Reviewerly/exHarmony huggingface dataset:
Interactive ATM For Visually Challenged

In Digital India ATM is used for financial transactions namely deposits, withdrawals, transfer funds and also it provides multiple facilities such as pay income tax, apply for personal loan, pay insurance premium at anytime without the help of bank authorities. People uses smartcard method for access control in ATM. But, it's difficult to check third person using some other's card, as well smartcard may be theft, missing or duplicated. The debit card issued through bank is activated by insertion of earphone jack into the ATM slot and exit the card to end the transaction. Since, it's not easy for them to put in a headphone into the jack by identifying the slot and to position the device. To defeat these complications, a device is provided with presenter direction to attain the ATM and with an earpiece arrangement for contact. At beginning stage of this device an user can access the door by handprint authentication. In the second stage of security requires the voice code recognition which verifies the user's voice password with already taped code. When it matches correctly, the user is termed as a legal person.
On Architecture of BodyInNumbers Exercise and Wellness Health Strategy Framework

They are many risk factors decreasing overall human physical and cognitive performance and increasing incidence of chronic diseases. It is very beneficial for any society to map, discuss and cope with these factors. This can be supported and evaluated by designing, developing, testing and using suitable self-management health systems. One of these systems is the BodyInNumbers exercise and wellness health strategy framework that allows experimenters to collect various heterogeneous health related data in a highly organized and efficient way. Thanks to its success and daily use, new requirements related to better security, scalability and maintainability of its architecture have emerged. The aim of this work is to present advances and changes in the architecture of the BodyInNumbers health strategy framework mainly focusing on new definition of user roles, optimization of the system deployment, and orchestration of the system components. As a proof of concept, a Kubernetes cluster prototype has been used to demonstrate the improved architectural solution.
PhishBench 2.0: A Versatile and Extendable Benchmarking Framework for Phishing

We describe version 2.0 of our benchmarking framework, PhishBench. With the addition of the ability to dynamically load features, metrics, and classifiers, our new and improved framework allows researchers to rapidly evaluate new features and methods for machine-learning based phishing detection. Researchers can compare under identical circumstances their contributions with numerous built-in features, ranking methods, and classifiers used in the literature with the right evaluation metrics. We will demonstrate PhishBench 2.0 and compare it against at least two other automated ML systems.
A New Blind Invisible and Semi-Fragile Colour Image Watermarking Scheme in Spatial Domain

As copyright protection is vital and indispensable, watermarking schemes are employed for securing digital data. We propose a novel (to the best of our knowledge) blind and invisible, semi-fragile image watermarking scheme for embedding coloured watermark inside a coloured cover image. The spatial domain embedding is implemented in such a way that modification made in the pixel values of the cover image is not noticeable by human perception. However, the watermark pixel values can be recovered using a unique decoding mechanism, which has been included in the paper. Extraction of the original cover image using the embedded image is also discussed. The obtained MATLAB implementation results are promising when compared to similar state-of-the-art research work.
Lexicometric Paradoxes of Frequency: Comparing VoBIS and NVdB

An emblematic “anti-spoken” canon, the linguistic variety offered as the standard model in Italian schools following national unification, while showing some signs of evolution over time, has remained relatively artificial and resistant to change. The lexical frequencies documented for scholastic Italian can therefore be intrinsically unaligned with those of the base vocabulary as well as with data for apparently similar varieties of Italian. This implies a need for interpretive models that assess quantitative data in the light of the complex paradigmatic relations among potential competing usages as well as the multi-layered connections between the number and type of meanings observed across different contexts of use. In this paper, I review the scholastic Italian modelled by teachers in the first 150 years after unification, with a view to assessing the strengths and weaknesses of applying lexicometric parameters to a linguistic variety that was targeted at an inexpert audience but specialist in nature, informed by lofty ideals but conditioned by practical educational needs, and constantly evolving yet resistant to the pull of contemporary living varieties.
The Combination of Foot Switch and Low-Cost IMU for a Wearable Mouse in Human-computer Interaction

A wearable mouse using upper arm movement/gesture is a promising pointing device that can be used by people with lower-arm disablement due to accident or prenatal disablement. The movement of the upper arm could be used to substitute the cursor movement. The use of an inertial measurement system is one way to capture the arm’s movement in 3 dimensional spaces. The purposes of this study are the development of a new wearable mouse using the nine degree-of-freedom of inertial measurement unit and a foot switch for clicking action; and the evaluation of its performance on a specified level of difficulties. The device was evaluated by comparing the proposed device and a standard mouse. The performance indicators were throughput, movement time, comfort, and fatigue, based on Fitts’ Law formula. The important finding is a significant (p < 0.01) difference in throughput between each level of difficulty in the proposed device by means of the target’s distances and diameter/width in graphical user input design. The other finding showed no neck fatigue related differences between the proposed device and a mouse.
Label Aggregation of Gradient Boosting Decision Trees

