Reviewerly/exHarmony huggingface dataset:
Micro-expression Video Clip Synthesis Method based on Spatial-temporal Statistical Model and Motion Intensity Evaluation Function

Micro-expression (ME) recognition is an effective method to detect lies and other subtle human emotions. Machine learning-based and deep learning-based models have achieved remarkable results recently. However, these models are vulnerable to overfitting issue due to the scarcity of ME video clips. These videos are much harder to collect and annotate than normal expression video clips, thus limiting the recognition performance improvement. To address this issue, we propose a micro-expression video clip synthesis method based on spatial-temporal statistical and motion intensity evaluation in this paper. In our proposed scheme, we establish a micro-expression spatial and temporal statistical model (MSTSM) by analyzing the dynamic characteristics of micro-expressions and deploy this model to provide the rules for micro-expressions video synthesis. In addition, we design a motion intensity evaluation function (MIEF) to ensure that the intensity of facial expression in the synthesized video clips is consistent with those in real -ME. Finally, facial video clips with MEs of new subjects can be generated by deploying the MIEF together with the widely-used 3D facial morphable model and the rules provided by the MSTSM. The experimental results have demonstrated that the accuracy of micro-expression recognition can be effectively improved by adding the synthesized video clips generated by our proposed method.
Signal Detection and Modulation Classification for Satellite Communications

Amateur ground stations are gaining increasing importance as both academic and hobby activities. However, due to the limited energy resources available in amateur satellites, ground stations need to be located in isolated places in order to establish a reliable communication. This usually implies limited Internet access. Hence, ground stations need to be able to recognize incoming signal without completely relying on an Internet connection. For this reason, we propose an algorithm to estimate parameters such as amplitude, center frequency, bandwidth and modulation type for amateur radio applications. For signal detection, we use an absolute-valued sinc approximation which estimates the center frequency and bandwidth of signals with signal-to-noise ratios over -6 dB with a precision of 5% and 2% respectively. In addition, Support Vector Machines (SVM) binary classifiers are used in series to classify the four most common modulation types used in amateur satellites. With accuracies over 90%, SVM outperforms solutions based on Artificial Neural Networks.
Insulator Fault Detection in Aerial Images based on the Mixed-grouped Fire Single-shot Multibox Detector

Abstract In a complex background, insulator fault is the main factor behind transmission accidents. With the wide application of unmanned aerial vehicle (UAV) photography, digital image recognition technology has been further developed to detect the position and fault of insulators. There are two mainstream methods based on deep learning: the first is the “two-stage” example for a region convolutional neural network and the second is the “one-stage” example such as a single-shot multibox detector (SSD), both of which pose many difficulties and challenges. However, due to the complex background and various types of insulators, few researchers apply the “two-stage” method for the detection of insulator faults in aerial images. Moreover, the detection performance of “one-stage” methods is poor for small targets because of the smaller scope of vision and lower accuracy in target detection. In this article, the authors propose an accurate and real-time method for small object detection, an example for insulator location, and its fault inspection based on a mixed-grouped fire single-shot multibox detector (MGFSSD). Based on SSD and deconvolutional single-shot detector (DSSD) networks, the MGFSSD algorithm solves the problems of inaccurate recognition in small objects of the SSD and complex structure and long running time of the DSSD. To resolve the problems of some target repeated detection and small-target missing detection of the original SSD, the authors describe how to design an effective and lightweight feature fusion module to improve the performance of traditional SSDs so that the classifier network can take full advantage of the relationship between the pyramid layer features without changing the base network closest to the input data. The data processing results show that the method can effectively detect insulator faults. The average detection accuracy of insulator faults is 92.4% and the average recall rate is 91.2%.
A Survey on Formal Verification of Separation Kernels

Introduction: In developing safety and security critical systems, separation kernel acts as a primary foundation, which provides spatial as well as temporal separation. Separation kernel offers highly assured partitions to the applications hosted on the fundamentally critical systems and can also control the flow of information between them. The industries, as well as academia, have developed several separation kernels that have been broadly applied in critical systems like military/defense secured applications, avionics/aerospace intelligent systems, healthcare units that deal with human lives and in many more areas. The increasing popularity of separation kernels demands the formal verification that assures the correctness of the functionalities in it. Further, formal verification of separation kernels has become mandatory by the security/safety certification authorities. Conclusion: This paper first presents the concept of separation kernel, and then it discusses the functionalities, design, and properties of it. The classification and analysis of the formal languages are being presented in this paper that has been used for writing the specifications of separation kernel and verifying it. The paper is an attempt towards the classification of formal languages being used for the verification of several separation kernels.
The SLUMBAT Demo of Blockchain Based Nuclear Safeguards

