Reviewerly/exHarmony huggingface dataset:
Research on OBE Online Teaching Mode Combined with Expression Recognition—Taking Digital Image Processing Course as an Example

Although the engineering education has been widely developed in China, the training for engineers cannot completely satisfy the requirements from the society. The output-based education (OBE) designs education process according to the results of education so that students can have a certain level of ability when they graduate. However, influenced by the COVID-19 pandemic, plenty of offline practical activities have changed to online ones. Thus, the digital image processing course is taken as an example, and the online class video is taken as the test data for students to do practical experiments. Moreover, an algorithm extracting harr-like features to detect face and recognize expression is applied. The eyes are further extracted from the detected face images to calculate the ratio of width and height, and then the students’ studying state can be determined. The experimental results demonstrate that the algorithm can help teachers understand the students’ state and improve the teaching efficiency. Moreover, the OBE online teaching mode can improve the students’ practical ability.
Speech Generation by Generative Adversarial Network

This paper takes a review of some recent state-of-the-art works in the field of speech generation through adopting adaptive Generative Adversarial Network (GAN). It firstly introduces what is GAN model and takes an overview of fields of applications. It then presents the theoretical foundations of this model, advantages, and disadvantages. After this, it reviews research works of Voice Conversion (VC) using GAN in the type of parallel framework and non-parallel framework. In addition, it provides readers with a more specific comparison between many of these works, in particular non-parallel ones. Finally, we propose future directions: research work might focus on a non-parallel framework and might especially focus on achieving sequence-to-sequence voice translations.
Contactless Attendance System

Attendance is a mandatory requirement of every institution. Managing and preserving the Attendance register every day is a troublesome and time-consuming task. There are many automated methods for the same like biometrics, RFID, Voice Recognition, etc. Nowadays, Machine Learning has been highly used for computer vision and its applications. So, it uses the concept of Machine Learning and deep learning in Face—Recognition for automatic attendance systems. In this proposed system it uses face Detection Algorithms that can correctly recognize up to 97% to provide the computer systems the ability to find and recognize human faces fast and precisely in images or videos so that the methods can be used in recording attendance.
Communication System Between People with Hearing Impairments and Their Patients

At the Technical University of Manabi, there is a group of students with hearing disabilities, among them are students who are studying clinical laboratory, who need to interact with patients and be able to communicate correctly at the time of carrying out their practices. The present research shows a system in which various interfaces are evaluated to achieve communication between people with hearing disabilities and their patients. To develop the system, the Extreme Programming methodology was applied, using the Android system as the base platform using the Android Studio IDE, under the Java and XML language, Text to Speech extension as support for the reproduction and conversion of text into audio, and SQLite as a data store for phrases, words, and images. The application makes it easier for hearing impaired students to instruct their patients on the clinical examination they carry out. Tests were carried out on the system by calculating the precision with different interfaces, and it was obtained that the one with the lowest margin of error is the interface where images and keywords are applied, this being the one used in the construction of the proposed system.
Identification and Mapping of Hydrocarbon Microseepage by Remote Sensors in the Majaguillar–Motembo Region, Cuba

The remote sensors allow the analysis of the terrain in order to identify indications of possible hydrocarbon microseepage in soils and sediments. Microseepage are invisibly hydrocarbon escapes that are manifested on the surface through changes in the reflectance, stress on vegetation, abnormal concentrations of kaolinite, iron oxides and carbonate alterations. The objective of the work is to identify areas of possible hydrocarbon microseepage from the study of the last four indirect indices through the analysis of optical images. The digital processing of multispectral images, Aster, Landsat 7 and 8 consisted of red green blue (RGB) combinations, band ratios and integration and analysis of the information in the geographic information system. Spatial–temporal studies of normalized difference vegetation index (NDVI), analysis of thermal reflectivity images of the surface related to the stress of the vegetation and studies of mineralogical anomalies were carried out at a scale of 1:50,000. Areas with microseepage near the Motembo oilfield and the zone of Menéndez in Villa Clara province were interpreted, being corroborated by data from surface geochemical surveys with the presence of gas in soils. Another area with microseepage was identified in the Majaguillar area, with similar patterns to the previous ones, which was supported by the interpretation of a complex of non-seismic and non-conventional methods. In addition to these, other areas were interpreted with a lower degree of confidence but with characteristics very similar to those already established.
Intelligent Automated Negotiation System in Business to Consumer E-Commerce

