Reviewerly/exHarmony huggingface dataset:
Automatic Speech Recognition for Low-Resource Languages: The Thuee Systems for the IARPA Openasr20 Evaluation

The paper introduces our Automatic Speech Recognition (ASR) systems for the IARPA Open Automatic Speech Recognition Challenge (OpenASR20) as well as some post explorations with speech pre-training. We compete in the Constrained training condition for the 10 languages as team THUEE, under which the only speech data permissible for training of each language is a 10-hour corpus. We adopt the hybrid NN-HMM acoustic model and an N-gram Language Model (LM) to construct our basic ASR systems. The acoustic model is proposed as CNN-TDNNF-A, which combines Convolution Neural Network (CNN), Factored Time Delay Neural Network (TDNN-F) and self-attention mechanism. As for low-resource condition, we apply speed and volume perturbation, SpecAugment and reverberation for data enhancement as well as data clean-up to filter interference information. A series of pre-and-post-processing procedures for the evaluation set, such as Speech Activity Detection (SAD), system fusion and results filtering are carried out to obtain the final results. Furthermore, we exploit wav2vec 2.0 pre-trained model to obtain more effective speech representations for the hybrid system with a series of explorations, which brings about evident improvements.
ϵ-Approximation of Adaptive Leaning Rate Optimization Algorithms for Constrained Nonconvex Stochastic Optimization

This brief considers constrained nonconvex stochastic finite-sum and online optimization in deep neural networks. Adaptive-learning-rate optimization algorithms (ALROAs), such as Adam, AMSGrad, and their variants, have widely been used for these optimizations because they are powerful and useful in theory and practice. Here, it is shown that the ALROAs are ϵ -approximations for these optimizations. We provide the learning rates, mini-batch sizes, number of iterations, and stochastic gradient complexity with which to achieve ϵ -approximations of the algorithms.
Key Part Detection of Transmission Line Fitting based on Keypoint Regression

The identification of metal defects in transmission lines can reduce the probability of power grid operation failure, which is very important for the stable operation of power grids. The location information of the key parts of fitting is helpful for target location and defect identification, especially defects caused by incorrect or incomplete key connections of parts. However, little research has been done on the location information of key parts of fittings. First, we combine the structure of the strain clamp and the function of key components, design keypoints of the key parts of the strain clamp, and make it into a standard format data set. Then, we design a keypoint feature extraction network based on the convolution layer according to the keypoint dataset that we make for strain clamp. Finally, we use HRNet32 and other networks to perform comparative experiments to prove the effectiveness of our method. Moreover, through the visual analysis of the results, the keypoints of the fittings can be well detected in the complex background or in the case of partial occlusion.
The Effect of Covid-19 Pandemic on the Regional Universities Research Culture and the Quality of the Engineering Education

In this paper we define university research culture and suggest a metric to evaluate the level of its maturity by comparing five universities in Russia, including the regional ones. We argue that strong research culture is pivotal for talent attraction and quality education, especially in engineering. By comparing the research productivity of universities with established and with immature research cultures during COVID-19 pandemic, we demonstrate that the mature research culture has helped universities to survive such global stressful situations with comparatively fewer loses, whereas the universities with currently undeveloped or emerging research culture have experienced a dramatic impact on their publication output. We suggest a set of activities to enhance research capacity of HE organizations by engaging local full-time faculty and report the results of implementation of a related faculty development program at one of the regional universities under study in late 2019 – early 2020. The implementation results show that such an intervention has contributed to enhancing research capacity and has advanced research culture at the University even at the time of pandemic as the result of increased research productivity of experienced and moderately experienced researchers and of forming new research groups.
Neural Metric Factorization for Recommendation

All current recommendation algorithms, when modeling user–item interactions, basically use dot product. This dot product calculation is derived from matrix factorization. We argue that an inherent drawback of matrix factorization is that latent semantic vectors of users or items sometimes do not satisfy triangular inequalities, which may affect the performance of the recommendation. Recently, metric factorization was proposed to replace matrix factorization and has achieved some improvements in terms of recommendation accuracy. However, similar to matrix factorization, metric factorization still uses a simple, linear fashion. In this paper, we explore leveraging nonlinear deep neural networks to realize Euclidean distance interaction between users and items. We propose a generic Neural Metric Factorization Framework (NMetricF), which learns representations for users and items by incorporating Euclidean metric factorization into deep neural networks. Extensive experiments on six real-world datasets show that, compared to the previous recommendation algorithms based purely on rating data, NMetricF achieves the best performance.
Self-Evolving Interval Type-2 Fuzzy Neural Network Design for the Synchronization of Chaotic Systems

