Reviewerly/exHarmony huggingface dataset:
Learning Adaptive Spatial Regularization and Temporal-Aware Correlation Filters for Visual Object Tracking

Recently, discriminative correlation filters (DCF) based trackers have gained much attention and obtained remarkable achievements for their high efficiency and outstanding performance. However, undesirable boundary effects occur when the DCF-based trackers suffer from challenging situations, such as occlusion, background clutters, fast motion, and so on. To address these problems, this work proposes a novel adaptive spatial regularization and temporal-aware correlation filters (ASTCF) model to deal with the boundary effects which occur in the correlation filters tracking. Firstly, our ASTCF model learns a more robust correlation filter template by introducing spatial regularization and temporal-aware components into the objective function. The adaptive spatial regularization provides a more robust appearance model to handle the large appearance changes at different times; meanwhile, the temporal-aware constraint can enhance the time continuity and consistency of this model. They make correlation filters model more discriminating, and also reduce the influence of the boundary effects during the tracking process. Secondly, the objective function can be transformed into three sub-problems with closed-form solutions and effectively solved via the alternating direction method of multipliers (ADMM). Finally, we compare our tracker with some representative methods and evaluate using three different benchmarks, including OTB2015, VOT2018 and LaSOT datasets, where the experimental results demonstrate the superiority of our tracker on most of the performance criteria compared with the existing trackers.
Imitative Reinforcement Learning Fusing Mask R-CNN Perception Algorithms

Autonomous urban driving navigation is still an open problem and has ample room for improvement in unknown complex environments. This paper proposes an end-to-end autonomous driving approach that combines Conditional Imitation Learning (CIL), Mask R-CNN with DDPG. In the first stage, data acquisition is first performed by using CARLA, a high-fidelity simulation software. Data collected by CARLA is used to train the Mask R-CNN network, which is used for object detection and segmentation. The segmented images are transformed into the backbone of CIL to perform supervised Imitation Learning (IL). DDPG means using Reinforcement Learning for further training in the second stage, which shares the learned weights from the pre-trained CIL model. The combination of the two methods is an innovative way of considering. The benefit is that it is possible to speed up training considerably and obtain super-high levels of performance beyond humans. We conduct experiments on the CARLA driving benchmark of urban driving. In the final experiments, our algorithm outperforms the original MP by 30%, CIL by 33%, and CIRL by 10% in the most difficult tasks, dynamic navigation tasks, and in new environments and new weather, demonstrating that the two-stage framework proposed in this paper shows remarkable generalization capability in unknown environments on navigation tasks.
An Interactive System Based on the IASP91 Earth Model for Earthquake Data Processing

System software for interactive human–computer data processing based on the IASP91 Earth model was designed. An interactive data processing system for visualizing earthquake data was designed and implemented via the Intel Fortran platform. The system reads and processes broadband seismic data acquired by field stations, mainly including the reading and import of raw data, pre-processing, identification of seismic phases and inter-correlation traveltimes picking. In the data processing step, shortcomings have been improved and functions have been gradually refined and enhanced, making it easier and faster to process data. It has already processed more than 1000 large seismic events received by the station from 2013 to 2018. The practical application shows that the human–computer interaction system is easy to operate, accurate, fast and flexible, and is an effective tool for processing seismic data.
Adaptive Dynamic Search for Multi-Task Learning

Multi-task learning (MTL) is a learning strategy for solving multiple tasks simultaneously while exploiting commonalities and differences between tasks for improved learning efficiency and prediction performance. Despite its potential, there remain several major challenges to be addressed. First of all, the task performance degrades when the number of tasks to solve increases or the tasks are less related. In addition, finding the prediction model for each task is typically laborious and can be suboptimal. This nature of manually designing the architecture further aggravates the problem when it comes to solving multiple tasks under different computational budgets. In this work, we propose a novel MTL approach to address these issues. The proposed method learns to search in a finely modularized base network dynamically and to discover an optimal prediction model for each instance of a task on the fly while taking the computational costs of the discovered models into account. We evaluate our learning framework on a diverse set of MTL scenarios comprising standard benchmark datasets. We achieve significant improvements in performance for all tested cases compared with existing MTL alternatives.
Securing Critical National Infrastructure Against Hacktivist

The acts of hacktivists in today's modern world might have extremely negative repercussions for the order and peace of countries. Hacktivists, by the very nature of their methodology, invariably target important government installations. This research focuses mostly on investigating the myriad of approaches that may be taken to cyber-secure important national infrastructure against the maneuvers that can be carried out by hacktivists. Inferences are drawn in this study from hacktivism episodes that occurred in the past and were documented by a variety of sources, some of which may be verified. A four-point cyber security plan for important national infrastructure has been developed as a way to provide direction for the execution of cybersecurity measures by key state facilities. This scheme was inspired by the ideas presented previously. Keywords: Critical National Infrastructure, Security, Hacktivists, Cyberspace, Hacking, Cybercrime
A Review of Various Data Security Techniques in Wireless Communication Systems

