Reviewerly/exHarmony huggingface dataset:
Automotive Ignition through a Face Recognition System: AutoTISow Framework Innovation

Interaction with any technological device should be increasingly carried out in the most natural way possible. For example: Gestures, voice or facial recognition. There are technological areas that are currently working in this address: robotic, domotic, A.I., among others. Particularly, we focus on the automotive sector. This article presents a facial recognition system (FaRecSys). It is used in order to turn an automotive fuel injection system on. This system is called SSoftCCom. Although this proposal is not new, given that some cars already behave in this way. FacRecSys is presented as an AutoTISow innovation strategy' s outcome.
Activities For A Stimulating School Environment

In the last decade the activities of the students’ council of the Secondary school for electrical engineering and technical gymnasium have spread to various areas of activities and interest. A lot of students mean a lot of interests, wishes and talents. I am going to present a few most successful activities and projects that have been realised since I became the council’s mentor. These are activities which have been going on for many years, such as students’ concerts, competitions among classes, helping to organise graduation dances and trips, taking care of healthy meals and the environment, blood donor sessions, as well as individual projects, such as charity events, making a music video for a group Dan D etc.
Robustness Verification Boosting for Deep Neural Networks

Deep Neural Network (DNN) is a widely used deep learning technique, and ensuring the safety of DNN-based systems is a critical and challenging problem. Robustness is an important safety property of DNN. However, existing work of verifying DNN's robustness is time-consuming and hard to scale. In this paper, we propose a boosting method for the falsification in DNN robustness verification, which aims to find counter-examples earlier. Our observation is that different inputs to a DNN have different possibilities of existing counter- examples around them, and in particular, the input with a small difference between the largest and the second largest output vaines tends to be the Achilles heel of the DNN. We have implemented our method and applied it on two state-of- the-art DNN verification tools and four DNN attacking methods. The results of the experiments on two benchmarks indicate the effectiveness of our boosting method.
Application of Semantic Modeling to Formalized Knowledge Assessment Based on Open Encyclopedic Resources

It is suggested to use semantically markup open encyclopedic resources as a source of knowledge for validation of distance learning outcomes. Evaluation of the results of distance learning is based on the creation of a personal ontology by students of the discipline, which includes the basic concepts and relationships between them. These concepts can be obtained from relevant encyclopedic resources and perform an automatic comparison of the student's ontology of knowledge with the standard ontology of the subject.The results of this comparison allow us to identify errors in the understanding of parts of domain knowledge (discipline, subject) and to improve the standard ontological model of the discipline as a whole.
Catalyst of Information Security in Malaysia Higher Learning Institutions

Conventional teaching and learning have met its revolution more than half a century with the existence of Internet and networking among computers. In higher learning institutions (HLI), this revolutionary has created significant impact towards learning contents, delivery, and methodology. Procedural paper-based correspondence and application of enrolment have been made easier through online platform which become simpler and better throughout the years. Effectiveness of teaching & learning become more reliant on virtual learning environment and becoming a trend among higher learning institutions (HLI). In Malaysia, all public universities already engaged with virtual learning environment which the classroom session is commonly delivered via online media. Meanwhile, as far as higher learning institutions (HLI) such as universities are enthusiastic in employing the latest technologies in the teaching and learning environment, a big challenge emerges while the technology becomes greater. Since almost all information is available online via internet transaction, the issues on Information Security (IC) becomes a fatal threat to an organization. Information Security (IC) is highly essential in protecting corporate information from unauthorized parties. As a result, Information Security (IC) Management Model for Malaysian Higher Education Institution is proposed to be the foundation of the any Information Security (IC) programme which required to be established in Malaysia Higher Learning Institutions.
Bitvectors with Runs and the Successor/Predecessor Problem

The successor and predecessor problem consists of obtaining the closest value in a set of integers, greater/smaller than a given value. This problem has interesting applications, like the intersection of inverted lists. It can be easily modeled by using a bitvector of size n and its operations rank and select. However, there is a practical approach [1], which keeps the best theoretical bounds, and allows to solve successor and predecessor more efficiently. Based on that technique, we designed a novel compact data structure for bitvectors with k runs that achieves access, rank, and successor/predecessor in O(1) time by consuming space O(sqrt(kn)) bits. In practice, it obtains a compression ratio of 0.04%-26.33% when the runs are larger than 100, and becomes the fastest technique, which considers compressibility, in successor/predecessor queries. Besides, we present a recursive variant of our structure, which tends to O(k) bits and takes O(log (n/k)) time.
Integrating Project Management Tools into the Course of English for Professional Purposes: Hands-on Approach to Academic Training of Future Managers

