Reviewerly/exHarmony huggingface dataset:
Offline Evaluation of Recommender Systems in a User Interface With Multiple Carousels

Many video-on-demand and music streaming services provide the user with a page consisting of several recommendation lists, i.e., widgets or swipeable carousels , each built with specific criteria (e.g., most recent, TV series, etc.). Finding efficient strategies to select which carousels to display is an active research topic of great industrial interest. In this setting, the overall quality of the recommendations of a new algorithm cannot be assessed by measuring solely its individual recommendation quality. Rather, it should be evaluated in a context where other recommendation lists are already available, to account for how they complement each other. The traditional offline evaluation protocol however does not take this into account. To address this limitation, we propose an offline evaluation protocol for a carousel setting in which the recommendation quality of a model is measured by how much it improves upon that of an already available set of carousels. We also propose to extend ranking metrics to the two-dimensional carousel setting in order to account for a known position bias, i.e., users will not explore the lists sequentially, but rather concentrate on the top-left corner of the screen. Finally, we describe and evaluate two strategies for the ranking of carousels in a scenario where the technique used to generate the two-dimensional layout is agnostic on the algorithms used to generate each carousel. We report experiments on publicly available datasets in the movie domain to show how the relative effectiveness of several recommendation models compares. Our results indicate that under a carousel setting the ranking of the algorithms changes sometimes significantly. Furthermore, when selecting the optimal carousel layout accounting for the two dimensional layout of the user interface leads to very different selections.
Behaviour Detection and Recognition of College Basketball Players Based on Multimodal Sequence Matching and Deep Neural Networks

This study fuses multimodal sequence matching with a deep neural network algorithm for college basketball player behavior detection and recognition to conduct in-depth research and analysis, analyzing the basic components of basketball technical action videos by studying the practical application of technical actions in professional games and teaching videos from self-published authors of short video platforms. The characteristics of the dataset are also analyzed through literature research related to the basketball action dataset. On the established basketball technical action dataset, combined with the SSD target detection algorithm, the video images with cropping of human motion regions to reduce the size of image frames, generating a basketball technical action dataset based on cropped frames, reduces the amount of network training and improves the efficiency of subsequent action recognition training. In this study, by analyzing the characteristics of basic camera motion, a univariate global motion model is proposed to introduce a quadratic term to accurately express the shaking transformation, while the horizontal and vertical motion are independently represented to reduce the model complexity. Comparative experimental results show that the proposed model achieves a good balance between complexity and accuracy of global motion representation. It is suitable for global motion modeling in behavior recognition applications, laying the foundation for global and local motion estimation. On this basis, the visual feature change pattern of the key area of the scene (basketball area) is combined with the group behavior recognition based on motion patterns and the success-failure classification based on key visual information to achieve basketball semantic event recognition. The experimental results at NCAA show that the fusion of global and local motion patterns can effectively improve group behavior recognition performance. The semantic event recognition algorithm combining motion patterns and video key visual information achieves the best performance.
Formation of Information Culture of Vocational Education Specialists

This article reflects the system of multilevel continuing professional teaching of vocational education specialists.The basic principles of this process are investigated and the structure of training of vocational education specialists is determined by means of the analysis of the state standards of preparation of graduates in a specialty "Professional education", educational programs and curricula.There is made comparative analysis of the maintenance of preparation of graduates of a specialty "Professional education" on specializations "Digital technologies" and "Sphere of service (Tourist service)" for formation of their information culture.
Genetic programming with diverse partner selection for dynamic flexible job shop scheduling

Dynamic flexible job shop scheduling (DFJSS) aims to make decisions for machine assignment and operation sequencing simultaneously to get an effective schedule under dynamic environments. Genetic programming hyper-heuristic (GPHH) has been successfully applied to evolve scheduling heuristics for the DFJSS problem. Parent selection plays an important role in GPHH for generating high-quality offspring. Traditional GPHHs select parents for crossover purely based on fitness (e.g., tournament selection). This might be too greedy to get good offspring and the selected parents might have similar structures/behaviours. In this paper, a GPHH method with a new diverse partner selection (DPS) scheme is proposed, namely GPDPS, for DFJSS. Specifically, we first define a new multi-case fitness to characterise the behaviour of each scheduling heuristic for DFJSS. Then, the newly proposed DPS method selects a pair of complementary high-quality parents for crossover to generate offspring. The experimental results show that GPDPS significantly outperforms the GPHH method on most of the DFJSS scenarios, in terms of both test performance and convergence speed.
Handwritten Digit Classification Using HOG Features and SVM Classifier