Sensitive data is constantly being transmitted particularly over wireless communication systems due to how convenient this wireless communication systems are. However, these communication systems are generally weak in terms of privacy protection and security as a whole. This is because anyone within the perimeter of a wireless network can attempt hacking into the network without physically connecting to it. This paper will focus on the two most common data security techniques in wireless communication systems – steganography and cryptography. Steganography is the practice of hiding information in another message or physical item such that its presence cannot be detected by human examination. Cryptography on the other hand is the science of encrypting information in a way that unintended recipients cannot interpret and then decrypting the said message back into plaintext. Keywords: Cryptography, Data Encryption, Data Decryption, Steganography, Wireless Communication
Automated Arabic-Arabic sign language translation system based on 3D avatar technology

&lt;p&gt;&lt;span&gt;Arabic sign language (ArSL) is the natural language of the deaf community in Arabic countries. Deaf people have a set of difficulties due to poor services available. They have problems accessing essential information or receiving an education, communicating with other communities, and engaging in activities. Thus, a machine translation system of Arabic to ArSL has been developed using avatar technologies. Firstly, a dictionary of ArSL was constructed using eSign editor Software. The constructed dictionary has three thousand signs. It can be adopted for the translation system in which written text can be transformed into sign language. The dictionary will be available as a free resource for researchers. It is complex and time-consuming, but it is an essential step in the machine translation of whole Arabic text to ArSL with 3D animations. Secondly, the translator has been developed. It performs syntactic and morphological analysis and then applies a set of rules to translate an Arabic text into ArSL text based on the structure and grammar of ArSL. The system is evaluated according to the parallel corpus that consists of 180 sentences using the metric for evaluation of translation with explicit ordering &lt;/span&gt;&lt;span lang="IN"&gt;metric for evaluation of translation with explicit ordering&lt;/span&gt;&lt;span&gt;(METEOR) our system achieves a relative score of (86%).&lt;/span&gt;&lt;/p&gt;
A new approach to hide texts into images and audio files using steganography and cryptography techniques

&lt;span&gt;These days there are many security issues facing users while they are using the internet for exchanging information among them. And, users use technology devices, such as mobiles and computers, and they connect them to the network and internet. Therefore, users always looking for a safe way to exchange information locally and globally, when they are connecting to a network. Also, these problems lead them to many further issues such as losing privacy, hacking and detecting personal information. Although, many security techniques have been used to solve these issues by creating many different software utilities; some of them worked perfectly to some extent, while some others still did not comply with the security environment. This research paper finds a new methodology to secure text information while exchanging among permitted users over the internet. This method is a combination of cryptography and steganography with audio and imaging multimedia, which works on hiding and encrypting information before sending it over a network. As result, this technique will add additional security processes to the data exchange, and it will provide a more reliable environment for the user to connect to the network. In addition, the quality of the data will not be altered or noticed during the encryption and decryption process.&lt;/span&gt;
Analysis of UMAP, the method for reducing the dimensionality of initial data in machine learning for the purpose of failure prediction in a motive power service

Aim. Feature transformation is one of the stages of machine learning application that has a significant effect on the quality of regression models. The paper aims to develop criteria for evaluating the quality of data dimensionality reduction at the stage of feature transformation and adaptation of the UMAP method to the problem of prediction of the number of days to failure in the locomotives of JSC RZD. Methods. The data transformation methods are divided into two groups, those that attempt to preserve the global data structure, and those that attempt to preserve the distances between points. The paper examines in detail the UMAP no-linear method of dimensionality reduction, whose low-dimensional data presentation is based on a transformation of a nearest neighbour graph retaining the data structure. The structure of the initial data manifold is examined using topological data analysis and simplified fuzzy set construction methods. Results. The analysis of UMAP theory conducted in the Russian language for the first time enabled a substantiated identification of the three primary parameters of the method, whose variation significantly affects the type of data obtained as the result of a transformation. In particular, that pertains to the quality of class separation over a two-dimensional space. Additionally, the characteristics of the input set of parameters were identified that affect the UMAP results. Practical results of UMAP application were demonstrated. Intermediate results included a list of nearest neighbours, a weighted graph of nearest neighbours. The fundamental result is a low-dimensional data representation (out of 44 initial measurements) over a two-dimensional space with class separation, which is confirmed both by calculations, and visually. Conclusions. It was identified that UMAP is an efficient and substantiated method of dimensionality reduction that allows – through parameter variation – transforming data in such a way as to improve the quality of data submitted to machine learning models by the criterion of “evident class separation”. The transformation is an intermediate stage of data preparation for regression model application, and class separation was performed for the purpose of eliminating the probability of gross regression errors.
SUSTAINABLE BANKING PRINCIPLES AND PERFORMANCE OF DEPOSIT MONEY BANKS IN NIGERIA: AN ECONOMETRIC ANALYSIS

