Reviewerly/exHarmony huggingface dataset:
Research and application analysis of typical recommendation methods

Authors:
raw_author_name: Mingyang Zhang
raw_affiliation_string: Beijing Huiwen Middle School, Beijing, 100061, China

Abstract: Recommendation systems are vital tools in the modern digital landscape for handling vast amounts of online data and are now employed across a variety of sectors. This manuscript underlines the importance of video recommendation algorithms and delves into the three main types: content-based, collaborative filtering, and hybrid algorithms. Content-based algorithms offer suggestions by assessing and matching the descriptive attributes of videos. These attributes could range from the genre, the actors involved, the director, or any other related metadata. On the other hand, collaborative filtering algorithms provide recommendations by comparing the user's historical data with that of others. They function on the principle that individuals who have agreed in the past are likely to agree again in the future. Hybrid recommendation algorithms, as the name suggests, are a combination of both content-based and collaborative filtering algorithms. They aim to harness the strengths of the two while mitigating their individual weaknesses, providing more balanced and accurate recommendations. This detailed exploration of these algorithms not only underscores their significance but also paves the way for the future, heralding the potential advancements and adaptations these systems could undergo. Continued innovation in this realm has the potential to revolutionize how information is processed and shared, ultimately enriching the user experience across the digital platform landscape.
Single‐Transistor Optoelectronic Spiking Neuron with Optogenetics‐Inspired Spatiotemporal Dynamics

Authors:
raw_author_name: Hanxi Li
raw_affiliation_string: School of Micro-Nano Electronics Zhejiang University Hangzhou 310027 China; ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou 310027 China
raw_author_name: Yishu Zhang
raw_affiliation_string: School of Micro-Nano Electronics Zhejiang University Hangzhou 310027 China; ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou 310027 China
raw_author_name: Yang Xu
raw_affiliation_string: Joint Institute of Zhejiang University and University of Illinois at Urbana-Champaign Zhejiang University Haining 314400 China; School of Micro-Nano Electronics Zhejiang University Hangzhou 310027 China; ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou 310027 China
raw_author_name: Bin Yu
raw_affiliation_string: School of Micro-Nano Electronics Zhejiang University Hangzhou 310027 China; ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou 310027 China

Abstract: Abstract The visual system is crucial for human perception and learning, and the retina is responsible for pre‐processing visual information. Mimicking the neurobiological functions of the retina provides the basis for neuromorphic vision hardware. Here, an optogenetics‐inspired prototype capable of modifying neuron‐spiking behavior in a retina‐like manner is demonstrated. In addition to basic information processing, neuronal signal transmission in the retina is bio‐faithfully emulated using optical modulation, that is, neuronal firing is inhibited in the dark and activated upon illumination. The proposed single threshold‐switch field‐effect transistor optoelectronic neuron features an ultra‐compact design by integrating a gate‐modulated 2D MoS 2 channel with a volatile threshold switch, and optoelectronic performance is achieved through a multi‐physics resistance‐matching mechanism. This study provides a promising pathway toward highly scalable and hierarchically designed spiking electronics, expanding the spectrum of neuromorphic hardware applications in the emerging field of biomimetic vision.
DECISION SUPPORT SYSTEM IN IMPROVING THE QUALITY OF BANJARMASIN TOURISM, GET TOUR APPLICATION USING THE SAW METHOD

Authors:
raw_author_name: Kamarudin .
raw_affiliation_string: a:1:{s:5:"en_US"; s:36:"Universitas Muhammadiyah Banjarmasin"; }

Abstract: Tourism is an activity that is liked by many people, even tourism is one of the important needs, especially regarding socio-economic activities which are seen as having good prospects in the future. In South Kalimantan, especially the city of Banjarmasin, there are many good tourist attractions such as the historical mosque of Sultan Suriansyah, Siring Park, Banjarmaisn City, and culinary tours of Arab villages. Of the tourist attractions that have been mentioned, tourists are still confused in determining tourist attractions because there are many places and a lack of information about tourist attractions in South Kalimantan. From this description, a Decision Support System (DSS) or Decision Support System (DSS) was created to determine tourist attractions in South Kalimantan called the Get Tour application. In addition to displaying information about tourist attractions, this application also displays information on tourist attractions in the form of a map. The results of calculations in the application are in accordance with the formula and expected results based on several criteria, namely distance, parking area, the first special criteria, the second special criteria and the third special criteria using the Simple Additive Weighting (SAW) method.
Graph neural network based method for robot path planning problem

