Reviewerly/exHarmony huggingface dataset:
Investigation of College Students' Intention to Accept Online Education Services: An Application of the UTAUT Model in Korea

This study acknowledges the necessity to make changes in the domestic education service market given the coronavirus (COVID-19) pandemic outbreak in the fourth industrial revolution era. Considering the change from traditional classroom education service to online education service, we measure the online education service quality and investigate its effects on its acceptance intention among college students. We employ the SERVQUAL measurement model, a universal method with the longest history. In measuring acceptance intention, we use the unified theory of acceptance and use of technology (UTAUT), an accurate measurement method for the effect on acceptance intention of introducing new technology. So, this study mainly focuses on SERVQUAL and UTAUT as these tools relate to online education. The research results show that all five sub-factors of the online education service quality have positive correlations with acceptance intention. The higher the empathy with the online education service quality and the higher the performance expectancy and social influence in the UTAUT model, the higher the college students' intention to accept the online education service. Given that the significance of online education service will likely further increase in the future, this study can serve as a reference for preparing a practical framework.
Novel load balancing approach based on ant colony optimization technique in cloud computing

The cloud is the framework in which communication is connected with virtual machines, data centers, hosts, and brokers. The broker searches for a highly reliable cloudlet virtual machine for execution. Vulnerability can occur in the network because of which framework gets overburden. A research strategy is introduced in this article to expand the fault tolerance of the framework. The proposed approach improvement depends on the algorithm of ant colony optimization (ACO) that can choose the better virtual machine on which is to migrate the cloudlet to reduce the execution time and energy consumption. The efficiency of the proposed approach simulated in terms of execution time, energy consumption and examined with CloudSim. The introduction is provided in this article with a detailed description of cloud computing and, in addition, green cloud computing with its models. This article also discussed the virtual machine (VM) in more depth in the introduction section, which allows cloud service providers to supervise cloud resources competently while dispensing with the need for human oversight. Then the article submitted and explained the related works with their discussion and then it explained the novel proposed load balancing based on ACO technique and concluded that the execution time and energy consumption of the proposed technique is better than the three-threshold energy saving algorithm (TESA) technique that is commonly used in cloud load balancing.
Design and Implementation of an Input Interface for Wearable Devices using Pulse Wave Control by Compressing the Upper Arm

In this paper we propose an input interface for wearable devices that enables simple command input by sensing blood flow changes using a pulse sensor installed in many wrist-worn wearable devices caused by body compression. The proposed method consists of four processes: pulse measurement, peak detection, calculation of the time interval between peaks, and command transmission. We proposed four input methods to determine a command based on the combination of compression patterns, and conducted an experiment in which four subjects were asked to choose one of several options with each input method.
Are My Business Process Models Compliant With LGPD? The LGPD4BP Method to Evaluate and to Model LGPD aware Business Processes

Context: Data privacy and data security became a priority among the problems faced by many Brazilian organizations that should be compliant with the Lei Geral de Proteção de Dados Pessoais (LGPD). This law defines the privacy rights on user data and penalties to the ones that break it. Problem: In a compliance program, business processes are of fundamental importance since they are the most important pillar of information security. However, an approach to guide companies to assess and achieve compliance with LGPD on their business processes is missing. Objective: This work proposes the LGPD4BP (LGPD for Business Process) method, which is composed by an evaluation questionnaire and a modelling method with a modelling patterns catalog. Method: To develop LGPD4BP, we carried out a literature review, an analysis of privacy laws, in particular the LGPD, and relevant works on the area. Results: The method was applied on a case study of Colégio de Aplicação from Federal University of Pernambuco and validated by a postgraduate class which applied the method and answered a questionnaire about easiness and completeness of the method. Conclusions: The results from students evaluations showed that the most hard step is the business process modeling and not the components from the proposed method.
Multi-Tensor Fusion Network with Hybrid Attention for Multimodal Sentiment Analysis

Multimodal sentiment analysis is a highly sought-after topic in natural language processing. In this paper, a multi-tensor fusion network with hybrid attention architecture for multimodal sentiment analysis is proposed. Firstly, Bi-LSTM is applied to encode contextual representation in different modalities. Following this, modalities features are extracted and interacted with by the hybrid attention mechanism. Finally, multi-tensor fusion approach is used to further enhance the effectiveness of fusing interaction features in different modalities. The proposed approach outperforms the existing advanced approaches on two benchmarks through a series of regression experiments for sentiment intensity prediction, as it improves F1-score by 3.4% and 2.1% points respectively. Our architecture would be open-sourced on Github <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> for researchers to use.
Timestamp-aligning and keyword-biasing end-to-end ASR front-end for a KWS system

