Reviewerly/exHarmony huggingface dataset:
Interface protocol inference to aid understanding legacy software components

Abstract High-tech companies are struggling today with the maintenance of legacy software. Legacy software is vital to many organizations as it contains the important business logic. To facilitate maintenance of legacy software, a comprehensive understanding of the software’s behavior is essential. In terms of component-based software engineering, it is necessary to completely understand the behavior of components in relation to their interfaces, i.e., their interface protocols, and to preserve this behavior during the maintenance activities of the components. For this purpose, we present an approach to infer the interface protocols of software components from the behavioral models of those components, learned by a blackbox technique called active (automata) learning. To validate the learned results, we applied our approach to the software components developed with model-based engineering so that equivalence can be checked between the learned models and the reference models, ensuring the behavioral relations are preserved. Experimenting with components having reference models and performing equivalence checking builds confidence that applying active learning technique to reverse engineer legacy software components, for which no reference models are available, will also yield correct results. To apply our approach in practice, we present an automated framework for conducting active learning on a large set of components and deriving their interface protocols. Using the framework, we validated our methodology by applying active learning on 202 industrial software components, out of which, interface protocols could be successfully derived for 156 components within our given time bound of 1 h for each component.
IDAR

Supergraph search is one of fundamental graph query processing problems in many application domains. Given a query graph and a set of data graphs, supergraph search is to find all the data graphs contained in the query graph as subgraphs. In existing algorithms, index construction or filtering approaches are computationally expensive, and search methods can cause redundant computations. In this paper, we introduce four new concepts to address these limitations: (1) DAG integration, (2) dynamic programming between integrated DAG and graph, (3) active-first search, and (4) relevance-size order, which together lead to a much faster and scalable algorithm for supergraph search. Extensive experiments with real datasets show that our approach outperforms state-of-the-art algorithms by up to orders of magnitude in terms of indexing time and query processing time.
Discrete Atomic Compression with Different Structures of Discrete Atomic Transform: Efficiency Comparison and Perspectives of Application to Digital Images Privacy Protection

Discrete atomic compression (DAC) is an algorithm for lossy compression of digital images. Discrete atomic transform (DAT) is a core of this algorithm. An important feature of DAT is the ability to vary its structure, as well as the atomic function on which it is based. In this paper, we consider DAC with several different structures of the DAT-procedure. We show that such changes have no significant effect on efficiency of the algorithm in terms of compression ratio and loss of quality measured by maximum absolute deviation and root mean square error. This feature opens up wide opportunities for application of DAC to digital image privacy protection. We also compare DAC with JPEG. It is shown that DAC with different structures of DAT provides higher memory savings with the same quality of the result.
A 1.15 TOPS/W Energy-Efficient Capsule Network Accelerator for Real-Time 3D Point Cloud Segmentation in Mobile Environment

An energy-efficient capsule network accelerator is proposed for real-time 3D point cloud segmentation in mobile devices. The proposed accelerator adopts the pipelined heterogeneous core architecture to achieve 1.55× throughput enhancement. Furthermore, the proposed dynamic route skipping controller predicts unimportant operations and skips them to reduce the external memory access by 39.1%. At last, the new squash activation function unit exploits the look-up table (LUT) based computing with L2-norm approximation to minimize the power and area overhead. The proposed architecture is implemented with the FPGA, Altera Cyclone V 5CEBA9F31C7 and we test capsule network-based 3D point cloud segmentation application. It consumes 2.15 W power and shows 0.05 TOPS/W energy-efficiency. Also, the architecture is simulated with the 65nm CMOS technology, showing 94.3mW power consumption and 1.15 TOPS/W energy-efficiency.
Deep neural network as deep feature learner

Features play an important role in image processing. But as not all features are comparable, relative features emerged. From the beginning, low-level features, extracted by experts, have been employed to create difficult models for learning the problem of relative attribute. Knowing these models ar e limited in generality of their applicability, deep learning models can be employed instead of them. A deep artificial neural network framework has been suggested for the task of relative attribute prediction in this article. The paper suggests to use a convolutional artificial neural network for learning the mentioned attributes through a peripheral auxiliary layer, called also a ranking layer, which is able to learn how to rank the images. A suitable ranking cost function is used to train the whole network in an end-to-end manner. The suggested method through this paper is experimentally superior to the state of the art methods on some well-known benchmarks. The experimental results indicate that the proposed method is capable of learning the problem of relative attribute.
Aquila: Adaptive Parallel Computation of Graph Connectivity Queries

