Reviewerly/exHarmony huggingface dataset:
Risk-Aware Model-Based Control

Model-Based Reinforcement Learning (MBRL) algorithms have been shown to have an advantage on data-efficiency, but often overshadowed by state-of-the-art model-free methods in performance, especially when facing high-dimensional and complex problems. In this work, a novel MBRL method is proposed, called Risk-Aware Model-Based Control (RAMCO). It combines uncertainty-aware deep dynamics models and the risk assessment technique Conditional Value at Risk (CVaR). This mechanism is appropriate for real-world application since it takes epistemic risk into consideration. In addition, we use a model-free solver to produce warm-up training data, and this setting improves the performance in low-dimensional environments and covers the shortage of MBRL’s nature in the high-dimensional scenarios. In comparison with other state-of-the-art reinforcement learning algorithms, we show that it produces superior results on a walking robot model. We also evaluate the method with an Eidos environment, which is a novel experimental method with multi-dimensional randomly initialized deep neural networks to measure the performance of any reinforcement learning algorithm, and the advantages of RAMCO are highlighted.
Attention-Guided Context-aware Emotional State Recognition

For effective communication between two humans, one needs to understand the emotional state of its fellow beings. Predicting human emotion involves several factors, which include, but not limited to, facial expression. Many researches in this direction are based on features extracted from facial expressions. In this paper we are proposing a model, which can predict human emotion by considering facial as well as context information. Our model not only extracts features from facial expressions but also is aware of the background context in the image dataset. We have shown the relevance of contextual information with facial expression and its impact on the predicted results. Our model captures facial expressions and contextual information with the most relevant part boosted up to capture feature and utilize it to predict human expressions. We are using Attention model in our architecture to boost relevant part, and learn what to boost to make relevant prediction. We have performed several experiments and compare the relevance of facial expressions with context and context free environments. Our proposed model is robust, capable of predicting emotions in real time with improved accuracy of 8% over state of the art accuracy to the best of our knowledge. In addition, it is implemented over dataset, which contains mostly spontaneous images and not posed one, leading to improved results.
Person Re-Identification Based on Attention Mechanism and Context Information Fusion

Person re-identification (ReID) plays a significant role in video surveillance analysis. In the real world, due to illumination, occlusion, and deformation, pedestrian features extraction is the key to person ReID. Considering the shortcomings of existing methods in pedestrian features extraction, a method based on attention mechanism and context information fusion is proposed. A lightweight attention module is introduced into ResNet50 backbone network equipped with a small number of network parameters, which enhance the significant characteristics of person and suppress irrelevant information. Aiming at the problem of person context information loss due to the over depth of the network, a context information fusion module is designed to sample the shallow feature map of pedestrians and cascade with the high-level feature map. In order to improve the robustness, the model is trained by combining the loss of margin sample mining with the loss function of cross entropy. Experiments are carried out on datasets Market1501 and DukeMTMC-reID, our method achieves rank-1 accuracy of 95.9% on the Market1501 dataset, and 90.1% on the DukeMTMC-reID dataset, outperforming the current mainstream method in case of only using global feature.
Novel phase unwrapping technique based on extended information filter

A general framework of a phase unwrapping procedure based on extended information filter is built. Two phase unwrapping algorithms with different observed models, are proposed by combining an extended information filter with an efficient phase gradient estimator, and an efficient quality-guided strategy based on heap-sort. The extended information filter that is a new type of filter with high efficiency and high robustness, is applied to acquire the unambiguous unwrapped phase of wrapped phase images for the first time to our knowledge. Through the application of the extended information filter, simultaneous phase unwrapping and noise suppression have been performed. The efficient phase gradient estimator that has been demonstrated well, is used to efficiently acquire phase gradient information required for the proposed algorithms. The efficient quality-guided strategy based on heap-sort is used to define the path of the proposed algorithms and to guarantee that they efficiently unwrap wrapped pixels along the path, from high-reliance to low-reliance regions. Synthetic and experimental data is used to demonstrate that our proposed algorithms can obtain robust solutions with reasonable time consumption, compared with some of the more traditional algorithms.
Right to Be Forgotten in the Age of Machine Learning

The right to be forgotten (RtbF) is considered as one of the fundamental human rights in many legal systems. However, given the popularity of the computer systems in our daily life, and particularly the rapid development of the machine learning techniques, the RtbF need to be considered again. In this study we review the definitions of RtbF in several major legal documents and the application of the right in practice. We then discuss the differential privacy as a framework to support the RtbF.
Symmetric 2-Adic Complexity of Ding-Helleseth Generalized Cyclotomic Sequences of Period pq