The study looked at sustainable principles and the performance of DMBs Nigeria for 2000-2020. The actual objectives include assessing the indicators of sustainable principles of banking, such as the ATM, CPS, LR and CR, as well as how DMBs’ performance measured by the ROA is affected by those factors. Data were retrieved from CBN documents such as bank supervisory annual report, statistical bulletin, and the NDIC annual reports for the period 2000-2019 were used as a secondary source of data (time series data) in this study. The findings revealed that ATM and CPS have a negative yet not significant effect on the ROA of DMBs, whereas LR and CR which also revealed a negative effect on ROA were significant after all. Following the outcome of this result, it was agreed that a mixed association exists between sustainable principles of banking and performance. Hence, the recommendation that modalities be put in place to effectively and efficiently adopt sustainable principles of banking in Nigeria, so as to ensure that banks deliver better services to their clients as well boosting profits. Keywords: Sustainable Banking Principles, Bank Performance, Econometric Analysis.
Correction: Moghimi et al. Automatic Relative Radiometric Normalization of Bi-Temporal Satellite Images Using a Coarse-to-Fine Pseudo-Invariant Features Selection and Fuzzy Integral Fusion Strategies. Remote Sens. 2022, 14, 1777

There was an error in the original publication [...]
Data Security in Cloud Computing: A Research Perspective

Security plays a very important role in the life of every living being. Today, in the ‘Technology’ engulfed businesses, data security is crucial for users and vendors alike. Cloud computing as a technology is the driving force behind all the financial transactions being completed every second across the globe. In the current context, Cloud data security is crucial and is of utmost importance. Data Security in the cloud refers to the technologies, policies, services, security controls that protect data in the cloud from loss, leakage or misuse through security breaches and unauthorized access. Securing data in the cloud is essential in e-commerce because attacks on the data can result in loss of revenue for businesses. Cyber criminals use advanced tools and techniques to steal information from the cloud servers for financial gain and other unscrupulous benefits. Securing data while it is at rest or in transit is most important for businesses. Efficient Data security tools and techniques must be applied to protect the data resident in the cloud. This paper closely examines the Issues and challenges of Data Security in Cloud Computing with reference to e-commerce. Keywords: Challenges, Cloud Computing, Data Security, E-Commerce, Public Cloud.
Facial Mask Detection Using Transfer Learning with Resent 18 and MobileNetV2

Identification of individuals using face masks has become a widespread concern as a result of the coronavirus pandemic. One critical method of preventing pandemic spread is for persons to constantly wear masks in public places. To achieve the maximum possible accuracy, new approaches for face mask detection are being developed that utilize convolutional neural networks. These novel techniques attempt to construct a binary face classifier capable of categorizing pixels in an image as either faces or non faces. In this study, we created a model using Resnet18 and MobileNetv2 in order to obtain trustworthy results for picture categorization. Additionally, we constructed our model for detecting live webcam feeds utilizing Transfer Learning on these deep convolutional neural networks, as well as ImageNet weights and OpenCV.
CHARACTERISTICS OF PSYCHOLOGICAL CONSULTATION AND COACHING TECHNOLOGY

The article describes the technology of psychological consultation and coaching as a new paradigm and modern technology of a person's professional activity. The position of application of coaching based on psychological approaches to organization is defined.
Chaos–hologram: a novel technique in encryption information

Abstract In this work, hologram-chaos technique was used to encrypt information as one of the hologram applications using MATLAB program. The results showed that the information to be transmitted in complete secrecy can be easily identified using computer generated holograms through the process of computer hologram reconstruction, so this technique is not able to guarantee the confidentiality of information transmission. When coupling the hologram technology with the chaotic Lorenz system by performing several arithmetic operations of addition, multiplication and division for the matrices of Fourier transforms of the information to be sent (the Mona Lisa image) and the hologram of the Lorenz system, the results showed a complete encryption of the images to be sent and the disappearance of their features using the division process.
Haze Removal Method for Efficient Visualization of Remotely Sensed Satellite Images

