Reviewerly/exHarmony huggingface dataset:
Impact of Alignment Edits on the Quality of Experience of 360° Videos

Optimizing user quality of experience (QoE) for 360° videos faces two major roadblocks: inaccurate viewport prediction and viewers missing the plot of a story. To tackle these issues simultaneously, alignment edits have emerged as a promising solution. These edits, also known as "re-targeting edits," work by aligning the user’s field of view with a specific region of interest in the video content. Despite their potential benefits, there is limited knowledge about the actual impacts of alignment edits on user experience (UX). Therefore, we conducted subjective experiments following ITU-T P.919 methodology to explore their effects on QoE. We proposed an alignment edit based on gradual rotation of the 360° frame, aiming to replicate natural viewing behavior. We tested this approach under various conditions and thoroughly analyzed its impacts using both head motion data and feedback from observers, focusing on their sense of presence, comfort, and perceived experience. The results of our experiments are encouraging. Our proposed gradual alignment technique achieved a level of comfort and presence comparable to that of instant edits. Furthermore, all alignment edits tested led to a noticeable reduction in head movement speed after the edit, affirming their potential utility for on-demand video streaming. Notably, the gradual edits, in particular, induced a significant reduction of approximately 8% in head movement speeds when compared to the instant alignment technique. These findings shed light on the positive effects of alignment edits on user experience and firmly establish the viability of the proposed gradual alignment technique to enhance QoE during video consumption.
CFDRN: A Cognition-Inspired Feature Decomposition and Recombination Network for Dysarthric Speech Recognition

As an essential technology in human–computer interactions, automatic speech recognition (ASR) ensures a convenient life for healthy people; however, people with speech disorders, who truly need support from such a technology, have experienced difficulties in the use of ASR. Disordered ASR is challenging because of the large variabilities in disordered speech. Humans tend to separately process different spectro-temporal features of speech in the left and right hemispheres of their brain, showing significantly better ability in speech perception than machines, especially in disordered speech perception. Inspired by human speech processing, this paper proposes a cognition-inspired feature decomposition and recombination network (CFDRN) for dysarthric ASR. In the CFDRN, slow- and rapid-varying temporal processors are designed to decompose features into stable and changeable features, respectively. A gated fusion module was developed to selectively recombine the decomposed features. Moreover, this study utilised an adaptation approach based on unsupervised pre-training techniques to alleviate data scarcity issues in dysarthric ASR. The CFDRNs were added to the layers of the pre-trained model, and the entire model is adapted from normal speech to disordered speech. The effectiveness of the proposed method was validated on the widely used TORGO and UASpeech dysarthria datasets under three popular unsupervised pre-training techniques, wav2vec 2.0, HuBERT, and data2vec. When compared to the baseline methods, the proposed CFDRN with the three pre-training techniques achieved 13.73% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 16.23% and 4.50% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 13.20% word error rate reductions on the TORGO and UASpeech datasets, respectively. Furthermore, this study clarified several major factors affecting dysarthric ASR performance.
Blockchain-Assisted Vehicle and Cargo Matching Using Optimal Fuzzy Restricted Boltzmann Machine in Autonomous Transport System

Blockchain (BC) technologies and cutting-edge artificial intelligence (AI) have the capability of reforming transport systems’ sustainability, safety, and efficiency. AI-powered solutions allow autonomous vehicles (AVs) to respond to potential hazards, navigate complex environments, make intelligent decisions, and examine real-time data. BC technologies offer secure and decentralized platforms for data management and sharing, fostering trust and cooperation among different stakeholders in the transportation system. An Autonomous Transport System (ATS) for secure vehicle and cargo matching intends to enhance and optimize transportation by using AVs and ensuring the secure transportation of goods. This mechanism combined AV technology, secure matching, and cargo tracking methods to securely and efficiently transport goods from one place to another. In this aspect, this article presents a Blockchain Assisted Vehicle and Cargo Matching using Optimal Fuzzy Restricted Boltzmann Machine (BAVCM-OFRBM) technique for Autonomous Transport System. The major aim of the BAVCM-OFRBM technique lies in the development of an automated vehicle and cargo matching approach using an optimal DL model. In addition, the BAVCM-OFRBM technique makes use of BC technology to secure transportation details such as order and shipment information. Moreover, the BAVCM-OFRBM technique exploits the FRBM model for the selection of appropriate vehicles and cargo for transportation purposes. Finally, the efficacy of the FRBM model can be improved by the use of Ingenious Crow Search Algorithm (ICSA) based hyperparameter tuning process. The simulation results of the BAVCM-OFRBM technique are tested under different aspects. The experimental outcomes emphasized the betterment of the BAVCM-OFRBM method in vehicle and cargo matching process.
Ancestral Kinship Structure and Country-Level Contemporary Financial Reporting Quality

