Reviewerly/exHarmony huggingface dataset:
A Robust Approach for Blur and Sharp Regions’ Detection Using Multisequential Deviated Patterns

Blur detection (BD) is an important and challenging task in digital imaging and computer vision applications. Accurate segmentation of homogenous smooth and blur regions, low-contrast focal regions, missing patches, and background clutter, without having any prior information about the blur, are the fundamental challenges of BD. Previous work on BD has emphasized much effort on designing local sharpness metric maps from the images. However, the smooth/blurred regions having the same patterns as sharp regions make them problematic. This paper presents a robust novel method to extract the local metric map for blurred and nonblurred regions based on multisequential deviated patterns (MSDPs). Unlike the preceding, MSDP extracts the local sharpness metric map on the images at multiple scales using different adaptive thresholds to overcome the problems of smooth/blur regions and missing patches. By using the integral values of the image along with image masking and Otsu thresholding, highly accurate segmented regions of the images are acquired. We argue/hypothesize that the local sharpness map extraction by using direct integral information of the image is highly affected by the threshold selected for distinction between the regions, whereas MSDP feature extraction overcomes the limitations substantially by using automatic threshold computation over multiple scales of the images. Moreover, the proposed method extracts the relatively accurate sharp regions from the high-dense blur and noisy images. Experiments are conducted on two commonly used SHI and DUT datasets for blur and sharp region classifications. The results indicate the effectiveness of the proposed method in terms of sharp segmented regions. Experimental results of qualitative and quantitative comparisons of the proposed method with ten comparative methods demonstrate the superiority of our method. Moreover, the proposed method is also computationally efficient over state-of-the-art methods.
Live Performance in VR

Virtual reality is a growing platform for live entertainment, offering the feeling of embodiment and immersion that is central to storytelling for both audience and performers. Find out how several different artists are bringing the magic of theater to digital playhouses around the world.
Uncertainty-Aware Resampling Method for Imbalanced Classification Using Evidence Theory

Class imbalance is a common issue in many real world classification problems. It refers to situations where the number of observations in the training dataset significantly differs for each class. Ignoring this issue will make it more challenging for classifiers to properly learn data characteristics, which results in poor performance. Many strategies have been proposed to deal with this issue. The most common one is tackling the imbalance at the preprocessing level, by re-sampling the training set. However, imbalanced classification can be affected by other data factors, such as uncertainty, i.e., ambiguous samples and noise. In this paper, we propose an uncertainty-aware hybrid re-sampling technique based on the theory of evidence to tackle imbalanced binary datasets in the presence of aleatoric uncertainty. A soft evidential structure is assigned to each object in the training set, which is later used to clean the dataset out of overlapping and noisy majority samples, and then selectively generate synthetic minority objects using a modified SMOTE algorithm. Experimental results on benchmark imbalanced datasets have shown significant improvement over popular re-sampling techniques.
Energy aware task scheduling using hybrid firefly - GA in big data

Task scheduling is important of research in big data and it is made in two traditions user level and system level; in user level issues with scheduling between the service provider and customer; in system level issues in scheduling with resources management in the data centre. The drawbacks of various existing methods to increase in power consumption of data centres have become a significant issue. Now the MapReduce clusters constitute a major piece of the data centre for big data applications. Simply the absolute size, high fault-tolerant nature and low utilisation levels make them less energy efficient. The complexity of scheduling increases when there is an increase in the size of the task, it becomes very tedious to perform scheduling effectively. The drawback with existing scheduling algorithm generates higher computational cost and less efficient; the multi-objective scheduling with cloud computing makes it difficult to resolve the problem in the case of complex tasks. These are the primary drawbacks of several existing works, which prompt us to manage this research on task scheduling in cloud computing.
A Conceptual Overview and Systematic Review on Artificial Intelligence and Its Approaches

Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing (NLP), speech recognition and machine vision. AI programming focuses on three cognitive skills: learning, reasoning and self-correction. This article proposes overall idea of artificial intelligence in various applications and domains.
ICT Professional Development Workshops and Classroom Implementation Challenges: Perceptions of Secondary School Teachers in Trinidad and Tobago

