Reviewerly/exHarmony huggingface dataset:
ProExtor: Mining API Protocols for Program Vulnerability Detection

API protocols play an important role in program verification, testing, evolution and other phases of the software development process. Many approaches have been proposed to mine API protocols automatically from programs. However, few tools are available, especially dynamical API protocol mining tools. In this paper, we present a dynamical API protocol mining tool for Java programs: ProExtor. Our tool mines API protocols in an online mode based on the instrumentation technique of Java agent. For each class, it produces two models: a probabilistic model and a deterministic model. The probabilistic model will be evolved persistently when more application programs are fed for mining. The deterministic model is transformed from the latest probabilistic model, which can be used for program verification, testing, evolution, etc. Both models can be visualized with the software Graphviz. We elaborate design and implementation details of our tool and an application to a real-world program. We believe our work is a good reference for the development of similar tools.
Efficient Training with Heterogeneous Data Distribution

In Edge Learning, training data are non-independently and identically distributed (non-IID). Applying the same learning strategy for all workers fails to work efficiently. In this chapter, we introduce federated learning, where the training data are always non-IID due to data isolation. Then, we summarize enabling technologies for efficient training with non-IID data. We also propose a reinforcement learning-based method that takes non-IID property and resource-constrain into consideration and adjusts the hyper-parameters to accelerate the loss descent efficiency.
Cybersecurity in Oil Storage and Transportation

ABSTRACT Cyber threats to the oil and gas industry have been existent in one form or another for as long as computing and networking systems have utilized to increase the efficiency of production and transportation operations. The number of systems that are utilizing internet-connected technology to aid the industry has risen dramatically over the past 20 years, seeing use on exploration, management of production systems, Supervisory Control and Data Acquisition (SCADA), and supply chain management. As the number of available exploits and attacks against these systems increases over time, it is more necessary than ever to ensure that cybersecurity is in facility and vessel plans. Incorporating cybersecurity measures into the existing security framework will be critical to ensuring that malicious actors do not impact communities and the environment through destructive attacks upon production and transportation. This paper will provide a look at the impact cyberattacks may have on the safe production, storage, and transportation of oil, as well as provide insight as to what industry standards and legal proposals exist to ensure that industry partners are operating securely throughout the US.
Trends In Natural Language Processing : Scope And Challenges

Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyse large amounts of natural language data. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them.NLP combines computational linguistics—rule-based modelling of human language—with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment. Challenges in natural language processing frequently involve speech recognition, natural language understanding, and natural language generation. This paper discusses on the various scope and challenges , current trends and future scopes of Natural Language Processing.
Augmented Reality Gamification of Intra- and Production Logistics

In intra- and production logistics, monotonous work processes reduce employee motivation and concentration. This leads to errors, decreasing willingness to perform and a decline in productivity. To reduce these deficits, gamification and innovative technologies such as augmented reality (AR) are attracting increasing attention in research and practice.In the context of this research field, three hypotheses are put forward that assume a positive influence of AR-supported gamification on the work experience, on productivity and on learning speed. To test the hypotheses, the training application TrainAR was gamified to support the learning of a sorting process. The gamification was evaluated by test subjects who conducted three test runs with the gamified application. The test runs each consisted of sorting ten packages for which the trainees achieved scores depending on their performance. Before and after the experiment they answered questionnaires. The results of these experiments supported the hypotheses or provided valuable information towards further research.Furthermore, a motivating effect of the game elements implemented in TrainAR - rank badges, points, feedback, humanity hero, ranking lists, progress bars, time measurement, achievements and story – were observed among subjects.
Modeling in the Process of Verifying a Suspects and a Defendants Alibis

The author examines the use of modeling in the verification of a suspect’s and a defendant’s alibis. In the conditions of digitization, there are wide prospects for the use of such a method. Alibi verification can be viewed as a special case of the criminalistic reconstruction of the event of the crime, and the modeling method is morphologically included in the process of reconstructing the investigated event or its part. Flowcharts are designed with the use of the working systems of alibi verification proposed by the author, they reflect forensically relevant features of the investigated object that should be selected from the flow of information as information blocks and properly organized. Information blocks are interconnected and correlate with each other. The more content each block contains, the more accurate the result produced by the constructed model will be. The author concludes that modeling should result in building an adequate model of an episode of the criminal event under investigation that will reflect data on the “history” of the alibi and will make it possible to predict its variants.
Innovative Methodologies for Mobile Security and Cryptography

