Reviewerly/exHarmony huggingface dataset:
An exploratory study on the introduction and removal of different types of technical debt in deep learning frameworks

To complete tasks faster, developers often have to sacrifice the quality of the software. Such compromised practice results in the increasing burden to developers in future development. The metaphor, technical debt, describes such practice. Prior research has illustrated the negative impact of technical debt, and many researchers investigated how developers deal with a certain type of technical debt. However, few studies focused on the removal of different types of technical debt in practice. To fill this gap, we use the introduction and removal of different types of self-admitted technical debt (i.e., SATD) in 7 deep learning frameworks as an example. This is because deep learning frameworks are some of the most important software systems today due to their prevalent use in life-impacting deep learning applications. Moreover, the field of the development of different deep learning frameworks is the same, which enables us to find common behaviors on the removal of different types of technical debt across projects. By mining the file history of these frameworks, we find that design debt is introduced the most along the development process. As for the removal of technical debt, we find that requirement debt is removed the most, and design debt is removed the fastest. Most of test debt, design debt, and requirement debt are removed by the developers who introduced them. Based on the introduction and removal of different types of technical debt, we discuss the evolution of the frequencies of different types of technical debt to depict the unresolved sub-optimal trade-offs or decisions that are confronted by developers along the development process. We also discuss the removal patterns of different types of technical debt, highlight future research directions, and provide recommendations for practitioners.
Knowledge-guided multiobjective particle swarm optimization with fusion learning strategies

Abstract Multiobjective particle swarm optimization (MOPSO) algorithm faces the difficulty of prematurity and insufficient diversity due to the selection of inappropriate leaders and inefficient evolution strategies. Therefore, to circumvent the rapid loss of population diversity and premature convergence in MOPSO, this paper proposes a knowledge-guided multiobjective particle swarm optimization using fusion learning strategies (KGMOPSO), in which an improved leadership selection strategy based on knowledge utilization is presented to select the appropriate global leader for improving the convergence ability of the algorithm. Furthermore, the similarity between different individuals is dynamically measured to detect the diversity of the current population, and a diversity-enhanced learning strategy is proposed to prevent the rapid loss of population diversity. Additionally, a maximum and minimum crowding distance strategy is employed to obtain excellent nondominated solutions. The proposed KGMOPSO algorithm is evaluated by comparisons with the existing state-of-the-art multiobjective optimization algorithms on the ZDT and DTLZ test instances. Experimental results illustrate that KGMOPSO is superior to other multiobjective algorithms with regard to solution quality and diversity maintenance.
Towards an efficient real-time kernel function stream clustering method via shared nearest-neighbor density for the IIoT

The rapid development of 5G communication technology will considerably help the expansion and gth Industrial Internet of things (IIoT). Indeed, both the data scale and dimension will significantly increase, leading to a challenging problem of the effective real-time stream clustering in the field of IIoT streaming mining. This paper proposes an efficient and novel real-time kernel function stream clustering method based on shared nearest-neighbor density for IIoT. In the proposed method, the projection technology is used to select the dimensions of high-dimensional data, while the Euler kernel function is used as the similarity measure. Furthermore, the micro-clusters are divided by the shared nearest-neighbor density, and the outliers are relearned. The main innovation lies in using the Euler kernel function to measure the similarity, reduce the sensitivity of outliers, and use the relearning strategy to improve the clustering quality of the data stream. The theoretical analysis and experimental comparisons on the simulated data sets show that the proposed method is very effective and represents a good solution for clustering real-time data streams of IIoT.
Weighted combination of per-frame recognition results for text recognition in a video stream

The scope of uses of automated document recognition has extended and as a result, recognition techniques that do not require specialized equipment have become more relevant. Among such techniques, document recognition using mobile devices is of interest. However, it is not always possible to ensure controlled capturing conditions and, consequentially, high quality of input images. Unlike specialized scanners, mobile cameras allow using a video stream as an input, thus obtaining several images of the recognized object, captured with various characteristics. In this case, a problem of combining the information from multiple input frames arises. In this paper, we propose a weighing model for the process of combining the per-frame recognition results, two approaches to the weighted combination of the text recognition results, and two weighing criteria. The effectiveness of the proposed approaches is tested using datasets of identity documents captured with a mobile device camera in different conditions, including perspective distortion of the document image and low lighting conditions. The experimental results show that the weighting combination can improve the text recognition result quality in the video stream, and the per-character weighting method with input image focus estimation as a base criterion allows one to achieve the best results on the datasets analyzed.
Market-oriented online bi-objective service scheduling for pleasingly parallel jobs with variable resources in cloud environments