This study presents a self-evolving interval type-2 fuzzy neural network using function-link network and asymmetric membership function, and it is used to synchronize the chaotic systems. The self-evolving algorithm is applied to autonomously construct the network structure. The function-link network and the asymmetric membership function are applied to enhance the learning capability of the proposed network. The parameters of the proposed network are online updated using the adaptive laws which are obtained by the gradient descent method. Finally, numerical simulation results of chaotic systems synchronization are conducted to illustrate the effectiveness of the proposed control method.
Cloud application importance and challenges: A Systematic Review

&#x201C;Computing for Use&#x201D; is a reality in the form of a private, public, or mixed Cloud. The raincloud ontology, its contributions, and their importance will be examined in depth as a result of our study. This new study is based on previously conducted studies, including the research questions and methods for insertion and deletion that have been used. Investigations into ontology&#x2019;s better security may be shown by stopping the incorporation exploration.
AIMS TALK: Intelligent Call Center Support in Bangla Language with Speaker Authentication

Call support centers operate over the telephone, connect between customers and receptionists to ensure customer satisfaction by solving their problems. Due to pandemics, customer call support centers have become a popular way of communication that has been used in different domains such as e-commerce, hospitals, banks, credit card support, government offices. Moreover, humans’ limitations to serve 24 hours a day and the fluctuation of waiting time makes it more challenging to satisfy all the customers over call center. So, customer service needs to be automated to handle customers by providing a domain-based response in the native language, especially in a developing country like Bangladesh, where call support centers are increasing. Although most people use the Bangla language to communicate, little work has been done in customer care automation in the native language. Our developed architecture, “AIMS TALK” can respond to that customer’s need by recognizing users’ voices, specifying customers’ problems in the standardized Bangla language, collecting customers’ responses to the database to give feedback according to the queries. Besides, the system uses MFCC feature extraction for speaker recognition with an average accuracy of 94.38% on 42 people in real-time testing, an RNN-based model for Bangla Automatic Speech Recognition (ASR) with a word error rate (WER) of 42.15%, and sentence summarization we used Sentence similarity measurement technique having an average loss of 0.004. Lastly, we used gTTS that works as Text to Speech Synthesis for the Bangla language in WavNet architecture.
Risk-sensitive Reinforcement Learning and Robust Learning for Control

We develop new foundations for Robust Reinforcement Learning for control, by exploring analytically the relation between the KL-regularized Reinforcement Learning and the Risk-sensitive Control "exponential of integral" criteria. We establish that the maximization of the risk-sensitive exponential criterion is equivalent to maximizing the KL-regularized objective jointly over the policy and the reference policy parameters. We show that the iterative procedure for optimizing the KL-regularized objective, by substituting the reference policy at each iteration with the optimal policy parameter obtained from the previous iteration, starting from some initial value, which is at the core of a number of well-known Reinforcement Learning algorithms, is an iterative approach for optimizing the risk-sensitive control exponential of integral criterion. We provide an interpretation of this iterative optimization procedure as the use of Minorization-Maximization (MM) algorithms. We offer a probabilistic interpretation of the iterative optimization procedure using Probabilistic Graphical Models to motivate the improved performance of such risk-sensitive objectives.
Examination of Fake Reviews of Product and Approach to Filter them from Legitimate Reviews

Product review branding and marketing is important for online retailers. They aid in the development of trust and loyalty, as well as establishing the distinction between your goods and those of competitors. Fake reviews pose a significant challenge to websites and product search engines because they may disrupt customer interest. Although fabricated assessments benefit everybody, the emergence of this dubious strategy is quite intriguing in the long run. The fact that enormous amounts of time, energy, and money are being put into fake review spamming demonstrates how beneficial a decent number of reviews can be. In this manuscript, a conventional method is proposed for the elimination of Fake Reviews on ecommerce sites. The solution is to use the reviewers’ behavior features with review parameters such as the username, IP address and duplicate reviews to mark fake users.
Explainable Health State Prediction for Social IoTs through Multi-Channel Attention

The core technology of Industry 4.0 is to enable the intelligence of manufacturing. One of the important tasks is anomaly detection. Although existing anomaly detection methods have achieved high accuracy, the basis of judgments cannot provide explainability, which greatly reduces the possibility for improving the model or facilitating human-machine cooperation. Therefore, in this paper, the goal is to provide the explainability for machine fault detection for social IoTs and realize the health monitoring and prognosis of the bearings simultaneously. Specifically, vibration signals from multiple sensors are transformed into spectrograms by short-time Fourier transform. Afterward, the features of frequency-domain data are extracted by the Squeeze-and-Excitation block and self-attention mechanism to assess the degradation of whole system. As such, when the process enters the early degradation, the source of components that causes the abnormality can be identified through the attention weight distribution. Experimental results show that the proposed approach achieves high accuracy in run-to-failure tests. Moreover, the proposed approach shows a better ability to explain the predicted results than the state-of-the-art bearing detection methods.
Unbalanced Encoding in Synchronous Weight Quantization-Compression for Low-Bit Quantized Neural Network

