Reviewerly/exHarmony huggingface dataset:
FACTORS INFLUENCING THE SUCCESS IN PROFESSIONAL SKILLS COMPETITIONS

This article deals with professional mastery competitions in Russian education as an important part of the national system of teacher growth. The annual wide participation in them and the constant rotation of the participants allow us to speak about the contests as the most eff ective way of the teachers’ professional development. The performance of a contestant depends on many factors, the degree of infl uence of which is studied on the basis of both open and collected by the author data of the contest “Teacher of the Year of Russia”, as the most status of all competitions held at the moment. The infl uence of such factors as participant’s region, teaching subject, gender, age, work experience and status of educational institution on the result is investigated. Changes in the concept of competitive tests and approaches to their evaluation throughout the history of the competition are discussed in detail. The author off ers his own approach to performance, considering not only absolute victory, as traditionally accepted in the competition movement, but also the achievement of the titles “laureate” and rizewinner”. This approach allows us to rethink stereotypes in the preparation of teachers for competitive tests and increase motivation to participate in such events.
EVENT MECHANISMS OF TRANSFORMATION OF VALUE-SEMANTIC ATTITUDES OF MODERN ADOLESCENTS

The study presents the results of a comparative analysis oft he structure of value-semantic attitudes of adolescents with diff erent experiences of immersion in the educational space of children’s creative associations (more than three years and less than one year), including the identifi cation of features of the realization of creative potential and life scenarios of modern adolescents. It is proved that the value of the subculture of the children’s creative team not only contributes to the transformation of the value-semantic attitudes of a doles cents, but also acts as a resource for the reproduction of value sand meanings that are signifi cant for this team.
A Module-Agnostic Reference Software Development Process for Different Levels of Higher-Education Study

Several software development methodologies and practices are taught in computer science and software engineering higher education degrees. This happens through individual modules and capstone projects and sometimes with participation from real clients. Different from industry, where processes are usually prescribed, students encounter company-agnostic artefacts and practises, often having to choose between the available options. Feedback from tutors, students and clients indicates this to be a challenge, with students often confused trying to mix-and-match different practices without the proper consideration of how they would work together. This paper introduces the SHU Development Process, covering all software development stages, that is instantiated into different levels of detail for students as they progress through their degree studies. The SHU Dev Process provides structured guidance to software development practices that can be followed through their chosen process flow or cherry-picked by students as needed. It has been created through a student-led project over multiple years. First applied during the academic year 2021/2 and iterated upon for 2022/3 in a capstone project module at Sheffield Hallam University, the process was evaluated annually by surveying students across different courses and levels of study. In initial surveys, students responded positively, and our experience provides valuable insight that other practitioners may draw upon to implement and evaluate a similar resource in the future.
"Embedding Dispositions in Peer Assessment for Software Teams: More than just a \"Product\" Focus"

Both the Computing Curricula 2020 (CC2020) and emerging Computer Science Curricula 2023 (CS2023) guidelines indicate a shift towards competency-based education, where learners apply their knowledge, dispositions and skills in tasks as part of their studies. This approach aims to further enhance graduates’ work-readiness and tackle global issues of unemployment and underemployment among computing graduates. Peer assessment is increasingly used in team software engineering and capstone projects to ensure fair recognition of individual contributions. As competency-based learning becomes more prevalent, peer assessment needs to not only consider project outcomes, but also aspects of knowledge, dispositions and skills. This poster presents preliminary work on how one UK university’s computer science undergraduate degree employs the Team-Q metric to address this challenge.
Quick Fixes for Novice Programmers: Effective but Under-Utilised

Professional software development environments typically provide “quick fixes” for common program errors: a common solution to an error that can be enacted with a single click. For example, an “unknown type” error can be fixed by adding an import for the type, or an “unknown variable” error can be fixed by changing a misspelt identifier to match the name of an already-declared variable. The BlueJ environment recently added some support for quick fixes, and in this paper we use the associated Blackbox dataset to investigate how they are used by novice programmers. We use a combination of automatic filtering of over 100 000 quick fixes, and manual categorisation of 900 programming session fragments. We find that acceptance of suggestions ranges from 1–17% for different quick fix types, usually within 3–5 seconds, and many manually performed alternatives are similar to the actions of unselected quick fixes. Furthermore, we find that users are faster and are more able to make productive progress when quick fix support is available, especially for fixing imports. This data can be used to inform design of future quick fix systems. There are pedagogical arguments for and against providing such a feature to novice programmers and we provide some initial discussion on the matter.
Resolution of Fuzzy Relation Equations with Constraints

