Reviewerly/exHarmony huggingface dataset:
Requirements for a Platform that Improves the Number of Young Women Entering Cybersecurity

The cybersecurity workforce is male-dominated, where females only make 11% of the cybersecurity workforce.There is, therefore, a need to encourage females to enter cybersecurity to promote a diverse and multiskilled workforce.The research aimed at promoting this by testing the hypothesis that increasing the visibility of female cybersecurity roles models would inspire young females to consider careers in cybersecurity, subsequently, the research also aimed to identify the requirements for a platform that will provide single-point access to the role model's career stories and related motivational features.
Automatic Orchestration for Automatic Composition

The automatic orchestration problem is that of assigning instruments or sounds to the notes of an unorchestrated score. This is related to, but distinct from, problems of automatic expressive interpretation. A simple algorithm is described that successfully orchestrates scores based on analysis of one musical structure -- the "Z-chain."
Multimedia sensor image detection based on constrained underdetermined equation

Abstract In order to study the image detection of multimedia sensor based on constrained under determined equation, this paper proposes the improvement of image sensor quality detection method based on image recognition algorithm. Firstly, the principle and steps of the under determined method are explained, and the complexity of the algorithm is analyzed. The image sensor is the most important part of the mobile camera, which determines the performance of the mobile camera, and its assembly quality largely determines the quality of the whole mobile camera. The case studied in this paper is model h mobile phone camera, and the first pass rate of good products is only 80%. In order to meet the 95% pass rate of good products required by customers, it is necessary to comprehensively improve the processes that have a great impact on product quality, so as to improve the first pass rate of products, achieve quality objectives and reduce manufacturing failure costs. In order to find out the threshold value of the height information measured by the improved method, to determine whether the product is good, and to find out the process that has a great impact on the surface flatness of the image sensor, the product measurement experiment is specially set. The improved war page test method was used to measure the products of each experimental group. Among them, No. 22, No. 39 and No. 40 products are poor due to changes in physical properties in subsequent production, and cannot flow to the next process for production. Therefore, the data of No. 22, No. 39 and No. 40 are missing in the test data. A total of 133 groups of experimental data were obtained in this experimental test. The results showed that the standard deviations of group 1, group 2, group 3 and group 4 were 0.172, 0.125 and 0.304 respectively. That is, after the same product is treated by false hardening, heating hardening and base installation processes, the false hardening and heating hardening processes have relatively little impact on the surface flatness of the image sensor. After the base opening and installation process, the surface flatness of the image sensor begins to change. The standard deviations of the measurement results of group 1 and groups 4, 5, 6 and 7 were 0.304, 0.381, 0.391 and 0.514 respectively. That is, after the base installation, calibration, bonding, soldering and testing of the product, the surface flatness of the image sensor and the initial surface flatness have changed greatly. Therefore, production technicians can focus on the process that starts to change, that is, the base installation process. The improved method has the advantages of automatic operation, low error, low error rate and so on.
A hybrid tournament differential evolution algorithm for solving optimisation problems and applications

The goal of this work is to propose a hybrid algorithm, combining differential evolution algorithm and tournamenting process, for solving constrained and bound-constrained optimisation problems. Considering different options of binary tournamenting, six variants of the proposed hybrid algorithm are developed. To test the efficiency and performance of the proposed hybrid algorithm, 12 benchmark optimisation problems are considered and solved. From the obtained results of these benchmark problems, the proposed six different variants of the hybrid algorithm are compared numerically as well as graphically. From these comparisons, the best variants of the hybrid algorithm for solving constrained and bound-constrained optimisation problems are identified separately. Then by using these best variants, three well known engineering design problems are considered and solved. The computational results are compared with the results of some of the existing algorithms available in the literature. In each case, it is observed that the proposed algorithm performs well.
Tensorial Evolutionary Computation for Spatial Optimization Problems

Spatial optimization problems (SOPs) refer to a class of problems where the decision variables require spatial organization. Existing methods based on evolutionary algorithms (EAs) fit conventional evolutionary operators by flattening the spatial representations of SOPs to one dimension, which hence loses crucial spatial structures of the potential solutions. To address this issue, the paper proposes a tensorial evolutionary algorithm (TEA) by formulating a tensor space of the SOPs, where the population of candidate solutions is represented as a third-order tensor. Accordingly, we design a tensor-product crossover operator and a two-phase mutation consisting of a local convolutional mutation operator and a global Gaussian mutation operator in the tensor space. These operators are all implemented based on tensor operations. Under the consideration of spatial context on individual solutions, we devise three types of evolutionary tensors, namely, the crossover tensor, the convolutional mutant tensor, and the Gaussian mutant tensor for the three tensorial evolutionary operators correspondingly. The resultant TEA is tested using two typical real-world SOPs, namely, the facility layout problem and the image steganography problem. Comparative experimental studies validate the effectiveness and robustness of TEA.
Transformer Networks for Future Person Localization in First-Person Videos

