Reviewerly/exHarmony huggingface dataset:
The need for a transition to a distance learning format: research results

Relevance. The article discusses the problems of the transition of universities to a distance learning format. The transfer of universities to a remote format due to the spread of the COVID-19 virus allowed students to continue to receive training, which varied significantly due to technologies, the choice of which was determined by the current capabilities of educational institutions. The article analyzes the result of a study of the attitude and assessment of teachers and students organizing and conducting training outside the educational institution with the use of distance technologies. The purpose of the study is to identify the features of teaching and learning in a distance format in higher education during the spread of a new coronavirus infection. The objectives of the study are to identify factors that have influenced the quality of the implementation of educational programs in full in the electronic information and educational environment. The results of the study. A serious influence of the distance learning format on the quality of the teacher-student interaction was revealed (it has significantly decreased), which is undoubtedly one of the factors that reduce studentsʼ motivation to learn. For many respondents, distance learning and teaching was the first experience of a distance learning format, and therefore the attitude towards it is rather complicated. A significant scatter in the format of the classes, their completeness and content within the framework of distance learning, the low proportion of the teaching staff prepared for its implementation, and the generally low level of technical, methodological and legal support show that the majority of Russian universities are not ready to introduce distance learning. The transition to an online training format caused the majority of respondents discomfort and negative emotional experiences.
Multiple oriented robots for search and rescue operations

Abstract Search and rescue (SAR) operations in a tragedy affected area is challenging. The rescue robots helps in the exploration of unknown, confined and cluttered environments. Multiple robots are developed will to explore the disaster affected region and will be able to detect any people or living beings present there. The robots have living beings as their targets in tragedy affected area. The multiple robots deployed in the field, traverse through the tragedy affected area. A novel algorithm has been developed which helps in finding the target. The rescue robots are programmed to find the shortest and less obstacle path to reach the target. Due to the tragedy affected environment, the robots decides the moving direction based on the information gathered by sensors such that the optimal path between start and goal positions can be found. The path to target by the robots are shared among them and best path is chosen among them. The robots explore and searches the region avoiding the obstacles and whenever it comes into contact with a living being it shares the information among other robots. The CO2 level of the particular region is also checked to know whether the person is in breathing state. The algorithm will be developed in order to increase the pace of the search and to locate the living being. The SAR robots have application over tragedy affected areas like earthquake, avalanche, etc.
A similarity for new meanings

Extraction is a way to get knowledge from information space, such as social networks. One efficient and concise tool for expressing knowledge related to meaning is to involve the concept of similarity. There are several similarity formulations to approach the same thing from the objects but have different tasks according to their functions. However, the measurement results reveal different meanings, although they remain in a mutually supportive position. Therefore, this paper aims to express the different meanings besides new meanings of the similarity function, which are proven based on the difference between the divisors of the similarity formulation. Different measurements of similarities produce different and new meanings with supporting simulation and clustering to manage big data.
Autonomous Systems, Trust, and Guarantees

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Editor’s notes:</i> Trustworthiness is key for the acceptance of autonomous systems. The authors advocate deterministic methods with hard-bounded corridors for operational parameters to guarantee dependable autonomy considering both functional and extra-functional properties. — <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Selma Saidi, TU Dortmund</i>
Image Steganography Based on Wavelet Transform and Color Space Approach

This paper presents a color image steganography scheme based on discrete wavelet transform (DWT) and YCbCr color space. The cover image is initially transformed from RGB color space into YCbCr and the Cb component is utilized for hiding a secret grayscale image. Two-level DWT is applied to the selected component to obtain the horizontal sub-band HL2 which is used for the embedding process based on the singular value decomposition (SVD) of the secret image and the obtained sub-band HL2 of the cover image. Structural Similarity Index Measure (SSIM) and Peak Signal to Noise Ratio (PSNR) are measured for evaluating the performance of the proposed scheme. Simulation results show that the proposed steganography method is reliable against several attacks without degrading the visual quality of the cover image. Further, comparative results for the proposed method in YCbCr and RGB color spaces show that the YCbCr space is more efficient in secret image retrieval
Research on application of an improved deep convolutional neural network in handwritten character recognition

Abstract The classical convolutional neural network has been widely used for handwritten digit character recognition with high accuracy. However, due to its small convolutional layer, fixed size of convolution kernel and few extracted features, the recognition accuracy of complex handwritten characters are reduced. In this paper, an improved deep convolutional neural network model is proposed, which can allocate different convolution kernels according to the different information amount in the handwritten character image area for convolution, so as to better extract the effective information of the image and is more suitable for complex handwritten character recognition applications. Experiments show that the recognition rate can be higher.
Applying PSO Algorithm to Determine the Scan Plan for the PA Ultrasonic Probe carried Robot in Testing the Corrosion of Fuel Tanks

