Reviewerly/exHarmony huggingface dataset:
Neural Networks with ESP32 - Are Two Heads Faster than One?

Processors with multiple cores have enabled machine learning to achieve the great success it has. In the last few years System on Chip devices became powerful enough to be used for machine learning tasks, and some of them have multiple cores. In this paper propagation speed of ESP32 SoC with two cores for feed-forward part of neural network tasks considering the number of used cores will be analysed.
Editing self-image

Technologies for manipulating our digital appearance alter the way the world sees us as well as the way we see ourselves.
A Comparison of Machine and Statistical Time Series Learning for Encrypted Traffic Prediction

This paper studies the utilization of machine and statistical learning methods for predicting encrypted user traffic. To this end, a reference system model and performance metrics for traffic prediction have been defined for enabling on-line training. Based on a collection of representative traffic data sets including various video and web traffic, two different classes of predictors have been evaluated. Our results show that very good prediction performance can be achieved using long short-term memory (LSTM) recurrent neural networks at the price of a significant computational cost for training.
Weakly Supervised Traffic Sign Detection in Real Time Using Single CNN Architecture for Multiple Purposes

We propose a new traffic-sign detection method based on a weakly-supervised multi-purpose single convolutional neural architecture. The base classification network used is the very light convolutional architecture, MobileNetv2, which is used as a region proposal network, in addition to being a classification network. The method is divided into two stages; in the first stage MobileNetv2 is trained to suggest certain regions within the image to classify, while in the second stage it is trained to be a traffic-sign classifier. The method attained few milliseconds of processing time for single image or frame testing and averaged about 55 milliseconds on 800x1300 resolution, while maintaining an acceptable accuracy. This method takes advantage of weak supervision which completely eliminates the time required for dataset annotation. We trained and tested our proposed technique on two datasets that have been broadly used for traffic-sign recognition and detection: the German Traffic Signs Recognition Benchmark (GTSRB) and the German Traffic Signs Detection Benchmark (GTSDB).
OMOPredictor: An Online Multi-Step Operator Performance Prediction Framework in Distributed Streaming Processing

Recently, with the development of distributed stream processing systems, the elastic resource scaling technique has been significantly improved. Many researchers focus on leveraging the approaches based on predicting the trend of data load to implement the elastic scaling. However, the existing predicting methods cannot track and predict the fluctuation of performance online accurately, and they need to utilize more dimensions of the raw data and resources to enhance the performance of prediction. To address these issues, we propose a framework named OMOPredictor to make an accurate prediction of operator performance online. The experimental results show that OMOPredictor can enhance the prediction of the operator performance on three real-world datasets.
Deep Attention-Based Review Level Sentiment Analysis for Arabic Reviews

Sentiment analysis is a branch of machine learning that concerns about classifying the polarity for a given text. Recently, it gained a lot of interest because of the availability of huge amount of opinionated data that needs to be analyzed and interpreted. Recently, using deep learning Artificial Neural Network (ANN) architecture has showed significant improvements with high tendency to reveal the underlying semantic meaning in the input text. However, the output of these models could not be explained and the efficiency could not be analyzed because ANN models are considered as a black box and the success of these models comes at the cost of interpretability. The main motivation of this work is to develop Arabic sentiment analysis system that understands review semantics, without using any linguistic resources. Different scenarios and architectures were examined to test the ability of the proposed model to extract salient words out of the input. The results proved the ability of the proposed model to understand a given review by highlighting the most informative words to the class label. In addition, proposed models are supporting visualization option to get intuitive explanation of the output. The effect of applying transfer learning technique on the problem of Arabic sentiment analysis is experimented as well.
Fully Homomorphic Encryption: An Antidote to Cloud Data Security and Privacy Concerns

Cloud Computing has witnessed tremendous growth in the last decades. Security and privacy of cloud data are two major concerns that have limited the growth and researches witnessed in the area of cloud computing. With Artificial Intelligence and Deep Learning taking prominent role in the decade, individuals and organizations are becoming more uneasy to the issues of security and privacy of cloud data. This article aims to provide an introduction to Fully Homomorphic Encryption as an antidote to the challenges of security and privacy of cloud data computation. The milestones and review of works in the area of Homomorphic Encryption was discussed. An insight into future research direction in the field of Fully Homomorphic Encryption was specified.
BPTrends Redesign Methodology (BPRM) for the Development Disaster Management Prevention Information System

