Reviewerly/exHarmony huggingface dataset:
The One-Eyed Leading the Blind: Understanding Differences Between IT Professionals and Non-IT Staff When Creating and Managing Passwords

Passwords remains the standard mechanism by which organisations protect their data from unauthorised entities accessing, changing or misusing their information. Organisations go to great lengths to educate their workers on the importance of creating and maintaining secure passwords. Extensive research has been conducted on how users create and manage their passwords. To date, there is limited insight on how the behaviour of IT workers may differ from that of non-IT workers. It is generally assumed that IT workers have a greater understanding of what a secure password entails and how insecure password behaviour may put an organisation's resources at risk by the nature of their roles. Consequently, they are expected to have a positive influence on non-IT workers' password behaviour. This research sets out to test this assumption. The findings suggest significant differences between the password practices applied when IT and non-IT workers create and manage their passwords. However, poor security behaviour by both IT and non-IT workers was evident.
Examining the Guji Oromo Ethnomathematical Games and Concepts

This chapter explores how the Guji Oromo people undertake ethnomathematical activities by applying their indigenous methods. Ethnomathematical activities include counting, locating (the activity of grouping, clustering, making network, etc.), measuring (the actions of quantifying, weighting, etc.), designing (planning, building, and pattern activities), playing (puzzles, paradoxes, models, games, hypothetical reasoning), and explaining (how to do things, activities [e.g., classifications, conventions, generalizations, and symbolic explanations]). This predominantly qualitative study identifies the indigenous ethnomathematical games and concepts and assesses the potential effectiveness of an integration of the ethnomathematics and formal mathematics on the learning/teaching experiences of pupils and teachers. Impacts of such integration on pupils' performance in mathematics assessment are examined. Such an action could enable to amalgamate the Western knowledge system with an African knowledge system to create synergy that might boost the quality of primary mathematics education in Ethiopia.
Refactoring Decision based on Measurements for IoHT Apps

Internet of Things (IoT) provides smart objects with the ability to connect to the Internet, allowing the exchange of information among them to provide a certain service and the development of innovative applications in several domains, including e-Health, in which it is called Internet of Health Things (IoHT). This domain can be critical specially when the application deals with the monitoring of the user health in real-time, what demands software quality assurance, even more than in other applications. Measures can be used to support that, for example, measures can suggest which components need refactoring to improve the software code, thus improving the application. In this work, we report how to do that with two existing measures that guide the refactoring process of an IoHT application for fall detection, called WatchAlert. These measures indicate that changes in both the architecture and the algorithms for fall detection should occur. After the refactoring, the app accuracy was improved from 73.3% to 92.7%. We believe that this work can contribute to other studies focusing on developing applications on the IoHT domain using a methodology, a set of refactoring techniques, and lessons learned that could be replicated to improve the quality of this type of application.
Dual Sequence Transformer for Query-based Interactive Recommendation

Interactive recommendation has drawn widespread attention from both academia and industry due to its effectiveness in real-world mobile applications. Instead of receiving message passively, customers can exploit further with less effort through generated queries. Usually, such systems mainly contain two main components: query generation and item recommendation. In this paper, we propose a novel framework that models both queries and items in shared latent embedding space via a dual sequence transformer structure, which captures customer's potential interest from the prospect of reconciling the historical queries and corresponding customers interactions. We propose a click-through-rate model to generate query candidates, and a session search model for further more precise information. Comprehensive offline and online experiments are conducted, and the results demonstrate that our proposed dual-sequence-transformer based model can better utilize interaction and improve the accuracy of recommendations.
DupM: a Data Replica Allocation Strategy for Distributed Mining

The mass data mining algorithm is usually sensitive to computing resources, the quantities of which (e.g., the quantities of memory and CPUs available for mining algorithms) determine the mining results. In this paper, data replicas as a resource were incorporated into the resource scheduling strategy for distributed data mining to offer a replica-aware resource scheduling strategy DupM. With data replicas as a type of data resources, the DupM strategy scheduled data replicas through dynamic programming so that a cloud platform could, based on the requirements of distributed mining, allocate replicas. The simulation test of test data sets in KDD CUP events and IBM synthesizer data sets demonstrated that the DupM resource scheduling strategy has more advantages than Hadoop’s built-in resource scheduling strategy.
A Compositional Cost Model for the λ-calculus