The paper shows an approach to determine the scanning plan (the shortest path) using the PSO algorithm based on the operating characteristics of the PA ultrasonic probe carryied robot, testing environment of fuel tanks and the requirements for collected ultrasonic images.The solution of the PSO problem has proposed an optimal scanning plan with shortest scanning time.Thescan plan has also been verifiedby robot movement on an area of 1000×1000 mm 2 .Experimental results showed that the simulation time is almost within 97% confidential limits of actual movement time.This means the PSO algorithm can be applied effectively into corrosion mapping for fuel tanks.
Evaluation of teaching quality in higher vocational colleges based on quantum behaviour particle swarm optimization

Abstract The implementation of teaching evaluation comprehensive evaluation system and effective management on teaching quality can scientifically manage the development of teachers’ teaching activities. At present, the teaching quality evaluation of teachers in higher vocational colleges has not yet realized algorithm operation based on quantum behavior particle swarm optimization. Therefore, it is necessary to study the teaching quality evaluation system consistent with the characteristics of higher vocational colleges. Quantum particle swarm optimization can construct a system model that can evaluate the quality of teaching. The system includes various functions like management and evaluation, which can be closely combined with the actual needs of each college. User name and password can effectively improve the security performance of the system. Convenient and fast database management operation, as well as intelligent and friendly database management interface, can ensure the timeliness and accuracy of query results. Research shows that, based on the practical application of quantum behavior particle swarm optimization in the field of teaching quality evaluation, the means of teaching evaluation are more scientific.
Path Planning using Neural A* Search

We present Neural A*, a novel data-driven search method for path planning problems. Despite the recent increasing attention to data-driven path planning, machine learning approaches to search-based planning are still challenging due to the discrete nature of search algorithms. In this work, we reformulate a canonical A* search algorithm to be differentiable and couple it with a convolutional encoder to form an end-to-end trainable neural network planner. Neural A* solves a path planning problem by encoding a problem instance to a guidance map and then performing the differentiable A* search with the guidance map. By learning to match the search results with ground-truth paths provided by experts, Neural A* can produce a path consistent with the ground truth accurately and efficiently. Our extensive experiments confirmed that Neural A* outperformed state-of-the-art data-driven planners in terms of the search optimality and efficiency trade-off. Furthermore, Neural A* successfully predicted realistic human trajectories by directly performing search-based planning on natural image inputs. Project page: https://omron-sinicx.github.io/neural-astar/
FACTORS AFFECTING THE FIRST-YEAR STUDENTS’ ADAPTATION TO LEARNING ACTIVITIES: A CASE STUDY OF A PUBLIC UNIVERSITY IN VIETNAM

Purpose of the study: Adaptation to learning activities plays a very important role and decides the first-year students’ academic results. In order to have appropriate solutions to improve the adaptation to the learning activities of the first-year students, it is necessary to identify the affecting factors. This study investigates the factors affecting adaptation to learning activities of the first-year students of the University of Labor and Social Affairs in Vietnam, thereby finding solutions that improve the adaptation to their learning activities.&#x0D; Methodology: This study used a quantitative method with primary data through questionnaires distributed to 300 samples of the first-year students of the University of Labor and Social Affairs in Vietnam. The Pearson correlation coefficient test and linear regression are also used to process the research results.&#x0D; Main Findings: The results of the study indicate that adaptability of students to learning activities affected by many different factors, subjective factors such as motivation, learning methods, and skills, and that there is a great influence on students' adaptation to learning activities.&#x0D; Applications of this study: The research provides a descriptive picture for the leadership of the University of Labor and Social Affairs to improve the adaptation to learning activities for first-year students.&#x0D; Novelty/Originality of this study: As far as we are concerned, there is no research on adaptation to the learning activities of the first-year students of the University of Labor and Social Affairs. This work is an attempt to fill in this gap.&#x0D;
Research and design of recommendation in book circle system

Abstract Reading is the ladder of human progress. It is the real needs of readers to quickly find good books and needed books in this fast-paced times. It will be more enjoyable if we can find the book friends who are interlinked with each other through reading. Digital reading records the whole process of reading, which contains readers’ reading preference. In this paper, we discussed the techniques based on the data related to reading: (1) book friends are personally recommended using books as the media; (2) good books are personally recommended taking readers as media; (3) reader recommendation model and book recommendation model are built based on the relationship between “book-reader” in reading events. A reading system with such a recommendation function can make it easy for readers to find the books they want and find book friends who have sympathetic thoughts.
Compact Elliptic Curve Scalar Multiplication with a Secure Generality