Authors:
raw_author_name: Wenzheng Chi
raw_affiliation_string: Robotics and Microsystems Center, School of Mechanical and Electric Engineering, Soochow University, Suzhou 215021, China
raw_author_name: Jiankun Wang
raw_affiliation_string: Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing 314000, China; Shenzhen Key Laboratory of Robotics Perception and Intelligence, Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen 518055, China

Abstract: Sampling-based path planning is widely used in robotics, particularly in high-dimensional state spaces. In the path planning process, collision detection is the most time-consuming operation. Therefore, we propose a learning-based path planning method that reduces the number of collision checks. We develop an efficient neural network model based on graph neural networks. The model outputs weights for each neighbor based on the obstacle, searched path, and random geometric graph, which are used to guide the planner in avoiding obstacles. We evaluate the efficiency of the proposed path planning method through simulated random worlds and real-world experiments. The results demonstrate that the proposed method significantly reduces the number of collision checks and improves the path planning speed in high-dimensional environments.
Deep Learning for Daily 2‐m Temperature Downscaling

Authors:
raw_author_name: Weijun Guo
raw_affiliation_string: Xiamen Air Traffic Management Station of China Civil Aviation Xiamen China

Abstract: Abstract This study proposes a novel method, which is a U‐shaped convolutional neural network that combines non‐local attention mechanisms, Res2net residual modules, and terrain information (UNR‐Net). The original U‐Net method and the linear regression (LR) method are conducted as benchmarks. Generally, the UNR‐Net has demonstrated promise in performing a 10× downscaling for daily 2‐m temperature over North China with lead times of 1–7 days and shows superiority to the U‐Net and LR methods. To be specific, U‐Net and UNR‐Net demonstrate higher Nash‐Sutcliffe Efficiency coefficient values compared to LR by 0.052 and 0.077, respectively. The corresponding improvements in pattern correlation coefficient are 0.013 and 0.016, while the root mean square error values are higher by 0.22 and 0.338, respectively. Additionally, the structural similarity index metric is higher by 0.033 and lower by 0.015. Furthermore, regions with significant errors are primarily distributed in complex terrain areas such as the Taihang Mountains, where UNR‐Net exhibits noticeable improvements. In addition, the 12 components‐based error decomposition method is proposed to analyze the error source of different models. Generally, the smallest errors are observed during the summer season and the sequence error component is proven to be the main source error of 2‐m temperature forecasts. Furthermore, UNR‐Net consistently demonstrates the lowest errors among all 12 error components. Therefore, combining the numerical weather prediction model and deep learning method is very promising in downscaling temperature forecasts and can be applied to routine forecasting of other atmospheric variables in the future.
Controlling chaotic maps using next-generation reservoir computing

Authors:
raw_author_name: Robert M. Kent
raw_affiliation_string: Department of Physics, The Ohio State University 1 , 191 W. Woodruff Ave., Columbus, Ohio 43210, USA
raw_author_name: Wendson A. S. Barbosa
raw_affiliation_string: Department of Physics, The Ohio State University 1 , 191 W. Woodruff Ave., Columbus, Ohio 43210, USA
raw_author_name: Daniel J. Gauthier
raw_affiliation_string: Department of Physics, The Ohio State University 1 , 191 W. Woodruff Ave., Columbus, Ohio 43210, USA; ResCon Technologies, LLC 2 , 1275 Kinnear Rd., Suite 238, Columbus, Ohio 43212, USA

Abstract: In this work, we combine nonlinear system control techniques with next-generation reservoir computing, a best-in-class machine learning approach for predicting the behavior of dynamical systems. We demonstrate the performance of the controller in a series of control tasks for the chaotic Hénon map, including controlling the system between unstable fixed points, stabilizing the system to higher order periodic orbits, and to an arbitrary desired state. We show that our controller succeeds in these tasks, requires only ten data points for training, can control the system to a desired trajectory in a single iteration, and is robust to noise and modeling error.
Effects of decades of physical driving experience on pre-exposure postural precursors of motion sickness among virtual passengers

Authors:
raw_author_name: Thomas A. Stoffregen
raw_affiliation_string: School of Kinesiology, University of Minnesota, Minneapolis, MI, United States
raw_author_name: Man Kit Lei
raw_affiliation_string: CIPER, Faculty of Human Kinetics, University of Lisbon, Lisbon, Portugal; Institute of Physical Education, Health and Leisure Studies, National Cheng Kung University, Tainan, Taiwan
raw_author_name: Kuangyou B. Cheng
raw_affiliation_string: Institute of Physical Education, Health and Leisure Studies, National Cheng Kung University, Tainan, Taiwan