Gradient boosting decision tree (GBDT) is one of the most widely used ensemble learning methods in both academia and industry. The core idea of GBDT is to consecutively fit new base learners to the residual errors between true and predicted values. However, GBDT is sensitive to noise in the data, especially label noise, which is very common in real-world data. Besides, due to the strong learning ability of GBDT, it is not easy to address this problem with simple ensemble method such as bagging. Thus we propose a new ensemble model of GBDT, called label aggregation of GBDTs (LA-GBDTs). The key points of our method lie on the random feature subspace and label aggregation, which lead to computational efficiency and robustness to noise. Experiments on several benchmark datasets show that label aggregation of GBDTs outperforms GBDT algorithms consistently in terms of accuracy. In addition, LA-GBDTs work well on datasets with a certain degree of label noise.
Localization from activity sensor data

In this work, we show that sensor data may leak a sensor's location even if the latter is not explicitly included in the data set. The sensors are localized by linking sensor data, in particular activity data, with publicly available environmental data such as weather data. We show that using a linkage attack a cow can be localized within an entire country with an average accuracy of up to 32.6 km solely from activity traces recorded with a tracker in the cow's stomach.
Decentralized Privacy Protection Approach for Video Surveillance Service

Cloud-based video surveillance service is being deployed ubiquitously, and the service normally stores video streams in the cloud over the Internet. The main problem of this service is that the user never knows how secure the video stream is transferred and stored. Although the video contains sensitive data that is related to personal or area-specific information, there is no way to objectively check whether the video is securely managed. Therefore, a user must trust that the service provider will keep their video secure without any evidence. Ensuring confidential information and privacy is protected, the sensitive data should be encrypted and traceable when they are transferred. In this paper, an architecture to build a key management system based on blockchain and public-key cryptography is proposed. The video encryption is possible by a block-cipher algorithm with a given symmetric key. In the proposed system, the symmetric key is also encrypted by a public key and stored as a ledger in the blockchain. Thus, when the symmetric key is requested, every operation is traceable, so a user can trust how a video stream is transferred and stored securely because of the nature of blockchain, decentralization, transparency, and the impossibility of being tempered. In addition, the user can monitor the request flow of the key, and then, the key is selectively distributable by the user’s intention. Therefore, the system meets the requirement of a fully secure method to manage the whole life cycle of video from creation to deletion. In this paper, we propose to apply this architecture to video surveillance service and also extend them to general-purpose systems.
Improvement of forecasting and classification in smart metering systems using a neural compute stick

Analyzing data on smart meters is a trend increasingly used by utility companies as it allows a better understanding of data directly from the source of origin. New distributed computing architectures like edge computing have given advance to improve data analytics. Generally, the capacity of such devices, including smart meters, is quite limited, so the use of specialized auxiliary hardware has begun to be used in these devices. The present work shows the results of using a neural stick compute for forecasting and data classification processes within smart metering systems. The results show that the processing times can be remarkably improved with the use of stick computers having a suitable model for artificial neural networks.
Information Mediation and Its Role in the Development of Complex Societies

Mediation has a fundamental role in the development of complex societies, since it interferes consciously or unconsciously in the way the subjects interact with and appropriate information. There are similarities between the theory of complexity and the conception of information adopted in this chapter; they are uncertainty and disorder. Both complexity and information are intertwined with these factors. Information, much more than satisfying a need or filling an information gap, raises new doubts and generates new information conflicts. The information mediation, in turn, seeks, among other purposes, to resolve such conflicts and aims for the subject to appropriate the information. Therefore, mediation needs a more complex and holistic discussion, considering the different factors and elements linked and resulting from it.
Strengthening human-computer interfaces: an automatic construction and evaluation of an annotated corpus

The human-computer interfaces exploits the annotated corpora to identify the meaning of the statements during dialogue management. The construction of an annotated corpus is an arduous task, usually manual involving high costs and limitations on the number of texts to annotate. This paper presents the automatic construction and evaluation of an annotated corpora for strengthening human-computer interfaces. The proposal is inexpensive and independent of domain information; therefore, it applies to other contexts with a low investment of resources.
Cost-Effective and Practical Countermeasure against the Template Side Channel Attack

