Reviewerly/exHarmony huggingface dataset:
On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methods

The increasing adoption of Reinforcement Learning in safety-critical systems domains such as autonomous vehicles, health, and aviation raises the need for ensuring their safety. Existing safety mechanisms such as adversarial training, adversarial detection, and robust learning are not always adapted to all disturbances in which the agent is deployed. Those disturbances include moving adversaries whose behavior can be unpredictable by the agent, and as a matter of fact harmful to its learning. Ensuring the safety of critical systems also requires methods that give formal guarantees on the behaviour of the agent evolving in a perturbed environment. It is therefore necessary to propose new solutions adapted to the learning challenges faced by the agent. In this paper, first we generate adversarial agents that exhibit flaws in the agent's policy by presenting moving adversaries. Secondly, We use reward shaping and a modified Q-learning algorithm as defense mechanisms to improve the agent's policy when facing adversarial perturbations. Finally, probabilistic model checking is employed to evaluate the effectiveness of both mechanisms. We have conducted experiments on a discrete grid world with a single agent facing non-learning and learning adversaries. Our results show a diminution in the number of collisions between the agent and the adversaries. Probabilistic model checking provides lower and upper probabilistic bounds regarding the agent's safety in the adversarial environment.
Codoc: Code-driven Architectural View Specification Framework in Python

Architectural views are expensive to maintain. Software systems continuously evolve, making architectural views outdated. We present Codoc, a continuous architectural documentation system. Codoc empowers users to utilize a code-driven architectural view specification language to create views that are continuously updated and accessible. Through qualitative studies, we find code driven view specification as a promising candidate to replace manual architectural visualization in software documentation.
Total Dual Dyadicness and Dyadic Generating Sets

A vector is dyadic if each of its entries is a dyadic rational number, i.e. of the form $$\frac{a}{2^k}$$ for some integers a, k with $$k\ge 0$$ . A linear system $$Ax\le b$$ with integral data is totally dual dyadic if whenever $$\min \{b^\top y:A^\top y=w,y\ge \mathbf {0}\}$$ for w integral, has an optimal solution, it has a dyadic optimal solution. In this paper, we study total dual dyadicness, and give a co-NP characterization of it in terms of dyadic generating sets for cones and subspaces, the former being the dyadic analogue of Hilbert bases, and the latter a polynomial-time recognizable relaxation of the former. Along the way, we see some surprising turn of events when compared to total dual integrality, primarily led by the density of the dyadic rationals. Our study ultimately leads to a better understanding of total dual integrality and polyhedral integrality. We see examples from dyadic matrices, T-joins, circuits, and perfect matchings of a graph.
Classification of Emotions Based on Text and Qualitative Variables

The aim of the paper is to compare the performance of classification of samples based on self-reporting emotions of subjects described by qualitative variables and textual description of situation in which it is experienced. The ISEAR data set was used in the research, which consists of 7666 samples classified in seven classes representing basic emotions (joy, fear, anger, sadness, disgust, shame, and guilt). For the text-based classification logistic regression (LR) was used as well as deep learning methods of convolutional neural networks (CNN), long short-term memory networks (LSTM) and convolutional long short-term memory networks (C-LSTM), while for classification based on qualitative variables LR and multilayer perceptron (MLP) were used. Performance of classification according to the textual description is similar for the LR and deep learning methods ranging from 53.65% in accuracy for CNN to 57.43% for LSTM, while the LR (accuracy of 77.19%) outperforms the MLP (accuracy of 51.83%) in classification according to qualitative variables. Also, we constructed an ensemble of classifiers based on text and qualitative variables showing improvement in performance compared to separate classifiers and accuracy ranging from 63.04% (CNN for text and MLP for qualitative variables) to 80.55% (LR for both text and qualitative variables).
An AI based Solution for Predicting the Text Pattern from Sign Language

A large social group get benefit from sign language detection through technology, but it is an overlooked concept. Communicating with others in society is a primary aim of learning sign language. Communication between members of this social group is rare due to limited access to technology. Hearing-impaired people are left behind. As normal people cannot make signs, they need to use texting methods to communicate with hearing-impaired people, which is less than ideal. Increasingly, deaf people must be able to communicate naturally no matter the practitioner's knowledge of sign language. An analysis of sign language is based on the patterns of movement generated by the hand or finger, commonly referred to as sign language.
The importance of formal modelling for the development of cognitive theory

