Reviewerly/exHarmony huggingface dataset:
The Comparison of Feature Selection Methods in Software Defect Prediction

One of the goal in software testing is to discover software defects before the software is used by customer. Successful software testing leads to high quality software. However, exposing a defect in software testing is very resources consuming. Therefore, an automated software defect prediction is needed. In order to build accurate model for prediction, a relevant subset of features must be carefully determined as an input to the classifier. Therefore, this research compares the performance of feature selection method between a kind of filter method, namely ReliefF and a kind of embedded method, namely SVM-RFE (Support Vector Machine - Recursive Feature Elimination). Those methods are free from the assumption of conditional independence among features. Then, SVM is applied as classification algorithm. Previously, SMOTE (Synthetic Minority Oversampling Technique) is used to balance the training data. The experiments are run on benchmark public dataset, NASA MDP dataset. The experiment results show that SVM-RFE perform better than ReliefF in term of g-mean, while ReliefF perform better SVM-RFE in term of accuracy. However, when using SVM-RFE feature selection, the best classifier performance can be achieved with smaller number of features as compared to ReliefF. Future research may explore ensemble feature selection method as an attempt to improve performance of the resulting classifier, both in g-mean and accuracy.
Speaker Verification Employing Combinations of Self-Attention Mechanisms

One of the most recent speaker recognition methods that demonstrates outstanding performance in noisy environments involves extracting the speaker embedding using attention mechanism instead of average or statistics pooling. In the attention method, the speaker recognition performance is improved by employing multiple heads rather than a single head. In this paper, we propose advanced methods to extract a new embedding by compensating for the disadvantages of the single-head and multi-head attention methods. The combination method comprising single-head and split-based multi-head attentions shows a 5.39% Equal Error Rate (EER). When the single-head and projection-based multi-head attention methods are combined, the speaker recognition performance improves by 4.45%, which is the best performance in this work. Our experimental results demonstrate that the attention mechanism reflects the speaker’s properties more effectively than average or statistics pooling, and the speaker verification system could be further improved by employing combinations of different attention techniques.
Understanding Intrinsic Characteristics and System Implications of DAG-based Blockchain

Blockchain is starting to be deployed in industries by enabling Internet of Things (IoT) for autonomous deviceto-device transactions through distributed blockchain-based immutable ledger technology. However, traditional block-based blockchain techniques, such as Bitcoin and Ethereum, are not suitable for IoT transactions due to its low throughput, high computation overhead, and costly transaction fee. To satisfy the requirements of IoT, DAG-based approaches, aiming to provide cheap blockchain services with low latency and high throughput, are emerging. In this paper, we present a set of comprehensive experimental studies on IOTA, a representative DAG-based blockchain, and aim to exhibit its unique characteristics mainly from two aspects: performance and security. We have developed a series of benchmark tools and judiciously selected typical configurations to perform experimental examinations with a real private IOTA network. Our studies reveal several interesting findings: 1) The throughput of IOTA is higher than the traditional block-based blockchain, but far less than the reported thousands TPS in its whitepaper, even with scaling-up configurations; 2) The database query heavily impacts the performance of IOTA, even more than its PoW (Proof of Work) process. We make our benchmark tools in public and expect our works can inspire system architects, application designers, and practitioners with new optimization directions and potential application cases for further exploration.
Noise-tolerant similarity search in temporal medical data

Temporal medical data are increasingly integrated into the development of data-driven methods to deliver better healthcare. Searching such data for patterns can improve the detection of disease cases and facilitate the design of preemptive interventions. For example, specific temporal patterns could be used to recognize low-prevalence diseases, which are often under-diagnosed. However, searching these patterns in temporal medical data is challenging, as the data are often noisy, complex, and large in scale. In this work, we propose an effective and efficient solution to search for patients who exhibit conditions that resemble the input query. In our solution, we propose a similarity notion based on the Longest Common Subsequence (LCSS), which is used to measure the similarity between the query and the patient's temporal medical data and to ensure robustness against noise in the data. Our solution adopts locality sensitive hashing techniques to address the high dimensionality of medical data, by embedding the recorded clinical events (e.g., medications and diagnosis codes) into compact signatures. To perform pattern search in large EHR datasets, we propose a filtering approach based on tandem patterns, which effectively identifies candidate matches while discarding irrelevant data. The evaluations conducted using a real-world dataset demonstrate that our solution is highly accurate while significantly accelerating the similarity search.
P4Fuzz: Compiler Fuzzer forDependable Programmable Dataplanes

