Reviewerly/exHarmony huggingface dataset:
Hand Down, Face Up: Innovative Mobile Attendance System Using Face Recognition Deep Learning

Computer Vision is considered as the science and technology of the machines that see. When paired with deep learning, it has limitless applications in various fields. Among various applications, face recognition is one of the most useful real-life problem-solving applications. We propose a technique that uses image enhancement and facial recognition technique to develop an innovative and time-saving class attendance system. The idea is to train a Convolutional Neural Network (CNN) using the enhanced images of the students in a certain course and then using that learned model, to recognize multiple students present in a lecture. We propose the use of deep learning model that is provided by OpenFace to train and recognize the images. This proposed solution can be easily installed in any organization, if the images of all persons to be marked this way are available with the administration. The proposed system marks attendance of students 100% accurately when captured images have faces in right pose and are not occluded.
Influence of the Applied Outlier Detection Methods on the Quality of Classification

This paper presents a comparison of a few chosen outlier detection methods and test quality of classification, both before and after the procedure of removing outliers. Using a few selected methods of outlier detection on several selected data sets, the process of elimination of atypical data was carried out. Atypical data may be of various nature. It can be noise or can be incorrect data. However, they can also be correct data, which for some reason are different from typical data. The removal of non-typical data may have a different effect on the classification quality. It may be dependent on the used method of removing unusual data but also on the nature of used data. Therefore, the classification process was carried out on the original data as well as with the outliers removed. The obtained results were compared and discussed.
Texture Classification Using Capsule Networks

Texture refers to the natural and external consistency of an object. Classification of images based on texture has always been an intricate problem drawing major research attention. Multiple methods based on image processing and deep learning were developed over the last century. In this paper, we studied the feasibility of solving this problem using capsule networks. Transfer learning was used in which the Xception network trained on ImageNet visual database was used as the primary model. The two layer capsule network served as the secondary model. Experiments on six standard texture datasets namely KTH-TIPS 2b, Kylberg, CURET, UIUC, DTD, FMD were performed. Translational and rotational invariance on the input images was incorporated during the training stage. Our results were observed to be comparable to the state of the art techniques.
Evaluation and Comparison of Agent-Oriented Methodologies: A Software Engineering Viewpoint

Numerous agent-oriented methodologies that offer a rich pool of resources to support developers of agent-based systems have been proposed. However, the use of existing methodologies in industrial settings is still limited due to the large volume of methodologies, diversity of covered scopes, ambiguity in concepts, and lack of maturity. This makes it difficult for agent technology practitioners to choose the appropriate methodology that best fits their given development context. To eliminate such agent-based development bottleneck, it is important to introduce suitable methods for evaluating, comparing, and classifying agent-oriented methodologies in order to leverage their usage among practitioners. Having systems to evaluate methodologies can effectively help developers better understand existing methodologies, realize their benefits, outline their pros and cons, and assist practitioners with selecting the best-fit methodology for a specific agent-based project. In response, this paper proposes a novel criteria-based evaluation that is influenced by software engineering practices to assess and compare agentoriented methodologies. The proposed evaluation is derived from the software engineering body of knowledge (SWEBOK) and provides a simplified method to assess the coverage degree of an agent-oriented methodology with respect to major software knowledge areas such as the requirements and testing phases. We demonstrate the applicability of the proposed evaluation by applying it to three agent-oriented methodologies (PASSI, MaSE, and Prometheus) in the software engineering requirements and testing phases.
A Hybrid Secure Watermarking Scheme Using Nonnegative Matrix Factorization and FastWalsh-Hadamard Transform

We present a robust and unnoticeable secure image watermarking approach using Nonnegative Matrix Factorization (NMF) and Fast Walsh-Hadamard Transform (FWHT). The core idea of the proposed scheme consist of four key steps: The original cover image is divided into small blocks. The NMF is then applied to each block separately, followed by using the FWHT to the generated weight matrix. Finally, the singular values of the watermark image are distributed over the transformed blocks. The experimental results certainly show enhanced visual imperceptibility and exceptional resiliency against various types of attacks.
Galois correspondence on linear codes over finite chain rings

Let S|T be a Galois extension of finite chain rings and consider H as its group of ring-automorphisms fixing the elements in T. In this paper we determine the stabilizer under such a group of any S-linear code. Using the so called Galois interior a correspondence between subgroups of H and a set of S-linear subcodes of B is determined. Some improvements of upper and lower bounds for the rank of the Galois-subring subcode and trace code are derived from this construction.
Computationally Efficient CPHD Fusion based on Generalized Covariance Intersection