Handwritten digit recognition is a process owe may say the ability of a computer to recognize the handwritten digits from several sources like papers, images, touch screens, etc. After recognizing the character, they classify them into 10 predefined classes (0–9). In the case of typed digits, getting high accuracy is quite possible nowadays. However, in the case of handwritten characters, this becomes a challengeable task. The major challenges are selecting a suitable feature extraction technique and a classifier. This work implements using an SVM known as Support Vector Machine classifier with the addition of using HOG features known as Histogram of Oriented Gradients for recognition of handwritten Odia numerals.
Big data analytics in the Indian banking sector: An empirical study

Banking and financial institutions produce and trade massive sums of money each minute all around the globe. As a result, this industry is by definition data-intensive. The data in this industry is complex, and it fits under the Big Data banner, which is described as “a set of enormous, varied, and sophisticated data that is developing at an accelerating rate. “Big data analytics is the difficult method for analyzing huge and diverse data sets or big data to uncover information such as patterns and insights, unrevealed correlations, market dynamics, and consumer trends that can aid businesses in making educated decisions. Data analytics tools and approaches, in general, show how to analyze and interpret sets of data and draw conclusions from them to assist businesses in making intelligent business decisions. This study has been conducted to check the role played by Big Data Analytics in banking organizations.
A Study on Instructors’ Perception and Educational Application of Metaverse-based Korean Language Education.

The ultimate purpose of this study is to comprehensively review teachers’ perceptions of metaverse -based Korean language education and views related to educational application, and to explore the possibility of metaverse application to Korean education through this. For this purpose, the instructor’s perception of metaverse-based education was studied, considering effect of their prior metaverse experience. In addition, educational application of metaverse was investigated from the perspective of the value and necessity of metaverse-based education, factors to consider when planning metaverse-based education, and expected difficulties and support directions of metaverse-based education. As a result of the study, the group of instructors who had experience with metaverse had higher understanding of the metaverse and were also statistically significant in terms of perceived usefulness and sense of efficacy. However, even in the unexperienced group, which showed a very low sense of efficacy, the behavioral intention to use was high, not showing a significant difference from the experienced group. In order to fully take advantages of metaverse in Korean language education, it was confirmed that speaking activities such as role play, simulation, and small group learning activities with interaction were the most effective. Furthermore, the necessity of metaverse-based education must be included in instructors’ training and re-training programs, as well as in the required courses for instructor qualification program.&#x0D;
Using the DiCoT framework for integrated multimodal analysis in mixed-reality training environments

Simulation-based training (SBT) programs are commonly employed by organizations to train individuals and teams for effective workplace cognitive and psychomotor skills in a broad range of applications. Distributed cognition has become a popular cognitive framework for the design and evaluation of these SBT environments, with structured methodologies such as Distributed Cognition for Teamwork (DiCoT) used for analysis. However, the analysis and evaluations generated by such distributed cognition frameworks require extensive domain-knowledge and manual coding and interpretation, and the analysis is primarily qualitative. In this work, we propose and develop the application of multimodal learning analysis techniques to SBT scenarios. Using these analysis methods, we can use the rich multimodal data collected in SBT environments to generate more automated interpretations of trainee performance that supplement and extend traditional DiCoT analysis. To demonstrate the use of these methods, we present a case study of nurses training in a mixed-reality manikin-based (MRMB) training environment. We show how the combined analysis of the video, speech, and eye-tracking data collected as the nurses train in the MRMB environment supports and enhances traditional qualitative DiCoT analysis. By applying such quantitative data-driven analysis methods, we can better analyze trainee activities online in SBT and MRMB environments. With continued development, these analysis methods could be used to provide targeted feedback to learners, a detailed review of training performance to the instructors, and data-driven evidence for improving the environment to simulation designers.
An overview of path planning technologies for unmanned aerial vehicles

