Reviewerly/exHarmony huggingface dataset:
Four-Stream Network and Nonsignificant Feature Learning for Visible–Infrared Person Re-Identification

Visible–infrared person re-identification (VI-ReID) is a current focused area in the field of re-identification. In order to reduce the gap between two modalities in VI-ReID and improve recognition accuracy, this paper proposes a four-stream network and nonsignificant feature learning (FS-NSF) method for VI-ReID. First, the dual-intermediate modality images of visible and infrared modalities are generated by two lightweight networks, and the labels are inherited from the visible and infrared images. Second, the ResNet50 backbone network is split in order to reconstruct the network adapted to shared feature learning of the four modalities. Finally, a multi-branch, multi-scale and multi-granularity feature extraction strategy is used to extract both significant and nonsignificant features. The comparison experiments are conducted on SYSU-MM01 dataset and RegDB dataset. The experimental results show that, compared with state-of-the-arts, our method has excellent performance on both datasets, especially on the SYSU-MM01 dataset, with an increase in performance of 1.9–6.28% for each index.
Decoding human behavior with big data? Critical, constructive input from the decision sciences

Abstract Big data analytics employs algorithms to uncover people's preferences and values, and support their decision making. A central assumption of big data analytics is that it can explain and predict human behavior. We investigate this assumption, aiming to enhance the knowledge basis for developing algorithmic standards in big data analytics. First, we argue that big data analytics is by design atheoretical and does not provide process‐based explanations of human behavior; thus, it is unfit to support deliberation that is transparent and explainable. Second, we review evidence from interdisciplinary decision science, showing that the accuracy of complex algorithms used in big data analytics for predicting human behavior is not consistently higher than that of simple rules of thumb. Rather, it is lower in situations such as predicting election outcomes, criminal profiling, and granting bail. Big data algorithms can be considered as candidate models for explaining, predicting, and supporting human decision making when they match, in transparency and accuracy, simple, process‐based, domain‐grounded theories of human behavior. Big data analytics can be inspired by behavioral and cognitive theory.
Correlation Power Analysis of SipHash

SipHash is ARX-based pseudorandom function optimized for short inputs. It was developed as a hash table lookup function, but it is also used for MAC generation. At the time of writing, there was no side-channel attack on SipHash known to us. This work is about application of CPA attack on SipHash. Attack was performed on ChipWhisperer CW308 UFO Board with STM32F0 target. Approximately 800 power traces were needed for succesful attack. Leakage information from XOR was used to attack cipher key. The main contribution of this work is power model of binary addition including carry propagation.
A Survey on Political Viewpoints Identification

Political viewpoints identification (PVI) is a task in Natural Language Processing that takes political texts and recognizes the writer's opinions towards a political matter. PVI reduces the ambiguity in texts by identifying the underlying meaning and clarifying the bias margin along the political spectrum (bias leaning). Thus, even non-experts can better understand political texts. For instance, they can identify misinformation, bias, and hidden political agendas. In this paper, we formally define the concept of political viewpoints identification, explain its importance and discuss to what extent current techniques can be used for extracting political views from text. Existing techniques address the problem of PVI inadequately. We outline their deficiencies and present a research agenda to advance PVI.
Effective Negation Handling Approach for Sentiment Classification using synsets in the WordNet lexical database

Good quality of data preprocessing will have a greater impact on improving the performance of the classification models. Negation is one of the most important features in textual data. Only one negation word can change the polarity of the whole sentence. It is often seen that words like ‘not’, ‘non’ or suffixes like “n't” are removed during noise removal thereby leading to blunders in Sentiment Classification. Effective Feature extraction is the cornerstone of effective Sentiment Analysis and Negation handling is simply essential for this purpose. In this paper, an effective function for handling negations based on First Sentiment Word (FSW) antonymy in the WordNet has been implemented on a set of IMDB movie reviews. The function for Negation Handling created for this paper increased the accuracy of Sentiment Classification by 4-8%. Experiments done in this paper show that improving the quality of the data gives higher results than implementing different state-of-the-art methods like n-grams and even deep learning methods like Word Embeddings, especially when used in an industry setting, where there is a need of quick deployments and changes with cost effectiveness and resource management.
An Effective Path Planning of Intelligent Mobile Robot Using Improved Genetic Algorithm

