Reviewerly/exHarmony huggingface dataset:
Multi-Scale Interactions Between Augmented Reality and Virtual Reality Users in Mixed Reality Environment

Multi-scale interaction in Mixed Reality (MR) allows the avatars to shrink down themselves into a small size or enlarge into giant size. Multi-scale in MR is important in some situations that required precise avatar’s measurements to interact and manipulate with the content. However, the correct or appropriate scaling for multi-scale still remain unsolved in MR, very less people work in Augmented Reality (AR). It is critical to align precisely the way humans perceive the virtual world with the ways perceiving the real world. Therefore, this paper aims to produce a correct multi-scale technique for VR and AR users in MR environment. This paper presents a preliminary study on the previous works multi-scale techniques for MR remote collaboration. Then it explains the development of a multi-scale interaction between VR and AR users for the MR environment. Finally, this paper shows the results and implement the technique into an application. The results used to measure how multi-scale interaction can be leveraged with MR technology. The remote MR application involves AR and VR users with different interaction techniques.
Integer Based Fully Homomorphic DSP Accelerator using Weighted-Number Theoretic Transform

Fully Homomorphic Encryption (FHE) has gained wide attention in cloud security as it allows computation on encrypted data. However, it requires a huge key size, resulting in impractical execution time. In this paper, we proposed an FHE hardware accelerator employing Weighted-Number Theoretic Transform (NTT) multiplier. NTT parameters are selected, in a way that the proposed design is executable on Digital Signal Processing (DSP) multiplier, to exploit its high clock rate. As the NTT kernel, is in general form, it can be pre-computed and stored in Look-up Tables (LUTs). The same LUTs are also usable for weight-factor as they both have symmetric periodicity properties. This optimization has saved 70% of LUTs utilization. Next optimization is proposed on reduction within NTT. The special prime moduli are exploited to accomplish a simple operation, where inverse Montgomery multiplication is replaced with shift and subtraction. The proposed optimizations are implemented for FHE encryption and realized on Kintex 7 platform. A magnitude of 93.2% speedup improvement is achieved for Toy, compared to benchmark software implementation. As the proposed design is targeted for full DSP implementation, it achieved a higher clock frequency (249.19 MHz), while consuming lower hardware resources.
A Machine Learning-based Approach for Medical Insurance Anomaly Detection by Predicting Indirect Outpatients' Claim Price

About 10% of insurance claims are fraudulent according to published reports. Insurance companies can take a very robust approach to detect anomalies using machine learning techniques. This study proposes a new model based on regression-based machine learning algorithms to predict the Total Price of a patient's claim based on the history of other patients, and then compare the estimated amount with the actual amount to obtain their price difference. The abnormal or fraud costs will be predicted in claims based on a threshold for the absolute price difference. A dataset of 99,440 records of RASA web portal is gathered for evaluation. Deep learning has the best mean absolute error (MAE) in the training phase, but the decision tree has the best MAE in the testing phase. So, the decision tree is used for anomaly detection, which can detect about 17% of records as abnormal with at least a 30% deviation. Expert human assessors check the results and approve more than 50% of reported anomalies.
An Evaluation of Modalities for Action Recognition

&lt;p&gt;Inductive biases of neural networks play a crucial role in their ability to learn robust representations for visual tasks. Human action recognition is a particularly challenging domain where inductive biases in the form of input modalities have profound impacts on the algorithms’ success. However, past research has not yet identified the optimal set of modalities to use for action recognition. In this thesis, a number of modalities are evaluated and compared using various metrics, including overall accuracy, per-class performance, and amount of contextual information encoded. This is done by training a deep 3D convolutional neural network on each input modality, while controlling variables like video length and dataset, which reveal characteristics of each modality. It is shown that certain modalities lend themselves better to specific network architectures, tasks, and data sets, which has relevance to real-world action recognition systems and future research in the field of multi-modal action recognition.&lt;/p&gt;
Effectiveness of Forensic Auditing on Fighting Fraud Among Non-Government Organisations in South Africa

This article sought to empirically establish the effectiveness of proactive forensic auditing techniques for 30 non-government organisations in the eThekwini region, South Africa. This article adopted quantitative and descriptive survey research. The data for this research was gathered via the use of the quantitative research method through an online questionnaire which was designed in a 5-point Likert scale and used for data analysis. Robustness analysis was entirely performed using SPSS software version 27 for statistical analysis. The study’s results showed that there exists a positive statistically significant relationship between the applicability of forensic auditing techniques and fraud detection, prevention and investigation among non-government organisations in the eThekwini region, South Africa. It also found that the application of forensic auditing techniques has its highest applications in proactive fraud detection. Based on the above, it was recommended that the application of forensic auditing skills and techniques among non-government organisations should be considered carefully.
Cross-age Face Image Similarity Measurement Based on Deep Learning Algorithms

