Reviewerly/exHarmony huggingface dataset:
Architectural Bad Smells for Self-Adaptive Systems: Go Runtime!

Self-adaptive systems (SAS) change their behavior and structure at runtime depending on environmental changes or user requests. For this purpose, the SASs combine architectural fragments or solutions in their adaptation process. However, this process may negatively impact the system’s architectural qualities, exhibiting architectural bad smells (ABS). Current studies perform ABS detection for SAS at design time, ignoring their intrinsic runtime variability. We demonstrate that this ignorance leads to inaccurate smell detections and possibly wrong maintenance decisions. We delineate the challenges runtime variability raise on ABS detection and argue that we should analyze SAS architectures at runtime.
Adding Product-Line Capabilities to Your Favourite Modeling Language

Software product lines are commonly adopted in industry to manage the development of complex families of software systems. Software engineering activities use models at their core, and extending a modeling language to support product lines is an expensive task. Moreover, many useful techniques and analyses such as model querying and model refactoring, are defined at the level of individual products. Before they can take advantage of a product line representation, they need to be lifted, i.e., reengineered to handle variability in a product line. Not only is this process non-trivial, it needs to be redone for each modeling language.
Uniquifying Architecture Visualization through Variable 3D Model Generation

Software visualization facilitates the interactive exploration of large-scale code bases, e.g., to rediscover the architecture of a legacy system. Visualizations of software structure suffer from repetitive patterns that complicate distinguishing different subsystems and recognizing previously visited parts of an architecture.
Task-aware asynchronous multi-task model with class incremental contrastive learning for surgical scene understanding

Surgery scene understanding with tool-tissue interaction recognition and automatic report generation can play an important role in intra-operative guidance, decision-making and postoperative analysis in robotic surgery. However, domain shifts between different surgeries with inter and intra-patient variation and novel instruments' appearance degrade the performance of model prediction. Moreover, it requires output from multiple models, which can be computationally expensive and affect real-time performance.A multi-task learning (MTL) model is proposed for surgical report generation and tool-tissue interaction prediction that deals with domain shift problems. The model forms of shared feature extractor, mesh-transformer branch for captioning and graph attention branch for tool-tissue interaction prediction. The shared feature extractor employs class incremental contrastive learning to tackle intensity shift and novel class appearance in the target domain. We design Laplacian of Gaussian-based curriculum learning into both shared and task-specific branches to enhance model learning. We incorporate a task-aware asynchronous MTL optimization technique to fine-tune the shared weights and converge both tasks optimally.The proposed MTL model trained using task-aware optimization and fine-tuning techniques reported a balanced performance (BLEU score of 0.4049 for scene captioning and accuracy of 0.3508 for interaction detection) for both tasks on the target domain and performed on-par with single-task models in domain adaptation.The proposed multi-task model was able to adapt to domain shifts, incorporate novel instruments in the target domain, and perform tool-tissue interaction detection and report generation on par with single-task models.
DDQR (dynamic DNA QR coding): An efficient algorithm to represent DNA barcode sequences

A DNA barcode is a short piece of standard DNA sequence used for species determination and discrimination. Representation of DNA barcodes is essential for DNA barcodes' applications in the transportation and recognition of biological materials. Previously, we have compared different strategies for representing the DNA barcodes. In the present study, we have developed a compression algorithm based on binary coding or Huffman coding scheme, followed by converting the binary digits into Base64 digits. The combination of this compression algorithm and the QR representation leads to the dynamic DNA QR coding algorithm (DDQR). We tested the DDQR algorithm on simulated data and real DNA barcode sequences from the commonly used plant and animal DNA barcode markers: rbcL, matK, trnH-psbA, ITS2, and COI. We compared the compression efficiency of DDQR and another state-of-the-art DNA compression algorithm GeCo3 for sequences with various base compositions and lengths. We found that DDQR had a higher compression rate than GeCo3 for DNA sequences shorter than 800 bp, which is the typical size range for DNA barcodes. We also upgraded a web server (http://www.1kmpg.cn/ddqr) that provides three functions: retrieval of DNA barcode sequences, encoding DNA barcode sequences to DDQR codes, and decoding DDQR codes to DNA barcode sequences. The DDQR algorithm and the webserver will be invaluable to applying DNA barcode technology in the food and traditional medicine industries.
What gets categorized counts: controlled vocabularies, digital affordances, and the international digital humanities conference

