Reviewerly/exHarmony huggingface dataset:
Toward Service Orchestration Through Software Capability Profile

Open-source applications are a gold mine of potential functions for reuse. However, the difficulty lies in the identification and characterization of these functionalities to meet specific business needs. In this work, we aim to decompose and externalize functionalities from open-source business applications for service orchestration. We propose a solution for characterizing a functionality in a standardized and readable way. Then we propose an automated process based on ISO 16100 capability profile, to externalize the characterized functionalities as RESTful APIs. As a result, we expose reusable components to be used for service orchestration in response to a specific business need.
Experiences With Replicable Experiments and Replication Kits for Software Engineering Research

Replications and replicable research are currently gaining traction in the software engineering research community. Our research group made an effort in the recent years to make our own research accessible for other researchers, through the provision of replication kits that allow rerunning our experiments. Within this chapter, we present our experiences with replication kits. We first had to learn which contents are required, how to structure them, how to document them, and also how to best share them with other researchers. While this sounds very straightforward, there are many small potential mistakes, which may have a strong negative impact on the usefulness and long-term availability of replication kits. We derive best practices for the content and the sharing of replication kits based on our experiences. Moreover, we outline how platforms for replicable research may further help our community, especially with problems related to the external validity of results. Finally, we discuss the lack of integration of replication kits into most current review processes at conferences and journals. We also give one example for a review process into which replication kits were well-integrated. Altogether, this chapter demonstrates that making research replicable is a challenging task and there is a long road ahead until our community has a generally accepted and enforced standard of replicability.
A Study of the Visualization Tool for Computer Science Majors’ Capability Assessment

IT industry burgeoning, more and more enterprises and universities pay attention to the comprehensive ability of computer science majors.However, the existing assessment method giving priority to test scores, could not greatly demonstrate computer science majors' capability and their development approaches, with drawbacks of serious one-sidedness, limitation and low perceptual intuition.Correspondingly, it's difficult to teach students according to their aptitude or interests.Moreover, the current research works show that this research field still requires theoretical foundations, appropriate models, effective tools, in-depth experimentations and practical implementations.In order to solve this problem, the paper proposes an index system of students' for evaluating computer science majors' ability, designs and implements the relevant visualization software of capability assessment based on dynamic radar charts and jitterplot, and then illustrates the application of this visual software through case studies.Based on the visualization tool, it has achieved good results in practical teaching.
Improving Application Integration by Combining Services and Resources

The main application integration approaches, the service-oriented architecture (SOA) and representational state transfer (REST) architectural styles, are rather different in their modeling paradigm, forcing application developers to choose between one and the other. In addition, both introduce more application coupling than required, since data schemas need to be common, even if not all instantiations of those schemas are used. This chapter contends that it is possible to improve this scenario by conceiving a new architectural style, structural services, which combines services and resources to reduce the semantic gap with the applications, allowing to tune the application integration between pure service-based and pure resource-based, or an intermediate mix. Unlike REST, resources are not constrained to offer a fixed set of operations, and unlike SOA, services are allowed to have structure. In addition, compliance is used to reduce coupling to the bare minimum required by the actually used application features.
Introduction and Implementation of Machine Learning Algorithms in R

Machine learning is one of the important areas in the field of computer science. It helps to provide an optimized solution for the real-world problems by using past knowledge or previous experience data. There are different types of machine learning algorithms present in computer science. This chapter provides the overview of some selected machine learning algorithms such as linear regression, linear discriminant analysis, support vector machine, naive Bayes classifier, neural networks, and decision trees. Each of these methods is illustrated in detail with an example and R code, which in turn assists the reader to generate their own solutions for the given problems.
Quantum query complexity of symmetric oracle problems

We study the query complexity of quantum learning problems in which the oracles form a group $G$ of unitary matrices. In the simplest case, one wishes to identify the oracle, and we find a description of the optimal success probability of a $t$-query quantum algorithm in terms of group characters. As an application, we show that $\Omega(n)$ queries are required to identify a random permutation in $S_n$. More generally, suppose $H$ is a fixed subgroup of the group $G$ of oracles, and given access to an oracle sampled uniformly from $G$, we want to learn which coset of $H$ the oracle belongs to. We call this problem coset identification and it generalizes a number of well-known quantum algorithms including the Bernstein-Vazirani problem, the van Dam problem and finite field polynomial interpolation. We provide character-theoretic formulas for the optimal success probability achieved by a $t$-query algorithm for this problem. One application involves the Heisenberg group and provides a family of problems depending on $n$ which require $n+1$ queries classically and only $1$ query quantumly.
Stabilization Control of a Two-Wheeled Triple Link Inverted Pendulum System with Disturbance Rejection

