Reviewerly/exHarmony huggingface dataset:
Combining Attention Mechanism and Dual-Stream 3D Convolutional Neural Network for Micro-expression Recognition

Society values micro-expressions, which are a type of facial expression that shows the true feelings that people try to hide, because they might provide potentially important information in crime detection, lie detection, and other areas. Micro-expressions differ from conventional facial expressions in that they are involuntary, have a short duration, and have a small amplitude. As a result, it is difficult to distinguish micro-expressions artificially, the correctness is impossible to guarantee, and it is dependent on professional knowledge. This research proposes a micro-expression recognition method that combines an attention mechanism with a dual-stream 3D convolutional neural network to solve this challenge. The optical flow method is used to model the action information of micro-expression videos to highlight the micro-expression spatial motion features. First, the micro-expression dataset is expanded by adding 3D BN layer batch normalization to perform data normalization, and then the optical flow method is used to model the action information of micro-expression videos to highlight the micro-expression spatial motion features. Second, to compensate for the loss of facial information caused by the optical flow method, the face feature map and optical flow feature map are passed into the network model as input for deep spatio-temporal feature extraction. Third, the attention module combines 3D temporal and 3D spatial attention to help the model pay greater attention to the time periods and places where micro-expressions occur during feature extraction. In this paper, simulation experiments were conducted on the open dataset CASME II, and an accuracy of 68.57% was achieved, which is better than the existing methods.
The Philos Trust Algorithm: Preventing Exploitation of Distributed Trust

The Philos Marketplace blockchain system is a proposed hierarchical blockchain architecture which allows a large number of individual blockchains to operate in parallel. These parallel chains achieve consensus among one another on a limited set of core operations, while allowing each on-chain application to manage its own data independently of others. This architecture addresses the scalability issues of traditional linear blockchains, but requires novel consensus mechanisms. A central feature of the Philos consensus mechanism is its trust algorithm, which assigns each network node a numerical trust value (or score) indicating the quality of recent past performance. This trust value is then used to determine a node's voting weight at the higher levels of consensus. In this paper, we formally define the Philos trust algorithm, and provide several illustrations of its operation, both theoretically and empirically. We also ask whether a misbehaving node can strategically exploit the algorithm for its personal gain, and show that this type of exploitation can be universally prevented simply by enforcing a mild limit on the number of participants in each of the parallel chains.
DevLeChain - An Open Blockchain Development Platform for Decentralized Applications

The development of smart contract has extended the usage of blockchain technology from a simple ledger database for decentralized cash transaction system into a platform that provides transaction intelligence, and further into a multifunctional state-sharing running platform for designing dependable decentralized applications. However, most current fast-booming blockchain platforms keep bringing new fashion features, which will bring the cost of breaking changes between different releases of the platform. This can be a barrier for developers especially beginners to start their development. To resolve such issue, in this work, we introduce a smart contract development platform called DevLeChain, which comes with unified access method, easy-to-understand development workflow, and constant design philosophy. DevLeChain can provide an easy toolset to customize and control the underlying blockchain's behavior, whereby developers can easily start developing blockchain-based systems. Also, the provided smart contract automation testing tools allow developers to quickly realize their code.
Secure Cloud-based E-Health System using Advanced Encryption Standard

As a result of cloud-based healthcare computing, healthcare has changed dramatically. The scalability of the desired service and the flexibility to scale up or down data storage, as well as Artificial Intelligence (AI) and machine learning collaboration, are the main advantages of cloud computing in healthcare. The existing system looked at a variety of research papers to see how intelligent techniques may be employed in health systems, with a particular emphasis on security and privacy issues in modern technologies Despite the numerous advantages of cloud computing for healthcare, Management, technological, security, and legal issues must all be evaluated and addressed. The Advanced Encryption Standard (AES) is one of the most widely used encryption algorithms in cloud computing (AES). While data deduplication is more effective in protecting any type of database from data influence. This study's current system is based on Health Care Systems. A huge number of data bases may be saved and retrieved on a daily basis with this technology. As a result, the issue emerges in the privacy and storage modules. Because the structure of data in the healthcare system has been substantially stored in recent times. In this study, the proposed approach solves the issues of privacy and storage in health-care systems. The easiest solution to avoid privacy issues is to use the Advanced Encryption Standard with data deduplication.
A Raspberry Pi based CNN model for Indian Currency detection for Visually Impaired People

