Reviewerly/exHarmony huggingface dataset:
Effect of an External Magnetic Field on Some Statistical Properties of the 2+1 Dirac–Moshinsky Oscillator

We map the 2+1 Dirac–Moshinsky oscillator (2+1 DMO) into the generalized Jaynes–Cummings model (GJCM), where an external magnetic field is coupled to an external isospin field. We solve analytically the basic equations of the model, where the coherent state is considered as an initial state. The results obtained show that the magnetic field strength and the coupling parameter of the isospin field play important roles when some statistical properties such as the entanglement, population inversion, and degree of coherence are considered. We show that these parameters are important for increasing the entanglement and also demonstrate the collapse and revival phenomena.
Combinatorial Optimization Approach for Arabic Word Recognition Based on Adaptive Simulated Annealing

The present paper proposes an approach based on a combinatorial optimization technique for Arabic word recognition, distinguished by its flexional nature and significant topological variability. We treat a large vocabulary of Arabic decomposable words, which we choose to factorize them by their roots and schemes. We adopt a structure that resembles a molecular cloud. This design rhymes well with the Arabic linguistic philosophy of constructing words from their roots. Each sub-vocabulary, corresponding to a sub-cloud, embodies neighboring words, which are derived from one root and follow different schemes and forms of derivation, flexion, and agglutination (proclitic and enclitic). Therefore, we propose to use the metaheuristic simulated annealing (SA) method, as a recognition approach, in this wide cloud. It's an algorithm based on elastic comparisons between their structures and primitives. As an extension of previous works, we opt to implement the SA algorithm by integrating linguistic knowledge. Preliminary experiments were conducted on Arabic word corpus including samples and agglutinated words from APTI database and yielded interesting outcomes.
Bespoke: a Methodology to design Software Factories - A preliminary approach

Software Product Line (SPL) field is meant to engineer families of related products to reduce development cost. Leveraging feature models for capturing variable and common aspects inside of a product family omits crucial concepts that need to be considered through designing system domain perimeter. At this level, Model Driven Engineering was integrated in SPL to facilitate variability implementation and management. MDE techniques rely on the DSML meta-model that describes the main concepts of the domain and their relations which allow system designers to work closer to the system domain as they can manipulate real concepts.The current work proposes a Methodology for Software Factories that aggregates both of the two aforementioned techniques to cover a large number of code generation strategies to derive products branching from a domain portfolio. The first phase consists in configuring a possible product line by transforming the domain metamodel DSML into a generic feature diagram. Secondly, the client uses the DSML to configure, by the guidances of the founded product line, the desired product. Hence, the Methodolgy Software Factory provides the engineer guidelines to derive the target product according to the configured product line.In this work in progress paper, we propose to extend metametamodel inspired from MOF in order to integrate crucial FD semantics element into the DSML metamodel and by the way facilitate their conversion. Besides, in this regard, a transformation method is presented to handle the extended DSML metamodel.
Modeling Machine Learning Agent for Interaction Conversational System Using Max Entropy Approach in Natural Language Processing

There are several service-oriented models, where services are deployed for the user. The user chooses any one of them. During the operational life cycle of these services, there are several issues that occur. User wants an interface for complaint. This paper uses the sentence boundary detection, NER, document categorization, and sentiment extraction methodologies. The natural language processing generates the training model which is a statistical representation of current system knowledge. When a user enters the input then training model extracts the value of a different parameter, these parameters used by call center model for better understanding of user input. The machine learning model used to generate a logical response. When the time exceeds the size of sample, data should be increased and the model understanding also more and more accurate. The accuracy of the model depends on the size of the sample training data. When training data size increases the statistical model for the call center is updated. When the user interacts with the call center agent then call center agent to extract the value of all parameters based on the current statistical model which is based on the sample training data. The paper uses different parameter such as NER, Document category, and sentiment for making a better user interaction. The probability of correct response is increase n time if n parameters are used for response generation. Call center module to take help from sentence detection, NER, document categorization and sentiment training model for extraction the value of the parameter. These parameter value helpful for extracting the NLP Text meaning. The response correctness also increases whenever anyone parameter is extracted correctly. The maximum entropy approach is used for making statistical modeling. The training data are taken from the heterogeneous source.
Breast Cancer Classification: Using Hybrid Technique (Association Rules and Deep Neural Network)