SLUMBAT is a framework for nuclear material accounting and reporting on a permissioned blockchain. Testing SLUMBAT for a nuclear fuel supply scenario involving two State systems of accounting for and control of nuclear material (SSAC), and their national and international regulators, is used to investigate the application of blockchain technology to nuclear non-proliferation. SLUMBAT shows how a permissioned blockchain allows for detailed access controls within a safeguards information management system, eliminates the need to report duplicate information, and simplifies transit matching of nuclear material transactions between facilities. It promises to enhance trust between participants in global nuclear supply chains by enhancing data integrity. SLUMBAT is a significant step towards showing how an SSAC based on blockchain information systems could work, and shows how complex reporting structures, both for this and other similar applications, would be readily described.
Improving the performance of the Proof-of-Work Consensus Protocol Using Machine learning

Blockchain technology has proven to be secured and reliable technology by bringing security, trust and data integrity to distributed systems. It brought a new paradigm that helps in the existence of the cryptocurrency and eliminating the third party in a financial transaction. It has the potential of optimising, enhancing streamlining many processes outside the cryptocurrency and financial sector but the adoption of the technology is limited by the hindering performance issues. These issues are mostly around the Proof-of-Work (PoW) consensus protocol that is used by Bitcoin and Ethereum and referred to as the most secured and decentralised protocol thus, the most reliable. Unfortunately, the protocol suffers a performance degrade with the increasing size and number of transactions because of its complexity. Many industries, researchers and organisation have been working on addressing these issues but most of the attempts result in facing another issue referred to as the scalability issue; having to trade off one of security or decentralisation to get speed. Other solution such as Bitcoin lighting network improved the transaction throughput of the Bitcoin without technically addressing the issue on the blockchain, therefore, didn't face the scalability issue. This paper presents a research work of a novel approach that propose using machine learning techniques to improve the performance by enhancing the mining efficiency of the protocol. It uses the Ethereum network as a case study. The paper also aims to compare the predictive accuracy and speed of some machine learning regression models against the traditional mining method starting from the Linear regression. The objective is to determine whether using machine learning in the mining process offers a faster way of achieving consensus, therefore improving performance by reducing the time and energy consumption of the protocol without sacrificing security or decentralisation. The proposed model results in improved accuracy and faster consensus from the early experiments.
A Systematic Identification of Pedagogical Conversational Agents

Conversational agents have been established to support educational practices, solving students' questions, recommending teaching materials, increasing the motivation of students who have little interest in certain content, among others. Although many studies are discussing the establishing of conversational agents, there is no global understanding of what has been investigated in this area. There are some previous secondary studies that have attempted to map the conversational agents used for educational purposes (i.e., pedagogical conversational agents). However, these studies are limited to include primary studies published in specific sets of conferences and journals. Therefore, an overview of the current state of the art associated with pedagogical conversational agents has not been produced yet. This research paper aims to contribute to the theme, through the presentation of planning, conduction, and results analysis of a systematic mapping on pedagogical conversational agents. We address the following questions: (i) in which areas have conversation agents been investigated?; (ii) at which education levels are conversational agents used?; (iii) which mechanisms do conversational agents use to interact with students?. We also present some discussions about research opportunities that can be explored in future works. This study aims to contribute to the topic of pedagogical conversational agents, helping researchers to have a deeper understanding of what has already been done and to guide their research to subjects not explored yet.
Breast Cancer Detection of Small Sample Based on Data Augmentation and Corner Pooling

Breast cancer is the most common cancer among women worldwide. The effective detection the location of breast cancer from the ultrasound images can assist doctors in diagnosing breast cancer. Diverse morphology, blurred edges, and small amount of data causes great difficulty in the detection of breast cancer. Deep learning is very advantageous when facing these problems. However, the problems of training on small sample datasets and the imbalance of positive and negative samples are problems that need to be solved. In order to improve the accuracy of ultrasound breast cancer detection, a small sample breast cancer detection method based on data augmentation and corner pooling is proposed in this paper. In this method, we propose a way for solving over-fitting of small samples and solving the imbalance problem of positive and negative samples. Data augmentation module based on geometric and noise transformation is proposed to solve the problem of small samples, and detection module based on focal loss and corner pooling is proposed to solve the problem of imbalance samples. The experiment found that the method used in this paper has more advantages than the mainstream methods in difficult to distinguish samples. The method used in this paper has an AP of 84.65%, which is higher than state-of-the-art methods.
Integration of Static and Dynamic Analysis Techniques for Checking Noninterference