Abstract Many end-to-end approaches have been proposed to detect predefined keywords. For scenarios of multi-keywords, there are still two bottlenecks that need to be resolved: (1) the distribution of important data that contains keyword(s) is sparse, and (2) the timestamps of the detected keywords are inaccurate. In this paper, to alleviate the first issue and further improve the performance of the end-to-end ASR front-end, we propose the biased loss function for guiding the recognizer to pay more attention to the speech segments containing the predefined keywords. As for the second issue, we solve this problem by modifying the force alignment applied to the end-to-end ASR front-end. To get the frame-level alignment, we utilize a Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) based acoustic model (AM) for auxiliary. The proposed system is evaluated in the OpenSAT20 held by the National Institute of Standards and Technology (NIST). The performance of our end-to-end KWS system is comparable to the conventional hybrid KWS system, sometimes even slightly better. With fusion results of the end-to-end and conventional KWS systems, we won the first prize in the KWS track. On the dev dataset (a part of SAFE-T corpus), the system outperforms the baseline by a large margin, i.e., our system with GMM-HMM aligner has a lower segmentation-aware word error rates (relatively 7.9–19.2% decrease) and higher overall Actual term-weighted values (relatively 3.6–11.0% increase), which demonstrates the effectiveness of the proposed method. For more precise alignments, we can use DNN-based AM as alignmentor at the cost of more computation.
Context Modelling in Ambient Assisted Living: Trends and Lessons

The current Internet of Things (IoT) development involves ambient intelligence which ensures that IoT applications provide services that are sensitive, adaptive, autonomous, and personalized to the users’ needs. A key issue of this adaptivity is context modelling and reasoning. Multiple proposals in the literature have tackled this problem according to various techniques and perspectives. This chapter provides a review of context modelling approaches, with a focus on services offered in Ambient Assisted Living (AAL) systems for persons in need of care. We present the characteristics of contextual information, services offered by AAL systems, as well as context and reasoning models that have been used to implement them. A discussion highlights the trends emerging from the scientific literature to select the most appropriate model to implement AAL systems according to the collected data and the services provided.
Use of educational platforms and student academic stress during COVID-19

This study aims to analyse the effects of the use of educational platforms and student academic stress generated during periods of social confinement by COVID-19. The convergent research design was used in two sections. In the first section, 160 validated and reliable surveys were administered to students to ascertain and contrast the quali-quantitative results. The second section consisted of 10 interviews with students at a Peruvian university, in which the stressful situation experienced by the students was revealed through interpretative hermeneutic analysis. The results show that there is no relationship between the use of educational platforms and academic stress among university students. The symptomatology presented at this stage is casual and temporary.
Overview of Image Encryption Based on Chaotic System

The upload and download of image data is a huge flow of traffic among internet, which means sensitive information are more vulnerable to be stolen and to be attacked by attackers. Therefore, it is urgently needed an image protection mechanism, namely image encryption schemes. Many image encryption schemes have been developed. Most of them adopt confusion-diffusion model by separating adjacent pixels and then spreading random substitution through all encrypted images. In particular, chaos-based algorithms are supportive to this structure. It is attractive to researchers owing to the unsteadiness and ramification structures. Chaotic maps are increasingly important in secure telecommunication because the security level of chaos-based algorithms are easily affected by the characteristic of chaotic maps. Generally, chaotic maps can be classified into one-dimensional (1D) and hyperdimensional (HD). In this paper, various kind of 1D and HD chaotic maps and their corresponding image encryption schemes have been reviewed. Based on the comparison results, related fields within the image encryption schemes have been discussed to promote practical application and make clear of the optimization direction in the sections of conclusion and future work.
Increasing Security of Database During Car Monitoring

Nowadays, a vast data flow causes quite a problem in several sectors. One of the issues related to information growth and data volume is their appropriate storage and the securing of the data processing related security and its protection. In this era, where the data are a very sensitive commodity, an attack or data loss can mean losing user trust. Various researchers have dealt precisely with these ideas. They figured out several effective methods of preventing data loss and how to reduce attack amount either on a database or on a web site. Big data, which currently has a significant role in many statistics, are susceptible to various data losses, steal, or deletion. Possible data deletion can lead to a wrong decision about a transport situation prediction task, inaccurate result in decision support, or even to lousy diagnosis prediction in medicine. This vast amount of the problems led to the significant data amount and their security problem solution. Our article relates to data security in situations with substantial real data amount in time, which are entering the system. We suggest effective data distribution methods and a mechanism for data transfer from a database to data storage according to individual records’ time parameters.
Cybersecurity Attacks: Regulatory and Practical Approach Towards Preventing Data Breach and Cyber-Attacks in USA