Reliably and accurately forecasting future trajectories of pedestrians is necessary for systems like autonomous vehicles or visual assistive devices to function correctly. While previous state-of-the-art methods relied on modeling social interactions with LSTMs, with videos captured with a static camera from a bird’s-eye view, our paper presents a new method that leverages the Transformers architecture and offers a reliable way to model future trajectories in first-person videos captured by a body-mounted camera, without having to model any social interactions. Accurately forecasting future trajectories is a challenging task, mainly due to how unpredictably humans move. We tackle this issue by using information about target persons’ previous locations, scales, and dynamic poses, as well as information about the camera wearer’s ego-motion. The model we propose predicts future trajectories in a simple way, modeling each target’s trajectory separately, without the use of complex social interactions between humans or interactions between targets and the scene. Experimental results show that our method overall outperforms previous state-of-the-art methods, and yields better results in challenging situations where previous state-of-the-art methods fail.
Classifying LPI Radar Waveforms With Time-Frequency Transformations Using Multi-Stage CNN System

As the number of radar waveforms in the cognitive electronic warfare applications increases, individual detection and classification performances of each waveform vary furthermore due to their different characteristics. To provide a supervised signal classification in an efficient framework, we propose a multi-stage waveform classification system, where multiple modular blocks are combined to classify 18 different radar waveforms. In the first stage, we transform the signals into time-frequency images (TFIs) using Fourier-based Synchrosqueezing Transform (FSST) and SqueezeNet to classify the signals into two subsets: P1-4 and others. Then, the subsets are used as inputs to two different systems. These systems use different TFI techniques such as FSST and Smoothed Pseudo Wigner Ville Distribution (SPWVD) for processing and convolutional neural network (CNN) architectures such as Squeezenet, ResNet-50, and ShuffleNet for classification. In experiments, we provide supervised classification results at different signal-to-noise ratio (SNR) levels and achieve 98.08% classification accuracy at 10-dB SNR on a diverse set of frequency and phase modulated signals.
Entrepreneurial Skills – a Pathway to Sustainable Social Innovations?

Along with globalization processes and adjustments – social and environmental problems that are challenging global sustainability in the labor market and society, it’s essential to ask whether the demand for education is also changing? Having regard to the fact that in 2015 United Nations (UN) member states adopted the 17th Sustainable Development Goal (SDGs) to address global challenges and emphasized the importance of quality education in Sustainable Development Goal 4 and The Organisation for Economic Co-operation and Development (OECD) countries have long recognized the need to develop skilled people through education. As they strive to find new sources of growth to underpin a solid and sustainable future, it is crucial to understand the best ways to advance social innovation, which is key to solving these increasing demands in the entrepreneurial process. It can manifest itself in various elements of pedagogical work. The study conducted content analysis to find out how social innovations and entrepreneurial skills show in the experience of four academic representatives of educational sciences to analyze various perspectives and opportunities of social innovation implementation by promoting entrepreneurial skills. To reach the aim of the research, three following questions were asked: How has the meaning of social innovation in the educational sciences changed since 2015, when UN member states adopted the 2030 Agenda for Sustainable Development and 17 Sustainable Development goals (SDGs)? What entrepreneurial skills help implement social innovation in the educational sciences? What are the most critical factors for the sustainable social innovations implemented in the educational sciences? The study results reveal diverse and ordinary comprehensions depending on professional academic experience, which has been listed and analyzed through the theories of social innovation.
Mask Recognition Based on Improved YOLOv5 Target Detection Algorithm