With the rapid development of the robotics industry, the problem of effective and fast path planning for intelligent mobile robots has always been one of the hot spots in the field of robotics research. Intelligent mobile robot path planning is divided into global path planning and local path planning, and its mathematical modeling and adaptive algorithms are different. Therefore, the research of robot path planning based on improved genetic algorithm is of great significance. This paper mainly studies the robot path planning problem based on improved genetic algorithm. Based on the research of the basic genetic algorithm, the improved genetic algorithm is applied to the mobile four-wheel robot to guide the four-wheel robot to complete path planning and other related tasks. Experiments show that the optimization probability and convergence speed of the genetic algorithm can be improved by improving the genetic algorithm. Studies have shown that evolutionary algebra and population size are inversely proportional to the optimal path length, so it is directly proportional to the search ability. However, as the evolutionary algebra and population size increase, the amount of calculation is also increasing, and the calculation time increases. Comprehensive considerations according to various factors, the best value of population size is 60, the best value of mutation probability is 0.09, the best value of crossover probability is 0.8, and the best value of evolutionary algebra is 150 generations.
Deep Learning of 3D High-Precision Model Digital Engraving of Next-Generation Games Based on Artificial Intelligence

In order to improve the intelligence of the next-generation game model, a new game artificial intelligence came into being. Through the in-depth understanding and research of the next generation of game model production technology, the current industry general production process is summarized, and a set of effective production methods is expounded. In the context of the rapid development of digital technology, the game industry increasingly recognizes the importance of digital carving technology for the design and production of the next generation of games. The design and production of the next-generation game model can make up for the lack of refinement of the traditional next-generation game character model and the unreal mapping effect. This paper focuses on two ways: behavior tree and machine learning, designing game AI with more anthropomorphic perception and flexible behavior. Finally, the basketball player competition AI designed with a behavior tree and machine learning demonstrates its intelligent behavior.
Suspicious Human Activity Recognition using 2D Pose Estimation and Convolutional Neural Network

Suspicious human activity detection is a major area of research and development that focuses on sophisticated machine learning techniques to reduce monitoring costs while enhancing safety. Since it is difficult for people to continually monitor public spaces, we need a real-time intelligent human activity recognition system that can identify suspicious activities. Current systems use low-accurate complex algorithms and techniques, making the system less reliable. This paper proposes a real-time suspicious human activity recognition with high accuracy by introducing a Convolutional Neural Network and using the 2D pose estimation technique to the system. This system can be used for home security, hospitals, and other areas of surveillance. Here, we are extracting skeletal images of humans from the input video frames using 2D pose estimation to identify the pose of humans in the videos. These poses are then passed to a pre-trained Convolutional Neural Network to classify different activities of humans like trespassing or not trespassing, fall or not fall, fighting, etc. After analyzing the pixels and activities, an alert can be produced through alarms, messages to phones, email the footage to the owner or security professional, and other techniques to prevent unusual activities. This system can be used in public places like shopping malls, railway stations, public roads, and even in homes, universities, and educational institutions.
5G and Artificial Intelligence Interactive Technology Applied in Preschool Education Courses

To explore the application of artificial intelligence interaction technology in preschool education practice courses, preschool education, 5G wireless network, artificial intelligence interaction technology, and other relevant theories are integrated, questionnaire survey is adopted as the research method, and six-year-old kindergarten students in a city of China are taken as the research objects. Adaptive optimization suggestions are put forward from the preschool children practice curriculum learning situation, cooperation and exchange situation, learning effect, and learning attitude research. The results show that in terms of learning of practical courses, students’ cognition of learning has changed after the implementation of wisdom teaching, but the average value of some <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> </math> values &lt; 0.05 is low in the real classroom adaptation stage. In the exchange and cooperation of practical courses, it is found that wisdom teaching has the greatest influence on helping other students solve problems, with a specific <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>P</mi> </math> of 0.009. In terms of learning effect and learning attitude, the effect is significant after the introduction of artificial intelligence technology, with an average of 3.56 before the introduction and 4.01 after the introduction. It means that to a certain extent, preschool children can realize the role and significance of wisdom classroom teaching, which plays a certain role in teaching practice. Therefore, research on the application of artificial intelligence interaction technology in preschool education practice courses under 5G wireless network environment provides a new theoretical basis and optimization direction for preschool education in the future.
Optimization of the Music Teaching Management System Based on Emotion Recognition