This paper studies the resolution of the max-min compositional fuzzy relation equation with the constraint of <inline-formula id="M1"> <math id="mathml_M1" display="inline" overflow="scroll"><munderover><mo movablelimits="false">∑</mo><mrow class="MJX-TeXAtom-ORD"><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>n</mi></munderover><mrow class="MJX-TeXAtom-ORD"><mrow class="MJX-TeXAtom-ORD"><msub><mi>x</mi><mi>i</mi></msub></mrow></mrow><mo>=</mo><mn>1</mn></math></inline-formula>. The solvability and the unique solvability of this constrained fuzzy relation equation are characterized. Furthermore, this paper presents a resolution of it and designs a corresponding tabular method. Finally, a numerical example is provided to illustrate the resolution procedure.
Semantic knowledge models for decision making in asset management: IM‐SAFE Knowledge Base

Abstract Against the backdrop of physical ageing, asset management organizations not only struggle with scattered object‐related data across incompatible software and models, but also dispersed, often missing, insights from domain experts. There is a large volume of data already available particularly at National Road Authorities i.e. object characteristics, inspection and measurement results, usage records. Yet this data is distributed, has complex and varying structures, it takes cumbersome effort to find and obtain for a particular asset management decision. Semantic knowledge models aim to capture domain information and represent it via class definitions, their attributes, relationships and in instances. Such models are used to create knowledge bases, where different technologies are utilized. In the IM‐SAFE project, a knowledge base is created based on a Semantic Wiki Platform. It captures asset information concerning their structural analysis and state on 99 civil structures across Europe. In this paper, a step‐by‐step methodology is provided to build the knowledge base. The paper involves knowledge acquisition, data collection, knowledge modelling, populating datasets and quality assurance steps. It will conclude future implications to make the base expandable and widely used for asset management.
Text Recognition for 2D Bridge Plans Using OCR‐Algorithms

Abstract The 2D bridge plans play an essential role in renovations or new replacements of transport infrastructures. As an indispensable component of plans, text serves as dimension labels, descriptions of materials, etc. Optical Character Recognition (OCR) can convert text in image into machine‐encoded format, and has high potential and invaluable practical worth for analyzing, managing, and extracting textual information from plans. First, the selection of OCR‐algorithms and the sampling of textual fragments from 2D bridge plans, including historic plans with handwritten text and digital plans with printed text, was carried out. Subsequently, the performances of different open‐source OCR‐algorithms on different textual fragments from 2D bridge plans are compared. In this process, the accuracy at character level and word level are chosen as two main factors to evaluate their performances. Finally, the most potential OCR‐algorithms for the text recognition on 2D bridge plans are proposed.
ERNIE-based Named Entity Recognition Method for Traffic Accident Cases

Abstract Traffic accident case named entity recognition, which helps mine key information in traffic accident texts, plays a vital role in downstream tasks such as the construction of knowledge graphs in road traffic and intelligent policing. In this paper, we construct a named entity recognition model based on the EDE (Entity Data Enhancement)-ERNIE-Bidirectional Gated Recurrent Unit Network (BiGRU)-Conditional Random Field (CRF) to address the current situation of low traffic accident case data and poor recognition of long-text entities. First, the amount of accident case data is enhanced using the entity random substitution method. Next, the text data of traffic accident cases are characterized as a dynamic word vector using the ERNIE pretraining model. Then, the BiGRU network learns the long-distance dependency relationship in the text to enhance the effect of the model on long-text entity recognition. Finally, the result sequence is constrained by the CRF layer to realize the named entity recognition model. The experimental part uses data related to real traffic accident cases in a domestic area. The data enhancement method increases the data volume three times compared to the original data volume. Experimental results show that the EDE-ERNIE-BiGRU-CRF model achieves better F1 values, recall and precision achieved better performance than the entity recognition methods of BERT-BiGRU-CRF, ERNIE-BiGRU-CRF, ERNIE-BiLSTM-CRF, ERNIE-CRF, ERNIE, BiGRU-CRF, ROBERTA-wwm-ext-BiGRU-CRF and verify its effectiveness for entity recognition in traffic accident cases.
Self-embedding reversible color-to-grayscale conversion with watermarking feature

