Reviewerly/exHarmony huggingface dataset:
Using Dynamic and Hybrid Bayesian Network for Policy Decision Making

The Bayesian network (BN) method is one of the data-driven methods which have been successfully used to assist problem-solving in a wide range of disciplines including policy making, information technology, engineering, medicine, and more recently biology and ecology. BNs are particularly useful for diverse problems of varying size and complexity, where uncertainties are inherent in the system. BNs engage directly with subjective data in a transparent way and have become a state-of-the-art technology to support decision-making under uncertainty.
Search of Software Artefacts Based on the Project Quantitative Characteristics

The search technique of software artefacts, important when searching of prototypes of the program system architecture or its components, is considered in this paper. The model of software projects focused on combined numerical and textual characteristics is provided. The two-stage search technique of similar software projects to a new one is proposed and is based on applying clustering of quantitative characteristics. The textual characteristics of the similar projects are the result of the technique. The proposed search is useful for the system design with reuse. It is focused on cost saving and cutting time.
Cryptography and Optimization-Driven Support Vector Neural Network to Mitigate DoS Attacks in E-Commerce

Nowadays, cloud computing has become a significant area for the business due to the high demand of people engaging in E-commerce. E-commerce applications are very popular for conducting business via the Internet which includes online banking, bill payment, and purchasing goods, etc. E-commerce is said to be more straightforward, easier to use, intuitive, and less threatening. The rapid development of the Internet over the past decade appeared to have facilitated an increase in the incidents of online attacks. One such powerful and harmful attack is the denial-of-service (DoS) attack, and hence, protecting the entities from such attacks is essential. Therefore, an effective DoS attack detection technique is required in the E-commerce transactions to offer security in this platform. Accordingly, in this paper, a technique is developed for DoS attack detection in the E-commerce transactions by proposing glowworm swarm optimization-based support vector neural network (GSO-SVNN)-based authorization. The security will be provided with elliptic-curve cryptography (ECC) and hashing function to show the strength of the security protocol against a DoS attacks. The performance of the proposed technique is evaluated using four metrics, like accuracy, precision, recall, and false positive rate (FPR).
Classification of Hourly Clearness Index of Solar Radiation in the District of Yamoussoukro

The exploitation of systems using solar energy as a source of energy is not fluctuations free because of short passage of clouds on solar radiation.The amplitude, the persistence and the frequency of these fluctuations should be analyzed with appropriate tools, instead of focusing on their location over time.The analysis of these fluctuations should use the instantaneous clearness index whose distribution is given as a first approximation which is independent not only of the season but also of the site.It is important to evaluate the potential solar energy in a region.Indeed such evaluation helps the decision-makers in their reflections on agricultural or photovoltaic solar projects.Then this study was conducted for a predictive purpose.The method used in our work combines the classification method which is the hierarchical ascending classification and two partitioning methods, the principal component analysis and the K-means method.The partitioning method enabled to achieve a number of well-known situations (in advance) that are representative of the day.The study was based on the data of a climatic weather station in the district of Yamoussoukro located in the center region of Côte d'Ivoire during the 2017 year.Using the clearness index, the study allowed the classification of the solar radiation in the region.Thus, it showed that only 346 days of the 365 days in 2017 were classified (95%).We identified three clusters of days, the cloudy sky (29%), the partly cloudy sky (32%) and the clear sky (39%).The statistical tests used for the characterization of these clusters will be detailed in a future study.
Multi-clustering Used as Neighbourhood Identification Strategy in Recommender Systems

This article describes clustering approach to neighbourhood calculation in collaborative filtering recommender systems. Precise identification of neighbours of an active object (a user to whom recommendations are generated) is very important due to its direct impact on quality of generated recommendation lists. Clustering techniques, although improving time effectiveness of recommender systems, can negatively affect quality (precision) of recommendations. In this article it is proposed a new algorithm based on multi-clustering, as well as author’s description of this term. Despite of various definitions in papers, a common distinctive feature of multi-clustering is its multiple point of view of one dataset. Various views discover their different aspects, selecting the most appropriate data model to solution of a current problem. The article contains experiments confirming advantage of multi-clustering approach over the traditional method based on single-scheme clustering. The results include recommendation quality and time effectiveness comparison, as well.
Dealing with Requirement Inconsistencies Based on ReqDL Language