Abstract: Previous research has shown that motion sickness associated with virtual vehicles is more common among passengers than among drivers. Separately, other studies have shown that postural precursors of motion sickness during virtual driving differ as a function of prior experience driving physical vehicles. We investigated the intersection of those prior effects: We asked whether decades of physical driving experience 1) would influence motion sickness among passengers in a virtual vehicle, and 2) would influence postural precursors of motion sickness among passengers in a virtual vehicle. In our study, middle-aged adults were exposed to a virtual vehicle as passengers. Some participants (Physical Drivers) had decades of experience driving physical automobiles, while others (Physical Non-Drivers) had rarely or never driven a physical vehicle. First, we measured head and torso movement as standing participants performed simple visual tasks. Then, each participant watched a recording of the motion of a virtual vehicle, which induced motion sickness in some participants. Afterward, neither the incidence nor the severity of motion sickness differed between Physical Drivers and Physical Non-Drivers. Our analysis of pre-exposure standing body sway revealed postural precursors of motion sickness in measures of the spatial magnitude and temporal dynamics of movement. In statistically significant interactions, these precursors (Well vs. Sick) differed as a function of physical driving experience (Physical Drivers vs. Physical Non-Drivers). Overall, our results indicate that, among virtual passengers, long-term real-world driving experience influenced the postural precursors of motion sickness, but not the incidence or severity of motion sickness. We discuss these results in terms of relationships between perception and motor control in theories of motion sickness etiology.
Associating UAS images through a graph‐based guiding strategy for boosting structure from motion

Authors:
raw_author_name: Masashi Matsuoka
raw_affiliation_string: Department of Architecture and Building Engineering Tokyo Institute of Technology Yokohama Japan

Abstract: Abstract Structure from motion (SfM) using optical images has been an important prerequisite for reconstructing three‐dimensional (3D) landforms. Although various algorithms have been developed in the past, they suffer from many image pairs for feature matching and recursive searching for the most suitable image to add to SfM reconstruction. Thus, carrying out SfM is computationally costly. This research proposes a boosting SfM (B‐SfM) pipeline containing two phases, indexing graph network (IGN) and graph tracking, to accelerate SfM reconstruction. The IGN intends to form image pairs presenting desirable spatial correlation to reduce the time costs spent for feature matching. Building on the IGN, graph tracking integrates ant colony optimisation and greedy sorting algorithms to encode an optimum image sequence to boost SfM reconstruction. Compared to the results derived from other available means, the experimental results show that the proposed approach can accelerate the two phases, feature matching and 3D reconstruction, by up to 14 times faster. The quality of the camera poses recovered is retained or even slightly improved. As a result, the developed B‐SfM can efficiently achieve SfM reconstruction by suppressing the time cost in the fashion of image pair selection for feature matching and image order determination for more efficient SfM reconstruction.
Modelling Data Poisoning Attacks Against Convolutional Neural Networks

Authors:
raw_author_name: Annapurna Jonnalagadda
raw_affiliation_string: School of Computer Science and Engineering, VIT University, Vellore 632 014, India
raw_author_name: Firuz Kamalov
raw_affiliation_string: Department of Electrical Engineering, Canadian University Dubai, Dubai, United Arab Emirates

Abstract: Cybersecurity has become a great concern in many real-world applications involving adversaries with Machine Learning (ML) algorithms being more widely used. This concern is more challenging in Internet of Things (IoT) platforms. As IoT-enabled applications are growing at a rapid pace in every sector there are growing security related incidents as well. ML algorithms are widely deployed to perform data analysis, reasoning and decision-making over the data emanating from IoT devices. Security of this data while collection, communication and computing is a major challenge. Various attackers are trying to find weaknesses in the ML algorithms and trying to deceive these ML algorithms to learn the wrong information from the data. Countermeasures need to be developed to evaluate the security of the ML models. To develop such countermeasures, one needs to understand all possible attacks on the ML models. Data poisoning attacks are a class of adversarial attacks on ML where an adversary has the power to alter a small fraction of the training data in order to make the trained classifier to satisfy certain objectives. In order to develop an attack-resistant ML model, one needs to know all possible attacks on these models. The recent data poisoning technique such as the Fast Gradient Sign Method (FGSM) is static and provides very micro control to the attacker on creating adversarial data. In this research, we develop a more robust data poisoning technique for deep neural networks using Generative Adversarial Networks (GANs) to create a data poisoning attack. We then evaluate the performance of the proposed algorithm and also compare the results obtained by FGSM.
Research on Network Teaching Collaboration Platform Using Flipped Classroom Teaching Mode

Authors:
raw_author_name: Haitao Mao
raw_affiliation_string: College of Asset Management Division, Xinyang Normal University, Xinyang 464000, P. R. China
raw_author_name: Junyong Hao
raw_affiliation_string: School of Education, Central China Normal University, Wuhan 430079, P. R. China