In this study, a multi-feature fusion and decoupling solution based on the RNN is proposed from a discriminative perspective.This method can address the identity and age information extraction losses in cross-age face recognition.This method not only constrains the correlation between identity and age using correlation loss but also optimizes identity feature restoration using feature decoupling.The model was trained and simulated in CACD and CACD-VS datasets.The single-task learning model stabilized after 125 iterations of training, while the multi-task learning model reached a stable and convergent state after 75 iterations.In terms of performance analysis, the DE-RNN model had the highest recognition accuracy with a mAP of 92.4%.The Human Voting model had a value of 90.2%.The mAP of the Human Average model was 81.8%, whereas the mAP of the DAL model was the lowest at 78.1%.Experiments proved that the model constructed in this study has effective recognition and application value in the cross-age face recognition scenario.
Attribute-based Access Control Model in Healthcare Systems with Blockchain Technology

Blockchain and the healthcare sector have a serious concern with context to scalability, which has a challenge of converting arbitrary values to fixed values.The transfer of arbitrary data coming from diverse resources has another point of concern in the blockchain.In this paper, the author proposed a model that will receive data from diverse sources and will convert it to a fixed type of value.The paper also proposes an access control scheme with various permission and consensus level protocols which will allow a reduction in block size with respect to scalability.The consensus level will allow access to the individual or a group of users and the permission level with respect to each block via considering the access granted to nodes of the blockchain.The addition of various permission and consensus levels will allow only a restricted type of data to pass the model.Once the data is verified and approved by various levels, then the data is all set to be part of the blockchain.The paper introduces a model where the time taken to create a new hash is 0.15625 microseconds.A total number of 64 transactions taken from the data set where the throughput is calculated for individual access are considered.After applying the formula, the calculated throughput is 32.5 microseconds.By the lighter block size data can be made available to the patients.The research is for the patients so they can keep track of their medical history and the deaths due to overdose of the medicines can be reduced.
Towards Analysis of Biblical Entities and Names using Deep Learning

Scholars from various fields have studied the translations of the Bible in different languages to understand the changes that have occurred over time.Taking into account recent advances in deep learning, there is an opportunity to improve the understanding of these texts and conduct analyses that were previously unattainable.This study used deep learning techniques of NLP to analyze the distribution and appearance of names in the Polish, Croatian, and English translations of the Gospel of Mark.Within the scope of social network analysis (SNA), various centrality metrics were used to determine the importance of different entities (names) within the gospel.Degree Centrality, Closeness Centrality, and Betweenness Centrality were leveraged, given their capacity to provide unique insights into the network structure.The findings of this study demonstrate that deep learning could help uncover interesting connections between individuals who may have initially been considered less important.It also highlighted the critical role of onomastic sciences and the philosophy of language in analyzing the richness and importance of human and other proper names in biblical texts.Further research should be conducted to produce more relevant language resources, improve parallel multilingual corpora and annotated data sets for the major languages of the Bible, and develop an accurate end-to-end deep neural model that facilitates joint entity recognition and resolution.
Deadline-aware Task Scheduling for Cloud Computing using Firefly Optimization Algorithm

Task scheduling poses a major challenge for cloud computing environments.Task scheduling ensures cost-effective task execution and improved resource utilization.It is classified as a NP-hard problem due to its nondeterministic polynomial time nature.This characteristic motivates researchers to employ meta-heuristic algorithms.The number of cloud users and computing capabilities is leading to increased concerns about energy consumption in cloud data centers.In order to leverage cloud resources in the most energy-efficient manner while delivering real-time services to users, a viable cloud task scheduling solution is necessary.This study proposes a new deadline-aware task scheduling algorithm for cloud environments based on the Firefly Optimization Algorithm (FOA).The suggested scheduling algorithm achieves a higher level of efficiency in multiple parameters, including execution time, waiting time, resource utilization, the percentage of missed tasks, power consumption, and makespan.According to simulation results, the proposed algorithm is more effective and superior to the CSO algorithm under HP2CN and NASA workload archives.
Systematic Analysis on the Effectiveness of Covert Channel Data Transmission