Emerging software-defined networks and programmable dataplanes promise to render communication networks more dependable, overcoming today’s manual and error-prone approach to operate networks. Indeed, programmable dataplanes such as P4 provide great opportunities for improving network performance and developing innovative security features, by allowing programmers to reconfigure and tailor switches towards their needs. However, extending programmability to the dataplane also introduces new threat models. In this paper, using a systematic security analysis, we identify a particularly worrisome vulnerability: the automated program compilers which lie at the core of programmable dataplanes. The dataplane compilers introduce a risk of persistent threats which are covert and hard to detect, and may be exploited for large-scale attacks, affecting many devices. Our main contribution is P4Fuzz, a compiler fuzzer to find bugs and vulnerabilities in P4 compilers, in an efficient and automated manner. We discuss the challenges involved in designing such a compiler fuzzer for P4, present our fuzzing and taming algorithms, and report on experiments with our prototype implementation, considering the standard compilers of BMv2, eBPF, and NetFPGA. Our experiments confirm that P4Fuzz is able to generate and test the validity of dozens of P4 programs per minute. Using P4Fuzz, we also successfully found several bugs which have been acknowledged and fixed by the community.
A survey of head pose estimation methods

For computer systems head pose estimating is still a challenge. In this paper, we discuss the inherent difficulties of head pose estimation and describe the evolution of the field in recent years. We simply classify these methods and focus on the advantages and disadvantages of each method, including related articles in the past 10 years. Our comparison of these systems focuses on their capabilities, estimates of rough and fine head pose, and the prominent method is very suitable for unconstrained environments.
A Blockchain-Based Multiplayer Transaction For Tourism Serious Game

A tourism serious game requires a transaction system to handle the transaction process's visualization between players. In this paper, we propose a serious game with a blockchain-based multiplayer transaction system. To integrate the transaction system with the game engine, we use the ethereum platform. In this serious game, ethereum handles the simulation of transaction activities between several characters' choices, including tourists, ticket sellers, jeep drivers, traders, and horse rental. The experimental results show that the gas price variable affects the speed of the transaction process. Meanwhile, setting the gas limit value in this serious game affects the transaction process's success rate between players.
Improved 2D Discrete Hyperchaos Mapping with Complex Behaviour and Algebraic Structure for Strong S-Boxes Generation

This paper proposes to present a novel method of generating cryptographic dynamic substitution-boxes, which makes use of the combined effect of discrete hyperchaos mapping and algebraic group theory. Firstly, an improved 2D hyperchaotic map is proposed, which consists of better dynamical behaviour in terms of large Lyapunov exponents, excellent bifurcation, phase attractor, high entropy, and unpredictability. Secondly, a hyperchaotic key-dependent substitution-box generation process is designed, which is based on the bijectivity-preserving effect of multiplication with permutation matrix to obtain satisfactory configuration of substitution-box matrix over the enormously large problem space of 256!. Lastly, the security strength of obtained S-box is further elevated through the action of proposed algebraic group structure. The standard set of performance parameters such as nonlinearity, strict avalanche criterion, bits independent criterion, differential uniformity, and linear approximation probability is quantified to assess the security and robustness of proposed S-box. The simulation and comparison results demonstrate the effectiveness of proposed method for the construction of cryptographically sound S-boxes.
Overparametrized Deep Encoder-Decoder Schemes for Inputs and Outputs Defined over Graphs

There is a growing interest in the joint application of graph signal processing and neural networks (NNs) for learning problems involving complex, non-linear and/or non-Euclidean datasets. This paper proposes an overparametrized graph-aware NN architecture able to represent a non-linear mapping between two graph signals, each defined on a different graph. The considered architecture is based on two NNs and a common latent space. Specifically, we consider an overparametrized graph-aware NN encoder which maps the input graph signal to a latent space, followed by an overparametrized graph-aware NN decoder that transforms the latent representation to the output graph signal. The parameters of the two NNs are jointly tuned by applying the back-propagation algorithm with an early stopping procedure to prevent overfitting. The overall architecture can be interpreted as an overparametrized graph-aware encoder/decoder NN operating over two different graphs. A key element in the encoder (decoding) scheme is the consideration of a nested collection of parametric graph-aware (down-) up-sampling operators, whose design will be studied in detail. We show by numerical simulations that the proposed scheme outperforms the corresponding benchmark NN architectures, previously introduced in the literature.
Recognition of Pedestrian Traffic Light using Tensorflow And SSD MobileNet V2

