Reviewerly/exHarmony huggingface dataset:
Bayesian Network-based GF-City Metropolitan Zone Modelling: an Approach Based on Location Quotient Resource Impulse Response Principle

Abstract Based on the evolution phenomenon and law of “location quotient”, this paper has studied the mapping relationship between image recognition of GF-City metropolitan area and the impulse response of local industrial resources. Based on the “location quotient” method, the level of local industry specialization in Foshan city was analysed, with the aid of VAR model. With the help of economic logic reasoning method, the VAR regression equation model improved design pattern analysis method was extracted in terms of the typical local industry semantics; a Bayesian network-based VAR model GF-City metropolitan area local industrial resources was constructed together with the impulse response network model. Based on the image recognition evaluation experiment of GF-City metropolitan area, the network combined Bayesian scoring function and search algorithm to carry out learning reasoning, and thus divided into local industrial grammar sub-network, image vocabulary sub-network and mapping relationship sub-network by modularization. According to the trend of node and conditional probability distribution, the image lexical structure and morphological organization characteristics were analysed, and the impulsive local industry resource impulse response knowledge rules were subsequently mined and characterized. Consequently, an example analysis proved the validity and applicability of the modelling method for the knowledge acquisition of the “location quotient” resource impulse response, so as to further analyse and perfect the rapid development of Foshan City with the drive of “GF-City metropolitan area” in the background of GHM Greater Bay Area.
The design of the brantas river ecological monitoring is real time with OpenCV

Abstract Brantas River is a river that is used by the people of East Java for drinking water sources, irrigation, regulating water discharge, and tourist attractions. However, the bad habits of the people around the Brantas River now have damaged the ecology of the Brantas River, one of these habits is throwing garbage. To protect the ecology of the Brantas river, a system is needed to monitor the activities of people who dispose of garbage in the Brantas river in real time using Open CV. One of our efforts is to establish a Closed Circuit Television system to monitor the activities of residents in the Brantas river area which is commonly used by people to dispose of garbage. The system applied is used to provide moral punishment to people who throw garbage. The way this system works is to record an area that is often used by people to dispose of garbage, if people find it throwing garbage, the admin system will take pictures and crop, then the photo is processed with Open CV. The introduction of people who throw garbage is done from the front view. From the results of the trial as many as 125 times, the system can recognize faces correctly as many as 117 times (93%).
Coordinate Systems: Level Ascending Ontological Options

A major challenge faced in the deployment of collaborating unmanned vehicles is enabling the semantic interoperability of sensor data. One aspect of this, where there is significant opportunity for improvement, is characterizing the coordinate systems for sensed position data. We are involved in a proof of concept project that addresses this challenge through a foundational conceptual model using a constructional approach based upon the BORO Foundational Ontology. The model reveals the characteristics as sets of options for configuring the coordinate systems. This paper examines how these options involve, ontologically, ascending levels. It identifies two types of levels, the well-known type levels and the less well-known tuple/relation levels.
Efficient Multiple Query Answering in Switched Probabilistic Relational Models

By accounting for context-specific independences, the size of a model can be drastically reduced, thereby making the underlying inference problem more manageable. Switched probabilistic relational models contain explicit context-specific independences. To efficiently answer multiple queries in switched probabilistic relational models, we combine the advantages of propositional gate models for context-specific independences and the lifted junction tree algorithm for answering multiple queries in probabilistic relational models. Specifically, this paper contributes (i) variable elimination in gate models, (ii) applying the lifting idea to gate models, defining switched probabilistic relational models, enabling lifted variable elimination in computations, and (iii) the switched lifted junction tree algorithm to answer multiple queries in such models efficiently. Empirical results show that using context-specific independence speeds up even lifted inference significantly.
JPEG Reversible Data Hiding with Matrix Embedding

Joint photographic experts group (JPEG) image is the most popular image format used in our daily life. Different from the reversible data hiding (RDH) in spatial domain image, JPEG RDH needs to consider not only the visual quality, but also the file storage size of the marked image. In this paper, we firstly implement the matrix embedding (which is originated from steganography) strategy into the field of JPEG RDH. Via considering the philosophy behind the JPEG encoder, those quantized discrete cosine transform (DCT) coefficients that may introduce less distortion are adaptively selected for modification in the embedding process. Experimental results demonstrate that higher visual quality and less storage size of the marked JPEG image can be obtained compared with the state-of-the-art methods.
Research On The Use of Software Specialties in Vocational Education and Training

