Reviewerly/exHarmony huggingface dataset:
Bridges Strengthening by Conversion to Tied-Arch Using Monarch Butterfly Optimization

The problemGardella, Orlando existingCrawford, Broderick in bridgesSoto, Ricardo thatLemus-Romani, José collapse due to undermining of the piers that sustain it, as well as collapses due to hydraulic action, generateAstorga, Gino highSalas-Fernández, Agustín costs that can be reduced, making the repair of its structures. It is possible to reinforce them by modification, incorporating cable-stayed arches, and tensioning of hangers to support them. In this paper, a new approach is presented to solve the problem effectively, using a modern metaheuristic based on nature called monarch butterfly optimization it works imitating the way of migration, who uses the monarch butterflies. The results obtained are compared with those provided by black hole algorithm through the use of a known statistical test.
Integrated Information System Based on Google Maps APIs: Design of Surabaya Public Transportation System

Public transportation is a passenger transportation service that can be used by the public, managed and operated on a designated route, and charged for each trip. Public transportations with fixed route often used by the people of Surabaya are share taxis (Angkutan Kota), city buses, bus rapid transit system (Suroboyo Bus), and commuter trains. There are many transportation modes in Surabaya, but there is no integrated information system of it. We propose a system designed to help passengers feel ease to access and choose any public transportation in Surabaya. The proposed system offers information to help passengers know the easier travel route, monitoring vehicle position, ticket price, and visualization on the map. A positioning device is installed on every public transportation, where it will send the vehicle's position to the server. The integrated information system is based on Android. It can help passengers who want to travel to find the best route, cheap ticket price, and arrival time while using public transportation in Surabaya. Outcome of this research is a mockup of the system.
The Development of Distance Learning in Ukrainian Liberal Arts Institutions Based on EU Experience

Distance education and blended learning has been becoming a major alternative to traditional teaching all over the world. The paper discloses the benefits and disadvantages of e-learning at Ukrainian Universities in particular at Ternopil V. Hnatiuk National Pedagogical University as the representative when compare to European models of distance education. Distance education at Ukrainian Universities bases on the principle of individuality and supports by tutor. Using multivariate statistics it has proven that self-motivation and both intrinsic and extrinsic goal orientation predict student success in distance education and e-learning. The perceived learning outcome is positively associated with to self-regulation, learning styles and success.
Algebraic Group Structure of the Random Number Generator: Theoretical Analysis of NTU Sequence(s)

An algebraic group is an essential mathematical structure for current communication systems and information security technologies. Further, as a widely used technology underlying such systems, pseudorandom number generators have become an indispensable part of their construction. This paper focuses on a theoretical analysis for a series of pseudorandom sequences generated by a trace function and the Legendre symbol over an odd characteristic field. As a consequence, the authors give a theoretical proof that ensures a set of subsequences forms a group with a specific binary operation.
An approach to the development of clustering algorithms with a combined use of the Variable Neighborhood Search and Greedy Heuristic Method

Abstract Authors propose a new approach to the development of clustering algorithms based on parametric optimization models with the combined use of search algorithms with variable randomized neighborhoods and greedy agglomerative heuristic procedures.
A pattern approach for identification of opportunities for personalisation and automation of user interactions for the IoT

This paper introduces the 'Personal Spark' pattern which can be used to identify relevant fragments of human-human interaction as an initial step to inform requirements for adaptive user interfaces and data processing, in this case for user interactions of consumer Internet of Things.
A Study of Ultrasonic Sensor Capability in Human Following Robot System

Abstract Human following robot is very common in this technology era. Human following is a technique used by robot and autonomous vehicles to follow a human within a specific range. In this case, communication between the human and the robot is the most significant factor where sensor is needed to ensure its successfulness. This paper discussed a human following robot system that utilized ultrasonic sensor. Ultrasonic sensor is preferred for human following robot due to its wide detection area, less light dependency, the ability to detect glass and shining wall, smaller in size, lightweight, use a very low memory, cheaper than Laser Range Finder (LRF) or camera and lower power consumption. However, being a specular type of sensor and not narrowly focused, this can cause wrong estimation and recognition of human, especially at the legs. Therefore, necessary accompanying algorithms should be developed to encounter this issue. Here, three pieces 40 kHz ultrasonic sensors are mounted on the four-wheeled mobile robot platform. Ultrasonic sensors used to detect the human’s leg, and the data collected from the sensors were used upon the design of sensor platform. In order to detect human’s leg, the sensor platform was tested repeatedly to obtain optimum data. The data which have been collected from the ultrasonic sensor will then be interfaced with Arduino software. Thus, this paper outlines a set of benchmarks for human following and briefly evaluates its performances. The human following robot was tested in a real laboratory environment. The results were good, where the implementation of this algorithm is able to produce an accurate decision.
Anomaly-Based Detection of System-Level Threats and Statistical Analysis

