Reviewerly/exHarmony huggingface dataset:
Conflict-Free Access Rules for Sharing Smart Patient Health Records

With an increasing trend in personalised healthcare provision across Europe, we need solutions to enable the secure transnational sharing of medical records, establishing granular access rights to personal patient data. Access rules can establish what should be accessible by whom for how long, and comply with collective regulatory frameworks, such as the European General Data Protection Regulation (GDPR). The challenge is to design and implement such systems integrating novel technologies like Blockchain and Data Lake to enhance security and access control. The blockchain module must deal with adequate policies and algorithms to guarantee that no data leaks occur when authorising data retrieval requests. The data lake module tackles the need for an efficient way to retrieve potential granular data from heterogeneous data sources. In this paper, we define a patient-centric authorisation approach, incorporating a structured format for composing access rules that enable secure data retrieval and automatic rules conflict checking.
Natural Language Interface for Covid-19 Amharic Database Using LSTM Encoder Decoder Architecture with Attention

The COVID-19 outbreak is still a challenge in most places because of lack of up-to-date information, primarily, to the people in the world who speak and use underrepresented local languages. Ethiopia is one example of a country where several in-digenous languages are under-represented and under-resourced. Thus, building an interactive interface that responds to users' query using their local language with organized information plays a significant role. In this study, attention-augmented Encoder-Decoder Long Short Term Memory(LSTM) network model has proposed to provide adequate information about the pandemic to the people of Ethiopia by their local language, Amharic. The model converts Amharic COVID-19 related questions into the corresponding structured query language (SQL). The model retrieves information from the Amharic COVID-19 database that has developed for this study. The database contains frequently referenced COVID-19 attributes such as symptoms, prevention, transmission and frequently asked questions. In addition, a parallel Amharic Question-SQL query dataset has been prepared to evaluate the model. The LSTM Network with augmented attention mechanism has shown a clear significant result. In this study, a user interactive interface has also developed. The interface uses the proposed model and provides information about the pandemic to the people with questions in Amharic.
Research on Six Degree of Freedom Robot Trajectory Planning Method Based on Improved Ant Colony Algorithm

With the rapid development of intelligent manufacturing, the control technology of industrial robots has been upgraded gradually, and the traditional six-degree-of-freedom industrial robots' teaching and control method can no longer meet the needs of industrial production. In order to make the robot adapt to the complex and changing environmental space and complete the established work smoothly and efficiently, this paper establishes the robot kinematic model and organizes the parameters using MD-H modelling method for the kinematic analysis problem of six-degree-of-freedom industrial robots, and improves the ant colony algorithm for the motion path planning problem of six-degree-of-freedom industrial robots, which improves the industrial robot production efficiency
Ontological-based ABAC and Blockchain Organizational Cooperation Framework for Security Management in AmI environments

IoT paradigm raises several critical challenges that are still difficult to achieve, such as message-integrity checking, authentication, and the lack of security. Blockchain as an emerging technology provides secure communication architecture. Moreover, relying on Attribute-Based Access Control (ABAC) model-based smart contract realizes a trans-organizational access control. Creating these Blockchain-based IoT applications raises challenging tasks, mainly related to knowledge exchange between various artifacts and humans. Since ontology facilitates the exchange of knowledge between users and systems and between the systems themselves, it constitutes de facto an ineluctable design to tackle the semantic interoperability issues by providing a unified representation of knowledge and sharing a common understanding of the semantic context structure. Thereby, using the ABAC mechanism and Blockchain paradigm with an ontological model promotes semantic contextual knowledge inferences and facilitates autonomous decision making without the intervention of a trusted third party, which could easily collect valuable private information unbeknownst to the users.
Cluster-Based Joins for Federated SPARQL Queries

Federated RDF systems allow users to retrieve data from multiple independent sources without needing to have all the data in the same triple store. The performance of these systems can be poor for large and geographically distributed RDF data where network transfer costs are high. This article introduces CBTP-OL and CBTP-Nhop, two novel join algorithms that take advantage of network topology to decrease the cost of processing Basic Graph Pattern (BGP) SPARQL queries in a geographically distributed environment. Federation members are grouped in clusters, based on the network communication cost between the members, and the bulk of the join processing is pushed to the clusters. Our CBTP-OL and CBTL-Nhop algorithms use an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">overlap list</i> and, respectively, an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N-hop overlap list</i> , to efficiently compute join results from triples in different clusters. We implement our algorithms in the OpenRDF Sesame federated framework and use Apache Rya triple store instances as federation members. Experimental evaluation results show the advantages of our approach over existing techniques.
Password Strength Assessment on a Laptop Device using an Online Password Recovery Tool — Cain &amp; Abel