Abstract This article explores how terms are incorporated into the conference submission and review process for the international digital humanities conference. This article provides an overview of the Alliance for Digital Humanities Organizations (ADHO) conference reviewing process and how the controlled vocabulary structures the review process. We show how expanding and rethinking the controlled vocabulary can impact the experience of those who submit, review, and attend the conference. We consider how ConfTool, the submission and reviewing portal used for the international digital humanities conference, processes the controlled vocabulary and algorithmically influences the review of submissions. Ultimately, we advocate for the ability to make intentional and careful changes to conference vocabularies including considering the adoption of a formal ontology. We also suggest that changes to the ConfTool algorithm are needed to ensure a diverse and equitable future for digital humanities.
Summary of the Tenth International Workshop on EyeMovements in Programming (EMIP 2022)

The study of eye movements in programming has many implications to software engineering and computing education in industry and academia. The 10th International Workshop on Eye Movements in Programming (EMIP 2022) was co-located with the 44th International Conference on Software Engineering (ICSE), with the goal to further develop the methodology of using eye tracking for programming and software engineering, both theoretically and in practice.
LARW: Network Representation Learning Algorithm Based On Long Anonymous Random Walks

Network Representation Learning (NRL) plays an important role in network analysis and aims to represent complex networks more concisely by transforming nodes into low-dimensional vectors. Network representation learning has become the focus of increasing research interest in academia and industry. Much of the work in one direction is based on learning network features based on random walk derived models. However, it is difficult to extract effective features in the face of long wandering sequences. Therefore, we propose a dynamic network characterization method based on Long Anonymous Random Walks(LARW). LARW incorporates the latest long series prediction method Informer, which allows more feature information to be retained. The model parameters are optimized in the process of comparing with the actual results, thus making the node embedding more predictive and causal. In our experiments, we compare our model with the existing NRL model on four real-world datasets. The experimental results show that LARW achieves superior results in tasks such as node classification and link prediction.
Component Comprehension in Context

A large number of commercial software solutions contain some functionality achieved using different APIs. To use these APIs correctly, programmers must understand what they do and how they work. However, research into the comprehension of APIs has so far been limited. The aim of this research is to expand the current knowledge of program comprehension to include APIs and to analyze API comprehension in the context of software engineering work.
On COVID-19 Pandemic-Induced Attitudinal Changes in Software Engineering Teaching and Learning

This note outlines certain notable changes observed in the attitudes towards and approaches in teaching and learning software engineeringrelated courses following the end of the COVID-19 pandemic. It views these changes from the interrelated perspectives of the primary stakeholders of software engineering education (SEE), namely administrators, educators, and students. It seems that while some changes may be ephemeral and transient, the others may be essential and permanent. From a students' perspective, the transformation of SEE appears to be generally for the better.
MaLTeSQuE 2022 Workshop Summary

Welcome to the sixth edition of the workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE 2022), held in Singapore, November 18th, 2022, co-located with ESEC / FSE 2022 [1]. Six papers from all over the world were submitted, five of them were accepted. The program also featured two keynotes by Yuriy Brun on the promise and perils of using machine learning when engineering software and Mike Papadakis on the best practices in assessment of deep learning testing methods.
ESG Equities and Bitcoin

Bitcoin is often criticized for excessive electricity consumption and resulting carbon footprint. We use wavelet analysis to examine its interconnectedness with environmental, social, and governance (ESG) equities. The results indicate their comovements are low. We further compute the optimal ratio and show that Bitcoin significantly reduces portfolio risk when combined with green stocks. Our findings are robust to alternate specifications. From an environmental perspective, this investment strategy offsets the negative impacts of bitcoin mining, providing a remedy to sustainability-conscious investors' dilemma of the economic payoff of Bitcoin versus adherence to green investment philosophy.
Technical process improvement through a systems based approach

: Abstract This paper focuses on a novel process maturity framework developed during the targeted improvement to the Integrated Design Review and Assurance (IDR&A) Process, by the Skanska Costain STRABAG (SCS) Joint Venture, for the delivery of HS2’s Main Works Civil Contract Southern (the Project). The Project has written processes and procedures for each discipline, with over 40 key technical procedures covering topics such as Design Management, Engineering Management and Quality Management. The project operates within HS2’s Technical Lifecycle Stages, such as Design, Construction and Handover, with all technical and environmental aspects of the Works passing through this lifecycle. The Lifecycle Stages weave together the activities from each technical process, which, if poorly integrated, will lead to inefficiencies. In the case of IDR&A Process, these delays were vocalised to be in region of £1M per day for unplanned overruns that prolonged the start of Construction. Adopting an Agile approach[1], and combining experience and good practice from other industries, the team developed a novel process improvement methodology to address the need for efficient procedures to reduce programme delays. The first iteration of this initiative removed 32 days from the IDR&A process. If that is repeated across all Detailed Design work packages it provides potential savings of 4200 days. This piece of work analysed the end-to-end process, from the perspective of the Lifecycle, to bring together activities from each single discipline process and will cover the methods and tools used to analyse the IDR&A process, focusing on our novel Multi-Dimensional Maturity Framework. Engagement with practitioners was critical to this activity and provided value across all technical process analyses. The analysis of the task maturity through a custom designed framework enabled the project to implement simple changes to processes, technology, and behaviours, subsequently opening the door to bigger tool and process enhancements.
Trajectory planning method of 6-DOF modular manipulator based on polynomial interpolation

