Reviewerly/exHarmony huggingface dataset:
A High Throughput Hardware CNN Accelerator Using a Novel Multi-Layer Convolution Processor

Convolutional Neural Network (CNN) is the state-of-the-art deep learning approach used in various computer vision algorithms due to their high accuracy. To ensure programmable flexibility and shorten the development period, FPGA is an appropriate platform to implement CNN models. However, the limited on-chip storage and memory bandwidth are the bottlenecks. In this paper, two different architectures are presented to implement a same model structure. One performs traditional computing, layer by layer, and the other one performs multiple-layer computing in a pipeline structure using a Multi-Layer Convolution Processor (MLCP) accelerator. In the latter one, the required on-chip memory and memory bandwidth are reduced. Implementation results on a Xilinx Zynq XC7Z020 chip under a frequency of 200 MHz shows that the MLCP accelerator achieves 12.9 GOP/s that is 2.6x higher than that of Single-Layer Convolution Processor (SLCP).
Web Folder Phishing Discovery and Prevention with Customer Image Verification

Phishing is a process ofimitating payment and other account login pages to get the actual user credentials. Phishing, attacks mostly executed on banking logins, because they are most valuable for money motive hackers. It's mostly targeted on senior citizens, Because they are not assertive on checking page SSL signatures and don't want to increase the steps to login account. So here phishing prevention is a idea to make dynamic signature on regular account authentication page. It's achieved by image as signature chosen by user. Every user account having this image as signature to verify at the time of login. Users no need to put more effort to identify the phishing page. It prevents generic phishing page and also can prevent from web bot scripts.
Classification of unknown Internet traffic applications using Multiple Neural Network algorithm

Traffic classification software is an important tool in complex environments like a cloud-based environment for network and device safety. The new methods of traffic classification attempt to benefit from numerical flow characteristics and computer teaching techniques, but minimal supervised knowledge and uncertain applications seriously affect classification efficiency. We propose a new way of dealing with an unknown application issue in the critical situation of a limited supervised training set to achieve an efficient network classification. The proposed model applied the multiple neural network algorithms to predict the unknown application that run through organization internet network. The advantage of the suggested approach is to filter and exclude the unknown internet applications that can be affecting into internet network performance. By Appling proposed method, the internet performance can be improved and the internet traffic and delay of transferred data can be reduced. The proposed method compared with other based line method in term of predication precision accuracy measure.
Scale-Aware Polar Representation for Arbitrarily-Shaped Text Detection

Arbitrarily-shaped text detection faces two major challenges: 1) various scales and 2) irregular angles. Previous works regress the text boundary in Cartesian coordinates as ordinary object detection. However, such grid space interleaves the unique scale and angle attributes of text, which seriously affects detection performance. The implicit disregard of text scale also impairs multi-scale detection ability. To better learn the arbitrary text boundary and handle the text scale variation, we propose a novel Scale-Aware Polar Representation (SAPR) framework. The text boundary is represented in Polar coordinates, where scale and angle of text could be both clearly expressed for targeted learning. This simple but effective transformation brings significant performance improvement. The explicit learning on separated text scale also promotes the multi-scale detection ability. Based on the Polar representation, we design line IoU loss and symmetry sine loss to better optimize the scale and angle of text with a multi-path decoder architecture. Furthermore, an accurate center line calculation is proposed to guide text boundary restoration under various scales. Overall, the proposed SAPR framework is able to effectively detect arbitrarily-shaped texts and tackle the scale variation simultaneously. The state-of-the-art results on multiple benchmarks solidly demonstrate the effectiveness and superiority of SAPR.
API-Net: Robust Generative Classifier via a Single Discriminator

Robustness of deep neural network classifiers has been attracting increased attention. As for the robust classification problem, a generative classifier typically models the distribution of inputs and labels, and thus can better handle off-manifold examples at the cost of a concise structure. On the contrary, a discriminative classifier only models the conditional distribution of labels given inputs, but benefits from effective optimization owing to its succinct structure. This work aims for a solution of generative classifiers that can profit from the merits of both. To this end, we propose an Anti-Perturbation Inference (API) method, which searches for anti-perturbations to maximize the lower bound of the joint log-likelihood of inputs and classes. By leveraging the lower bound to approximate Bayes' rule, we construct a generative classifier Anti-Perturbation Inference Net (API-Net) upon a single discriminator. It takes advantage of the generative properties to tackle off-manifold examples while maintaining a succinct structure for effective optimization. Experiments show that API successfully neutralizes adversarial perturbations, and API-Net consistently outperforms state-of-the-art defenses on prevailing benchmarks, including CIFAR-10, MNIST, and SVHN.(Our code is available at github.com/dongxinshuai/API-Net .).
Estimation of traffic matrices via super-resolution and federated learning

