Reviewerly/exHarmony huggingface dataset:
Efficient Real-Time Dense Pedestrian Detector Based on Improved YOLOv7-tiny

The state-of-the-art YOLO framework strikes an excellent balance between speed and accuracy and has become one of the most effective object detection algorithms. However, when performing dense pedestrian detection tasks, the network is computationally intensive, real-time is insufficient, and it is difficult to be applied to edge devices with weak computational power in practical use. Therefore, we propose a detection algorithm based on YOLOv7-tiny improvement. Based on the GhostNetv2 network, we use the parameter-free attention mechanism SimAM to construct the GhostNet-tiny structure and propose the C3Ghost-tiny module to be combined with the YOLO network in order to reduce the network computational parameters. We propose a new neck network structure to extract richer information with a bi-directional weighted pyramid structure and weighted connectivity of features at different scales. We also use WIoU as a coordinate loss function to improve the network's attention to small targets. We tested the effectiveness of the proposed method on the public dataset CrowdHuman, and the results show that our method can effectively reduce the network parameters while improving the accuracy. Notably, compared to the YOLOv7-tiny model, our model improves the accuracy value by 2.13%, reduces the amount of network parameters by 5.9%, reduces the amount of floating-point calculations by 60.3%, reduces the size of the network model file by 27.7%, and improves the detection speed by up to 46.64%.
Digital Eye on Endangered Wildlife: Crafting Recognition Datasets through Semi-Automated Annotation

Wildlife conservation, especially in Africa, faces significant challenges from hunting and poaching, endangering species like rhinos and elephants. Despite concerted efforts, the lack of annotated wild animal datasets has hampered the development of effective monitoring algorithms for reserves and parks. Although advances in technology have enabled institutions to upload images for monitoring purposes, manual annotation remains time-consuming and inefficient. To address this issue, the paper proposes a semi-automatic annotation framework for wild animal recognition dataset construction, focusing on six classes of common animals in danger in Africa (including rhino, elephant, lion, giraffe, cheetah, and zebra). The framework involves training a YOLOv5 model on a small manually annotated dataset of 10%, and using this model to semi-automatically label the remaining large-scale dataset. The framework results are convincing when compared to a semi-auto annotation tool (Bylabel) with an 86.16% improvement in terms of time consumption in seconds, which enhances the time efficiency for annotation. This is the first wild animal dataset constructed with boundary box labels based on semi-automatic annotation. The proposed framework has the potential to expedite research on wildlife protection, serve as a guide for semi-automatic annotation dataset construction, and have a significant impact on conservation communities. The dataset is publicly available at http://data.mendeley.com/datasets/vhmvfbgvxj.
Non-Fungible Tokens(NFTs): tokens of digital assets on the blockchain

Non-Fungible Token (NFT) is a carrier of unique identity as a virtual currency. Its indivisible, unique, and verifiable characteristics can truly reflect personal assets and virtual assets to a certain extent. It is widely applied by users in art, games, collectibles and other areas. NFT is stored on the blockchain. The user's digital signature is embedded into the NFT record, which is distributed through a smart contract and verified by the blockchain consensus mechanism, thus solving the problem of identity confirmation in digital asset transactions. This paper makes a review of the main processes and key technologies in NFT applications. First, it introduces the life of NFT: minting, distribution, storage and circulation. Then, the key technologies of NFT are explained, including the blockchain smart contracts and consensus mechanisms in NFT distribution and transaction, the token standards that the minting and transaction process of NFT needs to use, and the important role of NFT in the field of web3. Finally, through the evaluation system of NFT, the problems existing in the current NFT applications and the future research direction are drawn out.
How to Crack Complex, Ill-Defined, Nonimmediate Problems by Issue Trees: McKinsey on a Shoestring: Simple Patterns for Root Cause Analysis

Digital means have become ubiquitous in our lives. They offer tremendous capabilities to master various cognitive tasks typically performed by humans. However, knowledge workers often fall prey to the belief that the solution to numerous cognitive problems lies in just adopting a "powerful" digital tool. They take the beneficial nature of digital means for granted. But overconfidence in the power of digital capabilities frequently lead to bad or wrong results, frustration, and disappointment. Digital solutions are usually no silver bullets to problems - in some cases, they even create new ones. Thus, to make best use of available digital means, the competence to build personal problem-solving skills are a prerequisite. Lacking a comprehensive understanding of the problem, pursuing a digital solution could be futile or even risky. One approach to create a deep understanding of especially complex, ill-defined, nonimmediate (CIDNI) problems is to create Issue Trees. They present a systematic way to identify the right problem before resolving it right - possibly by help of powerful digital means. Issue trees break down a complex challenge into smaller, more manageable parts that can be understood at a human scale. They minimize the risk of missing critical information, eliminate overlaps, and ensure that key drivers of a problem are considered. In this paper, the author presents a pocket guide for knowledge workers seeking to improve their capability to really understand CIDNI problems by means of well-created issue trees. Seven patterns are laid down in detail, each addressing a best practice involved in constructing such issue trees to provide deeper insights into a problem space. By first creating issue trees, knowledge workers can easier identify and make much better use of digital means that finally resolve their problems.
API Refactoring to Patterns: Catalog, Template and Tools for Remote Interface Evolution

