Reviewerly/exHarmony huggingface dataset:
Automatic Recognition and Segmentation of Composite User Stories Based on Semantic Analysis

User stories are often used in agile development to express user needs. Accurate and rapid understanding of user stories is critical to product development. One of the major obstacles to accurate and efficient story understanding is the ambiguity of story statement caused by the existence of compound user stories. Therefore, the understanding of user stories is crucial to product development. One aspect that hinders the efficiency of story understanding is compound user stories, especially in Chinese, so identifying compound stories in the early stages of development is a major goal of this article. This paper proposes a method for identifying and segmenting compound stories based on semantic analysis, which includes analyzing the structural and semantic features of compound stories, building a feature dictionary, and automatically recognizing compound stories based on HanLP. This paper takes the user story of a game live broadcast system as an example, and the research proves that the method proposed in this paper can well identify common compound user stories.
The Role of Sketch Recognition in Engineering Education: Applications and Challenges

Sketch recognition is the automatic understanding of hand-drawn diagrams sketched through electronic mediums such as Tablet PCs or Smart Boards.In design, recognition algorithms and tools have been used to automatically generate computer-aided design (CAD) models from handdrawn, conceptual sketches.However, sketch recognition also lends itself to the field of education, especially in engineering domains.Hand-sketched diagrams aid in student learning and promote the creative process.Although having students draw diagrams to demonstrate knowledge of a concept is important, diagramming is typically omitted from the examination process as sketched diagrams take a significant time to grade.Because sketch recognition aims to automatically understand these hand-drawn diagrams, we are able to create tools that allow diagrams to be drawn and assessed in order to give immediate feedback to student and professor alike.In this talk, we will present our ongoing research and demonstrate some of the tools we have developed in the field of sketch recognition.In particular, we will discuss domains where sketch recognition can be used to aid in the educational process.Such domains include finite state machines and UML diagrams for computer science, circuit diagrams for electrical engineering, truss solving and recognition for civil engineering, physics simulations for mechanical engineering, and many others.We will also discuss some of the obstacles and challenges facing sketch recognition researchers, and give some insight into the future role and direction of sketch recognition in engineering education.
Peningkatan Kemampuan Menulis Surat Lamaran Pekerjaan Melalui Model Pembelajaran Think-Pair-Share

&#x0D; The purpose of the study was to find out how the ability to write job application letters for class XII students of SMK Negeri 2 Depok City in 2019-2020 and to find out whether cooperative learning with the Think-Pair-Share model could improve the ability to write cover letters for class XII students of SMK Negeri 2 Depok City. This study uses classroom action research (action research) in two rounds (cycles). Each round consists of four stages, namely: planning, activities and observations, reflection, and revision. The target of this research is Class XII Computer Network Engineering (TKJ) SMK Negeri 2 Depok City in 2019-2020. The data obtained in the form of subjective test results and observation sheets of teaching and learning activities. The results show, class XII students of TKJ SMK Negeri 2 Depok City in 2019-2020 are able to write job applications. This can be seen from the average score of students' ability to write job application letters and student learning completeness in cycle I and cycle II has increased. The average score of students' ability to write job application letters in the first cycle was 72.77 and learning completeness reached 64.70%. The average score of students' ability to write job application letters in cycle II was 78.38 and learning completeness reached 85.29%. The application of the Think-Pair-Share model of cooperative learning has a positive influence on Teaching and Learning Activities (KBM). Think-Pair-Share model of cooperative learning can improve student learning outcomes. This is indicated by the average student answers stating that students are interested and interested in the Think-Pair-Share cooperative learning method so that they become motivated to learn.&#x0D; Keywords: Writing Ability, Cooperative Learning, Think-Pair-Share Learning Model
Developing Independent Creativity in Pupils: Neuroscientific Discourse and Ukraine’s Experience

The article deals with the process of updating the content and methods of technological training. The latter aims to form pupils’ skills of independent creativity and educational and handicraft activities, which act as the means of improving education quality. The international relevance of the article lies in the objectification of Ukraine’s experience, which can be used in the post-Soviet countries, given that these countries follow conservative principles in planning and implementing educational content. The article proves that the effectiveness of developing pupils’ experience of independent creativity during handicraft and technology lessons is determined by the following organizational and methodological conditions: comprehending the need for independent creativity; developing pupils’ positive motivation towards independent creative handicraft and technology activities; improving educational and methodological support; using innovative pedagogical technologies and information and communication technologies. The article summarizes research findings of certain Ukrainian and foreign researchers, which adds up to the generalized contribution of its authors.
A New Identity-Based Encryption Scheme with Accountable Authority Based on SM9