Graph connectivity algorithms answer whether two nodes in a graph are connected under specific conditions, which are beneficial to a number of applications, such as pattern recognition and cybersecurity. Unfortunately, existing graph computing frameworks support only a small number of connectivity algorithms and achieve low computation parallelism. In this paper, we have designed an adaptive parallel computation framework, Aqila, that covers a wide range of different highly optimized graph connectivity algorithms. Given a graph, Aqila first transforms the query if it can be answered with partial computation. During the computation, Aqila is able to greatly reduce the workload by up to 98%. Furthermore, Aqila identifies the irregular tasks in the connectivity algorithms and applies different parallel strategies for different tasks. As a result, Aqila significantly outperforms existing systems such as Multistep, Galois, Ligra, GraphChi, X-Stream, DFS, and Boost, by average 13x, 53x, 264x, 364x, 1,369x, 45x, and 255x, respectively.
Towards Integrating True Random Number Generation in Coherent Optical Transceivers

The integration of quantum communication functions often requires dedicated opto-electronic components that do not bode well with the technology roadmaps of telecom systems. We investigate the capability of commercial coherent transceiver sub-systems to support quantum random number generation next to classical data transmission, and demonstrate how the quantum entropy source based on vacuum fluctuations can be potentially converted into a true random number generator for this purpose. We discuss two possible implementations, building on a receiver- and a transmitter-centric architecture. In the first scheme, balanced homodyne broadband detection in a coherent intradyne receiver is exploited to measure the vacuum state at the input of a 90-degree hybrid. In our proof-of-principle demonstration, a clearance of >2 dB between optical and electrical noise is obtained over a wide bandwidth of more than 11 GHz. In the second scheme, we propose and evaluate the re-use of monitoring photodiodes of a polarization-multiplexed inphase/quadrature modulator for the same purpose. Time-interleaved random number generation is demonstrated for 10 Gbaud polarization-multiplexed quadrature phase shift keyed data transmission. The availability of detailed models will allow to calculate the extractable entropy and we accordingly show randomness extraction for our two proof-of-principle experiments, employing a two-universal strong extractor.
Comparison of performance of k-nearest neighbor algorithm using smote and k-nearest neighbor algorithm without smote in diagnosis of diabetes disease in balanced data

Abstract According to the Indonesian Health Profile in 2017, diabetes is one of the causes of death for almost 70% in the world. The high mortality rate induces the need for making the effort to reduce the number of people with diabetes by conducting studies that lead to making a diagnosis so that can detect a person with diabetes accurately. This study tries to compare the performance of the K-Nearest Neighbors algorithm using Synthetic Minority Over-sampling Technique and the K-Nearest Neighbors algorithm without Synthetic Minority Over-sampling Technique in diagnosing diabetes on imbalanced datasets. The parameters tested are the k value of the K-Nearest Neighbors and Synthetic Minority Over-sampling Technique. The testing is carried out using the K-Fold Cross Validation strategy. The data used in this study were 3876 data from Pertamina Central Hospital. Based on the results of tests conducted, it shows that the value of accuracy produced in diagnosing diabetes by using Synthetic Minority Over-sampling Technique is better than the accuracy produced without using Synthetic Minority Over-sampling Technique with the highest accuracy increase of 8.25%. The highest average accuracy is obtained when the value of k = 3 in the K-Nearest Neighbors, k = 5 in the Synthetic Minority Over-sampling Technique, and fold = 10, which reaches 78.06%.
Human Activity Recognition Using Deep Learning: A Survey

Human activity recognition refers to predict what a person is doing from series of the observation of person’s action and surrounding conditions using different techniques. It is an active research area providing personalized support for various applications and its association with a wide range of fields of study like medicinal services, dependable automation developing, and smart surveillance system. This paper provides an overview which gives idea about some existing research methods on human activity recognition. It describes a general view on the state of the art for human activity recognition and shows comparative studies between existing research which consist of various methods, evaluation criteria, and features. It also comprises benefits and limitations of various methods to provide researchers to propose new approaches.
Handwritten Character Recognition Using KNN and SVM Based Classifier over Feature Vector from Autoencoder

Optical character recognition system is a necessity for the field of man-machine interaction. Handwritten character recognition is a subset of OCR technique by which computer classifies the handwritten alphabets as well as digits. In this work, we present four methods using a vanilla Autoencoder and a Convolutional Autoencoder. For classification purpose we have used KNN, SVM based classifiers such as hybrid KNN-SVM and ν-SVM. We evaluated our proposed models on different handwritten scripts such as EMNIST, Devanagari Handwritten Character, and Kannada-MNIST. Autoencoders are generally used to reduce the dimension of the dataset in a non-linear manner and hence extract features for efficient data representation. Baseline of our approach has been different combination of deep learning based feature extraction methods with classifiers. Our developed CNN models consists of less number of layers yet achieved results comparable to other state-of-the-art. A detailed justification for differences in accuracy of our proposed models are discussed in the main article.
Application of Neuroevolution in Autonomous Cars