We describe a (time) cost model for the (call-by-value) λ-calculus based on a natural presentation of its game semantics: the cost of computing a finite approximant to the denotation of a term (its evaluation tree) is the size of its smallest derivation in the semantics. This measure has an optimality property enabling compositional reasoning about cost bounds: for any term A, context C[_] and approximants a and c to the trees of A and C[A], the cost of computing c from C[A] is no more than the cost of computing a from A and c from C[a].Although the natural semantics on which it is based is nondeterministic, our cost model is reasonable: we describe a deterministic algorithm for recognizing evaluation tree approximants which satisfies it (up to a constant factor overhead) on a Random Access Machine. This requires an implementation of the λ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">v</inf> -calculus on the RAM which is completely lazy: compositionality of costs entails that work done to evaluate any part of a term cannot be duplicated. This is achieved by a novel implementation of graph reduction for nameless explicit substitutions, to which we compile the λ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">v</inf> -calculus via a series of linear cost reductions.
An Improved Particle Swarm Optimization with Dual Update Strategies Collaboration Based Task Allocation

The task allocation problem is a hot topic in the field of multiple unmanned aerial vehicle (UAV). In this paper, we consider the task allocation in rescue scenarios and establish the optimization model. Then, an improved particle swarm optimization with dual update strategies collaboration (DUCPSO) is proposed to solve it. In order to simplify the solution of the problem, a real vector coding method is adopted. In addition, the ring topology update method and the mutation update method are introduced to enhance the diversity of the population, and the effective collaboration of the two strategies is realized through the adaptive strategy conversion probability. The simulation results show that the proposed algorithm can effectively obtain the optimal task allocation scheme. Compared with other algorithms, the proposed algorithm is more feasible and efficient.
On the Use of a Sequential Deep Learning Scheme for Financial Fraud Detection

Forecasting fraud detection has never been more essential for the finance industry than today. The detection of fraud has been a major concern for the banking industry due to the high impact on banks’ revenues and reputation. Fraud can be related with an augmented financial risk, which is often underestimated until it is too late. Recently, deep learning models have been introduced to detect and forecast possible fraud transactions with increased efficiency compared to the conventional machine learning methods and statistics. Such methods gain significant popularity due to their ability to estimate the unknown distribution of the collected data, thus, increasing their capability of detecting more complex fraud events. In this paper, we introduce a novel multistage deep learning model that combines a feature selection process upon an Autoencoder model and a deep convolutional neural network to detect frauds. To manage highly unbalanced datasets, we rely on the Synthetic Minority Over-sampling Technique (SMOTE) to oversample our dataset and adjust the class distribution delivering an efficient classification approach. We describe the problem under consideration and our contribution that provides a solution for it. An extensive set of experimental scenarios are adopted to reveal the performance of the proposed scheme exposing the relevant numerical results. A comparative assessment is used for proving the superiority of our model compared with a Support Vector Machine (SVM) scheme, a classical CNN model and the results of two researches that use the same dataset.
Формирование цифровой компетентности будущих воспитателей детей дошкольного возраста средствами информационно-коммуникационных технологий

The article explains the relevance and necessity of forming the digital competence of future pre-school teachers. The role of information and communication technologies in the process of forming the digital competence of future pre-school teachers is considered. The characteristics of digital competence of future pre-school educators have been analyzed by means of information and communication technologies.
Two Modified NichePSO Algorithms for Multimodal Optimization

Multimodal function optimization (MMO) has seen a lot of interest and research over the past several years due to its many real world applications, and its complexity as an optimization problem. Several niching techniques proposed in past research have been combined with popular meta heuristic search algorithms such as evolutionary algorithms (EA), genetic algorithms (GA) and particle swarm optimization (PSO). The NichePSO algorithm was one of the first PSO algorithms proposed for utilizing niching methods and parallel swarms to apply PSO to MMO problems effectively. In this paper, two modified versions of the NichePSO algorithm are proposed, the NichePSO-R and NichePSO-S algorithms, in an attempt to improve its performance. Experimental results show that both proposed algorithms are able to locate more global optima on average than the NichePSO algorithm across several popular MMO benchmark functions.
DeVLBert: Out-of-distribution Visio-Linguistic Pretraining with Causality