The article presents the results of an experimental study of implementing the framework designed to integrate project management tools into the academic training of future managers. The research addresses the gap between the academic training of project managers at universities and actual requirements set by employers accounts for a steep learning curve once an alumnus finds the first job. The goal of the research is to study the results of the framework suggested to integrate project management software into the PM training, and the value of English course in this respect. The experimental module within the course of English for ProfessionalPurposes is studied for its efficiency in increasing the motivation of students for their further academic and professional activity.The article introduces the framework and the teaching materials used in the course of the experimental study, and singles out the project management tools that hold the most potential to provide the students with the hands-on approach to their professional training.
GSR

With the rapid development of Web services, more and more Web services are published on the Internet. A Mashup application that aggregates multiple Web APIs is also becoming more popular. But it also brings a problem that is how to find a suitable API among a wide variety of APIs has become a challenge. To this end, this paper proposes a web service recommendation algorithm that combines graph databases and semantics. In this algorithm, we propose to use graph database to build a two-layer structure resource model. First, we use LDA for topic classification and classify Mashup and API of the same classification into the same category respectively. This helps reduce the number of searches for Mashup and API. When a user enters a requirement document, Word2vec and WMD algorithms are used to find similar Web API description text. Finally, we use similarity and API history invokes to propose a ranking algorithm to generate a recommendation list. Through real-world data, this experiment has a better-recommended performance.
Time-Delay Multi-Agent Systems for a Movable Cloud

Adaptable splitting of calculations between movable devices and cloud is a significant and exciting research subject for movable cloud computing systems. Current work’s emphasis on the single-agent, neighbor-agent, or multi-agent systems, which aim to optimize the request achievement time for one particular agent. Due to the monopolistic competition for cloud resources among a large number of agents, the divested computations may be performed with a definite scheduling delay on the cloud. In this effort, we introduce three distributed dynamical systems, based on fractional calculus, describing the delay for single-agent, neighbor-agent, and multi-agent systems. We impose a new class of mixed-index time fractional differential equations. In addition, we investigate the asymptotic stability properties by using the fractional Cauchy’s method. We discuss the case of time-delay systems. A simulation is illustrated.
Gauntlet

In this position paper, we propose a novel wearable for capturing hand and finger movements accurately. The wearable uses a multimodal inference from rotary potentiometers and IMU sensor readings. We present various design decisions and challenges encountered in the development of the prototype of this wearable. We extensively evaluate the performance of this wearable by training it for identifying gestures using supervised learning. The results show great promise for this wearable to be used in variety of applications.
Compressing and Randomly Accessing Sequences (note)

In this paper we consider the problem of storing sequences of symbols in a compressed format, while supporting random access to the symbols without decompression. Although this is a well-studied problem when the data is textual, the kind of sequences we look at are not textual, and we argue that traditional compression methods used in the text algorithms community (such as compressors targeting k-th order empirical entropy) do not perform as well on these sequential data, and simpler methods such as Huffman-coding the deltas between sequence elements give better compression performance. We discuss data structures that allow random access to sequence elements that target such measures.
Tsunami Evacuation Planning as a tool for Tsunami Risk Reduction: A case study in Palu Bay, Central Sulawesi

Situated in one of the most complicated tectonic zones of the world, Palu is classified as one of the most susceptible places to earthquake and tsunami in Indonesia. This study aim to develop near-field tsunami evacuation simulation, as the primary strategy to reduce casualties in disaster risk reduction, based on daytime and night-time population scenarios in a tsunami-prone area in Palu Bay, Central Sulawesi. Least Cost Distance, a geospatial evacuation analysis approach in ArcGIS, is applied involving three main variables, namely population exposure, the arrival time of tsunami and walking speed of evacuees. These variables were analysed to calculate distribution of populations in daytime and night-time scenario; to identify and calculate the capacity of potential existing evacuation shelter buildings (ESB); and to analyse suitable locations, and to calculate the number and capacity of additional ESBs based on the most effective evacuation route. This study found that of the population in the study area, about 62.60 % cannot be sheltered in the seven existing ESB in the daytime as well as 63.98% of total population in the night-time scenario. Meanwhile,only 60.13% and 61.83% of the population in the service area of existing ESBs, can be evacuated in daytime and night-time scenarios, respectively. Therefore, eleven and twelve additional ESBs are proposed to be established for daytime and night-time scenarios, respectively, to accommodate people who currently cannot be sheltered.
Dashboard information system development as visualization of transaction reports in the application BackInd (backpacker reservation system)