Unmanned aerial vehicles, due to their superior maneuverability and reduced costs can easily perform tasks that are too difficult and complex to be performed with manned aircraft, under all conditions. In order to cope with various obstacles and operate in complex and unstable environmental conditions, the unmanned aerial vehicles must first plan its path. One of the most important problems to investigated in order to find an optimal path between the starting point and the target point of the unmanned aerial vehicles is path planning and choosing the appropriate algorithm. These algorithms find the optimal and shortest path, and also provide a collision-free environment for unmanned aerial vehicles. It is important to have path planning algorithms to calculate a safe path to the final destination in the shortest possible time. However, algorithms are not guaranteed to provide full performance in each path planning situation. Also, each algorithm has some specifications, these specifications make it possible to make them suitable in complex situations. Although there are many studies in path planning literature, this subject is still an active research area considering the high maneuverability of unmanned aerial vehicles. In this study, the most used methods of graph search, sampling-based algorithms and computational intelligence-based algorithms, which have become one of the important technologies for unmanned aerial vehicles and have been the subject of extensive research, are examined and their pros and cons are emphasized. In addition, studies conducted in the field of unmanned aerial vehicles with these algorithms are also briefly mentioned.
Signal recovery in optical wireless communication using photonic convolutional processor

Deep neural networks (DNNs) have been applied to recover signals in optical communication systems and have shown competence of mitigating linear and nonlinear distortions. However, as the data throughput increases, the heavy computational cost of DNNs impedes them from rapid and power-efficient processing. In this paper, we propose an optical communication signal recovery technology based on a photonic convolutional processor, which is realized by dispersion delay unit and wavelength division multiplexing. Based on the photonic convolutional processor, we implement an optoelectronic convolutional neural network (OECNN) for signal post-equalization and experimentally demonstrate on 16QAM and 32QAM of an optical wireless communication system. With system parameters optimization, we verify that the OECNN can achieve accurate signal recovery where the bit error ratio (BER) is below the 7% forward error correction threshold of 3.8×10-3 at 2Gbps. With adding the OECNN-based nonlinear compensation, compared with only linear compensation, we improve the quality (Q) factor by 3.35 dB at 16QAM and 3.30 dB at 32QAM, which is comparable to that of an electronic neural network. This work proves that the photonic implementation of DNN is promising to provide a fast and power-efficient solution for optical communication signal processing.
Assessing the Impact of Features on Probabilistic Modeling of Photovoltaic Power Generation

Photovoltaic power generation has high variability and uncertainty because it is affected by uncertain factors such as weather conditions. Therefore, probabilistic forecasting is useful for optimal operation and risk hedging in power systems with large amounts of photovoltaic power generation. However, deterministic forecasting is the mainstay of photovoltaic generation forecasting; there are few studies on probabilistic forecasting and feature selection from weather or time-oriented features in such forecasting. In this study, prediction intervals were generated by the lower upper bound estimation (LUBE) using neural networks with two outputs to make probabilistic modeling for predictions. The objective was to improve prediction interval coverage probability (PICP), mean prediction interval width (MPIW), continuous ranked probability score (CRPS), and loss, which is the integration of PICP and MPIW, by removing unnecessary features through feature selection. When features with high gain were selected by random forest (RF), in the modeling of 14.7 kW PV systems, loss improved by 1.57 kW, CRPS by 0.03 kW, PICP by 0.057 kW, and MPIW by 0.12 kW on average over two weeks compared to the case where all features were used without feature selection. Therefore, the low gain features from RF act as noise and reduce the modeling accuracy.
Analysis of Score-Level Fusion Rules for Deepfake Detection

Deepfake detection is of fundamental importance to preserve the reliability of multimedia communications. Modern deepfake detection systems are often specialized on one or more types of manipulation but are not able to generalize. On the other hand, when properly designed, ensemble learning and fusion techniques can reduce this issue. In this paper, we exploit the complementarity of different individual classifiers and evaluate which fusion rules are best suited to increase the generalization capacity of modern deepfake detection systems. We also give some insights to designers for selecting the most appropriate approach.
A DDQN Path Planning Algorithm Based on Experience Classification and Multi Steps for Mobile Robots