Elliptic curve cryptosystems (ECCs) are widely used because of their short key size. ECCs can ensure sufficient security with shorter keys, using less memory to reduce parameters. Hence, ECCs are typically used in IoT devices. The dominant computation of an ECC is scalar multiplication $Q = kP$ for $P \in E(\mathbb{F}_q)$. Thus, the security and efficiency of scalar multiplication are paramount. To render secure ECCs, complete addition (CA) formulae can be employed for secure scalar multiplication algorithms. However, this requires significant memory; thus, it is not suitable for compact devices. Several types of coordinates exist for elliptic curves such as affine, Jacobian, Projective and so on. The CA formulae are not based on affine coordinates and, thus, require considerable memory. In this study, we achieve a compact ECC by focusing on affine coordinates. In fact, affine coordinates are highly advantageous in terms of memory but require many if statements for scalar multiplication owing to exceptional points. We propose two scalar multiplication algorithms with the extended affine formulae to delete some exceptional inputs for scalar multiplication. Our two algorithms reduce memory cost up to 37% or 21%. In many cases such as NIST elliptic curves, our two algorithms are the most efficient if $\frac{I}{M}<12$, for the ratio of computational cost of inversion and multiplication. The experiment shows that our algorithms can compute the elliptic curve scalar multiplication correctly and efficiently.
Expiry-Date Recognition System Using Combination of Deep Neural Networks for Visually Impaired

Many drink packages have expiry dates written in dot matrix characters (digits and non-digits, e.g., slashes or dots). We collected images of these packages and trained two existing deep neural networks (DNNs) to combine and form a system for detecting and recognizing expiry dates on drink packages. One of the DNNs is an object-detection DNN and the other is a character-recognition DNN. The object-detection DNN alone can localize the characters written on a drink package but its recognition accuracy is not sufficient. The character-recognition DNN alone cannot localize characters but has good recognition accuracy. Because the system is a combination of these two DNNs, it improves the recognition accuracy. The object-detection DNN is first used to detect and recognize the expiry date by localizing and obtaining the size of the character. It then scans the expiry-date region and clips the image. The character-recognition DNN then recognizes the characters from the clipped images. Finally, the system uses both DNNs to obtain the most accurate recognition result based on the spacing of the digits. We conducted an experiment to recognize the expiry dates written on the drink package. The experimental results indicate that the recognition accuracy of the object-detection DNN alone was 90%, that of the character-recognition DNN alone was also 90%, and that combining the results of both DNNs was 97%.
EfficientDeRain: Learning Pixel-wise Dilation Filtering for High-Efficiency Single-Image Deraining

Single-image deraining is rather challenging due to the unknown rain model. Existing methods often make specific assumptions of the rain model, which can hardly cover many diverse circumstances in the real world, making them have to employ complex optimization or progressive refinement. This, however, significantly affects these methods' efficiency and effectiveness for many efficiency-critical applications. To fill this gap, in this paper, we regard the single-image deraining as a general image-enhancing problem and originally propose a model-free deraining method, i.e., EfficientDeRain, which is able to process a rainy image within 10~ms (i.e., around 6~ms on average), over 80 times faster than the state-of-the-art method (i.e., RCDNet), while achieving similar de-rain effects. We first propose the novel pixel-wise dilation filtering. In particular, a rainy image is filtered with the pixel-wise kernels estimated from a kernel prediction network, by which suitable multi-scale kernels for each pixel can be efficiently predicted. Then, to eliminate the gap between synthetic and real data, we further propose an effective data augmentation method (i.e., RainMix) that helps to train network for real rainy image handling.We perform comprehensive evaluation on both synthetic and real-world rainy datasets to demonstrate the effectiveness and efficiency of our method. We release the model and code in https://github.com/tsingqguo/efficientderain.git.
UNION: An Unreferenced Metric for Evaluating Open-ended Story Generation

Despite the success of existing referenced metrics (e.g., BLEU and MoverScore), they correlate poorly with human judgments for open-ended text generation including story or dialog generation because of the notorious one-to-many issue: there are many plausible outputs for the same input, which may differ substantially in literal or semantics from the limited number of given references. To alleviate this issue, we propose UNION, a learnable unreferenced metric for evaluating open-ended story generation, which measures the quality of a generated story without any reference. Built on top of BERT, UNION is trained to distinguish human-written stories from negative samples and recover the perturbation in negative stories. We propose an approach of constructing negative samples by mimicking the errors commonly observed in existing NLG models, including repeated plots, conflicting logic, and long-range incoherence. Experiments on two story datasets demonstrate that UNION is a reliable measure for evaluating the quality of generated stories, which correlates better with human judgments and is more generalizable than existing state-of-the-art metrics.
A Time-Critical Topic Model for Predicting the Survival Time of Sepsis Patients