As of today, at least 2.2billion people suffer from one or another visual impairment. India has around 40 million people, who are either blind or visually impaired. They face a lot of challenges regarding the monetary transaction which they require for their daily survival. This research study has proposed an Arduino-based currency detection system to assist them in resolving their issues. The currency note is captured through a camera which is then given to a Machine Learning model. The model is based on convolution neural network which helps in image processing and classification. Various filters like convolution 2D, ReLU, max pooling, flatten, and dense are used for feature extraction of the input image and finally, classification of the input image is done. Lastly, the results are delivered through a speaker.
Heatmap Visualization and Badminton Player Detection using Convolutional Neural Network

Badminton coaches and analysts are more interested in how well athletes do in games by watching video matches. However, they still keep watching the whole video by hand, which is inconvenient and might cause them missing important information in the video. Most studies for sports video analysis have been done on soccer and volleyball, but badminton has not been fully focused on. Based on this observation, in this work we aim to build an automated system that can track the position of a player from an input badminton broadcast video, and visualize its position statistics on a heatmap. Convolutional neural network is used to track players and their position is projected on 2D court map using homography. In this paper we validate our approach using videos collected from the Badminton World Federation (BWF) channel on YouTube.
Facial Recognition System using Decision Tree Algorithm

Face recognition technology is widely employed in a variety of applications, including public security, criminal identification, multimedia data management, and so on. Because of its importance for practical applications and theoretical issues, the facial recognition system has received a lot of attention. Furthermore, numerous strategies have been offered, each of which has shown to be a significant benefit in the field of facial and pattern recognition systems. Face recognition still faces substantial hurdles in unrestricted situations, despite these advancements. Deep learning techniques for facial recognition are presented in this paper for accurate detection and identification of facial images. The primary goal of facial recognition is to recognize and validate facial features. The database consists of 500 color images of people that have been pre-processed and features extracted using Linear Discriminant Analysis. These features are split into 70 percent for training and 30 percent for testing of decision tree classifiers for the computation of face recognition system performance.
Design of Secured Multifactor Fingerprint Authentication System for Bank Employee

Web banking and other financial administrations nowadays have turned into essential applications in the web, and these administrations are being offered by every bank around the globe. With the assistance of this administration, clients can make any exchange without going to the bank. Thus, for these web-based administrations there ought to be exceptionally close security and checking, for keeping away from the tricks and cheats. Therefore, there ought to be a framework for every client in the specific bank, and this venture gives this component to the banks so that banks can make their service better. In this exploration, a login and enlistment page for the banking framework is designed so that every client can log in according to the multi verification framework. There are various stages for login and each progression is significant. Stage 1 is the enrollment page in which bank can enlist the new client with the assistance of the fundamental data given by the client. Stage 2 login page permits the client to log in to the bank site, and stage 3 is the OTP confirmation where an OTP is sent to the given mobile number, and when the OTP is entered, the financial administration page is reached. Stage 4 is the unique identification mark check where the client needs to place his finger on the sensor and if the fingerprint matches, the client is checked and the login process is completed. With the assistance of this multi-confirmation process, the banking can be made 100% secure.
Transfer Learning for Classification of Histopathology Images of Invasive Ductal Carcinoma in Breast

Breast cancer is a leading cause of death in women all over the world. It is critical to diagnose it early in order to begin treatment. For the past decade, researchers have used machine learning algorithms to analyze medical images. The Deep learning concepts helped a lot in the medical field to detect the pattern, classification, etc. It aids in early cancer detection, helping us to save lives. In this paper, we talked about Invasive Ductal Carcinoma(IDC). With the improvement in the field of the neural network, this research work explores the use of the convolutional neural networks to detect IDC(+ve or -ve). The dataset used is from Kaggle and contains 277,524 total images of size 50x50. 198,738 images are non-cancerous and 78,786 images are cancerous. We proposed a transfer learning method for our classification of IDC negative(non-cancerous) and IDC positive (cancerous). Versions of Resnet, Efficientnet, and Densenet were used in this research. Resnet101 model is the one with the highest accuracy of 93.14% and an AUC of 0.9494 outperformed the other models we used.
Analyzing Sensor-based Technologies for Obstacle Avoidance Robot