E-Commerce is one of the world's most fast-paced industries where the significant aspect of these industries is that they are lacking Customer-Retailer Interaction. Due to the conventional human psychology of bargaining, a product with a lower price is still popular, and some of the products in this sector lack this kind of bargaining, which would be a cause for some of the products. With the advancement of machine learning, automated and Intelligent Agent negotiating system has become a prominent tool in E-Commerce. This paper presents a negotiation technique for establishing a mutually acceptable agreement between the negotiation system which represents supplier and customers, built using Minimum Profit Algorithm designed as per seller requirements and trained on UCI machine learning repository's online retailer dataset using XG Boost regressor for intelligence. This system outperforms the traditional way of negotiation and the model was able to achieve an accuracy of 91.53 percent.
Digital Educational Environment for Business: Risks and Trends of Sustainable Development

The article is devoted to the study and consideration of the features, the main directions of the formation of a digital educational environment for business in a period when the risks and unfavorable conditions are so great that they must be leveled by the presence of effective methods, techniques and technologies. On the example of the new mechanics of using the "case-tech" method proposed by the authors, the urgency of creating educational trends, an effective environment for interaction for subjects of the learning process, taking into account the goals of sustainable development, is substantiated. But, the basis of the method is universal. The main advantage is novelty and formalization. However, a person as an expert and mentor is the main role model in the process both at the start of creating a business and during its development. The important takeaway from this article is that technology does not have to be for technology's sake. But, at the same time, it is necessary to weigh the risks, take into account the goals of sustainable development in the formation of the digital educational environment as an ecosystem.
C++ Components and C++ Clients

In this chapter, we introduce the C++ codebase. This consists of a small set of statistical functions and classes. The intention is not to provide a fully fledged statistical library, but rather to provide some limited but useful functionality that we want to expose to clients. The functionality is only of interest insofar as it illustrates how we might expose it later to clients.
Downsampling Attack on Automatic Speaker Authentication System

Recent years have observed an exponential growth in the popularity of audio-based authentication systems. The benefit of a voice-based authentication system is that the person need not be physically present. Voice biometric system provides effective authentication in various domains like remote access control, authentication in mobile applications, customer care centers for call attests. Most of the existing authentication systems that recognize speakers formulate deep learning models for better classification. At the same time, research studies show that deep learning models are highly vulnerable to adversarial inputs. A breach in security on authentication systems are not generally acceptable. This paper exposes the vulnerabilities of audio-based authentication systems. Here, we propose a novel downsampling attack to the speaker recognition system. This attack can effectively trick the speaker recognition framework by causing inaccurate predictions. The proposed threat model achieved remarkable attack effectiveness of 75%. This system employs a custom human voice dataset recorded in real-time conditions to achieve real-time effectiveness during classification. We compare the attack accuracy of the proposed attack against the adversarial audios generated using the CleverHans toolbox. The proposed attack being a black box attack, is transferable to other deep learning systems also.
Next-item Recommendation with Bidirectional Encoder Representations from Transformer and Matrix Factorization

The next-item recommendation is a task based on sequential nature of users' behaviors. Existing approaches employ sequential neural networks with users' historical interactions for predicting next-item. However, these methods have the limitations including: (a) traditional unidirectional architectures overemp-hasize a rigidly order between items among sequence which is not always necessarily; (b) they often take users' short-term preferences from users' transient interests into consideration while neglect users' long-term intentions. In fact, consumers' purchasing intensions are not only affected by their recent preferences, but also by their long-term preferences for certain items. To this end, we propose a sequential recommendation model for next-item called BERTMF, which takes both users' recent intentions and long-term preferences into account for next-item recommendation. For users' current intentions, we employ bidirectional encoder representations from transformer (BERT) to model users' behavior sequences. To address users' diverse behavioral intentions tendency, attention mechanism is embedded into users' short-term intentions representations. In this work, we propose a generalized matrix factorization (GMF) method to model users' long-term interests and target it to the attention mechanism. As users' current intentions are changing and relative to users and target item, we introduce a fusion layer to better model both users' sequential behaviors and long-term intention representations. In the experiments on public datasets, our approach outperforms various origin next-item recommendation systems.
Explicit Attention Network for Face Alignment