This paper considers distributed multi-target tracking based on the Cardinalized Probability Hypothesis Density (CPHD) filter and the Generalized Covariance Intersection (GCI) fusion rule. For a distributed sensor network which has limited processing power and computational capability, the Gaussian-mixture-based CPHD (GM-CPHD) fusion is more parsimonious and practical compared to Monte Carlo-based CPHD fusion. Hence, the GM implementation is considered in this paper. Nevertheless, the GM-CPHD fusion is still characterized by a computational complexity that grows exponentially with the number of sensors, and high-order polynomially with the number of targets, This work focuses on devising a computationally efficient GM-CPHD fusion algorithm so as to enhance the practical applicability of CPHD fusion. To this end, the fused CPHD is approximated as a weighted sum of fused CPHDs, each obtained by performing fusion with respect to a smaller group of components. Based on the proposed approximation, we further devise a parallelizable CPHD fusion that can reduce the computational complexity of the original CPHD fusion, at the price of a slight performance loss. Further, by exploiting the Union-Find data structure, an efficient grouping procedure that can be performed at an early stage is proposed. The performance of the proposed method is demonstrated via simulation experiments in a challenging scenario.
Exploring the impact of transparency on the interaction with an in-car digital AI assistant

Nowadays, intelligent assistants, such as Amazon's Alexa, are widely available. Unsurprisingly, intelligent assistants find their way into cars, in some cases as a major way to interact with the car. We conducted a user enactment exploring the impact of transparency on a possible future user experience with a digital AI assistant in the car. The focus is on whether tasks should be performed in an opaque way, only involving the user when it is necessary, or in a transparent way, always offering the user insights into what is being done and how. We present initial findings indicating a slight preference towards more transparency.
Quantum Data Encryption as a Service on Demand: Eindhoven QKD Network Testbed

The widespread acceptance and deployment of quantum key distribution (QKD) for data encryption requires a better understanding of the technology concerning device-to-system interfacing and network performance from current vendors. Despite the existence of a few QKD testbeds worldwide, they are neither open-access nor capable of validating components from different system and component vendors business cases. User trials in testbeds have yet to yield a robust, open business case. This paper introduces the open-access QKD testbed currently being developed in the Eindhoven region which will focus on the creation of an open environment for end-to-end validation of distinct business cases, QKD security proofs, and technology certification and standardization. At this site, a quantum-to-the-home network scenario for providing quantum encryption as a service on demand, for end-to-end security to end-users, is also proposed. It is expected that this testbed will lead to a better understanding of what further developments are required for current networks to be augmented with QKD.
Calibration of multiple sparsely distributed cameras using a mobile camera

In sports science research, there are many topics that utilize the body motion of athletes extracted by motion capture system, since motion information is valuable data for improving an athlete’s skills. However, one of the unsolved challenges in motion capture is extraction of athletes’ motion information during the actual game or match, as placing markers on athletes is a challenge during game play. In this research, the authors propose a method for acquisition of motion information without attaching a marker, utilizing computer vision technology. In the proposed method, the three-dimensional world joint position of the athlete’s body can be acquired using just two cameras without any visual markers. Furthermore, the athlete’s three-dimensional joint position during game play can also be obtained without complicated preparations. Camera calibration that estimates the projective relationship between three-dimensional world and two-dimensional image spaces is one of the principal processes for the respective three-dimensional image processing, such as three-dimensional reconstruction and three-dimensional tracking. A strong-calibration method, which needs to set up landmarks with known three-dimensional positions, is a common technique. However, as the target space expands, landmark placement becomes increasingly complicated. Although a weak-calibration method does not need known landmarks, the estimation precision depends on the accuracy of the correspondence between image captures. When multiple cameras are arranged sparsely, sufficient detection of corresponding points is difficult. In this research, the authors propose a calibration method that bridges multiple sparsely distributed cameras using mobile camera images. Appropriate spacing was confirmed between the images through comparative experiments evaluating camera calibration accuracy by changing the number of bridging images. Furthermore, the proposed method was applied to multiple capturing experiments in a large-scale space to verify its robustness. As a relevant example, the proposed method was applied to the three-dimensional skeleton estimation of badminton players. Subsequently, a quantitative evaluation was conducted on camera calibration for the three-dimensional skeleton. The reprojection error of each part of the skeletons and standard deviations were approximately 2.72 and 0.81 mm, respectively, confirming that the proposed method was highly accurate when applied to camera calibration. Consequently, a quantitative evaluation was conducted on the proposed calibration method and a calibration method using the coordinates of eight manual points. In conclusion, the proposed method stabilizes calibration accuracy in the vertical direction of the world coordinate system.
Evaluation of the Complexity of Fully Homomorphic Encryption Schemes in Implementations of Programs