The introduction of Information and Communication Technologies (ICT) in the field of Greek education started in the form of a pilot project, called ODYSSEAS. This project was successfully completed after dividing it into 19 parts, each one of which played a significant part in the action, named ODYSSEY. During the IASONAS program, in 2008, twenty-six (26) Educational-Professional Software applications (EPS) were provided to the Vocational Education and Training (VET). Since then, these applications have had an immense impact on the way ICT implements in the VET. In the pilot survey, conducted in November–December 2017 among 133 (out of a total of 180) educators from schools of VET of the East Macedonia and Thrace, in Greece, instructors were inquired about their preference on the software applications during their lectures. The first results of the survey provide answers to diverse questions, regarding the EPS, contributing to the advocacy of the education, science and the overall upgrade of the VET.
Intelligent Image Analysis System for Position Control of People with Locomotor Disabilities

The paper presents a remote-controlled system mounted on the seats used by the persons with locomotor disabilities. In these cases, a multitude of problems may be avoided, such as obstacles or bumps, without the direct action of the human being, because the device adapts to each new situation, to reach the destination. The system used to guide or to move a wheelchair on the ground comprises several functional blocks that are distinct as structure, but interdependent. The movement area is monitored by a camcorder connected to the microcontroller, which transmits images to it. The microprocessor processes the images, calculates and sends signals to the communication interface of the equipment. The system receives the commands sent by the microcontroller, interprets them and carries out the movement, together with the transmission of various pieces of information towards the microcontroller such as: confirmations regarding data reception and their validity, data from the sensors, the image itself, etc.
Entanglement versus cooling in the system of a driven pair of two-level qubits longitudinally coupled with a boson-mode field

The relationship among the entanglement creation within coherently pumped and closely spaced two-level emitters longitudinally coupled with a single-mode boson field, and the subsequent quantum cooling of the boson mode is investigated. Even though the two-level qubits are resonantly driven, we have demonstrated an efficient cooling mechanism well below limits imposed by the thermal background. Furthermore, the cooling effect is accompanied by entanglement of the qubit pair components when the dipole-dipole frequency shift is close to the frequency of the boson mode. The maximum boson mode cooling efficiency realizes on the expense of the entanglement creation. Importantly, this occurs for rather weak external pumping fields protecting the sample from the deteriorations. Finally, the conditions to effectively optimize these effects are described as well.
Application of Case Based Reasoning for Student Recommendations Drop Out (Case Study: Adisutjipto College of Technology)

The data of non-active students is an obstacle in a university because it is counted as a student body, thus affecting the lecturer ratio. For that reason, in order to improve the lecturer ratio, a way is needed in addition to adding lecturers, but also by evaluating the data of students who are not active and active to be filtered back by looking at academic data that is known so that students can continue their studies or should be advised to resign or also drop out . To solve these problems a system model is needed that can recommend students as drop out students and can also provide other recommendations that can be used as evaluations. Case-based reasoning method is used to see new data matching with old data, where active student data to be evaluated will be matched with student data that has been dropped out or received a warning letter, so that it will be used as a new solution. Case-based reasoning methods can help in recommending students to drop out or get a warning letter.
Adaptation of the mathematical apparatus of the Markov chain theory for the probabilistic analysis of recurrent estimation of image inter-frame geometric deformations

The paper is devoted to the analysis of the possibilities of using Markov chains for analyzing the accuracy of stochastic gradient relay estimation of image geometric deformations. One of the ways to reduce computational costs is to discretize the domain of studied parameters. This approach allows to choose the dimension of transition probabilities matrix a priori. However, such a matrix has a rather complicated structure. It does not significantly reduce the number of computations. A modification of the transition probabilities matrix is proposed, it’s dimension does not depend on the dimension of estimated parameters vector. In this case, the obtained relations determine a recurrent algorithm for calculating the matrix at the estimation iterations. For the one-step transitions matrix, the calculated expressions for the probabilities of image deformation parameters estimates drift are given.
Requirements Analysis of a Serious Game for Deaf Players

Talk Town is a serious digital game to overcome social isolation in Deaf children by combating stigma associated with hearing impairments and providing modelling of social communication skills. This paper presents the research process and outcomes to ensure that Talk Town is engaging and useful for players, educators and parents. It is described and framed using Cooper’s Requirements Definition and qualitative methods. The motivation for the game-based intervention is illustrated with findings from a structured literature search and supported with insights gained from qualitative enquiries. Findings and themes from interviews and co-design activities are discussed, personas and artefacts illustrated, and relevant content, game and design recommendations for future work in this area presented.
Sports Video Summarization with Limited Labeling Datasets Based on 3D Neural Networks