Password cracking procedures are often done using password recovery tools, this has led to attackers using the same technique to obtain access to weak frameworks and infrastructures. The study conducted two trials of strength assessment for (1) weak password and (2) strong password using a Cain & Abel to show the potential risks of both passwords. Two procedures were used for the strength assessment: brute force and dictionary attacks. The findings of this study shows that passwords containing only uppercase and lowercase letters with numbers (3 to 8 characters) are easily exploited. The study concluded that using strong passwords prevent potential cybersecurity attacks because strong passwords require more resources, power, and time to hack.
Biorthogonal Wavelets

In this chapter, we introduce the concept of biorthogonal wavelets in a local field K of positive characteristic. We show that if \(\varphi \) and \(\tilde{\varphi }\) are the scaling functions of two multiresolution analyses (MRAs) such that their translates are biorthogonal, then the associated families of wavelets are also biorthogonal. Under mild decay conditions on the scaling functions and the wavelets, we also show that the wavelets generate Riesz bases for \(L^2(K)\). First, we find necessary and sufficient conditions for the translates of a function to form a Riesz basis for their closed linear span. We define the projection operators associated with the MRAs and show that they are uniformly bounded on \(L^2(K)\). Finally, we prove that the wavelets associated with dual MRAs are biorthogonal and generate Riesz bases for \(L^2(K)\).
Promoting Cybersecurity Compliance

In a global online economy, organizations are tasked with protecting their cybersecurity assets. Penalties from failing to protect assets, such as customer data, can severely harm an organization and even lead to bankruptcy. Cybersecurity governance programs need to be aware of the laws and regulations affecting their organizations and use applicable standards or frameworks to develop appropriate cybersecurity polices and controls. Compliance programs then need to monitor policy compliance on a continuing basis. This chapter discusses the laws, regulations, and standards that are used to create cybersecurity polices and the typical tools used to measure compliance. In addition, theoretical cybersecurity compliance research is reviewed to highlight supplementary techniques to improve compliance.
Using CNN Models to Identify Suspicious and Criminal Behavior - A Survey of Current Research Trends

In recent years there was significant progress in analyzing video recordings to identify criminal and suspicious behavior. Major types of machine learning architectures used in these studies are 2D and 3D Convolutional Neural Network (CNN) architecture. This article aims to review a notion of general CNN’s as well as their various modifications used for crime detection from video footage. In addition to the introduction to architectural details, this paper is focused on studying and comparing methods used in various studies on detecting criminal behavior using CNN neural architecture. Finally, the paper presents possible directions for future research in the given domain.
THE CONSTRUCTION METHODS FOR SOFTWARE COMPLEXES BY THE EXAMPLE OF OPEN CROSS-PLATFORM ONLINE SYSTEM FOR TEXT TRANSLATION

Popular online translators Google Translate, Bing Translator, DeepL Translator, and Apertium are reviewed. The goal is to compare the interfaces and functionality of these translators. The advantages of using the C ++ programming language are investigated. The choice of the Qt cross-platform framework among other analogues has been substantiated. Qt most of all meets the requirements set in the article for the development of its own software product. The basis for the development of the program is chosen methods of object-oriented programming using the application programming interface. The stages of development of the online «Crow Translate» system are presented. Its capabilities for text translation are described: an application programming interface for translation, peculiarities of the implementation of a C ++ programming system based on the Qt framework, basic commands of the console interface, functionality of the graphical interface for dubbing and translation, customization options, localization possibilities and ways of distributing the offered «Crow Translate» software. During the development of the system, the implementation of the translation process and the program interface were divided into two separate projects. A peculiarity of the project implementation is the development of a separate QOnlineTranslator library for text translation. This system is free software and is published on the GitHub page under the GPL v3 license for use in any similar projects. The details of the implementation of the suggested library and its main functions are described. Thanks to the use of the Google interface in the described program, a high-quality translation is ensured. The developed project provides for the ability to work in the dictionary mode when entering one word or a piece of text. Various translation options are additionally displayed for one word. There is support for displaying transliteration. When developing a software product, the possibility of receiving a translation in a pop-up window without additional transitions to the browser or other applications was taken into account. The result is an open cross-platform software product that has convenient access to the translation of a text selected in any application and takes up a small amount of RAM.
Dedark+Detection: A Hybrid Scheme for Object Detection under Low-light Surveillance