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An improving approach to analyzing change impact of C programs

A seemingly small change in the software can usually have a big impact. Impact analysis is an assessment of multiple risks associated with the changes. Research over the past 20 years has shown that impact analysis can decrease the time and effort needed in the development and regression testing and reduce the negative effects of change on other parts of the software system. These researches used program slicing, control flow analysis, call graph to analyze intraprocedural and interprocedural impact. However, most of them only analyze the methods affected by changes of simple variables or statements, which have coarse granularity and can provide limited help. To solve these problems, we propose an improving approach that can analyze the finer granularity change impact of multiple types of variables. This approach first breaks code changes into a combination of basic operations on variables and adopts corresponding analysis mechanisms for different types of variables. The code is preprocessed before analysis to gather essential information such as global variables table, function summaries, and pointer information map. In the intraprocedural analysis, we first divide the variables into two types: definition point and use point. For the use point, a data flow graph is used to analyze its change impact. For the definition point, we first find the paths where it is and analyze the variables on these paths that are controlled by it. In addition, to deal with the change impact of complex variables in the C programs, we have proposed a corresponding analysis procedure and algorithm. In interprocedural analysis, the impacted methods are searched according to their relationship and the global variable information table. A prototype system is developed based on our approach. In our evaluation, we demonstrate that the prototype system can be used to support various software development and maintenance tasks.
A survey of cybersecurity vulnerabilities in healthcare systems

The security of healthcare servers and their applications from unauthorized access and hacking effect of patient file safety as a result of missing or modified data. Therefore, it is necessary to protect those servers and health systems and to know the cyber weaknesses in them and other related services to prevent them before they occur and treat them if they occur as soon as possible to reach the highest level of security and confidentiality of patient data and their privacy in addition to the safety of systems from malfunctions as a result of the attack. This research revolves around the concept of penetration and the motives of the attackers, in addition to the methods of penetration and vulnerability with mentioning some common attacks and clarifying their method of operation, and determining their effects on the victim. The research also deals with the most important basics and requirements to achieve privacy and security for health systems at different levels, including systems, users, and devices, with some intrusion protection techniques mentioned, and some challenges and solutions will be discussed in addition to some security issues in general, the research aims to gain the best level of security and privacy in the infrastructure of health systems.
The Study of Various Methodologies in the Development of Recommendation System

Recommendation systems are becoming very popular due to their intelligence in recommending products or items to users or customers or consumers. Particularly, this concept became more popular due to E-commerce sites as these sites will attract customers by providing recommendations. In this article, the proposed framework deal with the development of recommendation systems using the methodologies such as priority-based recommendation systems, collaborative filtering recommendation systems, and user-item-based recommendation systems. The results obtained with these methodologies have also been mentioned in this article.
Methods and Technologies of Search, Explication and Analysis of Contextual Knowledge for Scientific Heritage Studies: A Case Study of Georgy F. Gause

The objective of this research is to study methods of search, explication and analysis of text data of scientific publications with information and communication technologies for use in scientific research. The research is based on Russian-language scientific publications reflecting the scientific heritage of G. F. Gause. The proposed study is based on the results of case studies conducted to assess the possibilities of using digital information resources in scientific research, extracting metadata from digital electronic resources using methods of their subsequent quantitative processing. The study examined the methods of explication and analysis of text data extracted from digital scientific resources (for example, Elibrary). For the analysis, the information system Sketch Engine was used, which provides natural language text processing (NLP) tools. Based on the analysis of the obtained results, conclusions are drawn about the possibility of using the studied methods not only in scientific research, but also in a wide range of scientific research on various topics.
Automated Approach for Computer Vision-Based Vehicle Movement Classification at Traffic Intersections

