Reviewerly/exHarmony huggingface dataset:
Image enhancement based on multi-scale transform domain technique for visual surveillance application

Image enhancement refers to processing images to make them more suitable for display or further image analysis. An enhancement procedure improves future automated image-processing steps (detection, segmentation, and recognition) for efficient system decision-making. This paper presents a new method of visual surveillance image enhancement that improves the visual quality of digital images that exhibit dark shadows due to the limited dynamic range of imaging. The proposed method base on 3-D block-rooting multi-scale transform domain technique, comprising: finding similar blocks in the image by block-matching; block-grouping for different block sizes; applying 3-D block-matching parametric image enhancement; calculating the quality measure of enhancement; optimizing parameters of image enhancement method through the quality measure of enhancement; fusing different enhanced images. Experimental results from test data set show that the proposed technique performs well and can improve the quality during the sharpening of the image details.
Deep Speaker Embedding with Multi-Part Information Aggregation in Frequency-Time Domain for ASV

Automatic speaker verification (ASV) is to verify the identity of speaker from a given speech utterance without direct supervision from outside entities. Majority of recent ASV systems with deep speaker embedding apply temporal pooling or similar techniques for frame-level feature aggregation in time domain. In this paper, we propose a deep speaker embedding network for adaptively modelling and fusing multi-part information in frequency-time domain, using a modified ResNet-SO to encode acoustic features into global information, a proposed multi-part information aggregator to distinguish global information and different part features for aggregating them with adaptive weight pooling to unified utterance-level embedding descriptors. More-over, we design a privacy-preserving manner and preliminarily implement it in prototype system. Experiments are conducted on three scale datasets. We demonstrate that the presented multi-part information aggregator with adaptive weight pooling is superior for producing discriminative and robust utterance-level embedding descriptors. We also show that our network achieves state-of-the-art performance by a significant margin on the popular VoxCelebl while requiring fewer parameters than previous approaches.
Attitudes of an international student cohort to the Quizlet study system employed in an advanced clinical health care review course

Computer-based learning applications and mobile technology have transformed many aspects of the educational experience over the last decade, producing software aimed at improving learning efficiency and streamlining the presentation of course materials. One such class of software, purpose-created to take advantage of spaced learning and spaced testing principles, are electronic flashcard applications. We provide a perspective on the novel use of the Quizlet flashcard application in a tertiary educational setting. To reduce cognitive load for international graduate dental students taking a pharmacology review course, we implemented Quizlet, which integrates both spaced learning and self-testing, to improve the student learning experience. This study assessed students' perceptions of the Quizlet flashcard system in a student cohort comprised of two consecutive years' classes (n = 51 students in total). Results indicated broad acceptance of Quizlet based on ease of use of the software and ease of study of the material. Our data provide insight into the use of this common software in a professional healthcare tertiary education setting and further demonstrate the successful application of electronic flashcards for a mixed international student cohort. Further research should include an assessment of the impact of flashcard on long-term knowledge retention in this setting.The online version contains supplementary material available at 10.1007/s10639-022-11371-3.
Convolutional spectral kernel learning with generalization guarantees

Kernel methods are powerful tools to capture nonlinear patterns behind given data but often lead to poor performance on complicated tasks compared to convolutional neural networks. The reason is that kernel methods are still shallow and fully connected models, failing to reveal hierarchical features and local interdependencies. In this paper, to acquire hierarchical and local knowledge, we incorporate kernel methods with deep architectures and convolutional operators in a spectral kernel learning framework. Based on the inverse Fourier transform and Rademacher complexity theory, we provide the generalization error bounds for the proposed model and prove that under suitable initialization, deeper networks lead to tighter error bounds. Inspired by theoretical findings, we finally completed the convolutional spectral kernel network (CSKN) with two additional regularizers and an initialization strategy. Extensive ablation results validate the effectiveness of non-stationary spectral kernel, multiple layers, additional regularizers, and the convolutional filters, which coincide with our theoretical findings. We further devise a VGG-type 8-layers CSKN, and it outperforms the existing kernel-based networks and popular CNN models on the medium-sized image classification tasks.
The Moderating Role of Thoughtfully Reflective Decision-Making on the Relationship between Information Security Messages and SMiShing Victimization: An Experiment