This paper presents a self-embedding reversible color-to-grayscale conversion (RCGC) algorithm that makes good use of deep learning, vector quantization, and halftoning techniques to achieve its goals. By decoupling the luminance information of a pixel from its chrominance information, it explicitly controls the luminance error of both the conversion outputs and their corresponding reconstructed color images. It can also alleviate the burden of the deep learning network used to restore the embedded chrominance information during the reconstruction of the color image. Luminance-guided chrominance quantization and checkerboard-based halftoning are introduced in the paper to encode the chrominance information to be embedded while reference-guided inverse halftoning is proposed to restore the color image. Simulation results verify that its performance is remarkably superior to conventional state-of-art RCGC algorithms in various measures. In the aspect of authentication, embedding the watermark and chrominance information is realized with context-based pixel-wise encryption and a key-based watermark bit positioning mechanism, which makes us possible to locate tampered regions and prevent unauthorized use of the chrominance information.
Associating multiple vision transformer layers for fine-grained image representation

- Accurate discriminative region proposal has an important effect for fine-grained image recognition. The vision transformer (ViT) brings about a striking effect in computer vision due to its innate multi-head self-attention mechanism. However, the attention maps are gradually similar after certain layers, and since ViT used a classification token to achieve classification, it is unable to effectively select discriminative image patches for fine-grained image classification. To accurately detect discriminative regions, we propose a novel network AMTrans, which efficiently increases layers to learn diverse features and utilizes integrated raw attention maps to capture more salient features. Specifically, we employ DeepViT as backbone to solve the attention collapse issue. Then, we fuse each head attention weight within each layer to produce an attention weight map. After that, we alternatively use recurrent residual refinement blocks to promote salient feature and then utilize the semantic grouping method to propose the discriminative feature region. A lot of experiments prove that AMTrans acquires the SOTA performance on four widely used fine-grained datasets under the same settings, involving Stanford-Cars, Stanford-Dogs, CUB-200-2011, and ImageNet.
A Definitive Technology Stack for Development of Smart Contracts for Energy Applications

In recent years, there has been a growing trend in research on smart contract applications. Smart contracts potential in the energy landscape is visible in their major applications such as peer-to-peer energy trading, electric vehicle charging, energy market management, and many more. Many studies have been conducted that produced a lot of literature in this area and many startups and companies have surfaced that exhibit large scope of its application in the energy sector. However, in comparison to other domains, there is still more development required. The literature available focuses on the different technical aspects and use cases, but there is no such scientific article providing gathered details of the smart contracts development process that invites the attention of researchers in the energy domain for development or provides basic knowledge of available tools. It is therefore necessary to contribute with academic articles that summarize this information, thus opening up paths of development in this field and strengthening the community. This paper is the first step towards the implementation of this idea and that is intended to be extended in the future.
Game Based Pigeon-Inspired Optimization with Repository Assistance for Stochastic Optimizations with Uncertain Infeasible Search Regions

Evolutionary algorithms have been widely used to solve stochastic optimization problems (SOP) in recent studies and their performances are largely determined by the reliabilty of evaluations as well as the exploration and exploitation behaviors. However, due to the black-box features and physical limitations to the research objects, evaluating most modern systems in real world may be expensive or even suffer from large numbers of infeasible solutions and high proportions of uncertain infeasible search regions (ISR), which arouses the expectations for less trials and errors during the entire optimization process. This article presents two variants of the pigeon-inspired optimization (PIO) algorithm respectively for the stochastic optimizations with and without ISR penalties. Both variants use a repository to maintain the historical individuals with local optima and a multi-strategy evolutionary game mechanism to promote the balances between explorations and exploitations in the corresponding search space. The second variant also uses the repository to reallocate infeasible solutions into feasible regions and motivate the pigeons moving faster to reduce the unneccessary trials during the optimizations. The performances of the first PIO variant have been evaluated on CEC'2017 benchmark functions, while a simple example of vehicular control is provided to demonstrate the effectiveness of the second variant on the SOP solving with minimal ISR touches.
Continual Learning of LSTM Using Ant Colony Optimization

The premise of continual learning is to continuously learn new tasks using the same model in an environment where multiple tasks are given sequentially while retaining the knowledge learned in previous tasks. Typical deep learning models suffer from catastrophic forgetting, in which knowledge of past tasks is drastically lost when learning new tasks, and LSTM (Long Short-Term Memory) is no exception. One of the promising methods for continual learning are replay-based methods. However, they are prone to overfitting, harming generalization. Meanwhile, Ant Colony Optimization (ACO) is an algorithm widely used for combinatorial optimization problems and has been applied to the structural optimization of LSTM. In this study, we propose a continual learning method for LSTM using ACO to reduce catastrophic forgetting. The method iteratively optimizes the internal structure of the LSTM in parallel with the training of the current task, using the model performance as fitness. It also extends the replay-based method and utilizes two kinds of memory buffers to reduce overfitting to the memory. The proposed method was tested on four benchmark problems, and the results indicate its effectiveness, especially in the small memory size scenarios.
3-sCHSL: Three-Stage Cyclic Hybrid SFS and L-SHADE Algorithm for Single Objective Optimization

