Reviewerly/exHarmony huggingface dataset:
How to introduce TD Management into a Software Development Process – A Practical Approach

This paper presents a process for management of technical debt (TD) and how it can be integrated sustainably into the existing software development process in an industrial context. A holistic approach is pursued where the development team and the management team agree on a common understanding of TD. From this, requirements for the process are derived, which should support the medium- and long-term planning of the roadmap. By iteratively evaluating the technical debt in the context of the feature roadmap, an internal development benefit (debt repayment) can be combined with an external benefit (new features), while optimizing development costs at the same time. The first results illustrate the positive effect of the continuous repayment of technical debt.
Automated Self-Admitted Technical Debt Tracking at Commit-Level: A Language-independent Approach

Software and systems traceability is essential for downstream tasks such as data-driven software analysis and intelligent tool development. However, despite the increasing attention to mining and understanding technical debt in software systems, specific tools for supporting the track of technical debts are rarely available. In this work, we propose the first programming language-independent tracking tool for self-admitted technical debt (SATD) – a sub-optimal solution that is explicitly annotated by developers in software systems. Our approach takes a git repository as input and returns a list of SATDs with their evolution actions (created, deleted, updated) at the commit-level. Our approach also returns a line number indicating the latest starting position of the corresponding SATD in the system. Our SATD tracking approach first identifies an initial set of raw SATDs (which only have created and deleted actions) by detecting and tracking SATDs in commits’ hunks, leveraging a state-of-the-art language-independent SATD detection approach. Then it calculates a context-based matching score between pairs of deleted and created raw SATDs in the same commits to identify SATD update actions. The results of our preliminary study on Apache Tomcat and Apache Ant show that our tracking tool can achieve a F1 score of 92.8% and 96.7% respectively.
Bibliometrix Analysis: Trade Process Information Systems in Tangerang Modern Land Market

Developing an information system that can facilitate the trading process at Pasar Modernland Tangerang. Pasar Modernland Tangerang is a modern market that has various types of traders and products offered to consumers. However, the trading process in this market still faces a number of obstacles, such as a lack of efficiency, lack of transparency, and difficulties in managing trade data. Therefore, this study aims to design and implement an information system that can help improve the trading process at Pasar Modernland Tangerang. Study This aim For develop system information that facilitates the trading process at Pasar Modernland Tangerang. Pasar Modernland Tangerang is a modern market that has various type merchants and products offered to consumer. The research method used in the given text is a systematic literature review. The text describes the process of conducting a bibliometric analysis to identify relevant publications for research. This study used a combination of keywords, filtering by time, type of publication, open access, language, and removal of duplicates to screen studies that fit the research objectives. This helps researchers narrow the range of studies to be synthesized and analyzed(Rojas-Sánchez et al., 2023). The information system designed and implemented in the study aimed to increase efficiency, strengthen transparency, and ease trading data management. The system covered several important features, such as trader registration and profile management, online ordering and payment, inventory management, and trading data reports and analysis. The system was expected to help increase the quality of service for consumers, make it easy for merchants to manage their business, and increase the potential growth of the economy in the market area
Synergetic Assessment of Quality and Aesthetic: Approach and Comprehensive Benchmark Dataset

Quantifications of image quality and aesthetic have been regarded as two independent fields in computer vision. Generally, image quality assessment aims at measuring image distortions and image aesthetic is judged by commonly established photography rules. However, either measuring image quality or aesthetic alone is not sufficient to qualitatively rank images. Therefore, this paper puts forward the synergetic assessment of quality and aesthetic to help understand the subjective human preferences of digital pictures more comprehensively. Specifically, considering that the images of existing benchmark datasets are only labeled with single attribute, we first establish a new dataset which contains 9042 real-world images with the corresponding human rated pair-wise quality-aesthetic scores. Previously, these images are only labeled with aesthetic score, and we evaluate the subjective quality score of them, so that it can make up the lack of image dataset with double attributes. Moreover, since the existing methods are mostly designed for individual attribute prediction. We then propose a two-stream learning network to assess both quality and aesthetic of images in parallel. This network follows the top-down perception mechanism which learns from both fined grained details and holistic image layout simultaneously. Furthermore, we introduce a Channel-Diversity loss, which can be deployed in grouped convolution operation, and can constrain channels to be mutually exclusive across the spatial dimensions. To some extent, this contributes to spotlight different local discriminative regions with a finer granularity. Finally, experiments demonstrate that our method outperforms the state-of-the-art methods on our established benchmark dataset and other benchmark datasets in terms of image quality and aesthetic assessment. We hope this paper could serve as a potent reference and be useful for future research on the study of image ranking. Both the benchmark dataset and the code will be publicly available to facilitate further research.
General deSouches Commands Multi-agent Army for Performing in Agents Assemble III Scenario: FIT-BUT at MAPC 2022