Abstract: Images captured in foggy weather conditions often suffer from poor visibility, which will create a lot of impacts on the outdoor computer vision systems, such as video surveillance, intelligent transportation assistance system, remote sensing space cameras and so on. In such situations, traditional visibility restoration approaches usually cannot adequately restore images due to poor estimation of haze thickness and the persistence of color cast problems. In our work, we propose a visibility restoration approach to effectively solve inadequate haze thickness estimation and alleviate color cast problems. By doing so, a high-quality image with clear visibility and vivid color can be generated to improve the visibility of single input image (with fog or haze), as well as the image’s details. Our approach stems from two important statistical observations about haze-free images and the haze itself. First, Wavelet decomposition is applied and LUM Filter is applied on the decomposed image. Finally, we can get the dehazed output.
Real Time Character Recognition using Convolution Neural Network

Abstract: Handwritten recognition of character (HCR) is a significant element in the current world and one of the focused fields in image processing and pattern recognition research. Handwritten recognition of character refers to the process of converting hand-written character into printed/word file character that in many applications may greatly enhance the interaction of man and machine. The styles, varied sizes and orientation angles of the current characters are tough to parse with large variances. In addition, it is hard to split cursive handwritten text as the edges cannot be clearly seen. Many ways of recognizing handwritten data are available. The proposed research is based on 5*5 convolution neural network where the performance of the system has been enhanced in terms of accuracy, precision and recall the data set. The research utilized the real time photos. The processing approaches are followed by binarization, skeletonisation, dilution, resizing, segmentation and extraction. The character characteristics are sent to CNN to train the models after preprocessing. The research achieved 92% accuracy and time delay while detecting the real time Images.
Development of Electromagnetic Acoustic Transducer System for Coin Classification

In this work, a method for identifying counterfeit coins based on an electromagnetic acoustic transducer (EMAT) to detect the difference in the coin's natural acoustic frequency response is presented. In the experimental system, the acoustic oscillation induced by a pulsed magnetic field is received by a microphone and recorded by an oscilloscope. The natural acoustic frequency of the coin is resolved by the fast Fourier transform (FFT) method on the computer. It is found that the natural frequencies of the possible counterfeit coins deviate significantly from the standard ranges of 16.9 to 17.4 kHz for the authentic 50 New Taiwan Dollar (NTD) coins. The observed natural frequencies of the coin are consistent with the values predicted by analytical estimation. We also built a prototype EMAT coin classification system to detect the natural acoustic frequency by direct frequency counting using a microcontroller. The prototype system demonstrates that a counterfeit coin can be identified by its natural frequency in less than 30 ms using the EMAT method. The proposed technique can be applied to the vending machine to improve the accuracy in discriminating between authentic and counterfeit coins.
Biomedical Knowledge Discovery From Unstructured Text Corpora Using Contextual Word Embeddings

Abstract Background: Unsupervised extraction of knowledge from large, unstructured text corpora presents a challenge. Mathematical word embeddings taken from static language models such as Word2Vec have been utilized to discover "latent knowledge" within such domain-specific corpora. Here, semantic-similarity measures between representations of concepts or entities were used to predict relationships, which were later verified using domain-specific scientific techniques. Static language models have recently been surpassed at most downstream tasks by pre-trained, contextual language models like BERT. Some have postulated that contextualized embeddings potentially yield word representations superior to static ones for knowledge-discovery purposes. To address this question, two biomedically-trained BERT models (BioBERT and SciBERT) were used to encode n = 500, 1000 or 5000 sentences containing words of interest extracted from a biomedical corpus. The n representations for the words of interest were subsequently extracted and then aggregated to yield static-equivalent word representations for words belonging to vocabularies of biomedical intrinsic benchmarking tools for verbs and nouns. Using intrinsic benchmarking tasks, feasibility of using contextualized word representations for knowledge discovery tasks can be assessed: Word representations better encoding described reality are expected to demonstrate superior performance. Results: The number of contextual examples used for aggregation had little effect on performance, however embeddings aggregated from shorter sequences outperformed those from longer ones. Performance also varied according to model used, with BioBERT demonstrating superior performance to static models for verbs, and SciBERT embeddings demonstrating superior performance to static embeddings for nouns. Neither model outperformed static models for both nouns and verbs. Moreover, performance varied according to model layer from which embeddings were extracted from, and depending upon whether a word was intrinsic to a particular model's vocabulary or required subword decomposition. Conclusions: Based on these results, static-equivalent embeddings obtained from contextual models may be superior to those from static models. Moreover, as n has little effect on embedding performance, a computationally efficient method of sampling a corpus for contextual examples and leveraging BERT's architecture to obtain word embeddings suitable for knowledge discovery tasks is described.