Reviewerly/exHarmony huggingface dataset:
Automation of Bug-Report Allocation to Developer using a Deep Learning Algorithm

Software bug maintenance is an important aspect of all software projects. The assignment of bug reports is essential in order to resolve bugs efficiently. In the case of open-source software developments and large projects, where many developers are engaged on different aspects of software development, it can be difficult to assign bug removal tasks to an appropriate developer. An increase in reported bugs, coupled with an increase in the number of software developers, will complicate the bug triage process. In these situations, bug triaging might be slow and may increase the Bug Tossing Length (BTL). An automated system to triage bug reports could potentially reduce BTL, as manual assignment of bug reports is tiresome, costly, and very time-consuming. The assignment of bug reporting to an irrelevant developer who does not possess sufficient experience to deal with the bug will adversely impact BTL and customer satisfaction. Text-based classification methods have the potential to make a strong contribution to automating the bug triaging process. In this research, different types of Information Retrieval and Machine Learning algorithms are used to determine the appropriate developer/s to rectify the reported bugs. This study used deep learning algorithms, such as the Bidirectional Long Short-Term Memory Network, to automate the bug triaging process. Bug reports contain textual data related to the bug information. In this research, the pretrained GloVe model is employed for word-to-vector representation of bug reports’ textual information. In this framework, developers’ activities are monitored based on their working history. To test the proposed approach, three large datasets, Net-Beans, Eclipse, and Mozilla, are used. It was observed that the proposed technique produced better results in terms of accuracy, recall, precision and f-measure compared to traditional Machine Learning algorithms for bug report recommendation.
Optical hiding based on single-input multiple-output and binary amplitude-only holograms via the modified Gerchberg-Saxton algorithm

Optics has provided a promising means for the development of information hiding in recent years. However, conventional optical information hiding systems can only hide a limited number of images, and optical implementation complexity is usually high in conventional methods. In this paper, we propose a new scheme to implement optical information hiding based on single-input multiple-output (SIMO) and binary amplitude-only holograms (AOHs) using the modified Gerchberg-Saxton algorithm (MGSA). Different from conventional optical hiding methods with the limited multiplexing capacity, the proposed scheme can retrieve a large number of different secret images from one single host image during optical retrieval. In addition, it is also illustrated that optical implementation complexity is reduced in the proposed method. Simulations and optical experiments are conducted to verify feasibility, security and robustness of the proposed method. It is expected that the proposed method could open up a different research perspective for optical multiple-image hiding.
Proof of Assets in the Diem Blockchain

A great challenge for distributed payment systems is their compliance with regulations, such as anti-money laundering, insolvency legislation, countering the financing of terrorism and sanctions laws. After Bitcoin's MtGox scandal, one of the most needed auditing functionalities for financial solvency and tax reporting purposes is to prove ownership of blockchain reserves, a process known as Proof of Assets (PoA). This work formalizes the PoA requirements in account-based blockchains, focusing on the unique hierarchical account structure of the Diem blockchain, formerly known as Libra. In particular, we take into account some unique features of the Diem infrastructure to consider different PoA modes by exploring time-stamping edge cases, cold wallets, locked assets, spending-ability delegation and account pruning, among the others. We also propose practical optimizations to the byte-size of PoA in the presence of light clients who cannot run a full node, including skipping Validator updates, while still maintaining the 66.67% Byzantine fault tolerance (BFT) guarantee.
Strategic Vectors of Library Development

Establishing of library development strategy is the most important stage in solving the tasks of adapting to the changing environment and increasing demand of users. The purpose of this study is to determine the vectors of strategic development of libraries (public libraries and libraries of scientific and educational institutions). Basing on the results of the analysis, the author identifies nine essential strategic directions and ten vectors, revealing the prospects for the development of libraries, being not only the information repositories and library and information centres, but also working, creative spaces, communication platforms, places for renting equipment, startup sites, etc. The article presents the specific features of public libraries, which are close interaction with the local community, promotion of intercultural and intergenerational dialogue, as well as socialization of the population. Strategies of libraries of scientific and educational institutions much attention pay to the integration of librarians in the research process, creation of analytical products, development of services for scientometric analysis and information support for open scientific research. Libraries strive to serve readers and visitors with the use of modern information technologies, organize spatial environment that ensure comfortable stay, provide resources and services that meet the changing preferences of users, which allows creating positive image of the library and attracting readers.
Accelerating Bayesian Structure Learning in Sparse Gaussian Graphical Models