Refactoring is an essential agile practice for software evolution. While program-internal code-level refactoring is well established, architecture-level refactoring has been researched but not yet widely adopted in practice. As a result, application programming interface (API) refactoring is not well understood, and practitioners consequently struggle with the evolution of distributed systems using Web APIs and other remoting technologies. To fill this knowledge gap, we propose to apply refactoring to the problem of designing and developing adaptive APIs. This paper introduces an Interface Refactoring Catalog (IRC) and presents eight of its refactorings. IRC has been available online since 2021 and collects 22 refactorings at present. Eleven of these patterns leverage Patterns for API Design, originating from our previous work; the remaining ones deal with the number and size of API endpoints and their operations, cover renaming of these API building blocks and message representation elements, and deal with architectural change.
USE OF THE RESULTS OF THE WORK OF THE UKRAINIAN METEOR NETWORK IN THE PROCESS OF TEACHING PHYSICS TO STUDENTS OF THE EDUCATIONAL AND SCIENTIFIC INSTITUTE OF ENERGY, AUTOMATION AND ENERGY SAVING NULES OF UKRAINE

Features of meteor fixation methods are presented by organizing a network of corresponding observation points - permanently operating observation stations located on the territory of Ukraine with appropriate technical and software support for conducting basic and one-way observations of meteors in the radio range of electromagnetic wavelengths. This contributes to solving the strategic problems of meteor research, the structure and evolution of meteor showers and streams, the interaction of meteoroids with the Earth's atmosphere and their chemical composition. Scientific and technical products of observation stations are the results of high-precision basic or one-sided observations of meteors in the radio range of electromagnetic wavelengths, fundamental and applied scientific and technical research. The purpose of the study is to consider the principle of operation of the Ukrainian meteor observation network, a separate meteor hardware and software complex for observing meteors in the radio range of electromagnetic waves using the method of direct scattering on meteor trails of signals from powerful FM radio broadcasting stations with the possibility of further processing and presentation of data by students of specialties 141 "Electric power, electrical engineering and electromechanics", 144 "Thermal power engineering", 174 "Automation, computer-integrated technologies and robotics" of the Educational and Scientific Institute of Energy, Automation and Energy Saving of NULES of Ukraine in the process of studying physics. Processing of experimental data was carried out by students of the specified specialties under the guidance of the author while studying physics. The results of statistical data processing of meteor invasions and their graphic representation by students of the specified specialties in the process of mastering physics are described. Prospects - putting into operation by the end of 2024 the seventh receiving station in the city of Kyiv (student campus of NULES of Ukraine) - the seventh meteor hardware and software complex, as a component of the Ukrainian meteor observation network.
Vetoing: Social, Logical and Mathematical Aspects

An alternative title of this chapter could be ‘On Vetoer in Language, Logic and Mathematics’ because this text can be considered as an example comparing definitions of a veto player in a natural language, i.e. in real world, in first-order logic and, generally, in mathematics. It is intuitively quite simple to understand how the vetoer can be defined by means of a natural language as a person with absolute negative power, i.e. a person who can impede any social initiative. Here we show how in first-order logic the existence of a vetoer is acknowledged by a first-order formula which is inconsistent with some well-known axioms of traditional Social Choice Theory. In addition, we present how the exact power of a vetoer can be mathematically calculated and transparently defined by means of a weighted voting system.
Trajectory Control Using an Information Engine

In Chap. 4 , we presented an information engine that could raise an optically trapped bead against gravity and store gravitational potential energy. We found that the average velocity of the bead depends on experimental parameters such as trap stiffness and bead diameter. Here we operate the information engine to convert thermal fluctuations into directed motion, without storing any gravitational energy. We term such an engine a transporter engine. Although the ensemble moves at an average velocity, individual trajectories have velocities that fluctuate independently about the average. As a consequence, the algorithm described in Chap. 4 cannot control individual trajectory paths. In this chapter, I explore a feedback algorithm that can make the bead trajectory track a desired path, with zero input trap work. I characterize the performance of the feedback algorithm, drawing on ideas from control theory. I find that the frequency range over which the heuristic feedback algorithm can track is comparable to the corner frequency of the trap.
Providing professional and pedagogical culture of future English teachers in the context of European integration

