Reviewerly/exHarmony huggingface dataset:
FSPose: A Heterogeneous Framework with Fast and Slow Networks for Human Pose Estimation in Videos

We propose a framework for the integration of heterogeneous networks in human pose estimation (HPE) with the aim of balancing accuracy and computational complexity. Although many existing methods can improve the accuracy of HPE using multiple frames in videos, they also increase the computational complexity. The key difference here is that the proposed heterogeneous framework has various networks for different types of frames, while existing methods use the same networks for all frames. In particular, we propose to divide the video frames into two types, including key frames and non-key frames, and adopt three networks including slow networks, fast networks, and transfer networks in our heterogeneous framework. For key frames, a slow network is used that has high accuracy but high computational complexity. For non-key frames that follow a key frame, we propose to warp the heatmap of a slow network from a key frame via a transfer network and fuse it with a fast network that has low accuracy but low computational complexity. Furthermore, when extending to the usage of long-term frames where a large number of non-key frames follow a key frame, the temporal correlation decreases. Therefore, when necessary, we use an additional transfer network that warps the heatmap from a neighboring non-key frame. The experimental results on PoseTrack 2017 and PoseTrack 2018 datasets demonstrate that the proposed FSPose achieves a better balance between accuracy and computational complexity than the competitor method. Our source code is available at https://github.com/Fenax79/fspose.
Using swarm intelligence optimization algorithms to predict the height of fractured water-conducting zone

The accurate calculation of the height of fractured water-conducting zone (FWCZ) is of great significance for mine optimization design, water disaster prevention, and safety production of the coal mines. In this article, a height-prediction model of FWCZ based on extreme learning machine (ELM) is proposed. To address the issues of low prediction accuracy and challenging parameter optimization, we optimized the ELM model using the gray-wolf optimization algorithm (GOA), whale optimization algorithm (WOA), and salp optimization algorithm (SOA). These optimization algorithms mitigate the issues of slow convergence, poor stability, and local optimality associated with traditional neural networks. The mining depth, mining height, overburden strata structure, working face length, and coal seam dip angle are selected as the main controlling factors for the height of FWCZ. A total of 42 fields-measured samples are collected and divided into 2 subsets for training and validating with a ratio of 36/6. The prediction capability of GOA-ELM, WOA-ELM, and SOA-ELM models are evaluated and compared, and the results show that the calculation results of the three models are optimized compared with the ELM model. The prediction capability of GOA and WOA are similar, while the prediction results of SOA-ELM are better than the other two models, and the relative errors of the test sets are all less than 10%. Therefore, the SOA-ELM model is finally applied to predict the height of FWCZ formed after the mining of No.15 coal seam in Xinjian Coal Mine. Finally, we verified the prediction results using measured data from the borehole television detection instrument, which showed good consistency. This provides further evidence of the effectiveness of the swarm intelligence optimization algorithm in predicting the height of FWCZ.
On the exceptionality of rational APN functions

Abstract We investigate APN functions which can be represented as rational functions and we provide non-existence results exploiting the connection between these functions and specific algebraic varieties over finite fields. This approach allows to classify families of functions when previous approaches cannot be applied.
Applying Predictive Data Mining to Discover Factors Associated to the Language Skill Performance from Elementary School Students

In this paper, predictive data mining techniques are applied to determine the academic performance from fifth grade students in the Saber 5° tests Language skill at Colombian elementary schools in 2017. We employed the CRISP-DM methodology. Socioeconomic, academic, and institutional information was available at the ICFES databases. A minable dataset was obtained using data cleaning and transformation techniques. A decision tree was built with the Weka tool J48 algorithm. Some of the predictors of the discovered patterns are the nature and location of the school, whether or not students failed a school year, the age group, the mother's educational attainment, and the rates of ICTs and household appliances. The findings of this research serve as quality information for the decision-making at the Ministry of National Education (MEN) and the secretaries of education, and for the directors of elementary educational institutions to define improvement plans that result in the quality of elementary school education in Colombia.
A decoupling method for solving the multi-agent path finding problem