Deep neural networks (DNNs) usually have thousands of trainable parameters to ensure high accuracy. Due to large amounts of computation and memory requirements, these networks are not suitable for real-time and resource-constrained systems. Various techniques such as network pruning, weight sharing, network quantization, and weight encoding have improved computational and memory efficiency. The synchronous weight quantization-compression (SWQC) technique applies both network quantization and weight encoding to realize weight compression in the process of network quantization. This technique generates a quantized neural network (QNN) model with a good trade-off between accuracy and compression rate by choosing the proper group size, retraining epoch number, and weight threshold. To further improve the compression rate of SWQC, a new strategy for weight encoding, unbalanced encoding, is proposed in this paper. This strategy is able to compress one or multiple quantized weights into one bit, thereby achieving a higher compression rate. Experiments are performed on a 4-bit QNN using the CIFAR10 dataset. The results show that unbalanced encoding achieves a higher compression rate for the layers with large-quantity parameters. By using mixed encoding which combines balanced and unbalanced encoding in different layers can achieve a higher compression rate than using one of them only. In the experiments with CIFAR10, unbalanced encoding gets the compression rate of over 13X in the fully connected layer. By comparison, the compression rate of SWQC with the incorporation of unbalanced encoding achieves more than 5X higher than using balanced encoding only.
Improved CNN classification accuracy with the addition of shallow cascading CNNs

A novel methodology of augmenting the design of an existing Convolutional Neural Network (CNN) is proposed to improve its accuracy over low accuracy classes, on any dataset. The proposed structure precedes the CNN and comprises shallow CNNs arranged in a novel cascading topology to minimize the inference time. The approach utilizes the confusion matrix of the input CNN on a specific dataset to identify sets of low accuracy classes that resemble each other with respect to the error distribution. The shallow networks operate in parallel to improve the accuracy of selected low accuracy classes, without increasing the inference time. Experimentation on benchmark datasets and established CNNs shows a significant increase, up to 36.8%, in the accuracy of selected classes over the input CNN, with practically no overhead on the inference time.
A detailed Analysis of Lightweight Cryptographic techniques on Internet-of-Things

The use of internet-of-things technology has risen due to the many advantages it offers. The IoT devices network collects and transmits data for processing and performs necessary actions after processing the data. The IoT devices do not have many security mechanisms due to limited functionalities, which can cause the data to be hacked while transmitting it to the server. To provide security to the data, cryptographic techniques can be used. The IoT devices cannot support traditional cryptographic algorithms as they cannot be implemented with the limited resources they possess. Therefore, lightweight cryptographic algorithms have been introduced that are designed to adhere to the restrictions of IoT devices. This paper conducts a study of various works of literature which implement lightweight cryptographic algorithms for heterogeneous IoT devices to provide security to the data being transmitted by constrained IoT devices.
INTEGRATED GEOLOGICAL INTERPRETATION FOR MODELING THE COMPLICATED RESERVOIRS; AN EXAMPLE FROM UPPER RUDEIS AND KAREEM RESERVOIRS, AMAL FIELD, SOUTHERN GULF OF SUEZ RIFT, EGYPT

This work is an attempt to convey the significant role of integrated geological interpretation techniques for constructing robust geological model in one of the most complicated and poorly seismically imaged reservoirs in the Southern Gulf of Suez rift basin.An Example has been taken from syn-rift Langhian and Early Serravallian reservoirs that are represented by Upper Rudeis and Kareem stratigraphic sequences.The bad seismic quality is the result of big thickness of Late Miocene evaporites.This hinders the construction of a reliable subsurface geological model of sub-salt reservoirs.As well, many geological and engineering challenges have been recognized particularly in these reservoirs.The 3D geological modeling techniques have been applied after the conventional geological interpretation to acquire more geological reasonability and geometric validity of the study reservoirs.The concerned section of Amal field has been subdivided into four different reservoirs; two of Upper Rudeis Member (UR2 & UR1) and two of Kareem Formation (Markha & Shagar).Only, the Upper Rudeis unit-one (UR1) records noticeable influence of syn-depositional fault movements on the thickness and facies pattern distribution.All these reservoirs exhibit different reservoirs connectivity that in turn affects the production performance behavior through the life of the field.This study could hold the key for the future field development plan and further exploration in and around of the main producing zones.
Sequences With Good Correlations Based on Circular Florentine Arrays