This study examines the effect of people’s ancestral kinship structure - the extent to which their ancestors were interdependent on extended family networks - on their country’s contemporary financial reporting quality. We argue that the culture of in-group trust (out-group trust) present in tightly knit (loose) ancestral kinship systems gets transmitted to future generations, and this culture and related institutional developments promote relationship-based (open market) transactions, which in turn influence financial reporting quality. Using an ancestral kinship tightness measure based on the social structures of pre-industrialization ethnic groups, we provide robust evidence suggesting that a country’s contemporary financial reporting quality is negatively associated with its people’s ancestral kinship tightness. Furthermore, controlling for ancestral kinship tightness significantly diminishes the explanatory power for financial reporting quality of many of the previously identified contemporary country attributes. These findings provide an explanation for the “high co-dependencies” observed by Isidro, Nanda, and Wysocki (2020) among these contemporary country attributes and address the call by Leuz and Wysocki (2016) to identify a “fundamental primitive” underlying the heterogeneity in country-level financial reporting quality.
BADANet: Boundary Aware Dilated Attention Network for Face Parsing

Over the past few years, deep learning techniques have revolutionized the field of face parsing by utilizing massive datasets to generate high-level features and achieve outstanding performance. Usually, these techniques involve Convolutional Neural Networks (CNNs) to derive features from the input image and then a decoder network to forecast the semantic labels for every pixel. Even with complex deep convolutional neural networks (DCNN), incorrect parsing leads to a semantic gap between identical features, especially at boundary levels. Face parsing has encountered additional challenges due to the inclusion of pose, illumination and facial expressions. In order to address these issues, a Boundary Aware Dilated Attention Network (BADANet) is introduced which explores the use of multi-scale techniques to improve the accuracy and robustness of the per-pixel frames. BADANet’s dilated attention module has been combined with a lightweight backbone to achieve exceptional results on LaPa, CelebAMask-HQ, and iBugMask. Extensive evaluations of the proposed method demonstrate its performance to be on par with various state-of-the-art techniques. The pipeline for the BADANet is available at https://github.com/abhigoku10/BADANet.
Application of KNCMA Algorithm Integrating Network Features in Data Resource Scheduling

To improve the efficiency of data resource scheduling in virtual data centers, this study analyzes the network structure of the data center, constructs a set of key nodes, and selects the importance of nodes. And a mapping algorithm based on key nodesis proposed as the mapping algorithm for the virtual data center. The effectiveness of virtual data center compression and network feature validation are analyzed. In the DCell structure, after compressing the virtual data center, the request acceptance rate has improved. The compressed virtual data center significantly consumes less physical bandwidth during mapping compared to the case of direct virtual data center mapping. In the BCube structure, after compressing the virtual data center, the request acceptance rate has also improved. A comparative experiment is conducted between the mapping algorithm based on key nodes and the mapping algorithm based ontherandom experiments. Under the three architectures of DCell, BCube, and Fattree, the acceptance rate of key node mapping algorithms is higher than that ofthe random experimental mapping algorithms. Its bandwidth utilization rate is also lower than the mapping algorithm in random experiments. After introducing network features, the request acceptance rate has improved to varying degrees. The bandwidth utilization rate increases with the increase of acceptance rate.
A Multilabel Learning-Based Automatic Annotation Method for Semantic Roles in English Text

With the increasing amount of textual information in the Internet, smart semantic comprehension is a practical demand. Among, automatic annotation for semantic roles remains the fundamental part for effective semantic comprehension. Although machine learning-based methods had received much attention in recent years, they mostly divided each sentences into separable parts for calculation. To deal with such challenge, this paper introduces multilabel learning to propose a novel automatic annotation method for semantic roles in English text. In the semantic representation of words, the method uses convolutional neural networks to extract local feature information of words from the character level. Such design can alleviate the problem of inconspicuous semantic features caused by random initialization of unregistered words. Secondly, in the process of implication recognition, by combining the interactive attention mechanism to construct a capsule for each implication relation separately, the recognition of the final implication relation is completed in the way of categorical learning. At last, some experiments are conducted on real-world data to verify the proposed method with being compared with several typical relevant methods. The obtained results show that the proposal achieves better Macro-F1 results on eight datasets compared to seven algorithms. Besides, the proposal also performs better than others in the sensitivity testing, as its performance can remain stable with the increase of noise input. In summary, the proposal can achieve good results and show strong capability in semantic role labeling tasks.
Text Segmentation Methods for Annotation on eHealth Consultation with Interview Function Labels: A Comparative Study