Abstract Many existing multi-agent path finding (MAPF) solvers focus on completeness, speed, or optimization. However, completeness and rapidity are usually in conflict with each other, which makes these algorithms far from satisfactory in practical applications. Motivated by this realistic requirement, we propose an efficient decoupling method to accelerate the solution of large MAPF problems. First, we define the concept of ‘non-essential vertex’-vertices which are not needed to solve a MAPF problem, and a scheme to identify them. Then, a decoupling scheme based on ‘non-essential vertex’ is proposed, which will assign higher priorities to agents whose goal positions are non-essential vertices and lower priorities to agents whose start positions are non-essential vertices. That is, invoking our decoupling algorithm can decouple any given MAPF problem into three subproblems while maintaining the completeness of the solution. All three sub-MAPF problems can be solved sequentially by a complete solver (e.g., CBS or EECBS, etc.), and two of them can also be solved by a prioritized planning algorithm. We have conducted several experiments in different workspaces, and the statistical results show that the proposed decoupling method significantly improves the speed and success rate of existing MAPF solvers with almost no degradation in solution quality when solving problems with high agent density. In addition, the solving efficiency can be further improved if the prioritized planning algorithm is invoked to solve the first and third sub-MAPF problems.
Application of Deep Reinforcement Learning in Quantum Control

Abstract Machine learning technology based on artificial neural network has been successfully applied to solve many scientific problems. One of the most interesting areas of machine learning is reinforcement learning, which has natural applicability to optimization problems in physics. In the quantum control task, it is necessary to find a set of optimal control functions to transfer a quantum system from the initial state to the target state with the highest fidelity possible, which is essentially an optimization task. In this paper, we use Deep Deterministic Policy Gradient algorithm (DDPG) to study the quantum control tasks. We use the algorithm to control the transfer of several quantum systems from one state to another. The results show that DDPG algorithm can find a control strategy to make the fidelity of the final state and the target state of the quantum system be maximum value 1. The results show the potential of DDPG in quantum control.
Effects of Footpad Slope, Movement Direction and Contact Part of Foot on Foot-Based Interactions

Feet cannot perform tasks as quickly and with as much dexterity as hands. However, due to the heavy workload placed on hands, there is potential for feet to replace or assist them . In order to use feet more effectively, this study aims to find ways to increase the speed and accuracy of directly touching a touchpad with the feet while in a seated position. The study investigates the effects of three factors: the slope of the touchpad, the direction of the foot movement, and the touch area of the foot used. Regarding the direction of the foot movement, the study found that the most effective direction for both accuracy and speed was at a 30° angle to the right when the front of the right foot was set at 0°. The 0° and 60° angles showed similar efficiencies, but were lower than the 30° angle. The study also found that using the big toe as the touch area resulted in the best speed, accuracy, and subjective satisfaction. The index toe was the second-best option, while using the ball of the foot was the least accurate and slowest option. Lastly, using an slope angle of 15° for the touchpad was found to increase work efficiency compared to using a 7° slope angle. These findings can serve as guidelines for designing foot interfaces.
Differential-Linear Cryptanalsis on SIMECK32/64 and SIMON32/64

Abstract In this paper, we give differential-linear cryptanalysis of SIMON, which is a family of lightweight block ciphers published by the National Security Agency, and SIMECK, which is a family of lightweight block ciphers proposed by Yang et al. Firstly, all input difference and output masks with one active bit are traversed to obtain a 9-round SIMON32/64 differential-linear distinguisher and a 10-round SIMECK32/64 differential-linear distinguisher. Then, a 12-round SIMON32/64 differential-linear distinguisher with bias 2 −12.69 and a 13-round SIMECK32/64 differential-linear distinguisher with bias 2 −14.03 can be obtained by searching one round of differential characteristics forward and two rounds of linear approximations backward. The dynamic key guessing technique proposed by Wang et al. has excellent advantages in the SIMON-like cipher key recovery process. Therefore, we have applied it to differential-linear cryptanalysis. Then, the 12-round SIMON32/64 differential-linear distinguisher is extended forward by four rounds and backward by four rounds to attack the 20-round SIMON32/64 with time complexity 2 55.68 and data complexity 2 28 . And the 13-round SIMECk32/64 differential-linear distinguisher is extended forward by four rounds and backward by four rounds to attack the 21-round SIMECK32/64 with time complexity 2 50.67 and data complexity 2 30 . These are the best differential-linear cryptanalysis results for SIMON32/64 and SIMECK32/64 in the open literature.
Effective and Interpretable Rule Mining for Dynamic Job-Shop Scheduling via Improved Gene Expression Programming with Feature Selection

