Reviewerly/exHarmony huggingface dataset:
Customized Federated Learning for Multi-Source Decentralized Medical Image Classification

The performance of deep networks for medical image analysis is often constrained by limited medical data, which is privacy-sensitive. Federated learning (FL) alleviates the constraint by allowing different institutions to collaboratively train a federated model without sharing data. However, the federated model is often suboptimal with respect to the characteristics of each client's local data. Instead of training a single global model, we propose Customized FL (CusFL), for which each client iteratively trains a client-specific/private model based on a federated global model aggregated from all private models trained in the immediate previous iteration. Two overarching strategies employed by CusFL lead to its superior performance: 1) the federated model is mainly for feature alignment and thus only consists of feature extraction layers; 2) the federated feature extractor is used to guide the training of each private model. In that way, CusFL allows each client to selectively learn useful knowledge from the federated model to improve its personalized model. We evaluated CusFL on multi-source medical image datasets for the identification of clinically significant prostate cancer and the classification of skin lesions.
Fast &amp; Furious: On the modelling of malware detection as an evolving data stream

Malware is a major threat to computer systems and imposes many challenges to cyber security. Targeted threats, such as ransomware, cause millions of dollars in losses every year. The constant increase of malware infections has been motivating popular antiviruses (AVs) to develop dedicated detection strategies, which include meticulously crafted machine learning (ML) pipelines. However, malware developers unceasingly change their samples' features to bypass detection. This constant evolution of malware samples causes changes to the data distribution (i.e., concept drifts) that directly affect ML model detection rates, something not considered in the majority of the literature work. In this work, we evaluate the impact of concept drift on malware classifiers for two Android datasets: DREBIN (about 130K apps) and a subset of AndroZoo (about 285K apps). We used these datasets to train an Adaptive Random Forest (ARF) classifier, as well as a Stochastic Gradient Descent (SGD) classifier. We also ordered all datasets samples using their VirusTotal submission timestamp and then extracted features from their textual attributes using two algorithms (Word2Vec and TF-IDF). Then, we conducted experiments comparing both feature extractors, classifiers, as well as four drift detectors (DDM, EDDM, ADWIN, and KSWIN) to determine the best approach for real environments. Finally, we compare some possible approaches to mitigate concept drift and propose a novel data stream pipeline that updates both the classifier and the feature extractor. To do so, we conducted a longitudinal evaluation by (i) classifying malware samples collected over nine years (2009-2018), (ii) reviewing concept drift detection algorithms to attest its pervasiveness, (iii) comparing distinct ML approaches to mitigate the issue, and (iv) proposing an ML data stream pipeline that outperformed literature approaches.
A digital mask to safeguard patient privacy

The storage of facial images in medical records poses privacy risks due to the sensitive nature of the personal biometric information that can be extracted from such images. To minimize these risks, we developed a new technology, called the digital mask (DM), which is based on three-dimensional reconstruction and deep-learning algorithms to irreversibly erase identifiable features, while retaining disease-relevant features needed for diagnosis. In a prospective clinical study to evaluate the technology for diagnosis of ocular conditions, we found very high diagnostic consistency between the use of original and reconstructed facial videos (κ ≥ 0.845 for strabismus, ptosis and nystagmus, and κ = 0.801 for thyroid-associated orbitopathy) and comparable diagnostic accuracy (P ≥ 0.131 for all ocular conditions tested) was observed. Identity removal validation using multiple-choice questions showed that compared to image cropping, the DM could much more effectively remove identity attributes from facial images. We further confirmed the ability of the DM to evade recognition systems using artificial intelligence-powered re-identification algorithms. Moreover, use of the DM increased the willingness of patients with ocular conditions to provide their facial images as health information during medical treatment. These results indicate the potential of the DM algorithm to protect the privacy of patients' facial images in an era of rapid adoption of digital health technologies.
Secure Sensitive Data Sharing Using RSA and ElGamal Cryptographic Algorithms with Hash Functions

With the explosion of connected devices linked to one another, the amount of transmitted data grows day by day, posing new problems in terms of information security, such as unauthorized access to users’ credentials and sensitive information. Therefore, this study employed RSA and ElGamal cryptographic algorithms with the application of SHA-256 for digital signature formulation to enhance security and validate the sharing of sensitive information. Security is increasingly becoming a complex task to achieve. The goal of this study is to be able to authenticate shared data with the application of the SHA-256 function to the cryptographic algorithms. The methodology employed involved the use of C# programming language for the implementation of the RSA and ElGamal cryptographic algorithms using the SHA-256 hash function for digital signature. The experimental result shows that the RSA algorithm performs better than the ElGamal during the encryption and signature verification processes, while ElGamal performs better than RSA during the decryption and signature generation process.
Blockchain in the supply chain – A comprehensive framework for theory-driven research

