Reviewerly/exHarmony huggingface dataset:
The Performance of Face Recognition Using the Combination of Viola-Jones, Local Binary Pattern Histogram and Euclidean Distance

Achieving low recognition time and high accuracy in real-time face recognition is challenging. This study implements Viola-Jones, Local Binary Pattern Histogram, and Euclidean Distance for real-time face recognition and calculates the face detection time. The face image is detected using the Viola-Jones method; its features are extracted using the Local Binary Pattern Histogram, and the face is recognized using Euclidean Distance. This study processes sample images from 1013 students as training data, with 20 images represent each student. The experiments show that 268 of 342 testing data are recognized correctly, resulting in an accuracy of 78.4%, with average real-time recognition time of 0.93 seconds.
Arithmetic and Boolean Secret Sharing MPC on FPGAs in the Data Center

Multi-Party Computation (MPC) is an important technique used to enable computation over confidential data from several sources. The public cloud provides a unique opportunity to enable MPC in a low latency environment. Field Programmable Gate Array (FPGA) hardware adoption allows for both MPC acceleration and utilization of low latency, high bandwidth communication networks that substantially improve the performance of MPC applications. In this work, we show how designing arithmetic and Boolean Multi-Party Computation gates for FPGAs in a cloud provide improvements to current MPC offerings and ease their use in applications such as machine learning. We focus on the usage of Secret Sharing MPC first designed by Araki et al [1] to design our FPGA MPC while also providing a comparison with those utilizing Garbled Circuits for MPC. We show that Secret Sharing MPC provides a better usage of cloud resources, specifically FPGA acceleration, than Garbled Circuits and is able to use at least a 10 × less computer resources as compared to the original design using CPUs.
Multimedia Teaching in Teaching of College English Reading

Abstract With the advancement of society and the continuous development of information technology, the medium of multimedia has become an indispensable part of college English teaching. Because the traditional Web-based college English has a single teaching method, poor classroom effect, and low interest of students, this paper designs a multimedia teaching system based on a browser/server (B/S) network model and model view controller (MVC) design pattern. It is based on B/S network mode and MVC design mode and composed of a Web service layer, client layer, and database service layer. The system adopts a B/S network mode and MVC design pattern fusion mode to realize hierarchical control and different data categories and refinement and completion of the rational division of teaching resources, thus greatly improving the ability of data circulation in the teaching system. The system function design includes four parts: user registration and login, teacher teaching, student learning, and administrator management. The teacher teaching module completes the teacher’s uploading and online answering function for college English reading materials. The student learning module enables students to obtain classroom content and courses. The design of the system has improved the students’ interest in learning English and the classroom effect has also been significantly improved.
Adaptive Model Predictive Control Using Iterative Fuzzy Broad Learning System for Nonlinear Digital Time-Delay Dynamic Systems

This paper presents a new learning control structure using fuzzy broad learning system (FBLS) for adaptive model predictive control (AMPC) of nonlinear digital time-delay dynamic systems. The proposed control method, abbreviated as FBLS-ANMPC, is novel in combining FBLS and model predictive control to develop a FBLS-ANMPC control law for high-performance setpoint tracking control and disturbance rejection. Comparative simulations are well used to demonstrate the effectiveness and merits of the proposed method in comparison with five existing methods. Experimental results on temperature control of a heating barrel in a plastic injection molding machine are conducted to show the practicability of the proposed method.
Earthquake detection in the Jiangsu region, China using graphics-processing-unit-based Match &amp; Locate and rapid earthquake association and location*

Earthquake detection and location are essential in earthquake studies, which generally consists of two main classes: waveform-based and pick-based methods. To evaluate the ability of two different methods, a graphics-processing-unit-based Match & Locate (GPU-M&L) method and a rapid earthquake association and location (REAL) method are applied to continuous seismic data recorded by 24 digital seismic stations from Jiangsu Seismic Network during 2013 for comparison. GPU-M&L is one of waveform-based methods by waveform cross-correlations while REAL is one of pick-based method to associate arrivals of different seismic phases and locate events through counting the number of P and S picks and travel time residuals. Twenty-six templates are selected from the Jiangsu Seismic Network local catalog by using the GPU-M&L. The number of newly detected and located events is about 2.8 times more than those listed in the local catalog. We both utilize a deep-neural-network-based arrival-time picking method called PhaseNet and a short-term/long-term average (STA/LTA) trigger algorithm for seismic phase detection and picking by applying the REAL. We then refine seismic locations using a least-squares location method (VELEST) and a high-precision relative location method (hypoDD). By applying STA/LTA and PhaseNet, 1006 and 1893 events are associated and located, respectively. The newly detected events are mainly clustered and show steeply dipping fault planes. By analyzing the performance of these methods based on long-term continuous seismic data, the detected catalogs by the GPU-M&L and REAL show that the magnitudes of completeness are 1.4 and 0.8, respectively, which are smaller than 2.6 given by the local catalog. Although REAL provides improvement compared with GPU-M&L, REAL is highly dependent on phase detection and picking which is strongly affected by signal-noise ratio (SNR). Stations at southeast of the study region with low SNR may lead to few detections in the same area.
Are Code Smell Co-occurrences Harmful to Internal Quality Attributes?

