Reviewerly/exHarmony huggingface dataset:
An Improvement for Background Modelling using a Mixture of Gaussian and Region Growing in Moving Objects Detection

Abstract Most of the research on object detection that uses discussing background is only necessary for the decision of the background model by assuming all statistical objects are part of the background. This caused a debate called “foreground aperture,” where statistical purposes that represent moving backgrounds, the position of the initial object will be detected as false detection. Related to the new background modeling is needed to overcome this problem. This paper proposes how to obtain an efficient background model that meets real requirements. As a background model, the Mixed-of-Gaussian Model (MOG) was adopted and refined with some improvements with the Region Growing method. Evaluation to measure the performance of the MOG algorithm and Region Growing by using a detection rate to calculate the number of right and wrong in the detection of moving objects.
Image Completion of Highly Noisy Images Using Deep Learning

Generating images from a noisy or blurred image is an active research problem in the field of computer vision that aims to regenerate refined images automatically in a content-aware manner. Various approaches have been developed by academia and industry which includes modern ones applying convolution neural networks and many other approaches to have more realistic images. In this paper, we present a novel approach that leverages the combination of capsule networks and generative adversarial networks that is used for generating images in a more realistic manner. This approach tries to generate images which are locally and globally consistent in nature by leveraging the low level and high-level features of an image.
Identification of applications running on smartphones using call detail records

The surge use in network traffic due to increased adoption of 4G LTE-capable portable devices put pressure on network providers to constantly upgrade their network infrastructures. At the same time, unlike traditional Internet access devices (e.g., computer desktops), whose network traffic can be easily tracked via their port numbers, applications of handheld devices typically communicate via HTTP and encrypted HTTPS. Additionally, for scalability purpose, most of their data are sent and received via Content Distribution Networks (CDNs). These communication characteristics obscure any attempt to monitor their network usage. This paper uses cellular network traffic generated by the applications running on a smartphone to predict a 62% prediction accuracy in distinguishing social network services (SNS) from other background applications.
Reasoning about sequential cyberattacks

Cyber adversaries employ a variety of malware and exploits to attack computer systems, usually via sequential or "chained" attacks, that take advantage of vulnerability dependencies. In this paper, we introduce a formalism to model such attacks. We show that the determination of the set of capabilities gained by an attacker, which also translates to extent to which the system is compromised, corresponds with the convergence of a simple fixed-point operator. We then address the problem of determining the optimal/most-dangerous strategy for a cyberadversary with respect to this model and find it to be an NP-Complete problem. To address this complexity we utilize an A*-based approach with an admissible heuristic, that incorporates the result of the fixed-point operator and uses memoization for greater efficiency. We provide an implementation and show through a suite of experiments, using both simulated and actual vulnerability data, that this method performs well in practice for identifying adversarial courses of action in this domain. On average, we found that our techniques decrease runtime by 82%.
Development of knowledge testing systems based on discrete optimization models

Abstract We present an approach to creating computer testing systems and an overview of our results obtained based on it. Special attention is paid to the problem of forming the optimal test content. This problem consists of finding a subset of the test tasks allowing us to get an objective conclusion about the degree of assimilation by a student the training course. Several formulations are described for solving this problem and the problem of forming an optimal test kit. Corresponding mathematical models are constructed on the base of the set covering problem and its generalizations. A specialized computer testing system, developed by us based on this approach, is described. The experience of this system use and the prospects of the proposed approach for creating knowledge testing systems are discussed.
The Correlation between Path Loss and Cutting Scenarios Based on Grey Correlation Degree

Accurate wireless channel modeling is the basis of reasonable estimation of railway wireless communication system. In order to further understand the relationship between scenario and path loss, based on the path loss exponent of cutting scenarios, the correlation between parameters of cutting scenarios, and ideal cutting scenarios is analyzed by using grey correlation degree algorithm. The preliminary conclusion is that the path loss exponent is more reasonable when the cutting scenarios parameters are within a certain range. The research results have some guiding significance for the collaborative work of cutting structure construction and wireless network construction.
Student Software Engineering Learning in HFOSS Projects

Becka Morgan takes great joy in teaching students computing languages, a subject she has been passionate about since she learned to program in 2006 as a non-traditional student.She is driven to create an inclusive environment.Consequently Dr. Morgan was drawn to teaching FOSS and HFOSS development based on work that is being done that suggests underrepresented groups are attracted to HFOSS participation.She teaches a one-term HFOSS
Procedural Behavior Model using Behavior Tree in Virtual Reality Applications