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Factors associated with women's exposure to mass media for Health Care Information in Ethiopia. A case-control study

IntroductionMass media are often essential to communicate health messages. Women's exposure to mass media remained low in Ethiopia. Evidence was scarce on underlined factors for enhancing women's exposure to mass media.ObjectiveThis study aimed to assess factors associated with women's exposure to mass media in Ethiopia.MethodsThe study was a secondary data analysis of the Ethiopian demographic and health survey 2016. A case-control study applied with frequency matching by place of residence and women's educational levels using a 1:1 ratio. A total of 9885 participants (4824 cases and 5061 controls) were involved in the study. Cases were women who had exposure and controls were who did not have exposure to mass media. Women's exposure to mass media is defined as the total number of women aged 15–49 who report exposure to either radio, television, newspapers, or magazines at least once a week. Logistic regressions analysis was conducted and the level of statistical significance was determined by a p-value of less than 0.05to identify factors associated with women's exposure to mass media.ResultBeing in Women's age group 30–34 years (AOR = 1.19,95% C.I:1.04–1.35), having own mobile phones (AOR = 1.92,95% C.I:1.71–2.15), women ever used internet (AOR = 1.56,95% C.I:1.13–2.13), Women in the female-headed household (AOR = 0.76,95%C.I:0.68–0.86), Women in middle (AOR = 1.48,95%C.I:1.25–1.76), richer (AOR = 2.13,95%C.I:1.82–2.49) and richest (AOR = 2.67,95% C.I:2.22–3.21) wealth index, and women visited by health care provider (AOR = 1.41,95% C.I:1.17–1.71) were factors affecting women's exposure to mass media.ConclusionWomen's access to mobile phones, internet, and care provider counseling were facilitating factors for exposure to mass media. Younger age women, lower wealth quantile, and female headship in the household were risk factors for the non-exposure of women to mass media. Empowering household wealth and improving access to ICT could improve women's exposure to mass media.
Distribution, Separation and Characterisation of Valuable Heavy Minerals from the Brahmaputra River Basin, Kurigram District, Bangladesh

Bangladesh’s heavy minerals deposits in river systems remain largely unexplored with past research restricted to beach placer deposits along the south-eastern coastline of the country. In this study, 64 samples were collected from stable sand bars from the northern Brahmaputra River. The average total heavy minerals (THM) from all sample sites was 10.73 wt%, with the THM concentrations generally highest at the outermost edges of the river and lowest within the mid-channel regions. There was no variation in THM content from north (upstream) to south (downstream). Valuable heavy minerals (VHMs) magnetite, ilmenite, garnet and zircon made up around 25–30 wt% of the THM content, with other heavy minerals including amphibole, pyroxene, kyanite, sillimanite, monazite, apatite and xenotime making up the remaining 70–75 wt%. A VHM distribution map showed that the upstream division of the studied area appeared to be the most prospective for valuable heavy minerals. A bench scale heavy mineral sands beneficiation flowsheet involving unit processes employing gravity, magnetic and electrostatic properties was designed and based on a detailed characterisation and phase analysis of the resulting fractions, it was determined that VHMs made up ~2 wt% of the material recovered. Of these, garnet and ilmenite made up the bulk of the VHMs at levels of 0.88 and 0.51 wt%, respectively, with recoveries of &gt;83%. Characterisation of the concentrates showed that further refinement of the processing conditions is required to generate concentrates that are potentially suitable for commercial applications.
Transforming Classic Learning Games with the Use of AR: The Case of the Word Hangman Game

Augmented Reality in combination with playful learning is used to enhance students' engagement in blended learning environments. This paper presents an AR game with hand tracking & hand interaction that transforms the classic Hangman word game. The thematic word areas of the game are recycling and Covid 19, two areas that are of great interest today. The game offers alternative interaction through a 3D hand model and 3D objects/letters. The player moves the 3D letters to complete words and collect paper cards with the words' image. These paper cards are then placed, in any order, by the student to create a picture story and a virtual maquette with AR objects is generated. This maquette allows player to create different picture-stories by shuffling the order of found words' paper cards. A field study has been contacted with two groups of students, one group used classic learning techniques and the other group used our AR game. Preliminary results of the study show that all students improved their language skills but only the group with the AR game evolve into being more active, involved, participative and engaged in the educational process and developed their creativity skills.
Characterizing the Impact of Network Delay on Bitcoin Mining