Increased cyber-attacks on the United States of America cyberspace has raised a lot of concerns on the integrity of the US cyberspace. The US government recently declared a state of emergency on cyber-attacks after its largest pipeline and meat supply companies were hit by a ransomware attack. These recent attacks show that, in addition to targeting government infrastructures, hackers now target key private companies and demand huge ransomware payments as consideration for not disclosing sensitive information and trade secrets accessed from the attack. This was the case with the attack on Colonial pipeline, who reportedly paid the hackers who shut down their systems the sum of $5million in ransom . The safety of the US cyberspace is a pre-requisite for a sustainable economic development in the US. Acknowledging this fact, the US Homeland security, in its publication, noted that “our daily life, economic vitality, and national security depend on a stable, safe, and resilient cyberspace” . It is important to note that the efforts of the US Homeland Security towards preventing cybersecurity attacks relates mainly to attacks targeted at federal government networks and critical infrastructure . The effect of this is that private companies are generally responsible to develop the capacity to protect their networks against cyber-attacks. Addressing the recent attacks on private companies, the US Senate Armed Services Committee during a hearing held on June 23, 2021, noted the lack of capacity on the part of private companies to withstand cyber-attacks . This acknowledgement came after President Biden had cautioned private companies to develop necessary infrastructure required to prevent future attacks. The Senate Committee also reinforced the need for the federal government to extend its cybersecurity prevention infrastructure to protect the entire country, including the private sector. To achieve a unified approach towards fighting cyber-attacks in the US, there is need for some level of dialogue and collaboration between the federal government and the private sector. The federal government must consider introducing laws and policies that will set the relevant standards required by companies to prevent cyber-attacks.
Automated Atlas-Based Segmentation of Breast Region in 3-D Magnetic Resonance Imaging (MRI) Using FCM Method

In this paper, an automated atlas-based segmentation approach for segmenting breast region in 3-D Magnetic Resonance Image (MRI) is proposed. This algorithm combines probabilistic atlas and atlas selection approaches and utilizes Fuzzy C-Means (FCM) classifier to classify atlases. Due to fuzzy nature of mentioned classifier, each pixel belongs to different classes by different membership values and sum of membership values which is derived from probabilistic theory, equals to 1. So, sum of the membership values for all classes of each data never equals to 0. Thus, there are not any empty classes. Proposed algorithm by utilizing 2240 MR images which are related to 40 women and are imaged in axial position, was evaluated. To represent similarities between automatic and manual segmentation results, average of Dice coefficients, average of Jaccard coefficients, average of FN and average of FP were calculated and obtained as 0.9166, 0.8481, 0.0816 and 0.0457, respectively.
Green power aware approaches for scheduling independent tasks on a multi-core machine

The energy consumption of large Information and Communications Technology structures such as data and computation centers along with the corresponding carbon footprint are on the rise. Green computing has become an indispensable solution to face the resulting economical and environmental challenges. Powering these centers with renewable energy sources is however a challenge since these sources cannot guarantee a constant power supply due to their fluctuating power production. We here tackle the problem of scheduling independent tasks on a multi-core machine within a predicted renewable power envelope that varies over time. We evaluate the complexity of different instances of the problem from a theoretical point of view. We propose several heuristics, including genetic algorithms, and we conduct experiments to assess their performance. For some particular cases we compare the performance of these heuristics to optimal solutions.
HUBUNGAN KEBIASAAN BELAJAR DAN DISIPLIN BELAJAR DENGAN HASIL BELAJAR IPS