Along with the development of social informatization, computer has been widely used in daily teaching and the technical support teaching management system, which can greatly improve the quality of students and teachers' information sharing and teaching, and is an important part of college information construction. Teachers' personal information, teaching information query, teaching file management, students' attendance, students' results query, teachers' evaluation, and so on constitute the music teaching information management system. Based on the Internet of things technology and emotion recognition, this paper mainly describes the design idea of the music teaching management system software, function design, and implementation scheme. The realization process of each subfunction module and hardware and software of the system is described in detail. In view of the characteristics of music teaching management itself, combined with the actual situation of music teaching, to form the system structure analysis, database design and data exchange, and other aspects of exploration, in order to establish a practical music teaching management system.
Medical MR Image Synthesis using DCGAN

Generative Adversarial Networks (GANs) have been extensively gained considerable attention since 2014. Irrefutably saying, their most remarkable success has been made in domains such as computer vision and medical image processing. Despite the noteworthy success attained to date, applying GANs to real world problems still posses significant challenges, one among which is diversity of image generation and detection of fake images from real ones. Focusing on the extend to which various GAN models have made headway against these challenges, this study provides an overview of DCGAN architecture and its application as a synthetic data generator and act an a binary classifier, which detects real or fake images using brain tumorous Magnetic Resonance Imaging (MRI) dataset.
Method of Selective Steganographic Data Hiding Based on Graphic Containers

An approach to the construction of a selective algorithm for steganographic information hiding based on graphic type containers is proposed. Unlike to most of existing approaches, proposed algorithm takes into account the container fragments content features. So, in the source container, initially performs search for a transformants set suitable for modification. In the next step, within each such identified transformants, a components set that are potentially acceptable for direct modification determines. At the same time, the search parameters for both the transformants set and the components can be modified using the stego key parameters. Encapsulating the secret message bits procedure considered. In this case used an approach based on indirect encapsulation, during which the component decimal value changes. Based on the fact that the algorithm does not make changes to the container that are characteristic of most known steganographic methods, and also due to the fact that the number of encapsulated bits within the container is insignificant, the proposed approach is potentially resistant to stegoanalysis methods.
Generation of Substitution Box Structures Based on Blum Blum Shub Random Number Outputs

One of the most important milestones in our transformation into a digital society is the effective adoption of Internet technologies by all stakeholders of the society. The critical factor in this adoption has been the trust model provided through cryptographic protocols. However, the secure environment that tries to be created with cryptographic algorithms is constantly under attack threat with technological developments. Therefore, it is necessary to develop new algorithms resistant to new attacks. In this study, a new approach is proposed for substitution box structures, which is a cryptographic component that can be used as a precaution against side channel attacks. The success of the obtained component has been analyzed for five basic substitution box design metrics. Successful experimental results indicate that the proposed approach have various potentials for new practical applications that can be designed in the future.
An Improved Method towards Multi-UAV Autonomous Navigation Using Deep Reinforcement Learning

Autonomous navigation is a key technology of multi-UAV systems, and deep reinforcement learning can endow UAVs with powerful autonomous decision-making capabilities. To improve the convergence speed and stability of reinforcement learning, this paper proposes a multi-agent deep deterministic policy gradient algorithm based on prioritized experience replay, namely PER-MADDPG. This algorithm makes the samples with higher priority have a higher probability of being chosen for the parameter update, which can speed up the algorithm convergence. Moreover, the actions of UAVs are generated utilizing parameter noise, which can improve the stability and robustness of the algorithm. Experiments show that PER-MADDPG has fast convergence speed and good convergence results, and has excellent autonomous navigation capabilities.
Application of principal component analysis and a spectrum-area fractal model to identify geochemical anomalies associated with vanadium mineralization in northeastern Jiangxi Province, South China

The Jiujiang region in northeastern Jiangxi Province is known for hosting many black shale-type vanadium deposits, which are mainly found in the Cambrian Hetang Formation and Sinian Piyuancun Formation. In this paper, based on stream sediment data, principal component analysis (PCA) and spectrum–area (S–A) fractal modelling are used to identify geochemical anomalies associated with vanadium (V) mineralization. First, a dataset containing 957 samples of stream sediments with 39 elements was processed using correlation analysis and cluster analysis to find the closest V-related elements, and Ag and Cd were obtained. Secondly, the raster maps of V, Ag and Cd were created using multifractal inverse distance-weighted interpolation. PCA was then employed to combine the concentration values of V, Ag and Cd into a single variable representing the internal relationships among the three elements. Finally, S–A analysis was used to decompose the first component pattern obtained through PCA and to extract anomalies from the complex background. The results show that the known V deposits are located within highly anomalous areas, which are well corelated with the distribution of black shales of the Hetang and Piyuancun Formations. It is suggested that the northeastern part of Jiangxi Province potentially hosts undiscovered V deposits, and the identified anomalies could be further used to guide mineral exploration. Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/cc/applications-of-innovations-in-geochemical-data-analysis
Cyber Attack Sequences Generation for Electric Power Grid