Abstract The accuracy and clarity of information becomes primary source for decision making process to bring effective and efficient of technology utilization. For this purpose, the ‘BackInd’ has been developed as reservation system to help travellers obtain complete information regarding the tourist attractions and any relevant information. Therefore, there are several problems that occur in term of presenting information, such as the use of complex tables as a means of detail information about business conditions, information overload in single page that become deterrent in term of identification of relevant data and unintuitive data presentation that bring exhaustive understanding. Thus, the implementation of dashboard can visualize the key performance and metric in real-time to support the executive gain valuable insight lead to quick and accurate decision making. The results of this study related to the process and context analysis by visualizing the data group into various sections such as transactions, finances, testimonials, visits and business performance to bring resourceful and usable information.
Software for Solving Fully Fuzzy Linear Systems with Rectangular Matrix

The article discusses the problem of solving a fully fuzzy linear system of equations with a fuzzy rectangular matrix and a fuzzy right-hand side described by fuzzy triangular numbers in a form of deviations from the mean. A solution algorithm based on finding pseudo-solutions of systems of linear equations and corresponding software is formed. Various examples of created software application for arbitrary fuzzy linear systems are given.
2015 개정 교육과정에 따른 초등학교 과학 교과서의 탐구 활동에 나타난 과학과 교과역량 분석

The purpose of this study was to analyze the key competencies of science subject in inquiry activities of elementary science textbooks based of 2015 revised national curriculum. They were examined to be presented in inquiry activities in elementary science textbooks according to grade and five areas such as material, movement and life, movement and energy earth and space, integration. Scientific inquiry ability and scientific thinking ability among science subject competencies were more emphasized than others in totally elementary science textbooks. As the grade level increases, the proportion of scientific inquiry ability in inquiry activities decreases slightly and that of scientific thinking ability increase. Regardless of grades and areas, scientific inquiry ability was the highest portion than other science subject competencies, but scientific thinking ability was the highest portion than others in material, life and integration areas of the 6th grade textbooks. It is necessary to explore ways to improve more evenly subject competencies reflected for elementary school students in science classes.
Research on the Development of Elementary School Physical Education Teaching Resources

The development and utilization of physical education teaching resources in elementary school is analyzed in this paper from the perspective of curriculum and teaching theory based on theoretical analysis; on the basis of establishing the awareness of physical education teaching resources development, the comprehensive utilization of venue facilities and sports project transformation, the introduction of emerging sports event and the use of information technology and other specific strategies are proposed.
Combining Multi-Agent Systems and Subjective Logic to Develop Decision Support Systems

Nowadays, the rise of the interconnected computer networks and the increase of processed data have led to producing distributed systems. These systems usually separate multiple tasks into other simpler with the goal of maintaining efficiency. This paradigm has been observed for a long time in different animal organisations as insect colonies and fish shoals. For this reason, distributed systems that emulate the biological rules that govern their collective behaviour have been developed. Multi-Agent Systems (MAS) have shown their ability to address this issue. This paper proposes Ant Colony based Architecture with Subjective Logic (ACA-SL). It is a bio-inspired model based on ant colony structures. It makes use of MAS to distribute tasks and Subjective Logic (SL) to produce Decision Support Systems (DSS) according to the combination of individual opinions. A system implementation based on the proposed architecture has been generated to illustrate the viability of the proposal. The proposed architecture is intended to be the starting point for developing systems that solve a variety of problems.
Creating a Model of Assessment of Mathematical Education Quality on the Example of Plekhanov Russian University of Economics

Educational process is inextricably connected with learning and the control of the level of acquired knowledge --- this is exactly what determines the relevance of the chosen topic. When checking up tests, taking exams it is relatively difficult to exclude the examiner's bias, lack of informational content in the tasks, personality traits of the student. The purpose of the work is, on the basis of existing models, to develop an approach to compiling student performance assessment systems, most informatively reflecting students 'knowledge levels, as well as to build and test a model for checking the level of knowledge for an objective assessment of students' level of preparedness in mathematical disciplines. In this paper, educational programs were analyzed for the depth of study of mathematical disciplines using syllabus and the proposed assessment tools; an experiment was conducted to study specific statistics of distributions of test results that reflect the correspondence of existing assessment tools and IRT (Item Response Theory); the results were evaluated in the context of other knowledge control tools. As a result of the study, the first outcomes were obtained, which can be further used in the preparation of control and measuring materials to assess the level of knowledge of students of Plekhanov Russian University of Economics (REU).
Beyond Relevance Feedback for Searching and Exploring large Multimedia Collections