Abstract: The quick development of intelligent information technology in modern society has accelerated education informatisation. Accordingly, the traditional modes of instruction and methods of instruction are no longer able to satisfy the educational needs of modern society, so the flipped classroom teaching method emerges at the historic moment. While injecting fresh vitality into education, this model can also reflect good teaching effects, which is of enormous practical relevance to the development of education. Additionally, flipped classroom teaching is made possible by the online teaching resources offered by colleges and institutions. In view of the unreasonable distribution of curriculum resources in the current university network teaching platform, this study proposes a university network platform combining flipped classroom teaching mode and improved Apriori algorithm. First of all, to achieve the acceptable allocation and suggestion of learning materials in accordance with students’ learning demands, the Apriori algorithm is introduced to address the university network teaching platform’s lack of adequate planning and organisation. After that, the flipped classroom teaching mode is introduced into the online teaching platform for empirical research. According to the trial findings, more than 90.22 % of the students had improved their study habits. The network teaching platform proposed by the research institute can stimulate college students’ learning interest and improve their learning ability to a certain extent.
Concentration‐number (C‐N) fractal models reveal the distribution pattern of the elements in ancient nephrite measured by portable X‐ray fluorescence: Based on nephrite objects excavated from different sites in Nanyang, Henan Province

Authors:
raw_author_name: Dian Chen
raw_affiliation_string: Department of Archaeology and Anthropology University of Chinese Academy of Sciences Beijing China; Key Laboratory of Vertebrate Evolution and Human Origin, Institute of Vertebrate Paleontology and Paleoanthropology Chinese Academy of Sciences Beijing China

Abstract: Abstract Changes in the composition of various chemical elements in ancient nephrite artifacts due to prolonged burial are critical factors that should not be underestimated. However, the increasingly stringent heritage management has made many techniques impractical. Consequently, portable X‐ray fluorescence (pXRF) has become an indispensable nondestructive field analysis method. This paper aims to make use of the pXRF dataset to distinguish which elements tested are endogenous or exogenous, as well as to gauge which elements have been affected by stronger burial effects. More specifically, we carry out this work on the example of 103 pieces of nephrite excavated from different cemeteries in the Nanyang area. In addition to traditional statistical techniques, a new tool, the concentration‐number (C‐N) fractal method can shed new light on the analysis of the distribution patterns of different elements in excavated nephrite. The anomalous boundaries generated by the method have clear geochemical significance and can be delineated between background zone and disturbed regions. The degree of resistance of different elements to fluctuations in external factors was assessed, which has a direct relationship with the content of the buried soil. Considering the richness of the model, it has the potential to be used in archaeometrics.
Unsupervised novelty detection for time series using a deep learning approach

Authors:
raw_author_name: Md Jakir Hossen
raw_affiliation_string: Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia
raw_author_name: Jesmeen Mohd Zebaral Hoque
raw_affiliation_string: Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia
raw_author_name: Nor Azlina binti Abdul Aziz
raw_affiliation_string: Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia
raw_author_name: Thirumalaimuthu Thirumalaiappan Ramanathan
raw_affiliation_string: Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia
raw_author_name: Joseph Emerson Raja
raw_affiliation_string: Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia

Abstract: In the Smart Homes and IoT devices era, abundant available data offers immense potential for enhancing system intelligence. However, the need for effective anomaly detection models to identify and rectify unusual data and behaviors within Smart Home Systems (SHS) remains a critical challenge. This research delves into the relatively unexplored domain of novelty anomaly detection, particularly in the context of unlabeled datasets. Introducing the novel DeepMaly method, this approach provides a practical tool for SHS developers. Functioning seamlessly in an unsupervised manner, DeepMaly distinguishes between seasonal and actual anomalies through a unique process of training on unlabeled pristine features extracted from time series data. Leveraging a combination of Long Short-Term Memory (LSTM) and Deep Convolutional Neural Network (DCNN), the model is primed to detect anomalies in real-time. The research culminates in a comprehensive data prediction and classification process into normal and abnormal data based on specified anomaly thresholds and fraction percentages. Notably, this function operates seamlessly unsupervised, eliminating the need for labeled datasets. The study concludes with a complete data forecasting and sorting method that divides data into normal and abnormal categories based on defined anomaly thresholds and fraction percentages. Working in an unsupervised mode reduces the requirement for labeled datasets. The results highlight the model's prowess in new detection, which has been successfully applied to benchmark datasets. However, there is a restriction since deep learning algorithms can recognize noise as abnormalities. Finally, the investigation enhances SHS anomaly detection, providing a crucial tool for real-time anomaly identification in the ever-changing IoT and Smart Homes scene.