Since the outbreak of the new crown epidemic, in order to curb the spread of the epidemic, the World Health Organization and others have carried out a series of work studies. At present, wearing a mask correctly when traveling in any public place is one of the effective means of protection. However, for example, in places with high traffic such as high-speed railway stations and airports, the efficiency of manual detection and supervision of mask wearing is low. Therefore, it is very necessary to use the automatic detection mask device to supervise the wearing of masks in real time. With the deepening of research on deep learning network models, in the real-time detection of masks Deep Learning-based network models, it is difficult to achieve satisfactory results in terms of precise and real-time in all performance. In order to improve the detection accuracy and other issues, based on the YOLOv5 object detecting algorithm, we first created a dataset MaskData for mask recognition by using the open source dataset downloaded from the Internet and adding various types of face mask datasets. Secondly, in the detection network, the DIOU_nms method is designed to be a replacement for the IOU in the NMS, and under the same parameters, the detection accuracy of occluded and overlapping targets is improved. Finally, replacing the GIOU loss function with the a-CIoU loss function can obtain higher-quality localized image regions faster and more accurately, generate bounding boxes and predict categories. The results of the experiment have demonstrated that the refined network can more accurately identify whether the face is wearing a mask, and to some extent, the detection accuracy was greatly improved. And using the designed GUI interface, the trained and improved YOLOv5 model can be directly called to perform real-time mask-wearing detection for videos and pictures.
Analysis of Potential Biases on Mammography Datasets for Deep Learning Model Development

The development of democratized, generalizable deep learning applications for health care systems is challenging as potential biases could easily emerge. This paper provides an overview of the potential biases that appear in image analysis datasets that affect the development and performance of artificial intelligence algorithms. Especially, an exhaustive analysis of mammography data has been carried out at the patient, image and source of origin levels. Furthermore, we summarize some techniques to alleviate these biases for the development of fair deep learning models. We present a learning task to classify negative and positive screening mammographies and analyze the influence of biases in the performance of the algorithm.
"Project smells"

Machine Learning (ML) projects incur novel challenges in their development and productionisation over traditional software applications, though established principles and best practices in ensuring the project's software quality still apply. While using static analysis to catch code smells has been shown to improve software quality attributes, it is only a small piece of the software quality puzzle, especially in the case of ML projects given their additional challenges and lower degree of Software Engineering (SE) experience in the data scientists that develop them. We introduce the novel concept of project smells which consider deficits in project management as a more holistic perspective on software quality in ML projects. An open-source static analysis tool mllint was also implemented to help detect and mitigate these. Our research evaluates this novel concept of project smells in the industrial context of ING, a global bank and large software- and data-intensive organisation. We also investigate the perceived importance of these project smells for proof-of-concept versus production-ready ML projects, as well as the perceived obstructions and benefits to using static analysis tools such as mllint. Our findings indicate a need for context-aware static analysis tools, that fit the needs of the project at its current stage of development, while requiring minimal configuration effort from the user.
Trust Based lot Access Control Using Blockchain

In order to solve the problems of single point of failure and lack of dynamic permission management in traditional centralized access control methods of Internet of Things, an Internet of Things access control method based on Hyperledger Fabric blockchain framework was proposed. The method on the basis of attribute-based access control, using the blockchain technology realize the decentralization of Internet equipment, automation and fine-grained access control. At the same time, the trust value is used as an additional attribute to evaluate the trust relationship between different domains and nodes, and the trust value is updated after each interaction to assign different access permissions to different nodes, so as to realize dynamic permission management. The results of theoretical analysis and experimental simulation shows that the scheme can maintain high throughput in a large number of requests while ensuring fine-grained and dynamic access control.
PERILAKU MENYIMPANG DI DUNIA PENDIDIKAN (Studi kasus Evaluasi pembelajaran di MA NW Dames)

Deviant behavior is a form of behavior or action that is not in accordance with the norms in the community. While deviant behavior in education Dimiyati (1980: 32) stated that the deviant behavior of children / adolescents in terms of education is that they are considered to interfere with the process of teaching and learning in school, do not obey the rules that have difficulties in association and other aspects that interfere and harm themselves or harm others. As a result, all of these behavioral irregularities were repeated repeatedly by them. The method in this study is a qualitative research method with a descriptive approach. The type of research used in this study is a case study (case study) From the studies conducted, this study resulted in deviant behavior in the world of education that often occurs during evaluation of learning. In the process of evaluating learning deviations carried out by students that is working together in working on exam questions (cheating).
A Fuzzy-Based Multimedia Content Retrieval Method Using Mood Tags and Their Synonyms in Social Networks