While previous works have discussed the network delay upper bound that guarantees the consistency of Nakamoto consensus, measuring the actual network latencies and evaluating their impact on miners/pools in Bitcoin remain open questions. This paper fills this gap by: (1) defining metrics that quantify the impact of network latency on the mining network; (2) developing a tool, named miner entanglement (ME), to experimentally evaluate these metrics with a focus on the network latency of the top mining pools; and (3) quantifying the impact of the current network delays on Bitcoin&#x0027;s mining network. For example, we evaluated that Poolin, a Bitcoin mining pool, was able to gain between 0.5&#x0025; and 1.9&#x0025; of blocks in addition (i.e., from 36.27 BTC to 137.83 BTC) per week thanks to its low network latency. Moreover, as pools are rational in Bitcoin, we model the strategy a pool would follow to improve its network latency (e.g., by leveraging our ME tool) as a two party game. We show that a Bitcoin mining pool could improve its effective hash rate by up to 4.5&#x0025;. For a multi-party game, we use a state-of-the-art Bitcoin mining simulator to study the situation where all pools attempt to improve their network latency and show that the largest mining pools would improve their revenue and reach a Nash equilibrium while the smaller mining pools would suffer from a decreased access to the network, and therefore a decreased revenue. These conclusions further incentivize the centralisation of the mining network in Bitcoin, and provide an empirical explanation for the observed tendency of pools to design and rely on low latency private networks.
Personalized Privacy Protection Solutions

In this chapter, we demonstrate personalized privacy protection techniques that applied to secure real-world applications such as cyber-physical systems, social networks, smart homes, and location-based services.
DEGAS: differentiable efficient generator search

Network architecture search achieves state-of-the-art results in various tasks such as classification and semantic segmentation. Recently, a reinforcement learning-based approach has been proposed for generative adversarial networks (GANs) search. In this work, we propose an alternative strategy for GAN search by using a proxy task instead of common GAN training. Our method is called differentiable efficient generator search, which focuses on efficiently finding the generator in the GAN. Our search algorithm is inspired by the differential architecture search strategy and the global latent optimization procedure. This leads to both an efficient and stable GAN search. After the generator architecture is found, it can be plugged into any existing framework for GAN training. For consistency-term GAN, which we use in this work, the new model outperforms the original inception score results by 0.25 for CIFAR-10.
A data-driven explainable case-based reasoning approach for financial risk detection

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Task Scheduling Algorithms in Cloud Computing: A Survey

In the cloud architecture, the virtual machines (VMs), data centers, hosts and brokers are composed to establish the communication. The most reliable VM is searched through brokers in order to execute the cloudlet. When working in a cloud environment the aim is to schedule tasks properly for system to work efficiently and this is a major issue in the cloud computing, because if the tasks are not scheduled properly the system performance may degrade. In this paper number of existing task scheduling algorithms are studied and compared on the basis of different metrics. Some approaches are provided in order to improve the performance of a particular algorithm.
Challenges and Opportunities on the Application of Heuristic Evaluations: A Systematic Literature Review

Heuristic evaluation belongs to the usability inspection methods and is considered one of the most popular methods since it allows to discover over 75% of the total usability problems involving only 3 to 5 usability experts, in comparison with user tests. However, certain problems and challenges have been identified at the time of their execution. In this study we present the results of conducting a Systematic Literature Review (SLR) to identify case studies, challenges, problems, and opportunities on the execution of heuristic evaluations in the context of a research for the automation and formalization of the process. For this SLR, we have employed the protocol proposed by Kitchenham and Charters. The research was carried out on September 7 of 2020 and retrieved a total of 167 studies of which 37 were selected for this review. The results show that the main challenges are related to the low suitability of the chosen set of heuristics and the low expertise of usability evaluators. Additionally, we have identified that exist very few software solutions that support and automate the process. Finally, we have found that there were many protocols to follow when applying a heuristic evaluation, like the definition of new usability heuristics for a given case of study. According to the results obtained, we can conclude that it is necessary to develop and validate a tool based on a formal protocol that supports and automate the heuristic evaluation process, that gives solutions to the challenges and opportunities identified in this research.
Using FSMs to Find Patterns for Off-Chain Computing