This paper presents various parameters for the analysis of threats to any network or system. These parameters are based on the anomalous behavior of the system. To characterize the behavior of the system connected to the Internet, we need to consider a number of incoming and outgoing packets, the process running in the background and system response which include CPU utilization and RAM utilization. Dataset is collected for the above-mentioned parameter under the normal condition and under the condition of any cyber-attack or threat. Based on the deviation in the values under two conditions, another statistical parameter entropy is calculated. This will helps us to identify the type of threats.
Named Entity Recognition Using Part-of-Speech Rules for Telugu

Part-of-Speech (POS) Gorla, SaiKiranmai plays an important role in identifying and classifyingVelivelli, Sriharshitha Named Entities (NEs) for any language, especially, forSatpathi, Dipak Kumar inflected and agglutinating languages likeMurthy, N. L. Bhanu Telugu. The main objective of our work is to generateMalapati, Aruna POS-based rules to identify named entities in Telugu language using Decision Tree classifier. The corpus is generated by crawling through Telugu newspaper websites, which consists of 54457 words and each of these words are manually annotated with NEs tags, namely person, location, organization, and others. We have achieved competent performance by the generated POS rules and they help to get deeper insights into NER for Telugu.
ProLFA: Representative prototype selection for local feature aggregation

Given a set of hand-crafted local features, acquiring a global representation via aggregation is a promising technique to boost computational efficiency and improve task performance. Existing feature aggregation (FA) approaches, including Bag of Words and Fisher Vectors, usually fail to capture the desired information due to their pipeline mode. In this paper, we propose a generic formulation to provide a systematical solution (named ProLFA) to aggregate local descriptors. It is capable of producing compact yet interpretable representations by selecting representative prototypes from numerous descriptors, under relaxed exclusivity constraint. Meanwhile, to strengthen the discriminability of the aggregated representation, we rationally enforce the domain-invariant projection of bundled descriptors along a task-specific direction. Furthermore, ProLFA is also provided with a powerful generalization ability to deal flexibly with the semi-supervised and fully supervised scenarios in local feature aggregation. Experimental results on various descriptors and tasks demonstrate the proposed ProLFA is considerably superior over currently available alternatives about feature aggregation.
Design of High Performance Convolutional Neural Network for Lightweight Platform

Machine learning plays an important role in the field of data science. With the further promotion of deep learning, it is becoming more and more valuable to study deep learning network models that can efficiently run in distributed computing, cloud services and mobile platforms. However, due to the limited bandwidth, storage and operation performance, it is still a huge challenge to accurately and efficiently complete neural network computing on these platforms. In this paper, a lightweight convolution neural network called FccNet(fusion cross convolution network) is proposed. This new convolution neural network architecture extracts multi-scale features by small-scale convolution, and combines the characteristics of parallel crossover, identity mapping and feature fusion. In order to evaluate the performance of FccNet comprehensively, this paper conducted experiments on the Food-101, Caltech-256 and GTSRB Image Classification Benchmark datasets. FccNet can significantly reduce the model parameters and greatly improve the performance.
Analysis of the GCR Communication Channel For Image Steganography

Main motivation behind this investigation is to determine how and to what extent Gray Component Removal (GCR) influences a color image. Specifically how GCR influences images in spatial domain, as well as in Fourier and Cosine domains. This is important to determine in order to develop a data hiding technique that can be used in GCR communication channel. To evaluate the influence of GCR on color images a large image dataset is first converted from RGB to CMYK color space with very aggressive GCR. Then the original and the converted images are compared in Fourier and Cosine domains. The results show that the GCR communication channel has a nonlinear impact on the image that depends exclusively on the content of the image. The impact in spatial domain is too severe for watermarking techniques in spatial domain. In contrast, the extent of the impact in frequency domain is low enough so GCR can be used for data hiding techniques based on both DFT and DCT transforms.
Image Quality Detection Using The Siamese Convolutional Neural Network