This paper focuses on the robust controller for triple Links inverted pendulum on two-wheeled system. The development of triple Link inverted pendulum on two-wheeled model using CAD based soft-ware, SimWise 4D is proposed. Interval Type-2 Fuzzy Logic Control (IT2FLC) is used as control algorithm for the system. This system is multi input and multi output system which means each motor in this system is controlled by one controller to achieve stability or upright position for these three links. The robustness of the controller is tested by applying disturbance to the model to observe the response from the model to handle the uncertainties. The performance of IT2FLC is compared with Type-1 Fuzzy Logic Control (T1FLC) to demonstrate best controller for the system. The experiment results concerning the angular position for each three Links and the maximum value of disturbance rejection for both controllers are obtained by using heuristic tuning for input and output gain control.
User Goals, Behaviours and Attitudes: Developing Web User Personas of Art and Design Students

User personas are one of the most understandable deliverables which used to improve user experience and enhance design communication in web design process.Myriad studies and design research were conducted in this area which was largely based on qualitative research methods.This posted a challenge empirically in the validation of the personas created.To set on this gap, a qualitative-quantitative validation method was adapted with the focus in the three main attributes of personas: user goals, behaviours, and attitudes in this research to create student web user personas in the School of The Arts, Universiti Sains Malaysia web site.As an outcome, a set of art and design student web user personas were presented.
Video Stabilization for High-Quality Medical Video Compression

A medical video analysis provides a forum for the dissemination of diverse results in the area of medical field. The swiftly sprouting changes in the health care entail the modern advances with imaging. The effect of camera motion from a video stream leads to improper diagnosis in case of medical video processing. Hence, video stabilization technique enables the video stream to generate a stabilized video. Compression also plays a very vital role in the field of clinical and image analysis, since it carries massive information thereby reducing the file size. To further ensure effectiveness in diagnosis, a video stabilization is performed before compression. The results proved that the proposed methodology offered a substantial increase in the quality of video by 1.25 dB peak-signal-to-noise ratio and 0.06 structural similarity index.
IoT-Based Embedded Smart Lock Control Using Face Recognition System

Smart home security and remote monitoring have become vital and indispensable in recent times, and with the advent of new concepts like Internet of Things and development of advanced authentication and security technologies, the need for smarter security systems has only been growing. The design and development of an intelligent web-based door lock control system using face recognition technology, for authentication, remote monitoring of visitors and remote control of smart door lock have been reported in this paper. This system uses Haar-like features for face detection and Local Binary Pattern Histogram (LBPH) for face recognition. The system also includes a web-based remote monitoring, an authentication module, and a bare-bones embedded IoT server, which transmits the live pictures of the visitors via email along with an SMS notification, and the owner can then remotely control the lock by responding to the email with predefined security codes to unlock the door. This system finds wide applications in smart homes where the physical presence of the owner at all times is not possible, and where a remote authentication and control is desired. The system has been implemented and tested using the Raspberry Pi 2 board, Python along with OpenCV are used to program the various face recognition and control modules.
Using AI-Planning to Solve a Kinodynamic Path Planning Problem and Its Application for HAPS

This work emphasizes on the ability of a domain-independent AI-planner to solve a kinodynamic path planning problem by recapitulating the encoding in the PDDL+ modelling language and by showing the easy extension for multiple HAPS. The advantage of the approach is highlighted with the concept of an implementation framework that incorporates tools to validate the problem model and to explain the plans to the operator. Some flight path plans are illustrated as well as the validation of plans are described.
The Capsize Band Concept Revisited

A[aut] Tsakalakis, N. concept for analytical representation of the capsize rate, a measure directly related to damage ship survivability, has attracted attention ever since the first attempts were made to explain the behaviour of a damaged ship in waves. Attempts in the late 1990s helped to enhance understanding and facilitate characterisation of phenomena pertaining to capsize probability and time to capsize in given environments and loading conditions, but a consistent verifiable formulation is still lacking. In this respect, pursuing an analytical approach to express the capsize rate offers many advantages, time efficiency being amongst the most important. In an era when stability/survivability calculations are required to be carried out in real time, there is a need for a model accounting for the random nature of capsize whilst achieving accuracy close to that of time-domain simulations with simple hydrostatic calculations. This study is an attempt to establish a new methodology for survivability assessment by means of a multivariable analytical model based on numerical simulations, validated against the results of physical model tests.
Ontology-Based Fragmented Company Knowledge Integration: Possible Approaches