Abstract As a government needs to facilitate the whole public including traffic public facilitate. A pedestrian traffic light should be perceived by pedestrian to safe them to cross the street including someone with the vision deficiency problem. Nowadays, a technology supports human being to build some innovations or application more practical. An innovation which adopt the computer vision and deep learning technology can be built to recognize the pedestrian traffic light which is portable and practical in the context of light computing. The paper aim to evaluate threshold value and data parameters to recognition pedestrian traffic light by Tensorflow and SSD MobileNet V2. The sample are green light and red light on pedestrian traffic light. Based on result, more number of data train than data test and higher of threshold value, the accuracy obtained is more better. Object detection accuracy level reaches 97,98%. On the other result, earphone can produce sounds based on detected objects with output accuracy level reaches 100%.
Large cryptocurrency-portfolios: efficient sorting with leverage constraints

Using daily data of the 100 largest cryptocurrencies, we construct the efficient sorting portfolios and the quantile-based sorting portfolios based on ten factors. We find two price factors that can well predict cryptocurrency returns. The efficient sorting portfolios outperform the traditional quantile-based portfolios and the naive 1/N portfolios. The outperformance is largely due to the use of DCC-NL estimator, which captures the dynamic of covariance matrix and meanwhile addresses the curse of dimensionality. In addition, leverage constraints are important for cryptocurrency portfolios to control their risks.
Accelerating Distributed Inference of Sparse Deep Neural Networks via Mitigating the Straggler Effect

Once a Deep Neural Network (DNN) is trained, an inference algorithm retains the learning and applies it to batches of data. The trained DNN can be sparse because of pruning or following a preset sparse connectivity pattern. Inference in such sparse networks requires less space and time complexities compared to dense ones. Similar to dense DNNs, sparse DNNs can be parallelized using model or data parallelism, whereby the former partitions the network and the latter partitions the input among multiple threads. Model parallelism efficiently utilizes the Last Level Cache (LLC) but has a heavy synchronization cost because of compulsory reductions per layer. In contrast, data parallelism allows independent execution of partitions but suffers from a straggler effect due to a load imbalance among partitions. We combine data and model parallelisms through a new type of parallelism that we denote data-then-model. In data-then-model, each thread starts with data parallelism, thus mitigating the per-layer synchronization cost of model parallelism. After it finishes its partition, it switches to model parallelism to support a slower active thread, hence, alleviating the straggler effect of data parallelism. We compare data-then-model parallelism with data and model parallelisms as well as task-based parallelisms using the IEEE HPEC sparse DNN challenge dataset. On average, we achieve up to 10 to 65% speedup compared to these parallelisms.
A Comparative Study on the Maritime Object Detection Performance of Deep Learning Models

With the increasing volume of maritime traffic, the need for maritime surveillance is also increasing. In this situation, unlike the land environment where various data sets are built, the marine environment lacks data, so the progress of technology research is insufficient. Several object detection methods have been proposed and show excellent performance in areas where sufficient data is secured, but performance verification of existing techniques has not been performed on marine images. In this paper, we compare the performance of marine object detection in various marine images with the latest object detection methods and propose an object detection method suitable for marine environments.
Vehicle Density Analysis and Classification using YOLOv3 for Smart Cities

Incorporation of the digital technologies in the surveillance of urban mobility such as monitoring the traffic density will help in improving the quantity of vehicles/arrangements to be provided for the public commutation, facility to be incorporated in reducing the traffic, infrastructure to be provided such as road widening, pedestrian path, over bridge, under pass etc., where traffic and transport is an issue. This can be implemented in the city, at which it is recognized to be developed as a smart city. The proposed research work analyzes the vehicle density using python-OpenCV and YOLOv3. Real time videos are recorded in four directions from Sony HD IP cameras in a designated area. Image frames from video sequence are used to detect moving vehicles. The background extraction method is applied for each frame which is used in subsequent analysis to detect and count all the vehicles. The blobs are detected for each vehicle which helps to track the vehicle in motion. The center of each vehicle with blob gives the count of vehicle based on the lanes considered. This work not only counts the vehicle in real time but also classifies the different vehicles using deep learning technique. YoloV3(You only look once) object detection system is used along with a pretrained model called darknet to classify the vehicle into different categories (bus, car, motorcycle etc). This deep learning method showed better classification and detection rate compared to blobs and morphological method used for counting the vehicles. Classification is shown for vehicle and also person classification is considered to analyze the percentage of people and vehicles. The analysis of percentage of vehicles is shown using pie chart.
An Optimized Implementation of AES-GCM for FPGA Acceleration Using High-Level Synthesis