Security assessment of cyber-physical energy systems (CPESs), such as the electric power grid, is a critical operation to maintain availability, reliability, and quality of service in the presence of persistent threats from malicious cyber actors. Existing security assessment approaches, such as penetration testing and red teaming, rely on subject matter experts’ experience and forensic analysis of historical events to perform realistic, threat-informed assessments of CPES defense. CPESs have a large attack surface because of the heterogeneity and complexity of underlying topology, devices, measurements, and vulnerabilities. The aforementioned approaches lead to partial coverage of the attack surface with a large set of unknown but possible exploits. There is a need to automate an CPES’s attack surface discovery and contextualize it for relevant, highly probable, real-world attack scenarios. We propose a methodology and framework to facilitate the discovery of the CPES attack surface. We generate multilayer attack graphs with ranked attack sequences to describe CPES failure scenarios. We present a work-in-progress framework, CAGen, that lists key components to automate the attack modeling and sequence generation. We demonstrate the published National Electric Sector Cybersecurity Organization Resource (NESCOR) CPES failure scenario to highlight the trustworthiness of generated attack sequences.
Emergency transition to remote learning: DoIt@Home Lab in engineering

Purpose The interruption of on-campus teaching and learning, due to the COVID-19 pandemic, forced universities around the globe to rethink their pedagogical models and adopt innovative strategies and approaches that enabled continuity of learning. Engineering schools and faculties were faced with the challenge of how to continue to engage students with the practical component of coursework, especially in terms of lab work and experimentation, which are mandatory requirements for degree awards. Design/methodology/approach This study documents how the Faculty of Engineering in a university in Oman engaged students with the practical component of their course during the pandemic by launching the remote DoIt@Home Lab. The DoIt@Home Lab approach included the design and development of video recorded labs, virtual labs, simulation exercises and DoIt@Home experiments which were provided to students as teaching tools and guides to conducting home experiments remotely. Findings This study presents the DoIt@Home Lab approach introduced to Year 2 Chemistry for engineering students. Students' grades improved by 11% over the previous year when the course was delivered face-to-face. Failure rates dropped by 8% while the number of students earning a 3.25 grade point average (GPA) or higher increased by 18%. Originality/value The DoIt@Home Lab for engineering courses could enhance students' learning experience and create an effective remote learning environment. While the DoIt@Home Lab was created to supplement on-campus activity in the event of a temporary disruption, it can also be used to supplement regular face-to-face program delivery.
Evaluation of Various Tree-Based Ensemble Models for Estimating Solar Energy Resource Potential in Different Climatic Zones of China

Solar photovoltaic (PV) electricity generation is growing rapidly in China. Accurate estimation of solar energy resource potential (Rs) is crucial for siting, designing, evaluating and optimizing PV systems. Seven types of tree-based ensemble models, including classification and regression trees (CART), extremely randomized trees (ET), random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), gradient boosting with categorical features support (CatBoost) and light gradient boosting method (LightGBM), as well as the multi-layer perceotron (MLP) and support vector machine (SVM), were applied to estimate Rs using a k-fold cross-validation method. The three newly developed models (CatBoost, LighGBM, XGBoost) and GBDT model generally outperformed the other five models with satisfactory accuracy (R2 ranging from 0.893–0.916, RMSE ranging from 1.943–2.195 MJm−2d−1, and MAE ranging from 1.457–1.646 MJm−2d−1 on average) and provided acceptable model stability (increasing the percentage in testing RMSE over training RMSE from 8.3% to 31.9%) under seven input combinations. In addition, the CatBoost (12.3 s), LightGBM (13.9 s), XGBoost (20.5 s) and GBDT (16.8 s) exhibited satisfactory computational efficiency compared with the MLP (132.1 s) and SVM (256.8 s). Comprehensively considering the model accuracy, stability and computational time, the newly developed tree-based models (CatBoost, LighGBM, XGBoost) and commonly used GBDT model were recommended for modeling Rs in contrasting climates of China and possibly similar climatic zones elsewhere around the world. This study evaluated three newly developed tree-based ensemble models of estimating Rs in various climates of China, from model accuracy, model stability and computational efficiency, which provides a new look at indicators of evaluating machine learning methods.