Reviewerly/exHarmony huggingface dataset:
Performance Evaluation of Distributed Linear Regression Kalman Filtering Fusion

This article studies the performance evaluation of distributed linear regression Kalman filtering fusion for nonlinear systems. Sufficient conditions are established for the convergence of the centralized fusion (CF) under the assumption of bounded estimation error covariance, and a measure of performance is derived from the convergence conditions. By the performance analysis, it can be found that the CF has a better performance than the distributed fusion with feedback, especially at the beginning of the estimation. Moreover, the performance of the local estimator can be improved by receiving the fused estimate from the fusion center, which is different from the fusion estimation in linear systems. Finally, by simulations of a target tacking example, the comparisons of the centralized fusion and the distributed fusion with and without feedback are presented to show the accuracy of the performance analysis.
Iterated Adaptive Entropy-Clip Limit Histogram Equalization for Poor Contrast Images

Poor contrast and hidden details in images owing to low camera quality, bad illumination, and poor setting for environment capture are the main challenges in the contrast enhancement process.Histogram equalization (HE) based methods are commonly used to reduce these problems.However, resultant images produced by existing methods often look unnatural, are affected by unwanted artifacts, and suffer from washed-out effects.Therefore, this study proposes a local type of HE based contrast enhancement method called iterated adaptive entropy-clip limit histogram equalization (IAECHE).First, IAECHE divides the image into multiple sub-images to fine-tune the local details.Each sub-image will be applied with a different number of enhancement iterations, which are adaptively determined by IAECHE.This process ensures the best enhanced resultant sub-images, which are then merged to produce the final resultant image.A quantitative analysis of 820 images from three databases shows that IAECHE successfully produces better resultant images with better contrast, clearer details and less affected by noises.The proposed method also successfully preserves the details and structures of the images compared to several state-of-the-art methods.These findings are supported and validated by quantitative analysis.IAECHE produces high average values of peak signal-to-noise ratio, discrete entropy, absolute mean brightness error, structure similarity index, and contrast improvement index.From the analyses, the proposed IAECHE technique is proven to be capable of enhancing images' contrast, details, and structure.Thus, it has high potential for applications in the machine vision, industrial, and medical imaging fields.
Consortium Blockchain Smart Contracts for Musical Rights Governance in a Collective Management Organizations (CMOs) Use Case

Private and permissioned blockchains are conceptualized and mostly assembled for fulfilling corporations’ demands and needs in the context of their own premises. This paper presents a complete and sophisticated end-to-end permissioned blockchain application for governance and management of musical rights endorsed by smart contract development. In a music industry use case, this disclosed solution monitors and regulates conflicting musical rights of diverse entities under a popular permissioned distributed ledger technology network. The proposed implementation couples various and distinct business domains across the music industry organizations and non-profit blockchain associations.
AgglutiFiT: Efficient Low-Resource Agglutinative Language Model Fine-Tuning

Text classification tends to be difficult when data are inadequate considering the amount of manually labeled text corpora. For low-resource agglutinative languages including Uyghur, Kazakh, and Kyrgyz (UKK languages), in which words are manufactured via stems concatenated with several suffixes and stems are used as the representation of text content, this feature allows infinite derivatives vocabulary that leads to high uncertainty of writing forms and huge redundant features. There are major challenges of low-resource agglutinative text classification the lack of labeled data in a target domain and morphologic diversity of derivations in language structures. It is an effective solution which fine-tuning a pre-trained language model to provide meaningful and favorable-to-use feature extractors for downstream text classification tasks. To this end, we propose a low-resource agglutinative language model fine-tuning AgglutiFiT , specifically, we build a low-noise fine-tuning dataset by morphological analysis and stem extraction, then fine-tune the cross-lingual pre-training model on this dataset. Moreover, we propose an attention-based fine-tuning strategy that better selects relevant semantic and syntactic information from the pre-trained language model and uses those features on downstream text classification tasks. We evaluate our methods on nine Uyghur, Kazakh, and Kyrgyz classification datasets, where they have significantly better performance compared with several strong baselines.
Memory-Based Jitter: Improving Visual Recognition on Long-Tailed Data with Diversity in Memory