There have been several existing text segmentation methods. Nevertheless, no study has provided the experimental result on the performance of those methods in segmenting text of health consultation data based on sentence context and provided automatic annotation on the segmented results in the form of interview function labels. This study compares four methods with different text segmentation approaches and analyzes their performances based on their reliability concerning human expert judgment. The methods are Content Vector Segmentation (CVS), GraphSeg, K-Means, and Latent Dirichlet Allocation (LDA). This study used a greedy similarity approach to perform automatic annotation by selecting candidate labels based on the maximum value. The annotation results were evaluated using Gwet's AC1 method to assess the integrity among evaluators in clinical research. The evaluation results indicate that the CVS method outperforms other methods and has a substantial level of agreement (0.67). It is also relatively stable, with a standard deviation of 0.12.
optimization of Configuration Hybrid Power Generation System at Oil Refinery

The oil refinery industry has a potential emission reduction by combining its fossil fuel with Renewable Energy usage. One alternative to reduce carbon emissions is the hybrid power system using solar panels for electricity generation. This research will observe the optimum scenario for utilizing a hybrid power system. The research subject is observed refinery facilities that will be built in Indonesia, where the electricity source used formerly comes from fossils. The monitored parameters are Cost of Energy (COE) and carbon emission. optimization involves three scenarios involving basic and hybrid configurations. The basic scenario is the initial configuration of the electricity supply with power source from the generator and PLN (national’s utility). The first scenario is a hybrid power plant utilizing the roofs of existing buildings in the refinery area. The second is a hybrid power plant with a battery to supply electricity at night, at the capacity of renewable energy (RE) of 20% and 40%. Compared to the basic scenario, scenario 1 increases COE by 0.17% and reduces carbon emission at 0.3% compared to the basic scenario. Scenario 2 with 20% and 40% of renewable energy are obtained at COE of 4.7% and 44.3%, while reducing emissions occurred at 38% and 52.7%, respectively. The scenario of a hybrid power system with a 20% renewable energy is the optimal result compared to the other scenarios.
Optimization of Data Protection in IoT Field of Cloud Computing Using Compiler Privacy Calculation Method

This paper aims to enhance cloud computing security and mitigate privacy risks by investigating optimized compiler models. Through extensive literature review and analysis, we have selected a compiler model based on the Porcupine privacy computing approach and a component called Quill, which translates plain text specifications into equivalent implementations in homomorphic cryptography. Concurrently, we propose additional optimization strategies that improve the efficiency and cost effectiveness of homophobic encryption. Our work signifies a step forward in the realm of secure, private cloud computing
A Deep Learning-Based Framework for Automatic Detection of Defective Solar Photovoltaic Cells in Electroluminescence Images Using Transfer Learning

The utilization of electroluminescence (EL) imaging has proven to be a reliable and precise method for inspecting photovoltaic (PV) modules, due to its high spatial resolution, which allows for the detection of various types of defects. However, the manual analysis of EL images is both expensive, and time-consuming, and requires a specialist with extensive knowledge to identify a wide range of defects. In this study, we propose a deep learning-based technique for the automatic detection of defective solar cells from EL images. Specifically, we employed two convolutional neural network (CNN) architectures in our proposed framework. The first architecture is a transfer learning-based VGG16 model that has been fine-tuned with custom fully connected neural network layers to classify defective and non-defective solar cells. The second architecture is a lightweight CNN model that was created from scratch and was used as a baseline for classification comparison with the VGG16 fine-tuned model. The models were trained on a publicly available monocrystalline solar cell image dataset. To address overfitting and to increase the dataset size, we utilized data augmentation techniques. Our proposed method achieved a 95.2% accuracy on the test dataset, which is higher than in previous studies. The implementation of our proposed method will enable continuous, rapid, and precise quality inspection of solar PV plants. Proper maintenance of solar PV panels can significantly improve their efficiency, safety, and power output.
Determining the Best Weightage Feature in Parameterization Process of GCCD Model for Clone Detection in C-Based Applications