Indonesia's forest fire disaster is still much happening and has become a national problem; if it is not handled thoughtfully, it can be a threat to worry about, and can disrupt the Indonesian economy. According to BMKG, Pekanbaru meteorological station declared a forest fire had struck 11 regencies in Riau Province on July 2019. Indications of the fire were detected from the existence of 38 hot spots in the area. Previous research has been conducted to develop the SI/TI framework for the mitigation of forest fire disaster using TOGAF, which result is a reference in business development to technology design for disaster management. This paper proposes a recommendation for business process development and the design of disaster management technology. BPTrends Redesign method outlines activity and improvement in business process consisting of activity, role, result, technique, and meta-model in the development of disaster management prevention information system, thus obtained a more comprehensive recommendation in its application development. The results of the discussion obtained technology architecture from the Organization Database to Emergency Management information System and overview of the business model in the form of Disaster integration and Safety Information system which explains about internal and external access.
Evaluating Convolution Neural Network optimization Algorithms for Classification of Cervical Cancer Macro Images

In this study, Convolution Neural Network (CNN)-based learning method, which is well-used in deep learning is applied to evaluate optimization algorithms for improved accuracy in cervix cancer detection. The convolutional layer in this study is made up of numerous convolution kernels which are used to compute different feature maps for representations of the inputs. As a result, the architecture of the model consisting of convolutional layers, pooling layers, and fully-connected layers is designed by stacking the network layers. The model is evaluated firstly by increasing the amount of image data through image augmentation. Furthermore, the hyper parameters for optimum performance are chosen. Finally, analysis is performed for Stochastic gradient descent (SGD), Root Mean Square Propagation (RMSprop) and Adaptive Moment Estimation (Adam) optimizers to determine which improves the networks performance for the classification of cervix cancer.
Sample Compression, Support Vectors, and Generalization in Deep Learning

Even though Deep Neural Networks (DNNs) are widely celebrated for their practical performance, they possess many intriguing properties related to depth that are difficult to explain both theoretically and intuitively. Understanding how weights in deep networks coordinate together across layers to form useful learners has proven challenging, in part because the repeated composition of nonlinearities has proved intractable. This paper presents a reparameterization of DNNs as a linear function of a feature map that is locally independent of the weights. This feature map transforms depth-dependencies into simple tensor products and maps each input to a discrete subset of the feature space. Then, using a max-margin assumption, the paper develops a sample compression representation of the neural network in terms of the discrete activation state of neurons induced by s “support vectors”. The paper shows that the number of support vectors s relates with learning guarantees for neural networks through sample compression bounds, yielding a sample complexity of O(ns/E) for networks with n neurons. Finally, the number of support vectors s is found to monotonically increase with width and label noise but decrease with depth.
Increasing validity through replication: an illustrative TDD case

Abstract Software engineering (SE) experiments suffer from threats to validity that may impact their results. Replication allows researchers building on top of previous experiments’ weaknesses and increasing the reliability of the findings. Illustrating the benefits of replication to increase the reliability of the findings and uncover moderator variables. We replicate an experiment on test-driven development (TDD) and address some of its threats to validity and those of a previous replication. We compare the replications’ results and hypothesize on plausible moderators impacting results. Differences across TDD replications’ results might be due to the operationalization of the response variables, the allocation of subjects to treatments, the allowance to work outside the laboratory, the provision of stubs, or the task. Replications allow examining the robustness of the findings, hypothesizing on plausible moderators influencing results, and strengthening the evidence obtained.
Prediction Method of Energy Consumption Based on Multiple Energy-Related Features in Data Center

As the scale of cloud data center continues to expand, issues such as energy consumption and resource utilization are becoming more and more prominent. To improve the energy efficiency of the data center, energy consumption prediction methods are critical to data center energy conservation efforts. However, due to complexity and heterogeneity in cloud computing scenarios, it is difficult to precisely estimate the energy consumption using conventional approaches. To this end, this work presents an energy consumption method based on multiple energy-related features to cope with low energy efficiency. Unlike other methods that focus only on a few performance features, the proposed method screens out 12 key features related to energy and uses the deep learning model for adequate training. In particular, this approach is composed of three main phases including (i) energy-related features acquisition, (ii) essential feature selection, and (iii) energy consumption model establishment. The experimental evaluation shows that the proposed energy consumption prediction method is superior to other energy prediction methods in most cases.
Adaptive Containerization for Microservices in Distributed Cloud Systems