Managing requirement for complex systems requires a rigorous methodology practice in all requirement engineering process. Poorly written requirements result in wasted effort and rework [1]. Requirements inconsistencies could occur at all levels of abstraction. Therefore, detecting inconsistencies between requirements is important; particularly in requirements statements. Finding precisely this inconsistency in the textual description requirement is a difficult task. In this paper, we introduce DSL-base approach for managing inconsistent requirements which aims to improve the requirement text description with very fine granularity. Then we use those pieces of grains to detect possible inconsistencies among them.
CSI4FS®

This chapter describes the development of a game-based markerless augmented reality smartphone application (CSI4FS®) that complements traditional crime scene investigation training. The intent is to make a strong case for the use of augmented reality in a forensic science training environment. It includes a brief outline of the issue followed by a history of augmented reality in education and training, simulation use in high-risk professions generally and in education specifically, and augmented reality use in crime scene investigation. Both marker-based and markerless technologies are discussed, followed by a description of the augmented reality application and some of the challenges involved in the creation of that application. Overall, the purpose of this chapter is to introduce a potential solution that will help college students learn crime scene investigation techniques more effectively than with the more traditional training methods.
Intelligent Agent for Weather Parameters Prediction

The paper shows how the typical and not sophisticated topology of the neural network trained by easily implemented gradient method can fulfil the practical needs of the intelligent agent to be useful for weather parameters prediction. If we are able to accumulate the significant set of weather events recording temperature, atmospheric pressure, wind speed, etc. we have the real input for correct prediction in the future. The size of the training vectors can be limited as well as the number of the training epochs. Better results of prediction we can expect when we use the combination of weather events for the training vectors creation. It is possible to create the type of intelligent agent to predict the value of the weather parameters with acceptable low-level error at different climate zones. This way the idea of the weather Complex Event Processing systems seems to be sensible where Event Processing Agents (EPAs) can typical sensors to test the values of the weather parameters as well as intelligent tools created based on big data sets stored year by year.
Dependability of Multichannel Communication System with Maintenance Operations for Air Traffic Management

Air Traffic Management (ATM) systems are one of the key element of critical for flight safety infrastructure. Among ATM systems communications play a special role as a vital part in information exchange between air traffic controllers and pilots, especially in hazardous situations. In the modern ATM systems each controller has independent direct communication channel with separate radio frequency. For reliability increasing of such ATM communication channels the redundancy with common set of standby radio stations is used. Traditionally, maintenance is additional tool to increase the efficiency of radio stations operation. This paper examines the effectiveness of the ATM communication channels with the simultaneous using both of the above methods—redundancy and periodic maintenance of radio stations. Mathematical model of the channel reliability for this case is developed.
A Dispersed Multi-Value Attribute-Based Encryption With Mediated Obfuscation

The Multi-authority attribute-based encryption (MA-ABE) scheme can provide fine-grained data access control in many management domains monitored by different authorities for the cloud storage system. However, existed schemes do not support the flexible management of authorities. So, we present a new dispersed multi-value MA-ABE scheme with mediated obfuscation (DMV-ABE-WMO). In this scheme, each of attribute authorities (AAs) works independently without any collaboration with other AAs. For a data user (DU), data owner (DO) individually creates ciphertext in each management domain and each attribute authority (AA) also individually generates a private key in each management domain, respectively. We use the mediated obfuscation (MO) model to embed a secret related to DU into all target components which are used to encrypt the plaintext. In MO model, an especial function program is coded into the form of a group element, which is obfuscated to make other participants know nothing about the inner program but can evaluate the value of the function program. Our scheme is based on multi-value access structure and is proved secure against chosen plaintext attack. Compared with the related schemes, our scheme is more suited for realizing the dynamic management in many domains.
Graph-Based Registration and Blending for Undersea Image Stitching

Summary Image stitching is important for the perception and manipulation of undersea robots. In spite of a well-developed technique, it is still challenging for undersea images because of their inevitable appearance ambiguity caused by the limited light in the undersea environment, and local disturbance caused by moving objects, ocean current, etc. To get a clean and stable background panorama in the undersea environment, this paper proposes an undersea image-stitching method by introducing graph-based registration and blending procedures. Specifically, in the registration procedure, matching the features in each undersea image pair is formulated and solved by graph matching, to incorporate the structural information between features. In the blending procedure, an energy function on the indirect graph Markov random field is proposed, which takes both image consistency and neighboring consistency into consideration. Coincidentally, both graph matching and energy minimization can be mathematically formulated by integer quadratic programming problems with different constraints; the recently proposed graduated nonconvexity and concavity procedure is used to optimize both problems. Experiments on both synthetic images and real-world undersea images witness the effectiveness of the proposed method.
An Image Captioning Method for Infant Sleeping Environment Diagnosis

