Reviewerly/exHarmony huggingface dataset:
A METAHEURISTIC APPROACH FOR TETROLET BASED MEDICAL IMAGE COMPRESSION

Over recent times, medical imaging plays a significant role in clinical practices. Storing and transferring the huge volume of images becomes complicated without an efficient image compression technique. This paper proposes a compression algorithm that uses a Haar based wavelet transform called Tetrolet transform, which reduces the noise on the input images and decomposes with a 4 x 4 blocks of equal squares called tetrominoes. It opts for a decomposing using optimal scheme for achieving the input image into a sparse representation which gives a much-detailed performance for texture and edge information better than wavelet transform. Set Partitioning in Hierarchical Trees (SPIHT) is used for encoding the significant coefficients to achieve efficient image compression. It has been investigated with various metaheuristic algorithms. Experimental results prove that the proposed method outperforms the other transform-based compression in terms of PSNR, CR, and Complexity. Also, the proposed method shows an improved result with another state of work.
Customer perceptions of value, satisfaction, and loyalty in the social media era: insights from electrical home appliances in an emerging economy

Customer loyalty (CS) is an outcome of all brands, which is hard to come by in the era of social media because customers are very volatile and tend to switch. Thus, this study investigated the influence of quality (product quality, delivery and installation service quality), customer perceived value, and customer satisfaction on loyalty (attitudinal and behavioural) among customers in the marketplace of electronic home appliances, as well as the equivalent moderating influence of brand trust and social media usage in those relationships. This study was conducted in Dhaka and collected data from 486 customers using a structured questionnaire and analysed in partial least squares structural equation modelling (PLS-SEM). The findings revealed that all hypotheses were accepted except delivery service quality on customer satisfaction. These findings contributed to stimulus-organism-response theory and provided several insights to practicing managers in their developing marketing strategies to maintain customer loyalty in electronic home appliances.
A Vietnamese Festival Preservation Application

Preservation and development of intangible cultural heritage are one of the burning problems in rich cultural countries. They are significant characteristics to express the substantial identity of a nation. At present, leading artificial intelligence (AI) into the cultural development strategy is a promising approach to introduce remarkable features to the world community, especially in tourism. However, detecting and discovering the festival knowledge at a local place in a country is not only an exciting problem for travelers but also a challenge for researchers and managers. We, therefore, propose a festival information preservation framework that applied AI techniques to seek and exploit the festival knowledge in Vietnam. We provide a Vietnamese festival lightweight ontology. We investigate and implement a practical application for identifying and mining the Vietnamese festival knowledge in Mekong Delta for the first work. Our initial approach concentrates on providing a Web application that classifies the snapshot and automatically answers questions.
Simulation of impact azimuth angle on specific energy output of a fixed mounting rooftop PV system in Jakarta, Indonesia

One of the key performances of a PV system is specific energy output, which is calculated based on energy output compared to the input solar irradiation under operating conditions. The specific energy output is affected by many factors, mainly by the amount of solar irradiation received by the module. This work presents the simulation of impact azimuth angle on specific energy output by the fixed mounting rooftop PV System in Jakarta, Indonesia. Simulation is carried out using SolarGIS PVPlanner by varying roof orientation, which is correlated with module azimuth angle. The tilted of the module is assumed 30°, fixed with the common residential roof tilt in Jakarta, Indonesia. It is found that the highest annual specific energy output is about 1,243 kWh/kWp produced by module orientation of Northeast, while the lowest is produced by module facing South with specific energy output about 1,098 kWh/kWp.
Checkpoint-Based Blockchain Approach for Securing Online Transaction

Industry 4.0 is the revolutionary technology to incorporate evolutionary and self-learning structures in the concept of the Internet of Things (IoT). Also, blockchain technology has become more and more appropriate in many areas. Few application programs now transfer details to ledger storing data which guarantees the safety, quality control, and resilience of its components. Many applications use blockchain-based digital data for trustworthiness in the coming years. Checkpoints were known to improve reliability. Generally, applications also save information in blockchain-based storing of decentralized software. Although in emerging bitcoin blockchain for general usage, there are a few uncomfortable points. The design of decentralized consensus mechanisms primarily causes these issues. Nowadays, majorly of blockchain protocols reach accuracy just “ultimately.” The results of stalemate structures on clustered checkpoints are presented here, actively involved in creating an empirical model to test downtime and revenue collection of a decentralized control strategy for blockchain-based processing. The initial step is to build a simulation to monitor a blockchain system's behavior, secure the data in the network from unauthorized access, or avoid financial fraud.
CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator

With the prevalence of wearable devices, inertial measurement unit (IMU) data has been utilized in monitoring and assessment of human mobility such as human activity recognition (HAR). Training deep neural network (DNN) models for these tasks require a large amount of labeled data, which are hard to acquire in uncontrolled environments. To mitigate the data scarcity problem, we design CROMOSim, a cross-modality sensor simulator that simulates high fidelity virtual IMU sensor data from motion capture systems or monocular RGB cameras. It utilizes a skinned multi-person linear model (SMPL) for 3D body pose and shape representations, to enable simulation from arbitrary on-body positions. A DNN model is trained to learn the functional mapping from imperfect trajectory estimations in a 3D SMPL body tri-mesh due to measurement noise, calibration errors, occlusion and other modeling artifacts, to IMU data. We evaluate the fidelity of CROMOSim simulated data and its utility in data augmentation on various HAR datasets. Extensive experiment results show that the proposed model achieves a 6.7% improvement over baseline methods in a HAR task.
Voice Interaction Drive-through Self-service Station

Due to the rapid development of the artificial intelligence technology, a fast and reliable ASR (Automatic Speech Recognition) system finally became a reality. Neural network models capable of converting audible speech into written text for later parsing are now available to almost everyone on the globe, in hundreds of different languages. They are used in intelligent assistant services, such as Siri and Google Assistant, telephone customer services, and in many other fields. The next logical step is to move the dialogue with a customer from a mobile app to a voice-controlled device such as Amazon Echo or Google Home. See Figure 5-1.
Information-Based Laws of Feature Learning

What are the mechanisms behind learning to see? This is what we address this chapter, where the underlying computational process does in fact characterize the agent’s life in his own visual environment. This is built up on the neural architecture described in the previous chapter that is properly chosen for the incorporation of the motion consistent and abstraction constraints coming from the I and the II Principles. We begin addressing the simplest case of feature conjugation that arises when we estimate the optical flow and continue by considering the canonical set of ODE that express all the visual constraints.
Unsupervised Domain Adaptation Method for Segmenting Cross-Sectional CCA Images

Background and Objectives: Automatic vessel segmentation in ultrasound is challenging due to the quality of the ultrasound images, which is affected by attenuation, high level of speckle noise and acoustic shadowing. Recently, deep convolutional neural networks are increasing in popularity due to their great performance on image segmentation problems, including vessel segmentation. Traditionally, large labeled datasets are required to train a network that achieves high performance, and is able to generalize well to different orientations, transducers and ultrasound scanners. However, these large datasets are rare, given that it is challenging and time-consuming to acquire and manually annotate in-vivo data. Methods: In this work, we present a model-based, unsupervised domain adaptation method that consists of two stages. In the first stage, the network is trained on simulated ultrasound images, which have an accurate ground truth. In the second stage, the network continues training on in-vivo data in an unsupervised way, therefore not requiring the data to be labelled. Rather than using an adversarial neural network, prior knowledge on the elliptical shape of the segmentation mask is used to detect unexpected outputs.Results: The segmentation performance was quantified using manually segmented images as ground truth. Due to the proposed domain adaptation method, the median Dice similarity coefficient increased from 0 to 0.951, outperforming a domain adversarial neural network (median Dice 0.922) and a state-of- the-art Star-Kalman algorithm that was specifically designed for this dataset (median Dice 0.942).Conclusions: The results show that it is feasible to first train a neural network on simulated data, and then apply model-based domain adaptation to further improve segmentation performance by training on unlabeled in-vivo data. This overcomes the limitation of conventional deep learning approaches to require large amounts of manually labeled in-vivo data. Since the proposed domain adaptation method only requires prior knowledge on the shape of the segmentation mask, performance can be explored in various domains and applications in future research.
Fast Retargetable Goals Driven Approach to Deal with Plan Failures of Spacecraft

Plan repair is more preferred over replanning when the agent suffers from plan failures. Most of the existing research work on autonomous plan repair regards the planned action as an instantaneous point, preventing it from being directly applied to the field of spacecraft operation, where is full of concurrent actions with varying duration and resource consumption. In this chapter, a reactive rapid autonomous plan repair algorithm based on retargetable goals, Retargetable Goals Plan Repair Method (ReGPR), is proposed. In ReGPR, a mechanism for transforming a mission plan into a state queue and a method of determining the optimal states, i.e., retargetable goals, based on evaluation are proposed. For transformation, ReGPR discretizes the concurrent actions into two state nodes distributed at their beginnings and ends, which encapsulates much information such as logic, numeric, and duration. Then, the plan repair problem is transformed into the puzzle of state transition by mapping them into the same timeline, which forms the retargetable goals. To determine the optimal goal for recovery, an evaluation criterion including goal reachability and search time estimation is designed. With the help of evaluation, ReGPR gets rid of searching for the recovery plan by the try-error method and finds the possible solution quickly. Several experiments with either logic failures or energy shortage or both were done in the modified Satellite Complex domain to show the performance of ReGPR by comparing it with the corresponding results of the replanning methods and other plan repair methods. And the results desmonstrate that in the repair progress, ReGPR explores fewer state nodes in no more than one round, and its advantage is greater when the plan repair problem becomes more complex.
Developing Instrument for Investigation of Blockchain Technology