Abstract Early detection of any disease is very important since it aids curable with a few of effort. A lot of people fail to detection their disease before it be chronic. This causes an increase in mortality about the world. One of these diseases is breast cancer that can be cured when identified in the early stages before it spreads throughout the body. Develop techniques that can aide physicians to get accurate diagnosis is significantly important in early detection for this disease. The goal is design a hybrid approach (class association rules and deep neural network). In this paper, we design efficient methodology for classifying breast cancer using hybrid approach techniques. Where used a CARs to discover all the interesting relationship in a large database, while the DNN is used for classification purpose. In this study, use Wisconsin Breast cancer dataset from UCI machine learning repository to evaluate the performance of the proposed system. The experiment show that proposed system achieves good results, with high accuracy of 100% and less mean square error rate 0.0002.
Concert Hall Acoustics’ Influence on the Tempo of Musical Performances

The acoustics of a concert hall is an integral and significant part of a musical performance as it affects the artistic decisions made by performer. Still, there are few systematic studies on the p ...
Comparison of Milne-Simpson Method and Hamming Method in Logistic Equation Settlement on Pert Prediksi the People of Bandar Lampung City

Abstract Milne-Simpson method and Hamming method are a numerical method that can be applied in the calculation of population prediction in the future as a reference government in building an area. Beginning with the calculation of growth rates with logistic equations then processed into the fourth-order Runge-Kutta method. The recalculation process continues to search for the smallest error between the Milne-Simpson method and the Hamming method. Error calculation is done with population growth rate 5.2% with step size h = 1 and capacity of Bandar Lampung city 1.500.000 soul. Numerical solutions show population growth annually and Milne-Simpson method is the best method seen from the smaller error than the Hamming method error.
Connecting Language and Vision

For most humans, understanding multimedia content is easy, and in many cases images and videos are a preferred means of augmenting and enhancing human interaction and communication. Given a video, humans can discern a great deal from this rich information source and can interpret and describe the content to varying degrees of detail. For computers however, interpreting content from image and video pixels and associating them with language is very challenging. Research in the recent past has made tremendous progress in this problem of visual language grounding, i.e. interpreting visual content, from images and videos, and associating them with language. This progress has been made possible not only by advances in object recognition, activity recognition, and language generation, but also by developing versatile and elegant ways of combining them. However to realize the long-term goal of enabling fluent interaction between humans and computers/robots, it is also essential to ground language in action in addition to vision. In this respect embodied, task-oriented aspect of language grounding has emerged as a research direction that is garnering much attention. Current research focuses on developing new datasets and techniques for linking language to action in the real world, such as agents that follow instructions for navigation tasks or manipulation tasks. Following the exciting progress in this space, we expect research in connecting language and vision to continue to accelerate in the coming years towards the development of embodied agents that learn to navigate the real world through human interaction.
Thiết kế bộ điều khiển mờ lai cascade áp dụng cho robot rắn

Nowadays, PID controller have been used to regulate a process in most of the industrial control systems. This is commonly designed by the Ziegler-Nichols tuning method. However, this method has the disadvantage that it depends on actual processes, so it is difficult to find optimal parameters of the controller by manual turning. This paper is to proposes a combination of a fuzzy controller to generate a signal to compensate for the PID controller, so called fuzzy-PID controller. The characteristics of the Fuzzy-PID controller are illustrated with simulation results in the control of direction and speed of a snake robot. Simulation results were implemented basing on MATLAB/Simulink software as follows The overshoot, rise time and setting time of the system with fuzzy-PID controller are superior to those of the PID controller. Besides, the controller also responded to noise interference on the systems.
Facilitating Physical Contact in Collocated Virtual Reality and Real-World Player Games through Wearable Touchable Interfaces

Virtual Reality (VR) gaming environments can recreate dangerous or even physically impossible situations in an immersive way. However, VR players are isolated from the real world and the experience lacks some features compared to real games, i.e. the physicality of touching objects or other players. Furthermore, in traditional VR gaming, VR players cannot interact with real world (RW) players. Here, we are exploring how to collocate VR and RW players to play simultaneously in the same physical space using wearable touch-sensitive patches that can be attached to objects, walls or players and serve as a nexus between both players. We present the design of a gaming system, two games mechanics supported by it and a preliminary user evaluation to assess in which way physicality was enhanced.
Implementation of Myanmar Handwritten Recognition