Movement-specific vehicle classification and counting at traffic intersections is a crucial component of various traffic management activities. In this context, with recent advancements in computer-vision-based techniques, cameras have emerged as a reliable data source for extracting vehicular trajectories from traffic scenes. However, classifying these trajectories by movement type is quite challenging, as characteristics of motion trajectories obtained this way vary depending on camera calibrations. Although some existing methods have addressed such classification tasks with decent accuracies, the performance of these methods significantly relied on the manual specification of several regions of interest. In this study, we proposed an automated classification method for movement-specific classification (such as right-turn, left-turn and through movements) of vision-based vehicle trajectories. Our classification framework identifies different movement patterns observed in a traffic scene using an unsupervised hierarchical clustering technique. Thereafter, a similarity-based assignment strategy is adopted to assign incoming vehicle trajectories to identified movement groups. A new similarity measure was designed to overcome the inherent shortcomings of vision-based trajectories. Experimental results demonstrated the effectiveness of the proposed classification approach and its ability to adapt to different traffic scenarios without any manual intervention.
Design Approach in Document Management System: The Development of EZDESK Dashboard

Document Management System is a concept of planning and managing a document in various activities simultaneously, which one of common activity is publication from before the conference starts to after the conference finished. Pre-conference activities include the process of registering a conference, the admin process of verifying the registered conference, until the conference has been published. In this case, user experience (UX) orientation and paradigm was investigated through the simplified RMS (Recognize, Materialize and Scrutinize) design approach by generating the prototype with the usability criteria and aspects to comply with the user demand as the critical factors and trigger for comprehensive execution. The EzDesk application is an application designed to assist users in registering conferences and verifying conference registration documents. There are 2 actors, primarily in this module, which are chair as the conference organizer and the admin plays a role in conference verification. To verify a conference to be held, the admin must check the required documents for holding a conference. After going through the verification stage, a conference can be distributed via visualization on the main page of document management system application.
Comparison of the Performance of NewSQL Databases Based on Linux OS

In the field of cloud technology and big data a new kind of databases arose - NewSQL databases. In the meantime there are many different suppliers and databases available. In this article the most common NewSQL databases in the field of Linux OS are presented and their performance is measured and compared to each other. Therefore a data set with weather data was prepared and used. Furthermore the test setting is explained. The results of the different tests are illustrated and discussed. Recommended actions are given at the end.
Digital-Body (Avatar) Library for Textile Industry

Personalized garment design and virtual assembly application of a garment is one of the most important goals for almost all textile companies. Aligning the designing of dress on the virtual garment with the feature points of the human body and can be considered as a success factor. For this reason, it is best solution to measure the human body and to find the dimensions and to produce tailor made garment according to sizing of body. Clothing sizes have been a challenge for the apparel industry. This is because, difficulties arising from to find the right size when purchasing an outfit. Customers are often disappointed with their product deliveries, causing them to be disappointed and less likely to buy clothes online. Increased returns and reduced customer loyalty have negative effects on profit margins. Considering the fit dress problem, it is necessary to create a body database that every company can use. The sensible solution of this problem is to create a common digital body library. The aim of Digital-body (avatar) library is an ongoing process from measuring the customer's body to dressing according to the customization garment, and dramatically to reduce return rates due to non-fitting dresses over internet sales. It is needed a redesign for the sizing system, and it will be new paradigm shift in tailor made production for apparel industry system and to thrive the appeal industry. Digital Body Library will affect the dress which every sale in the future.
A Placement Angle Detection Method of Recyclable Object for Garbage Power Generation

With the rapid development of China's economy, the number of population increases year by year, and the municipal solid waste (MSW) increases rapidly. Garbage incineration power generation can realize the harmlessness and resource of domestic garbage, thus realize the green construction of our country and promote the further development of our economy. In this paper, Yolo network is used to detect household garbage, improved on the basis of the original network to solve the problem that Yolo network can not detect the the angle. And a recyclable garbage dataset containing the angle of the object is constructed.
Dynamic Programming Inspired Global Optimal Path Planning for Mobile Robots

Optimal path planning for mobile robots can improve the output in a warehouse and therefore have the potential to greatly increase the profit of a warehouse. Dynamic programming has long been used for optimal path planning tasks. Different from most works in this research area, which use dynamic programming with grid discretization to approximate the global optimal solution, in this paper, we propose an efficient dynamic programming inspired global optimal path planning solution for the continuous state space. The proposed algorithm can find the exact global optimal solution or the shortest path to the goal position from any starting point in the map.
RGB Image Edge Detection Using Fuzzy Logic to Infer The Function Of Triangle And Trapezoidal Key Members