As mobile phones are evolving; security problems are increasing. On one side, attackers are constantly trying to crack all encryption algorithms and security procedures followed to access the users&#x2019; data, and on the other side, providers are continuously trying to respond to those attacks. This article discusses the algorithms used to encrypt the data on mobile phones, the weaknesses they have, the attacks they encountered, and the enhancements done on them.
A Selection Support System for Enterprise Resource Planning Package Components using Ensembles of Multiple Models with Round-trip Translation

An enterprise resource planning (ERP) package consists of software to support day-to-day business activities and contains multiple components. System engineers combine the most appropriate software components for system integration using ERP packages. Because component selection is a very difficult task, even for experienced system engineers, there is a demand for machine-learning-based systems that support appropriate component selection by reading the text of requirement specifications and predicting suitable components. However, sufficient prediction accuracy has not been achieved thus far as a result of the sparsity and diversity of training data, which consist of specification texts paired with their corresponding components. We implemented round-trip translation at both training and testing times to alleviate the sparsity and diversity problems, adopted pre-trained models to exploit the similarity of text data, and utilized an ensemble of diverse models to take advantage of models for both the original and round-trip translated data. Through experiments with actual project data from ERP system integration, we confirmed that round-trip translation alleviates the problems mentioned above and improves prediction accuracy. As a result, our method achieved sufficient accuracy for practical use.
Using Regularity Unit As Guidance For Summarization-Based Image Resizing

In this paper, we propose a novel algorithm for summarization-based image resizing. In the past, a process of detecting precise locations of repeating patterns is required before the pattern removal step in resizing. However, it is difficult to find repeating patterns which are illuminated under different lighting conditions and viewed from different perspectives. To solve the problem, we first identify the regularity unit of repeating patterns by statistics. Then we can use the regularity unit for shift-map optimization to obtain a better resized image. The experimental results show that our method is competitive with other well-known methods.
Parallel Planar Approximation for Large Networks

Planarity is an extensively studied topic within the graph theory domain. Planarity describes the potential for a graph to be embedded on a Euclidean plane without having any of its edges cross, and planarity is consequently a subject of particular interest in the context of graphs with nodes representing entities that are subject to physical constraints. While graph planarity has been extensively studied as a theoretical topic, it has also been successfully practically applied to architectural design [1] and circuit board design [2]. Our prior work found planarity to be a topic of interest in biological networks, specifically with regard to protein-protein interaction and domain-domain interaction networks. This work presents a novel parallel algorithm that produces planar approximations of large networks, which may be used to assess the relative planarity of large networks, for further analyses or ensemble methods, and visualization.
Why security needs to stop blaming the end user

Abstract To protect the most vulnerable, security education and awareness campaigns need also to target leaders, managers, architects, operators, administrators and developers. Oscar O’Connor FBCS presents his views and recommendations.
A More Scalable Deep-learning Processing Unit For Depthwise Separable Convolution

Due to the excellent energy efficiency and real-time performance, FPGA has gradually become an important computing platform for CNN inference. However, most FPGA based Deep-learning Processing Units (DPU) are not scalable enough to cope with the rapid changes in both operator type and network structure of convolutional neural networks (CNNs). To solve this problem, we proposed the Dataflow Driven Multi-core Architecture to improve the scalability of DPU. It implements different computational functions in various modules, which are connected by the streaming structures. Firstly, we designed the Basic-DPU based on the architecture, which is very efficient for standard convolution such as VGG, SSD, etc. To verify the architecture&#x2019;s scalability, we then added a function module into Basic-DPU to obtain the Extended-DPU, which can accelerate both standard convolution and depthwise separable convolution in high computational efficiency. Finally, the Basic-DPU and Extended-DPU are implemented and evaluated on Xilinx xczu9eg. The experimental results show that their FPGA resource consumptions are almost the same. For the standard convolution, the actual performance reaches 470.3GOPS and 471.5GOPS. With the same test algorithm, the computational efficiency is over 90&#x0025; for both of them, which is almost 1.69 times higher than the equivalent FPGA implementation. For depthwise separable convolution, their actual performance reaches 183.3GOPS and 245.2GOPS. The computational efficiency of Extended-DPU is 1.3 times that of Basic-DPU and 2.1 times that of the peer FPGA implementation.
Technological trends of higher education development