In this paper, we study the market-oriented online bi-objective service scheduling problem for pleasingly parallel jobs with variable resources in cloud environments, from the perspective of SaaS (Software-as-as-Service) providers who provide job-execution services. The main process of scheduling SaaS services in clouds is: a SaaS provider purchases cloud instances from IaaS providers to schedule end users’ jobs and charges users accordingly. This problem has several particular features, such as the job-oriented end users, the pleasingly parallel jobs with soft deadline constraints, the online settings, and the variable numbers of resources. For maximizing both the revenue and the user satisfaction rate, we design an online algorithm for SaaS providers to optimally purchase IaaS instances and schedule pleasingly parallel jobs. The proposed algorithm can achieve competitive objectives in polynomial run-time. The theoretical analysis and simulations based on real-world Google job traces as well as synthetic datasets validate the effectiveness and efficiency of our algorithm.
Diffusions on a space of interval partitions: Poisson–Dirichlet stationary distributions

We introduce diffusions on a space of interval partitions of the unit interval that are stationary with the Poisson–Dirichlet laws with parameters α,0 and α,α. The construction has two steps. The first is a general construction of interval partition processes obtained previously by decorating the jumps of a Lévy process with independent excursions. Here, we focus on the second step which requires explicit transition kernels and, what we call, pseudo-stationarity. This allows us to study processes obtained from the original construction via scaling and time-change. In a sequel paper we establish connections to diffusions on decreasing sequences introduced by Ethier and Kurtz (Adv. in Appl. Probab. 13 (1981) 429–452) and Petrov (Funktsional. Anal. i Prilozhen. 43 (2009) 45–66). The latter diffusions are continuum limits of up-down Markov chains on Chinese restaurant processes. Our construction is also a step toward resolving longstanding conjectures by Feng and Sun on measure-valued Poisson–Dirichlet diffusions and by Aldous on a continuum-tree-valued diffusion.
Media technologies and virtual practices in creative approaches to educational training of a social worker

Abstract The educational system has undergone significant transformations in recent decades, the main catalyst of which is the development of the information sphere of society. Both individual elements and the structural, substantive content are modified and updated. Among the vectors that determine the dynamics and direction of this process is the growing socio-cultural role of modern media, which are based on digital technologies as a globalization platform and the main type of communication. The change of the information code - digitalization of the media sphere, creation of new media platforms of social interaction initiates the need to build an educational system that meets the demands of today. Among the many factors that are currently relevant in educational approaches are media technologies. Their role in the training of specialists in various fields is growing symptomatically, as it is associated with the functions of “new media”, which for the subject of activity become transgressive guidelines for constructing reality. Educational and further professional activity of a social worker needs to be reconsidered in the context of innovative strategies, first of all formation of media technologies in social work. Their formation is based on modern principles of new media: interactivity of communication and gamification of virtual practices. In social work at different levels of activity of the representatives of this sphere, media competencies are in demand, which include mastery of instrumental methods of implementing game, virtual, art practices as a motivational resource, technology of initiating social activity, cultural-adaptive and creative orientation of social assistance clients. This issue includes the specifics of the profession of social worker through the prism of the possibilities of filling his case with a creative resource - the tools of media technology in social work. The article outlines both the potential of gamification and virtualization of media practices in the educational and further professional activities of a social worker, and points out the possible dangers of implementing these technologies for certain categories of people in need. The article is a research exploration in the formation of a professional strategy of a social worker based on the involvement of the latest media practices as an expansion of media tools, the implementation of a creative approach to helping the client of social services.
Analysis of Object Detection Performance Based on Faster R-CNN

Abstract The related regions with convolutional neural networks (R-CNN) models have been widely used in the field of object detection. Faster R-CNN significantly improves the overall performance by adding RPN, especially in terms of detection speed. However, the application of different pre-training models will result in a great difference in the performance of Faster R-CNN. This paper analyzed the performance of Faster R-CNN models based on different pre-training models and conducted a comprehensive evaluation of the performance of Faster R-CNN. The experimental results showed the accuracy and detection speed of R-CNN, fast R-CNN and faster R-CNN based on three different data sets. They can objectively and comprehensively evaluate the performance of R-CNN, fast R-CNN, and faster R-CNN.
Analysis The Effect of Website Quality on User Satisfaction with The WebQual 4.0 Method and Importance-Performance Analysis (IPA) (Case Study: SPMB Sebelas Maret University’s Website)