This chapter presents an approach to the research of a number of aspects of blockchain formation. The main goal of the work is to create a research tool for blockchain technology and analyze its capabilities. Such an analysis allows us to determine the scope of the possible use of the blockchain, depending on the underlying construction methods, in various fields of application, such as the Internet of things (IoT), cyber-physical systems (CPS) with a specific application orientation. To perform the analysis, those aspects that explain the basic principles of blockchain formation are selected. Some minor tasks are specially simplified so as not to distract attention from the main ones. The chapter also demonstrates the main possibilities of placing information in a distributed registry. A study of the specific implementation of the blockchain formation system with the previously indicated features has been carried out. A model of a multinode system is constructed; an analysis of the functioning of a number of software solutions in the field of research modeling of blockchain construction is carried out. The time characteristics of the system depending on the specified parameters are investigated.
Extractive Text Summarization

In this fast paced technological era, where huge quantity of information is generating on the internet day by day. Since the dotcom bubble burst back in 2000, technology has radically transformed our societies. So, it is necessary to provide the better mechanism to extract the useful information fast and most effectively. Automatic text summarization is one of the methods of identifying the important meaningful information in a document or set related document and compressing them into a shorter version preserving its overall meanings. It reduces the time required for reading whole document and also it reduces space that is needed for storing large amount of data. Automatic Text summarization has two approaches 1) Abstractive text summarization and 2) Extractive text summarization. In extractive text summarization only important information or sentence are extracted from the given text file or original document. Here we will discuss on extractive text summarization using sentence scoring and sentence ranking method.
PVO Based Reversible Secret Data Hiding Technique in YCbCr Color Space

In the current paper, it is proposed that PVO based secret data hiding technique is outperformed in different color space, such as YCbCr color space in comparison to RGB color space. The R, G, and B color components in the color image are correlated to each other, so any change in one color component may also change in other color components, which ultimately results in severe distortion in the color image. However, if the color image is processed in YCbCr color space, distortion does not become additive. This is due to the non-correlation nature of Y (luminance) component and Cb, Cr (chrominance) components. In addition to this, the image in YCbCr color space provides a narrowed and high peak histogram of prediction errors, which is more suitable for embedding additional secret data using block-based PVO technique. To improve the quality of the stego image in the current technique, we calculate the standard deviation of surrounding pixels of the PVO block and accordingly classify the PVO block into a complex region and a smooth region. Then, only a smooth region is processed to embed secret data while leaving the complex region. Therefore, the current paper proposes a secret data hiding technique in YCbCr color space, which not only improves data hiding capacity but also enhances image quality.
A Study of the Characteristics and Extraction of Buzzwords Based on DCC

The definition, characteristics, categories, curve of the evolution of buzzwords and three methods of extracting buzzwords are discussed in the DCC dynamic circulation corpus. The buzzword refers to a word which is widely and rapidly spread in a certain period or a certain range. Nominal buzzwords account for a vast majority of buzzwords, while verbs account for only a minority. The proportion of two syllables is not very high, and the proportion of four syllables is the highest, followed by three syllables, and then five syllables. The proportion of nine syllables and above is also on the rise. Words with different number of syllables must be segmented to extract buzzwords. The full segmentation method to extract buzzword candidates can achieve more ideal results.
Spectral Ranking Regression

We study the problem of ranking regression, in which a dataset of rankings is used to learn Plackett–Luce scores as functions of sample features. We propose a novel spectral algorithm to accelerate learning in ranking regression. Our main technical contribution is to show that the Plackett–Luce negative log-likelihood augmented with a proximal penalty has stationary points that satisfy the balance equations of a Markov Chain. This allows us to tackle the ranking regression problem via an efficient spectral algorithm by using the Alternating Directions Method of Multipliers (ADMM). ADMM separates the learning of scores and model parameters, and in turn, enables us to devise fast spectral algorithms for ranking regression via both shallow and deep neural network (DNN) models. For shallow models, our algorithms are up to 579 times faster than the Newton’s method. For DNN models, we extend the standard ADMM via a Kullback–Leibler proximal penalty and show that this is still amenable to fast inference via a spectral approach. Compared to a state-of-the-art siamese network, our resulting algorithms are up to 175 times faster and attain better predictions by up to 26% Top-1 Accuracy and 6% Kendall-Tau correlation over five real-life ranking datasets.
AN ATTITUDE OF TEACHER TRAINEES TOWARDS INTERNSHIP

