Reviewerly/exHarmony huggingface dataset:
BengaliNet: A Low-Cost Novel Convolutional Neural Network for Bengali Handwritten Characters Recognition

As it is the seventh most-spoken language and fifth most-spoken native language in the world, the domain of Bengali handwritten character recognition has fascinated researchers for decades. Although other popular languages i.e., English, Chinese, Hindi, Spanish, etc. have received many contributions in the area of handwritten character recognition, Bengali has not received many noteworthy contributions in this domain because of the complex curvatures and similar writing fashions of Bengali characters. Previously, studies were conducted by using different approaches based on traditional learning, and deep learning. In this research, we proposed a low-cost novel convolutional neural network architecture for the recognition of Bengali characters with only 2.24 to 2.43 million parameters based on the number of output classes. We considered 8 different formations of CMATERdb datasets based on previous studies for the training phase. With experimental analysis, we showed that our proposed system outperformed previous works by a noteworthy margin for all 8 datasets. Moreover, we tested our trained models on other available Bengali characters datasets such as Ekush, BanglaLekha, and NumtaDB datasets. Our proposed architecture achieved 96–99% overall accuracies for these datasets as well. We believe our contributions will be beneficial for developing an automated high-performance recognition tool for Bengali handwritten characters.
Knowledge transfer-based distributed differential evolution for dynamic database fragmentation

Database fragmentation can protect the distributed database's privacy by dividing attributes of sensitive associations into different fragments. Previous database fragmentation algorithms are designed for the initialization of the distributed database. However, the initial database fragmentation cannot maintain its effect during the distributed database's entire life cycle. This paper defines a dynamic database fragmentation problem with privacy preservation requirements, in which both the privacy preservation degree and the communication cost are considered during the optimization. For this problem, a knowledge transfer-based distributed differential evolution algorithm (KT-DDE) is proposed to achieve the optimal communication cost and maintain privacy preservation. The proposed KT-DDE algorithm includes a distributed framework and a differential evolution-based optimizer. In the proposed distributed framework, the fragmentation knowledge is transferred between different database fragmentation subproblems. The fragmentation information of various individuals is exchanged in the optimizer and used to generate trial individuals. After the selection, competitive trial individuals are kept in the population. Experimental results show that the proposed algorithm can outperform the other competitors in terms of solution accuracy, convergence speed, and computation efficiency. In addition, the effectiveness of the proposed components is verified.
A landmark article on nonlinear time-domain modeling in musical acoustics

The Reflections series takes a look back on historical articles from The Journal of the Acoustical Society of America that have had a significant impact on the science and practice of acoustics.
Machine learning and artificial intelligence: applications in healthcare epidemiology

Abstract Artificial intelligence (AI) refers to the performance of tasks by machines ordinarily associated with human intelligence. Machine learning (ML) is a subtype of AI; it refers to the ability of computers to draw conclusions (ie, learn) from data without being directly programmed. ML builds from traditional statistical methods and has drawn significant interest in healthcare epidemiology due to its potential for improving disease prediction and patient care. This review provides an overview of ML in healthcare epidemiology and practical examples of ML tools used to support healthcare decision making at 4 stages of hospital-based care: triage, diagnosis, treatment, and discharge. Examples include model-building efforts to assist emergency department triage, predicting time before septic shock onset, detecting community-acquired pneumonia, and classifying COVID-19 disposition risk level. Increasing availability and quality of electronic health record (EHR) data as well as computing power provides opportunities for ML to increase patient safety, improve the efficiency of clinical management, and reduce healthcare costs.
Fusing Heterogeneous Factors with Triaffine Mechanism for Nested Named Entity Recognition

Nested entities are observed in many domains due to their compositionality, which cannot be easily recognized by the widely-used sequence labeling framework.A natural solution is to treat the task as a span classification problem.To learn better span representation and increase classification performance, it is crucial to effectively integrate heterogeneous factors including inside tokens, boundaries, labels, and related spans which could be contributing to nested entities recognition.To fuse these heterogeneous factors, we propose a novel triaffine mechanism including triaffine attention and scoring.Triaffine attention uses boundaries and labels as queries and uses inside tokens and related spans as keys and values for span representations.Triaffine scoring interacts with boundaries and span representations for classification.Experiments show that our proposed method outperforms previous span-based methods, achieves the state-of-the-art F 1 scores on nested NER datasets GENIA and KBP2017, and shows comparable results on ACE2004 and ACE2005.
High-speed photonic reservoir computer based on a delayed Fano laser under electrical modulation