A covert channel is a communication channel that allows parties to communicate and transfer data indirectly.Covert channel types are storage, timing, and behavior channels.Covert channels can be used for malicious and benign applications.A covert channel is a mechanism for violating the communication security policy that was not anticipated by the system creator.Recently, covert channels are used to transfer text, video, and audio information between entities.This article, discusses studies related to the development of covert channels as well as the research works that focus on improving the performance/throughput of covert channels.Also, it analyzes the previous studies in terms of publication type, year of publication, article title, article purpose, transferring file format used in covert channel, coding technique, throughput performance, time needed to transfer files, and article limitations.
Krill Herd Algorithm for Live Virtual Machines Migration in Cloud Environments

Green cloud computing is a modern approach that provides pay-per-use information and communication technologies with a minimal carbon footprint.Cloud computing enables users to access computing resources without the need for local servers or personal devices to operate applications.It allows businesses and developers to access infrastructure and hardware resources conveniently.Consequently, this results in a growing demand for data centers.It becomes crucial in maintaining economic and environmental sustainability as data centers use disproportionate energy.This points to sustainability and energy consumption as being important topics for research in cloud computing.This paper introduces a two-tiered VM placement algorithm.A queuing model is proposed at the first level to handle many VM requests.Models such as cloud simulation are easily implemented and validated using this model.It also provides an alternate method for allocating tasks to servers.Next, a multi-objective VM placement algorithm is proposed based on the Krill Herd (KH) algorithm.Basically, it maintains a balance between energy consumption and resource utilization.
Recommendation System Based on Double Ensemble Models using KNN-MF

In today's digital environment, recommendation systems are essential as they provide personalised content to users, increasing user engagement and enhancing user satisfaction.This paper proposes a double ensemble recommendation model that combines two collaborative filtering algorithms, K Nearest Neighbour (KNN) and Matrix Factorization (MF).KNN is a neighbourhood-based algorithm that uses the similarity between users or items to make recommendations.At the same time, MF is a model-based algorithm that decomposes the user-item rating matrix into lower-dimensional matrices representing the latent user and item factors.The proposed double ensemble model uses KNN and MF to predict missing ratings matrix and combines their predictions using stacking.To evaluate the performance of the proposed ensemble model, we conducted experiments on three datasets i.e.Movielense, BookCrossing dataset and Hindi Movie dataset and compared the results with those of single algorithm approaches.The experimental results demonstrate that the double ensemble model outperforms the single algorithm approaches regarding accuracy metrics such as Mean Square Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE).The results indicate that stacked KNN and MF lead to a more robust and more accurate recommendation system.
Quantum Fisher information of an N-qubit maximal sliced state in decoherence channels and Ising-type interacting model

Abstract Various quantum states have been suggested to realize high-precision measurement and quantum Fisher information (QFI) is one of the most important indicators to evaluate the performance. Here, we systematically investigate the QFI of an N-qubit maximal sliced (MS) state in different environments. In the ideal situation, we present the analytical QFI and it decays from N 2 to (N − 1) 2 as the parameter α changed from 0 to 1, which denotes the decreased metrological ability. In the amplitude damping channel, the variation trends of QFI with respect to p are similar and it decreases to the number of qubits N at p = 1, meaning the vanished entanglement superiority. However, it is different in the phase damping channel where with the increase of the number of qubits a turning point of QFI equal to N is found at p closing to 0.1. In the Ising-type interacting model, with the increasing interaction strength γ, the QFI with respect to α shows a crossover from decreasing to increasing. Particularly, at the critical point γ = 1, the QFI of an N-qubit MS state is found analytically equal to N 2 and regardless of α. This indicates a great potential in achieving the Heisenberg-limited metrology and may shed some new light on quantum information science. PACS: 03.67.-a, 03.65.Ud
The Dynamics of Innovation on Firm Performance: Empirical Analysis of Pharmaceutical Manufacturing Firms in Sindh Pakistan

Innovation is defined as the process of producing developing new products and services for significant positive change, innovation is a crucial driver of economic growth and societal progress. Aim of this paper is to analyze the direct, indirect, and total effects of exogenous variables product innovation, process innovation, and organizational innovation, with the mediating effects of organizational innovation capability and dynamic capability on endogenous variable firm performance. In the context of Resource Based view theory organizational achievement is defined with reference to utilization resources, proficiencies to achieve competitive advantage, sustainable and viable. In the framework of this concepts, innovation which leads to shape resources capabilities for better performance in pharmaceutical manufacturing firms of Sindh. Path analysis approach is incorporated for analyzing multivariate causal effect, a five-point Likert scale, and a pretested, closed ended questionnaire was used for feedback of responses. Cluster sampling technique is incorporated for compilation of data, by using SPSS 25 version software for demographic statistics, and PLS 3.5.V for inferential statistics. Direct, indirect, and total effects is significantly positive, except PI &gt; to DC &gt; is insignificantly associated with firm performance. Partial mediation is established by the, product innovation, and organizational innovation through organization innovation capability and dynamic capability with firm performance.The limitation only cover the pharmaceutical manufacturing firms al so limitations are expelled various variables of firm performance because of limited time. Beside these limitations, this study provides direction to top management and policymakers for enhancing of performance of manufacturing firms of Sindh.
Learning English through YouTube videos to foster speaking skills: Public Administration students’ perceptions