Security messages, as a form of information security awareness training, are designed to encourage individuals to make an informed security decision, reducing their susceptibility to online victimization. To date, no known study has assessed the effectiveness of security messages or whether the effectiveness of these messages varies based on the recipients' characteristics. Using a randomized controlled trial, this study tests the efficacy of security messages on reducing SMiShing victimization, then assesses whether thoughtfully reflective decision-making (TRDM) moderates the effect of these messages on victimization by launching simulated attacks against participants of the study. Findings reveal that neither security messages nor TRDM directly affect SMiShing victimization. However, security messages effectively prevent victimization when the message recipient has higher TRDM levels. The interaction between TRDM and security messages demonstrates the relevance of criminological theory and the importance of interdisciplinary scholarship in understanding how human behavior influences the effectiveness of cybersecurity practices.
Autonomy and Turnover in Distributed Software Development Projects: a Systematic Literature Review

Distributed teams have become a tendency among software companies. However, companies that apply Distributed Software Development (DSD) suffer from high developer turnover. Research shows that autonomy factors can mitigate or prevent team turnover. In this research, we investigate the role of autonomy on turnover in distributed teams through a Systematic Literature Review (SLR). Our findings revealed a set of autonomy dimensions that influence turnover in DSD projects. Besides, we identified the main factors related to autonomy linked to turnover in DSD projects. These findings provide software companies and organizations with a better understanding of the importance of autonomy factors to battle the DSD projects turnover problem.
Benefits and Limitations of Using Modern Technologies for Teaching STEM Subjects to Students with Intellectual Disabilities

The paper presents results of a case study that aimed to investigate teachers’ opinion regarding the benefits that Augmented Reality and Gamification can bring when educating students with typical development disability and/or intellectual disability. The study concluded that the use of modern technologies in the education of students with intellectual disabilities is extremely effective given that it is used sparingly, adapted to the students’ needs and supported by teachers.
Hyper-Tuned Ensemble Machine Learning Model for Credit Card Fraud Detection

Credit card fraud detection is a matter of concern in online financial transactions. This can be done with the help of the Machine Learning (ML) techniques trained by the huge data. Sometimes it is possible that data may suffer from the class imbalance which itself is an issue. To handle this class imbalance issue, this research study proposes a Synthetic Minority Oversampling Technique (SMOTE) with different ML models. After class balancing, an ensemble model is proposed which is hyper-tuned with a metaheuristic approach named Particle Swarm Optimization (PSO). Here, PSO is used to obtain the global optimum solution. The proposed model is evaluated with the performance matrices Precision (P), Recall (R), F1-Measure, and Accuracy (ACC). The model is evaluated and compared with baseline ML models utilized for ensemble considering with and without S MOTE. The results indicate that the proposed model is robust and good enough for the detection of fraudulent transactions.
Knowledge-aware document summarization: A survey of knowledge, embedding methods and architectures

Knowledge-aware methods have boosted a range of natural language processing applications over the last decades. With the gathered momentum, knowledge recently has been pumped into enormous attention in document summarization, one of natural language processing applications. Previous works reported that knowledge-embedded document summarizers excel at generating superior digests, especially in terms of informativeness, coherence, and fact consistency. This paper pursues to present the first systematic survey for the state-of-the-art methodologies that embed knowledge into document summarizers. Particularly, we propose novel taxonomies to recapitulate knowledge and knowledge embeddings under the document summarization view. We further explore how embeddings are generated in embedding learning architectures of document summarization models, especially of deep learning models. At last, we discuss the challenges of this topic and future directions.
A Taxonomy of the Risks and Challenges of Embracing Blockchain Smart Contracts in Facilitating Renewable Electricity Transactions

Environmental imperatives and global energy supply crisis are driving interests in the renewable electricity industry. Yet, the revenue risks of renewable generators make it challenging for them to attract finance from traditional investors. Several mechanisms have been proposed to minimize these risks but have their limitations. Blockchain smart contract arrangements have emerged as a new marketplace, addressing the shortcomings of these traditional electricity hedging mechanisms. However, they have their peculiar challenges, potentially impeding their mainstream adoption in the renewable electricity industry. Hence, this paper develops a novel taxonomy of the risks and challenges of embracing blockchain smart contracts in facilitating renewable electricity transactions. Examining these issues indicates that the adoption of blockchain smart contracts in the renewable energy industry can be facilitated by cooperation and partnerships between technology developers and researchers, renewable energy companies, as well as governments.
Multi-task Ranking with User Behaviors for Text-video Search