In this article, we present an overview of recent combinations of deductive program verification and automatic test generation on the one hand and static analysis on the other hand, with the goal of checking noninterference. Noninterference is the non-functional property that certain confidential information cannot leak to certain public output, i.e., the confidentiality of that information is always preserved. We define the noninterference properties that are checked along with the individual approaches that we use in different combinations. In one use case, our framework for checking noninterference employs deductive verification to automatically generate tests for noninterference violations with an improved test coverage. In another use case, the framework provides two combinations of deductive verification with static analysis based on system dependence graphs to prove noninterference, thereby reducing the effort for deductive verification.
Local Binary Pattern Histogram for Face Recognition in Student Attendance System

Student attendance record has an important role in the educational process. Universitas Bhayangkara Jakarta Raya, as a case study, uses attendance record as the factor for final grade calculation. Many attendance recording systems were developed using biometrics, e.g. face recognition, iris recognition, and fingerprint recognition. In this study, face recognition was proposed since the face cannot be duplicated and can eliminate fraud committed by students. In addition, this contactless method could minimize the risk of COVID-19 spread with some additional treatments. The local binary pattern (LBP) was proposed in this study. This method has the ability to describe the texture and shape of an image by dividing the image into small portions of feature extraction. The result showed that the proposed system can identify students with 86% accuracy.
Steady states, squeezing, and entanglement in intracavity triplet down conversion

Triplet down conversion, the process of converting one high-energy photon into three low-energy photons, may soon be experimentally feasible in the optical regime, due to advances in optical resonator technology. We use quantum phase-space techniques to analyze the process of degenerate intracavity triplet down conversion by solving stochastic differential equations within the truncated positive-P representation. We simulate the time evolution of both intracavity fields, and examine the resulting steady-states as a function of the pump intensity. Quantum effects are most pronounced in the region immediately above the semi-classical pumping threshold, where our numerical results differ significantly from semi-classical predictions. We calculate steady-state fluctuation spectra and identify regimes of squeezing, bipartite entanglement, and non-Gaussianity measurable in the cavity output fields. We also validate the truncated positive-P description against Monte Carlo wave function simulations, finding good agreement for low mode populations.
Performance Analysis of Secure Real-time Transport Protocol Using Elliptic Curves

The growing popularity of streaming audio/video is enhancing the popularity of real-time media applications and services. People use real-time media applications for voice/video calls as well. Real-time Transport Protocol is a well-known protocol solely used for real-time data transmission, which uses the Voice over IP technology. Nevertheless, like other Internet applications, the security aspect has become a significant issue for real-time media applications. To protect users' privacy, real-time data such as streaming video or audio/video calls can be encrypted during the conversation. Some real-time applications use symmetric key encryption to protect the media data. However, this cannot ensure the security of real-time media. An asymmetric key encryption technique can be a better choice in this context. Among other asymmetric key encryption schemes, Elliptic Curve Cryptography (ECC) performs better due to its comparatively smaller key size and lesser computation time. We must choose the elliptic curves judiciously to encrypt the voice call, because audio encryption may increase end-to-end delay, which worsens the quality of the call. The appropriate elliptic curves can be chosen from a set of elliptic curves by analyzing their performance on real-time audio encryption. In this work, we have implemented an ECC based encryption technique in a softphone to encrypt real-time voice calls. We have used 15 elliptic curves to measure the performance of audio calls, and based on the result, have proposed some suitable elliptic curves for real-time audio encryption.
Crypto-Governance: The Ethical Implications of Blockchain in Public Service

Blockchain systems are positioned to revolutionize many aspects of governance and public service. This paper explores the ethical implications of blockchain for government ethics leaders implementing and managing blockchain systems. Blockchain is a unique challenge and opportunity for democratic governance because of its transparency, security, efficiency, and decentralization. In particular, blockchain could significantly change public entities ethical relationship to citizen privacy, social equity, and administrative evil.
WITHDRAWN: Probability learned neural model for human behavior analysis based on language cognition