Constrained by the numbers of action space and state space, Q-learning cannot be applied to continuous state space. Targeting this problem, the double deep Q network (DDQN) algorithm and the corresponding improvement methods were explored. First of all, to improve the accuracy of the DDNQ algorithm in estimating the target Q value in the training process, a multi-step guided strategy was introduced into the traditional DDQN algorithm, for which the single-step reward was replaced with the reward obtained in continuous multi-step interactions of mobile robots. Furthermore, an experience classification training method was introduced into the traditional DDQN algorithm, for which the state transition generated by the mobile robot–environment interaction was divided into two different types of experience pools, and experience pools were trained by the Q network, and the sampling proportions of the two experience pools were updated through the training loss. Afterward, the advantages of a multi-step guided DDQN (MS-DDQN) algorithm and experience classification DDQN (EC-DDQN) algorithm were combined to develop a novel experience classification multi-step DDQN (ECMS-DDQN) algorithm. Finally, the path planning of these four algorithms, including DDQN, MS-DDQN, EC-DDQN, and ECMS-DDQN, was simulated on the OpenAI Gym platform. The simulation results revealed that the ECMS-DDQN algorithm outperforms the other three in the total return value and generalization in path planning.
Compositional Generalization in Multilingual Semantic Parsing over Wikidata

Abstract Semantic parsing (SP) allows humans to leverage vast knowledge resources through natural interaction. However, parsers are mostly designed for and evaluated on English resources, such as CFQ (Keysers et al., 2020), the current standard benchmark based on English data generated from grammar rules and oriented towards Freebase, an outdated knowledge base. We propose a method for creating a multilingual, parallel dataset of question-query pairs, grounded in Wikidata. We introduce such a dataset, which we call Multilingual Compositional Wikidata Questions (MCWQ), and use it to analyze the compositional generalization of semantic parsers in Hebrew, Kannada, Chinese, and English. While within- language generalization is comparable across languages, experiments on zero-shot cross- lingual transfer demonstrate that cross-lingual compositional generalization fails, even with state-of-the-art pretrained multilingual encoders. Furthermore, our methodology, dataset, and results will facilitate future research on SP in more realistic and diverse settings than has been possible with existing resources.
THE IMPORTANCE OF CYBERSECURITY AWARENESS FOR CHILDREN

Online school during the COVID-19 pandemic makes the children prone to cyberattacks and malware. Therefore, cybersecurity awareness among children is critical. Online games and stories caused children to neglect their studying time. Therefore, this paper aims to describe the importance of cybersecurity awareness among children. Analyses were performed by using a literature review. Inclusion criteria are review and research articles. Inclusion criteria are review and research articles that were read twice to reduce the bias, then were summarized and narrated descriptively. Results revealed that cybersecurity awareness could be increased by digital literacy training. Children should be taught how to avoid risky behavior online. The cybersecurity awareness program is suitable for children to avoid phishing, pornography, cyberbullying, identity theft, and breaking of privacy. Children must be taught how to keep their passwords safe and private. They also need to be more cautious when playing online games. In conclusion, digital literacy and cybersecurity programs can be taught to children. These programs are suitable for raising cybersecurity among children.
Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty  Estimation for Facial Expression Recognition

Due to the subjective annotation and the inherent interclass similarity of facial expressions, one of key challenges in Facial Expression Recognition (FER) is the annotation ambiguity. In this paper, we proposes a solution, named DMUE, to address the problem of annotation ambiguity from two perspectives: the latent Distribution Mining and the pairwise Uncertainty Estimation. For the former, an auxiliary multi-branch learning framework is introduced to better mine and describe the latent distribution in the label space. For the latter, the pairwise relationship of semantic feature between instances are fully exploited to estimate the ambiguity extent in the instance space. The proposed method is independent to the backbone architectures, and brings no extra burden for inference. The experiments are conducted on the popular real-world benchmarks and the synthetic noisy datasets. Either way, the proposed DMUE stably achieves leading performance.
SparkGC: Spark based genome compression for large collections of genomes

Abstract Since the completion of the Human Genome Project at the turn of the century, there has been an unprecedented proliferation of sequencing data. One of the consequences is that it becomes extremely difficult to store, backup, and migrate enormous amount of genomic datasets, not to mention they continue to expand as the cost of sequencing decreases. Herein, a much more efficient and scalable program to perform genome compression is required urgently. In this manuscript, we propose a new Apache Spark based Genome Compression method called SparkGC that can run efficiently and cost-effectively on a scalable computational cluster to compress large collections of genomes. SparkGC uses Spark’s in-memory computation capabilities to reduce compression time by keeping data active in memory between the first-order and second-order compression. The evaluation shows that the compression ratio of SparkGC is better than the best state-of-the-art methods, at least better by 30%. The compression speed is also at least 3.8 times that of the best state-of-the-art methods on only one worker node and scales quite well with the number of nodes. SparkGC is of significant benefit to genomic data storage and transmission. The source code of SparkGC is publicly available at https://github.com/haichangyao/SparkGC .