The Ministry of Education (MoE) of The Republic of Trinidad and Tobago has recently embarked on another teacher training program to equip secondary school teachers with the necessary competencies for ICT infusion in their physical classrooms. This study aims to investigate the impact and usefulness of that program, and challenges that the teachers have been facing in its implementation. Thirteen interviews (eight one-on-one and five focused group) were conducted from forty trained teachers. The findings showed that almost all the teachers were quite content and even ecstatic about the workshops and were willing to use the acquired knowledge and skills. Those who were able to use the knowledge and skills attained in the workshops reported improved students’ interest, classroom interaction, and academic performance. However, many expressed disappointments due to the multiple obstacles at their schools which prevented them from fully and effectively using the knowledge and skills from the workshops. They specifically reported insufficient number of computers, poor and inapt ICT environment and most importantly, unreliable internet access and connectivity. This study identifies three important elements in the successful implementation of any ICT in education program: willingness (mainly of administrators, teachers and students), training (especially for the teachers) and infrastructure (physical, hardware and software). Although, there was an overwhelming willingness from the teachers (even excitement) and adequate training, issues with infrastructure appear to be preventing the policy from yielding desired results. In order to ensure that the current policy brings the desired results, the MoE must not only provide effective professional development training for teachers, but also furnish schools with the necessary ICT resources and infrastructure. Only then, the MoE may revolutionize the education system through ICT.&#x0D; Keywords: ICT in education, professional development, teacher training, 21st century skills, Trinidad
Quality improvement of the e-commerce website using integration of Kano model-ipa with QFD approach

This study was conducted to analyze the quality of e-commerce websites, find out which items need improvement, and make improvement design e-commerce websites. This study uses the kano model and Importance Performance Analysis approach based on 7 dimensions with 39 attributes. This study used the survey results from 103 respondents who regularly use the e-commerce website Tokopedia.com. The results of the questionnaire through the validity and reliability tests were used to analyze the reliability of items and the feasibility of the results of the questionnaire. After that, the results of the questionnaire through the corellation and hypothesis tests were used to know the relationship between variables. The findings show that 7 attributes influence customer satisfaction. However, the findings are still nowhere near the expectations. Thus, it requires being the focus of improvement. Improvements in design of 7 attributes in the form of House of Quality with 6 technical features needed to improve the quality of e-commerce website Tokopedia.com. Therefore, effective management strategies can be applied to overcome the intense competition in the e-commerce industry.
Investigation conditions for inclusion of gamification elements in study of the basics of algorithmization and programming to improve the quality of educational results of pupils

The problem and the aim of the study. Formation of programming skills, communication with artificial intelligence and other people, the level of thinking development are the basic indicators that determine effectiveness of the didactic system. Within the framework of e-learning, digital technologies, gamification elements, cyber-physical devices, etc. are actively used to support the quality of learning. The authors investigate conditions for effective inclusion of gamification elements in algorithmization and programming activities to increase the level of educational achievements of pupils. Research methods. The analysis and generalization of scientific literature on the problems of gamification of learning and cognition processes, the study of the basics of algorithmization are used. For development of game projects, debugging and testing applications, the ideas of structural programming, the method of step-by-step detailing are used. The programming technology is supported by the principles and techniques of gamification. Empirical methods that were used are observation, analysis of the results of work in the Logo program (user interface, game design, choice of algorithmic constructions, etc.). The study involved 48 pupils who study in grades 5-6 in Kirov school №11. The Pearson’s χ2 (chi-square) criterion was used as a method for statistical processing of the experimental data. Results. The pupils of the experimental group were involved in the purposeful cognitive activity on development of game projects using the Logo programming language (studying fundamental concepts, graphical capabilities, managing a Turtle in the object-oriented environment, research, collaboration, social activity). Statistically significant differences were found between the experimental and control groups in the level of educational achievements χ2emp.2 &gt; χ2crit0.05 (7.885 &gt; 7.815). In conclusion, the conditions under which the gamification of learning the basics of algorithmization and programming is maximally effective for improving the quality of academic achievements of schoolchildren are summarized: correlation of the didactic goal and the result of the game form of work, the choice of the plot for the game project, thinking through the mechanisms of feedback and interactivity, the distribution of roles, etc.
Balancing Performance and Human Autonomy With Implicit Guidance Agent