In this paper, we consider Ding-Helleseth generalized cyclotomic sequences of length pq where p and q are odd distinct primes. We derive symmetric 2-adic complexity of these sequences for any p, q and show that they have high symmetric 2-adic complexity. These results generalize known conclusions of Yan et al. (IEEE Access, 2020, https://doi:10.1109/ACCESS.2020.3012570 ) about 2-adic complexity of Ding-Helleseth sequences of order 2 and of length pq.
A new fusion framework for motion segmentation in dynamic scenes

Motion segmentation in dynamic scenes is currently widely dominated by parametric methods based on deep neural networks. The present study explores the unsupervised segmentation approach that can be used in the absence of training data to segment new videos. In particular, it tackles the task of dynamic texture segmentation. By automatically assigning a single class label to each region or group, this task consists of clustering into groups complex phenomena and characteristics which are both spatially and temporally repetitive. We present an effective fusion framework for motion segmentation in dynamic scenes (FFMS). This model is designed to merge different segmentation maps that contain multiple and weak quality regions in order to achieve a more accurate final result of segmentation. The diverse labelling fields required for the combination process are obtained by a simplified grouping scheme applied to an input video (on the basis of a three orthogonal planes: xy, yt and xt). Experiments conducted on two challenging datasets (SynthDB and YUP++) show that, contrary to current motion segmentation approaches that either require parameter estimation or a training step, FFMS is significantly faster, easier to code, simple and has limited parameters.
Teaching Course on Algorithms and Data Structures during the Coronavirus Pandemic

At the beginning of spring 2020, the life in Slovakia and in other European countries has slowed down due to coronavirus disease 2019 caused by SARS-CoV-2. Slovakia and many other countries have tried to stop or slow down the spread of the coronavirus by minimizing face-to-face interactions between people. This has resulted in reduction of public transport, closing churches, shops, restaurants, and a lot of other services, including education. During the second half of March 2020, most education institutions, including universities, were closed to the public. Students and teachers could not visit them, and the process of education had to move from a classical physical space to a virtual space. Traditional face-to-face education supported by online learning had to be replaced by distance learning very quickly. However, most of the education institutions in Slovakia were not be prepared for this since distance learning had not been actively used by them before. Also universities, which provide education that requires much more self-study than in case of primary and secondary schools, challenged to problems how the distance learning should be realized in such a short term. In this paper, we present how some of the challenges and issues related to fast transformation of face-to-face education to education based on distance learning were solved by us within a course on algorithms and data structures taught at the Faculty of Management Science and Informatics of University of Zilina. Results presented in the paper indicate that a course requiring active participation of a student in the learning process by solving real world problems can be taught effectively using distance learning and the quality of education and knowledge grasped by students do not suffer from absence of face-to-face lectures and practical classes.
A Power Transformer Fault Diagnosis Method Based on Random Vector Functional-Link Neural Network

The random vector functional link (RVFL) network is suitable for solving nonlinear problems from transformer fault symptoms and different fault types due to its simple structure and strong generalization ability. However, the RVFL network has a disadvantage in that the network structure, and parameters are basically determined by experiences. In this paper, we proposed a method to improve the RVFL neural network algorithm by introducing the concept of hidden node sensitivity, classify each hidden layer node, and remove nodes with low sensitivity. The simplified network structure could avoid interfering nodes and improve the global search capability. The five characteristic gases produced by transformer faults are divided into two groups. A fault diagnosis model of three layers with four classifiers was built. We also investigated the effects of the number of hidden nodes and scale factors on RVFL network learning ability. Simulation results show that the number of implicit layer nodes has a large impact on the network model when the number of input dimensions is small. The network requires a higher number of implicit layer neurons and a smaller threshold range. The size of the scale factor has significant influence on the network model with larger input dimension. This paper describes the theoretical basis for parameter selection in RVFL neural networks. The theoretical basis for the selection of the number of hidden nodes, and the scale factor is derived. The importance of parameter selection for the improvement of diagnostic accuracy is verified through simulation experiments in transformer fault diagnosis.
Research on Big Data Storage Method based on IPFS and Blockchain