Blockchain research in the supply chain management field is growing. The complexities of blockchain as a technology and its potential implications for business and digital ecosystems have expanded the spectrum of research areas that need to be investigated to further develop this emerging field. The increasing number of applications of the technology in supply chain practice calls for theory-driven research to develop better understanding of blockchain and its implications for the supply chain. However, theory-based research on blockchain adoption, deployment and use is scarce, limiting theory development in the field. There is a lack of guidance on the range of theories that can be used to undertake theory-driven research that investigates different facets of blockchain adoption, deployment and use. We address this need in this research. We draw on the existing literature to develop a comprehensive list of factors and barriers for blockchain applications in the supply chain. We present a framework for theory-driven research capturing four distinct facets of supply chain research studies - intra-organizational, inter-organizational, technical, and external environment. Considering the multidisciplinary nature of blockchain research, we propose different theoretical perspectives that can be used alone or in combination to advance theory development in each area as the pace of blockchain adoption in the supply chain increases.
The Effect of Caputo Fractional Variable Difference Operator on a Discrete-Time Hopfield Neural Network with Non-Commensurate Order

In this work, we recall some definitions on fractional calculus with discrete-time. Then, we introduce a discrete-time Hopfield neural network (D.T.H.N.N) with non-commensurate fractional variable-order (V.O) for three neurons. After that, phase-plot portraits, bifurcation and Lyapunov exponents diagrams are employed to verify that the proposed discrete time Hopfield neural network with non-commensurate fractional variable order has chaotic behavior. Furthermore, we use the 0-1 test and C0 complexity algorithm to confirm and prove the results obtained about the presence of chaos. Finally, simulations are carried out in Matlab to illustrate the results.
eHealth: A Survey of Architectures, Developments in mHealth, Security Concerns and Solutions

The ramifications of the COVID-19 pandemic have contributed in part to a recent upsurge in the study and development of eHealth systems. Although it is almost impossible to cover all aspects of eHealth in a single discussion, three critical areas have gained traction. These include the need for acceptable eHealth architectures, the development of mobile health (mHealth) technologies, and the need to address eHealth system security concerns. Existing survey articles lack a synthesis of the most recent advancements in the development of architectures, mHealth solutions, and innovative security measures, which are essential components of effective eHealth systems. Consequently, the present article aims at providing an encompassing survey of these three aspects towards the development of successful and efficient eHealth systems. Firstly, we discuss the most recent innovations in eHealth architectures, such as blockchain-, Internet of Things (IoT)-, and cloud-based architectures, focusing on their respective benefits and drawbacks while also providing an overview of how they might be implemented and used. Concerning mHealth and security, we focus on key developments in both areas while discussing other critical topics of importance for eHealth systems. We close with a discussion of the important research challenges and potential future directions as they pertain to architecture, mHealth, and security concerns. This survey gives a comprehensive overview, including the merits and limitations of several possible technologies for the development of eHealth systems. This endeavor offers researchers and developers a quick snapshot of the information necessary during the design and decision-making phases of the eHealth system development lifecycle. Furthermore, we conclude that building a unified architecture for eHealth systems would require combining several existing designs. It also points out that there are still a number of problems to be solved, so more research and investment are needed to develop and deploy functional eHealth systems.
Reinforcement-learning-based parameter adaptation method for particle swarm optimization

Particle swarm optimization (PSO) is a well-known optimization algorithm that shows good performance in solving different optimization problems. However, PSO usually suffers from slow convergence. In this article, a reinforcement learning-based online parameters adaption method(RLAM) is developed to enhance PSO in convergence by designing a network to control the coefficients of PSO. Moreover, based on RLAM, a new RLPSO is designed. In order to investigate the performance of RLAM and RLPSO, experiments on 28 CEC 2013 benchmark functions are carried out when comparing with other online adaption method and PSO variants. The reported computational results show that the proposed RLAM is efficient and effictive and that the the proposed RLPSO is more superior compared with several state-of-the-art PSO variants.
Refined Quantum Gates for Λ‐Type Atom‐Photon Hybrid Systems

Abstract High‐efficiency quantum information processing is equivalent to the fewest quantum resources and simplest operations by means of logic qubit gates. Based on the reflection geometry of a single photon interacting with a three‐level Λ‐type atom‐cavity system, the authors present some refined protocols for realizing controlled‐not (CNOT), Fredkin, and Toffoli gates on hybrid systems. The first control qubit of gates is encoded on a flying photon, and the remaining qubits are encoded on the atoms in the optical cavity. Moreover, these quantum gates can be extended to the optimal synthesis of multi‐qubit CNOT, Fredkin, and Toffoli gates with optical elements without auxiliary photons or atoms. Further, the simplest single‐qubit operations are applied to the photon only, which make these logic gates experimentally feasible with current technology.
Learning of rules that have high-frequency exceptions: New empirical data and a hybrid connectionist model