It is an undeniable fact that image quality is closely related to the performance and accuracy of both verification and identification biometric systems. As an example, we can mention official documents, for instance passports or identity cards. Portraits in such documents must satisfy specific requirements in several categories. This paper describes our approach to determining the quality of images using artificial neural networks. We decided that we would use an approach different to most of the previously used ones - our system consists of two separate, but identical sub-networks, which together form a so called siamese neural network. The result of using such a network is that we are able to decide which one of a pair of images is of higher quality. As this paper later describes, the network can determine the better image in specific categories which affect the overall quality too.
Measuring Developer Work to Support the Software Project Manager

Software project management is an essential practice to achieve the goal of success in these projects and a challenging task for the Project Manager (PM). Therefore, information about developers' work can be valuable in supporting PM. Several studies have addressed this topic and suggested different strategies for obtaining such information. Given the variety of existing strategies, the need arises to know the state of the art regarding the theme. In this paper, what relevant information for PM and how that information can support the project management practices are presented, especially regarding risk management and people management. So, an exploratory study was performed applying Systematic Mapping of Literature technique. Contributions include the identification of 58 metrics, 4 sources of information, and 7 PM's activities supported by the measurement of developer work. In addition, aspects to be explored on the subject are presented, inspiring new studies in the field of Software Engineering.
Towards Better Understanding Developer Perception of Refactoring

Refactoring is a critical task in software maintenance. It is typically performed to enforce best design practices or to cope with design defects. Research in refactoring has been driven by the need to improve system structures. However, recent studies have shown that developers may incorporate refactoring strategies in other development-related activities that go beyond improving the design. Unfortunately, these studies are limited to developer interviews and a reduced set of projects. In this context, we aim at exploring how developers document their refactoring activities during the software life cycle, we call such activity Self-Affirmed Refactoring (SAR), by understanding developers perception of refactorings so that we can bridge the gap between refactoring and automation in general. We aim in more accurately mimicking the human decision making when recommending better software refactoring and remodularization.
Vague Gesture Control

In the rehabilitation of burns patients, remedial exercises are an important part of maintaining and regaining range-of-motion. To encourage patient rehabilitation and reduce discomfort, this paper presents the preliminary stages of a project which makes use of both vision based sensors and virtual reality technologies to create an immersive environment. The goal of our research is to design and develop effective vague gesture control. Hand gesture detection may lack clarity/resolution or otherwise be vague due to various reasons including patients' difficulty to enact gestures fully due to injuries, interference with the visual detection of gestures due to bandages, and the difficulty that vision based sensors have detecting small details like fingers at range. Extra environmental and situational information can enhance gesture recognition and provide better gestural classification. To achieve this, temporal machine learning is used to develop an application which uses past environmental interactions to help clarify present vague gestures. A gesture recognition system that can handle imperfect gestures in an intuitive way contributes towards naturalistic computer human interactions, in particular for gamified burns rehabilitation systems.
A Cognitive Semantic-Based Approach for Human Event Detection in Videos

Surveillance systems are mainly used to monitor activity and behaviour for the purpose of managing small human group. In particularly, the depicted approach focused on the small human groups stayed in the same place for a while and characterizing the behaviour of the group. The described approach has wide functions in several areas such as surveillance, group interface and behaviour classification. The video surveillance mainly deals with the recognition and classification of group behaviour with respect to several activities such as Normal speech, kicking, hugging and punching. The processing steps include frame generation, segmentation using Fuzzy C-Mean (FCM) clustering; features are extracted using Local Binary Pattern (LBP) and Hierarchical Centroid (HC). Convolution Neural Network (CNN) is used to classify the features. The proposed model is better than existing methods by achieving 80% accuracy.
Thickness Measurement of Water Film/Rivulets Based on Grayscale Index

This study proposed a nonintrusive and cost-efficient technique to measure the thickness of a thin water film/rivulet based on the grayscale index. This technique uses millions of probes and only needs a digital camera, fill lights, and pigment. For water colored with diluted pigment, the grayscale index of the water captured by a digital camera depends on the water thickness. This relationship can be utilized to measure the water thickness through digital image processing. In the present study, the relationship between the grayscale index and water thickness was theoretically and experimentally investigated. Theoretical derivation revealed that when the product of water thickness and the color density approaches to 0, the grayscale index is inversely proportional to the thickness. The experimental results show that under the color density of 0.05%, the grayscale index is inversely proportional to the thickness of water film when the thickness is less than 6 mm. This linear relationship was utilized to measure the distribution and profile of a water rivulet flowing on the lower surface of a cable model.
Performance Evaluation of Dynamic and Static WordPress-based Websites