One of the most efficient attacks for exploiting the secret key is a template attack. Recent researches show that regular countermeasures against these attacks are costly. Randomized shuffling in the time domain is known as a cost-effective countermeasures against side-channel attacks are widely used. In this article, we implemented a real template attack and proposed an efficient countermeasure against it. We practically evaluated security improvement with this method in the AES algorithm on a template attack and reported the analysis results of our countermeasure. According to these results, template attack will be hardened significantly after the proposed protection such that the grade of the key recovery increases from 1 with 350K traces in unprotected design to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">100</sup> with 700K traces in the protected circuit. This security improvement gains in the cost of about 7% delay overhead.
Conformal transformation of the metric for k-nearest neighbors classification

The paper introduces a new method for improving the nearest neighbors classifier. Our approach is based on the idea of replacing the constant metric with a variable and conformally equivalent one that is data dependent, and therefore it is more informative. We define a family of conformal transformations that, under some assumptions, induces distance functions that are efficiently computable. Using the intuition that the distances between points near a class boundary should be larger, a simple method for selecting a transformation is proposed.We perform experiments on two datasets. The first set of experiments are with a sentiment prediction dataset, and in this case our method offers some improvements over the standard k-NN algorithm. In the second empirical analysis, we apply the method to a news categorisation problem. In this case the results are mixed.We conclude with a discussion of the advantages and weaknesses of the method, and propose a number of possible improvements.
Machine Learning Method Based on Stream Homomorphic Encryption Computing

This study proposes a machine learning method based on stream homomorphic encryption computing for improving security and reducing computational time. A case study of mobile positioning based on k nearest neighbors ( kNN) is selected to evaluate the proposed method. The results showed the proposed method can save computational resources than others.
Identification of monogeneans parasite using gray level co-occurrence matrix and artificial neural network

Abstract flatworms that are parasitic and attack the skin and gills of fish are called monogenean parasites. In this study 3 monogenic parasites were used, namely grandis, liewi, and johorensis. GLCM (Grey Level Co-occurrence Matrix) used in digital image processing to obtain feature extraction on images. ANN (Artificial Neural Network) used to classification mongenean parasites based on features extracted. This research resulted overall accuracy of 86,67%. Studying monogenean parasites is needed because it is related to the health of fish that we need in our daily lives, so that this research can ease and assist the work of ecologists in classifying monogenean parasites more quickly and efficiently.
Post-Quantum cryptographic schemes library for Android operating system

Nowadays the Institute of Standards and Technology (NIST) goes in the third round of the standardization process of asymmetric key post-quantum cryptographic schemes. Such schemes are said to be resistant to attacks from quantum computers. With this, the need to start experimenting and implementing with these schemes has arisen. In addition to that, there is a variety of post-quantum cryptographic schemes libraries written in different programming languages and compiled for different architectures. However, in order to a programmer can integrate them into an Android application, it is necessary to compile the source code, perform the integration and test this last one. As a consequence it is not possible to have a ready-to-use library. Based on the aforementioned, in this paper we present the design of a post-quantum scheme library for Android operating system that has the characteristic to be ready-to-use. Moreover, obtained results shows our library has the three main aspects that make a library good.
Based on Microblog Emotions Analysis to Study the Impact of the Sade Event on the Chinese and South Korean Stock Markets

Social media plays an increasingly important role in people's lives, transforming people from passive recipients to active creators of information and influencing all aspects of society in a unique way. Behavioral finance studies have shown that stock investors are prone to making decisions that are not in line with expectations due to external emotions. This paper studies the impact of the Sade incident on the economy of China and South Korea, and studies and predicts the change of the overall stock prices in China and Korea through the microblog emotions related to Sade events. This paper uses text mining technologies to analyze the text of the related microblog of Sade incident from July 2016 to May 2017 in Sina Weibo. The time series of emotional evaluation index and emotional tendency of blog posts are generated. Based on VAR model and Granger analysis, the correlation between the SSE/KOSPI index and the emotional tendency is tested, and further the support vector machine model, optimized by the particle swarm optimization, is established to predict the price change of the stock market in China and South Korea, and to analyze the influence of the Sade event on China and Korea. The experimental results show that the SVM model achieves higher prediction accuracy after introducing the emotional feature. The accuracy of the forecast in South Korea is especially significant, indicating that the impact of the Sade event on the Korean stock market is more severe than that in China.
A Case Study for Clustering Characteristics Evaluation and Clustering Dimension Selection by Using UCI News Data