The aim of this paper is to present and analyse the Internet of Things and Technology-Supported Learning Environments, which represent current technological trends of higher education development. The method used in the paper is content analysis of available scientific literature - theoretical and empirical. It is shown how the application and integration of these technologies trigger significant socio-technological changes in the conditions in which higher education operates. The results show that the application of these technologies enables material savings, that it can result in increasing of the efficiency and effectiveness of the higher education processes and in adapting of the teaching process to the individual needs of students. The paper further discusses the challenges and risks that these technological change can bring to higher education and society as a whole. In the conclusion, it is pointed out that despite the numerous advantages that are being realized, it is necessary to develop human resource competencies and find solutions related to the ethical use of data.
BEV Detection and Localisation using Semantic Segmentation in Autonomous Car Driving Systems

In autonomous vehicles, the perception system plays an important role in environment modeling and object detection in 3D space. Existing perception systems use various sensors to localize and track the surrounding obstacles, but have some limitations. Most existing end-to-end autonomous systems are computationally heavy as they are built on multiple deep networks that are trained to detect and localize objects, thus requiring custom, high-end computation devices with high compute power. To address this issue, we propose and experiment with different semantic segmentation-based models for Birds Eye View (BEV) detection and localization of surrounding objects like vehicles and pedestrians from LiDAR (light detection, and ranging) point clouds. Voxelisation techniques are used to transform 3D LiDAR point clouds to 2D RGB images. The semantic segmentation models are trained from the ground up on the Lyft Level 5 dataset. During experimental evaluation, the proposed approach achieved a mean average precision score of 0.044 for UNET, 0.041 for SegNet and 0.033 for FCN, while being significantly less compute-intensive when compared to the state-of-the-art approaches.
Survey on Virtualization of High Performance Computing

With the increase of the user's demand for High Performance Computing (HPC), the hardware resources have been continuously developed and expanded. The interconnections between the various parts of HPC systems are increasingly complex. How to maximize the performance of the system and how to maximize the utilization of resources have become the major problem. The integration of virtualization technology improves the resource utilization of HPC system, but loses some performance. The multi-user usage scenarios and the pursuit of performance in HPC system put forward high requirements for virtualization technology. The highly customized nature of HPC systems also adds to the challenges of virtualization. This paper introduces operating system-level and application-level virtualization in HPC system, and discusses the current problems and the future development direction.
Contextualization of Design Qualities in Interactive Story-Based Visualization Applied to Engineering

Visualization is widely employed in the engineering industry to fulfill the increasing demand for robust data exploration and insight enhancement tools. It is valued for its ability to improve the performance and efficiency of various engineering tasks. Narrative visualization is a growing interdisciplinary field with a great potential for mission-critical, pressure-filled, and time-sensitive operations. Because of the novelty of the discipline, there is a lack of research focused on design strategies of story-based data representation in the engineering domain. Based on the existing visualization research, we identified four prominent design qualities: aesthetics, usability, novelty, and complexity; and examined them in the context of interactive story-focused engineering visualization. We concluded that: 1) Terminology needs to be re-evaluated in the new context; 2) All four design dimensions we analyzed are subjective and prone to personal interpretation; 3) All four design qualities can be both positive and negative, depending on the context; 4) Trade-off between design qualities has to be made; 5) There is a lack of completed case studies in the field. We hope this research will be beneficial for bridging the gap between storytellers, visualization designers, and engineers, as well as establishing design strategies for this emerging visualization form.
CNN Based Architecture for Automatically Detecting People without Face Mask

COVID-19 emerged as one of the major outbreak to human society and has been declared as pandemic by World Health Organization (WHO). The first phase was almost over in the month of February 2021 in India, but soon after second wave emerged out with greater impact. The whole world is struggling hard to contain the spread of virus but finding it difficult, as new mutations and variants are taking shape continuously. Many mathematical models have been designed to predict spread of COVID-19, but prediction fails due to evolution of virus as well as its behavior. WHO provided many guidelines to prevent spread of COVID-19 which includes social distancing, wearing of masks in public places and frequent sensitization of hands. Wearing of mask has been proved to be the most effective in preventing the spread of corona virus. Wearing masks perhaps is the most important life style change which could help contain the spread of virus specially in offices, malls, theaters, restaurants and other public places. Though the administration frequently issues guidelines to wear masks but it is really very difficult to identify the peoples without mask in large gatherings. In the present manuscript, an automatic mask detection system has been proposed using machine learning to automatically identity the people without masks.
Relational and NoSQL Databases: The Appropriate Database Model Choice