This paper proposes the offline Myanmar handwriting recognition system which uses the combination of deep learning approaches- convolutional neural networks to extract features from the input images and recurrent neural networks to recognize these features. To improve the performance of the system, some pre-processing techniques such as binarization, noise removing, image segmentation and resizing, are also applied before training the scanned images. The experiment is carried out on the own dataset related with office staff leave form documents since there is no free resources for Myanmar language. Performance of the proposed system is evaluated using both the character error rate (CER) and the word error rate (WER).
Evaluation of adaptive algorithms effectiveness for cancellation of power line interferences in gamma waves band of EEG signal

The paper presents the results of investigations devoted to evaluation of effectiveness of adaptive algorithms employed for suppressing power line interferences appearing in electroencephalography (EEG) signals. Predominantly the power line interferences are cancelled with use of a notch filter with the central frequency 50 or 60 Hz. Due to removal of a part of EEG signal frequency band, a part of information about EEG signal, especially from gamma waves band (35-100 Hz), is lost. The paper focuses on comparison of the adaptive algorithms (LMS, RLS) and the notch filter. The comparison is based on the dedicated measures allowing to assess the distortions (related to loss of information) introduced by the analysed methods both in the whole EEG signal and in the gamma waves band. The results of preliminary studies show that depending on the power of interferences the adaptive filters may introduce less distortions in gamma-band than the notch filter, which may be significant for accurate gamma waves analysis.
A Survey on Crowd Video Exploration Using Physical Enthused Approaches

There is a great demand for automated systems which can easily investigate the behavior of crowd using video surveillance. Crowd exploration is critical for human comportment investigation, well-being science, and computational recreation and computer vision applications. Numerous techniques have been proposed in this area by many researchers. In aggregation of the activities, the analysis of human behavior in crowd has numerous substantial qualities like speed, heading of movement, cooperation power, and vitality. In this way, a ton of techniques and models inspired from such physical thoughts are connected in numerous systems for identifying crowd comportment investigation. This paper reviews some of methods based on physical techniques for crowd investigation in detail. On the basis of such physics, here crowd analysis is outlined into three major classes, viz., interaction force, complex situations of crowd movement frameworks and fluid dynamics. Some crowd-based databases are analyzed in this paper along with future work directions.
Incremental Label Propagation on Facial Images

The increasing computational complexity of label propagation-based facial image annotation when applied on multimedia data whose cardinality increases over the time (e.g., when analyzing video or movie content on-line), can be reduced by using an incremental approach. In this paper, a method for incremental label propagation on facial images is described. The similarity matrix is incrementally constructed by employing the kd-tree nearest neighbor algorithm. Furthermore, the matrix inversion, which is included in the label propagation solution, is calculated with a block-wise inversion formula involving the Woodbury matrix identity. Experiments show significant computational savings when the incremental approach is applied on a dataset of three full length movies. Moreover, the classification accuracy was improved in most cases.
Suggested guidelines for developing sustainable leadership of private science school principals

Abstract The research aimed to study the guidelines for developing the sustainable leadership of private school principals, under jurisdiction of Khon Kaen Provincial Education Office. The participant included Ten experts for focus group discussion selected by Purposive Sampling including the Lecturers teaching Leadership Course in the university, the Provincial Education Office administrators, the school director, the expert, and the academic receiving doctoral degree in Educational Administration. Methodology regarded qualitative paradigm. The guidelines of developing the sustainable leadership of private school principals were interpreted through Focus Group Discussion from the experts selected by Purposive Sampling. Data were analyzed by using the Content Analysis based on conceptual framework and other issues being studied, and concluded into main issues. The findings revealed that the guidelines for developing the sustainable leadership of private school principals, under jurisdiction of Khon Kaen Provincial Education Office were provided into 9 categories. These included 1) outcome based oriented, 2) strategic administration, 3) the process based oriented (instead of planning), 4) Ethical and moral based administration, 5) professional administrors, 6) strategic abandonment or detachment, 7) cooperative development potentiality, 8) strategies measure for continual success, 9) building up sustainability. The paper will discuss about these guidelines. The paper may have implications for developing professional development of sustainable leadership in Thailand.
Procedural Sound Generation for Soft Bodies in Video Games

We propose a real-time, data-driven method to synthesize the sound of typical soft bodies (such as cloth and rope) in video games. For a given soft body, we perform a geometric analysis of its shape at each frame. These data are used to calculate a variety of motion events at run time that would produce sounds. We then record a database of sounds, which contains sequences of segmented sound units. Finally, we use a concatenative synthesis method to select and synthesize the actual soft-body sounds according to the extracted motion signals. Our approach is more computationally efficient compared to existing soft-body sound synthesis methods and is compatible with any particle-based soft-body physics in video games. We implement our method in Unreal Engine 4 and demonstrate its efficiency with several examples.
Smart TV Study—System Errors