The article presents the features of ensuring the professional and pedagogical culture of future English language teachers in the context of European integration. By professional and pedagogical culture we understand the integral quality of the teacher’s personality, which is a prerequisite for effective pedagogical activity, a generalized indicator of professional competence and the goal of professional self-improvement. A modern future teacher must be comprehensively prepared for various aspects of the future work and demonstrate the results of learning – “knowledge, skills, ways of thinking, skills, views, values and other personal qualities. Modern education and upbringing of an active and independent creative personality, competitive in the conditions of the information society, requires from the future teacher not only interactive skills, but also the ability to independently acquire knowledge, thoughtfully build a schedule, navigate information and prepare oneself for the use of these technologies in future professional activities. This approach determines the organization of pedagogical practice of students, which contributes to the formation of motivational-emotional, cognitive, activity and regulatory structural-criterial components of the intellectual culture of the future teacher. Educators assure that sensitivity to suggestions and the value of accepting one’s own opinion dominate during education. They noted that in an educational institution it is very important to teach the material in an accessible way, as well as to encourage an increase in motivation for better assimilation of knowledge with words and support. They need such encouragement and motivation in writing papers of various research types, supervision and mentoring. Such support will contribute to the formation of the professional and pedagogical culture of future teachers of the English language and the possibility of applying the acquired skills, knowledge and skills in professional activities.
Increasing the efficiency of public management of cryptocurrency circulation in Ukraine: challenges and prospects

This article is devoted to the problem of improving the efficiency of public management of cryptocurrency circulation in Ukraine. Prospects for solving these problems are presented, such as the development of clear regulation, promotion of innovation, protection of the rights and interests of investors and educational programs. Optimizing public administration will help create stable and transparent conditions for the development of the trade market in Ukraine.
A State Diagnosis Method for Distribution Transformer Device Based on Cloud Edge Collaboration

A distribution transformer device status diagnosis method based on cloud edge collaboration is proposed to address the difficulty and time-consuming issue of device status diagnosis in new power systems. Firstly, a distribution network device status diagnosis system was designed based on cloud edge collaboration architecture, which improves diagnostic efficiency through data preprocessing in the vicinity. Then, principal component analysis is used on the edge side to extract the main parameters of the distribution transformer, reducing data redundancy. Finally, the Softmax objective function in the XGBoost model was optimized using Focal loss and deployed in the cloud center to analyze the main parameter samples of distribution transformers, thereby obtaining transformer status diagnosis results. Based on the Pycharm platform, experimental analysis was conducted on the proposed method, and the results showed that its diagnostic accuracy reached 96.13%, and the diagnostic time was 12.51s, providing strong guarantees for the safe and stable operation of distribution transformers.
DANet: Enhancing Small Object Detection through an Efficient Deformable Attention Network

Efficient and accurate detection of small objects in manufacturing settings, such as defects and cracks, is crucial for ensuring product quality and safety. To address this issue, we proposed a comprehensive strategy by synergizing Faster R-CNN with cutting-edge methods. By combining Faster R-CNN with Feature Pyramid Network, we enable the model to efficiently handle multi-scale features intrinsic to manufacturing environments. Additionally, Deformable Net is used that contorts and conforms to the geometric variations of defects, bringing precision in detecting even the minuscule and complex features. Then, we incorporated an attention mechanism called Convolutional Block Attention Module in each block of our base ResNet50 network to selectively emphasize informative features and suppress less useful ones. After that we incorporated RoI Align, replacing RoI Pooling for finer region-of-interest alignment and finally the integration of Focal Loss effectively handles class imbalance, crucial for rare defect occurrences. The rigorous evaluation of our model on both the NEU-DET and Pascal VOC datasets underscores its robust performance and generalization capabilities. On the NEU-DET dataset, our model exhibited a profound understanding of steel defects, achieving state-of-the-art accuracy in identifying various defects. Simultaneously, when evaluated on the Pascal VOC dataset, our model showcases its ability to detect objects across a wide spectrum of categories within complex and small scenes.
Load Decomposition Method Based on Improved EEMD-Person-PCA