Robots are widely used in all walks of life, and their excellent work efficiency has been paid attention to. As the key component of robot, manipulator plays an important role in the running performance of robot. In order to effectively improve the trajectory accuracy and efficiency of the manipulator, a six degree of freedom (6-DOF) modular manipulator trajectory planning method based on polynomial interpolation is proposed, and its feasibility and effectiveness are verified by experiments. At the same time, the performance of the method is compared with two other methods of the same type. The experimental results show that the six degree of freedom modular trajectory planning method has a shorter running time, and the shortest running time is 1.62 s. Compared with the directions in previous studies, the planning trajectory of the proposed method is more practical and its accuracy is higher. In the iterative process, the running time of the proposed method is also the shortest. In addition, the minimum error of the three methods is about 1%, which is lower than the other two methods. It is concluded that the six degree of freedom modular trajectory planning method has high feasibility and performance, which is of great significance to improve the operating efficiency and stability of the robot.
Knowledge enhancement BERT based on domain dictionary mask

Semantic matching is one of the critical technologies for intelligent customer service. Since Bidirectional Encoder Representations from Transformers (BERT) is proposed, fine-tuning on a large-scale pre-training language model becomes a general method to implement text semantic matching. However, in practical application, the accuracy of the BERT model is limited by the quantity of pre-training corpus and proper nouns in the target domain. An enhancement method for knowledge based on domain dictionary to mask input is proposed to solve the problem. Firstly, for modul input, we use keyword matching to recognize and mask the word in domain. Secondly, using self-supervised learning to inject knowledge of the target domain into the BERT model. Thirdly, we fine-tune the BERT model with public datasets LCQMC and BQboost. Finally, we test the model’s performance with a financial company’s user data. The experimental results show that after using our method and BQboost, accuracy increases by 12.12% on average in practical applications.
Distilled Meta-learning for Multi-Class Incremental Learning

Meta-learning approaches have recently achieved promising performance in multi-class incremental learning. However, meta-learners still suffer from catastrophic forgetting, i.e., they tend to forget the learned knowledge from the old tasks when they focus on rapidly adapting to the new classes of the current task. To solve this problem, we propose a novel distilled meta-learning (DML) framework for multi-class incremental learning that integrates seamlessly meta-learning with knowledge distillation in each incremental stage. Specifically, during inner-loop training, knowledge distillation is incorporated into the DML to overcome catastrophic forgetting. During outer-loop training, a meta-update rule is designed for the meta-learner to learn across tasks and quickly adapt to new tasks. By virtue of the bilevel optimization, our model is encouraged to reach a balance between the retention of old knowledge and the learning of new knowledge. Experimental results on four benchmark datasets demonstrate the effectiveness of our proposal and show that our method significantly outperforms other state-of-the-art incremental learning methods.
Emotional intelligence in the activities of law enforcement officers Kyrgyz Republic

The article is devoted to the study of the emotional intelligence of employees of the internal affairs bodies of the Kyrgyz Republic. It is a well-known fact that service in the internal affairs bodies is of a specific nature. It is extremely important for employees of internal affairs bodies to be able to work with society, build trusting relationships, be flexible in various situations, in order to effectively perform official tasks and maintain the image of their profession. Emotional intelligence is one of the necessary qualities of a person, especially in the activities of the police, since it involves the ability of a person to perceive, recognize emotions, understand intentions, motivation, both one's own and other people. A high level of emotional intelligence among police officers also implies the ability to adapt to various situations and find a common language with society. The study involved 78 employees of various departments of the internal affairs bodies of the Kyrgyz Republic.
Automated Classification of Snow-Covered Solar Panel Surfaces Based on Deep Learning Approaches