The human-agent team, which is a problem in which humans and autonomous agents collaborate to achieve one task, is typical in human-AI collaboration. For effective collaboration, humans want to have an effective plan, but in realistic situations, they might have difficulty calculating the best plan due to cognitive limitations. In this case, guidance from an agent that has many computational resources may be useful. However, if an agent guides the human behavior explicitly, the human may feel that they have lost autonomy and are being controlled by the agent. We therefore investigated implicit guidance offered by means of an agent’s behavior. With this type of guidance, the agent acts in a way that makes it easy for the human to find an effective plan for a collaborative task, and the human can then improve the plan. Since the human improves their plan voluntarily, he or she maintains autonomy. We modeled a collaborative agent with implicit guidance by integrating the Bayesian Theory of Mind into existing collaborative-planning algorithms and demonstrated through a behavioral experiment that implicit guidance is effective for enabling humans to maintain a balance between improving their plans and retaining autonomy.
Modified Neural Architecture Search (NAS) Using the Chromosome Non-Disjunction

This paper proposes a deep neural network structuring methodology through a genetic algorithm (GA) using chromosome non-disjunction. The proposed model includes methods for generating and tuning the neural network architecture without the aid of human experts. Since the original neural architecture search (henceforth, NAS) was announced, NAS techniques, such as NASBot, NASGBO and CoDeepNEAT, have been widely adopted in order to improve cost- and/or time-effectiveness for human experts. In these models, evolutionary algorithms (EAs) are employed to effectively enhance the accuracy of the neural network architecture. In particular, CoDeepNEAT uses a constructive GA starting from minimal architecture. This will only work quickly if the solution architecture is small. On the other hand, the proposed methodology utilizes chromosome non-disjunction as a new genetic operation. Our approach differs from previous methodologies in that it includes a destructive approach as well as a constructive approach, and is similar to pruning methodologies, which realizes tuning of the previous neural network architecture. A case study applied to the sentence word ordering problem and AlexNet for CIFAR-10 illustrates the applicability of the proposed methodology. We show from the simulation studies that the accuracy of the model was improved by 0.7% compared to the conventional model without human expert.
LAE-GAN-Based Face Image Restoration for Low-Light Age Estimation

Age estimation is applicable in various fields, and among them, research on age estimation using human facial images, which are the easiest to acquire, is being actively conducted. Since the emergence of deep learning, studies on age estimation using various types of convolutional neural networks (CNN) have been conducted, and they have resulted in good performances, as clear images with high illumination were typically used in these studies. However, human facial images are typically captured in low-light environments. Age information can be lost in facial images captured in low-illumination environments, where noise and blur generated by the camera in the captured image reduce the age estimation performance. No study has yet been conducted on age estimation using facial images captured under low light. In order to overcome this problem, this study proposes a new generative adversarial network for low-light age estimation (LAE-GAN), which compensates for the brightness of human facial images captured in low-light environments, and a CNN-based age estimation method in which compensated images are input. When the experiment was conducted using the MORPH, AFAD, and FG-NET databases—which are open databases—the proposed method exhibited more accurate age estimation performance and brightness compensation in low-light images compared to state-of-the-art methods.
Stability of non-linear filter for deterministic dynamics

&lt;p style='text-indent:20px;'&gt;This papers shows that nonlinear filter in the case of deterministic dynamics is stable with respect to the initial conditions under the conditions that observations are sufficiently rich, both in the context of continuous and discrete time filters. Earlier works on the stability of the nonlinear filters are in the context of stochastic dynamics and assume conditions like compact state space or time independent observation model, whereas we prove filter stability for deterministic dynamics with more general assumptions on the state space and observation process. We give several examples of systems that satisfy these assumptions. We also show that the asymptotic structure of the filtering distribution is related to the dynamical properties of the signal.&lt;/p&gt;
5D-fusion sensing via interference illumination and polarization imaging

This study proposes a polychromatic interferometric illumination and polarimetric sensor-based imaging method for spectrum, polarization, and 3D shape, which are significant physical parameters of feature analysis for target detection. 5D-fusion sensing refers to the joint detection and fusion of the above 5D information, which is currently a great challenge. The method generates a polychromatic interference pattern using a Sagnac lateral shearing interferometer and projects it to the target. Then, interferograms modulated by the target are acquired during scanning. Fast Fourier transform (FFT) is performed on the interferograms to obtain their frequency spectra. The spectral and polarization information is extracted from the moduli of the frequency spectra. The 3D shape is recovered from the phase of the frequency spectra using the calibration data. The theory of 5D-fusion sensing is investigated, and verification experiments are then performed. The experiments indicate that the proposed method can fulfill 5D-fusion sensing in one scanning and with FFT using only one device compared with other separate methods. Consequently, the proposed method can improve the sensing and recognition ability of optical imaging technology, which provides great application potential in biomedicine, food safety, material analysis, criminal investigation, archeology, and other fields.
Question Classification of CoQA (QCoC) Dataset