As more and more information generated, recorded, and distributed daily, the video summarization allows the spectators to focus on the clips mostly containing the important events for watching time saving without scarifying the video quality. The purpose of this work is to use the deep learning for removing the redundant video clips, whereas keeping the critical content exhaustive. We would try to bridge the semantic gap between the low-level video features and the high-level human perception, which is prone to be modeled by the neural network systems. The advantages will also be verified by comprehensive experiments for MLB (Major League Baseball) video summarization in terms of reduced size and expressional consistency.
Analyzing Modular Smoothness in Video Game Music

This article provides a detailed method for analyzing smoothness in the seams between musical modules in video games. Video game music is comprised of distinct modules that are triggered during gameplay to yield the real-time soundtracks that accompany players’ diverse experiences with a given game. Smoothness—a quality in which two convergent musical modules fit well together—as well as the opposite quality, disjunction, are integral products of modularity, and each quality can significantly contribute to a game. This article first highlights some modular music structures that are common in video games, and outlines the importance of smoothness and disjunction in video game music. Drawing from sources in the music theory, music perception, and video game music literature (including both scholarship and practical composition guides), this article then introduces a method for analyzing smoothness at modular seams through a particular focus on the musical aspects of meter, timbre, pitch, volume, and abruptness. The method allows for a comprehensive examination of smoothness in both sequential and simultaneous situations, and it includes a probabilistic approach so that an analyst can treat all of the many possible seams that might arise from a single modular system. Select analytical examples from The Legend of Zelda: The Wind Waker and Portal 2 provide a sampling of the various interpretive gains available through this method.
Analysis of Human Error of EHS in Healthcare Industry Using TISM

Human error plays a key role in healthcare industry. The risk factors for human error involved in environmental health and safety section is extremely high when compared to other sections. The objective of this paper is to recognize the most influential factors for human error of EHS in healthcare industry. The risk factors are identified from the literature survey as well as inputs from industrial experts. The identified factors are analyzed using one of the soft computing tools known as total interpretive structural modeling (TISM). The model of TISM clearly shows the driving power and reliance power of the factor. A case empirical study is conducted in an Indian healthcare industry for verifying the suggested model. The outcomes of the paper will help industrial managers for implementing the framework of EHS and also will enhance the managerial excellence. Finally, to verity the TISM results, a statistical validation is done using covariance-based structural equation modeling (CBSEM).
A Joint Bitrate and Buffer Control Scheme for Low-Latency Live Streaming

Live video streaming has experienced explosive growth on the mobile Internet. Unlike on-demand streaming, live video streaming faces more challenges due to the strong requirement of low latency. To balance several inherently conflicting performance metrics and improve the overall quality of experience (QoE), the adaptive bitrate algorithm is widely used under time-varying network conditions. However, it does not perform well at low latency. In this paper, we present a joint bitrate and buffer control scheme (JBBC) for low-latency live streaming based on latency-constrained bitrate adaptation and playback rate adaptation. Experiments demonstrate that the proposed algorithm has better performance on overall QoE than most existing adaptive schemes, achieving a more stable bitrate selection and relatively lower delay on the premise of almost no rebuffering.
Irrelation of Mathematical and Functional Aspects of Descriptive Image Algebras with One Ring Operations Interpretability

The study is continued investigation of mathematical and functional/physical interpretation of image analysis and processing operations used as sets of operations (ring elements) in descriptive image algebras (DIA) with one ring. The main result is the determination and characterization of interpretation domains of DIA operations: image algebras that make it possible to operate with both the main image models and main models of transformation procedures that ensure effective synthesis and realization of the basic procedures involved in the formal description, processing, analysis, and recognition of images. The applicability of DIAs in practice is determined by the realizability—the possibility of interpretation—of its operations. The interpretation is considered as a transition from a meaningful description of the operation to its mathematical or algorithmic implementation. The main types of interpretability are defined, and examples of interpretability of operations of the descriptive image algebras with one ring, are given.
Binocular Infrared Camera Calibration Based on Calibration Checkerboard