In recent years, the use of advanced robotic systems has increased significantly, making machinery quite effective and productive. A robot is an advanced machine that can perform a task autonomously or under supervision. This paper analyzes various sensors that can be used for collision avoidance and introduces a robot with built-in intelligence that can direct itself and avoid obstacles. This robot is constructed using an Arduino Microcontroller, a Motor shield, a Sensor, and a DC (Direct Current) Motor. To carry out the required operation, the ATmega328 microcontroller is used. The Arduino UNO is used as the controller for the robot. It receives commands from a computer that is connected via a USB cable. The commands are sent in a text format via serial communication. The Arduino UNO then interpret these commands and send signals to the other components of the robot to execute them accordingly. The Sensor and the DC motor are used to scan for obstacles. The servo motor is used to change the direction of the robot when it detects an obstacle. In conclusion, the findings of this paper will provide us with a completely automated robot, that can navigate effectively in unfamiliar environments without colliding.
Blockchain based Reliable Electronic Voting Technology

In this digital era, developing a secure as well as democratically responsible vote casting system is becoming an increasingly challenging task. This research study attempts to integrate the blockchain technology to develop a secured and distributed electronic voting system. The primary objective of this research study is to develop a decentralized voting system, which can easily support an open, rational, and easily verifiable democratic plan. The obtain results prove that the proposed smart and reliable electronic voting machine is beneficial for the current and future democratic framework by implementing an essential E-vote casting features along with a provision to allow the citizens to change/update their vote within the given time slot.
Credit Card Fraud Detection based on Ensemble Machine Learning Classifiers

Credit card is considered as one of the most popular paying methods for online and regular purchases, due to the advancement in communication and electronic commerce systems. Thus, the fraud associated with these transactions increased significantly. The great utilization of electronic payment is highly affected by this fraudulent transactions, which requires urgent detection to solve this issue. Therefore, effective and efficient approaches to detect fraud in credit card transactions are needed. To catch the fraudulent transaction, a good fitting model is needed, hence researchers recommends the use of various Machine Learning (ML) techniques, because of its beneficial characteristics. The main aim of the research work is to implement an ensemble based ML techniques for Credit Card Fraud Detection (CCFD). The strength of our model is a combination of the forces of the three subsystems; Recursive Feature Elimination (RFE), CCFD's using ensemble classifiers, and Synthetic Minority Oversampling (S MOTE) to deal with the problem of unbalanced data to identify the most effective prediction features. The proposed model run typical tests on two real databases of public credit card transactions, including fraudulent and official ones. Based on the comparison of other ML methods, the extra tree classifier has performed better and achieved high efficiencies such as 96% of accuracy and 57.95% of F1-measure.
Learning Robotic Skills through Reinforcement Learning

The application of modern robots has increased dramatically in recent years making systems more productive and efficient. Some of the traditional control approaches used for teaching movement to robots are PID (Proportional Integral Derivative) and LQR (Linear Quadratic Regulator) feedback controllers. These controllers have proven to help keep a system in a stable condition. However, as the model dynamics become more complex and sensitive, these techniques become more difficult to implement. Thus, traditional control techniques for adaptive robotic locomotion with multiple degrees of freedom render the task of maintaining a stable state impossible. This paper addresses the problem of traditional control approaches and proposes reinforcement learning for handling complex states as RL (Reinforcement Learning) is a robust framework and can tackle these problems by testing sample states in a simulated environment and will optimize the robot in each state to achieve for the maximum reward. To train the proposed robot in a realistic environment, this study applies the exploration and exploitation approach, as well as algorithms like curriculum learning, shape reward, input and reward normalization, etc. The findings of this study will generate novel ideas for locomotive robotic motion control and will encourage the use of artificial intelligence in robotic and other automated machines.
Wearable Sensor based Authentications using Red-Tacton

Biometric identification using smart wearable devices is becoming more popular. They will, however, continue to provide authentication services to the fitness and medical industries. If someone claims to be who they say they are, biometric authentication can be used to verify their identification. The physical or behavioural attributes of an individual are compared to a database using biometric authentication technology to ensure their legitimacy. If the two sets of biometric data match, the user is confirmed to be real. Access to computers, buildings, and other electronic equipment is commonly restricted via biometric authentication. The fingerprints or retinal scans of a person are used in biometric identification. Smart sensor-based wearable authentications have been evaluated using Red-tacton in this work. The sensors that measure heartbeat, temperature, and breathing events are sent to the PiC microcontroller. The RS232 level converter is used to convert TTL.
From Zero to One Hundred