Abstract The digital era describes the presence of technology where everyone can exchange information so quickly by the internet. For an institution, a website has its own benefits, among others, as a media for promotion, presenting information, and providing online services. This study aims to evaluate and improve website performance by analyzing the quality of the Sebelas Maret University selection of new student admissions (SPMB) website by utilizing a combination of WebQual 4.0 and Importance Performance Analysis (IPA) methods. In its analysis, WebQual method looks at 3 main dimensions: Usability Quality, Information Quality, and Service Interaction Quality. This research, which involved students in UNS, found that the website’s gap analysis still has a negative value on all attributes and there are several attributes that require improvement related to website design, presentation of detailed and up-to-date information and navigation on the website. The results from this study are expected could be used as evaluation for the SPMB website’s development to meet user expectation.
Tax Regulation on Blockchain and Cryptocurrency: The Implications for Open Innovation

Blockchain is a technology that will change the relationships between the different actors in society, individuals, companies and administration, in aspects as important as taxation, by implementing concepts such as Self-sovereign identity (SSI) and Smart Contracts; which support, for example, virtual currencies, that are not controlled by any state, financial institution or centralized company. Hence, the growing interest of researchers, investors, traders, marketers, enterprises, and administrations to know the scope of this new technology and its tax implications. The main objective of this work is to clarify the status of these studies, explore issues, methods, findings, and trends as well as to define their meaning within the current research scenario. To achieve these objectives, bibliometric analysis was carried out, retrieving 349 research papers, and analyzing 343 papers published between 2015–2019 based on the results of the Web of Science (WoS).
Crypto assets regulation in the UK: an assessment of the regulatory effectiveness and consistency

Purpose The UK authority published its first regulatory guidance on crypto-assets in July 2019. This paper aims to critically evaluate the effectiveness of the crypto-asset regulation in the UK and the consistency of the existing regulatory scheme. Design/methodology/approach This paper adopts comparative methods to carry out the analysis. The paper begins by elaborating the development of crypto-assets alongside the financial innovation in the world and pinpointing the core Acts and Regulations applied to crypto-assets in the UK. The paper also discusses a court case in the EU to highlight an argument among legal professions concerning crypto-assets classification. Findings Through carefully analysing relevant primary and secondary legislation of the UK and EU, this paper identifies some unclarified issues in the regulatory framework and discovers three flaws in the regulatory system. The paper concludes that the effectiveness of the current regulatory scheme is poor and room for improvement exists. Originality/value The paper provides the first review and a thorough analysis of the Laws and Acts applied to the crypto-asset regulation in the UK. It also calls on a simpler and clearer regulatory scheme from the perspectives of market participants and consumers. The discovered issues in the crypto-asset regulation in the UK may urge authorities to improve the existing regulatory frameworks and legal provisions.
Personalized Image Recoloring for Color Vision Deficiency Compensation

Several image recoloring methods have been proposed to compensate for the loss of contrast caused by color vision deficiency (CVD). However, these methods only work for dichromacy (a case in which one of the three types of cone cells loses its function completely), while the majority of CVD is anomalous trichromacy (another case in which one of the three types of cone cells partially loses its function). In this paper, a novel degree-adaptable recoloring algorithm is presented, which recolors images by minimizing an objective function constrained by contrast enhancement and naturalness preservation. To assess the effectiveness of the proposed method, a quantitative evaluation using common metrics and subjective studies involving 14 volunteers with varying degrees of CVD are conducted. The results of the evaluation experiment show that the proposed personalized recoloring method outperforms the state-of-the-art methods, achieving desirable contrast enhancement adapted to different degrees of CVD while preserving naturalness as much as possible.
Impact of Climate Change on Wind and Photovoltaic Energy Resources in the Canary Islands and Adjacent Regions

The progressive energy transition to systems with higher shares of renewable energy is particularly important in islands regions, which are largely dependent on energy imports. In this context, to assess the impact of climate change on renewable energy resources during the 21st century is crucial for polycimakers and stakeholders. In this work, we provide an overview of wind and photovoltaic (PV) resources, its variability and complementarity between them, as well as their future changes, in the Canary Islands and surrounding areas. Variability is assessed through the analysis of energy droughts (low-productivity periods). In addition, a sensitivity test is performed to find the optimal combination of PV (photovoltaic) and wind that reduce energy droughts and the persistence of that conditions at a local scale. A set of climate simulations from the MENA-CORDEX runs are used, in present and future climate (2046–2065, 2081–2100) for two different scenarios (RCP2.6, RCP8.5). Results show different changes in wind productivity depending on the scenario: a decrease in RCP2.6 and an increase in the RCP8.5. PV experienced a subtle decrease, with some exceptions. Changes in variability are small and the complementarity test shows that high shares of PV energy (above 50%) reduce both, energy droughts and the persistence of drought conditions.
Why Is MBTI Personality Detection from Texts a Difficult Task?