Gene expression programming (GEP) is frequently used to create intelligent dispatching rules for job-shop scheduling. The proper selection of the terminal set is a critical factor for the success of GEP. However, there are various job features and machine features that can be included in the terminal sets to capture the different characteristics of the job-shop state. Moreover, the importance of features in the terminal set varies greatly between scenarios. The irrelevant and redundant features may lead to high computational requirements and increased difficulty in interpreting generated rules. Consequently, a feature selection approach for evolving dispatching rules with improved GEP has been proposed, so as to select the proper terminal set for different dynamic job-shop scenarios. First, the adaptive variable neighborhood search algorithm was embedded into the GEP to obtain a diverse set of good rules for job-shop scenarios. Secondly, based on the fitness of the good rules and the contribution of features to the rules, a weighted voting ranking method was used to select features from the terminal set. The proposed approach was then compared with GEP-based algorithms and benchmark rules in the different job-shop conditions and scheduling objectives. The experimentally obtained results illustrated that the performance of the dispatching rules generated using the improved GEP algorithm after the feature selection process was better than that of both the baseline dispatching rules and the baseline GEP algorithm.
Research on Att-NFM Recommendation Algorithm Based on Attention Mechanism

Abstract Attentional mechanisms play an important role in artificial intelligence, and inspired by other work, we apply them in the field of recommendation algorithms. By combining factorization machine and neural network together through parallel structure, the linear model of factorization machine is used for binary feature interaction, and the nonlinear relationship between features is explored in using neural network. We propose the Att-NFM model to learn attention coefficients in neural networks to characterize the importance between features. Through experiments, we use AUC and LogLoss as evaluation metrics, and the results show that the evaluation metrics are improved by 3% and 2%, respectively.
Automated issue assignment using topic modelling on Jira issue tracking data

Abstract As more and more software teams use online issue tracking systems to collaborate on software projects, the accurate assignment of new issues to the most suitable contributors may have significant impact on the success of the project. As a result, several research efforts have been directed towards automating this process to save considerable time and effort. However, most approaches focus mainly on software bugs and employ models that do not sufficiently take into account the semantics and the non‐textual metadata of issues and/or produce models that may require manual tuning. A methodology that extracts both textual and non‐textual features from different types of issues is designed, providing a Jira dataset that involves not only bugs but also new features, issues related to documentation, patches, etc. Moreover, the semantics of issue text are effectively captured by employing a topic modelling technique that is optimised using the assignment result. Finally, this methodology aggregates probabilities from a set of individual models to provide the final assignment. Upon evaluating this approach in an automated issue assignment setting using a dataset of Jira issues, the authors conclude that it can be effective for automated issue assignment.
Locating Two-Level Systems in a Superconducting Xmon Qubit

One significant source of decoherence in superconducting circuits is known as two-level systems (TLSs), found in amorphous oxide layers. These circuits can, however, also be utilized as spectral and temporal TLS probes. Comprehensive investigations on the physics of TLSs are now possible thanks to recent advancements in superconducting qubits. Here, we simultaneously measure the tunable Xmon qubit decoherence time as well as the resonance frequency for more than 3 days to investigate stochastic fluctuations. Time-domain Allan deviation and frequency-domain power spectral density analysis indicate that two TLSs in near resonance with the qubit are responsible for the fluctuations. From the extracted oscillation in T1 decay, we locate the two TLSs near the junctions.
Video based behavior detection algorithm

Abstract Action recognition based on video surveillance becomes possible because of the rapid development of action recognition, temporal action recognition and spatial-temporal action recognition technology. A video-based behavior detection algorithm designed to find information of interest from videos. In the process of video detection, feature extraction is often carried out from space and time dimensions. However, the calculation amount of videos sent into the deep convolutional network is much higher than that of pictures. Therefore, the design of lightweight convolutional network is conducive to reducing training costs, realizing efficient calculation, and enabling researchers to quickly apply it to practical scenarios.
Analysing sentiment change detection of Covid-19 tweets

The Covid-19 pandemic made a significant impact on society, including the widespread implementation of lockdowns to prevent the spread of the virus. This measure led to a decrease in face-to-face social interactions and, as an equivalent, an increase in the use of social media platforms, such as Twitter. As part of Industry 4.0, sentiment analysis can be exploited to study public attitudes toward future pandemics and sociopolitical situations in general. This work presents an analysis framework by applying a combination of natural language processing techniques and machine learning algorithms to classify the sentiment of each tweet as positive, or negative. Through extensive experimentation, we expose the ideal model for this task and, subsequently, utilize sentiment predictions to perform time series analysis over the course of the pandemic. In addition, a change point detection algorithm was applied in order to identify the turning points in public attitudes toward the pandemic, which were validated by cross-referencing the news report at that particular period of time. Finally, we study the relationship between sentiment trends on social media and, news coverage of the pandemic, providing insights into the public's perception of the pandemic and its influence on the news.
Distributed Generation Forecasting Based on Rolling Graph Neural Network (ROLL-GNN)