Network measurement and telemetry techniques are central to the management of today's computer networks. One popular technique with several applications is the estimation of traffic matrices. Existing traffic matrix inference approaches that use statistical methods, often make assumptions on the structure of the matrix that may be invalid. Data-driven methods, instead, often use detailed information about the network topology that may be unavailable or impractical to collect.
OpenCollBench - Benchmarking of Collision Detection &amp; Proximity Queries as a Web-Service

We present a server-based benchmark that enables a fair analysis of different collision detection & proximity query algorithms. A simple yet interactive web interface allows both expert and non-expert users to easily evaluate different collision detection algorithms’ performance in standardized or optionally user-definable scenarios and identify possible bottlenecks. In contrast to typically used simple charts or histograms to show the results, we additionally propose a heatmap visualization directly on the benchmarked objects that allows the identification of critical regions on a sub-object level. An anonymous login system, in combination with a server-side scheduling algorithm, guarantees security as well as the reproducibility and comparability of the results. This makes our benchmark useful for end-users who want to choose the optimal collision detection method or optimize their objects with respect to collision detection but also for researchers who want to compare their new algorithms with existing solutions.
A Conservative Sharpening Filter for Interpolated Images

Interpolation methods are computationally efficient and rather effective for magnification from low-resolution images in order to generate higher resolution images. However, blur and other undesirable visual artifacts are often produced during the process. This work evaluates a sharpening filter that enhances the quality of interpolated images by preserving information from the original low-resolution images. The novel filter was shown to achieve statistically significant increments in both the PSNR and SSIM of magnified RGB images using two image sets from the literature.
Reliability Evaluation of Pruned Neural Networks against Errors on Parameters

Convolutional Neural Networks (CNNs) are widely used in image classification tasks. To fit the application of CNNs on resource-limited embedded systems, pruning is a popular technique to reduce the complexity of the network. In this paper, the robustness of the pruned network against errors on the network parameters is examined with VGG16 as a case study. The effects of errors on the weights, bias, and batch normalization (BN) parameters are evaluated for the network with different pruning rates based on error injection experiments. The results show that in general networks with more weights pruned are more robust for a given error rate. The effect of multiple errors on bias or BN parameters is almost the same for the networks with different pruning rates that are lower than 90%. Further experiments are performed to explain the bimodal phenomenon of the network performance with errors on the parameters, to find that only errors on 6% of the parameter bits will cause large degradation of the neural network performance.
Featherweight Swift: a Core calculus for Swift’s type system

Swift is a modern general-purpose programming language, designed to be a replacement for C-based languages. Although primarily directed at development of applications for Apple's operating systems, Swift's adoption has been growing steadily in other domains, ranging from server-side services to machine learning. This success can be partly attributed to a rich type system that enables the design of safe, fast, and expressive programming interfaces. Unfortunately, this richness comes at the cost of complexity, setting a high entry barrier to exploit Swift's full potential. Furthermore, existing documentation typically only relies on examples, leaving new users with little help to build a deeper understanding of the underlying rules and mechanisms.
Development of Kazakh Named Entity Recognition Models

Named entity recognition is one of the important tasks in natural language processing. Its practical application can be found in various areas such as speech recognition, information retrieval, filtering, etc. Nowadays there are a variety of available methods for implementing named entity recognition. In this work we experimented with three models and compared the performances of machine learning based models and probabilistic sequence modeling method on the task of Kazakh language named entity recognition. We considered three models based on BERT, Bi-LSTM and CRF baseline. In the future these models can be parts of an ensemble learning system for name entity recognition in order to achieve better performance results.
Image Defogging Algorithm Based on Fisher Criterion Function and Dark Channel Prior

For images with sky region, we propose a method of image defogging based on pattern recognition and dark channel priori. First, the transmittance is obtained by reducing the minimum filter window, and then the edge of transmittance is smoothed by V transformation. Since the transmittance histogram has the feature of two peaks, we used Fisher linear classifier to identify sky and non-sky regions, and then recalculated the transmittance of sky regions. Finally, the atmospheric scattering model is used to get the new luminance component, and the gamma correction is carried out. After the saturation component is adjusted appropriately, the defogged image is converted back to RGB space. The experiment shows that the proposed algorithm can get better haze removal effect compared with the traditional defogging algorithm based on dark channel prior and other state-of-the-art methods developed in recent years, and to a certain extent has solved the problem of color distortion and the whole brightness being darker in the sky region when using the DCP based algorithm.
Exploring the Bug Investigation Techniques of Intermediate Student Programmers