This paper considers deep visual recognition on long-tailed data. To make our method general, we tackle two applied scenarios, i.e. , deep classification and deep metric learning. Under the long-tailed data distribution, the most classes (i.e., tail classes) only occupy relatively few samples and are prone to lack of within-class diversity. A radical solution is to augment the tail classes with higher diversity. To this end, we introduce a simple and reliable method named Memory-based Jitter (MBJ). We observe that during training, the deep model constantly changes its parameters after every iteration, yielding the phenomenon of weight jitters. Consequentially, given a same image as the input, two historical editions of the model generate two different features in the deeply-embedded space, resulting in feature jitters. Using a memory bank, we collect these (model or feature) jitters across multiple training iterations and get the so-called Memory-based Jitter. The accumulated jitters enhance the within-class diversity for the tail classes and consequentially improves long-tailed visual recognition. With slight modifications, MBJ is applicable for two fundamental visual recognition tasks, i.e., deep image classification and deep metric learning (on long-tailed data). Extensive experiments on five long-tailed classification benchmarks and two deep metric learning benchmarks demonstrate significant improvement. Moreover, the achieved performance are on par with the state of the art on both tasks.
AutoHash: Learning Higher-order Feature Interactions for Deep CTR Prediction

Feature combinations are essential for the success of many web applications, such as personalised recommendation and online advertising. State-of-the-art methods usually model explicit feature interactions to help neural networks reduce the number of parameters and achieve better performance. However, their explicit feature interactions are often restricted to the second-order due to computational complexity. In this work, we propose efficient ways to represent explicit high-order feature combinations as well as prune redundant features in the mean time. To begin with, we make novel use of the Count Sketch algorithm within a DNN classifier such that high-order feature combinations can be compactly represented. After that, to combat the problem of redundant features which degrade the prediction performance, we introduce an adaptive hashing algorithm, AutoHash, which can automatically select meaningful features to interact at high orders according to the specific dataset in question. This is an AutoML approach. Experiments on three well-known public datasets demonstrate that AutoHash is significantly superior to state-of-the-art methods. Meanwhile, due to its efficient scheme of automatically selecting useful high-order feature interactions, AutoHash has less model complexity and can be trained in an end-to-end manner with less training time than state-of-the-art methods.
TSPR: Deep network-based blind image quality assessment using two-side pseudo reference images

We present a deep network-based blind image quality assessment (BIQA) using two-side pseudo reference (TSPR) images. Different from a traditional reference with the perfect quality, two opposite images are introduced as pseudo references, which are generated via a restoration model and a degradation model, respectively. The bilateral distance (error) maps between the TSPR images and the pristine distorted images are first extracted, and then the regression neural network is utilized to simulate how the human brain processes information, i.e., dialectical thinking, in which both positive information and negative information are taken into consideration for predicting the quality of images. To avoid the complexity and differentiation of each optimization objective in the end-to-end strategy, the step-by-step strategy is adopted to make the model clearer and more explainable. Finally, four common distortions on the LIVE-2D, TID2013, and CSIQ datasets are evaluated using the proposed model, and the experiment results demonstrate the effectiveness and robustness of the algorithm, which delivers superior performance over the state-of-the-art NR methods.
Dynamic power management for value-oriented schedulers in power-constrained HPC system

High performance computing (HPC) systems are confronting the challenge of improving their productivity under a system-wide power constraint in the exascale era. To measure the productivity of an HPC job, researchers have proposed to assign a monotonically decreasing time-dependent value function, called job-value, to that job. These job-value functions are used by the value-based scheduling algorithms to maximize the system productivity where system productivity is the accumulation of job-value for the completed jobs. In this study, we first show that the relative performance of the competing state-of-the-art static power allocation strategies interchange based on the level of the power constraint when applied to the value-based algorithms. We then investigate the limitations of these static strategies by relating the job completion rate to the resource utilization, and expose that there is non-negligible amount of unused resources for the scheduler to utilize. Even though the system is oversubscribed, these unused resources are insufficient to schedule new high-value jobs. Based on this observation, we propose a novel dynamic power management strategy for the value-based algorithms. Our dynamic allocation policy maximizes the system productivity, resource utilization, and job completion rate by utilizing application power-performance models to reallocate power from running jobs to newly arrived jobs. We simulate a large-scale system that uses job arrival traces from a real HPC system. We demonstrate that the dynamic-variant of each value-based algorithm earns up to 16% higher productivity and completes 13% more jobs compared to its static variants when power becomes a highly constrained resource in the system.
The Effects of Transformational Leadership and Job Satisfaction on Commitment to Organisational Change: A Three-Component Model Extension Approach