This paper shows a thorough examination of fully homomorphic encryption schemes and their performance in programs. We do an extensive analysis of one specific, widely-used scheme -- the Gentry-Sahai-Waters scheme, and its variations -- the libraries TFHE and FHEW. We aim to devise an abstraction level that enables us to assess the real-world speed performance of fully homomorphic operations without actually working with encrypted data. We propose an algorithm that uses the gathered statistical data combined with our mathematical model to evaluate the performance of fully homomorphic implementations of arbitrary computer programs.
Activity event recommendation and attendance prediction

The recommendation problem has been widely studied and researchers are constantly searching for better methods. Recommending events is an even more difficult problem because there is no information such as ratings from past events. In this paper, we introduce a method for recommending activity events: activities hosted by one or more individuals which involve movement: walking, running, cycling, cross-country skiing, and driving to users who have location history such as trajectories, meetings, POI visits, and geo-tagged photos. We tested the method in a real environment in Mopsi platform: http://cs.uef.fi/mopsi/events. Although there are many location-based event recommendation systems in literature, this is to our knowledge the first system that recommends activity events like bicycle and skiing trips. The experiments show that we can predict whether a user is attending the event or not with 80% accuracy, which is significantly better than random chance (50%).
First-order composition-nominative logics with predicates of weak equality and of strong equality

Development of the new software-oriented logical formalisms is a topical problem. The paper introduces lo­gics of partial predicates with predicate complement and equality predicates, we denote them LCE. They ex­tend logics of quasiary predicates with equality and logics with predicate complement. The composition of the predicate complement is used in Floyd-Hoare pro­­gram logics’ extensions on the class of partial predi­cates. We define predicates of weak equality and of strong equality. Thus, LCE with predicates of weak equality (denoted by LCEw) and LCE with predicates of strong equality (denoted by LCEs) can be specified. LCE can be studied on the first order and renominative levels. We consider composition algebras of LCE, investigate properties of their compositions and describe first order languages of such logics. We concentrate on the properties related to the equality predicates and the composition of the predicate complement. Various variants of logical consequence relations for the first order LCE are introduced and studied: P |= T , P |= F , R |= T , R |= F , P |= TF , R |= TF , P |= IR . In particular, we obtained that LCEw are somewhat degenerate, as for them all the relations are incorrect except for the irrefutability logical consequence relation under the conditions of undefinedness |= IR ^ . At the same time, all of the listed relations are correct for LCEs. Properties of the logical consequence relations are the semantic basis for con­struction of the respective calculi of sequential type. Further investigation of logical consequence relations for LCE includes adding the conditions of undefined­ness and constructing the corresponding sequent calcu­li; it is planned to be displayed in the forthcoming ar­ticles. Problems in programming 2019; 3: 28-44
Sentiment Extraction and Analysis using Machine Learning Tools-Survey

Abstract Sentiment search is clearly abstract cornerstone and essential administer in identifying user’s importance preferences. To get the quality of the product, position in evaluations is precondition. Normally, if item’s studys express constructive idea, the produce perhaps with bigger rating to some populous qualification. By analyzing the user considerations, their sentiments suggest unique experts to some target user in agreement the user culture. LDA is truly a Bayesian approach represented particularly to create the unification of studies, topics and discussions. In this paper, we have discussed about various machine learning tools and techniques for the better understanding of the concepts and efficient processing of sentiments from the huge data sets.
Computationally Intensive Functions in Designing and Operating Distributed Cyber Secure and Resilient Systems

Increasing incorporation of advanced information technologies makes business and public organisations more effective and efficient, while often introducing exploitable vulnerabilities. The efficient provision of security of interconnected, and interdependent, processes and sectors against cyberattacks requires deep understanding of vulnerabilities, exposure, potential negative impact, as well as the contribution existing and emerging organisational and technological solutions will potentially have on preventing attacks, reducing vulnerabilities, protecting digital infrastructures, response and recovery, and resilience. Such understanding will allow minimisation of risks against a spectrum of plausible cyber threats and reducing negative consequences of one or a series of cyberattacks.
Exploratory Analysis of the Research Literature on Evaluation of In-Vehicle Systems