Bug investigation – testing and debugging – is a significant part of software development. Ineffective practices of bug investigation can greatly hinder project development. Therefore, we seek to understand bug investigation practices among intermediate student programmers. To this end, we used a mixed-methods approach to study the testing and debugging practices of students in a junior-level Data Structures course with 3–4 week long projects. First, we interviewed 12 students of varying project performances. From the interviews, we identified five techniques that students use for both testing and debugging: 1) writing diagnostic print statements, 2) unit testing, 3) using source-level debugger, 4) submission to online auto-grader, and 5) manual tracing. Using the Grounded Theory approach, we developed four hypotheses regarding students' use of multiple techniques and their possible impact on performance. We used clickstream data to analyze the level of use of the first four of these techniques. We found that over 92%, 87%, and 73% of the students used JUnit testing, diagnostic print statements, and the source-level debugger, respectively. We found that the majority of the students (91%) used more than one technique to investigate bugs in their projects. Moreover, students who used multiple techniques had overall better performance in the projects. Finally, we identified some ineffective practices correlated with lower project scores. We believe that the findings of our research will help understand, characterize, and teach better practices of bug investigation.
Interactive English Language Mobile Application

Mobile devices are becoming more common among adults when compared to the traditional desktop computers. In order to make the students motivated to learn a foreign language and tailor learning materials to their needs, foreign language teachers with the help of the faculty programmers at the University of Hradec Kralove in the Czech Republic have decided to develop and implement a smartphone application (app) for teaching English to the students of Management of Tourism (MT). Firstly, the literature on the topic, i.e., on the use of smartphone apps in the teaching and learning process is reviewed. Its purpose is to detect the areas of English skills and knowledge that would remain as the most appropriate feature to learn via the smartphone app. Secondly, the solution for the development of a smartphone app for the students of MT is described and its advantages and disadvantages are highlighted.
Use of adaptive weighted echo state network ensemble for construction of prediction intervals and prediction reliability of silicon content in ironmaking process

The silicon content of molten iron is one of the most important molten iron quality parameters. However, the silicon content cannot be measured directly, therefore, accurate prediction for silicon content is of great significant to blast furnace (BF) iron making process. Aiming at the problem of low accuracy, an adaptive weighted echo state network (AW-ESN) based ensemble model is proposed in this paper to construct the prediction intervals (PI) and predict the silicon content of molten iron in BF. First, bootstrap method is utilized to resample the training set to construct subsets, AW-ESN is proposed to estimate silicon content and the corresponding PI is constructed. Then, the correspondence between the width of PI and reliability is established. Finally, the prediction results and the reliability can be obtained. In order to verify the effectiveness of the proposed method, industrial experiments were carried out by using process data of BF. The results demonstrate that the proposed method has higher prediction accuracy and the reliability can be realized, which provide more information to the on-site operators.
Noise-tolerant and context-aware structural vibration based activity monitoring

Automated monitoring of humans and animals can facilitate better health and productivity. Prior monitoring approaches use video, which requires line-of-sight and high processing power, or motion detection, which has difficulty separating subtle activities. Wearable sensors can address these issues but are vulnerable to animal destructiveness and human forgetfulness. We present a system that uses structural vibration to monitor animal or human behavior. We use domain knowledge to adapt this system to different environments, and evaluate it on humans in a home office environment and on pigs at an operational pig farm.
An Investigation Into The Level Of Valence Offered By Different Pointing Devices Against Challenging Tasks

Pointing devices are the primary media of interac-tion between humans and computers. The three most popular pointing devices used in computers (both portable and non-portable) are mouse, touchpad and nubs (joystick). They have their different advantages and use cases while being targeted to different user groups. The aim of this study was to investigate whether the aforementioned pointing devices have different effects on human valence. A total of 12 participants were recruited for the experiment. Each participant completed a pointing reaction test with every pointing device aforementioned, where they selected as many randomly appearing circles as possible in a given amount of time. Then, subjective ratings of emotional valence and arousal were collected, and the effects of the pointing device used on these ratings were investigated. Our study shows that the valence rating of using the mouse were significantly higher in challenging scenarios, compared to the likes of touchpad and nub.
Architectural Solutions for Self-Adaptive Systems

Increasingly adopted in critical application domains, selfadaptive systems (SaSs) present a particular ability to modify their behavior or configuration at runtime autonomously. The architectural decision-making activity in an SaS can be supported by reusing proven software structure configuration.
Multi-Candidate Electronic Voting Scheme Based on Fully Homomorphic Encryption