We propose and numerically demonstrate a high-speed photonic reservoir computing (RC) system using a compact Fano laser (FL) with optical feedback under electrical modulation. Benefiting from its insensitivity to external feedback, an FL has a wider dynamic steady-state region compared with a conventional Fabry-Perot laser, which significantly extends the ranges of desirable RC implementation. Interestingly, we observe two separate regions of good RC performances corresponding to two scenarios of the dynamic steady state of the FL, respectively. Moreover, the robust RC performance versus the feedback phase can be achieved in one of the steady-state regions, where the laser is not destabilized for lower external reflectivity. Owing to the ultra-short photon lifetime in the FL, the information processing rate of our proposed RC system may reach 10 Gbps. More importantly, as a specific type of microscopic laser, the FL offers potential applications to RC-based integrated neuromorphic photonic systems.
Leveraging Smart Contract in Project Procurement through DLT to Gain Sustainable Competitive Advantages

Project delivery on time, with agreed quality and assigned budget, is the desire of project-based companies. Time, quality, and cost are determinants of project success; however, organisations suffer from achieving these three success criteria at the same time. Failures in project delivery cause the loss of the competitive advantage. The recent digital technologies introduce smart contracts to supply chain (SC) operations for improving SC processes. Project procurement is the area for smart contract implementation to deliver successful projects and gain sustainable competitive advantages. The aim of this study was in explaining how smart contracts benefit project organisations through project procurement. Qualitative research design guided this research with phenomenology. Semi-structured interviews generated the data. The obtained research data were analysed with thematic, textual, and discourse analysis. Published industry reports were used to triangulate the data. This study demonstrated an integrated relationship model to answer the research question. The research findings initially identified the fact that smart contracts improve procurement efficiency through cost, time, and quality. Secondly, smart contracts build a trust-less platform where reliability is delivered and reinforced with transparency, traceability, and security. This study found that enhanced procurement efficiency and reliability meet requirements to gain sustainable competitive advantages. This study intends to contribute to industry practices and future research. The correlation of project procurement management success, smart contracts, and sustainable competitive advantages are expected to guide feature research and business practices.
Adoption of Blockchain With 5G Networks for Industrial IoT: Recent Advances, Challenges, and Potential Solutions

It has been proven that Internet of Things (IoT) platforms can improve the performance and efficiency of a wide range of processes. With the acceptance of IoT as a major part of the technology of Industry 4.0, the notion of leveraging the Internet in industries to enable automation and reconfigure existing industrial processes has greatly evolved. By introducing smart technology and intelligent processes, the Industrial Internet of Things (IIoT) is committed to bringing high operational efficiency, enhanced productivity, and effective management to industrial assets. Despite this, the reliance of IIoT on central architecture presents numerous challenges, including the security and maintenance of smart devices, privacy issues owing to third-party participation, and massive computations conducted by a central entity, all of which prevent its widespread adoption in businesses. Emerging blockchain technologies have the potential to transform IIoT platforms and applications. A distributed and decentralized approach followed by blockchain might offer interesting solutions to the challenges raised by IIoT. Furthermore, 5G networks are expected to deliver excellent solutions to meet the demands of decentralized systems, with a focus on application-specific vulnerabilities. Blockchain and IIoT, enabled by 5G, is a viable option to fully explore the potential of contemporary industry. In this context, this article analyzes and examines recent achievements to highlight the major obstacles in blockchain&#x2013;IIoT convergence and presents a framework for potential solutions. A well-organized literature review by analyzing the existing work in three primary areas: blockchain consensus algorithms used in existing IoT and IIoT applications, blockchain for 5G-enabled IoT networks, and blockchain in industry have been performed, with major findings summarized in each area. Directions for the future are also provided and intend to assist researchers in understanding the full potential of these innovations.
Predicting Victories in Video Games - IEEE BigData 2021 Cup Report

We summarize the results of IEEE BigData 2021 Cup: Predicting Victories in Video Games - a data mining challenge organized at the KnowledgePit platform in association with the IEEE BigData 2021 conference. We describe the competition task, as well as the data acquisition and preprocessing steps. We also provide a brief overview of the top-performing solutions submitted by participants. Finally, we present results of the post-competition data analysis, in which we consider the similarity of solutions submitted by various teams in terms of their errors on the test data. We conclude this analysis by demonstrating a method for constructing an ensemble of submitted solutions. Such an ensemble performs better than any of the individual solutions submitted during the competition.
A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm (AI) for Optimal Task Scheduling