The main purpose of this study was to examine the attitude of teacher trainees towards Internship. The participants of the study were 70 students of Faculty of Education of Gujarat Vidyapith. Self-constructed Five Point Likert type scale was used for data collection. In this study, random sampling method was adopted. The main objective of this research was to examine an attitude of Teacher Trainees towards Internship Programme in the context of their gender, area of residence, social category and parents’ education level. The findings of the study revealed that Teacher Trainees had positive attitude towards Internship Programme. There was no significant difference in Teacher Trainees attitude towards Internship Programme in the context of their gender, area of residence, social category mother and father’s education level
Syntactical Parsing of Kannada Text Based on CYK Algorithm

Computational linguistics is one of the attractive research topics in Natural Language Processing (NLP) and Artificial Intelligence domains. This paper presents a particular syntactical parsing technique on Kannada texts which is one of the South Indian languages. Cocke–Younger–Kasami (CYK) parsing technique has been adopted to parse Kannada sentences and identify their grammatical structure. Currently, very less NLP tools are available to parse several Indian languages. Hence, an effort has been made by us to efficiently parse the structure of the complex sentences in Kannada text using CYK algorithm. It is a bottom-up dynamic programming approach which functions only with the grammar in Chomsky normal form (CNF). We are giving annotated Kannada sentences as input to the parser model and obtaining the grammatical correctness of each sentence as the output. The parser model generates NLTK parse tree only for grammatically correct sentences in the output. The error messages have been displayed for incomplete and incorrect Kannada sentences. This work has been implemented and tested on 1000 annotated Kannada sentences. The input dataset contains both simple and compound sentences in which many sentences are 20–22 words in length. We obtained considerable results from the proposed syntax parser model which is implemented on Kannada text.
Relying on discourse analysis to answer complex questions by neural machine reading comprehension

In this chapter, we enable machine reading comprehension (MRC) with linguistic knowledge to improve answering long, complex questions. The traditional MRC is integrated with information retrieval-based candidate answer selection component which ranks answers relying on syntactic and semantic generalization. We also explore an MRC answer-correction scenario where the resulting MRC answer is evaluated by generalization and updated if necessary. We observe a performance boost of 7%–10% by enabling MRC with syntactic, semantic, and discourse features depending on the question complexity; the greater the question complexity, the stronger the boost.
Design and Development of Simulation Software Based on AR-Based Torricelli Experiment

This paper proposes to use AR technology to design and develop simulation software based on the Torricelli experiment of AR. In the physics experiment teaching, Torricelli experiment clearly confirmed the existence of atmospheric pressure with simple devices and easy-to-understand principles and measured that the size of a standard atmospheric pressure is about 760 mm Hg. However, due to the possibility of causing mercury hazards and other problems during the experiment, it is not recommended to demonstrate the actual operation of the Torricelli experiment. This paper first uses smartphones to scan device pictures to obtain virtual three-dimensional models, and to conduct interactive learning on the screen of the mobile phone, so as to ensure the personal safety of teachers and students and the premise of environmental safety. Next, we improve students’ learning motivation and mastery of physics knowledge. In the development process of this system, Unity3d software is used as the main development tool, Vuforia is used technically to realize AR recognition, and the current mainstream development language C# is selected for programming.
Robust Dynamic Pricing in Online Markets with Reinforcement Learning

In online markets, reinforcement learning (RL) is a promising way for dynamic pricing, due to its ability in maximizing long-term cumulative return. However, directly optimizing RL policies in the online markets can be costly since RL requires trial-and-error with the environment, which may lead to drastic revenue loss. In this paper, we propose a robust dynamic pricing algorithm using RL. The main idea is to train the dynamic pricing policy in an adversarial simulation environment built with a generative adversarial framework. In this framework, the generator is trained to: 1) imitate real customers behaviors; 2) generate adversarial behaviors. The algorithm is proved to converge under certain assumptions. The experiment results show that our algorithm can be comparable with the algorithm directly trained in the real environment. Moreover, it outperforms other baseline significantly in different scenarios.