Accountable authority identity-based encryption (A-IBE) is an extension of identity-based encryption (IBE) in which private key’s source can be traced, i.e., whether the key comes from a private key generator or a user. SM9 is an official cryptography standard of China which defines a practical IBE scheme. In this paper, we construct a practical A-IBE scheme from the SM9-IBE scheme. Our A-IBE scheme has public traceability and is proven secure if the based SM9-IBE scheme is secure. Compared with other A-IBE schemes, our A-IBE scheme has better efficiency in encryption and decryption.
Two-Stage Frame Extraction in Video Analysis for Accurate Prediction of Object Tracking by Improved Deep Learning

Recently, the information extraction from graphics and video summarizing using keyframes have benefited from a recent look at the visual content-based method. Analysis of keyframes in a movie may be done by extracting visual elements from the video clips. In order to accurately anticipate the path of an item in real-time, the visible components are utilized. The frame variations with low-level properties such as color and structure are the basis of the rapid and reliable approach. This research work contains 3 phases: preprocessing, two-stage extraction, and video prediction module. Besides, this framework on object track estimation uses the probabilistic deterministic process to arrive at an estimate of the object. Keyframes for the whole video sequence are extracted using a proposed two-stage feature extraction approach by CNN feature extraction. An alternate sequence is first constructed by comparing the color characteristics of neighboring frames in the original series to those of the generated one. When an alternate arrangement is compared to the final keyframe sequence, it is found that there are substantial structural changes between consecutive frames. Three keyframe extraction techniques based on on-time behavior have been employed in this study. A keyframe extraction optimization phase termed as "Adam" optimizer, dependent on the number of final keyframes is then introduced. The proposed technique outperforms the prior methods in computational cost and resilience across a wide range of video formats, video resolutions, and other parameters. Finally, this research compares SSIM, MAE, and RMSE performance metrics with the traditional approach.
Determination and Application of Sample Size Using Computer Mathematical Statistics and Standard Deviation

The determination of sample size is an important part of the sample survey. This paper determines the sample size mainly considering the influence of factors such as the population size, absolute error limit, confidence level and population variance. At the same time, this paper also considers the method of obtaining the value of the population variance. At last, we give a real example to show the method. The example is about the analysis of the drip irrigation belt about the sample size.
VOLGSTU GRADUATE ASSOCIATION: DEBUT IN A PANDEMIC BACKGROUND

Alumni associations are a relatively new phenomenon for Russia, typical, as a rule, for large metropolitan universities with a long history, rich traditions and extensive international relations. In recent years, alumni associations have begun to appear in regional universities. The authors analyze the situation with the development of alumni associations in the flagship universities of the country and in the universities of Volgograd (the chronology and forms of activity are considered). The article summarizes the results of Volgograd State Technical University Alumni Association's first year. Plans, goals and tasks for the further development of the VolgSTU Alumni Association have been developed. The authors show that the dynamic formation of alumni associations can be promising for regional universities as well, serving as platforms for the interaction of regional elites, centres of strategic decisions for regional development and providing communication between generations of alumni.
Automatic vectorization of point symbols on archive maps using deep convolutional neural network

Abstract. Archive topographical maps are a key source of geographical information from past ages, which can be valuable for several science fields. Since manual digitization is usually slow and takes much human resource, automatic methods are preferred, such as deep learning algorithms. Although automatic vectorization is a common problem, there have been few approaches regarding point symbols. In this paper, a point symbol vectorization method is proposed, which was tested on Third Military Survey map sheets using a Mask Regional Convolutional Neural Network (MRCNN). The MRCNN implementation uses the ResNet101 network improved with the Feature Pyramid Network architecture and is developed in a Google Colab environment. The pretrained network was trained on four point symbol categories simultaneously. Results show 90% accuracy, while 94% of symbols detected for some categories on the complete test sheet.
Cybernetic Sparks and Philosophical Feedback Loops

Gregory Bateson observed that cybernetics is not essentially about "exchanging information across lines of discipline, but in discovering patterns common to many disciplines" (Bateson, 1971, p. 23). This paper adopts his line of thought to join the dots between cybernetics and the philosophy of Existentialism, and then interconnect both with contemporary art. It demonstrates that while terminologies may differ, many of the three fields' primary concerns closely cohere. The world's most ground-breaking artists are found to apply and fuse cybernetic paradigms and Existentialist themes, from Robert Rauschenberg and Marina Abramović to Damien Hirst, Stelarc and Anish Kapoor. The research offers the first detailed comparison between cybernetics and Existentialism, and reveals surprising commonalities. Feedback loops, circular causality and negative entropy are not only central tenets of cybernetics, but also of Existentialism. Autonomy, autopoiesis and interactivity equally unite both fields, and each is visionary and forward looking in seeking radical change and transformations. Both explored artistic endeavours, with Existentialists Jean-Paul Sartre and Albert Camus equally renowned for their powerful novels and plays as their philosophical works, while cybernetic art became a major phenomenon in the 1960s following the landmark exhibition Cybernetic Serendipity: the Computer in the Arts (1968), and influenced artistic practices thereafter.
Information Visualization for the Construction Industry