The search algorithm based on symbiotic organisms' interactions is a relatively recent bio-inspired algorithm of the swarm intelligence field for solving numerical optimization problems. It is meant to optimize applications based on the simulation of the symbiotic relationship among the distinct species in the ecosystem. The task scheduling problem is NP complete, which makes it hard to obtain a correct solution, especially for large-scale tasks. This paper proposes a modified symbiotic organisms search-based scheduling algorithm for the efficient mapping of heterogeneous tasks to access cloud resources of different capacities. The significant contribution of this technique is the simplified representation of the algorithm's mutualism process, which uses equity as a measure of relationship characteristics or efficiency of species in the current ecosystem to move to the next generation. These relational characteristics are achieved by replacing the original mutual vector, which uses an arithmetic mean to measure the mutual characteristics with a geometric mean that enhances the survival advantage of two distinct species. The modified symbiotic organisms search algorithm (G_SOS) aims to minimize the task execution time (makespan), cost, response time, and degree of imbalance, and improve the convergence speed for an optimal solution in an IaaS cloud. The performance of the proposed technique was evaluated using a CloudSim toolkit simulator, and the percentage of improvement of the proposed G_SOS over classical SOS and PSO-SA in terms of makespan minimization ranges between 0.61-20.08% and 1.92-25.68% over a large-scale task that spans between 100 to 1000 Million Instructions (MI). The solutions are found to be better than the existing standard (SOS) technique and PSO.
New Related-Tweakey Boomerang and Rectangle Attacks on Deoxys-BC Including BDT Effect

In the CAESAR competition, Deoxys-I and Deoxys-II are two important authenticated encryption schemes submitted by Jean et al. Recently, Deoxys-II together with Ascon, ACORN, AEGIS-128, OCB and COLM have been selected as the final CAESAR portfolio. Notably, Deoxys-II is also the primary choice for the use case “Defense in depth”. However, Deoxys-I remains to be one of the third-round candidates of the CAESAR competition. Both Deoxys-I and Deoxys-II adopt Deoxys-BC-256 and Deoxys-BC-384 as their internal tweakable block ciphers.In this paper, we investigate the security of round-reduced Deoxys-BC-256/-384 and Deoxys-I against the related-tweakey boomerang and rectangle attacks with some new boomerang distinguishers. For Deoxys-BC-256, we present 10-round related-tweakey boomerang and rectangle attacks for the popular setting (|tweak|, |key|) = (128, 128), which reach one more round than the previous attacks in this setting. Moreover, an 11-round related-tweakey rectangle attack on Deoxys-BC-256 is given for the first time. We also put forward a 13-round related-tweakey boomerang attack in the popular setting (|tweak|, |key|) = (128, 256) for Deoxys-BC-384, while the previous attacks in this setting only work for 12 rounds at most. In addition, the first 14-round relatedtweakey rectangle attack on Deoxys-BC-384 is given when (|tweak| &lt; 98, |key| &gt; 286), that attacks one more round than before. Besides, we give the first 10-round rectangle attack on the authenticated encryption mode Deoxys-I-128-128 with one more round than before, and we also reduce the complexity of the related-tweakey rectangle attack on 12-round Deoxys-I-256-128 by a factor of 228. Our attacks can not be applied to (round-reduced) Deoxys-II.
Impact of the industrial revolution 4.0 on higher education in Vietnam: challenges and opportunities

With a lofty goal and mission, it is the mission of "cultivating people". Higher education has an important role to play in the development of society. Education not only trains people with good knowledge and expertise, but also trains people with good moral and physical qualities, effectively serving the socio-economic development associations of each country, each ethnic group. The industrial revolution 4.0 with its rapid development speed and profound impacts on all areas of the social life of each country, if left behind of this revolution, the backward development is also inevitable. On the contrary, if you make good use of the advantages of this revolution, the opportunities are huge. In the digital era, higher education will change profoundly from the educational environment, the role of teachers and learners to teaching methods. Currently, not only Vietnam but also many countries around the world are facing the big challenge of a shortage of highly qualified, specialized and skilled workers. Therefore, in order to renovate higher education to meet the requirements of the labor market, it is necessary to raise awareness and renew thinking on higher education development; renovating training programs and methods.
MAGMA: An Optimization Framework for Mapping Multiple DNNs on Multiple Accelerator Cores

As Deep Learning continues to drive a variety of applications in edge and cloud data centers, there is a growing trend towards building large accelerators with several sub-accelerator cores/chiplets. This work looks at the problem of supporting multi-tenancy on such accelerators. In particular, we focus on the problem of mapping jobs from several DNNs simultaneously on an accelerator. Given the extremely large search space, we formulate the search as an optimization problem and develop an optimization framework called M3E. In addition, we develop a specialized optimization algorithm called MAGMA with custom operators to enable structured sampleefficient exploration. We quantitatively compare MAGMA with several state-of-the-art methods, black-box optimization, and reinforcement learning methods across different accelerator settings (large/small accelerators) and different sub-accelerator configurations (homogeneous/heterogeneous), and observe MAGMA can consistently find better mappings.
RepSR: Training Efficient VGG-style Super-Resolution Networks with Structural Re-Parameterization and Batch Normalization