Field-programmable gate array (FPGA) is growing as a new platform for accelerating heavy computational tasks such as machine learning and cryptography. To making FPGA acceleration as easy as conventional one by graphics processing units, FPGA vendors are providing high-level synthesis tools, such as Xilinx's SDAccel, that synthesize a circuit from a program written by languages such as C, C++, and OpenCL. The benefit of high-level synthesis, however, comes with the stronger abstraction that makes optimization challenging compared to conventional development using a hardware description language, and there is only a limited publications on how to optimize the performance of a synthesized circuit. In this paper, we take the authenticated encryption algorithm AES-GCM as an example, and show the case study on how to optimize the performance: starting from a naive baseline implementation and achieving a fully pipelined implementation that accepts 128-bit message block every cycle. The optimized implementation achieves 392.173 MB/s, which is 50 times faster than the baseline implementation.
An efficient approach for paper submission recommendation

Nowadays, there is a rapidly increasing number of conferences and journals in computer science that make a lot of challenges for researchers to find an appropriate venue to submit their scientific work. There is a need for a recommendation system that can support researchers for a better process of paper submission. In this paper, we present an efficient approach for building such a recommendation model by using embedding methods, Global Vector (GloVe) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> created by Pennington et al. [1] and FastText <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> proposed by Facebook [2], Convolutional Neural Network (CNN) [3], and LSTM. The experimental results show that the combination of CNNs and FastText, CNN + FastText, can achieve the best performance in terms of the Top 1 Accuracy compared with other techniques, including the S2RSCS model, as presented in [4]. Moreover, the performance by using GloVe or FastText is much better, faster, and more stable than S2RSCS in most cases.
On the generalization of the construction of quantum codes from Hermitian self-orthogonal codes

Abstract Many q -ary stabilizer quantum codes can be constructed from Hermitian self-orthogonal $$q^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>q</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:math> -ary linear codes. This result can be generalized to $$q^{2 m}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>q</mml:mi> <mml:mrow> <mml:mn>2</mml:mn> <mml:mi>m</mml:mi> </mml:mrow> </mml:msup> </mml:math> -ary linear codes, $$m &gt; 1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>m</mml:mi> <mml:mo>&gt;</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> . We give a result for easily obtaining quantum codes from that generalization. As a consequence we provide several new binary stabilizer quantum codes which are records according to Grassl (Bounds on the minimum distance of linear codes, http://www.codetables.de , 2020) and new q -ary ones, with $$q \ne 2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>q</mml:mi> <mml:mo>≠</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> , improving others in the literature.
An enhanced approach to the robust discriminant analysis and class sparsity based embedding

In recent times, feature extraction attracted much attention in machine learning and pattern recognition fields. This paper extends and improves a scheme for linear feature extraction that can be used in supervised multi-class classification problems. Inspired by recent frameworks for robust sparse LDA and Inter-class sparsity, we propose a unifying criterion able to retain the advantages of these two powerful linear discriminant methods. We introduce an iterative alternating minimization scheme in order to estimate the linear transformation and the orthogonal matrix. The linear transformation is efficiently updated via the steepest descent gradient technique. The proposed framework is generic in the sense that it allows the combination and tuning of other linear discriminant embedding methods. We used our proposed method to fine tune the linear solutions delivered by two recent linear methods: RSLDA and RDA_FSIS. Experiments have been conducted on public image datasets of different types including objects, faces, and digits. The proposed framework compared favorably with several competing methods.
Design of evaluation system of physical education based on machine learning algorithm and SVM

The evaluation system of physical education is limited by many factors, so the reliability of the quantitative results of its intelligent scoring system is not high. In order to improve the teachingeffect ofphysical education major, this paper combines a machine learning algorithm and SVM to build anevaluation system of physical education. The system uses optimized machine learning as the system algorithm. In order to improve the operating efficiency of the system, this study optimizes the system physical layer certification to improve the system data processing speed and accuracy and uses a three-layer structure to build a basic model of the system structure and analyze its functional modules. Moreover, this study uses a database based on an expert evaluation system for data processing to achieve physical education evaluation and puts forward corresponding improvements. In addition, system performance verification is carried out on the basis of building the system. Through various experimental verifications, we know that the model constructed in this paper has good performance and can be applied to actual physical education.