The traditional monolithic on-premises model of application deployment is fast being replaced by a cloud-based microservices paradigm, driven in part by the rise of numerous cloud infrastructure providers providing seamless access to a variety of computing hardware, and the need for applications to serve an ever-increasing audience necessitating scalability. While container-based virtualization has been the preferred method of microservice deployment, Cloud Consumers have not had much opportunity for cost and resource optimization thus far. To this end, this paper introduces a resource allocation framework for the containerized deployment of microservices, called Adaptive Containerization for Microservices in Distributed Cloud Systems, which helps reduce operating costs while ensuring a minimum guaranteed level of service. Further, a variety of performance evaluation metrics have been provided to reinforce the validity of the proposed framework.
Big Data analysis using a metaheuristic algorithm: Twitter as Case Study

Analyzing big data is very common requirement of today; all such requirements become difficult to access when analyzing bulk of data source such as social networking sites which are having a lot of information on daily basis. Twitter is the micro blogging and popular site providing social networking services today. Analyzing big amounts of tweets from twitter to get different patterns and extract relevant information is a big challenge. Apache Spark is the platform that is used to analyze big data efficiently. Therefore, this paper proposes a novel metaheuristic method based on Particle Swarm Optimization and K-means (PSOK). The main idea of our proposed method is to find the optimum cluster-heads from the streaming tweets. The efficiency of our proposed method has been tested on different Twitter datasets by comparing our results with Particle Swarm Optimization (PSO) and Cuckoo Search with K-means (CSK) methods.
A High Capacity Gray Code Based Security Scheme for Non-Redundant Data Embedding

This paper proposes a double layer technique, used to improve the level of security by deploying both cryptography and steganography. The Blowfish algorithm was chosen for the cryptography layer, while an enhanced steganography technique was used for the steganography layer. Embedding is done according to a flag that avoids embedding a redundant bit by highlighting whether one or two bits are embedded per pixel, depending on the redundancy. If the bit to be embedded is similar to the already existing bit, the next bit is embedded in the 7th significant bit position and the flag is modified. The flag position varies depending on whether the pixel is odd or even and its values vary for each color plane. In the steganography layer, a Gray code sequence is used to scramble the order of the pixels in which data will be embedded. This is utilized to make it insurmountable for a third party to detect. 13 different performance measurements are computed and evaluated, to ensure that the image quality remains preserved. The proposed scheme is compared with its counterparts in the literature, in terms of mean square error (MSE), structure similarity index (SSIM), peak signal to noise ratio (PSNR), structural content (SC), maximum embedding capacity, extraction time and maximum Difference. Outstanding numerical results are achieved, showing superiority of the proposed schemes in terms of performance, capacity, and security.
Linear-time Erasure List-decoding of Expander Codes

We give a linear-time erasure list-decoding algorithm for expander codes. More precisely, let r > 0 be any integer. Given an inner code C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> of length d, and a d-regular bipartite expander graph G with n vertices on each side, we give an algorithm to list-decode the expander code C = C(G, C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ) of length nd from approximately δδ,.nd erasures in time n·poly(d2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sup> /δ), where δ and δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> are the relative distance and the r'th generalized relative distance of C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> , respectively. To the best of our knowledge, this is the first linear-time algorithm that can list-decode expander codes from erasures beyond their (designed) distance of approximately δ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> nd. To obtain our results, we show that an approach similar to that of (Hemenway and Wootters, Information and Computation, 2018) can be used to obtain such an erasure-list-decoding algorithm with an exponentially worse dependence of the running time on r and δ; then we show how to improve the dependence of the running time on these parameters.
A Recommender System of Extended Reality Experiences

Interest in Extended Reality (XR) technologies - such as virtual, augmented, and mixed reality - has increased, due to the opportunities offered by such technologies to provide users with live immersive digital experiences. In particular, these technologies for simulations have been widely used in recent years in entertainment and training industries, but have often been limited by the predictability of the simulation, and the lack of personalization in terms of simulation suggestions. This project proposes an Extended Reality simulator that uses Artificial Intelligence to suggest Extended Reality experiences (or scenarios) to users, through a collaborative filtering approach.
Effective Selection of a Compact and High-Quality Review Set with Information Preservation