With the popularity of digital cryptocurrency such as bitcoin, blockchain, as a new distributed framework with decentralization, non rewriting and traceability, has sprung up rapidly and has been applied in many industries such as finance, medical treatment, information security, etc. In order to ensure the security of transaction data, all key information in the business needs to enter the blockchain network. In the field of artificial intelligence, model data (i.e. effective feature point data set) will be the key information and will be used frequently. However, these feature point datasets may be megabytes, auspicious, or even terahertz. Then, the performance of big data transaction in blockchain network will be a problem worthy of study. Therefore, this paper proposes a blockchain big data storage method based on IPFs. This method mainly solves the transaction performance problem of large text data in the blockchain network. The data larger than 100 megabytes are stored in IPFs to obtain the hash certificate of text. The hash code is the only transaction voucher in the blockchain network. It greatly improves the transaction efficiency of blockchain network. In this paper, a comparative experiment is set up to further prove the efficiency of our method.
Research Issues &amp; State of the Art Challenges in Event Detection

Event detection is a cutting-edge problem in video image processing which is not that straight forward to solve. Hence, we perform a thorough search for state of the art in solving the issues in this area. There are many applications of event detection in this age of cybercrime. We target to propose a best solution to outperform existing research and make a positive development in it. We have discussed quite a long list of existing literature reviews in this hope that it would help us and anyone else working on the same as a guideline and one stop reading.
Novel Modulation Recognizer for Frequency-Hopping Signals Based on Persistence Diagram

Modulation recognition or classification has drawn a lot of research interest for military and civilian communication applications. However, modulation recognition (classification) for frequency-hopping (FH) signals remains very difficult due to its unique time-varying spectral characteristics. In this work, a novel modulation-recognition scheme using the topological data analysis is proposed as the first attempt to solve this difficult problem. In our proposed scheme, the received frequency-hopping signal is sampled and these samples are mapped into the embedded phase space. Then the expected persistence diagrams (EPDs) are established to construct the features. We treat such resulted EPDs as probability distribution functions. Consequently, the discrepancy between any two EPDs can be measured by Kullback-Leibler divergence (KLD). During the recognition stage, a test signal will thus be converted to an EPD and the corresponding KLDs to all training features (EPDs) can be calculated for modulation recognition decision. Preliminary results from Monte Carlo simulations have demonstrated that the recognition accuracy of our proposed new approach is above 95% when the signal-to-noise ratio is greater than 30 dB.
A Comparative Study on Load Balancing Algorithms in Cloud Environment

Cloud computing is an emerging computational model for delivering services over the internet to clients at any time and any location on a pay-per-use basis. Load balancing is a set of techniques used to improve resource utilization and affect the overall performance of the cloud computing platform.
Quality of Experience Eye Gaze Analysis On HbbTV Smart Home Notification System

Recent studies have started to consider the HbbTV as the central hub to handle notifications regarding the smart home devices. The HbbTV integrates into the multimedia TV contents the notifications coming from various smart home devices such as the smartphone, video door-phone, sensors, washing machine, etc. Therefore, there is a need to evaluate the impact on-screen notifications would have on the user's perceived Quality of Experience (QoE). In some cases, notifications may annoy the user and negatively impact the QoE perceived for the watched video contents. For these reasons, a subjective quality assessment is presented in order to evaluate the impact of onscreen notifications while users are watching TV video contents. Test participants were recorded while watching the video because, using the eye's gaze direction, the attention of the participants towards notifications was measured.
Implementation and Optimization of Data Prefetching Algorithm Based on LLVM Compilation System

Abstract In order to reduce the problem of mismatch between high-performance processors and DRAM speeds, current processors have added a cache structure, but the low cache hit rate also seriously affects the actual performance of the program. Data prefetching technology can alleviate the problems of memory access latency and low hit rate caused by the speed difference between high-performance processors and DRAM. Based on the LLVM open source compiler, this article first implements the data prefetch module on the Shenwei platform. This paper improves the prefetch distance algorithm, proposes a new prefetch scheduling algorithm, introduces a cost model to evaluate the prefetch revenue, and accurately determines the insertion timing of the prefetch instruction to improve the cache hit rate. SPEC2006 performance test results show that after optimization, Shenwei 1621 processor single-core can achieve a maximum performance improvement of 50%, and an average performance improvement of 11%.
Non-discrete k-order additivity of a set function and distorted measure

In this study, we generalize the concept of the k-order additivity of a set function. First, we discuss the Möbius transform for a non-discrete set function. Next, we generalize the definition of the k-order additivity of a set function using the Möbius transform and provide the equivalent conditions for the k-order additivity. Furthermore, we consider the k-order additivity of the distorted monotone measure. We prove that under certain conditions, a distorted measure is k-order additive if and only if the distortion function is a polynomial of k-th order.
Accelerated Modified Sine Cosine Algorithm for Data Clustering