This paper presents a new method of image captioning, which generate textual description of an image. We applied our method for infant sleeping environment analysis and diagnosis to describe the image with the infant sleeping position, sleeping surface and bedding condition, which involves recognition and representation of body pose, activity and surrounding environment. In this challenging case, visual attention as an essential part of human visual perception is employed to efficiently process the visual input. Texture analysis is used to give a precise diagnosis of sleeping surface. The encoder-decoder model was trained by Microsoft COCO dataset combined with our own annotated dataset contains relevant information. The result shows it is able to generate description of the image and address the potential risk factors in the image, then give the corresponding advice based on the generated caption. It proved its ability to assist human in infant care-giving area and potential in other human assistive systems.
U-DADA: Unsupervised Deep Action Domain Adaptation

The problem of domain adaptation has been extensively studied for object classification task. However, this problem has not been as well studied for recognizing actions. While, object recognition is well understood, the diverse variety of videos in action recognition make the task of addressing domain shift to be more challenging. We address this problem by proposing a new novel adaptation technique that we term as unsupervised deep action domain adaptation (U-DADA). The main concept that we propose is that of explicitly modeling density based adaptation and using them while adapting domains for recognizing actions. We show that these techniques work well both for domain adaptation through adversarial learning to obtain invariant features or explicitly reducing the domain shift between distributions. The method is shown to work well using existing benchmark datasets such as UCF50, UCF101, HMDB51 and Olympic Sports. As a pioneering effort in the area of deep action adaptation, we are presenting several benchmark results and techniques that could serve as baselines to guide future research in this area.
From harmonic oscillation to chaotic motion of a compass

Abstract With secondary school students at the age of 17-20 years old, we described compass motion in uniform-immobile, uniform rotating and their superposed magnetic fields. As the students have already known the kinematics, dynamics and energy analysis of a simple pendulum in harmonic case, we tried to find analogies between pendulum and compass. Using Dynamic Solver freeware software and two representation types, phase-plane and stroboscopic projection, we simulated and analysed compass motions in more and more complex cases. Finally, we searched chaotic attractors, the sign of chaotic motion, on stroboscopic projections. A new type of representation, a video where every picture is a stroboscopic projection, helped to understand our results. This study was funded by the Content Pedagogy Research Program of the Hungarian Academy of Sciences.
iLid

The ability to monitor eye closures and blink patterns has long been known to enable accurate assessment of fatigue and drowsiness in individuals. Many measures of the eye are known to be correlated with fatigue including coarse-grained measures like the rate of blinks as well as fine-grained measures like the duration of blinks and the extent of eye closures. Despite a plethora of research validating these measures, we lack wearable devices that can continually and reliably monitor them in the natural environment. In this work, we present a low-power system, iLid, that can continually sense fine-grained measures such as blink duration and Percentage of Eye Closures (PERCLOS) at high frame rates of 100fps. We present a complete solution including design of the sensing, signal processing, and machine learning pipeline and implementation on a prototype computational eyeglass platform.
Video Information Hiding Based on Feature Points and Arnold Cat Algorithm

Information hiding has brought a lot of attention in recent years. In much of this research tasks, different techniques were considered where data could be prevented from stealing. This will avoid any possible risk it may happen to our data. Due to the challenges involved in designing different security algorithms using hiding based aspects such as the nature of wireless communication, literature has proposed new algorithms in order to increase the security of the data. Steganography has been used to hide data using media cover to deal with the right target. However, these techniques have different issues regarding the visual quality, capacity, and robustness. Based on this, we proposed a new method of steganography to make the balance between all factors by using the feature points. The proposed method also improves the layer of security for the hidden data due to applying Arnold Cat algorithm on secret message prior to the embedding stage.
Convolutional Encoder-Decoder Networks for Robust Image-to-Motion Prediction

A deep encoder-decoder network was previously proposed for learning a mapping from raw images to dynamic movement primitives in order to enable a robot to draw sketches of numeric digits when shown images of same. In this paper, the network architecture, which was previously constructed entirely with fully-connected linear layers, is modified to include convolutional layers in order to improve the image encoder component and make the network more robust to noise. The convolutional layers are pre-trained as part of an MNIST digit classifier and adapted for use in the encoder-decoder network, before the network is trained using a dataset composed of digit images and corresponding writing trajectories. This architecture was tested on several challenging noisy digit datasets and the use of convolutional layers is shown to provide a robust improvement in results.
A New Method for Computing the Principal Point of an Optical Sensor by Means of Sphere Images

