Reviewerly/exHarmony huggingface dataset:
Optimal Kalman-like filter for a class of nonlinear stochastic systems

This paper deals with an optimal Kalman-like filter for nonlinear discrete-time systems aided with auto and cross-correlated noises and stochastic parameter matrices involved in state and measurement equations, and random nonlinearity. The random variables are proposed by their statistical characteristics while the inquiry is focused on stochastic multivariate analysis and calculation. For the nonlinear system with the auto and cross-correlated noises and stochastic parameter matrices, an equivalent system is first reconstructed by decomposing stochastic parameter matrices and introducing uncorrelated pseudo-noises. Then a recursive filter that ensures unbiasedness and minimizes the error variance is designed for the newly transformed equivalent system. Finally, the filter is verified by applying it to some numerical simulations.
Design and Application of Intelligent Processing Technology for Animation Images Based on Deep Learning

The origins of anime can be traced all the way back to the Homo sapiens period of human civilization. Nowadays, anime is a record of life as well as a popular kind of entertainment and a source of ideal trust for many individuals. Children and individuals of all classes and ages enjoy anime. In the opinion of most people, anime is not simply a form of amusement and pleasure, but it can also express deeper meanings, transmit other cultures, and inspire individuals to pursue their aspirations. Image stylistic migration based on convolutional neural networks has developed as a central research path in recent years, and attempts on style migration have evolved as well. However, there are few studies on style migration. In this paper, we propose a deep learning-based solution to the problem of anime-style migration. Experiments on a relevant database show that our proposed method is effective and accurate and has commercial and academic significance.
Ink Recognition Using TDNN and Bi-LSTM

The proposed work utilises a combined architecture of time delay neural networks (TDNN) and multi-layered bidirectional long short-term memory (Bi-LSTM) network for the ink recognition from the handwritten text. We added a Trie beam search decoder with three smoothing algorithms such as Kneser–Ney Back off, Kneser–Ney Interpolated, and Stupid Back off to improve the performance of the model. This paper discusses the performance of the combined TDNN and Bi-LSTM network with the above-stated decoder for the ink recognition task. This paper also reports a comparison with the existing models that were implemented for this task. The analysis showed that the model with TDNN and Bi-LSTM architecture with an additional Trie beam search decoder with Kneser–Ney Interpolated smoothing algorithm using 10,000-word lexicon performed better than the model without a decoder.
Bangla sign digits recognition using depth information

Sign Language Recognition (SLR) targets on interpreting the sign language into text or speech, so as to facilitate the communication between deaf-mute people and ordinary people. The task has broad social impact, but is still very challenging due to the complexity and large variations in hand actions. Existing dataset for Sign Language Recognition (SLR) in Bangla Sign Language (BdSL) is based on RGB images. Recent research on sign language recognition has shown better recognition accuracy using depth-based features. In this paper, we present a complete dataset for Bangla sign digits from Zero (Shunno in Bangla) to Nine (Noy in Bangla) using MediaPipe, a cross-platform depth-map estimation framework. The proposed method can utilize hand skeleton joint points containing depth information in addition to x, y coordinates from RGB images only. To validate the effectiveness of our proposed approach, we have run MediaPipe on a benchmark American Sign Language (ASL) dataset. Running different classifiers in our proposed dataset we got 98.65% using Support Vector Machine (SVM). Moreover, we compared our dataset with the existing Bangla digit dataset Ishara Bochon using deep learning based approach and achieved significantly higher accuracy.
SAVE‐AS: Accelerating convergence in network traffic prediction through adaptive optimized variational mode decomposition and an integrated extreme learning machine

Summary Nonlinearity and nonstationary data affect the prediction accuracy of network traffic. A practical solution is to use an integrated modeling method based on time‐series decomposition and an extreme learning machine (ELM). The original network traffic data series are decomposed in this paper using variational mode decomposition (VMD), and then each subdata series is subjected to phase space reconstruction (PSR). Finally, ELM trains a model for predicting network traffic. We used scalable artificial bee colony (SABC) algorithms to optimize parameter selection throughout the modeling process. However, several processes, such as VMD decomposition, PSR, and ELM training, will be repeated during iterative model optimization, resulting in a significant increase in computational complexity. This article proposes an adaptive selection mechanism for decomposing time series, which named is SABC‐VMD‐ELM prediction method with an adaptive selection (SAVE‐AS) method. From gray correlation degree and approximate entropy, an adaptive selection operator is constructed that can adaptively select the number of data sets after VMD optimization decomposition. The ELM model is trained using several subdata sequences that satisfy the conditions rather than all subdata sequences to minimize computational complexity in modeling. Two publicly available data sets of Mackey–Glass and Lorenz chaotic time series were used; in addition, actual network traffic data from the MAWI Working Group's WIDE Backbone was simulated. It is demonstrated that the prediction model can reduce computational complexity and accelerate convergence speed while maintaining the model's predictive accuracy. In three data sets, training time was reduced by 25.25%, 23.87%, and 41.36%, respectively.
Financial Risk Control Model Based on Deep Neural Networks