In artificial intelligence, data mining is a process that automatically discover valuable information from huge amounts of data in order to obtain knowledge. The most important unsupervised technique of data mining is the clustering technic, which his main task is dividing the dataset into homogeneous groups. Metaheuristics based clustering is an actual research area where optimization algorithms have demonstrated their efficiencies to provide near optimal solutions to this problem in a reasonable time, including the recent Sine Cosine metaheuristic Algorithm (SCA). However, its convergence rate is still rather slow. In this paper, an upgraded adaptation of SCA is proposed to improve the exhibition capacities of the quest strategy for ideal results for data Clustering problem, named AMSCAC. In this algorithm, both the local and global search procedures are enhanced by additional strategy. The experimental results on five standard datasets are promising and confirm the superiority of AMSCAC, for the clustering results over SCA, cuckoo search algorithm (CS), differential evolution algorithm (DE), and genetic algorithm (GA).
Automatic Video summarization with Timestamps using natural language processing text fusion

In videos, description and keywords play an important role in the choosing process of the right video to watch. The main idea of the proposed approach is to generate descriptions and timestamps for videos automatically. Our approach plays an essential role in reducing the time consumed searching for the proper video. It aims to save time for users watching wrong unwanted videos and saves their time using timestamps. Timestamps would help to find and watch only the desired part of the video. One of the main goals of our approach is actual keyword extraction. Extracted keywords help finding videos with the significant video's keywords. The summarizing of the video depends on frames, emotions and speech. Firstly the video content appears in the frame and output a summarized text for the video content. Secondly, emotion and how it changes during a specific period merged with the outputted summarization of the frames. Thirdly, the audio transcribing into text occurs and output an abstractive summarization of the audio track. Finally, the fusion happens between all summarizations (audio, video, emotion) using natural language processing techniques. Techniques such as tokenization, sentence segmentation and lemmatization & stemming, and then abstractive summarization. Video summarization occurs to get a meaningful accurate description of the video. Having an accurate description helps finding the inquired content matching the description. The implemented experiment showed that on average 87% of the participants found generated text well representing the video.
Single Exposure Optical Image Watermarking Using a cGAN Network

A single exposure optical image watermarking framework based on deep learning (DL) is proposed in this paper, and original watermark image information can be reconstructed from only single-frame watermarked hologram by using an end-to-end network with high-quality.First, the single exposure watermarked hologram is acquired with our presented phase-shifted interferometry based optical image watermarking (PSOIW) frame, and then all holograms and corresponding watermark images are constructed to the train datasets for the learning of an end-to-end conditional generative adversarial network (cGAN), finally retrieved the watermark image well with the trained cGAN network using only one hologram.This DL-based method greatly reduces the recording or transmitting data burden by 1/4 compared with our presented PSOIW technique, and may provide a new way for the real-time 3D image/video security applications.The feasibility and security of the proposed method are demonstrated by the optical experiment results.
Pose recognition in sports scenes based on deep learning skeleton sequence model

Human skeleton extraction is a basic problem in the field of computer vision. With the rapid progress of science and technology, it has become a hot issue in the field of target detection such as pedestrian recognition, behavior monitoring, and pedestrian gesture recognition. In recent years, due to the development of deep neural networks, modeling of human joints in acquired images has made progress in skeleton extraction. However, most models have low modeling accuracy, poor real-time performance, and poor model availability. problem. Aiming at the above-mentioned human target detection problem, this paper uses the deep learning skeleton sequence model gesture recognition method in sports scenes to study, aiming to provide a gesture recognition method with strong noise resistance, good real-time performance and accurate model. This article uses motion video frame images to train the VGG16 network. Using the network to extract skeleton information can strengthen the posture feature expression, and use HOG for feature extraction, and use the Adam algorithm to optimize the network to extract more posture features, thereby improving the posture of the network Recognition accuracy. Then adjust the hyperparameters and network structure of the basic network according to the training results, and obtain the key poses in the sports scene through the final classifier.
Knowledge, diffusion and interest towards blockchain technology in SMEs

Blockchain technology could can lead to radical changes by improving business model and process. Employing this technology in small and medium enterprises (SMEs) still represents a novel idea. Moreover, not much is actually known about the benefits brought by technology to SMEs. This research focuses on how SMEs perceive the new technology, in doing that we analyze the degree of knowledge, diffusion, and interest of the blockchain by SMEs. We designed and addresses a questionnaire to a sample of 300 SMEs in Italy. Results show that blockchain technology is quite well known, but the level of knowledge is quite limited. Moreover, the research reveals that the rate of adoption is very low. Regarding the interest to implement blockchain in the future results are optimistic.