One of the problems arising in using blockchains is their size-constraints regarding performance. This paper proposes a new algorithm for blockchain software developers and architects to use for determining what computations of a smart contract can be effectively done off-chain without loss of trust. Our algorithm uses FSMs or HSMs in order to create smart contract patterns using graphs and then uses pattern recognition to identify which parts of the smart contracts should be considered for moving off-chain. The pattern recognition property used is that once software execution transits into the pattern's entry state, off-chain execution continues until the execution transits into the pattern's exit state, when execution continues on-chain. The software developer reviews each pattern together with information, such as anticipated overhead cost due to off-chain execution, and she is provided with guidance on decision making whether to execute the smart contract pattern under consideration off-chain. Expert software developer inspection, in the context of a Trade Finance use case, validates that our algorithm finds optimal patterns for moving computations off-chain and improve blockchain software performance.
DeepQSC: a GNN and Attention Mechanism-based Framework for QoS-aware Service Composition

When several Web services with simple functions need to be combined to provide more complex functions, how to choose from a large number of Web services with the same functions but different quality of service is a QoS-based service composition problem. Currently, there are many classical methods and reinforcement learning methods applied to the QoS-based service composition problem. However, these methods require long computation time. We address three challenges in building an end-to-end supervised learning framework. 1) The number of Web services composing different composite services varies. 2) The topological relationships among Web services are difficult to express and difficult to integrate into neural networks. 3) The number of Web services providing each sub-function in composite services varies. Finally, we propose DeepQSC, a deep supervised learning framework based on graph convolutional networks and attention mechanisms. The framework can form high QoS composite services with limited computation time. We conducted experiments on a real-world dataset. The experiments show that DeepQSC has a significant advantage over six current state-of-the-art algorithms.
Understanding Task Differences to Leverage the Usability and Adoption of Voice Assistants (VAs)

Voice Assistants (VAs) are becoming increasingly popular, but evidence shows that users' utilization of features is limited to few tasks. Although the literature has shown that usability impact VA adoption, little is known about how usability varies across VA tasks and its relation to task adoption by users. To address this gap, we conducted usability tests followed by debriefing sessions with Siri and Google Assistant users, assessing usability measures of six features and uncovering reasons for task usage. The results showed that usability varied across tasks regarding task completeness, error number, error types, and user satisfaction. Checking the weather and making phone calls had the best usability measures, followed by playing songs and sending messages, whereas adding appointments to a calendar and searching for information were the most incomplete and frustrating interactions. Furthermore, usability-related factors such as perceived ease of use and the interaction's hands/eyes-free nature influenced task adoption. Nevertheless, we also identified other task-independent factors that affect VA usage, such as use context (i.e., place, task content), VAs' personality, and preferences for settings. Our main contributions are recommendations for VA design, highlighting that attending to tasks separately is paramount to understanding specific usability issues, task requirements, users' perceptions of features, and developing design solutions that leverage VAs' usability and adoption.
A Network Resource Aware Federated Learning Approach using Knowledge Distillation

Federated Learning (FL) has gained unprecedented growth in the past few years by facilitating data privacy. This poster proposes a network resource aware federated learning approach that utilizes the concept of knowledge distillation to train a machine learning model by using local data samples. The approach creates different groups based on the bandwidth between clients and server and iteratively applies FL to each group by compressing the model using knowledge distillation. The approach reduces the bandwidth requirement and generates a more robust model trained on the data of all clients without revealing privacy.
A Blockchain-based Framework for Information Management in Internet of Vehicles

The existing data management solutions for Internet of Vehicles (IoV) data cannot guarantee data source credibility verification and data security. The decentralized technical architecture of blockchain provides new ideas for solving these problems. However, it faces challenges in computing resource consumption and data segmentation between on-chain and off-chain. This paper proposes a blockchain-based information management system that can reliably manage data related to vehicles. The system adopts a sharding blockchain structure, which means IoV nodes in a limited area form a sub-chain to increase the calculation speed and reduce the burden on the main-chain. Besides, an on-chain and off-chain computing scheme based on trusted execution environment is proposed to solve data source credibility and computing resource consumption of smart contract. A case which is the driving data management in vehicle insurance scenarios is studied and implemented as an example of application of the system proposed. Experiments show that the resource and time consumption of the smart contract designed is acceptable.