This paper proposes a new dataset for question classification process. Named QCoC (Question Classification of CoQA), this dataset is created based on Stanford’s CoQA (A Conversational Question Answering Challenge) dataset. The total of QCoC datapoint is 116630 (total of combined question-answer pairs in CoQA training and evaluation dataset). Common question classification datasets are classifying question based on its paired answer’s knowledge (the semantic of answer’s context). For QCoC, classification is done differently that is per answer’s feature (semantic and syntactic of answer’s type). This paper discusses the question classification datasets, QA datasets, and justification of CoQA as selected base for QCoC. Then QCoC specification is discussed with class definition, classification method and result subsections. To the author’s knowledge, such dataset is still nonexistent to date. This paper suggests that this type of dataset is useful in solving abstractive answers issue in Question-Answering (QA) system. While factual answers can be directly produced by regular QA system, abstractive answers need some additional components. Although it is a recognizable issue, lack of suitable dataset perhaps is the reason why such direction is not being pursued. With QCoC dataset made publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , hopefully such direction is open for further exploration.
Content-based book recommendations

The content-based book recommendation approach developed by Bookarang yields many improvements over existing systems, not only in recommendation quality and consistency, but also in flexibility, depth and transparency. Content-based book recommenders can be implemented in a large array of products, from personalised newsletters to conversational AI systems.
How Neurons in Deep Models Relate with Neurons in the Brain

In dealing with the algorithmic aspects of intelligent systems, the analogy with the biological brain has always been attractive, and has often had a dual function. On the one hand, it has been an effective source of inspiration for their design, while, on the other hand, it has been used as the justification for their success, especially in the case of Deep Learning (DL) models. However, in recent years, inspiration from the brain has lost its grip on its first role, yet it continues to be proposed in its second role, although we believe it is also becoming less and less defensible. Outside the chorus, there are theoretical proposals that instead identify important demarcation lines between DL and human cognition, to the point of being even incommensurable. In this article we argue that, paradoxically, the partial indifference of the developers of deep neural models to the functioning of biological neurons is one of the reasons for their success, having promoted a pragmatically opportunistic attitude. We believe that it is even possible to glimpse a biological analogy of a different kind, in that the essentially heuristic way of proceeding in modern DL development bears intriguing similarities to natural evolution.
Discovering State and Action Abstractions for Generalized Task and Motion Planning

Generalized planning accelerates classical planning by finding an algorithm-like policy that solves multiple instances of a task. A generalized plan can be learned from a few training examples and applied to an entire domain of problems. Generalized planning approaches perform well in discrete AI planning problems that involve large numbers of objects and extended action sequences to achieve the goal. In this paper, we propose an algorithm for learning features, abstractions, and generalized plans for continuous robotic task and motion planning (TAMP) and examine the unique difficulties that arise when forced to consider geometric and physical constraints as a part of the generalized plan. Additionally, we show that these simple generalized plans learned from only a handful of examples can be used to improve the search efficiency of TAMP solvers.
Barcode Technology and its Application in Libraries

The library is one of the essential place in any academic institution. With the invention of ICT (Information and Communications Technology), every sector now a day are trying to automate their system and library is also one of them. We can find that the technologies have taken over and helped the library in various ways. Now a day’s libraries need new tools that will allow them to increase their productivity and improve user service without adding personnel. Application of Bar-code technology in libraries is a way to process client requests from fast to fastest. Bar-code technology is mostly used in the circulation system of a library and most successful due to its speed, accuracy and reliability. Bar-coding though comparatively an old technology is one of the significant steps in library automation and is still not widely used in libraries.This article briefly discusses the description of the application of Bar-code technology in libraries, its working mechanism and also the advantages and disadvantages of this technology. The researcher has tried to explore the technical aspects of Bar-code technology in the library. The main aim of the article is to create awareness among the librarians to use Bar-code technology in the library. Bar-code is very cost-effective technology can be used by every library. The paper also discusses the creation of Bar-code with Glabels open-source software which is absolutely free of cost.