This paper proposes a novel hybridization of two metaheuristic algorithms to solve the real-parameter single objective numerical optimization problems. The proposed Three-stage Cyclic Hybrid SFS and L-SHADE (3-sCHSL) algorithm integrates the high-level interactions of Stochastic Fractal Search (SFS) and L-SHADE algorithms in a single framework. Furthermore, a guided control population initialization strategy is injected in 3-sCHSL to address the stagnation and diversity loss issues as the evolution process evolves. The performance of the proposed algorithm is tested under different complexity levels of different transformations of the CEC 2021 benchmark suite with dimension 20. The experimental results demonstrated the efficiency and competitiveness of the proposed algorithm against recent state-of-the-art algorithms. In addition, 3-sCHSL achieved a superior performance when evaluating two engineering design problems.
Demystifying deep learning in predictive monitoring for cloud-native SLOs

The complexity inherent in managing cloud computing systems calls for novel solutions that can effectively enforce high-level Service Level Objectives (SLOs) promptly. Unfortunately, most of the current SLO management solutions rely on reactive approaches, i.e., correcting SLO violations only after they have occurred. Further, the few methods that explore predictive techniques to prevent SLO violations focus solely on forecasting low-level system metrics, such as CPU and Memory utilization. Although valid in some cases, these metrics do not necessarily provide clear and actionable insights into application behavior. This paper presents a novel approach that directly predicts high-level SLOs using low-level system metrics. We target this goal by training and optimizing two state-of-the-art neural network models, a Short-Term Long Memory - LSTM, and a Transformer-based model. Our models provide actionable insights into application behavior by establishing proper connections between the evolution of low-level workload-related metrics and the high-level SLOs. We demonstrate our approach to selecting and preparing the data. We show in practice how to optimize LSTM and Transformer by targeting efficiency as a high-level SLO metric and performing a comparative analysis. We show how these models behave when the input workloads come from different distributions. Consequently, we demonstrate their ability to generalize in heterogeneous systems. Finally, we operationalize our two models by integrating them into the Polaris framework we have been developing to enable a performance-driven SLO-native approach to Cloud computing.
PolarisProfiler: A Novel Metadata-Based Profiling Approach for Optimizing Resource Management in the Edge-Cloud Continnum

Resource provisioning is vital in large-scale, geo-graphically distributed, and hierarchically organized infrastructures, and, at the same time, it represents one of the stiffest challenges in their management. The goal is to optimally allocate infrastructure resources to jobs, ensuring jobs' Service Level Objectives (SLOs) while retaining high resource utilization across the entire resource pool. In this context, accurate workload profiling is crucial to achieving optimal resource management, giving more context to the system. However, approaches either make static guesses or use runtime profiling - that may be delayed by sandbox testing - and fall short in providing fast and accurate information. We aim to overcome these challenges with a novel profiling approach and methodology, the PolarisProfiler. We discard the consistency assumptions and assume a broader and less influenced perspective. We use apriori available, static metadata to enable generic and immediate job profiling based on historic execution traces. The PolarisProfiler proposes a novel dynamic profiling model, a generic workload profile generator, and a metadata-based profile classifier. We illustrate the practical feasibility of our approach by evaluating the PolarisProfiler in a case study. We target machine learning workloads, leveraging a publicly available dataset from Alibaba. We offer a reference implementation of our profiling methodology, combining a density-based hierarchical clustering technique and an interpretable decision-tree model for the classifier. We test the PolarisProfiler for job duration estimation. Despite being based solely on static, apriori metadata, we obtain convincing results compared to the state-of-the-art, yielding an estimation error rate of 5% for the 80% of profiled jobs.
Differential Evolution with Clustering-based Niching and Adaptive Mutation for Global Optimization

This paper proposes a differential evolution with clustering-based niching and adaptive mutation (CMDE) for global optimization. In the proposed algorithm, a clustering method is first employed to adaptively divide the population into niches according to the stage of evolution as well as the diversity of population. Based on the obtained niches, an adaptive mutation scheme is then devised such that encouraging high potential niches for exploitation while low potential niches for exploration, thus appropriately search the space. The performance of proposed algorithm has been evaluated on CEC'2015 test suit and compared with related methods. The results show that the proposed clustering-based niching and adaptive mutation schemes could be promising to enhance the DE for optimization.