Consumers increasingly make informed buying decisions based on reading online reviews for products and services. Due to the large volume of available online reviews, consumers hardly have the time and patience to read them all. This article aims to select a compact set of high-quality reviews that can cover a specific set of product features and related consumer sentiments. Selecting such a subset of reviews can significantly save the time spent on reading reviews while preserving the information needed. A unique review selection problem is defined and modeled as a bi-objective combinatorial optimization problem, which is then transformed into a minimum-cost set cover problem that is NP-complete. Several approximation algorithms are then designed, which can sustain performance guarantees in polynomial time. Our effective selection algorithms can also be upgraded to handle dynamic situations. Comprehensive experiments conducted on twelve real datasets demonstrate that the proposed algorithms significantly outperform benchmark methods by generating a more compact review set with much lower computational cost. The number of reviews selected is much smaller compared to the quantity of all available reviews, and the selection efficiency is deeply increased by accelerating strategies, making it very practical to adopt the methods in real-world online applications.
SwitchAgg: A Further Step Towards In-Network Computing

Many distributed applications adopt a partition/aggregation pattern to achieve high performance and scalability. The aggregation process, which usually takes a large portion of the overall execution time, incurs large amount of network traffic and bottlenecks the system performance. To reduce network traffic, some researchers take advantage of the emerging programmable RMT (Reconfigurable Match-Action Table) switch to commit In-Network Aggregation. However, we argue that the architecture of RMT switch is not well-suited for In-Network Aggregation since it is designed primarily for implementing traditional network functions. In this paper, we first show the weakness of RMT switch in handling aggregation tasks, and then we propose SwitchAgg, which is an In-Network Aggregation system that overcomes the shortcomings of RMT. We evaluate the performance improvement we have gained from SwitchAgg. Our results show that, SwitchAgg can aggregate data at line rate and give a high data reduction ratio, which helps us cut down network traffic and alleviate pressure on server CPU. In the system performance test, the job completion time can be reduced as much as 44%.
Effects of Image Augmentation and Dual-layer Transfer Machine Learning Architecture on Tumor Classification

Breast tumor (BT) is the second most common health problem for women. Traditional diagnosis methods can be very labor-intensive and time-consuming with the risk of making a wrong diagnosis. Computer vision and imaging processing techniques using machine learning (ML) methods are emerging to aide in clinical diagnosis. Some machine learning methods have yielded an accuracy of 85% using a single-layer classifier. In this study Inception-V3, a two-layer classifier of transfer machine learning tool was used for image processing with enhancement technologies and for the classification of breast tumor histopathological types. Results showed that image augmentation with dual-layer transfer machine learning algorithms yielded an accuracy of 95.6% in identification of breast tumor pathologic types, which was higher than previously reported methods in the literature. Different image preprocessing methods, dataset preparing methods, and classifier architectures were also studied to identify the optimal algorithm. Results showed that multiple-layer processing algorithms using color images, instead of black and white images, yielded a better accuracy in histopathological type classification.
Analyzing The Security of The Cache Side Channel Defences With Attack Graphs

Note that very limited work is proposed to analyze the security of defenses against the cache side channel attacks on micro-architecture. In this paper, we propose a model based method to generate a visual attack graph and analyze the security of micro-architecture security designs in the early stages of processor design. The experiments indicate that our method can identify the special attack paths that some common security designs fail to defend against and show them in an attack graph.
Low-light Image Enhancement Using Dual Convolutional Neural Networks for Vehicular Imaging Systems

This paper presents a low-light image enhancement method using a convolutional neural network (CNN). Given a lowlight input image, the proposed method converts RGB color space to CIELAB color space. The luminance and chrominance components are separately enhanced. The luminance channel is enhanced using a CNN to enhance the brightness. On the other hand, the chrominance channels are enhanced using a dilated CNN to reduce the color distortion. Experimental results demonstrate that the proposed method can successfully enhance low-light images of a vehicular imaging system without color distortion.
Indexing Biosignal for Integrated Health Social Networks

Rising medical costs and aging populations are major concerns for most countries, including developed countries. Some studies are now mining Health Social Networks (HSNs) as a way of dealing with these concerns. HSN provides a scalable, cost-effective, and fast method for collecting a large amount of user-generated data. However, patients usually have difficulty finding relevant information from social networks. This study aims to develop an Internet of Things (IoT) approach to find keywords to describe medical conditions using patients' biosignals. This study uses the Convolutional Neural Network (CNN) to encode ECG signals into word embedding vectors. Word embedding is a vector projection of words' sentimental features from a context. Similar keywords can be extracted given a vector. Therefore, keywords can be used to search for information from HSN. The average number of keywords correctly predicted is 2 to 3 out of 5. This approach improves the efficiency and effectiveness of information searching in HSNs using biosignal. This study is the first time that index biosignal in HSN.