This paper introduces a study for procedurally generating the behavior of objects in a virtual environment at runtime.This study was initiated to enable the behavioral model of objects in virtual reality applications to evolve in response to user behavior at runtime.Our approach is to describe the behavior of an object as a behavior tree, and to make a node of the behavior tree change to another type if a certain condition is satisfied.We defined four types of node changes: "parameterized", "probabilistic", "alternate", and "variant".We experimented with a virtual environment that includes a variety of simple procedural elements to explore the possibilities of our approach .As a result of the implementation, if an optimization algorithm that can select and apply the optimized procedural elements in response to the user's behavior is complemented, it is confirmed that more intelligent objects and agents can be implemented in virtual reality applications.
Multimedia Teaching Reform of Skiing Based on the VR Technology

In view of the unfavorable factors of the unreasonable allocation of the class hours, the restriction of the site conditions and the high difficulty of the action technologies in the traditional skiing teaching methods, this paper puts forward the idea of using the modern education and multimedia technologies to overcome the shortcomings of the traditional skiing teaching methods, aiming to break the traditional teaching methods and effectively improve the quality of the skiing teaching. Through the analysis of the traditional skiing teaching mode and teaching methods, this paper expounds the positive role of the computer and multimedia courseware in the skiing teaching with examples, probes into the characteristics and advantages of using the multimedia courseware in the skiing teaching, and puts forward concrete opinions and suggestions.
Randomizing Vigenere Cipher in Polyalphabetic Substitution Scheme

Data security is the most threatening challenge that the world is facing today. Encrypting and storing data is one of the pivotal solutions to it. Encrypted data being hacked and getting decrypted is another threat to it. A secure encryption algorithm can overcome this issue to an extent. A secure algorithm is usually very complicated. One of the oldest and simplest encryption algorithms is Vigenere Algorithm. This paper proposes an efficient method to improve the security of Vigenere cipher.
Eyes on you: field study of robot vendor using human-like eye component “Akagachi”

Eye gaze is an important non-verbal behavior for communication robots as it serves as the onset of communication. Existing communication robots have various eyes because design choices for an appropriate eye have yet to be determined, so many robots are designed on the basis of individual designers' ideas. Thus, this study focuses on human-like eye gaze in a real environment. We developed an independent human-like eye gaze component called Akagachi for various robots and conducted an observational field study by implementing it to a vendor robot called Reika. We conducted a field study in a theme park where Reika sells soft-serve ice cream in a food stall and analyzed the behaviors of 984 visitors. Our results indicate that Reika elicits significantly more interaction from people with eye gaze than without it.
An optimized model for classification of appropriate technology products using neural networks and genetic algorithms

Abstract The background of this research is the difficulty faced when classifying the result of the assesment in the event Competition Appropriate Technology Tegal City. In this paper, the classification result of the assesment is carried out by categorizing the result of technology product that are suitable for implementation in small to medium industries by using the model. The model proposed in this study is using the Neural Network (NN) method to be able to classify products whether into appropriate technology for small/medium industries. Neural Network can produce a low level of accuracy, but Neural Network optimized based on Genetics Algoritm can produced a level of accuracy more higher than without optimation. The genetic algorithm (GA) is proposed to optimize the value of Neural Network parameters such as the number of hidden layers and learning rates. Based on the results of the experiments that have been produced, and system validation using the 10 Fold cross-validation trial shows that the Neural Network method that optimizes the genetic algorithm produces an accuracy rate of 91.37%, better than the Neural Network method without optimization which is equal to 90, 14%.
Task Planning with Manual Intervention Using Improved JSHOP2 Planner

In real-world task planning, such as automatic vehicles dispatch, often face the arrival of new tasks and uncertain factors in the process of task execution. As a typical implementation of Hierarchical Task Network (HTN) planning, JSHOP2 planner is suitable for complex task planning. Given the fact that JSHOP2 planner fails to get planning results in the global level when facing uncertain factors, an improved JSHOP2 planner is proposed to solve this problem. The improved JSHOP2 planner with manual intervention supplements the planning result and eliminates the impact of uncertain factors with the help of human experience. In addition, we conducted comparative experiments based on improved planner. The simulation results show the effectiveness and the ability to emergency response of improved planner.
Il Corpus parallelo italiano-russo per lo studio del causativo in chiave contrastiva