Existing neural network attention modules are learned in an implicit way, thus losing the clarity and interpretability of its function. In this paper, we propose a novel Explicit Attention network for Face Alignment (EAFA), which applies explicit supervision on attention feature maps. Specifically, facial landmark heatmaps are employed as a simple and direct choice for supervising spatial attention maps. A multi-scale fusion method is further introduced to fully integrate the explicit attention maps with backbone network features, which enhances accuracy by a large margin. To promote network optimization, we present a dynamic loss balancing strategy that allows the network to automatically balance the constraints on attention maps and final prediction. Experiments on several benchmark datasets validate the effectiveness of our method.
Seal Imprint Verification Using SVM Classifier and Unmatched Key Point Features

Seal imprint verification is widely used in Asian countries for authenticating the identity of a person or an organization, particularly organization. Recent studies show good performance using the edge difference images. However, context information is lost in the edge difference images. Other work uses matched SIFT key points in an SVM classifier. In this study, we propose to use an LBP feature at the unmatched SIFT key points to exploit the context information and the abundant information in the left and right SIFT difference images. An SVM classifier is used. Experiment results show good improvements in accuracy, particularly on unseen seal images.
An Autoencoder Based Approach to Enable High Fidelity Video Conferencing over Low Bandwidth Networks

Data lagging and distortion has been the issue for the majority of us since the usage of online video conferencing platforms has become a routine of our daily life. In this paper, we attempted to design a solution for this by specifically for the image part of the videos, by building a convolution neural network based autoencoder, which will compress the images being sent from one end to another in a batch of 5 frames, and stretch it back to its original size on the receiver end. We calculated the accuracy and loss obtained for the same for comparison purposes.
An Analysis on the Variants of the RSA Cryptography

Recent developments suggest RSA suffers from integer factorization issues because it depends on two prime numbers. In this paper, we analyze the issues of integer factorization and suggest possible future solutions for RSA for conventional computers. Recent developments in Quantum Computing make integer factorization easier than ever. Therefore, RSA cryptography will obsolete soon after the launching of the complete quantum computer. Still, quantum computers are designed especially for particular purposes, but not for general purposes. Therefore, we analyze the enhancement mechanism of RSA till the arrival of general-purpose quantum computers. We analyze the linear RSA, multiple RSA, recursive RSA, and multiple recursive RSA in this paper. We describe these variants of RSA mathematically and analyze the feasibility of these RSA variants for the future. However, we found that reRSA is the most suitable variant of RSA.
Generating Factoid Questions with Question Type Enhanced Representation and Attention-based Copy Mechanism

Question generation over knowledge bases is an important research topic. How to deal with rare and low-frequency words in traditional generation models is a key challenge for question generation. Although the copy mechanism provides significant performance improvements, the original copy mechanism weakens the focus on aspect generation in the overall representations. In this article, we present a novel method to improve question generation with a question type enhanced representation and attention-based copy mechanism. The proposed method exploits the advantages of the generate mode in the copy mechanism and replaces objects in the factual triples with question types, which attempts to improve the output quality in the generate mode and effectively generate questions with proper interrogative words. We evaluate the proposed method on two standard benchmark datasets. The experimental results demonstrate that our proposed method can produce higher-quality questions than these of the Encoder-Decoder-based and CopyNet-based methods.
Thermal biometric face recognition (TBFR): A noncontact face biometry

Face of an individual is an important biometric trait that can be used to perform real-time computer-based biometric authorization of an individual. In order to create such a system, the data of human faces are taken as input in various domains: visual, thermal, sketch, or fused images are available or can be generated. Each of these domains has its own pros and cons. As in the case of visual images, the system suffers mainly from the problems associated with varied light intensity, poses, accessories, aging, etc. On the other hand, the thermal domain’s insensitivity toward light gives it an upper hand toward these issues. Because of such advantages, it has been attracting not only the computer scientists or researchers but also the psychologists and neuroscientists into this area of research and development. In this work, a novel face recognition algorithm for thermal infrared images has been proposed. It is named as the thermal biometric face recognition (TBFR) system for its adaptability in noncontact face biometry technology. Thermal face recognition requires prior aligned faces based on canonical coordinates with the help of fiducial landmarks, which is a very stiff step. Low resolution, low contrast, and presence of few textual information in thermal images have manifested a critical hurdle to detect the fiducial landmarks required for image alignment. To overcome this problem, 73 biometric landmark points have been manually annotated over NVIE dataset images. These landmark information are then used to train a deep-learning-based landmark detection architecture for automatic face and landmark prediction. The UCH Thermal Temporal Face (UCH-TTF) and the PUCV Thermal Temporal Face (PUCV-TTF) databases are chosen as probes, and the NVIE database is taken as gallery image corpus. Next, the annotated facial image is cut into several quadrangular blocks, and feature vectors are extracted from each quadrilateral cutting using a deep convolution neural network. Finally, subspace geometry and several clustering algorithms are incorporated to distinguish these feature vectors according to their biometry. Experimental results have proven that the performance and accuracy of TBFR are found to be better than the state-of-the-art methods.
Securing images using robust chaos based masking and scrambling in the fractional Fourier domain