Automatic detection of the four MBTI personality dimensions from texts has recently attracted noticeable attention from the natural language processing and computational linguistic communities.Despite the large collections of Twitter data for training, the best systems rarely even outperform the majority-class baseline.In this paper, we discuss the theoretical reasons for such low results and present the insights from an annotation study that further shed the light on this issue.
Incentive Mechanism Design for Distributed Coded Machine Learning

A distributed machine learning platform needs to recruit many heterogeneous worker nodes to finish computation simultaneously. As a result, the overall performance may be degraded due to straggling workers. By introducing redundancy into computation, coded machine learning can effectively improve the runtime performance by recovering the final computation result through the first k (out of the total n) workers who finish computation. While existing studies focus on designing efficient coding schemes, the issue of designing proper incentives to encourage worker participation is still under-explored. This paper studies the platform's optimal incentive mechanism for motivating proper workers' participation in coded machine learning, despite the incomplete information about heterogeneous workers' computation performances and costs. A key contribution of this work is to summarize workers' multi-dimensional heterogeneity as a one-dimensional metric, which guides the platform's efficient selection of workers under incomplete information with a linear computation complexity. Moreover, we prove that the optimal recovery threshold k is linearly proportional to the participator number n if we use the widely adopted MDS codes for data encoding. We also show that the platform's increased cost due to incomplete information disappears when worker number is sufficiently large, but it does not monotonically decrease in worker number.
Word Senses as Clusters of Meaning Modulations: A Computational Model of Polysemy

Abstract Most words in natural languages are polysemous; that is, they have related but different meanings in different contexts. This one‐to‐many mapping of form to meaning presents a challenge to understanding how word meanings are learned, represented, and processed. Previous work has focused on solutions in which multiple static semantic representations are linked to a single word form, which fails to capture important generalizations about how polysemous words are used; in particular, the graded nature of polysemous senses, and the flexibility and regularity of polysemy use. We provide a novel view of how polysemous words are represented and processed, focusing on how meaning is modulated by context. Our theory is implemented within a recurrent neural network that learns distributional information through exposure to a large and representative corpus of English. Clusters of meaning emerge from how the model processes individual word forms. In keeping with distributional theories of semantics, we suggest word meanings are generalized from contexts of different word tokens, with polysemy emerging as multiple clusters of contextually modulated meanings. We validate our results against a human‐annotated corpus of polysemy focusing on the gradedness, flexibility, and regularity of polysemous sense individuation, as well as behavioral findings of offline sense relatedness ratings and online sentence processing. The results provide novel insights into how polysemy emerges from contextual processing of word meaning from both a theoretical and computational point of view.
Compact Zero-Knowledge Proofs for Threshold ECDSA with Trustless Setup

Threshold ECDSA signatures provide a higher level of security to a crypto wallet since it requires more than t parties out of n parties to sign a transaction. The state-of-the-art bandwidth efficient threshold ECDSA used the additive homomorphic Castagnos and Laguillaumie (CL) encryption based on an unknown order group G, together with a number of zero-knowledge proofs in G. In this paper, we propose compact zero-knowledge proofs for threshold ECDSA to lower the communication bandwidth, as well as the computation cost. The proposed zero-knowledge proofs include the discrete-logarithm relation in G and the well-formedness of a CL ciphertext.When applied to two-party ECDSA, we can lower the bandwidth of the key generation algorithm by 47%, and the running time for the key generation and signing algorithms are boosted by about 35% and 104% respectively. When applied to threshold ECDSA, our first scheme is more optimized for the key generation algorithm (about 70% lower bandwidth and 85% faster computation in key generation, at a cost of 20% larger bandwidth in signing), while our second scheme has an all-rounded performance improvement (about 60% lower bandwidth, 46% faster computation in key generation without additional cost in signing).