The term “code clone” relates to code that has been replicated many times in a program. Primarily, Type-1, Type-2, Type-3, and Type-4 serve as the four distinct categories for the classification of code clones. Distinct code clone approaches and tools have been implemented for identifying code clones over the years. To overcome the limitation of generalization in recognizing all types of clones, Generic Code Clone Detection (GCCD) model is developed. The five procedures that make up the GCCD model's foundational structure are pre-processing, transformation, parameterization, categorization, and match detection. However, the preceding GCCD model can only detect all types of code clone in Java applications. In light of this limitation, the study proposes a code clone detection model based on the GCCD model, which has the capability to support other programming languages in various applications. The primary objective of this proposed research is to enhance the process in Generic Code Clone Detection (GCCD) model that can improve the code clone detection result, specifically in C-based applications. To achieve the desired objective, some enhancements in the GCCD model have been recommended which are to propose a constant and weightage for Pre-processing and Parameterization process in GCCD model. The proposed work will be tested in a case study involving four C applications. As determined by the code clone detection results from the proposed enhancement, void with its weightage is the preeminent constant and weightage for the Generic Code Clone Detection Model in C-based applications.
Investigation on Insurance Purchase Classification for Insurance Recommendation Using Deep Learning and Class Propagation

A majority of insurers are in the opinion that Artificial Intelligence (AI) has potential to create a better customer experience, improve direction-making and achieve cost savings. One area of interest for AI in insurance is in the area of recommendation of on-demand insurance policies. This automated recommendation can expedite the process of insurance recommendation as it does not involve any agents. This research involves the utilization of insurance dataset, comprising 9422 records and 86 customer features related to social-demographics and insurance purchases. The data is transformed into a graph-based representation using Stellar Graph to capture the interrelationships between features and fit into a Graph Convolutional Network (GCN). Additionally, other AI techniques such as Long Short-term Memory Networks (LSTM), Decision Tree (DT), and Support Vector Machines (SVM) were also considered. To address the issue of imbalanced data in the dataset, various class propagation techniques such as Borderline-SMOTE, Adaptive Synthetic Sampling Approach (ADASYN), and Gaussian Copula were employed with different data size ratio distributions. Overall, GCN performs well with 50:50 ratio distribution in three of the different oversampling techniques, indicating that the graph- based structure and processing algorithm of GCN are suitable for recommendation systems.
Design and Implementation of a LCDP with Enhanced Functionality

Low-code application development platforms (LCDPs) have been widely used recently to replace typical application development with lower development costs and speed up application delivery. However, LCDP in the market is still evolving, with several limitations. This research aims to demonstrate a new LCDP implementation that can generate an application with improved functionality practically used in real-world business applications. The invention starts from the concept of model-driven engineering (MDE) theory and designs an intelligent code generator engine. This LCDP implementation is a software-as-a-service platform allowing users to design their custom applications with the visualized designer. The output of the design then is to generate an executable source code that enables to build of the software applications with minimum limitation, less code for development, future extensibility, and unleash from vendor locked-in of a proprietary platform. The research has also extended the study by comparing functionality with benchmark commercial applications so that the generated software applications from this LCDP research can perform similarly.
Improving Sarcasm Detection in Mash-Up Language Through Hybrid Pretrained Word Embedding

Sarcasm detection is an imperative undertaking within the realm of natural language processing, albeit one that poses considerable challenges when confronted with mash-up languages, characterized by the amalgamation of multiple distinct languages. In response to the intricacies of sarcasm detection in mash-up languages, with a specific focus on the Indonesian-English language mash-up, this study introduces the Hybrid Pretrained Word Embedding approach as a means to enhance sarcasm detection. The primary objective of this research is to augment the precision of sarcasm detection in mash-up languages by amalgamating suitable word embeddings tailored to the employed terms. The present study combines two prevalent pretrained word embeddings, i.e Glove and Fasttext, wherein Glove is utilized to extract semantic context vectors for English words, while Fasttext is employed to extract semantic context vectors for Indonesian words. The classification process in this research leverages the deep learning methodology known as Bidirectional Gated Recurrent Unit (BiGRU). To assess the efficacy of the proposed approach, an extensive dataset comprising sarcastic and non-sarcastic tweets, written in a hybrid language of Indonesian and English, is acquired from the Twitter platform. The results unequivocally demonstrate that the Hybrid Pretrained Word Embedding approach significantly enhances sarcasm detection in mash-up languages, attaining a commendable classification accuracy of 93.57% and an F-measure of 97.94%. By offering an effective methodology to identify sarcasm in mash-up languages, this study yields a substantive contribution to the field of natural language processing.