Human Behavior modeling and Language Cognition is a significant component in the application domain, like social, behavioral, and healthcare research. However, accurate prediction to understand the human behavior determinant roles and cognitive states helps analyze humans' behaviors as a critical challenge by various conventional methods. Hence in this research, the Probability learned Neural Model (PLNM) had been proposed to address the critical issues related to testing, training, and classify the cognitive states for accurate prediction of roles and rules of the human mind. This conceptual model describes the user's psychological behavior by utilizing activities, action, inter and intra activity behavior, and language modeling. Furthermore, the architecture shows how the probability model enables users to predict the next steps and effectively detect user psychological anomalies. The lab-scale numerical results show that accurate prediction in human psychological behavior and its quality shows the proposed framework's stability.
Robot communication system based on OIO middleware

Coordination and control of actions using sensors on the robot is the key technology for robots to become intelligent. Aiming at the communication and control problems of existing sensor robots, an object-oriented data communication framework based on OIO middleware is proposed. Through the research of serialization and deserialization of communication data, the problems of coordinated advancement, climbing, and turning of sensor robot clusters are explored, and the law of object data and intelligent control instruction transmission is revealed. The research in the paper provides new ideas for the development of sensor robot motion coordination and control modules, and promotes the intelligentization of sensor robots. The paper designs OIO middleware that can be used for sensor robot cluster communication and control, and regards the sensor robot as an object that encapsulates attributes and methods, and performs intelligent control based on object-oriented data. Experimental research on dual-robot and multi-robot collaborative climbing, collaborative turning, and collaborative obstacle avoidance based on OIO middleware was carried out. The experimental results show that this method simplifies the communication and control of the robot cluster and improves the control effect.
Cartoon Zero-Watermark Method Based on Edge Contourlet Feature and Visual Cryptography

By analyzing the characteristics of the cartoon images, we propose a new method by using edge feature. In this method, image normalization is applied to better resistance to geometric attacks firstly. Then the secondary contourlet transform are applied to extract the general contour and edge features effectively. Next, the non-negative matrix factorization is applied to obtain better feature information, after binary processing, the matrix which is basic obtained is used as the standard sequence of cartoon images. In order to make the algorithm proposed in this paper more secure, visual cryptography and other technologies are applied for the original watermarking image. Finally, the feature sequence extracted from the cartoon image and the watermark sequence representing copyright information are logically XOR operated to obtain the zero-watermark. Analysis of the test results of this paper can be seen that the cartoon zero-watermark algorithm is robust and can resist noise attacks, geometric transformation attacks, JPEG compression attacks.
Driver Glance Classification In-the-wild: Towards Generalization Across Domains and Subjects

Distracted drivers are dangerous drivers. Equipping advanced driver assistance systems (ADAS) with the ability to detect driver distraction can help prevent accidents and improve driver safety. In order to detect driver distraction, an ADAS must be able to monitor their visual attention. We propose a model that takes as input a patch of the driver's face along with a crop of the eye-region and classifies their glance into 6 coarse regions-of-interest (ROIs) in the vehicle. We demonstrate that an hourglass network, trained with an additional reconstruction loss, allows the model to learn stronger contextual feature representations than a traditional encoder-only classification module. To make the system robust to subject-specific variations in appearance and behavior, we design a personalized hourglass model tuned with an auxiliary input representing the driver's baseline glance behavior. Finally, we present a weakly supervised multi-domain training regimen that enables the hourglass to jointly learn representations from different domains (varying in camera type, angle), utilizing unlabeled samples and thereby reducing annotation cost.
Towards the Definition of Patterns and Code Smells for Multi-language Systems

Developers often combine multiple programming languages to build large-scale applications. They choose programming languages properly for their tasks at hand instead of solving all of their problems with a single language. Foreign Functions Interface allow code written in one programming language to access features available in another programming language. Multi-language systems benefits from several advantages. However, they also introduce challenges related to the development, comprehension, and maintenance of such systems. Software quality is achieved partly by following good practices---architectural styles, design patterns, idioms---and avoiding bad practices---design anti-patterns and code smells. Yet, a review of the literature shows that there are a few works that study developers' practices among multi-language systems. The heterogeneity of components introduces code smells at the source code level. While design patterns are defined as good solutions to a recurrent problem, code smells are defined as poor design and coding choices that can negatively impact the quality of a software program despite satisfying functional requirements. In this paper, we report four patterns and five code smells related to multi-language systems. Those patterns and code smells were extracted from open-source systems, developers' documentation, and bug reports. We encoded these practices in the form of patterns and code smells in the context of Java Native Interface systems.