The Multi-agent Programming Contest provides a good opportunity to compare different approaches to creating a multi-agent system for given scenarios. In this edition, the FIT-BUT team tested solutions based on their own design of a multi-agent system, both on its organisational levels and on the actual architectures of the agents used. The three-level organisation of our multi-agent system was based on the principle of central strategic planning, which results in strategic goals for some subset of agents. These agents execute the goals as their mission and are coordinated by the agent that lies between them and the central element in the hierarchy. The resulting system has stood up to competition and some aspects of it may inspire the design of other multi-agent systems.
Quantum Computing Reliability: Problems, Tools, and Potential Solutions

Quantum computing is a new computational paradigm, expected to revolutionize the computing field in the next few years. Qubits, the atomic units of a quantum circuit, exploit the quantum physics properties to increase the parallelism and speed of computation. Unfortunately, qubits are both intrinsically noisy and highly susceptible to external sources of faults, such as ionizing radiation. The latest discoveries highlight a much higher radiation sensitivity of qubits than traditional transistors and identify a much more complex fault model than bit-flip. The observed error rate is so high that researchers are questioning the large-scale adoption of quantum computers. The reliability and dependability community is asked to act to find innovative solutions to improve the reliability of quantum applications. This tutorial aims at providing the DSN community with the tools to do so and to train the attendees on quantum fault injection.
Sequential Deep Learning for Mars Autonomous Navigation

Recent advances in computer vision for space exploration have handled prediction uncertainties well by approximating multimodal output distribution rather than averaging the distribution. While those advanced multimodal deep learning models could enhance the scientific and engineering value of autonomous systems by making the optimal decisions in uncertain environments, sequential learning of those approximated information has depended on unimodal or bimodal probability distribution. In a sequence of information learning and transfer decisions, the traditional reinforcement learning cannot accommodate the noise in the data that could be useful for gaining information from other locations, thus cannot handle multimodal and multivariate gains in their transition function. Still, there is a lack of interest in learning and transferring multimodal space information effectively to maximally remove the uncertainty. In this study, a new information theory overcomes the traditional entropy approach by actively sensing and learning information in a sequence. Particularly, the autonomous navigation of a team of heterogeneous unmanned ground and aerial vehicle systems in Mars outperforms benchmarks through indirect learning.
Implementation of a secret sharing-based masking scheme against side-channel attack for ultra-lightweight ciphers in IoT

IoT applications consist of a group of small physical devices with sensing capabilities, working collaboratively to provide a specific functionality. Collaboration is realised by sending data from one or more devices in a network to another device or group of devices. Data stored or processed across an IoT ecosystem is likely to contain sensitive information, requiring strong confidentiality. Cryptographic algorithmic modules embedded on these physical devices are particularly vulnerable to side channel analysis. The most common countermeasure for block cipher implementations is masking, which basically randomises the variables to be protected by combining them with numerous random values. In this paper, masked implementation of lightweight block ciphers PRESENT and BORON is demonstrated. In the framework, secret sharing-based masking procedure is adapted as an alternative to Boolean masking. We then conduct a security analysis and empirical observations of our framework. To prove the novelty and practical adaptability of the proposed framework, implementation and obtained results are also presented in the paper.
An empirical study of improved ant colony clustering algorithm in English composition review

The scoring analysis method of English composition review lacks flexibility. To solve this problem, this paper proposes an analysis method based on the improved ant colony clustering algorithm, where cosine distance and Euclidean distance were combined to determine the conversion function. The empirical results show that compared with the previous standard ant colony clustering algorithm, the traditional k-means algorithm and IGKA algorithm, the improved ant colony clustering algorithm can realise the comprehensive evaluation of English composition review. It can be seen that the proposed method is reasonable and feasible, which can effectively conduct cluster analysis on English composition review, and has a higher accuracy rate of 89.33%. Therefore, in order to achieve the clustering analysis of English composition rating more precisely, the next step is to improve the ant colony clustering algorithm by repeated experiments on experimental data.
On the effect of particle update modes in particle swarm optimisation