In this paper, we propose to investigate out-of-domain visio-linguistic pretraining, where the pretraining data distribution differs from that of downstream data on which the pretrained model will be fine-tuned. Existing methods for this problem are purely likelihood-based, leading to the spurious correlations and hurt the generalization ability when transferred to out-of-domain downstream tasks. By spurious correlation, we mean that the conditional probability of one token (object or word) given another one can be high (due to the dataset biases) without robust (causal) relationships between them. To mitigate such dataset biases, we propose a Deconfounded Visio-Linguistic Bert framework, abbreviated as DeVLBert <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , to perform intervention-based learning. We borrow the idea of the backdoor adjustment from the research field of causality and propose several neural-network based architectures for Bert-style out-of-domain pretraining. The quantitative results on three downstream tasks, Image Retrieval (IR), Zero-shot IR, and Visual Question Answering, show the effectiveness of De-VLBert by boosting generalization ability <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
Voiceprint Recognition Based on Big Data and Gaussian Mixture Model

In order to overcome the disadvantages of voiceprint recognition technology in application, for example, it is difficult to accurately identify the voice of different groups of people, it is difficult to accurately label the voice of each individual, and so on, this paper proposes a novel voiceprint recognition technology based on big data technology and Gaussian mixture model. The technology adopts big data technology, introduces big data thinking, gives full play to the processing and storage capacity of big data technology, and can quickly process and store different sound data. Based on this, Gaussian mixture model is used to analyze the voiceprint differences of different individuals, and EM model is used to label different samples accurately. At the same time, the technology also uses Matlab algorithm to realize the optimal strategy of identifying individual voice differences. The research results show that, different from the traditional voiceprint recognition technology, the new voiceprint recognition technology proposed in this paper can achieve 95% accuracy whether recording or loading.
Geometry of Outdoor Virus Avoidance in Cities

We present low complexity and efficient path selection algorithms for limiting the exposure to pandemic virus during and between lock-downs. The average outdoor exposure rate reduction is around 3.
Onion Pass: Token-Based Denial-of-Service Protection for Tor Onion Services

The Tor network is widely recognized as an important tool to preserve online privacy. In addition to anonymous Internet access, it allows hosting anonymous services, i.e., Onion Services. However, connecting to an Onion Service is realized in a way that makes them vulnerable to Denial-of-Service attacks (DoS). In this work, we propose Onion Pass, an extension of the Tor protocol that utilizes anonymous cryptographic tokens to mitigate the issue. Clients can solve a challenge to acquire tokens that later can be presented to the Onion Service. The Onion Service can thus differentiate between valid and malicious requests when under attack. Please note that Onion pass is agnostic on the specific challenge-response scheme and follows a design philosophy that puts Onion Services in control of the Onion Pass protocol. We implemented a prototype of Onion Pass and present experimental results that indicate its potential to prevent DoS attacks on Onion Services by reducing their CPU usage required to identify malicious requests by a factor of 47.
papagenoReQ: Generation of Embedded Systems from Application Code Requirements

At the beginning of every embedded system is a set of requirements to its software and hardware. In the common case, these requirements change a lot throughout the development cycle and are highly dependent on the software being coded. This already hints at a major problem of the state-of-the-art system development process, in which the hardware platform is established before the software engineering starts: changes to the hardware platform are very costly not only by means of money, but also time. To counteract this problem, we propose a concept where the hardware platform can be directly generated out of a set of requirements, making an inversion of this system development process possible. Using a query language to map requirements to the system to properties of modules that are composed into system configurations, we highly improve the flexibility within embedded systems design and development. The present work uses a set of requirements as an input and generates printed circuit boards of an embedded system matching those requirements based on a developer's module library.
Peculiarities of Psychologists’ and Social Pedagogues’ Work in Terms of Quarantine

The article is devoted to the study of peculiarities of work of psychologists and social teachers in quarantine conditions. The analysis of the mental state of specialists and the possibilities of conducting professional activities remotely is carried out. The ability of nature therapy to influence the restoration of the social and psychological state of children is considered. It is noted that the perspective direction of rehabilitation of children with special educational needs is inclusive tourism, namely, excursions, hikes, travels that contribute to the general process of socialization, integration and development of communication skills. Taking into account the increased demand for services, psychologists and social teachers can effectively work remotely in quarantine.
Image Segmentation Using the EM/MPM Algorithm

In this article we propose to place our work in a Markovian framework for unsupervised image segmentation. We give one of the procedures for estimating the parameters of a Markov field, we limit the work to the EM estimation method and the Posterior Marginal Maximization (MPM) segmentation method. Estimating the number of regions who compones the image is relatively difficult, we try to solve this problem by the K-means Histogram method.
Development and Evaluation of a Technical Information Sharing System for International Remote Medical Education