Text-video search has become an important demand in many industrial video sharing platforms, e.g., YouTube, TikTok, and WeChat Channels, thereby attracting increasing research attention. Traditional relevance-based ranking methods for text-video search concentrate on exploiting the semantic relevance between video and query. However, relevance is no longer the principal issue in the ranking stage, because the candidate items retrieved from the matching stage naturally guarantee adequate relevance. Instead, we argue that boosting user satisfaction should be an ultimate goal for ranking and it is promising to excavate cheap and rich user behaviors for model training. To achieve this goal, we propose an effective Multi-Task Ranking pipeline with User Behaviors (MTRUB) for text-video search. Specifically, to exploit the multi-modal data effectively, we put forward a Heterogeneous Multi-modal Fusion Module (HMFM) to fuse the query and video features of different modalities in adaptive ways. Besides that, we design an Independent Multi-modal Input Scheme (IMIS) to alleviate competing task correlation problems in multi-task learning. Experiments on the offline dataset gathered from WeChat Search demonstrate that MTRUB outperforms the baseline by 12.0% in mean gAUC and 13.3% in mean [email protected] We also conduct live experiments on a large-scale mobile search engine, i.e., WeChat Search, and MTRUB obtains substantial improvement compared with the traditional relevance-based ranking model.
A method for diagnosing rolling bearing faults based on SDAE-ADHKELM

Abstract Aiming to extract useful features from bearing signals for fault classification, an intelligent fault diagnosis method is proposed with a stacked denoising auto-encoder (SDAE) and adaptive deep hybrid kernel extreme learning machine (ADHKELM). The deep network architecture of the SDAE is used automatically to extract deeply important features, and a new HKELM is constructed by combining a polynomial with a wavelet kernel function to overcome a single kernel function not being universal. After that a DHKELM, from stacking multiple HKELMs, and the sparrow search algorithm are introduced to iteratively determine the optimal value of core hyper-parameter combinations of the DHKELM to generate the final fault classifier ADHKELM to enhance the performance of the model. Two experimental verification results show that the SDAE-ADHKELM has better fault classification precision, robustness and generalizability than other related methods.
Photonic reservoir computing with non-linear memory cells: interplay between topology, delay and delayed input

Photonic reservoir computing is an emerging topic due to the possibility to realize very fast devices with minimal training effort. We will discuss the reservoir computing performance of memory cells with a focus on the impact of delay lines and the interplay between coupling topology and performance for various benchmark tasks. We will further show that additional delayed input can be beneficial for reservoir computing setups in general, as it provides an easy tuning parameter, which can improve the performance of a reservoir on a range of tasks.
Designing a Component-Based Throttled Load Balancing Algorithm for Cloud Data Centers

Cloud services are accessed from different geographical locations where client migration or switching from one server to another based on the loads is a common phenomenon. One of the most critical challenges the cloud data centers face is managing the loads over geographically dispersed data centers and their virtual machines (VMs). VMs need to be balanced with the varied loads or dynamics of traffic. There are possibilities of the highest loads to be tolerated by the VMs over the cloud servers without crashing. Load balancing issues are managed by load balancing algorithms. Load balancing algorithms have varied issues of efficiency due to certain parameters like the capability of the lowest resource utilization, response time, higher overhead while checking the idle or normal nodes, and many others. Throttled load balancing algorithm manages loads of the virtual machines by dividing the virtual machines into two segments, that is, “available” and “free.” To do this, the throttled algorithm uses a single component to assign the virtual machines and other tasks. The throttled algorithm utilizes only the first VMs available, the next, and so on. These strategic issues most often degrade the performance of the applied load balancing algorithm. Such issues create a curiosity to enhance this algorithm’s performance for efficiently managing the dynamic loads of the cloud VMs. This research paper proposes a component-based throttled load balancing algorithm with VM reader, free VM holder, and free VM manager components. The VM reader component reads all available VMs. The free VM component holds free VMs temporarily until they are moved to the free VM manager component. For the performance test, the cloud analyst simulation tool was used. Based on the comparative analysis with the other five popularly used load balancing algorithms, the component-based algorithm’s performance is significantly enhanced. The proposed algorithm resulted in 325.30-microsecond response time and 27.12-microsecond processing time by the closest data center service broker policy. The newly proposed “component-based throttled load balancing algorithm” is found to be better than the existing throttled algorithm and the other five selected algorithms in terms of response time, processing time, and resource utilization.
Exploration of Erdaohe Silver Polymetallic Deposit in Inner Mongolia based on Induced Polarization Method