Previous studies demonstrated how code smells (i.e., symptoms of the presence of system degradation) impact the software maintainability. However, few studies have investigated which code smell types tend to co-occur in the source code. Moreover, it is not clear to what extent the removal of code smell co-occurrences -through refactoring operations - has a positive impact on quality attributes such as cohesion, coupling, inheritance, complexity, and size. We aim at addressing these gaps through an empirical study. By investigating the impact of the smells co-occurrences in 11 releases of 3 closed-source systems, we observe (i) which code smells tend to co-occur together, (ii) the impact of the removal of code smell co-occurrences on quality internal attributes before and after refactoring, and (iii) which are the most difficult co-occurrences to refactoring from the developers' perspective. Our results show that 2 types of code smell co-occurrences generally tend to co-occur. Moreover, we observed that the removal of code smells co-occurrences lead to a significant reduction in the complexity of the systems studied was obtained. Conversely, cohesion and coupling tend to get worse. We also found that two code smells cooccurrences (God Class-Long Method and Disperse Coupling-Long Method) as the most difficult to refactor indicating that attention is needed not to insert these anomalies in the source code. Based on our findings, we argue that further research is needed on the impact of code smells co-occurrences on internal quality attributes.
Compactly Committing Authenticated Encryption Using Tweakable Block Cipher

Message franking is a cryptographic scheme introduced in the Facebook end-to-end encrypted messaging system. It enables users to report abusive messages to Facebook in a verifiable manner. Grubbs, Lu and Ristenpart initiated theoretical study of message franking. They formalized the notion and introduced a new primitive called compactly committing authenticated encryption with associated data (ccAEAD) in 2017. They also presented provably secure ccAEAD schemes. Dodis, Grubbs, Ristenpart and Woodage introduced a new primitive called encryptment as a core building block of ccAEAD in 2018. They presented a provably secure encryptment scheme using a Merkle-Damgård hash function and transformations to ccAEAD from it. In this paper, we present a provably secure encryptment scheme using a tweakable block cipher (TBC). Then, we present a ccAEAD scheme using a TBC by showing a transformation from encryptment using a TBC. Similar to the previous schemes, our scheme requires a collision-resistant pseudorandom function. We adopt a double-block-length construction using TBC for it.
ParsiPayesh: Persian Plagiarism Detection based on Semantic and Structural Analysis

In recent years, the rapid increase of Persian electronic resources and facility of access to them has seriously triggered the plagiarism problem of the Iranian scientific community. Despite the automatic systems of plagiarism detection, like Turnitin, Eve2, this problem has strongly remained due to lack of support from Persian. The main purpose of this article is to detect exact plagiarisms and re-writings in Persian science texts. In our proposed method, after the candidate retrieval based on the statistical characteristics, in the text alignment step, structural analysis and semantic analysis of expression has been performed to detect re-writing plagiarisms. Firstly, data-driven dependency parser has been improved with the help of a deep learning model for Persian language to analyze the structure of the expression, and then the degree of structural similarity of the expression is evaluated through the analysis of the dependency tree. In this paper, our suggestion to examine the semantic similarity of expression is to use the semantic role labeling obtained from the deep learning model presented. The experiments have been performed on the corpus prepared in the AAIC2015 and corpus of the PAN2015 competitions. The results indicate that structural and semantic information improves the performance of the proposed method. ParsiPayesh is available on http://www.parsipayesh.ir.
Highly Immersive Telepresence with Computation Offloading to Multi-Access Edge Computing

COVID-19 has raised the demand for non-contact interactions in most areas of human activities. One of the core technologies for non-contact interactions is telepresence that can provide truly immersive interactions between remote participants. Many telepresence approaches rely on avatar technology to provide realistic and immersive telepresence experience and hence a lot of research has been conducted on the topic of avatar creation. However, it is difficult to host avatar technology on user devices because of extremely large data and computation requirements. Cloud-based services can provide sufficient resources to manage avatars but the physical distance between the user and the cloud server introduces latencies that adversely affect service quality. In this paper, we propose a telepresence system design suitable for the multi-access edge computing (MEC) environment that can provide large processing power for avatar management with low latency so as to offer immersive telepresence experiences with high quality avatars.
MultiANet: a Multi-Attention Network for Defocus Blur Detection