WordPress is a popular website Content Management System (CMS) based on the PHP programming language and MySQL or MariaDB Relational Database Management System (RDBMS). Since PHP is not compiled but interpreted programming language, every time a WordPress-based web page is requested from the server, it needs to load core CMS files and custom theme files, read related content from associated database and generate final HTML output that will be sent to the user's web browser. This significantly prolongs a website's loading time because a user's web browser is normally not able to display any part of web page's interface until the server sends the complete front-end code. In this paper, we have setup a standard WordPress-based website and its static version on a virtual machine. Using the Apache Bench program, we have tested their performances by observing chosen metrics. The experiment results indicate that usage of a static version of a normally dynamic WordPress-based website can have great benefits for both server-side and client-side website operation processes.
Binary Owl Search Algorithm for Feature Subset Selection

Feature subset selection is one of the essential preprocessing tasks for numerous classification problems. This is because good feature subset can reduce overfitting of data, enhance the accuracy, and lessen the training time of a classifier model. However, finding the optimum feature subset is computationally expensive when the number of features is relatively high. Therefore, stochastic approaches are often used in attaining good feature subset within a feasible time frame. In this paper, we propose a binary variant of recent stochastic optimization algorithm Owl Search Optimization (OSA) for optimum feature subset selection. In this approach, six different transfer functions of S-shaped and V-shaped families were employed for generating six different binary Owl Search Optimization (BOSA) models. The proposed mechanism then employed on eleven publicly available datasets and performances were compared with popular approaches including, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Harmony Search (HC). Results reveal that, for most of the datasets, BOSA-based approaches can produce optimal feature subset with reduced number of features and improved classification accuracy compared to other approaches.
Preventing zero-day exploits of memory vulnerabilities with guard lines

Exploitable memory errors are pervasive due to the widespread use of unsafe programming languages, such as C and C++. Despite much research, techniques for detecting memory errors at runtime have seen limited adoption due to high performance overhead, incomplete memory safety, or non-trivial microarchitectural changes.
Improving Recommendations by Using a Heterogeneous Network and User's Reviews

A recommendation system is an information filtering technology that seeks to predict and recommend items to its users through an analysis of their history of interactions with the system. An important point to consider is the analysis of user's reviews, which allows the aggregation of valuable information in the recommendation process. However, commom data representations for recommender systems usually are not enough to capture all the relationships between the entities of the problem. Therefore, in this work, we propose a heterogeneous network that aggregates information of users, items and reviews in a single representation for the recommendation task. Using the proposed network, recommendations were generated by a network regularization method. The experiments showed that the proposed method is very promising.
RAPTOR: Release-Aware and Prioritized Bug-Fixing Task Assignment Optimization

Over a decade, many bug assignment methods have been proposed in order to assist developers to read bug reports submitted daily and numerously, and to assign an appropriate developer. However, they tend to concentrate their assignments on a small number of particular developers. Applying the methods to the projects which have releases would reduce the number of bugs that developers can fix by the next release date because the time that developers can devote to bug-fixing is limited. In this study, we propose the release-aware bug-fixing task assignment method to mitigate the task concentration and increase the number of bugs that developers can fix by the next release date. This method employs mathematical programming to find the best combination at project level while the traditional methods find the best pair of a bug and a developer (at individual level).
Modal Type Theory Based on the Intuitionistic Modal Logic $$\mathbf{IEL}^{-}$$

The modal intuitionistic epistemic logic $$\mathbf{IEL}^{-}$$ was proposed by Artemov and Protopopescu as the intuitionistic version of belief logic. We construct the modal lambda calculus which is Curry-Howard isomorphic to $$\mathbf{IEL}^{-}$$ as the type-theoretical representation of applicative computation widely known in functional programming.
Mathematical Modeling of Power Generation from Day-Night Temperature Drop Using Neural Networks and Artificial Intelligence

Modern thermal energy converters are engineered for energy conversion from exhaustible resources, such as gasoline, light oil, coil, etc.
Performance Analysis of the Symmetric Proxy Re-encryption Scheme

The use of cloud system's storage service has been increasing in recent years. A way to secure the stored data is to use cryptographic techniques. One of the techniques that can be used when sharing data accesses with other users, is proxy reencryption, which means converting data from its unreadable form (ciphertext) corresponding to one key into another ciphertext of a different key. Many proxy re-encryption schemes often only use asymmetric key cryptography, and not symmetric key. In this paper we analyze the performance of a symmetric proxy re-encryption scheme's implementation. In measuring the performance of the re-encryption function, the authors compare the running time of the program with that of AES CBC's reencryption's running time, to compare a naïve re-encryption with the selected schemes. We also propose some improvement to the implementation of the proxy re-encryption algorithm.