Changing network landscapes and rising security threats have imparted a sense of urgency for new approaches to security. Zero trust has been proposed as a solution to these problems, but some regard it as a marketing tool to sell existing best practice while others praise it as a new cybersecurity standard. This article discusses the history and development of zero trust and why the changing threat landscape has led to a new discourse in cybersecurity. Drivers, barriers, and business implications of zero trust provide a backdrop for a brief overview of key logical components of a zero trust architecture and implementation challenges.
Chaotic time-delay signature suppression in lasers using phase-controlled dual optical feedback

We experimentally study a semiconductor laser subject to two optical feedbacks in a free space setup. We show that control over the feedback phase is essential to adjust the time delay signature in the chaotic output intensity while affecting also the chaotic bandwidth. By optimizing the feedback phase, the time delay signature can be reduced by a factor of more than 2 while maintaining a large chaotic bandwidth.
Measuring Impact of Dependency Injection on Software Maintainability

Dependency injection (DI) is generally known to improve maintainability by keeping application classes separate from the library. Particularly within the Java environment, there are many applications using the principles of DI with the aim to improve maintainability. There exists some work that provides an inference on the impact of DI on maintainability, but no conclusive evidence is provided. The fact that there are no publicly available tools for quantifying DI makes such evidence more difficult to be produced. In this paper, we propose two novel metrics, dependency injection-weighted afferent couplings (DCE) and dependency injection-weighted coupling between objects (DCBO), to measure the proportion of DI in a project based on weighted couplings. We describe how DCBO can serve as a more meaningful metric in assessing maintainability when DI is also considered. The metric is implemented in the CKJM-Analyzer, an extension of the CKJM tool to perform static analysis on DI detection. We discuss the algorithmic approach behind the static analysis and prove the soundness of the tool using a set of open-source Java projects.
Development of Youth Communities in the University Ecosystem: Resources, Projects, and Risks

The article explores the development of youth communities in the ecosystem of the Orenburg State University. The modern university is designed to create new conditions for the development of youth communities. The study of students’ creative challenges demonstrates a wide range of aspirations and needs, their uncertainty and spontaneity. The answer to creative challenges is timely competent navigation and organizational and pedagogical support of young people for integration into socially positive communities. A regional university can become a center, space and mobilizer for the development of youth communities. Youth communities are dynamically changing, their reality goes beyond educational and scientific activities. In this regard, the traditional scientist-oriented model of the university is being transformed into an ecosystem model, which equally reveals the areas of training, development and education. Modern education at the empirical level masters the ecosystem approach. The article aims at revealing resources, projects, and risks of youth communities development in the conditions of the formation of a regional university ecosystem. The subject of study is the structure, axiological, organizational and procedural foundations of the university ecosystem. The resources, methods and risks of developing modern youth communities also represent the perspective of the study of the humanities. Ecosystem resources of the Orenburg State University are presented in many aspects: axiology and pedagogical technologies, projects of the Priority 2030 program, integration with the Orenburg University (Educational) District Association, international educational cooperation with universities in Kazakhstan. The article identifies external and internal risks specific to the development of youth communities in the logic of “potential applicants – university ecosystem – potential employers” based on the study of accelerator programs, streams, online surveys.
URL Classification Using Convolutional Neural Network for a New Large Dataset

In today’s world, methods for real-time web page classification are in need due to the tremendous increase in the number of web pages and Internet usage of the people . To address these problems, in the literature, URL-based methods have been proposed which have advantages in classification speed and computational effectiveness over content-based approaches. This work proposes a CNN-based method using URLs only as input. We extract word-level tokens from the URLs alone, feed them into a word embedding layer and then hyper-tunned CNN layers. Our experiments demonstrate that this method can archive an F1-score of 0.9759 and outperforms many existing methods for a new large dataset.
Pre-Occupancy evaluation of buildings in VR: development of the prototype and user studies

The overarching goal of this project is to enable pre-occupancy evaluation in architecture. This project uses computational techniques and Virtual Reality (VR) technologies to develop and test an innovative method for decision-making enabled by collaborative multi-sensory pre-occupation of designs. A gaming engine was used to develop an immersive VR environment that allowed users to explore architectural spaces within a range of time-specific spatial conditions. This paper presents comparative user studies between VR and paper-based evaluation approaches. Two experimental streams were carried out including architecture professionals and students. This manuscript presents background studies, proposed methods, and results of user testing. Significant differences in responses between architecture practitioners and architecture students were identified, including satisfaction levels of visual and acoustic qualities of architectural spaces; and the ease of using VR for pre-occupancy assessments. The professional population was also more likely to change their original choice after being immersed in the VR experience.