For over four decades, Relational database management systems RDBMS have been the primary model for data storage, retrieval and management. However, due to the continuous information growth in current organizations and the increasing needs for scalability and performance, specially while handling a very huge amount of data that generated by various new generation real time applications or social networking sites that could be unstructured or semi-structured data, poses a set of challenges to the existing RDBMS Vendors. Such challenges have created a need for adaptation alternative technologies in the field of data storage and manipulation. NoSQL technology is the alternative category of Database Management Systems that have been emerged as the solution to the ever-growing data requirements. In this paper, the advantages and the limitations of relational databases we will be presented. The NoSQL data model, types of NOSQL data stores, characteristics of each data store, advantages and disadvantages of NoSQL over RDBMS will also be discussed. The paper helps the interest users to take a review of the different database model solutions, which can serve as a base for selecting the proper database model that can satisfy their application requirements.
Predictive BPaaS management with quantum and neural computing

Abstract With the increasing adoption of cloud computing, the deployment and management of business processes over cloud environments have become an essential operation for most enterprises, leading to the emergence of BPaaS (Business Process as a Service) as a new cloud service model. This SaaS‐like service, like its ancestors, should be strategically distributed and managed over multiple cloud zones, while taking into account several constraints and conditions (e.g., sensitivity of BPaaS fragments, insecure and untrusted cloud zones, lack of resources, and workload changes). However, current BPaaS approaches are static, which means that they are no longer suitable to manage such enterprise‐oriented cloud service model and to deal with the uncertain and dynamic nature of cloud availability zones. To fill this gap, we adopt a predictive BPaaS management strategy by proposing a model that forecasts the next‐short time overload of cloud zones. These latter, as hosting environments for the managed BPaaS, are categorized as overloaded or underloaded, which triggers the migration of BPaaS fragments to high‐performance cloud zones. The proposed neural network prediction model (called QGA‐NN) is enhanced with a quantum genetic algorithm to optimize the prediction of cloud zones' overload. QGA‐NN is evaluated using a BPaaS placement algorithm, which we defined as a triggered management operation. Experimental results have proved the accuracy and effectiveness of our predictive approach, compared with state‐of‐the‐art solutions.
A Feature Extraction Approach Based on LBP Operator and Complex Networks for Face Recognition

Face recognition is one of the most important tasks in computer vision research. Despite the advances achieved, novel approaches are needed to extract meaningful information and produce interpretable results. This work presents a new approach to feature extraction, called LBP-CN, based on Local Binary Pattern (LBP) and Complex Network (CN) measurements for face recognition. Initially, the face region is subdivided into smaller regions and the network nodes are defined from the pixels of these sub-regions. The LBP operator is applied to the pixels of the subregions and the edges are defined by the distances between all pixels. Therefore, measurements of complex networks are extracted from the network and thresholds are applied to remove edges with higher values. Five face data sets and four competing methods were adopted to evaluate the proposed approach. The results show the adequacy and robustness of the proposed approach to facial recognition, getting better accuracy and smaller standard deviation than the competing methods in a relatively simple way, using only two parameters. In addition, the LBP-CN results for the HPI and FEI data sets also showed the adequacy of the proposed approach to angle variations.
Cloud-Based Smart Energy Framework for Accelerated Data Analytics with Parallel Computing of Orchestrated Containers: Study Case of CU-BEMS

This paper proposes a practical smart energy framework for data analytic on energy management system at Chulalongkorn University, called CU-BEMS. This serves as an example of demand-sided smart energy application that copes with the challenges of big data analytic and real-time processing needs. The framework is based on the divide and conquer paradigm to accelerate data analytics with parallel computing. The workload is containerized and deployed on the Kubernetes cloud facility of our internationally collaborated [email protected] playground. With this playground, the workload scalability and portability can be achieved. Applying the proposed framework, this paper reports on a practical data log analysis to determine the wasted energy consumption. Based on the experimental result, the wasted energy consumption of the whole data set of CU-BEMS's communication research laboratory area from March 2014 to August 2017 can be computed within 81 seconds by using 32 cores running in parallel. The framework is expected to serve as a basis template for further research ongoing at CU-BEMS and smart energy applications that can be computationally enhanced by data analytic pipelining with containerized services as orchestrated by Kubernetes.