Part IV of this work investigates factors which influence state reactanceState reactance and the effect that state reactanceState reactance has on interaction. The first study investigated state reactanceState reactance as a consequence of system errors. System errors were the only item on the list of identified situations from the expert survey, described in Chap. 5 , that has not been investigated in the context of human–computer interactionHuman–computer interaction in the literature before. The study measured state reactanceState reactance and acceptability of a smart television set (TV) that could be controlled via voice commands. The smart TV was able to display several steps of the processing chain of a command via different feedback types. Conditions were manipulated in a way, that artificial errors were introduced. In one error condition, the error could be identified as an interpretation error. In another error condition, the interpretation was correct and participants could not explain why the error had occurred. Results show that participants experience significantly higher levels of state reactanceState reactance when the smart TV produced errors. However, when the participants could understand why the error had occurred, their state reactanceState reactance levels were lower, compared to when they could not. This trend is also visible in the ratings of acceptability, which highly correlated with the state reactanceState reactance ratings.
Using Speech Emotion Recognition to Preclude Campus Bullying

Campus bullying could have extremely adverse impact on pupils, leading to physical harm, mental disease, or even ultra behaviour like suicide. Hence, an accurate and efficient anti-bullying approach is badly needed. A campus bullying detection system based on speech emotion recognition is proposed in this paper to distinguish bullying situations from non-bullying situations. Initially, a Finland emotional speech database is divided into two parts, namely training-data and testing-data, from which MFCC (Mel Frequency Cepstrum Coefficient) parameters are garnered. Subsequently, ReliefF feature selection algorithm is applied to select the useful features to form a matrix. Then its dimensions is diminished with PCA (Principle Component Analysis) algorithm. Finally, KNN (K-Nearest Neighbor) algorithm is utilized to train the model. The final simulations show a recognition rate of 80.25%, verifying that this model is able to provide a useful tool for bullying detection.
Characterizing Strongly Normalizing λGtz-terms via Non-Idempotent Intersection Types

Idempotent and non-idempotent intersection type systems have been used in a series of papers for characterizing strong normalization in natural deduction style. Regarding the sequent calculus, only idempotent type systems have been used so far. Strong normalization in the non-idempotent intersection type system follows without requiring reducibility techniques. Non-idempotent intersection type systems are just based on the decrease of a non-negative integer measure. In this paper, we introduce a non-idempotent intersection system for λGtz-calculus, which is in sequent calculus style, to show that a λGtz-term is typeable if and only if it is strongly normalizing.
Extracting High-Level System Specifications from Source Code via Abstract State Machines

We are interested in specifications which provide a consistent high-level view of systems. They should abstract irrelevant details and provide a precise and complete description of the behaviour of the system. This view of software specification can naturally be expressed by means of Gurevich's Abstract State Machines (ASMs). There are many known benefits of such an approach to system specifications for software engineering and testing. In practice however, such specifications are rarely generated and/or maintained during software development. Addressing this problem, we present an exploratory study on (semi) automated extraction of high-level software specifications by means of ASMs. We describe, in the form of examples, an abstraction process which starts by extracting an initial ground-level ASM specification from Java source code (with the same core functionality), and ends in a high-level ASM specification at the desired level of abstraction. We argue that this process can be done in a (semi) automated way, resulting in a valuable tool to improve the current software engineering practices.
Adaptive Controller Based on IF-THEN Rules and Simultaneous Perturbation Stochastic Approximation Tuning for a Robotic System

This study presents an adaptive controller based on neuro-fuzzy networks and stochastic approximation techniques. The algorithm assumes that the mathematical model of the plant is unknown. An adaptive Fuzzy Rule Emulated Network (FREN) structure is implemented as the main controller. While, a modified version of the Simultaneous Perturbation Stochastic Approximation (SPSA) technique is added as the adaptation algorithm, which estimates the gradient of the plant with respect to the control effort. The proposed FREN+SPSA performance for position control is compared to conventional FREN and classical PID controllers. Experimental tests were performed on a cartesian robotic system, regulating the frequency of a DC motor to follow a desired trajectory. Experimental results show better performance of the proposed FREN+SPSA controller than the conventional FREN and PID controller.