In this paper I will show how a parallel corpus can be used to compare Russian and Italian causative constructions. While the first is mainly lexical, causation in Italian is almost exclusively expressed through the construction ‘make + infinitive’. This corpus-based analysis will show, on the one hand, how Italian causative often loses its primary function and, on the other, how Russian, that does not always mark the opposition between direct and indirect causation, could be ambiguous when expressing the actants of a given causative situation. This confirms, once again, the predominantly synthetic nature of Russian compared to Italian.
Three-pass Protocol Scheme on Vigenere Cipher to Avoid Key Distribution

Information exchange is a prevalent thing in computer networks. The information sent is confidential, and some are ordinary. In sending confidential information, many concerns occur. The sender is afraid of the information being stolen, especially if the information contains confidential data. In sending information, the sender and recipient must know the key or password to be able to read the information correctly. However, sending the key is very deep concern because if others know the key, then the data and information contained in the file will be successfully read. Key exchanges must be avoided. The technique for avoiding key exchange is to use the Three-pass Protocol scheme. This scheme allows the sender and receiver to encrypt and decrypt using their respective keys without having to exchange keys. Three-pass Protocol is a mechanism. There are two algorithms needed in the cryptographic process. The algorithm used is Vigenere Cipher. By implementing the Three-pass Protocol scheme, data security will be more guaranteed.
Line-scan detection system to identify rare earth elements in rocks

We develop an integrated sensor system to detect rare earth elements (REE) in natural minerals on-site. The system combines reflectance and photoluminescence (PL) spectroscopy in order to present a noninvasive alternative to conventional time-consuming and costly chemical analysis of drill cores in mineral exploration. The major benefit lies in the rapid gathering of continuous spatial information on the type and abundance of the REEs in drill cores. Additionally the sample material remains unharmed during the whole process. To maximise scan speed and sample throughput, our system operates in continuous line scan mode, with continuous sample flow beneath the detector.
Automated Vehicles Path Modification for Designated Via-Point Using Nonlinear Simplex Optimizer

Path generation and modification are very important issues in the operation of automated vehicles. The pertaining processing time and accuracy in real time often depend on the type of solution algorithm. In this paper, a gradient-free optimization algorithm has been used for Bezier curve-based base path modification for passing through the designated via-point. The strength of nonlinear simplex optimizer in the limited three-dimensional solution space is added with the features of no requirement of computing derivatives and smooth objective function. The performance of the proposed method is compared with that of the method using gradient-based optimizer through computer simulation results.
Sobel-edge Detection Algorithm in a Mobile Application for Detecting Fake Money

This paper used an existing algorithm called Sobel Edge Detection through image segmentation to detect fake money using a mobile application. The most common form of detecting money if counterfeit or not, is through the use of manual inspection using touch, ocular inspection by sight, or the use of ultraviolet light. The mobile application developed used a photo taken by the cellphone camera and with the use of Sobel Edge Detection Algorithm, using image segmentation for determining counterfeit money. Image of the money to be tested is captured by the digital camera of the phone which is then subjected to the process of edge detection in order to determine if it is fake or not. This study will be the first to utilize the cellphone camera that will use picture to be analyzed through a mobile application to determine fake Philippine money. Java language was used in the development process.
BLASTNEL

In this paper, we propose "BLASTNEL", a collision sensation display for VR games using highly compressed air. BLASTNEL has multiple nozzles on a panel, and it can control the phase and duration of the jetting air from each nozzle. The player standing in front of the panel receives the jetting air when an object is collided to the player's character in the virtual world. The outline and duration of the jetting air are synchronized with those of the collision. Thus, BLASTNEL can improve the immersive feeling of the player. We developed a prototype and conducted a simple evaluation experiment. In this paper, we describe the responses of the subjects and the revealed issues.
Combining Deep Deterministic Policy Gradient with Cross-Entropy Method

This paper proposes a deep reinforcement learning algorithm for solving robotic tasks, such as grasping objects. We propose in this paper a combination of cross-entropy optimization (CE) with deep deterministic policy gradient (DDPG). More specifically, where in the CE method, we first sample from a Gaussian distribution with zero as its initial mean, we now set the initial mean to DDPG's output instead. The resulting algorithm is referred to as the DDPG-CE method. Next, to negate the effects of bad samples, we improve on DDPG-CE by substituting the CE component with a weighted CE method, resulting in the DDPG-WCE algorithm. Experiments show that DDPG-WCE achieves a higher success rate on grasping previously unseen objects, than other approaches, such as supervised learning, DDPG, CE, and DDPG-CE.
Automatic Classification Method for Academic Characterization of Computer Programming Students