Technical issues with videoconferencing can often disturb the learning process of international remote medical education. In this study, a system to share technical information among technical support staffs and accelerate their collaboration was developed and evaluated for the purpose of preventing technical issues. The system design incorporates two types of websites. The first site enables each institution to share their technical information. The second site enables the chief engineer to browse the shared information and provide tailored technical direction. The system was evaluated via online questionnaires and online interviews. The first site enables each institution to document their technical environments by visually choosing elements. Pictograms are displayed on the second site for the chief engineer to offer suitable technical direction. The result of questionnaires and interviews were positive. The system contributes to sharing technical information more easily and helps technical staff use the collective information to improve connectivity testing.
emyConnect: Building a Smart Public Service for Young Mobile Europeans

The paper presents and discusses the results of a European project ‘Empowerment of mobile youth in the EU’. It studied the information and communication needs of European mobile youth (EMYs) living in Austria and Estonia to help them better participate in social and political life in their new host countries. These results have been used to develop an emyConnect chatbot as a tool to facilitate such participation and eventually help realize the European citizenship with the help of modern technology. The project outcomes suggest that the chatbot technologies suit well meeting the personalized public service in the domain of life events. From that perspective, chatbot technology demonstrates potential to effectively bridge the information deficit and communication gaps that mobile young people face on a daily basis when living away from home.
Modeling Cardinality in Image Hashing

Cardinality constraint, namely, constraining the number of nonzero outputs of models, has been widely used in structural learning. It can be used for modeling the dependencies between multidimensional labels. In hashing, the final outputs are also binary codes, which are similar to multidimensional labels. It has been validated that estimating how many 1's in a multidimensional label vector is easier than directly predicting which elements are 1 and estimating cardinality as a prior step will improve the classification performance. Hence, in this article, we incorporate cardinality constraint into the unsupervised image hashing problem. The proposed model is divided into two steps: 1) estimating the cardinalities of hashing codes and 2) then estimating which bits are 1. Unlike multidimensional labels that are known and fixed in the training phase, the hashing codes are generally learned through an iterative method and, therefore, their cardinalities are unknown and not fixed during the learning procedure. We use a neural network as a cardinality predictor and its parameters are jointly learned with the hashing code generator, which is an autoencoder in our model. The experiments demonstrate the efficiency of our proposed method.
WASTE MANAGEMENT EVALUATION IN ARORO IRORO MARKET SERUI YAPEN ISLANDS AREAS

The market is one of the public places that is the center of public transactions as well as economic activities that have the potential to generate waste. The purpose of this research is to evaluate the implementation of waste management which consists of institutional, financial, operational, legal and regulatory aspects, as well as community participation in waste management in Aror Iroro Serui Market. The research was conducted using qualitative quantitative descriptive method with cross sectional approach. Data collection techniques used observation and measurement sheets, questionnaires, and in-depth interviews. The object of research was a waste management system starting with observation, then data collection was carried out through questionnaires to traders and data synchronization was carried out with in-depth interviews with market managers then narrated and carried out SWOT analysis (Strength, Opportunities, Weakness, Threats). The results show that the average waste generation reaches 98.1 m3. SWOT analysis shows that the existing strategy is included in quadrant II or a diversification strategy with a value of X, Y (0.91; -0.8). Based on the SWOT analysis, a solid waste management program strategy is obtained in the form of diversification or diversification of waste management regulations, programs to increase obedience of officers in carrying out their obligations, procurement of adequate infrastructure, and forms of effective approaches for the community.
Local energy markets, commercially available tools

This chapter is about the tools commercially available to enable local markets. It deals with the available technologies through a contextual framework. It is a toolbox of capabilities, which can be adapted to set up a market. We start from the market level. What are the tools available to run the actual market? We then look at the participant level. What tools enable participants to run the needed internal processes to be able to participate and what connectivity is available to connect the participant to the market? Finally we take a look at the endpoint, which can mean looking at a single asset or even a group of different assets. The levels presented are role levels that do not preclude single companies or legal entities performing any multiple or grouping of them. Enjoy the chapter and let your mind explore the possibilities presented to discover what creates value for you.
Toward a World with Intelligent Machines That Can Interpret the Visual World

In the previous chapter, we introduced the idea of directly comparing computational models versus human behavior in visual tasks. For example, we assess how models classify an image versus how humans classify the same image. In some tasks, the types of errors made by computational models can be similar to human mistakes. Here we will dig deeper into what current computer vision algorithms can and cannot do. We will highlight the enormous power of current computational models, while at the same time emphasizing some of their limitations and the exciting work ahead of us to build better models.