Object detection under low-light surveillance is a crucial problem that less efforts have been made on it. In this paper, we proposed a hybrid method that jointly use enhancement and object detection for the above challenge, namely Dedark+Detection. In this method, the low-light surveillance video is processed by the proposed de-dark method, and the video can thus be converted to appearance under normal lighting condition. This enhancement bring more benefits to the subsequent stage of object detection. After that, an object detection network is trained on the enhanced dataset for practical applications under low-light surveillance. Experiments are performed on 18 low-light surveillance video test sequences, and superior performance can be found when comparing to state-of-the-arts.
Live coding during lockdown

An inside look at developer advocacy during the COVID-19 pandemic---from creating authenticity to keeping the social life around code alive while the world stayed home.
Evolutionary Multitasking via Artificial Neural Networks

Evolutionary Multi-Tasking (EMT), which solves multiple optimization tasks simultaneously, is a burgeoning topic in the area of evolutionary computation. As the EMT transfers useful knowledge across tasks to guide the search while the optimization process progresses online, superior search performance has been obtained in many recent attempts. Autoencoding evolutionary multitasking is a recently proposed EMT algorithm, which employs a single-layer denoising auto-encoder for knowledge transfer. However, since the autoencoding evolutionary multitasking (AEEMT) algorithm learns the relationships between tasks through a linear auto-encoder, it may lead to negative transfer in cases that the linear variable relationship does not hold across tasks. Taking this cue, we propose an evolutionary multitasking algorithm with artificial neural networks for transferring knowledge across tasks in this paper, which possesses higher modeling capability of variable relationships. To test and verify the efficiency and effectiveness of the proposed model, we conducted comprehensive empirical studies on the well-known single-objectives multi-task optimization benchmarks. The experimental results have shown that the proposed method has a remarkable effect on the evolutionary search in terms of both search speed and solution quality and comparing to the autoencoding evolutionary multitasking algorithms and the single-task evolutionary counterpart.
Multi-Encoder Sequential Attention Network for Context-Aware Speech Recognition in Japanese Dialog Conversation

Japanese automatic speech recognition (ASR) in conversation is considered challenging and highly ambiguous because the subjects are usually omitted and the number of homonyms is quite large. Since the speaker&#x0027;s utterance alone may not provide enough information, the context of the previous utterance from the dialog partner might help. A limited number of studies have addressed the idea of incorporating dialog-context information in end-to-end ASR tasks. However, these have mainly focused on the English language. Furthermore, although such models exploit dialog context history, the decoder handles the current input and the context simultaneously, ignoring the sequence of a conversation in the process. In this study, we propose a Japanese ASR that considers the dialog partner&#x0027;s conversation context in a sequential manner. Specifically, we use a multi-encoder sequential attention network and investigate several possible architectures. The experimental results reveal that context information helps to improve recognition performance over standard ASR and systems that process the context at the same time.
Software Defect Prediction for Specific Defect Types based on Augmented Code Graph Representation

In a software life cycle, improving quality and identifying and repairing defects has become an important research topic. Previous studies have proposed defect prediction based on artificial measurement features, a method whose quality is unfortunately difficult to guarantee. On the other hand, many current studies have attempted to predict all types of defects using a single model, which is difficult to achieve. In this paper, Augmented-CPG, a new code graph representation, is proposed. Based on this representation, a defect region candidate extraction method related to the defect type is proposed. Graphic neural networks are introduced to learn defect features. We carried out experiments on three different types of defects, and the results show that our method can effectively predict specific types of defects.
A Dummy Location Selection Algorithm Based on Location Semantics and Physical Distance

With the development of smart devices and mobile positioning technologies, location-based services (LBS) has become more and more popular. While enjoying the convenience and entertainments provided by LBS, users are vulnerable to the increased privacy leakages of locations as another kind of quasidentifiers. Most existing location privacy preservation algorithms are based on region cloaking which blurs the exact position into a region, and hence prone to inaccuracies of query results. Dummy-based approaches for location privacy preservation proposed recently overcome the above problem, but did not consider the problem of location semantic homogeneity, query probability and physical dispersion of locations simultaneously. In this paper, we propose a dummy location selection algorithm based on location semantics and physical distance (SPDDS) that takes into account both side information, semantic diversity and physical dispersion of locations. SPDDS solves a simplified problem of single objective optimization by uniting the three objectives (location semantic diversity, query probability and physical dispersion of locations) together. The efficiency and effectiveness of the proposed algorithms have been validated by a set of carefully designed experiments. The experimental results also show that our algorithms significantly improve the privacy level, compared to other dummy-based solutions.
Peningkatan Kualitas Pembelajaran Explanation Text Menggunakan Model Problem Based Learning Berbasis Lesson Study