Computer programming is a fundamental subject that lays down conceptual basis and tends to develop important abilities for the student's software development path. Nevertheless, in a computer programming course there are several obstacles that prevent students from committing and take computer programming as a professional future or even as a useful tool, this is evidenced in the lack of motivation to learn coding. The difficulties that arise in computer programming's learning process together with the high demand for software developers, highlight the importance to trace the critical fact that influences student's performance and lay down strategies that allow us to improve the possibilities for each student in his learning process. Automatic classification methods are proposed to find patterns to relate personal features with computer programming performance.
Recognition of Flight Operation Action Based on Expert System Inference Engine

Flight operation action recognition is the basis to judge the flight operation ability. The completion of tasks by fighter-pilots needs to comprehensively investigate the interception operation skill, precise timing, the establishment of mutual support, the application of radar equipment, the application of deception tactics and so on. The establishment of flight operation action recognition method based on expert system inference engine aims at building a low-cost and efficient manoeuvre platform for the training of pilots and tactics and military strategy deduction by virtue of the recognition technology and in combination with the emulation technique, display panel control and network environment.
A Project-based PMiner Algorithm in Uncertain Databases

Existing works considered the level-wise method to find the high average-utility sequential patterns at different levels, which may cause higher computations and the huge memory requirements. To solve the previous problem of high average-utility sequential pattern mining, a project-based algorithm called PMiner is presented here to speed up the mining efficiency compared to the previous work. Also, the developed PMiner concerns not only the average-utility on the sequential data but also the data in the uncertain environment (i.e., Internet of Things). Several experiments are then evaluated to show that the proposed algorithm achieves promising performance compared to the level-wise model.
Circular feature and non-coplanar point based monocular pose measurement

This paper proposed a novel monocular pose measurement method based on circular feature and non-coplanar point feature. The proposed method extracts the circular feature in the first frame using the Orientation Transform based circle detection algorithm (OTCircles). For the subsequent frames, a global optimal searching mechanism is adopted to track the circular feature. The circular feature tracking enables the proposed method to measure the pose efficiently and robustly for the image sequence. In order to eliminate the uncertainty of the recovered parameters of the circular feature, a non-coplanar point is introduced in the proposed method. The point feature is detected via template matching. Based on the detected circular feature and non-coplanar point feature, the proposed method measures the six-degree-of-freedom (6DoF) pose. Experiments on simulated data and real images indicate that the proposed method measures the 6DoF pose parameters efficiently and accurately.
Professional development: Perspective of Business English teachers in vocational high schools in Serbia

This paper addresses professional development of teachers who run Business English courses in vocational high schools in Serbia. The analysis of professional development programmes offered in Serbia indicated that Business English teachers' needs are not sufficiently met. Therefore, by investigating teachers' attitudes, experience, and needs with regard to professional development, it is possible to gain valuable guidelines which might help improve the overall situation. The research, which was of both quantitative and qualitative nature, was conducted with 31 respondents who were asked to complete the questionnaire which centered on the topics such as frequency of attending professional development (PD) programmes, criteria for selecting them, types of PD activities that teachers undertake, attitudes towards features of PD programmes such as quality, relevance, applicability, etc., teachers' needs in terms of strengthening various professional competences (e.g. field competences, curricular competences, ICT competences, etc.), ways of improving professional development programmes and the like. The teachers' responses confirmed that the current situation regarding professional development in Serbia is unfavourable, but also reflected their understanding and awareness of the importance of this process for building their professional identity and improving their pedagogical practices. The respondents expressed a high level of need for developing both general and specific teaching competences. They also highlighted the importance of being provided with contemporary, relevant and applicable programmes which are delivered by educators who are well acquainted with both pedagogical theory and teachers' working environment. Taking into account teachers' needs while choosing the location and determining the time of PD activities could additionally increase PD attendance. Another factor that could improve the overall situation regarding professional development is the greater presence of virtual PD programmes.
Pronunciation of Italian and English words by Japanese subjects of different ages: The results of an experiment

The paper reports an experiment aimed at verifying the performance in the pronunciation of foreign sequences (in Italian, English and invented words) by Japanese in preschool, school and adult age. The results show that progress through age, even for the same phonetic sequences, is greater in English words, suggesting that the word, rather than the phonetic sequence, is the language unit that speakers learn to pronounce correctly. It is proposed that there may be a minor disturbing role of the katakana syllabic writing, which causes a slight decrease in the performance of school-age children. A strong interference is observed from the most frequent English loanwords, which leads to pronouncing the original English word with the same adaptation it receives in Japanese phonology.