This paper explores training efficient VGG-style super-resolution (SR) networks with the structural re-parameterization technique. The general pipeline of re-parameterization is to train networks with multi-branch topology first, and then merge them into standard 3x3 convolutions for efficient inference. In this work, we revisit those primary designs and investigate essential components for re-parameterizing SR networks. First of all, we find that batch normalization (BN) is important to bring training non-linearity and improve the final performance. However, BN is typically ignored in SR, as it usually degrades the performance and introduces unpleasant artifacts. We carefully analyze the cause of BN issue and then propose a straightforward yet effective solution. In particular, we first train SR networks with mini-batch statistics as usual, and then switch to using population statistics at the later training period. While we have successfully re-introduced BN into SR, we further design a new re-parameterizable block tailored for SR, namely RepSR. It consists of a clean residual path and two expand-and-squeeze convolution paths with the modified BN. Extensive experiments demonstrate that our simple RepSR is capable of achieving superior performance to previous SR re-parameterization methods among different model sizes. In addition, our RepSR can achieve a better trade-off between performance and actual running time (throughput) than previous SR methods. Codes are available at https://github.com/TencentARC/RepSR.
PF-SMOTE: A novel parameter-free SMOTE for imbalanced datasets

Class imbalance learning is one of the most important topics in the field of machine learning and data mining, and the Synthetic Minority Oversampling Techniques (SMOTE) is the common method to handle this issue. The main shortcomings of the classic SMOTE and its variants is the interpolation of potential noise and unrepresentative examples. This paper is devoted to proposing a novel parameter-free SMOTE mechanism to produce sufficient representative synthetic examples while avoiding interpolating noisy examples. Specifically, two types of minority class examples are defined, namely boundary and safe minority examples. The synthetic examples generation procedure fully reflects the characteristics of the minority class examples with filling the region dominated by the minority class and expanding the margin of the minority class. To verify the effectiveness and robustness of the proposed method, a thorough experimental study on forty datasets selected from real-world applications is carried out. The experimental results indicate that our proposed method is competitive to the classic SMOTE and its state-of-the-art variants.
Breast DCE-MRI segmentation for lesion detection using Chimp Optimization Algorithm

The high prevalence of breast cancer in women has increased dramatically in recent times. Physician’s knowledge in breast cancer diagnosis and detection using computerized algorithms for extraction and segmentation of features can help. Image segmentation is a critical component of image analysis that has a direct impact on the quality of the results. This article presents Kapur’s entropy-based multilevel thresholding using Chimp Optimization Algorithm (ChOA) to estimate optimal values for the lesion segmentation of breast DCE-MRI. An improved ChOA is also developed by incorporating Opposition based-learning (OBL) in it, termed as ChOAOBL, and applied to solve the same problem. The proposed methods are evaluated using 200 Sagittal T2-Weighted fat-suppressed DCE-MRI images of 40 patients. The proposed methods are compared with Improved ChOA (IChOA), Particle Swarm Optimization (PSO), Multi-verse Optimizer (MVO), Slime Mould Algorithm (SMA), Arithmetic Optimization Algorithm (AOA), Tunicate Swarm Algorithm (TSA), Multilevel Otsu Threshold (MLOT), Conventional Markov Random Field (CMRF), Hidden Markov Random Field (HMRF), and Improved Markov Random Field (IMRF). The high sensitivity, accuracy, and Dice Efficient Coefficient (DSC) level of the proposed ChOA-based method are achieved at 90.75%, 98.24%, and 87.09% respectively. The accuracy value of 99.02%, sensitivity 95.73%, and DSC 93.25% are achieved using another proposed ChOAOBL-based segmentation method. The results are analyzed using a one-way ANOVA test followed by Tukey HSD, and Wilcoxon Signed Rank Test. We have also analyzed the overall performance using Multi-Criteria Decision Making based on accuracy, precision, specificity, F-measure, sensitivity, false-positive rate, Geometric-Mean (G-mean), and DSC. The proposed methods outperform other compared methods, according to both quantitative and qualitative outcomes.
Do contagion cues shape customers’ willingness to adopt hospitality service robots?

During the COVID-19 pandemic, service robots have been widely used to maintain social distancing in the hospitality industry. It is therefore important to explore the factors that drive customers’ adoption of service robots under health concerns. This study focuses on how disease contagion cues shape customers’ willingness to adopt service robots in hospitality by using restaurant as the representative scenario. Drawing on the theory of the behavioral immune system, we proposed an underlying process in terms of social avoidance and infection-combating action. The results of three experiments demonstrate that contagion cues motivate customers to choose service robots over human servers. Service robots are associated with less interpersonal contact, which is aligned with the social avoidance tendency triggered by the contagion cues. However, such effect is eliminated when infection-combating action has been taken. This study makes theoretical and practical contributions to research on hospitality service encounters and the application of robotics.