The preferences of Web information purchasers are rapidly evolving.Cost-effectiveness is now becoming less regarded than cost-satisfaction, which emphasizes the purchaser's psychological satisfaction.One method to improve a user's cost-satisfaction in multimedia content retrieval is to utilize the mood inherent in multimedia items.An example of applications using this method is SNS (Social Network Services), which is based on folksonomy, but its applications encounter problems due to synonyms.In order to solve the problem of synonyms in our previous study, the mood of multimedia content is represented with arousal and valence (AV) in Thayer's two-dimensional model as its internal tag.Although some problems of synonyms could now be solved, the retrieval performance of the previous study was less than that of a keyword-based method.In this paper, a new method that can solve the synonym problem is proposed, while simultaneously maintaining the same performance as the keyword-based approach.In the proposed method, a mood of multimedia content is represented with a fuzzy set of 12 moods of the Thayer model.For the analysis, the proposed method is compared with two methods, one based on AV value and the other based on keyword.The analysis results demonstrate that the proposed method is superior to the two methods.
Neighborhood-Adaptive Multi-Cluster Ranking for Deep Metric Learning

Deep metric learning methods generally concentrate on designing distance-based losses to learn sample embeddings, which tacitly presuppose the neighborhood structure around each sample (e.g., hypersphere for Euclidean distance). However, this supposition is overly optimistic: 1) visual data is often located on low-dimensional manifolds curved in high-dimensional space, and all regions of the manifold may hardly share the same local structures in the input space; 2) it is unlikely that the local structure in the output embedding space is as homogeneous as assumed due to the non-linearity of neural networks. Hence, simply characterizing sample embeddings while ignoring the respective neighborhood structures leads to limitations. To address this problem, this paper presents a Neighborhood-Adaptive Multi-cluster Ranking (NAMR) framework by leveraging the heterogeneity of local structures. Specifically, considering that indexing algorithms are usually required in large-scale retrieval, NAMR characterizes an image from two kinds of embeddings (i.e., sample embedding and structure embedding). The sample embedding can be trained using any distance-based loss, while the structure embedding representing the neighborhood structure can be jointly learned with the sample embedding in a self-supervised multi-cluster ranking manner. In this way, existing indexing algorithms can seamlessly support large-scale retrieval employing NAMR embeddings without any modifications. We evaluate the proposed model on five standard benchmarks, consistently and explicitly improving four baselines (especially the simplest triplet loss) and achieving state-of-the-art performance.
A Preference Learning System for the Autonomous Selection and Personalization of Entertainment Activities during Human-Robot Interaction

Social robots assisting in cognitive stimulation therapies, physical rehabilitation, or entertainment sessions have gained visibility in the last years. In these activities, users may present different features and needs, so personalization is essential. This manuscript presents a Preference Learning System for social robots to personalize Human-Robot Interaction during entertainment activities. Our system is integrated into Mini, a social robot dedicated to research with a wide repertoire of entertainment activities like games, displaying multimedia content, or storytelling. The learning model we propose consists of four stages. First, the robot creates a unique profile of its users by obtaining their defining features using interaction. Secondly, a Preference Learning algorithm predicts the users’ favorite entertainment activities using their features and a database with the features and preferences of other users. Third, the prediction is adapted using Reinforcement Learning while entertainment sessions occur. Finally, the robot personalizes Human-Robot Interaction by autonomously selecting the users’ favorite activities. Thus, the robot aims at promoting longer-lasting interactions and sustaining engagement.
Developing E-book Batang Heaven of Asia as Descriptive Text Teaching Material

Technology in this era as a friend for human. It can be good or bad depending on the user. Now almost of all generation prefer the practical thing to use in their life, included in learning process. Both of teachers and students prefer to have an effective and efficient learning process. The researcher use the situation to conduct research and development. The research “Developing Ebook “Batang Heaven of Asia” as Descriptive Text Teaching Materials” was the utilization of technology in learning process. The e-book can help the teachers and the students as learning source. The research answered two questions (1) How is the need of developing e-book “Heaven of Asia” as descriptive text teaching materials? (2) How is developing e-book “Batang Heaven of Asia” as descriptive text teaching materials? The development procedures that used in this research was PPE from Richey and Klein 2005 . Those procedures were planning, production, and evaluation. In planning, the researcher distributed the questionnaire to get teachers’ and students’ needs. From the data result, the researcher analyzed the data and designed the product based on teachers’ and students’ needs. The process of producing this e-book, the researcher observed the tourism destination before writing the text. The title of e-book was Let’s Explore Batang. It contained the basic standard and learning goals, the materials, the texts, worksheet and glossary. This RND arrange e-book as descriptive text teaching material based on description of tourism destination in Batang Regency. This e-book was not only the texts but there were some pictures of the destination described. The e-book validated by some experts, they were English lecturers and English teacher. Keywords: e-book, RND, needs analysis, materials