The chapter introduces the topic by contextualizing, historically and conceptually, the use of visual properties to convey data and information for the built environment. It presents how design affects the visualization process, highlighting the authorial choices that influence how readers perceives information, showing why visualizations are not the result of a mechanical process of data encoding, but rather the outcome of an authorial process of representation. This is done by presenting the elements composing a visualization: the visual variables, the layout elements, and contextual information. Visual variables can be seen as “construction blocks” used to encode data dimensions, and strengths and weaknesses of their usage are exposed. The chapter then discusses visual models as “blueprints” featuring specific mappings of data onto visual variables, presenting a selection of useful ones and highlighting their context of use as well as issues in their usage. Finally, it is highlighted the relevance of contextual elements (such as titles, legends, and annotations) in the process of encoding/decoding of information and their role in providing readers the implicit knowledge needed to understand the subject and to get insights from the visualizations.
Image Captioning for Spatially Rotated Images in Video Surveillance Applications Using Neural Networks

Video Surveillance has become an essential tool in the Security industry because of the sophisticated and fool-proof technology. Recent developments in image recognition and captioning have enabled us to adopt these technologies in the field of video surveillance. The biggest problem in image captioning is that it is variant on the rotation angle of the image. Different angles of the same image generate different captions. We aim to address and eliminate the rotation variance of image captioning. We have implemented a custom image rotation network using a Convolutional Neural Network (CNN). The input image is rotated to the original angle using this network and passed on to the image captioning. The caption of the image is generated and sent to the user for the situation analysis.
Comparative Study of Abalone Age Prediction Using Classical Machine Learning Algorithms and Extreme Learning Machine

Abalone age prediction is a multi-class classification problem using the Abalone dataset from UCI machine learning repository. Extreme Learning Machine (ELM) is one of the randomization algorithm which uses the idea of least square estimation. In this paper, a comparative study of the performance of Extreme Learning Machine and three classical machine learning algorithms—MLP, KNN and random forests, is performed on the Abalone dataset. The performance of the algorithms is evaluated based on their accuracy scores and training times.
Anime4You: An Intelligent Analytical Framework for Anime Recommendation and Personalization using AI and Big Data Analysis

In recent years the popularity of anime has steadily grown. Similar to other forms of media consumers often face a pressing issue: “What do I watch next?”. In this study, we thoroughly examined the current method of solving this issue and determined that the learning curve to effectively utilize the current solution is too high. We developed a program to ensure easier answers to the issue. The program uses a Python-based machine learning algorithm from ScikitLearn and data from My Animelist to create an accurate model that delivers what consumers want, good recommendations [9]. We also carried out different experiments with several iterations to study the difference in accuracy when applying different factors. Through these tests, we have successfully created a reliable Support vector machine model with 57% accuracy in recommending users what to watch.
Development and testing of a model of distance education

This paper aims to present a universal model applicable at all levels of distance learning, in a region and particular educational organization. The authors applied a systematic approach to consider the introduction of distance education as an activity, which result depends on the current sociocultural conditions in the region, including the specifics of the population, the labor market, the socioeconomic and political situation, and the quality of teaching in educational organizations. The scientific novelty of the research includes the theoretical and methodological substantiation of a universal model for implementing distance education. It involves mandatory monitoring and support measures at all levels of administration: the micro- (an educational organization), meso- (regional and municipal administration), and macroadministration (the State). Exploring the research problem, the authors revealed that distance learning effectiveness is directly connected with the quality of the regional analysis of sociocultural factors at the planning and forecasting stage.
Reflections on Choosing a New Requirements Management Tool

Requirements engineering practitioners in industry are well-versed in the challenge of building and maintaining practice integrity while continually monitoring the landscape—our industry peers as well as our researcher colleagues—for new developments. This challenge is often overshadowed by the daunting effort of choosing and maintaining a requirements management (RM) tool. Vendors are always happy to share their wares, and users typically have a raft of requirements—some of which are functional, but others are more qualitative, subjective, and difficult (if not impossible!) to reach agreement on! Michael Panis moves from requirements authoring to creative writing in this issue’s “Requirements” column, sharing a fictionalized account of a tool selection process that highlights many of the factors that must be considered before investing in RM infrastructure.—Sarah Gregory
A quantum processor based on coherent transport of entangled atom arrays