Defocus blur detection is a challenging task because of obscure homogenous regions and interferences of background clutter. Most existing deep learning-based methods mainly focus on building wider or deeper network to capture multi-level features, neglecting to extract the feature relationships of intermediate layers, thus hindering the discriminative ability of network. Moreover, fusing features at different levels have been demonstrated to be effective. However, direct integrating without distinction is not optimal because low-level features focus on fine details only and could be distracted by background clutters. To address these issues, we propose the Multi-Attention Network for stronger discriminative learning and spatial guided low-level feature learning. Specifically, a channel-wise attention module is applied to both high-level and low-level feature maps to capture channel-wise global dependencies. In addition, a spatial attention module is employed to low-level features maps to emphasize effective detailed information. Experimental results show the performance of our network is superior to the state-of-the-art algorithms.
The Visual Quality of Teleoperated Driving Scenarios How good is good enough?

Teleoperated Driving can be a solution for situations that can not be solved by automated vehicles on their own. A human driver resolves the situation by controlling the vehicle remotely. Thus, drivers need to get an impression of the vehicle's environment. This is typically achieved by streaming video data through existing cellular networks. However, the available bitrate and the respective stream quality can vary drastically. This paper utilizes an user study to investigate, which video qualities are sufficient for different driving scenarios and how they are correlated with the required bitrates. Results show, that scenarios have a strong influence on the users' perceived quality ratings.
Single Image Super Resolution Using Convolutional Neural Networks for Noisy Images

In this paper, we address a problem of image super resolution to obtain a noise-free and high resolution image from a noisy and low resolution image. In recent years, deep learning-based approaches have been achieved a lot of progress to the image restoration problems. However, it is still not trivial to generate a high quality image when the input image is both noisy and low-resolution, because it is difficult to disambiguate the fine texture components from noise components for the input image. In this case, conventional super-resolution algorithms usually amplify the noise along with the details. To deal with this problem, we propose a super-resolution network that is robust to noisy images by constructing multi-modules in parallel architecture. The experimental results show that our proposed network restores a noise-free and rich-texture image from the low-resolution and noisy input image, while other methods fail.
The Projected Belief Network Classifier: both Generative and Discriminative

The projected belief network (PBN) is a layered generative network with tractable likelihood function, and is based on a feed-forward neural network (FF-NN). It can therefore share an embodiment with a discriminative classifier and can inherit the best qualities of both types of network. In this paper, a convolutional PBN is constructed that is both fully discriminative and fully generative and is tested on spectrograms of spoken commands. It is shown that the network displays excellent qualities from either the discriminative or generative viewpoint. Random data synthesis and visible data reconstruction from low-dimensional hidden variables are shown, while classifier performance approaches that of a regularized discriminative network. Combination with a conventional discriminative CNN is also demonstrated.
Generation of Figures with Controllable Posture using Ss-InfoGAN

With the increasing interest of social media, individual content production has become widespread. However, producing quality content will require much time and skills for individuals. On the other hand, recent generative adversarial networks (GAN) can easily generate images by learning. We aim to support or assist the video production and lower the threshold of content production by making the output results of the deep generation model more controllable. However, in conventional GANs, the correspondence between input and output was not easy for humans to interpret. As a premise, one interpretable example is that each input corresponds to each element of the output image. Therefore, in this research, we aim to control poses that are easy to interpret for images generated from 3D models of people using Ss-InfoGAN. Each input of Ss-InfoGAN is associated with the inclination of each joint, or only one input is moved. Experiments are conducted to check whether only the expected joint changes, and succeeded in actually associating the input with the joint state.
A Survey of Interpretability of Machine Learning in Accelerator-based High Energy Physics

Data intensive studies in the domain of accelerator-based High Energy Physics, HEP, have become increasingly more achievable due to the emergence of machine learning with high-performance computing and big data technologies. In recent years, the intricate nature of physics tasks and data has prompted the use of more complex learning methods. To accurately identify physics of interest, and draw conclusions against proposed theories, it is crucial that these machine learning predictions are explainable. For it is not enough to accept an answer based on accuracy alone, but it is important in the process of physics discovery to understand exactly why an output was generated. That is, completeness of a solution is required. In this paper, we survey the application of machine learning methods to a variety of accelerator-based tasks in a bid to understand what role interpretability plays within this area. The main contribution of this paper is to promote the need for explainable artificial intelligence, XAI, for the future of machine learning in HEP.
Farklı türdeki ağ ataklarının GNS3 platformunda analizi