Under the background of global economic integration, commercial banks are facing more and more complex business environment. As one of the major financial risks faced by commercial banks, liquidity risk determines and reflects the safety and profitability of bank operation. Based on joint-stock commercial banks as the research object, this paper, respectively, from the angle of the static and dynamic measurements and projections for liquidity risk and based on the current situation of four joint-stock commercial banks liquidity level study, tries to explore the change law of commercial banks liquidity risk and financial risk control of commercial bank and puts forward reasonable suggestions. In this paper, an AHP neural network model combining subjective and objective methods is proposed. This method can not only overcome the defects of the single evaluation method but also improve the data accessibility by using the qualitative data and quantitative data of AHP. In the aspect of financial risk control system, this paper tries to establish a more comprehensive and practical financial risk control model by combining the previous research of scholars, the business model process, and the experience of practical workers.
Two-mode quantum entanglement dynamics of underwater quantum communication

Quantum communication provides a powerful tool for communication with information-theoretic security and high private capacity. However, until now, almost all studies on underwater quantum communication have been based on the polarization or orbital angular momentum of the single-photon state. Because quantum entanglement is an important resource in quantum communication, it is an interesting but still open question as to whether quantum entanglement still exists after interaction with water and underwater objects. In this study, we use the air-water-metal three-layer model as an example and investigate the dynamics of the output entangled state when both modes are sent through the water. Our results show that strong entanglement exists even after interaction with shallow water, and underwater quantum communication with two-mode quantum entanglement is thus feasible in typical quantum communication scenarios.
Efficient Localization of Multitype Barcodes in High-Resolution Images

Barcode positioning technology is one of the important components of barcode technology. However, most of the current algorithms are only applicable to a single type of barcode positioning or limited to low-resolution images due to a large amount of calculation, and thereby it is still a challenge to locate multitype barcodes in high-resolution images. In response to the above problems, this paper proposes a reliable multitype barcode localization method for multibarcode localization in high-resolution images where one-dimensional (1D) barcodes, two-dimensional (2D) barcodes, or multitype barcodes are present simultaneously. The method consists of three main steps: first, extracting multiple types of barcode features through a joint edge detection algorithm; next, marking target barcode regions with a bidirectional contour labeling method; and finally, extracting barcode regions by an improved affine transformation. The experimental results show that, in terms of localization accuracy, the proposed method has a better accuracy of 97.83% than existing algorithms in low-resolution images and can locate multitype barcodes in high-resolution images with an accuracy rate of 98.04%. Besides, in terms of time cost, the proposed method effectively reduces the time cost by 50% and improves the barcode localization efficiency.
CAMBI

Banding is manifested as false contours in otherwise smooth regions in an image or a video. There are many different reasons for banding artifacts to occur, however, one of the most prominent causes is the quantization inside a video encoder. Compared to other types of artifacts common in video processing, e.g. blur, ringing, or blockiness, only a relatively small change of the original pixel values can produce an easily noticeable and visually very annoying case of banding. This property makes it very difficult for banding to be captured by generic objective quality metrics such as PSNR or VMAF [10] which brings a need for a distortion specific detector targeted directly at banding artifacts.
Intelligent Surveillance System Using Sum of Absolute Difference Algorithm

In the present world due to increase in unusual activities the security is becoming more and more important. The security in restricted areas such as borders, public places is more important and the security is more concerned for military, illegal migrants and many terrorist attacks. Monitoring those places may require huge security and it is not ancient. In order to reduce the errors made by human due to different reasons we can use automatic monitoring system. In our real time monitoring system it is used to detect motion in a live video. It also shows how many times the person crossed the threshold which shows the unusual activities. Our proposed method gives the processed video from where the motion has detected. It reduces the time and also more efficient. Here the moving object is identified using the image subtraction method. This can be done by using SAD algorithm. It provides relief to regular surveillance systems which consumes more time and less efficient.
Features Calculation of Closed Curve and its Application in Leaf Discrimination