Abstract In this paper, in order to accurately calibrate binocular infrared camera, a method was proposed. It contains a calibration checkerboard based on thermal infrared radiation and the Zhang method. After the calibration experiment, we accurately obtained its internal parameters and stereo parameters. The results show that: the mean reprojection error (MRE) is less than 0.15pixels, and the calibration precision of baseline length reached 0.35%. It proves the effectiveness of the calibration checkerboard and the feasibility of our proposed method.
Ensemble Undersampling to Handle Unbalanced Class on Cross-Project Defect Prediction

Abstract There has been much research which proposed for cross-project software defect prediction models but no models that perform very well with various datasets in general. Software defect dataset usually imbalanced because it contains far more the not defected modules than the defected modules. Class imbalances in the dataset can reduce the performance of classifiers in the software defect prediction model. In this study proposed a Random Undersampling algorithm to balance classes and ensemble techniques to reduce misclassification. The ensemble technique used is the AdaBoost and Bagging algorithm. The results showed that the software defect prediction model that integrates the Random Undersampling algorithm and AdaBoost provides better performance and can find more defects than other models.
Two Recognition Models for Thai Dancing Data Set

Human activities recognition has been developed using various techniques for optimal recognition and many data sets have also been developed for this task. However, some Thai culture activities cannot be recognized by the techniques developed by oversea data sets. That is the main motivation for collecting a new data set about Thai dancing in this work. We compare the performance between 2 recognition techniques, i.e. 3D CNN and LSTM against this task. Five classes from 16 volunteers are collected in this data set. Then, two existing models from each technique are trained by the Thai dancing data set with 16 batch size and 50 epochs using 7:3 to represent the ratio between the number of training data and test data. The final results show that LSTM model achieved 0.1250 accuracy which is better than 3D CNN obtaining the accuracy score of 0.0800.
Detection of spam using email signatures

Currently, unwanted emails are actively sent to the Internet. Millions copies of emails are sent simultaneously to various users. Often e-mails undergo minor modifications to complicate the detection of spam. The paper proposes options for determining the signature of e-mails that allow identify letters with the same content and structure. Content signature of the letter includes the basic phrases in the text of the e-mail with the exception of names, numeric codes, suspicious words that are not included in the dictionary. Structure signatures incorporate the same type of e-mails, such as paragraphs, tables, images. The paper shows the results of using signatures to detect e-mail spam.
Visualizing Socio-techno-natural Processes: Issues and Challenges

Contemporary models and systems of data visualization, implemented on the basis of information technology, individually cover social, technological and natural processes of the world development. Modern world is developing in the conditions of transformational transitional processes, when the artificial shell, the technosphere, created by the society becomes the leading life-support system instead of the biosphere. It is a full-fledged participant in the exchange processes between a globalizing technogenic society and transforming nature, which allows us to talk about forming socio-techno-natural laws of developing the world and life. There are no integrative visual models in the world that represent evolutionary changes in three systems simultaneously – society (and man), the technosphere and the technologically transformed biosphere, on the basis of which it is possible to predict the formation of a sustainable future for humanity in connection with the expansion of socio-technonatural processes. The generally recognized visualization methodology must be supplemented by the methodology of the philosophy of the world socio-technogenic development and the change in the evolution of life for a better visual representation and explanation of the transformation processes taking place in the changing world that is becoming post-biospheric. Interdisciplinary philosophical view allows capturing the world in the integrity of its diverse characteristics and at the same time forming a problem field for visualizing transformational processes. The fundamental role of the integration link belongs to information technology, which allows us to represent visually heterogeneous data with the aim of further developing models for the safe development of mankind in the technosphere.
Automated Worker’s Skill Evaluation System Based on the Time Series Elemental Processes for Improving Productivity

In order to enhance and improve labor productivity, we have developed an automated system for evaluating the worker’s skills by using labeled latent Dirichlet allocation (L-LDA). Since the L-LDA learns the characteristic motions automatically, we do not need to find and define any features of the motion. The elemental processes are analyzed by the L-LDA. The worker’s skills are evaluated based on the analyzed time series elemental process data. The evaluated worker’s skills are correctness, stability, speed, and rhythm of the work. The results confirmed that our proposed evaluation system is capable of automatically providing a new analysis over the conventional evaluation method with only working time. For example, an evaluation experiment was done to one subject. This result showed the speed category to be lower than the other categories. Then, we know that the subject lacks parallelism work skill. These results give new knowledge that never obtained by the conventional evaluation method.