For some applications it can be preferable to use images of spheres in order to calibrate a 2D camera. All published sphere-based algorithms need the complete knowledge of the elliptic sphere image, i.e. 5 geometric parameters, in particular the ellipse orientation. Because sphere images tend to be close to circular shapes, this orientation is often very noise-sensitive. For example, it is common to compute the principal point as the intersection of the lines through the major axes of the elliptic images, but this procedure is quite unstable. We present a new method for computing the principal point by means of three sphere images, without making use of the ellipse orientation. By mean of simulations and real experiments we demonstrate that the proposed method is more accurate and stable in finding the principal point as compared to sphere-based calibration algorithms that use the complete ellipse geometry.
Variable Handling and Compositionality: Comparing DRT and DTS

This paper provides a detailed comparison between discourse representation theory (DRT) and dependent type semantics (DTS), two frameworks for discourse semantics. Although it is often stated that DRT and those frameworks based on dependent types are mutually exchangeable, we argue that they differ with respect to variable handling, more specifically, how substitution and other operations on variables are defined. This manifests itself in two recalcitrant problems posed for DRT; namely, the overwrite problem and the duplication problem. We will see that these problems still pose a challenge for various extended compositional systems based on DRT, while they do not arise in a framework of DTS where substitution and other operations are defined in the standard type-theoretic manner without stipulating any additional constraints. We also compare the notions of contexts underlying these two kinds of frameworks, namely, contexts represented as assignment functions and contexts represented as proof terms, and see what different predictions they make for some linguistic examples.
Acceptance and perceptions of interactive location-tracking displays

Tracking the location of people and their mobile devices creates opportunities for new and exciting ways of interacting with public technology. For instance, users can transfer content from public displays to their mobile device without touching it, because location tracking allows automatic recognition of the target device. However, many uncertainties remain regarding how users feel about interactive displays that track them and their mobile devices, and whether their experiences vary based on the setting. To close this research gap, we conducted a 24-participant user study. Our results suggest that users are largely willing - even excited - to adopt novel location-tracking systems. However, users expect control over when and where they are tracked, and want the system to be transparent about its ownership and data collection. Moreover, the deployment setting plays a much bigger role on people's willingness to use interactive displays when location tracking is involved.
“Attention Beneficiary…!”

This chapter identifies the various types and features of scam emails as a genre of computer-mediated communication. The types identified include money transfer, investment scam, inheritance claim, next-of-kin claim, charity donation scam, foreign aid scam, foreign lottery scam and email account lottery scam. The study also describes the linguistic and discourse features of these types of scam emails and argues that the more knowledge of online financial crimes that is created and disseminated, the more people are informed and empowered to protect themselves against them. This study hopes to contribute significantly to literature on phishing attacks and online financial crimes.
Showboater

This paper describes Showboater, a simple system architecture for rural community display networks. We outline the context of our 2-year longitudinal study and outline five design goals: a functional, sustainable, scalable, resilient networked display solution which affords roles for the distribution of governance. We describe the design and implementation of Showboater and how it aligns to the design goals, as well as describing two separate deployments. We reflect on evaluation feedback and provide insight into the implications of deploying Showboater as rural community display system, respective of the initial design goals, and present our recommendations for future improvements.
Boosting speed- and accuracy of gradient based dark pupil tracking using vectorization and differential evolution

Gradient based dark pupil tracking [Timm and Barth 2011] is a simple and robust algorithm for pupil center estimation. The algorithm's time complexity of O(n4) can be tackled by applying a two-stage process (coarse center estimation followed by a windowed refinement), as well as by optimizing and parallelizing code using cache-friendly data structures, vector-extensions of modern CPU's and GPU acceleration. We could achieve a substantial speed up compared to a non-optimized implementation: 12x using vector extensions and 65x using a GPU. Further, the two-stage process combined with parameter optimization using differential evolution considerably increased the accuracy of the algorithm. We evaluated our implementation using the "Labelled pupils the wild" data set. The percentage of frames with a pixel error below 15px increased from 28% to 72%, surpassing algorithmically more complex algorithms like ExCuse (64%) and catching up with recent algorithms like PuRe (87%).