Object discrimination is one of the applications of visual intelligence technology. The shape and size of an object can be roughly judged by its contour. View-dependent contour can be shown by a discrete closed curve. In this paper, we proposed a discrimination method for two-dimensional discrete closed curves, using both curvature features and shape features. Firstly, the curvature of each point on the discrete closed curve is calculated using the micro-central difference algorithm. Then four statistical indexes, that are curvature mean, standard deviation of curvature, curvature skewness and curvature kurtosis, are estimated. Based on the sigma criterion, the curvature sharpness is obtained. The shape features are calculated and standardized. Finally, the support vector machine is employed to classify and recognize the discrete closed curve. The experimental results show that the proposed method works well for classifying those closed curves.
Influence of Sensor Noise and Latency on Navigational Safety of Deep-Reinforcement-Learning-based Planners

Recently, mobile robots have become important tools in various industries, like logistics, healthcare, and delivery. Deep Reinforcement Learning (DRL) emerged as an end-to-end approach, which maps raw laser scan observations to robot actions and promises flexible and efficient navigation. Various works have incorporated DRL for navigation in highly dynamic environments. However, these approaches are often trained within simulation with optimal assumptions and perfect modeling of observations, which makes the simulationto-reality gap a relevant issue. In this work, we evaluate the impact of noise and delay modules on the performance of DRL-based navigation approaches. Therefore, we introduce noise and delay modules into a 2D simulation environment - arena-rosnav, which makes it feasible to train and test DRL-based approaches under realistic circumstances. Subsequently, we evaluate the approaches with the different noise modules extensively and demonstrate a strong correlation between the level of noise and the success rate. The results could be valuable to design safe DRL-based navigation approaches.
Analysis of Impact of Industry 4.0 on Africa, Eastern Europe and US: A Case Study of Cyber-Security and Sociopolitical Dynamics of Nigeria, Russia and USA

This article explores the technological innovations associated with industry 4.0 and how it has altered virtually every aspect of the human life. Even though in the process of redefining technology, the insatiable and complex needs of man can be met quite considerably, there are various challenges observed with its usage by individuals, groups, business organizations and countries. Some of the consequences highlighted in this study include cyber-threat or attacks against businesses and individuals, political figures and government. Therefore the author examines critical cases and problems which are not just socioeconomic but also sociopolitical. The qualitative method and NCSI Index were used to analyze the cases in this study. In addition, the solutions proposed in this study includes that top technological figures, institutions and companies in Nigeria, Russia and the US need to put their cyber differences aside, move beyond the firewall protection approach and critically assess how data breach can be massively reduced if not avoided in its entirety.
Research on the Millionaires’ Problem under the Malicious Model Based on the Elliptic Curve Cryptography

With the rapid development of blockchain, big data, cloud computing, and artificial intelligence, the security of multisource data collaborative computing has become increasingly prominent. Secure multiparty computing has become the core technology of privacy collaborative computing. Millionaires’ problem is the cornerstone of secure multiparty computation. Firstly, this paper proposes a 0-1 coding rule, which is used to solve the millionaires’ problem under the semihonest model. Aiming at the possible malicious behaviors of the protocol under the semihonest model, the millionaires’ problem protocol under the malicious model based on the elliptic curve cryptography is designed by using cryptographic tools such as the zero-knowledge proof and the cut-choose method. This protocol not only can effectively solve the millionaires’ problem but also can safely and effectively prevent malicious behaviors. Meanwhile, the security ordering designed by the protocol can be effectively applied to a quality evaluation in the blockchain.
A Fine-Grained Medical Data Sharing Scheme with Ciphertext Reencryption

With the fantastic development of cloud computing technology, cloud storage has been widely applied in the field of medical data sharing due to its efficiency and flexibility. Attribute-based encryption (ABE), as a promising technology, plays an important role in the fine-grained sharing of medical data. However, there are two issues: (1) an access policy specified in the encryption phase may need to be updated after a period of time, and (2) the considerable decryption overhead in ABE is too heavy for resource-limited devices. To address these issues, a fine-grained medical data sharing scheme with ciphertext reencryption is proposed. Users with decryption permission can produce reencryption keys and cloud server can reencrypt initial ciphertext. The cloud server can predecrypt ciphertext, which reduces user’s decryption overhead. Moreover, the traditional centralized server is replaced with a decentralized blockchain that performs system initialization, key management, and user revocation. We compared the proposed scheme with some existing schemes in terms of function and efficiency; the results show that the efficiency of the proposed scheme outperforms other related schemes. The security analysis shows that the proposed scheme is security and correctness.
Chaotic Ant Swarm based Feature Subset Selection with Concept Drift Detection and Classification Model for Data Streaming Applications