The exploration of Erdaohe Cu-Ag-Pb-Zn deposit in Zhalantun City, Inner Mongolia has entered the stage of blind exploration. It is a difficult problem at this stage whether common geophysical methods can find anomalies caused by 300∼1000 m or deeper Pb-Zn deposits in this area. Whether the problem is solved or not has a strong practical significance for the current and future prospecting work in this area. According to the theory of induced polarization sounding in time domain, it is considered in this paper that when the parameters such as high power, suitable device, and time delay are well coupled with the deep blind ore, it is possible to find anomalies caused by deep lead-zinc ore larger than 300 m. In order to prove the feasibility of the deep application of this method, the high-power induced polarization sounding experiment with different devices and different power supply cycles was carried out in Erdaohe copper-silver lead-zinc mine. The test results show that by adopting the measurement method of the encrypted equipotential device and selecting the appropriate polar distance, when the power supply period is 32 s, the time delay is 200 s and the sampling width is 200 s, a good deep lead-zinc mine anomaly can be found. The equipotential symmetrical quadrupole sounding MN/AB = 1/10 and the depth correction coefficient is 0.41 for inversion can accurately determine the anomaly location, which is worthy of popularization and application in this area. The ore bodies in this area are controlled by structures and distributed in lenticular layers along the fault footwall interlayer fracture zone. The ore bodies may change with the occurrence of the fracture surface. The characteristics of low resistivity, low slow, and high polarization of IP and single peak anomaly of IP sounding are important signs of lead-zinc prospecting in this area.
Techno-Economic Analysis of a Rooftop Grid-connected Photovoltaic Solar System: A case study of Jomo Kenyatta University of Agriculture and Technology (SAJOREC)

Solar photovoltaic (PV) system installation can play an essential role in reducing conventional energy consumption and carbon footprint, helping create a green and sustainable environment worldwide. Techno-economic assessment of installing a grid-tied rooftop solar PV (RTSPV) plant at SinoAfrica Joint Research Center (SAJOREC), part of Jomo Kenyatta University of Agriculture and Technology (JKUAT), was done. The energy demand profile for 2019 for the site was determined, and it was found that a 31.7 kWp RTSPV system could meet the demand. The estimated energy produced by the RTSPV system was 4S.S2SMWh/year. The specific production, capacity utilization factor, and the performance ratio obtained were 1541 kWh/kWp/year, 17.5 %, and S3%, respectively. The economic analysis of the project showed that the total project cost was USD 30066. S5. The Levelized Cost of Electricity (LCOE) of 0.10 USD/kWh and Discounted Payback Period ($\mathrm{T}_{\mathrm{D}\mathrm{P}}$) of 4 years obtained indicated that the development of a grid-connected rooftop solar PV system for the JKUAT, SAJOREC area would be technically and economically viable.
An Analysis of Pre-Service Teachers’ Needs for Teacher Competence and Teacher Education in the Era of the 4th Industrial Revolution: Focusing on the Case of a University

The purpose of this study was to analyze the perceptions and needs of pre-service teachers for teacher competence and teacher education in the era of the 4th industrial revolution. A total of 106 pre-service teachers participated in this study. They were asked to complete a questionnaire. The data collected from the survey was analyzed using the paired sample t-test, the Borich priority formula, and the Locus for Focus Model.&#x0D; The main results of this study are as follows. First, the required level was statistically significantly higher than the current performance level in all areas of teacher competence. Second, the most highly needed teacher competence was the ability to provide an integrated learning experience. In addition, knowledge and application ability of intelligent information, ethical awareness of intelligent information technology, ability to organize inquiry-type learning activities, ability to provide and perform learning experiences linked to social values, ability to collaborate with experts in other fields or outside the school, and ability to cope with emotional problems were identified as highly needed competencies. Third, pre-service teachers showed a slightly higher perception than the middle on whether the current teacher training education is appropriate for the new era. Also, pre-service teachers reported that the teacher training education have had problems such as lack of connection with the school field, knowledge-oriented teaching methods, and insufficient curriculum for the new era. Furthermore, they recognized that new teaching methods and media application education, experience-centered curriculum, character education and self-management education, and class research-related subjects were necessary for developing teacher competencies in the 4th industrial revolution.
Split body: Extending self-location by splitting a body left and right

Is it possible to feel part of one’s own body even when the body part is separated from the body? If so, we could exist in different locations by splitting the self-body and using our body in each location. In a study in which the illusion that two bodies are one’s own body was induced using virtual reality (VR), the participants felt as if they were at two locations through the two bodies. However, this illusion was weak and reported only subjectively. We hypothesized that this was because two presented bodies moved synchronously with one participant’s movement or the simultaneous stroking of one participant’s body and two presented bodies switched their attention and weakened body ownership. In this study, we investigated whether splitting one body into left and right in VR could induce body ownership and extend the self-location while maintaining a one-to-one correspondence between the participant’s body and the presented body. The results showed that weaker body ownership was induced in the split body than in the normal body and self-location was extended to the right side of the body. The participants did not report a sense of having more than one body but reported a sense of body spreading, suggesting that the split bodies were perceived as a single body extending to the right side.