In this paper, we present a robust and secure image encryption scheme that comprises of two major steps of masking of image pixels using piecewise nonlinear chaotic map and scrambling using the chaotic standard map in the fractional Fourier transform domain. The proposed scheme possesses features of both optical signal processing (fast and parallelism) as well as excellent nonlinear confusion and diffusion (due to the use of robust chaos) as desired in an ideal encryption system. The initial conditions and parameters of chaotic maps and the orders of fractional Fourier transform together constitute the secret key. The performance analysis results show that the proposed image encryption scheme is robust and usable in the real-time image and video transmissions.
Research on hierarchical Linkage threat response Model of Power Information Network

Nowadays,a lot of work has been done on network security protection. Only by fully identifying security risks can effective security measures be taken. However, a large number of continuous operation of security protection equipment produces a huge amount of alarm, which makes it difficult for security administrators to understand the security threat status of the system in the face of a large number of alarm information, and can not take appropriate response measures in a timely manner. can not integrate multi-dimensional security information, linkage of multi-type security equipment for threat defense means and measures. Therefore, by studying the threat response mechanism, this paper establishes a hierarchical linkage defense threat response model, and puts forward an intelligent decision-making method of linkage strategy on the basis of the threat response model to optimize the security policy.
Blind obstacle avoidance system based on improved YOLOv3

In order to facilitate outdoor travel for blind people, this paper proposes a blind person's obstacle avoidance system based on the improved YOLOv3 algorithm. The system design is based on C/S architecture, the user device consists of STM32F103, serial module, camera module, 4G module, etc.; the server software system mainly includes two parts: data sending and receiving and target detection. First of all, the pictures taken by the camera module are stored in the data buffer, and the 4G module is used to transmit the picture data to the server, after the server receives the picture data, it will be reduced to pictures, and then the trained F-YOLOv3 model is used to detect the pictures, and if an obstacle is detected, the result will be returned to STM32F103, and STM32F103 will output this result to the user by voice, so as to achieve the purpose of obstacle avoidance. The STM32F103 will output this result to the user, thus achieving the purpose of obstacle avoidance. In this paper, we propose an improved F-YOLOv3 based on YOLOv3, which increases the original 3 feature scales in YOLOv3 to 4, thus reducing the miss detection rate of small target objects; replaces some convolutional layers in Darknet53 with improved deep separable convolutional blocks. The depth-separable convolutional block in Darknet53 is replaced with an improved depth-separable convolutional block, which speeds up the detection speed.
Toward a Smart Approach of Migration from Relational Database System to NoSQL System: Analyzing and Modeling

In recent years, data has taken a strategic and decisive position in the existence and evolution of different organizations, and in this perspective several institutions have used to store and manage its data from several sources. At the time, the relational model and systems reigned in the management and exploitation of this data thanks to their strict mathematical principles, efficiency and profitability. The web2.0 revolution has caused an exponential increase in the amounts of data received and coming from many sources and in many forms by building Big Data, and as long as the data is more important, then the related data processing systems, will be more reliable. But relational systems have major limitations in managing and exploiting Big Data, which has given rise to the emergence of NoSQL systems to overcome these limitations. This situation pushes institutions to adopt the new system, without losing their data accumulated for years, and builds their present and their future, expressing the need for the migration of their databases from the relational system to another NoSQL. With this in mind, several studies and approaches have been created to meet this need, but their effectiveness and closely depend on its analytical logic and the methodology applied. In this paper, we will propose a methodology and analysis method to identify the elements of relational databases, that we need to migrate to the NoSQL system, as well as the relational and NoSQL systems models and meta-models, which will be used in a proposed approach for migration, respecting sequence based on layers and parallelism.