With the onset of electric vehicles, and them becoming more and more popular, autonomous cars are the future in the travel/driving experience. The barrier to reaching level 5 autonomy is the difficulty in the collection of data that incorporates good driving habits and the lack thereof. The problem with current implementations of self-driving cars is the need for massively large datasets and the need to evaluate the driving in the dataset. We propose a system that requires no data for its training. An evolutionary model would have the capability to optimize itself towards the fitness function. We have implemented neuroevolution, a form of genetic algorithm, to train/evolve self-driving cars in a simulated virtual environment with the help of Unreal Engine 4, which utilizes Nvidia’s PhysX Physics Engine to portray real-world vehicle dynamics accurately. We were able to observe the serendipitous nature of evolution and have exploited it to reach our optimal solution. We also demonstrate the ease in generalizing attributes brought about by genetic algorithms and how they may be used as a boilerplate upon which other machine learning techniques may be used to improve the overall driving experience.
Bayesian Model Selection of Gaussian Directed Acyclic Graph Structures

Summary During the last years, graphical models have become a popular tool to represent dependencies among variables in many scientific areas. Typically, the objective is to discover dependence relationships that can be represented through a directed acyclic graph (DAG). The set of all conditional independencies encoded by a DAG determines its Markov property. In general, DAGs encoding the same conditional independencies are not distinguishable from observational data and can be collected into equivalence classes, each one represented by a chain graph called essential graph (EG). However, both the DAG and EG space grow super exponentially in the number of variables, and so, graph structural learning requires the adoption of Markov chain Monte Carlo (MCMC) techniques. In this paper, we review some recent results on Bayesian model selection of Gaussian DAG models under a unified framework. These results are based on closed‐form expressions for the marginal likelihood of a DAG and EG structure, which is obtained from a few suitable assumptions on the prior for model parameters. We then introduce a general MCMC scheme that can be adopted both for model selection of DAGs and EGs together with a couple of applications on real data sets.
Machine learning control — explainable and analyzable methods

Recently, the term explainable AI came into discussion as an approach to produce models from artificial intelligence which allow interpretation. For a long time, symbolic regression has been used to produce explainable and mathematically tractable models. In this contribution, we extend previous work on symbolic regression methods to infer the optimal control of a dynamical system given one or several optimization criteria, or cost functions. In earlier publications, network control was achieved by automated machine learning control using genetic programming. Here, we focus on the subsequent path continuation analysis of the mathematical expressions which result from the machine learning model. In particular, we use AUTO to analyze the solution properties of the controlled oscillator system which served as our model. As a result, we show that there is a considerable advantage of explainable symbolic regression models over less accessible neural networks. In particular, the roadmap of future works may be to integrate such analyses into the optimization loop itself to filter out robust solutions by construction.
Entrepreneurship, Innovation, and Economic Growth: Evidence from Saudi Arabia

There have been growing interests on studies focusing on the shifting from resource economy to knowledge economy, and entrepreneurship and innovation seem to take leading roles in such discourse. Despite the fact that entrepreneurship has been recognized for the last few decades as an essential component of economic growth, which is facilitated by technological innovation, yet several studies could not establish such, especially in developing economies. Since Saudi Arabia government is dedicated to the achievement of Vision 2030 among which are diversifications of the economy from oil, increase the participation of small and medium enterprises and reduction of unemployment, there is a need for this study. This study examines the impact of entrepreneurship and innovation on economic growth in Saudi Arabia using the data obtained from World Bank Development Indicators (WDI) for the period 2005–2016. The result of the regression analysis revealed that innovation positively and significantly impacted on entrepreneurship. The results of the endogenous growth model further reveal that while entrepreneurship and innovation positively influence economic growth, the impact of entrepreneurship is significant but that of innovation is not significant on economic growth. This implies that entrepreneurship directly impacted on economic growth but innovation only impact on economic growth through entrepreneurship. Saudi government should be committed to further create a more suitable entrepreneurship ecosystem whereby improved entrepreneurial framework conditions, such as increasing the level of R&D transfers and innovation, finance for entrepreneurs, enhancing post-school entrepreneurship training and encouraging the participation of the private sector are achieved.
Risk Management in Information Systems Projects