Image preparation is the process of obtaining organizable visual data from a computerized PC for tasks such as robotics, as well as for analyzing wasteland and satellite symbols, remote sensing, and clinical purposes. The important ability of image preparation is to reduce a large amount of information, while maintaining valuable data is to eliminate confusion and further expand the communication channels of subtleties. The shaded boundary position map is more attractive than the grayscale image because it has a higher success rate in selecting unique items. In the case where the gray-scale power estimate remains stable, the boundaries of different regions will be distinguishable from each other. By combining these two functions, consistent image processing with two unmistakable shadow magnification characteristics is being created: one that does not completely exclude all tones, and the other allows certain tones to be filtered. In this article, the RGB shading model ventures into three channels and uses edge discrimination and smooth registration calculations to determine edges.
Phish What You Wish

IT professionals have no simple tool to create phishing websites and raise the awareness of users. We developed a prototype that can dynamically mimic websites by using enriched screenshots, which requires no additional programming experience and is simple to set up. The generated websites are functional and remain up-to-date. We found that 98% of the hyperlinks in mimicked websites are functional with our tool, compared to 43 % with the best competitor, and only two participants suspected phishing attempts at the time they were performing tasks with our prototype. This work intends to raise awareness for phishing attempts especially with local websites by providing an easy to use prototype to set up such phishing sites.
A Systematic Review Through Intellectual Based Blockchain-Intermediary

Decentralized ecosystems are generally still rarely used in various disciplines. Of all the existing fields, decentralization is only applying to solve problems requiring various partners' organizing. Blockchain has unique characteristics and reflects multiple values related to distinctive and prevalent influences is often referred to by the online community as crypto. This study describes Blockchain-mediated decentralization based on a systematic review on decentralization. This review covers the features of decentralization in a variety of disciplines starting with public administration to science. Blockchain as an intermediary referred to in various scientific articles as an opponent of the binary position interpreted, decentralized and centralized. The author proposes a dialectical design and argues that system jurisdiction allotment is a standard located on the scale amongst pure or centralized decentralization and then notes how a Blockchain arrangement can simultaneously have entities that are significantly centralized whereas others are not. Through a systematic review, we offer a design of Blockchain-mediated decentralization comprehension by having the authors document the findings based on their systematic review. Their proposed framework for generating Blockchain-mediated decentralization comprehension recommends a description and then drawing a new direction for future research on humans into distributed ledger technology.
Peripheral-Free Device Pairing by Randomly Switching Power

With the growing popularity of the Internet-of-Things (IoT), a massive amount of purpose-specific, heterogeneous, inexpensive devices have been deployed. To allow these devices to perform their duties and collaborate efficiently, designing a secure and dependable communication channel is necessary. Pairing, as the fundamental procedure for establishing a trustworthy communication channel, has received extensive attention from security researchers. Previous secure pairing protocols depend on auxiliary peripherals (e.g., displays, speakers) to share the secret message, while for those products featuring with low-price, manufacturers would probably adopt insecure pairing methods to reduce the cost, so the devices may be subject to various attacks. To mitigate such a situation, we design a peripheral-free secure pairing protocol, termed SwitchPairing. Our protocol only requires users to connect the pre-pairing devices to the same power source, then randomly presses and releases the switch to generate a shared secret. It does not require additional peripherals and can defense eavesdropping and replay attacks innately. We implement a prototype via two CC2640R2F development boards and invite volunteers to participate in the experiments about bench-marking security and usability. The result of our experiments show that our protocol can fulfill the security and efficient requirement of various IoT applications.
Rhythmanalysis, Concrete Abstraction and the Quantified Self: Sonification and Performance Research as Remediation of Data

This paper explores the potential of Henri Lefebvre's rhythmanalysis to understand data as an appearance assumed by the quantitative abstraction of everyday life, which negates a qualitative disjuncture between different natural and social rhythms – specifically those between embodied circadian and biological rhythms and the rhythms of working life. It takes as a case study a prototype performance research method investigating the methodological and practical potential of quantified self technologies to reconnect the body to its forms of abstraction in a digital age by means of the collection, interpretation and sonification of data using wearable tech, mobile apps, synthesised music and modes of visual communication. Quantitative data were selectively ‘sonified’ with synthesisers and drum machines to produce a 40-minute electronic symphony performed to a public audience. The paper theorises the project as an intervention reconnecting quantitative data with the qualitative experience it abstracts from, exploring the potential for these technologies to be used as tools of remediation that recover the embodied social subject from its abstraction in data for critical self-knowledge and understanding.