Sequences and their correlation properties have been extensively studied due to their broad applications. In this paper, we develop a connection between sequences and well-studied combinatorial objects, circular Florentine arrays. This connection allows us to derive two types of sequences with good correlation properties. The first type consists of sequences having optimal correlation with respect to the Sarwate bound. Our constructions are based on perfect polyphase sequences. The number of perfect sequences with optimal correlation depends on the existence of circular Florentine arrays, which improves the previous known results. The second type is about multiple ZCZ sequence sets with low inter-set cross-correlation. Each generated ZCZ sequence set is optimal with respect to the Tang-Fan-Matsufuji bound, and each sequence in each set is perfect. In addition, any two sequences from distinct ZCZ sequence sets possess optimal inter-set cross-correlation with respect to the Sarwate bound. Compared with the previous results, the number of ZCZ sequence sets with optimal inter-set cross-correlation property is improved, because of the existence of circular Florentine arrays.
Slice Image Particle Segmentation Technology Based on Multi Light Sources and Multi Angles

Rock particle segmentation is the basis of intelligent identification of slices, which can provide quantitative data for mineral composition identification, diagenesis evaluation and pore-throat extraction etc. The main difficulty of particle segmentation in a rock slice is that it contains more particles that are densely distributed, and the particles themselves have the characteristics of irregular shape, inconsistent size, rich texture and so on. In view of this difficulty, this paper proposes a semiautomatic rock slice particle segmentation method. The method uses both single polarized image and orthogonal polarized sequence images of a rock slice. Firstly, the HSV color space algorithm is used to extract the pores in the original single polarized image. Then K-means algorithm and SRM algorithm are used to extract particles from the orthogonal polarized sequence images, and the extracted particles are screened manually. Thirdly, OTSU algorithm is used to segment the selected particles combined with the original single polarized image. Finally, the graph cut algorithm is applied to segment the remaining particles. Experimental result shows that this method greatly improves the efficiency of image segmentation, and can ensure good segmentation results.
CS-Siam: Siamese-Type Network Tracking Method with Added Cluster Segmentation

In the visual target tracking, the template image often contains background information because of manual box selection, which increases the difficulty of feature extraction and the complexity of calculation when the image goes through feature extraction. The existing method has achieved satisfactory results by optimizing the internal of the Siamese network model. However, it fails to consider pre-processing the template image, which is an important process to improve the performance of the target tracking model. Thus, we use image segmentation to extract the target from the template image and then propose a method that introducing the clustering segmentation into the Siamese network to reduce the background information on the tracker. Introducing our present into SiamFC, SiamRPN, SiamRPN++, and SiamFC++ frameworks, we achieve performance improvements on both OTB2015 and VOT2018 challenging benchmarks.
Expert-augmented Data-driven Safety Level Assessment Scheme with Incremental Learning

In this paper, the data-driven safety level assessment problem for dynamic systems with only normal scenario data is studied, i.e. no abnormal scenarios data are available. An expert-augmented data-driven safety level assessment framework is proposed, which combines expert knowledge and data-driven algorithms to tackle the problem. The user-defined high-level description of the system is introduced firstly, which consists of the attribute that denotes specific subsystem is abnormal or not. In this way, expert knowledge can be represented as the attributed description prototype matrix with corresponding safety levels. Considering the data is non-stationary and usually comes in the stream, the fast incremental support vector data description (FISVDD) algorithm, which can be trained incrementally, is used to detect the attribute description based on the online measured variables. At last, the nearest neighbor search is used to determine the safety level by calculating and sorting similarities between the attribute description and prototypes. A practical case study based on JiaoLong deep-sea manned submersible demonstrates the effectiveness of the proposed scheme.
A pinball loss support vector machine based regression model

The Pin-SVM model (Huang et al., [1]) has gained popularity among researchers in recent days. Bi and Bennett [2] have developed a geometrical framework, in which they have derived the optimization problem of the SVR model from a related SVM model. In this paper, we have used the result of Bi and Bennett in the Pin-SVM model to obtain its corresponding SVR model. We have termed the resulting model with ‘Pinball loss Support Vector machine-based Regression’ (Pin-SVR) model and briefly described its different properties. Further, we have compared the prediction of the proposed Pin-SVR model with the existing standard $\epsilon$-SVR model and its L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -norm variant on several artificial and real-world benchmark datasets. Our numerical results conclude that the proposed Pin-SVR model can obtain the better generalization ability than the existing $\epsilon$-SVR model and its variant.