Information technologies (IT) and information systems (IS) are the backbone of any developed business, and organizations without them cannot compete. In recent decades, many best practices standards, and guides have been made available to project managers and organizations aimed to improve project management. Unfortunately, IS projects continue to show a poor track record, and problems related to project management performance persists. Risk management has a vital role in this context since it can increase the likelihood and impact of positive events, and decrease the likelihood and impact of adverse events in the project. This article presents the results of an international web-based survey, studying if risk management processes are being implemented consistently in IS project management. The obtained results show low levels of risk management processes implementation and reinforce the idea that “it can be risky not to do risk management,” demanding more research in this area.
Sentiment Digitization Modeling for Recommendation System

As the importance of providing personalized services increases, various studies on personalized recommendation systems are actively being conducted. Among the many methods used for recommendation systems, the most widely used is collaborative filtering. However, this method has lower accuracy because recommendations are limited to using quantitative information, such as user ratings or amount of use. To address this issue, many studies have been conducted to improve the accuracy of the recommendation system by using other types of information, in addition to quantitative information. Although conducting sentiment analysis using reviews is popular, previous studies show the limitation that results of sentiment analysis cannot be directly reflected in recommendation systems. Therefore, this study aims to quantify the sentiments presented in the reviews and reflect the results to the ratings; that is, this study proposes a new algorithm that quantifies the sentiments of user-written reviews and converts them into quantitative information, which can be directly reflected in recommendation systems. To achieve this, the user reviews, which are qualitative information, must first be quantified. Thus, in this study, sentiment scores are calculated through sentiment analysis by using a text mining technique. The data used herein are from movie reviews. A domain-specific sentiment dictionary was constructed, and then based on the dictionary, sentiment scores of the reviews were calculated. The collaborative filtering of this study, which reflected the sentiment scores of user reviews, was verified to demonstrate its higher accuracy than the collaborative filtering using the traditional method, which reflects only user rating data. To overcome the limitations of the previous studies that examined the sentiments of users based only on user rating data, the method proposed in this study successfully enhanced the accuracy of the recommendation system by precisely reflecting user opinions through quantified user reviews. Based on the findings of this study, the recommendation system accuracy is expected to improve further if additional analysis can be performed.
How do they find us? A study of geolocation tracking techniques of malicious web sites

Geolocation cloaking is a process in which varying and customised web content is delivered to visiting users based on the geographical information derived from the users' system and network variables. Geolocation cloaking allows a malicious web site to: 1) Increase the success rate of an attacks by targeting a specific population using sociocultural attributes of the visiting user and, perform targeted social engineering attacks. 2) Deliver benign content to requesting users (or detection systems) who do not reside in the geographical location specified by the attacker and, subsequently limit exposure and bypass detection entirely regardless of the detection engine utilised. In this paper we provide an overview of the range of geolocation detection techniques which could potentially be used to estimate the location of a visiting user and perform geolocation cloaking attacks. We discuss these systems in terms of their operation and feasibility to be utilised by a malicious web site.
Optimal video communication strategy for intelligent video analysis in unmanned aerial vehicle applications

For Unmanned Aerial Vehicles (UAV), the intelligent video analysis is a key technology in intelligent autonomous control, real-time navigation and surveillance. However, poor UAV wireless links would degrade the quality of video communication, leading to difficulties in video analysis. To meet the challenges of packet-loss and limited bandwidth in adverse UAV channel environments, this paper proposes a parameter optimization mechanism for UAV intelligent video analysis. In the proposed method, an Optimal Strategy Library (OSL) is designed to optimize the parameters for video encoding and forward error correction. Adapted to the packet-loss rate and bandwidth in practical UAV wireless network, the proposed OSL can facilitate the encoding of video sequences and the recovery of degraded videos with optimal performance. Experimental results demonstrate that the proposed solution can keep intelligent video analysis working efficiently with adverse UAV wireless links, and is capable of maximizing the inference accuracy of Multi-Object Tracking (MOT) algorithms in various scenarios.
Developing an Educational Framework for Using WhatsApp Based on Social Constructivism Theory

Social media applications are one of the promising tools in facilitating the educational process. Among these applications, WhatsApp was recently suggested as one of the most popular applications used by students. However, there is a scarce of a framework that can fully utilize the WhatsApp for educational purposes. To bridge this gap and to draw upon the social constructivism framework, the aim of this study is to develop a social constructivism framework for WhatsApp (SCFW) that enables both formal (in-class) and informal (off-class) learning environment. The SCFW framework is based on the principle that educators can use WhatsApp closed groups to enable learners to construct and share knowledge with their peers. This has been accomplished through the integration of four aspects, including device aspect, social aspect, learner aspect, and teacher aspect.