Companies have multiple business process, some of which are supported by knowledge described via ontologies. However, due to their nature, the processes use different knowledge notation what causes a problem of integrating such fragmented heterogeneous knowledge. The paper investigates the problem of developing a single multi-domain ontology for integrating company knowledge taking into account differences between terminologies and formalisms used in various business processes. Different options of designing ontologies covering multiple domains are considered. Three of them: (i) ontology localization/multilingual ontologies, (ii) granular ontologies, and (iii) ontologies with temporal logics are considered in details and analyzed.
Human Activity Recognition without Vision Tracking

This work describes the recognition of human activity based on the interaction between people and objects in domestic settings, specifically in a kitchen.The difference between this and other proposals is that considers a human activity in a process without vision tracking.Videos are a sequence of photographs.Taking this into account, if you analyze an orderly sequence of images it could be based on the objects present in each scene so that you can understand the possible activity performed.However, it is not enough to consider the objects present in the scene; it is necessary to determine if those objects are employed or not by the humans present.If they are used, it is evident that they are necessary to carry out the activity; if they are not used they would only provide noise to the recognized activity.Therefore, it is necessary to generate a conceptualization of objects in the scene with characteristics (definition of an object, motion detector, object recognition, object position, object action) that allows you to recognize them and to determine the degree of use (unchanged, added, removed, moved, and indeterminate) and influence the possible recognized activity.
Design and Implementation of a Domain Specific Rule Engine

Security strings are often needed in identity authentication mechanism. Security strings recovery is a reverse process, which does much calculations on a large amount of possible strings to find the right one, so that we can recover lost or forgotten strings and regain access to valuable information. In this reverse process, we need first process basic strings based on transformation rules, so as to generate new ones quickly. Rule processing is complex, which has high requirements for computing power, processing time, especially system power consumption. In response to the above requirements, this work puts forward the idea of accelerating the processing of rules using hardware for the first time, and a domain specific rule engine is designed and implemented on the existing FPGA platform. The experimental results show that the performance of the rule engine on a single Xilinx Zynq 7z030 FPGA is better than that of CPU, its performance power ratio is 3 times higher than that of GPU, and 50 times higher than that of CPU. The speed and energy efficiency of the rule processing is improved effectively.
A Smart Mobile Application for Learning English Verbs in Mauritian Primary Schools

Learning English verbs in primary schools in Mauritius may sometimes be difficult for students as English is not their mother tongue. Integrating information technology to traditional way of teaching can help to improve the teaching process. In this paper, an AI-enabled mobile application which has been developed to help the learning Mauritian students is discussed. The application provides for personalised learning and an artificial neural network analyses the learning pattern of the students to readapt the type of learning material proposed to them. To bridge the gap due to language issues, the interaction between the students and application is in Creole. The student can say the verbs in Creole and then get the translated version in the various tenses. Instructions on how to use the application is also given in Creole. Quizzes are also given to help the learning process.
Models of Research and the Dissemination of Research Results: the Influences of E-Science, Open Access and Social Networking

In contrast with practice in recent times past, computational and data intensive processes are increasingly driving collaborative research in science and technology. Large amounts of data are being generated in experiments or simulations and these require real-time, or near real-time, analysis and visualisation. The results of these evaluations need to be validated and then published quickly and openly in order to facilitate the overall progress of research on a national and international basis. Research is increasingly undertaken in large teams and is also increasingly interdisciplinary as many of the major research challenges lie at the boundaries between existing disciplines. The move to open access for peer reviewed publications is rapidly becoming a required option in the sector. At the same time, communication and dissemination procedures are also utilising non-traditional forms facilitated by burgeoning developments in social networking. It is proposed that these elements, when combined, constitute a paradigm shift in the model of research and the dissemination of research results.
Meet-in-the-Middle Key Recovery Attacks on a Single-Key Two-Round Even-Mansour Cipher