An exploratory literature review method was applied to publications from several sources on Human-Computer Interaction (HCI) for In-Vehicle Information Systems (IVIS). The novel approach for bibliographic classification uses a graph database to investigate connections between authors, papers, used methods, and investigated interface types. This allows the application of algorithms to find similarities between different publications and overlaps between different usability evaluation methods. Through community detection algorithms, the publications can be clustered based on similarity relationships. For the proposed approach several thousand papers were systematically filtered, classified, and stored in a graph database. The survey shows a trend for usability assessment methods with direct involvement of users, especially the observation of users and performance-related measurements, as well as questionnaires and interviews. However, especially methods usually applied in early stages of development based on the assessment through models or experts, as well as collaborative and creativity methods do not seem very popular in automotive HCI research.
How to Optimize the Input Efficiency of Keyboard Buttons in Large Smartphone? A Comparison of Curved Keyboard and Keyboard Area Size

Smartphone has been constantly optimizing the user experience of viewing content by increasing screen size. However, larger screen brings about unsatisfactory input issue, especially for one-handed users. Curved QWERTY keyboard and reduced soft keyboard area are proposed to solve the input inefficiency issue of application design in the large smartphones. Following the design of existing curved keyboards, we designed a keyboard application, which could collect all the usage data, to test whether the curved keyboard or reduced-area keyboard could indeed solve the input inefficiency issue. By using within-subject design. we compared 2 screen sizes (5.0 in. vs. 6.5 in.), 2 area sizes (small-area: letter key area is 4.9 mm × 7 mm vs. large-area: letter key area is 6.3 mm × 9 mm), and 2 keyboard layouts (curved QWERTY vs. traditional QWERTY). The results show that the large-area keyboard is significantly better in terms of pairs per minute and reaction time between two keys, at the same time, the curved keyboard performs worse than the traditional keyboard. It indicates that the two design elements are not a common practice.
Enhancing Social Closeness between Drivers by Digital Augmentation

Driving is a social activity: Drivers need to coordinate and cooperate with each other to share the infrastructure. The relationship between drivers influences their driving behavior and experience. Lights, horn and speed are the most frequently used means to exchange information, limiting both the range and the bandwidth of the connectivity and leading to isolation, loneliness, and competition. We present “iSticker” and “MusicHound”, two concepts that aim to establish a connection by presenting similarity information between drivers. The two concepts were prototyped and evaluated with users in a driving simulator. The results showed that iSticker and MusicHound enhance drivers’ social closeness with each other and belongingness during the journey.
A Qualitative Study of Teaching Requirements Engineering in Universities

Requirement Engineering education shows itself as relevant when considering its possible benefits to the training of students of Computer Science courses, such as Software Engineering, Computer Engineering, Information Systems and Computer Science. In the literature, many challenges that come from the process of Requirements Engineering in the industry are described. Regarding the academic environment, the initial training of Requirements Engineers still happens in the undergraduate programs and many challenges may happen in this context. Considering that context, this paper presents how the discipline of Requirements Engineering is taught in undergraduate courses on the Computer Science area in Brazil, from the perspective of the lecturers. The research shows the teaching methodologies, the evolution of the discipline over the years, the forums used and the challenges faced by lecturers when teaching requirements documentation. Based on that purpose, a survey with open questions was conducted with lecturers from different institutions. The results showed the similarity in the way the discipline is taught, challenges common to the lecturers, their desire for studies to diversify teaching methodologies, and the creation of exercises and more efficient context simulations. Besides that, it is listed different methodologies used by lecturers to mitigate the challenges and problems faced, as well as research sources for subjects related to Requirements Engineering that can support the planning and teaching-learning processes of the discipline, according to the experience of those lecturers.
The Computational Relevance of Formal Logic Through Formal Proofs

The construction of correct software, i.e. a computer program that meets a given specification, is an important goal in Computer Science. Nowadays, not only critical software (the ones used in aircraft, hospitals, banks, etc.) is supposed to provide additional guarantees of its correctness. Nevertheless, this is not an easy task because proofs are often long and full of details. In this sense, a strong background in logical deduction is essential to provide Computer Science (CS) professionals the necessary competencies to understand and provide mathematical proofs of their programs. Logic courses for CS tend to follow old precepts without emphasizing mastering deduction itself. In our institution, for several years we have followed a more pragmatical approach, in which the foundational aspects of both natural deduction and deduction à la Gentzen are taught and, in parallel, the operational premises of deduction are put into practice in proof assistants. Thus, CS students with a minimum knowledge in programming are challenged on providing correctness certificates for simple algorithms. “Putting their hands in the dough” they acquire a better understanding of the value and importance of deductive technologies in computing. Here we show how this is done relating natural deduction and sequent calculus deduction and using the proof assistant PVS in the simple context of a library of sorting algorithms.