In this study, DDoS, SQL injection and XSS attacks that hackers use most in cyber attacks are modeled on GNS3 emulator platform and network security is analyzed. A network scenario was designed using Graphical Network Simulator (GNS3), virtual machines, VMware workstation, firewall, router, and switches in order to examine the attacks on networks in real environment. Attacks were performed on this network with different techniques and target servers and devices were affected by the attacks. At the time of the attack, network traffic between the attacker and the target device was recorded with Wireshark software. Network traffic records and traces were examined and evaluations of attacks were made.
Automatic Deep Understanding of Tables in Technical Documents

In order to achieve a deep understanding of the information presented in technical documents, one must first understand the document's individual modalities, such as tables, graphics, diagrams, etc. Accurate detection and recognition of these individual modalities constitute key processing steps to ensure efficient understanding as well. In this paper, we present a methodology for understanding the deeper associations of tabular information and expressing them in stochastic Petri-net graphs. This research is focused on tables that follow strictly the IEEE format rules. We divide this work into three distinct steps 1) table detection, 2) table recognition, 3) table understanding. For the part of detection, we study different machine learning and rule-based methodologies for classifying images as tables and compare the results from their evaluation. During the table recognition step, we extract all the necessary information from the table and recognize associations between them. We then convert the recognized associations into attributed graphs, natural language text, and SPN graphs. Finally, we present the preliminary results from the conversion of tabular information into the other forms of information representation.
Convolutional Long Short Term Memory Model for Credit Card Detection

At present times, credit card frauds appears as a major issue of stealing and fraud obligation using payment cards such as credit or debit cards. To prevent credit card frauds, several financial organizations were initiated to design effective models to detect the existence of frauds. The growth of Deep Learning (DL) models finds useful to examine the usual and unusual patterns along with separate transactions for raising an alert for probable frauds. This paper presents a novel DL based convolutional long short term memory (C-LSTM) model for credit fraud detection. The proposed C-LSTM model involves preprocessing, and classification. The preprocessed data undergo classification using the C-LSTM model to detect the occurrence of credit card frauds or not. The performance of the C-LSTM model is validated using German Credit and Kaggle's Credit Card Fraud Detection dataset. The attained experimental values portrayed that the C-LSTM model has shown proficient performance with the accuracy of 94% and 94.65% on the applied German credit and Credit card fraud detection dataset.
High-speed train state monitoring method with Broad Learning System

In this paper, based on Broad Learning System (BLS) that is proposed recently, a real-time monitoring method is designed for the high-speed train (HST) braking system. Due to the high efficiency of the BLS, it is possible to update the state monitoring model in time to adapt to the changes during the HST operation. Moreover, the actual data is highly imbalanced, thus boosting ensemble learning framework is applied to optimize the model to obtain a higher generalization accuracy. In the monitoring system based on BLS integrated with boosting algorithm (noted as B-BLS), the data collected by sensors are added to the model training process in real-time, which makes the anomaly detection more suitable for the current state of the HST braking system. Compared with off-line training models, i.e. artificial neural networks and convolutional neural networks, experimental results demonstrate that the B-BLS has relatively higher adaptivity and efficiency, showing that the proposed monitoring method is feasible.
A Comparison of Blockchain-Based Wireless Sensor Network Protocols

Wireless sensors are often deployed in environments where it is difficult for them to discern friend from enemy. An example case is a military tactical scenario, where sensors are deployed to map the location of an item but where some of the nodes have been compromised or where there are other malicious nodes present. In this scenario, sharing data with other network nodes may present a critical security risk to the sensor nodes. Blockchain technology, with its ability to house a secure distributed ledger, offers a possible solution. However, blockchain applications for Wireless Sensor Networks suffer from poor latency in block propagation which in turn decreases throughput and network scalability. Several researchers have proposed solutions for improved network throughput. In this work, a comparison of these existing works is performed leading to a taxonomy of existing algorithms. Characteristics consistently found in algorithms reporting improved throughput are presented and, later, these characteristics are used in the development of a new algorithm for improving throughput. The proposed algorithm utilizes a proof-of- authority consensus algorithm with a node trust-based scheme. The proposed algorithm shows strong results over the base case algorithm and was evaluated with blockchain network simulations of up to 20000 nodes.