Particle swarm optimisation has been successfully applied in various single- and multi-objective optimisation problems. Through the literature review, it is shown that in PSO-based algorithms particles are updated mainly in two different modes. Specifically, the first mode denoted as PSO-a uses random vectors in [0, 1]n in the particle update process. The second mode denoted as PSO-b uses random variables in [0, 1]. This study systematically analysed the effect of different modes on a varied set of benchmarks. Experimental results show that the PSO-a mode is more suitable for single-objective optimisation while the PSO-b has certain advantages for multi-objective optimisation due to the regularity of multi-objective problems. Also, the introduction of a mutation operator into PSO-b can overcome the limit of dimension. Moreover, to guarantee finding the optimal solution, the swarm size must be larger than the problem dimensionality when PSO-b is purely adopted.
Movement Tracking in Augmented and Mixed Realities Impacting the User Activity in Medicine and Healthcare

The paper deals with the issue of comparing trajectories in augmented and mixed realities incorporated in medical and healthcare systems. The comparison process brings to a sequence of comparing pairs of elements of these trajectories. We match up pairs using either time-domain mapping or projection in Euclidean space. The suitability of matched up pairs are assessed based on gold standard Dynamic Time Wrapping method. Finally, we propose two similarity relations which incorporate the idea of coordination space and position vectors.
Architecting Cloud-Based Business Software—A Practitioner’s Perspective

The recent advances in software engineering, such as CI/CD, cloud computing, containerization, and microservices have transformed the architectures of software systems. We analyzed architectures of two such cloud-based systems and retrospectively reviewed how crucial architectural decisions were made. This enabled us to identify the set of key architectural issues that architects found challenging as well as drivers and risks that shaped the architects’ decisions. This, in turn, provides a basis for the insights into how architecture decision-making has changed in the cloud era and what has triggered these changes.
Deployment of Deep Models in NLP Infrastructure

The paper presents the deployment of transformer based deep models in CLARIN-PL, an open language technology infrastructure for Social Sciences and Humanities. We discuss possible methods for implementing deep models, together with an experimental analysis of their performance, which led to the final solution based on the ONNX format and the AMQP communication protocol. We illustrate the obtained solution with an exemplary application to the analysis of a corpus of literary texts. In the application, we have used the Sentence-BERT model to embed text passages and UMAP to project the embeddings, and thus the text, onto the 2D plain.
Pay Attention to the Robustness of Chinese Minority Language Models! Syllable-level Textual Adversarial Attack on Tibetan Script

The textual adversarial attack refers to an attack method in which the attacker adds imperceptible perturbations to the original texts by elaborate design so that the NLP (natural language processing) model produces false judgments.This method is also used to evaluate the robustness of NLP models.Currently, most of the research in this field focuses on English, and there is also a certain amount of research on Chinese.However, to the best of our knowledge, there is little research targeting Chinese minority languages.Textual adversarial attacks are a new challenge for the information processing of Chinese minority languages.In response to this situation, we propose a Tibetan syllablelevel black-box textual adversarial attack called TSAttacker based on syllable cosine distance and scoring mechanism.And then, we conduct TSAttacker on six models generated by fine-tuning two PLMs (pre-trained language models) for three downstream tasks.The experiment results show that TSAttacker is effective and generates high-quality adversarial samples.In addition, the robustness of the involved models still has much room for improvement.
Social Justice and the Response to the COVID-19 Crisis in European Countries

In European countries during the pandemic, measures such as lockdowns that strongly restrict people’s freedom have been widely implemented, and mandatory masks and vaccines have been promoted. In these countries, freedom of assembly, freedom to go out, and freedom to conduct business, etc. were restricted across the board, and people’s voluntary actions were greatly restricted. In addition, despite the EU’s commitment to the “free movement” of citizens within the EU, EU member states introduced a full range of border controls that greatly undermined that “freedom” and imposed strong restrictions on the entry of foreigners.These policies were developed in contrast to Japan’s emphasis on “self-restraint.” The Japanese government, which is believed to have interventionist bureaucracy, was reluctant to adopt mandatory measures to contain the outbreak. This “unexpected” contrast of policy outcomes between Western countries and Japan is an interesting puzzle. This chapter looks at the impact of the pandemic on national, political, and social systems from an international comparative perspective, focusing mainly on European countries. Through comparisons and contrasts, this chapter will clarify the ways in which the state and society are organized in countries around the world and contribute to solve the “puzzle” mentioned above.
Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering

Large Language Models (LLMs) are capable of performing zero-shot closed-book question answering tasks, based on their internal knowledge stored in parameters during pre-training.However, such internalized knowledge might be insufficient and incorrect, which could lead LLMs to generate factually wrong answers.Furthermore, fine-tuning LLMs to update their knowledge is expensive.To this end, we propose to augment the knowledge directly in the input of LLMs.Specifically, we first retrieve the relevant facts to the input question from the knowledge graph based on semantic similarities between the question and its associated facts.After that, we prepend the retrieved facts to the input question in the form of the prompt, which is then forwarded to LLMs to generate the answer.Our framework, Knowledge-Augmented language model PromptING (KAP-ING), requires no model training, thus completely zero-shot.We validate the performance of our KAPING framework on the knowledge graph question answering task, that aims to answer the user's question based on facts over a knowledge graph, on which ours outperforms relevant zero-shot baselines by up to 48% in average, across multiple LLMs of various sizes.
Fluency Matters! Controllable Style Transfer with Syntax Guidance

Unsupervised text style transfer is a challenging task that aims to alter the stylistic attributes of a given text without affecting its original content.One of the methods to achieve this is controllable style transfer, which allows for the control of the degree of style transfer.However, an issue encountered with controllable style transfer is the instability of transferred text fluency when the degree of the style transfer changes.To address this problem, we propose a novel approach that incorporates additional syntax parsing information during style transfer.By leveraging the syntactic information, our model is guided to generate natural sentences that effectively reflect the desired style while maintaining fluency.Experimental results show that our method achieves robust performance and improved fluency compared to previous controllable style transfer methods.
Going the distance: project management issues in a cycle hire project

Research methodology The case was devised using both primary and secondary data sources. Primary sources of data consisted of in-depth interviews with individuals using the cycle hire project. The researcher also had first-hand experiences of using the cycles. The case study has been tested with undergraduate and graduate students taking management information systems courses. Case overview/synopsis This teaching case study charts the London cycle hire project, mostly from its first inception in July 2010, right through to the planned expansion of electric cycles from Summer 2022. The main aim of the case is to introduce students to project management challenges which are part of the London cycle hire project. While the project was filled with enthusiasm from its early beginnings, various challenges were encountered including issues associated with the project procurement/sourcing process, software and technical problems, as well as other project management issues. Problems became so severe in 2011 that the service provider was hit with a penalty and had to make critical project improvements. Would these accountability measures prompt the service provider to resolve these issues? How would the service provider go about undertaking a fact-finding exercise to verify the existence of the challenges and address them to ensure renewed project success? Complexity academic level The case was written for classes at both the undergraduate and graduate levels. The focus of the case is particularly well suited for exploring topics and issues relating to types of information systems, project management and accountability, multiple global supplier procurement, as well as challenges associated with hardware integration and software design. While the case was targeted at MIS students, the case study would also be effective for an introductory level project management course or a general management course. The subject of the case, the bicycle rental program, is likely to appeal to students, and the basic underlying business issues, processes and objectives of the project are easily understood.
Music Quotation in Contemporary Non-Academic Art: Aspects of Genre and Style Transformation

"The problematic field of the article focuses on aspects of genre and style transformation of well-known musical content that is recognizable and stylistically significant. In contemporary non-academic art, the established semantics of the original source is subject to change under the influence of a new creative rethinking, the possibilities of which are only increasing in the context of the technologies of the twenty-first century. These possibilities are embodied in various visual formats: from direct citation to borrowing or processing with its variations. The relevance of this topic lies in the need to study the phenomenon of citation as such, as well as to research and systematize other means of genre and style transformation of musical material. The direct object of the study is mostly music of the so-called “light” genres, music of mass culture, due to more mobile, transparent and visual observation opportunities. The author offers a self-made classification of possible methods of borrowing someone else’s text as a means of compositional technique, as well as of individual genres: cover, remake, borrowing, allusion, sampling. In the context of the study of the phenomenon of “musical text”, the peculiarities of realization and perception of the artistic and integral result are traced. As a result, several types of citation that have become widespread in contemporary music are distinguished. Keywords: quote, citation, borrowing, autocitation, samples, cover, remake, collage, intertextuality. "