Abstrak&#x0D; Tujuan penelitian ini adalah untuk menguji hubungan kebiasaan belajar dan disiplin belajar dengan hasil belajar IPS siswa kelas IV SDN Gugus Sadewa Kecamatan Petanahan Kabupaten Kebumen. Penelitian ini adalah penelitian kuantitatif jenis korelasi. Teknik sampel yang digunakan adalah Proportional Random Sampling sebanyak 103 siswa. Teknik pengumpulan data menggunakan angket, dokumentasi, dan wawancara. Teknik analisis data dengan analisis statistik deskriptif dan analisis product moment. Hasil penelitian menunjukkan bahwa: (1) terdapat hubungan yang positif dan signifikan antara kebiasaan belajar dengan hasil belajar IPS, nilai rhitung = 0,749 dan termasuk kategori kuat serta berkontribusi sebesar 54,2%; (2) terdapat hubungan yang positif dan signifikan antara disiplin belajar dengan hasil belajar IPS, nilai rhitung = 0,777 dan termasuk kategori kuat serta berkontribusi sebesar 57,6%; (3) terdapat hubungan yang positif dan signifikan antara kebiasaan belajar dan disiplin belajar secara bersama-sama dengan hasil belajar IPS, nilai rhitung = 0,790 termasuk kategori kuat serta berkontribusi sebesar 62,3%. &#x0D; Abstract&#x0D; The purpose of this research was to examine the correlation between study habits and learning discipline with social studies learning outcomes of 4th grade students in Cluster Sadewa Elementary School Petanahan Kebumen. This is a quantitative research correlation design. The sample technique was proportional random sampling of 103 participants. Data collection technique were questionnaires, interview, and documentation. These data were analyzed by having descriptive statistic analyzed and product moment analyzed. The result of this research showed that (1) there was a positive and significant correlation between study habits and social studies learning outcomes, rcount = 0.749 which included in strong category and contribution of 54.2%; (2) there was a positive and significant correlation between learning discipline and social studies learning outcomes, rcount = 0.777 which included in strong category and contribution of 57.6%; (3) there was a positive and significant correlation study habits and learning discipline toward social studies learning outcomes, rcount = 0.790 which included in strong category and contribution of 62.3%.
A Data Center Demand Response Policy for Real-World Workload Scenarios in HPC

Demand response programs offer an opportunity for large power consumers to save on electricity costs by modulating their power consumption in response to demand changes in the electricity grid. Multiple types of such programs exist; for example, regulation service programs enable a consumer to bid for a sustainable amount of power draw over a time period, along with a reserve amount they are able to provide at request of the electricity service provider. Data centers offer unique capabilities to participate in these programs since they have significant capacity to modify their power consumption through workload scheduling and CPU power limiting. This paper proposes a novel power management policy and a bidding policy that enable data centers to participate in regulation service programs under real-world constraints. The power management policy schedules computing jobs and applies server power-capping under both the constraints of power programs and the constraints of job Quality-of-Service (QoS). Simulations with workload traces from a real data center show that the proposed policies enable data centers to meet both the requirement of regulation service programs and the QoS requirement of jobs. We demonstrate that, by applying our policies, data centers can save their electricity costs by 10% while abiding by all the QoS constraints in a real-world scenario.
RF-RISA: A novel flexible random forest accelerator based on FPGA

Recently, FPGA has been utilized to accelerate the Random Forest prediction process to meet the speed requirements of real-time tasks. However, the existing accelerators impose restrictions on the parameters of the accelerated model. The accelerators have to be reconfigured to adapt to a model whose parameters exceed the predefined restrictions. When these accelerators are applied in the scenarios where the model updates or switches frequently, non-trivial time overhead and maintenance costs may be introduced. To solve the above problem, a flexible accelerator RF-RISA, Random Forest Reduced Instruction Set Accelerator, is presented in this paper. Compared with the existing accelerators, RF-RISA eliminates all the restrictions by decoupling the model parameters from its hardware implementation. Specifically, RF-RISA encodes the information of the model into a group of instructions, then the instructions are stored in the memory rather than are hardcoded in the hardware. Meanwhile, a mapping scheme is proposed to map the instructions into the memory dynamically. Finally, a new hardware architecture is designed to support the pipelined computing. The theoretical analysis and experimental results show that the proposed RF-RISA can accelerate a wide range of RF models without reconfiguration. At the same time, it can achieve the same throughput as the state-of-the-art.
Deriving Contextualised Semantic Features from BERT (and Other Transformer Model) Embeddings

Models based on the transformer architecture, such as BERT, have marked a crucial step forward in the field of Natural Language Processing. Importantly, they allow the creation of word embeddings that capture important semantic information about words in context. However, as single entities, these embeddings are difficult to interpret and the models used to create them have been described as opaque. Binder and colleagues proposed an intuitive embedding space where each dimension is based on one of 65 core semantic features. Unfortunately, the space only exists for a small data-set of 535 words, limiting its uses. Previous work (Utsumi, 2018, 2020; Turton et al., 2020) has shown that Binder features can be derived from static embeddings and successfully extrapolated to a large new vocabulary. Taking the next step, this paper demonstrates that Binder features can be derived from the BERT embedding space. This provides two things; (1) semantic feature values derived from contextualised word embeddings and (2) insights into how semantic features are represented across the different layers of the BERT model.