability and semantic visibility in Web search engines increases accordingly. The generation of structured data in the Web environment through this schema is deemed to promote the concept of the Semantic Web, and make data and knowledge retrieval easier.
Exploration in Recommender Systems

In the era of increasing choices, recommender systems are becoming indispensable in helping users navigate the million or billion pieces of content on recommendation platforms. Most of the recommender systems are powered by ML models trained on a large amount of user-item interaction data. Such a setup however induces a strong feedback loop that creates the rich gets richer phenomenon where head contents are getting more and more exposure while tail and fresh contents are not discovered. At the same time, it pigeonholes users to contents they are already familiar with. We believe exploration is key to break away from the feedback loop and to optimize long term user experience on recommendation platforms.