Research Aims: The present study aimed to examine the relationship between transformational leadership, job satisfaction and employee commitment to organisational change. Design/Methodology/Approach: Extension of a three-component model was employed. The study sample consisted of 474 employees in organisations undergoing a significant organisational change in Vietnam. The data were analysed using a structural equation model (SEM). Research Findings: The study results show that transformational leadership has a positive and significant influence on job satisfaction and organisational commitment to change (affective commitment, normative commitment, continuance commitment). Job satisfaction is found to be positively and significantly related to employee affective commitment and normative commitment to organisational change. Theoretical Contribution/Originality: This study indicated the critical role of transformational leadership and job satisfaction on the commitment to organisational change in the context of an emerging economy. Managerial Implications in the Southeast Asian Context: Organisations should maintain employees' positive attitudes and behaviour by applying the transformational leadership style and ensuring job satisfaction, which plays a central role in the organisation by orienting employee psychology, motivation and positive behaviour to change. Research limitation & implications: This study has certain limitations due to its sample and self-report questionnaire scale. The research model did not simultaneously test multiple antecedents (e.g., personality, context) and consequences of employee commitment to organisational change. There is a lack of empirical studies addressing the relationship between demographics and antecedents/consequences of commitment to change.
Use of Eye-tracking in Artworks to Understand Information Needs of Visitors

This study examined which accompanying information elements were noticed by visitors while they were looking at artworks, using eye-tracking experiments. First, we conducted an online survey to grasp the types of information that visitors wanted to know, and five elements were obtained. Second, we collected information on these five elements through interviews with one artist. Third, eye-tracking experiments were performed with semi-structured interviews. We set the information delivery media as follows: wall and mobile texts as commonly used in art museums. The results showed that patterns of eye movement of visitors were different according to the information delivery media. Also, we found that there was a correlation between the results of the eye-tracking experiment and visitor interest. This study has limitations in that it is an experiment limited to small sample size and artwork genre; however, it is meaningful in that it was able to grasp the information needs of visitors through eye-tracking.
Tracking the Insider Attacker: A Blockchain Traceability System for Insider Threats

The insider threats have always been one of the most severe challenges to cybersecurity. It can lead to the destruction of the organisation’s internal network system and information leakage, which seriously threaten the confidentiality, integrity and availability of data. To make matters worse, since the attacker has authorized access to the internal network, they can launch the attack from the inside and erase their attack trace, which makes it challenging to track and forensics. A blockchain traceability system for insider threats is proposed in this paper to mitigate the issue. First, this paper constructs an insider threat model of the internal network from a different perspective: insider attack forensics and prevent insider attacker from escaping. Then, we analyze why it is difficult to track attackers and obtain evidence when an insider threat has occurred. After that, the blockchain traceability system is designed in terms of data structure, transaction structure, block structure, consensus algorithm, data storage algorithm, and query algorithm, while using differential privacy to protect user privacy. We deployed this blockchain traceability system and conducted experiments, and the results show that it can achieve the goal of mitigating insider threats.
An Embedded-Based Weighted Feature Selection Algorithm for Classifying Web Document

With the exponential increase in a number of web pages daily, it makes it very difficult for a search engine to list relevant web pages. In this paper, we propose a machine learning-based classification model that can learn the best features in each web page and helps in search engine listing. The existing methods for listing have lots of drawbacks like interfacing the normal operations of the website and crawling lots of useless information. Our proposed algorithm provides an optimal classification for websites which has a large number of web pages such as Wikipedia by just considering core information like link text, side information, and header text. We implemented our algorithm with standard benchmark datasets, and the results show that our algorithm outperforms the existing algorithms.
Bit-Parallel Vector Composability for Neural Acceleration

Conventional neural accelerators rely on isolated self-sufficient functional units that perform an atomic operation while communicating the results through an operand delivery-aggregation logic. Each single unit processes all the bits of their operands atomically and produce all the bits of the results in isolation. This paper explores a different design style, where each unit is only responsible for a slice of the bit-level operations to interleave and combine the benefits of bit-level parallelism with the abundant data-level parallelism in deep neural networks. A dynamic collection of these units cooperate at runtime to generate bits of the results, collectively. Such cooperation requires extracting new grouping between the bits, which is only possible if the operands and operations are vectorizable. The abundance of Data-Level Parallelism and mostly repeated execution patterns, provides a unique opportunity to define and leverage this new dimension of Bit-Parallel Vector Composability. This design intersperses bit parallelism within data-level parallelism and dynamically interweaves the two together. As such, the building block of our neural accelerator is a Composable Vector Unit that is a collection of Narrower-Bitwidth Vector Engines, which are dynamically composed or decomposed at the bit granularity. Using six diverse CNN and LSTM deep networks, we evaluate this design style across four design points: with and without algorithmic bitwidth heterogeneity and with and without availability of a high-bandwidth off-chip memory. Across these four design points, Bit-Parallel Vector Composability brings (1.4× to 3.5×) speedup and (1.1× to 2.7×) energy reduction. We also comprehensively compare our design style to the Nvidia's RTX 2080 TI GPU, which also supports INT-4 execution. The benefits range between 28.0× and 33.7× improvement in Performance-per-Watt.
Towards Blockchain for Suitable Efficiency and Data Integrity of IoT Ecosystem Transactions