Recently, the demand for renewable energy has increased due to its environmental and economic needs. Solar panels are the mainstay for dealing with solar energy and converting it into another form of usable energy. Solar panels work under suitable climatic conditions that allow the light photons to access the solar cells, as any blocking of sunlight on these cells causes a halt in the panels work and restricts the carry of these photons. Thus, the panels are unable to work under these conditions. A layer of snow forms on the solar panels due to snowfall in areas with low temperatures. Therefore, it causes an insulating layer on solar panels and the inability to produce electrical energy. The detection of snow-covered solar panels is crucial, as it allows us the opportunity to remove snow using some heating techniques more efficiently and restore the photovoltaics system to proper operation. This paper presents five deep learning models, -16, -19, ESNET-18, ESNET-50, and ESNET-101, which are used for the recognition and classification of solar panel images. In this paper, two different cases were applied; the first case is performed on the original dataset without trying any kind of preprocessing, and the second case is extreme climate conditions and simulated by generating motion noise. Furthermore, the dataset was replicated using the upsampling technique in order to handle the unbalancing issue. The conducted dataset is divided into three different categories, namely; all_snow, no_snow, and partial snow. The five models are trained, validated, and tested on this dataset under the same conditions 60% training, 20% validation, and testing 20% for both cases. The accuracy of the models has been compared and verified to distinguish and classify the processed dataset. The accuracy results in the first case show that the compared models -16, -19, ESNET-18, and ESNET-50 give 0.9592, while ESNET-101 gives 0.9694. In the second case, the models outperformed their counterparts in the first case by evaluating performance, where the accuracy results reached 1.00, 0.9545, 0.9888, 1.00. and 1.00 for -16, -19, ESNET-18 and ESNET-50, respectively. Consequently, we conclude that the second case models outperformed their peers.
A Note on the Transformation Behaviors Between Truth Tables and Algebraic Normal Forms of Boolean Functions

Let f be a Boolean function in n variables. The Möbius transform and its converse of f can describe the transformation behaviors between the truth table of f and the coefficients of the monomials in the algebraic normal form representation of f. In this letter, we develop the Möbius transform and its converse into a more generalized form, which also includes the known result given by Reed in 1954. We hope that our new result can be used in the design of decoding schemes for linear codes and the cryptanalysis for symmetric cryptography. We also apply our new result to verify the basic idea of the cube attack in a very simple way, in which the cube attack is a powerful technique on the cryptanalysis for symmetric cryptography.
Retracted: Design of an Intelligent Nursing Information Management System for Critically Ill Patients in Neurosurgery

[This retracts the article DOI: 10.1155/2022/2848255.].
Routing Analysis in The Emergency Call Application By Using Hill Climbing and Greedy Algorithm

Emergency calls in Indonesia are still implementing satellite call technology to call emergency agencies. Indonesia already has a universal emergency number, but its implementation is still not evenly distributed in all cities/districts, where the emergency number is 112. Most Indonesians rarely know this number because the information from this number is less published, and people tend to use the Internet more than conventional calls. The author can build a calling application that combines with Google Maps to provide an image display from a digital map by utilizing an internet-based application. The application automatically routes the caller’s location to the agency’s location that is called directly. Based on the test results, Hill Climbing and Greedy routing algorithms can be implemented properly by using SAW (Simple Additive Weighting) weights of 30% distance and 70% duration, which has a total range value between nodes is 0.022. The Greedy algorithm gives the best results based on the average response time is 0.912, while Hill Climbing Algorithm is 1.59. As a result, the best route in this research comparing Hill Climbing and the Greedy algorithm is the Greedy Algorithm.
Detecting Malicious Accounts in Online Developer Communities Using Deep Learning

Online developer communities like GitHub allow a massive number of developers to collaborate. However, the openness of the communities makes them vulnerable to different types of malicious attacks, since attackers can easily join these communities and interact with legitimate users. In this work, we propose GitSec, a deep learning-based solution for detecting malicious accounts in online developer communities. GitSec distinguishes malicious accounts from legitimate ones based on the account profiles, dynamic activity characteristics, as well as social interactions. First, GitSec introduces two user activity sequences and applies a parallel neural network design with an attention mechanism to process the sequences. Second, GitSec constructs two graphs to represent the interactions between users according to their repository operations. Especially, graph neural networks and structural hole theory are employed to deal with the two constructed graphs. Third, GitSec makes use of the descriptive features to enhance the detection performance. The final judgement is made by a decision maker implemented by a supervised machine learning-based classifier. Based on the real-world data of GitHub users, our comprehensive evaluations show that GitSec achieves a better performance than state-of-the-art solutions, with an AUC value of 0.916.