Sepsis is a leading cause of mortality in intensive care units and costs hospitals billions of dollars annually worldwide. Predicting survival time for sepsis patients is a time-critical prediction problem. Considering the useful sequential information for sepsis development, this paper proposes a time-critical topic model (TiCTM) inspired by the latent Dirichlet allocation (LDA) model. The proposed TiCTM approach takes into account the time dependency structure between notes, measurement, and survival time of a sepsis patient. Experimental results on the public MIMIC-III database show that, overall, our method outperforms the conventional LDA and linear regression model in terms of recall, precision, accuracy, and F1-measure. It is also found that our method achieves the best performance by using 5 topics when predicting the probability for 30-day survival time.
Acoustic Analysis of Timbre of Sarasvati Veena in Comparison to Simple Sonometer

We characterize the timbre of the Sarasvati Veena, a traditional Indian string instrument, by analysing the sound spectrum and the energy distribution with different frequencies as a function of time. We compare the acoustic analysis with that of a simple sonometer. We find that while the spectrum consists of the fundamental and harmonics of a plucked string under tension, the Veena shows a prominent formant near the resonant frequency of the body. But more notable is the behaviour of the higher harmonics (upper partials) which share substantial energy and show a characteristic revival with time, in contrast to the sonometer spectrum.
GRAC: Self-Guided and Self-Regularized Actor-Critic

Deep reinforcement learning (DRL) algorithms have successfully been demonstrated on a range of challenging decision making and control tasks. One dominant component of recent deep reinforcement learning algorithms is the target network which mitigates the divergence when learning the Q function. However, target networks can slow down the learning process due to delayed function updates. Our main contribution in this work is a self-regularized TD-learning method to address divergence without requiring a target network. Additionally, we propose a self-guided policy improvement method by combining policy-gradient with zero-order optimization to search for actions associated with higher Q-values in a broad neighborhood. This makes learning more robust to local noise in the Q function approximation and guides the updates of our actor network. Taken together, these components define GRAC, a novel self-guided and self-regularized actor critic algorithm. We evaluate GRAC on the suite of OpenAI gym tasks, achieving or outperforming state of the art in every environment tested.
ENAS4D: Efficient Multi-stage CNN Architecture Search for Dynamic Inference

Dynamic inference is a feasible way to reduce the computational cost of convolutional neural network(CNN), which can dynamically adjust the computation for each input sample. One of the ways to achieve dynamic inference is to use multi-stage neural network, which contains a sub-network with prediction layer at each stage. The inference of a input sample can exit from early stage if the prediction of the stage is confident enough. However, design a multi-stage CNN architecture is a non-trivial task. In this paper, we introduce a general framework, ENAS4D, which can efficiently search for optimal multi-stage CNN architecture for dynamic inference in a well-designed search space. Firstly, we propose a method to construct the search space with multi-stage convolution. The search space include different numbers of layers, different kernel sizes and different numbers of channels for each stage and the resolution of input samples. Then, we train a once-for-all network that supports to sample diverse multi-stage CNN architecture. A specialized multi-stage network can be obtained from the once-for-all network without additional training. Finally, we devise a method to efficiently search for the optimal multi-stage network that trades the accuracy off the computational cost taking the advantage of once-for-all network. The experiments on the ImageNet classification task demonstrate that the multi-stage CNNs searched by ENAS4D consistently outperform the state-of-the-art method for dyanmic inference. In particular, the network achieves 74.4% ImageNet top-1 accuracy under 185M average MACs.
An Improved Bacterial Foraging Algorithm for Multi-modal Problems

Abstract In order to improve the performance of BFO (bacterial foraging optimization) algorithm in multi-modal problems, the algorithm is optimized and improved in this paper. In the chemotaxis operation, the gradient accumulation variable swimming step size is realized, and the convergence speed of the algorithm is accelerated. In the reproduction operation, a new population competition mechanism is added, and a double-index sorting is realized, which improves the ability of the algorithm to jump out of the local optimum and the diversity of the population. In the migration operation, the vitality index of bacteria is added to dynamically affect the migration probability, ensure the survival probability of elite individuals, and further improve the global optimization ability of the algorithm. Finally, a multi-modal function is selected to test. Compared with the genetic algorithm, the improved BFO algorithm is better than the genetic algorithm in speed and quality.