Students' problems in writing explanatory texts start from understanding the structures of explanatory texts, determining topics, making themes so that they become an essay outline, to developing an essay outline that is directed at making texts. In addition, students' interest in writing is still low. The purpose of this study was to analyze the problem-based learning model on the quality of explanation text learning in English subjects for high school students. This research is qualitative. This study uses a problem-based learning model based on lesson study. There are three stages carried out in lesson study activities, namely: planning (plan), implementation (do), and reflection (see). The subjects selected were 11th-grade students in high school. The results showed that giving the text model to students, dissecting the text, and summarizing the text could be followed by students smoothly, however, 15 minutes to respond to the content of the video presented was felt to be lacking. In general, student participation was good during the learning activities, and the skills that were trained were also successfully seen from the worksheet results. Based on the results of the study, it can be concluded that the problem-based learning model based on lesson study is considered effective in teaching explanation text in high school students.
Learning to Detect: A Semi Supervised Multi-relational Graph Convolutional Network for Uncovering Key Actors on Hackforums

Cybercriminals who interact extensively on underground forums, often, exchange illegal commodities and indulge in discussions on unwarranted topics. To facilitate the disruption of these highly proficient criminals, we propose a deep learning based multi-relational graph convolutional network approach to analyse the underground forum and identify key actors. We first modeled the hackforum into a homogeneous graph of users, where the multiple edges between users are captured based on their involvement in private conversations, group discussions and other miscellaneous activities. In addition, we also encode the textual content shared among users’ in form of distributed feature representation generated from BERT. To obtain ground truth labels for training data, we propose a hypothesis to calculate the scores for each user based on the quality and quantity of their involvement in the underground forum. The proposed framework jointly embeds the users’ and multi relational information to learn the nodes embeddings in the graph. We demonstrate the effectiveness of the proposed model on a neonazi underground forum, Iron March. We conducted an ablation study on the model parameters to generate the best results and achieved a classification accuracy of 82% which validates the proposed hypothesis for score computation and class labelling. To establish the robustness of our model, we compare its performance against state-of-art models. Though we used an underground forum as a showcase, the proposed model can be implemented to identify influential users’ on other social media platforms.
Credit Score Improvisation through Automating the Extraction of Sentiment from Reviews

Credit rating firms like D&amp;B, A.M Best Company, etc., usually give scores to companies based on their bank records, scanning the failure to repay the loan, etc. since they only look into the financial details of whether the company defaulted or not in repaying their loans. Text/sentimental analysis improves decisions made by the banks before lending loans to their customers. Also enables businesses to grow profitably by providing information-based intelligence tools. The mission of the work has been to extract the unstructured data from websites (i.e., Glassdoor, Indeed) housing company reviews. The objective is to automate the extraction of the aspects and their corresponding sentiments and cumulate a credit score. This proposed prototype will accept a text input manually or via a text file stacking review. These reviews will be tokenized into words and categorized by noun and adjective. The adjectives are assigned the respective class values/polarity (binary form). The entire goal was to make use of company information stored on the Internet, since it was unaccounted. This kind of information has been extracted from public websites like kanoon.com, 166Glassdoor, Indeed.in, etc. So, the rating now is not only based on the bank records but also on how the company operates its employees, sanitation issues, the pay problems if any, beneficial perks given to the employees’, etc. Even the consumer whoever given review about the company performance in the market also considered for processing. The sentiments/adjectives given to all noun forms are recorded and given binary score values. The cumulative score of a sentence or a paragraph is then presented in a database. Then a pivot table is generated, which displays a frequency table of the noun forms and their respective sentiment used to describe them. The number of times a noun form has a positive/negative sentiment gets recorded and a score get displayed to the user. Accuracy values for both the text analysis algorithms have been analyzed, and the best one, that is, the TextBlob Analyzer has been put to use since it had accuracy values above 95% for positive sentiments and 91% for the mixed sentiments.