Abstract In recent years, multi-candidate electronic voting has attracted tremendous attention and been put into lots of practice because of its outstanding effectiveness and convenience. As a result, it has become an urgent task to achieve the anonymity and public verifiability of electronic voting. Several schemes have been proposed so far, but most of them are either inefficient or too demanding of the security of the entities involved. Focusing on this problem, a multi-candidate electronic voting scheme based on fully homomorphic encryption and digital signature algorithm is proposed in the paper. To encrypt votes efficiently and reduce the dependence of different entities, the Identity Based Full Homomorphic Encryption(IBFHE) scheme is used in the paper. A homomorphic addition ticket counter is then used to realize the homomorphism calculation of encrypted votes. To settle the problem of identity authentication in electronic voting with high efficiency and security, Elliptic Curve Digital Signature Algorithm(ECDSA) is utilized. The capability of batching votes is achieved by making use of Single Instruction Multiple Data(SIMD) technology, which is designed for improving the efficiency of the voting system. Finally, the paper makes an analysis based on six security features of e-voting, which reveals the scheme’s safety and feasibility.
Analysis of Measurements of Direct Normal and Global Solar Radiation for Maceió, Alagoas State, Northeastern Brazil

Abstract Studying solar radiation is essential for human knowledge, since it is present in practically all its activities. Thus, the aim of this work was to analyze the climatic and seasonal variation of direct normal and global solar radiation in the region of Maceió, Alagoas State, Northeastern Brazil with sky conditions characterized by clearness index (Kt). The Kt was determined by the ratio between global solar irradiance and solar irradiance at the top of the atmosphere. The highest occurrences of daily direct normal solar irradiance under conditions of Kt ≥ 0.6 were recorded between 400 W m−2 and 700 W m−2 for all seasons. Under conditions of 0.4 ≤ Kt &lt; 0.6, the daily direct normal solar irradiance occurred between 200 W m−2 and 500 W m−2 and for conditions of Kt &lt; 0.4, its maximum value was 200 W m−2. It was observed that the levels of solar incidence in the study region depend on cloud cover conditions, with little influence of seasonality.
Optimization of characteristics using Artificial Neural Network for Classification of Type of Lung Cancer

Early detection of lung cancer is a challenging problem because it is associated with the unique structure of cancer cells. In West Sumatra, Radiology Semen Padang Hospital and M Djamil general hospital of Padang as the hospital with the most complete facilities, the number of radiologists is only 4 radiologists. Even though they operate 24 hours, it condition is not enough in handling cancer detection. Accumulated CT scan results makes radiologists work not optimal. Human factors (human error) such as fatigue, not focusing on making the diagnosis wrong. For this reason, a system is needed that can assist radiologists in diagnosing CT scans that can help the radiologist to diagnose faster and reduce errors caused by human error. This paper presents the system using artificial neural network backpropagation method. This study resulted the artificial neural network backpropagation classification system to diagnose CT scans of lung cancer patients. This system has several steps and methods as part of the Computer Aided Diagnosis (CAD) system including segmentation of cancer images for simplifying data input, then feature extraction is done by processing data from the pixel value of the segmentation results by taking five characters, namely the number of areas, mean, standard deviation, curtosis and skewness as features or characteristics of the data. Then using the ANN backpropagation algorithm for the classification stage of cancer types. System testing shows that the results of the system accuracy with hospital diagnosis have training data accuracy of 88.89% and test data of 83.33%.
A mathematical model for image saturation with an application to the restoration of solar images via adaptive sparse deconvolution

Abstract In this paper we introduce a mathematical model of the image saturation phenomenon occurring in a charged coupled device (CCD), and we propose a novel computational method for restoring saturated images acquired by the atmospheric imaging assembly (AIA) telescope. The mathematical model takes into account both primary saturation, when the photon-induced charge reaches the CCD full well capacity, and the blooming effect, when the excess charge flows into adjacent pixels. The restoration of AIA saturated images is then formulated as an inverse problem with a forward operator encoding the standard diffraction of light rays by a convolution, the primary saturation by an upper limit to the number of photons and the blooming effect by the conservation of the photon-induced charge spilled over adjacent pixels. As a result of this theoretical formulation we propose an adaptive ℓ 1 regularized inversion method improving the desaturation capabilities of the existing SE-DESAT method [Guastavino S et al 2019 Astrophys. J. 882 109]. We prove that this method has the consistency estimation property also in the case that a fixed unknown background is considered. We test the adaptive method both in the case of synthetic and real data, comparing the performance with the one of the SE-DESAT method, showing that the proposed method avoids edge effects and artifacts in reconstructions even when the background solar activity is particularly intense.