This article analyzes the effectiveness of deploying the Blockchain technology in the implementation of the IoT ecosystem database. To this end, we assess the processing efficiency of transactions originated by smart devices and the storeddata integrity. The processing-efficiency evaluation is carried out through queue-theory-based analytical modeling, in which the average time for transaction confirmation is estimated. By its turn, the data-integrity is measured through simulations, where the probability of fraudsters altering already-stored data is estimated. Moreover, the experiments consider a set of scenarios related to different application domains. Final results show that the Blockchain technology may meet IoT efficiency requirements, besides providing adequate data integrity. Lastly, general conclusions and avenues for further research close this article.
Prediction Confidence based Low Complexity Gradient Computation for Accelerating DNN Training

In deep neural network (DNN) training, network weights are iteratively updated with the weight gradients that are obtained from stochastic gradient descent (SGD). Since SGD inherently allows gradient calculations with noise, approximating weight gradient computations have a large potential of training energy/time savings without degrading accuracy. In this paper, we propose an input-dependent approximation of the weight gradient for improving energy efficiency of training process. Considering that the output predictions of network (confidence) changes with training inputs, the relation between the confidence and the magnitude of weight gradient can be efficiently exploited to skip the gradient computations without accuracy drop, especially for high confidence inputs. With a given squared error constraint, the computation skip rates can be also controlled by changing the confidence threshold. The simulation results show that our approach can skip 72.6% of gradient computations for CIFAR-100 dataset using ResNet-18 without accuracy degradation. Hardware implementation with 65nm CMOS process shows that our design achieves 88.84% and 98.16% of maximum per epoch training energy and time savings, respectively, for CIFAR-100 dataset using ResNet-18 compared to state-of-the-art training accelerator.
RETRACTED: Public welfare organization management system based on FPGA and deep learning

Public welfare organization system Promoting social support is one of the government's influence relieving benevolent associations. To qualify as exempt organizations, community or government support, the association exercises should simply replace the network edge, welfare organization is conducive to select another person, and every person should be registering for details. The social ground considered as network partner to the exercise of social, government support element in any case, despite all the affiliation, if the network is advantageous. Costs associated social modernization typically does not include support groups. Long-term support will be used to understand the impact of decisions and related efforts in the political mission exercises. So, the government's social support groups altruistic relationship. A Field-Programmable Gate Array (FPGA), and a Graphics Processing Unit (GPU) to improve the throughput of the cellular neural network. Rather, Field-Programmable Gate Array (FPGA) accelerating the depth learning network is not just one reason, but also because of its ability in energy efficiency, the maximum parallelism. In this article, we review recent prior art deeply accelerated learning networks in the FPGA. We stress the importance of using a variety of techniques to improve the acceleration performance of important features. We also offer suggestions for improving the use of FPGA accelerated cellular neural networks. In this paper, research methods represent FPGA-based accelerator's recent trends in depth learning networks. Therefore, this review is expected to be useful in depth study researchers' direct and efficient hardware accelerator future development.
Awareness, Current Educational State and Educational Requirements of Virtual Reality and Augmented Reality among Nursing Students

Purpose: This study aimed to investigate the awareness, current educational status, and educational requirements of the nursing students regarding virtual reality (VR) and augmented reality (AR). Methods: A total 307 nursing students were included in this study using an online survey. Questionnaires were made to investigate the awareness of VR and AR. The final scale consisted of thirteen items and its validity and reliability were verified. Among the 203 nursing institution, 162 institutions were surveyed regarding education for VR and AR. Results: Among the participants, 99.7% knew about VR and AR, 83.7% had direct experiences, and 17.3% had made relative purchases. The awareness of VR and AR was about 4.35±0.73 (maximum scale of 6). Most participants thought that it would be favorable to use VR and AR for educational purposes in classes once or twice a week. Of the 162 nursing institutions, 30 invested in related facilities, and 13 invested in education in the form of cultural studies, lectures, or programs. Conclusion: The awareness of VR and AR among nursing students was positive and showed need for future education adoption. However, the current educational infrastructure is lacking. Therefore, there is a need to develop relevant educational programs in the future.