Many teachers use YouTube as an internet-based application to share their areas of expertise, such as sharing writing skills, English pronunciation, English speaking, and so on. In this research, the lecturer uses YouTube as an audiovisual application to help Public Administration students learn English speaking. This research fills the gap of previous research in higher education, precisely about learning to support their careers in the near future. This research aims to understand YouTube as a medium for speaking English based on Public Administration students’ perceptions in class. Therefore, the qualitative method was ideal to answer the purpose of the research. This study involved 15 students, and the researcher served as the primary instrument because of his capacity to provide clarity about the phenomenon utilizing questionnaires and semi-structured interviews. The finding showed that YouTube was the right tool to support their speaking skill. YouTube enhances students’ understanding of Public Interest vocabulary. However, a stable internet connection was the most common problem in learning. Furthermore, there must be similar research in the Department of Elementary School Teacher Education to fill the gap in this research and enrich the understanding of English-speaking skills.&#x0D;
The impact of vocabulary selection ability on EFL students’ communication skills

To gain proficiency in English, the students in Bali should have proficiency in the language, not only through correctly understanding and using sentence structure or vocabulary, but also by managing the flow of language in broader culture-based expressions in standardized communication. Recent studies have focused solely on the phenomenon of language proficiency, however, without considering the integrative elements of language proficiency as a learning achievement. Therefore, this study aimed to explore any causal-impact relationship between students' ability to discriminate vocabulary and their communication skills. The researcher conducted an ex post facto research design as the purpose was only to accurately see the correlation between the variables. This study involved 40 students of tenth-grade students of the SMK PGRI 3 Badung as participants using a targeted sampling technique. Data were collected by running two types of tests. Test A was used to measure students' ability to distinguish vocabulary usage in listening activities and Test B was a speaking and writing proficiency test. The data were analyzed with SPSS 25.0. The results showed that the ability to distinguish vocabulary statistically affected the clear flow of English communication, although some grammatical errors could not be ignored. The results showed that word selection ability enabled students to maintain the flow of communication better. Therefore, this study suggests that teachers adopt a teaching strategy to integrate all elements of communication for better language proficiency.&#x0D; &#x0D;
A narrative inquiry into the cultivation of EFL students’ critical thinking skills through multimodal texts

This article provides a story of two female undergraduate students in learning speaking with multimodal texts to elevate their critical thinking skills. Specifically, this study tried to investigate their lived story and experience in learning with multimodal texts following with the analysis activity to foster their critical thinking skill. In addition, their perceptions towards the use of multimodality and the benefits of multimodality were also portrayed. The study was carried out in a speaking class of Foreign Language for Specific Purposes (FLSP) subject in one of private universities in Malang, East Java, Indonesia. The materials taught consisted of e-picture of advertisement shared via WhatsApp group and YouTube videos about advertisement. This study employed qualitative method with a narrative inquiry. The study findings reveal that students interviewed had positive attitudes towards learning English with multimodal texts. Moreover, the use of multimodality benefits students to raise their awareness on critical thinking. The results of study contribute to the body of knowledge regarding the use of multimodality for speaking purposes.
Gaze differences in Multiple Object Tracking vs Multiple Identity Tracking

In dynamic environments, a central task of the attentional system is to keep track of objects changing their spatial location over time. In some instances, it is sufficient to track only the spatial locations of moving objects (i.e., multiple object tracking; MOT). In other instances, however, it is also important to maintain distinct identities of moving objects (i.e., multiple identity tracking; MIT). Despite previous research, it is not clear whether MOT and MIT performance emerge from the same tracking mechanism. In the present report, we study gaze coherence (i.e., the extent to which participants repeat their gaze behaviour when tracking the same object locations twice) across repeated MOT and MIT trials. We observed more substantial gaze coherence in repeated MOT trials compared to the repeated MIT trials or mixed MOT-MIT trial pairs. A subsequent simulation study suggests that MOT is based more on a grouping mechanism than MIT; whereas, MIT is based more on a target-jumping mechanism than MOT. It thus appears unlikely that MOT and MIT emerge from the same basic tracking mechanism.