The ability to engineer parallel, programmable operations between desired qubits within a quantum processor is central for building scalable quantum information systems. In most state-of-the-art approaches, qubits interact locally, constrained by the connectivity associated with their fixed spatial layout. Here, we demonstrate a quantum processor with dynamic, nonlocal connectivity, in which entangled qubits are coherently transported in a highly parallel manner across two spatial dimensions, in between layers of single- and two-qubit operations. Our approach makes use of neutral atom arrays trapped and transported by optical tweezers; hyperfine states are used for robust quantum information storage, and excitation into Rydberg states is used for entanglement generation. We use this architecture to realize programmable generation of entangled graph states such as cluster states and a 7-qubit Steane code state. Furthermore, we shuttle entangled ancilla arrays to realize a surface code with 19 qubits and a toric code state on a torus with 24 qubits. Finally, we use this architecture to realize a hybrid analog-digital evolution and employ it for measuring entanglement entropy in quantum simulations, experimentally observing non-monotonic entanglement dynamics associated with quantum many-body scars. Realizing a long-standing goal, these results pave the way toward scalable quantum processing and enable new applications ranging from simulation to metrology.
Decision-based Black-box Attack Against Vision Transformers via  Patch-wise Adversarial Removal

Vision transformers (ViTs) have demonstrated impressive performance and stronger adversarial robustness compared to Deep Convolutional Neural Networks (CNNs). On the one hand, ViTs' focus on global interaction between individual patches reduces the local noise sensitivity of images. On the other hand, the existing decision-based attacks for CNNs ignore the difference in noise sensitivity between different regions of the image, which affects the efficiency of noise compression. Therefore, validating the black-box adversarial robustness of ViTs when the target model can only be queried still remains a challenging problem. In this paper, we propose a new decision-based black-box attack against ViTs termed Patch-wise Adversarial Removal (PAR). PAR divides images into patches through a coarse-to-fine search process and compresses the noise on each patch separately. PAR records the noise magnitude and noise sensitivity of each patch and selects the patch with the highest query value for noise compression. In addition, PAR can be used as a noise initialization method for other decision-based attacks to improve the noise compression efficiency on both ViTs and CNNs without introducing additional calculations. Extensive experiments on ImageNet-21k, ILSVRC-2012, and Tiny-Imagenet datasets demonstrate that PAR achieves a much lower magnitude of perturbation on average with the same number of queries.
Fast and Accurate Span-based Semantic Role Labeling as Graph Parsing

Currently, BIO-based and tuple-based approaches perform quite well on the span-based semantic role labeling (SRL) task. However, the BIO-based approach usually needs to encode a sentence once for each predicate when predicting its arguments, and the tuple-based approach has to deal with a huge search space of $O(n^3)$, greatly reducing the training and inference efficiency. The parsing speed is less than 50 sentences per second. Moreover, both BIO-based and tuple-based approaches usually consider only local structural information when making predictions. This paper proposes to cast end-to-end span-based SRL as a graph parsing task. Based on a novel graph representation schema, we present a fast and accurate SRL parser on the shoulder of recent works on high-order semantic dependency graph parsing. Moreover, we propose a constrained Viterbi procedure to ensure the legality of the output graph. Experiments on English CoNLL05 and CoNLL12 datasets show that our model achieves new state-of-the-art results under both settings of without and with pre-trained language models, and can parse over 600 sentences per second.
Towards Federated Learning on Time-Evolving Heterogeneous Data

Federated Learning (FL) is an emerging learning paradigm that preserves privacy by ensuring client data locality on edge devices. The optimization of FL is challenging in practice due to the diversity and heterogeneity of the learning system. Despite recent research efforts on improving the optimization of heterogeneous data, the impact of time-evolving heterogeneous data in real-world scenarios, such as changing client data or intermittent clients joining or leaving during training, has not been well studied. In this work, we propose Continual Federated Learning (CFL), a flexible framework, to capture the time-evolving heterogeneity of FL. CFL covers complex and realistic scenarios -- which are challenging to evaluate in previous FL formulations -- by extracting the information of past local datasets and approximating the local objective functions. Theoretically, we demonstrate that CFL methods achieve a faster convergence rate than \fedavg in time-evolving scenarios, with the benefit being dependent on approximation quality. In a series of experiments, we show that the numerical findings match the convergence analysis, and CFL methods significantly outperform the other SOTA FL baselines.