The purpose of this study is to research and explore the clustering algorithm, and provide methods for clustering characteristics evaluation and clustering dimension selection, in order to help the user to understand the impact and meaning of clustering parameters and data dimensions on clustering, thereby strengthening the use of clustering algorithm. In previous studies, many scholars have proposed various types of clustering algorithms. Most of these algorithms need to set the clustering parameters, and the selection of clustering parameters will affect the results after clustering. Therefore, the user must fully understand the meaning of clustering parameters for clustering and select appropriate clustering parameters for clustering, then the clustering algorithm can be effectively used to help solve decision-making problems. Based on the above factors, this study focus on doing further analysis and description on the meaning of the clustering data distribution & the meaning of parameters to the clusters, and the relationship among the clusters, find out the important clustering feature and propose a new clustering evaluation formula, and expect to assist the decision-maker to find appropriate clustering parameters effectively.
Copyright and protection of scientific results: the experience of Russia, the United States and the countries of the Near East

Abstract In this article, the authors analyze the legal regulation of the copyright protection of the results of scientific activity in Russia, the United States and the countries of the Near East. Considerable attention is paid to the review of key regulatory acts of the states operating in the designated area, as well as international treaties affecting aspects of the copyright protection of intellectual rights in the field of science. The authors consider the main ways of protecting the scientific results by means of copyright. Special attention is paid to the analysis of the judicial practice of the states, which plays a vital role in defining approaches to the legal regulation of the scientific results. The authors emphasized the similarity and difference between the systems of copyright protection of the results of scientific activity, the role of the judiciary in the functioning of such systems. In the end the conclusion is made about the prospects for harmonization of the approaches to the legal regulation of the results of scientific activity by means of copyright. The article will be relevant to practicing lawyers, researchers, students and everyone who is interested in IP law.
Heterogeneous-Graph-Based Video Search Reranking Using Topic Relevance

In this paper, we address the problem of analyzing topics, included in a social video group, to improve the retrieval performance of videos. Unlike previous methods that focused on an individual visual aspect of videos, the proposed method aims to leverage the “mutual reinforcement” of heterogeneous modalities such as tags and users associated with video on the Internet. To represent multiple types of relationships between each heterogeneous modality, the proposed method constructs three subgraphs: user-tag, video-video, and video-tag graphs. We combine the three types of graphs to obtain a heterogeneous graph. Then the extraction of latent features, i.e., topics, becomes feasible by applying graph-based soft clustering to the heterogeneous graph. By estimating the membership of each grouped cluster for each video, the proposed method defines a new video similarity measure. Since the understanding of video content is enhanced by exploiting latent features obtained from different types of data that complement each other, the performance of visual reranking is improved by the proposed method. Results of experiments on a video dataset that consists of YouTube-8M videos show the effectiveness of the proposed method, which achieves a 24.3% improvement in terms of the mean normalized discounted cumulative gain in a search ranking task compared with the baseline method.
FEAST'20: Fifth Workshop on Forming an Ecosystem Around Software Transformation

The Fifth Workshop on Forming an Ecosystem Around Software Transformation (FEAST) provides a forum for presentation and discussion of new tools, methodologies, and techniques facilitating the automated or semi-automated transformation and analysis of software executables for improving their security and efficiency without the benefit of any original source code whence they were developed. Late-stage software customization of this form is of particular benefit to security-conscious software consumers who must use closed-source or source-free binary software components in mission-critical settings, or who must harden software against newly emerging attacks not anticipated during the software's original design and development. However, code analysis and transformation becomes much more difficult without the aid of source-level information to provide a context for its intended operation. This outstanding challenge motivates the FEAST Workshop's goal of forming a robust ecosystem of strategies and tools for accomplishing source-free binary code transformation reliably and on-demand.
DISTANCE EDUCATION IN BULGARIA DURING COVID-19 IN A SMALL EDUCATIONAL ORGANIZATION – METHODOLOGY AND TOOLS

DISTANCE EDUCATION IN BULGARIA DURING COVID-19 IN A SMALL EDUCATIONAL ORGANIZATION – METHODOLOGY AND TOOLS
Stroke Correction in Interactive Arabic Handwriting Learning System

In this research, we present a method for locating and correcting broken strokes in Arabic handwritten words. The method is applied in an interactive Arabic online handwriting learning system. In such a system, a word written by a learner is matched with the model word having the same characters or shapes. The learner is given feedback on his writing by locating writing errors in the word he wrote. Locating errors in handwritten strokes is itself a challenging task, as broken and/or touching characters may appear. In this paper, we present our progress in this field to identify the broken strokes in Arabic online handwritten words based on n-gram models of stroke descriptors.