With the rapid development of economy and society, the power demand of the whole society is becoming more and more huge and the types are becoming more and more complicated. The traditional power load modeling method is single, which leads to the problems of low classification of load modeling area decomposition and difficult detailed separation of features in the current situation. To solve these problems, a load decomposition method based on Ensemble Empirical Mode Decomposition (EEMD) -Person correlation coefficient - Principal Component Analysis (PCA) was proposed. It has good engineering significance for separating load characteristics in complex cases. First, a series of intrinsic mode functions (IMF) with mode aliasing were obtained by decomposing the original load data through EEMD. Then, the Person correlation coefficient and PCA method were used to decompose and reduce the dimension of IMF, eliminate the IMF component with weak correlation and obtain decomposition parameters with high similarity, thus alleviating the degree of modal aliasing and reducing the difficulty of parameter determination. The proposed method is applied to the typical load parameter decomposition problem, and the simulation results show that the proposed method has obvious advantages in the precision scale in the centralized modeling of multiple decomposition problems, indicating that the designed method can effectively deal with the typical load decomposition modeling problem.
Intent Classification of Nepali Factoid Questions

Question Classification plays a vital role in identifying the accurate answer to any question, and is considered as a core component of Question Answering Systems. In Nepali Natural Language Processing, the area of Question Answering has not even been initiated due to a lack of dataset. The dataset of Nepali factual questions has been created by collecting questions from general knowledge books and labelling them with developed taxonomy. The problem of Nepali factoid question classification has been studied with a Support Vector Machine, which works as a baseline classification algorithm and obtained a 0.74 weighted F1-score in the imbalance dataset. The experimental results of the proposed model show the effectiveness of the approach in classifying factoid questions accurately compared with the state-of-the-art approaches.
A Data-Driven Short-Term Prediction Method for Photovoltaic Power Generation

This paper describes a data-driven photovoltaic power generation prediction strategy based on grey correlation analysis and long and short term memory (LSTM) model. The strategy utilizes grey correlation analysis to select main influencing factors of photovoltaic power generation, and utilizes LSTM regression network to predict intraday power sequences. The proposed method is only based on data, avoiding complex modeling processes. And it is validated with actual photovoltaic power data.
Unbiased and Robust: External Attention-enhanced Graph Contrastive Learning for Cross-domain Sequential Recommendation

Cross-domain sequential recommenders (CSRs) are gaining considerable research attention as they can capture user sequential preference by leveraging side information from multiple domains. However, these works typically follow an ideal setup, i.e., different domains obey similar data distribution, which ignores the bias brought by asymmetric interaction densities (a.k.a. the inter-domain density bias). Besides, the frequently adopted mechanism (e.g., the self-attention network) in sequence encoder only focuses on the interactions within a local view, which overlooks the global correlations between different training batches. To this end, we propose an External Attention-enhanced Graph Contrastive Learning framework, namely EA-GCL. Specifically, to remove the impact of the inter-domain density bias, an auxiliary Self-Supervised Learning (SSL) task is attached to the traditional graph encoder under a multi-task learning manner. To robustly capture users’ behavioral patterns, we develop an external attention-based sequence encoder that contains an MLP-based memory-sharing structure. Unlike the self-attention mechanism, such a structure can effectively alleviate the bias interference from the batch-based training scheme. Extensive experiments on two real-world datasets demonstrate that EA-GCL outperforms several state-of-the-art baselines on CSR tasks. The source codes and relevant datasets are available at https://github.com/HoupingY/EA-GCL.
Density Operator Description for Superconducting Traveling-Wave Parametric Amplifiers

In this paper, we present a density operator description for a dissipative-dispersive Josephson traveling-wave parametric amplifier. A quantum master equation has been derived for the description of dissipation and thermal fluctuations that arise from lossy dielectric substrates. The formalism is especially effective in the dispersive case. Based on the equation of motion of the system density operator, we investigate the amplification performance by predicting the signal gain and the total photon number, considering amplifier phase-mismatch present in real devices. In addition to the gain investigation, we perform a two-mode squeezing analysis for such structures.
Detecting Important Video Shots Using Video Summarization and Feature Extraction Techniques

Video Shot Detection is one of the techniques used in detecting changes in video scenes. Shots can be defined as the fundamental units of analyzing video files in a variety of applications, including online advertisements and e-commerce. The important shot determines how much a shot influences the video analysis process. Because of their low substance, some video shots should be discarded in order to reduce video raw data, using the summarization technique. Video summarization provides frames that are of use in creating conceptual information about a certain video. The present work proposes the coordination of both concepts: shot detecting and video summarizing for obtaining better outcomes. Feature extraction is performed via the frames so as to draw a comparison with consecutive frames used in video shot detection. This process involves the use of histogram feature algorithm, Histogram of Oriented Gradients (HOG) and Expectation Maximization (EM), after which video summarization is performed to find the best frame shot using Euclidian distance between two neighboring frames. The shot frames obtained during video shot detection are used in the video summarizing process in order to find or detect the important shots in a video. The obtained results indicate that the method introduced in this work is robust and sufficient.