Relevance feedback was introduced over twenty years ago as a powerful tool for interactive retrieval and still is the dominant mode of interaction in multimedia retrieval systems. Over the years methods have improved and recently relevance feedback has become feasible on even the largest collections available in the multimedia community. Yet, relevance feedback typically targets the optimization of linear lists of search results and thus focuses on only one of the many tasks on the search - explore axis. Truly interactive retrieval systems have to consider the whole axis and interactive categorization is an overarching framework for many of those tasks. The multimedia analytics system MediaTable exploits this to support users in getting insight in large image collections. Categorization as a representation of the collection and user tasks does not capture the relations between items in the collection like graphs do. Hypergraphs are combining categories and relations in one model and as they are founded in set theory in fact are closely related to categorization. They, therefore, provide an elegant framework to move forward. In this talk we highlight the progress that has been made in the field of interactive retrieval and in the direction of multimedia analytics. We will further consider the promises that new results in deep learning, especially in the context of graph convolutional networks, and hypergraphs might bring to go beyond relevance feedback.
A Regularization Algorithm of Improved Data Fidelity Term and Weight Adaptive Bilateral Total Variation

In view of the existing shortcomings of super-resolution that data fidelity term in MAP framework obtained from L1, L2 and other norm functions and super-resolution total variation (TV) or bilateral total variation (BTV) for regularization, Tukey norm function is used to construct fidelity term in the MAP framework which is combined with the weight adaptive BTV regularization term to improve the regularization algorithm based on TV or BTV, and a regularization super-resolution image restoration algorithm based on the improved fidelity term and the weight adaptive bilateral total variation (WABTV-Tukey) is proposed. Through comparative analysis of multiple simulation experiments, WABTV-Tukey norm and bilinear interpolation, BTV-L1 norm, BTV-L2 norm, BTV-Tukey norm, feature-driven prior MAP block, Lorentzian-Tikhonov Regular analysis, generalized total variation regularization based on neighborhood pixel expansion and other algorithms are used for comparative analysis. Subjective evaluation of visual analysis and objective quantitative evaluation of peak signal-to-noise ratio (PSNR) indicators verify the adaptability of WABTV-Tukey algorithm to different types of noise models, and the superior edge detail retention characteristics.
FuzzyNet

This paper addresses the pertinent object localization problem in deep convolutional neural networks by introducing a spatial fuzzy post-processing function which allows the smooth transition of object edges within individual pixel's neighborhood. We accomplish the task of semantic segmentation by first computing class weights as a means of avoiding class bias or imbalance training. Our proposed FuzzyNet runs a convolutional encoderdecoder network architecture with the following novel features: (i) It incorporates a new Global Context Spatial Module (GCSM) (ii) It exploits the atrous spatial pyramid structure for enriching the semantic encoding (iii) It incorporates the transfer of lower level features connected to higher levels with contextual spatial feature maps (iv) It effectively achieves an attention component with an extensive focus on objects of interest. Thus, the fusion of spatial fuzzy function enables normalization of intensity variation at different object boundaries, avoidance of poor localization and ultimately resulting in quality semantic segmentation. The evaluation of our proposed FuzzyNet model achieves improved performance with respect to the accuracy and object boundary refinement on the PASCAL VOC 2012 and CamVid benchmark datasets.
Novel Watermarking Technique Using Encryption Steganography, Chaotic Logistic Map and Multiple Embedding

Copyright of patented digital objects such as digital audio, image, video or multimedia information are safe guarded by watermarking. The manufacturer’s logo, used as the watermark or secret information, is watermarked or embedded in the digital object by various techniques. Small size watermark is preferred as it alters the quality of the digital object which may be considered undetectable. However, a large size watermark may be embedded in such way that it does not affect the quality at all, and also a small size watermark is embedded with small effect on the quality of the digital object. In this paper we propose a novel and highly secure watermarking technique using a full size and a half size watermark, the latter is obtained from the first one. The proposed technique consists of two stages. In the first stage we actually embed in the encrypted digital object with encrypted half size watermark, and in the second stage we virtually watermark the de-encrypted digital object from the first stage, by multiple times embedding with encrypted full size watermark. The stage is called virtual as the multiple embedding is cleaned off and the result is the de-encrypted digital object that was obtained from the first stage. Cleaning or reverting the multiple embedding, multiple derived secret information is obtained which are safely stored in the company for utilization against adversaries claiming rights or unauthorized usages. The retrieval of the half size watermark and multiple derived secret information strongly protects the copyright of the digital object.