We propose new key recovery attacks on the two-round single-key n-bit Even-Mansour ciphers (2SEM) that are secure up to 22n/3 queries against distinguishing attacks proved by Chen et al. Our attacks are based on the meet-in-the-middle technique which can significantly reduce the data complexity. In particular, we introduce novel matching techniques which enable us to compute one of the two permutations without knowing a part of the key information. Moreover, we present two improvements of the proposed attack: one significantly reduces the data complexity and the other reduces the time complexity. Compared with the previously known attacks, our attack first breaks the birthday barrier on the data complexity although it requires chosen plaintexts. When the block size is 64 bits, our attack reduces the required data from 245 known plaintexts to 226 chosen plaintexts with keeping the time complexity required by the previous attacks. Furthermore, by increasing the time complexity up to 262, the required data is further reduced to 28, and DT=270, where DT is the product of data and time complexities. We show that our data-optimized attack requires DT=2n+6 in general cases. Since the proved lower bound on DT for the single-key one-round n-bit Even-Mansour ciphers is 2n, our results imply that adding one round to one-round constructions does not sufficiently improve the security against key recovery attacks. Finally, we propose a time-optimized attacks on 2SEM in which, we aim to minimize the number of the invocations of internal permutations.
Reasoning about the disruption patterns for train system using Bayesian Network and Prolog

We construct a Prolog-based expert system to reason about the disruption patterns for train system using Bayesian network and Prolog. The disruptions dependencies are modeled using Bayesian network and the reasoning is carried on using Prolog. We choose Bayesian network because it is one of the most efficient and elegant framework to represent and reason probabilistic graphical model. The causative relationship among disruptions is represented using Directed Acyclic Graph (DAG). We use Prolog to improve the efficiency of the reasoning process by defining Bayesian network and its probabilistic information into a knowledge base. The causative relationships among disruptions are also modeled in terms of Prolog rules. Our Prolog-based expert system combines the statistical reasoning capability of Bayesian network and logic programming efficiency. The system provides comprehensive reasoning regarding the causative probability of events, the causative relationship among disruptions, as well as the most triggering and triggered disruptions in train system.
Integrating Heterogeneous Enterprise Data Using Ontology in Supply Chain Management

Many industries prefer worldwide business operations due to the economic advantage of globalization on product design and development. These industries increasingly operate globalized multi-tier supply chains and deliver products and services all over the world. This global approach produces huge amounts of heterogeneous data residing at various business operations, and the integration of these data plays an important role. Integrating data from multiple heterogeneous sources need to deal with different data models, database schema, and query languages. This chapter presents a semantic web technology-based data integration framework that uses relational databases and XML data with the help of ontology. To model different source schemas, this chapter proposes a method based on the resource description framework (RDF) graph patterns and query rewriting techniques. The semantic translation between the source schema and RDF ontology is described using query and transformational language SPARQL.
Real-Time Head Action Recognition Based on HOF and ELM

Head action recognition, as a specific problem in action recognition, has been studied in this paper. Different from most existing researches, our head action recognition problem is specifically defined for the requirement of some practical applications. Based on our definition, we build a corresponding head action dataset which contains many challenging cases. For action recognition, we proposed a real-time head action recognition framework based on HOF and ELM. The framework consists of face detection based ROI determination, HOF feature extraction in ROI, and ELM based action prediction. Experiments show that our method achieves good accuracy and is efficient enough for practical applications.
User-Based Adaptive Multimedia Delivery Over 5G Network

The increase in multimedia traffic is likely to pose challenges for the 5G networks. The data bundles are likely to increase but also open the door to new applications which will increase the data consumption. This chapter discusses how multimedia adaptation could be improved in this context by considering the user and the contextual factors for multimedia adaptation. This could be used to deliver a more accurate trade-off between quality and price. As a result, this could reduce the network consumptions, reduce the cost the user has to pay, and improve user satisfaction with the multimedia services.
A Platform for Assessing Physical Education Activity Engagement

Physical activity is an important part of the healthy development of children, improving physical, social and emotional health. One of the main challenges faced by physical educators is the assembling of a physical education program that is compelling to all individuals in a diverse group. Recent advances in Human Activity Recognition (HAR) methods and wearable technologies allow for accurate monitoring of activity levels and engagement in physical activities. In this work, we present a platform for assessing the engagement of participants in physical education activities, based on a wearable IoT device, a machine learning HAR classifier and a comprehensive experiment involving 14 diverse volunteers that resulted in about 1 million data samples. Targeting at a replicable research, we provide full hardware information and system source code.