The future power grid will have more distributed energy sources, and the widespread access of distributed energy sources has the potential to improve the energy efficiency, resilience, and sustainability of the system. However, distributed energy, mainly wind power generation and photovoltaic power generation, has the characteristics of intermittency and strong randomness, which will bring challenges to the safe operation of the power grid. Accurate prediction of solar power generation with high spatial and temporal resolution is very important for the normal operation of the power grid. In order to improve the accuracy of distributed photovoltaic power generation prediction, this paper proposes a new distributed photovoltaic power generation prediction model: ROLL-GNN, which is defined as a prediction model based on rolling prediction of the graph neural network. The ROLL-GNN uses the perspective of graph signal processing to model distributed generation production timeseries data as signals on graphs. In the model, the similarity of data is used to capture their spatio-temporal dependencies to achieve improved prediction accuracy.
Sentiment Analysis of Social Network Text Based on HDBSCAN and SO-PMI

Abstract With the rapid development of the Internet, the length and themes of text content in social network environments are becoming more and more abundant. The probability and degree of semantic similarity between different words in different texts are also increasing. It is difficult to effectively identify text topics and their sentiment tendencies. This paper takes the text data of the Ford Forum of the PCauto as the research object, builds a word vector model based on Word2vec, uses HDBSCAN to classify social network context, and calculates the sentiment value of different topic through SO-PMI. It aims to explore the complexity of the semantic evolution and feature extraction process and its generating text topics, based on which we further analyze the variability of sentiment tendencies between words and topics and then summarize their characteristics.
Development of modification of the post-quantum public-key cryptosystem NTRUENCRYPT

This work is devoted to the development of a modification of the post-quantum public-key cryptosystem NTRUEncrypt. Given that two main requirements for modern cryptographic algorithms are resistance to attacks (including quantum attacks) and performance, the developed modification offers an improvement in both aspects. Karatsuba algorithm for fast polynomial multiplication is employed to achieve better performance. The modification also includes additional protection against a chosen ciphertext attack that can be effectively against standard NTRUEncrypt. Performance test of the developed modification confirmed that less time is required for key generation, encryption and decryption in comparison with the classical algorithm. The modified algorithm is then applied to implement an asymmetric encryption system with a graphical user interface that allows establishing communication between two users with resistance to both classical and quantum attacks.
A Shadowed Type-2 Fuzzy Approach for Crossover Parameter Adaptation in Differential Evolution

The shadowed type-2 fuzzy systems are used more frequently today as they provide an alternative to classical fuzzy logic. The primary purpose of fuzzy logic is to simulate reasoning in a computer. This work aims to use shadowed type-2 fuzzy systems (ST2-FS) to dynamically adapt the crossing parameter of differential evolution (DE). To test the performance of the dynamic crossing parameter, the motor position control problem was used, which contains an interval type-2 fuzzy system (IT2-FS) for controlling the motor. A comparison is made between the original DE and the algorithm using shadowed type-2 fuzzy systems (DE-ST2-FS), as well as a comparison with the results of other state-of-the-art metaheuristics.
Document-level Event Extraction - A Survey of Methods and Applications

Abstract Event extraction aims to detect occurrences of specified types and extract corresponding event arguments from unstructured data input, which is an integral part of information extraction [1]. Many downstream tasks, such as text summarization, causal relationship identification, and event reasoning, make extensive use of event extraction. However, most of the existing research on event extraction remains at the sentence level. Document-level event extraction is still under exploration and lacks relevant review. Focusing on the task of document-level event extraction, this survey first divides the existing technologies into three categories and introduces the most representative models; Then, we list some commonly used datasets for document-level event extraction with their usage messages; Finally, we analyse the current research gaps of document-level event extraction and future research trends.
Psikhologicheskie i organizatsionnye osobennosti pedagogicheskogo soprovozhdeniia obuchaiushchikhsia vysshei shkoly

The authors actualized the issues of the organization of pedagogical support of students of higher school. Modern processes in the economic, political and social spheres of our state have a direct impact on the formation of higher education as a social institution. The main role of higher education is the formation of professional human capital. In this regard, there is a need to search for new effective technologies that would ensure the formation of a specialist as a professional and as a person. One of the most effective technologies that solves the tasks set is pedagogical support of students of higher school. The authors revealed the psychological and organizational features of accompanying students. The historical background and evolution of the development of support as a pedagogical technology are presented.