Recently, high dimensional data stream poses additional challenges to statistical classification process owing to the varying distribution of the training and target data over time, which is termed concept drift. The concept drift explores the considerable modifications in data tuple which has the significance of raising the contextual problems in the expected output of the methods depending upon the corresponding data tuples. Though several concept drift detection models are available in the literature, it is required to enhance the drift detection performance. In this view, this study develops a Chaotic Ant Swarm based Feature Subset Selection with Concept Drift Detection and Classification (CASFS-CDDC) technique. The major aim of the CASFS-CDDC technique is to choose an optimal subset of features prior to concept drift and classification processes. The proposed CASFS-CDDC technique involves the design of CASFS technique for the selection of feature subsets. Moreover, early drift detection (EDD) technique is applied for the detection of concept drift. Furthermore, auto encoder (AE) is used for the classification of data into appropriate classes. In order to ensure the enhanced performance of the CASFS-CDDC technique, an extensive experimental analysis is carried out and the comparative study reported the better performance over the existing techniques.
DEVELOPING STUDENTS' LANGUAGE SKILLS THROUGH INDEPENDENT WORK

The Kazakh language course conducted in higher educational institutions for russian groups has its own specific. There is a need for further development and independent improvement of the language skills acquired within the framework of the school blueprint. Achieving average-level, above-average results requires serious qualifications. Some of the optimal approaches to language acquisition have a positive impact on students. Creative adaptive learning is one of the manifestations of language literacy requisite by modern youth. It is characterized by the fact that, as a result of its consistency, purposefulness, it can have a direct impact on the quality of teaching the subject, clearly and accurately reflects the level of knowledge and skills of the student, optimizes the assessment, gives direction to determine the extant of shape of various competencies of the student and the development of his capabilities in the future. We can also motivate to perfect knowledge of the Kazakh language by systematically providing cognitive, educational, informational tasks as independent work. Such approaches are of great importance for acquisition the Kazakh language in the audience with other languages in the direction of educating students of the intelligence and mind, the culture of Kazakh thinking and speech, the formation of personal abilities.
Algorithm/architecture solutions to improve beyond uniform quantization in embedded DNN accelerators

The choice of data type has a major impact on speed, accuracy, and power consumption of deep learning accelerators. Quantizing the weights and activations of neural networks to integer based computation is an industry standard for reducing memory footprint and computation cost of inference in embedded systems. Uniform weight quantization can be used for tasks where accuracy drop can be tolerated. However, the drop in accuracy due to a uniform quantization might be non-negligible especially when performed on shallow networks, complex computer vision tasks, or with lower-bit integers. In this paper, we introduce a software and a hardware solution to improve on a baseline integer based uniform quantization so that it can be run on lower power systems and with even less bits. We also introduce a novel encoding technique on top of our software solution specific for partial sums to significantly reduce memory footprint, latency and energy consumption due to movement of partial sums. The proposed SW solution exploits non-uniform piece-wise linear quantization to improve accuracy by capturing bell shaped distribution of weights while still using INT-based computation units. The proposed partial sum encoding can be applied to the partial sums regardless of uniform or non-uniform quantization. The proposed HW solution can either combine integers to make larger integers or turn them into Floating-Point operations so that various levels can have various precisions or data types, if necessary. To do so, we studied upper limits of precision we need in our compute units to support floating point inner product operations. It turns out that we can improve upon integer IPUs to perform accurate floating-point operations without introducing large shift units or wide adder trees. Our proposed SW solution (PWLQ) achieves the state-of-the-art results on all cases and it outperforms all other methods with a large margin. The proposed partial sum encoding technique effectively compresses the partial sum of networks like Resnet-50 down to 12bits (from Int64/32) without loss in accuracy. The proposed HW architecture achieves area improvements of up to 46% in TOPS/mm2 with power efficiency improvements of up to 63% in TOPS/W when compared to state of the art mixed precision implementation.
Development of a medical expert system: disease staging by a fuzzy classifier

Abstract The article considers an approach to the processing and analysis of biomedical data from clinical examination. An automated diagnosis system is presented to be applied in liver disease diagnosis by the results of the initial examination of patients using a classifier based on fuzzy inference. The research is carried out by modeling the proposed system in the environment MATLAB FuzzyLogic. The regularities are revealed making it possible to normalize the results of laboratory tests depending on gender characteristics and to increase the percentage of correctly determined disease stages.