Theorists of human learning, in domains as various as category learning and language acquisition, have grappled with the issue of whether learners induce rules or remember exemplars, or both. In this article we present new data that resect both rule induction and exemplar encoding, and we present a new connectionist model that speciÞes one way in which rule-based and exemplar-based mechanisms might interact. Our empirical study was motivated by analogy to past tense acquisition, and speciÞcally by the previous work of Palermo and Howe (1970). Human subjects learned to categorize items, most of which could be classiÞed by a simple rule, except for a few frequently recurring exceptions. The modeling was motivated by the idea of combining an exemplar-based module (ALCOVE, Kruschke, 1992) and a rule-based module in a connectionist architecture, and allowing the system to learn which module should be responsible for which instances, using the competitive gating mechanism introduced by Jacobs, Jordan, Nowlan, and Hinton (1991). We report quantitative Þts of the model to
Florida International University - University of Miami Trecvid 2014

This paper demonstrates the framework and results from the team “Florida International University - University of Miami (FIU-UM)” in TRECVID 2014 Semantic Indexing (SIN) task [Smeaton09]. Two runs were submitted, and the summary of these four runs are as follows:
Stochastic Planning and Lifted Inference

The paper argues that (1) stochastic planning should be used as a core problem domain for relational probabilistic models providing problems of interest that are challenging for current approaches and significant scope for extending their capabilities, (2) that symbolic dynamic programming solving such problems can be seen as a prime example of lifted inference in relational probabilistic problems, (3) that first order decision diagrams provide a useful tool to drive such lifted computations, and (4) that the resulting lifted inference is qualitatively different from what other approaches are providing. As a result, this relationship can be studied to the benefit of developing foundations for relational probabilistic models and to the benefit of stochastic planning.
Training auto-encoders effectively via eliminating task-irrelevant input variables

Auto-encoders are often used as building blocks of deep network classifiers to learn feature extractors, but task-irrelevant information in the input data may lead to bad extractors and result in poor generalisation performance of the network. In this paper, via dropping the task-irrelevant input variables, the performance of auto-encoders can be obviously improved. Specifically, an importance-based variable selection method is proposed to aim at finding the task-irrelevant input variables and dropping them. It firstly estimates importance of each variable, and then drops the variables with importance value lower than a threshold. In order to obtain better performance, the method can be employed for each layer of stacked auto-encoders. Experimental results show that when combined with our method the stacked denoising auto-encoders achieve significantly improved performance on three challenging datasets.
Robust Multiplication-Based Tests for Reed–Muller Codes

We consider the following multiplication-based tests to check if a given function $f: \mathbb{F}_q^n\to \mathbb{F}_q$ is a codeword of the Reed-Muller code of dimension $n$ and order $d$ over the finite field $\mathbb{F}_q$ for prime $q$ (i.e., $f$ is the evaluation of a degree-$d$ polynomial over $\mathbb{F}_q$ for $q$ prime). * $\mathrm{Test}_{e,k}$: Pick $P_1,\ldots,P_k$ independent random degree-$e$ polynomials and accept iff the function $fP_1\cdots P_k$ is the evaluation of a degree-$(d+ek)$ polynomial (i.e., is a codeword of the Reed-Muller code of dimension $n$ and order $(d+ek)$). We prove the robust soundness of the above tests for large values of $e$, answering a question of Dinur and Guruswami [Israel Journal of Mathematics, 209:611-649, 2015]. Previous soundness analyses of these tests were known only for the case when either $e=1$ or $k=1$. Even for the case $k=1$ and $e>1$, earlier soundness analyses were not robust. We also analyze a derandomized version of this test, where (for example) the polynomials $P_1,\dots,P_k$ can be the same random polynomial $P$. This generalizes a result of Guruswami et al. [SIAM J. Comput., 46(1):132-159, 2017]. One of the key ingredients that go into the proof of this robust soundness is an extension of the standard Schwartz-Zippel lemma over general finite fields $\mathbb{F}_q$, which may be of independent interest.
Hidden Markov Models in Time Series, with Applications in Economics

Markov models introduce persistence in the mixture distribution. In time series analysis, the mixture components relate to different persistent states characterizing the state-specific time series process. Model specification is discussed in a general form. Emphasis is put on the functional form and the parametrization of timeinvariant and time-varying specifications of the state transition distribution. The concept of mean-square stability is introduced to discuss the condition under which Markov switching processes have finite first and second moments in the indefinite future. Not surprisingly, a time series process may be mean-square stable even if it switches between bounded and unbounded state-specific processes. Surprisingly, switching between stable state-specific processes is neither necessary nor sufficient to obtain a mean-square stable time series process. Model estimation proceeds by data augmentation. We derive the basic forward-filtering backward-smoothing/sampling algorithm to infer on the latent state indicator in maximum likelihood and Bayesian estimation procedures. Emphasis is again laid on the state transition distribution. We discuss the specification of state-invariant prior parameter distributions and posterior parameter inference under either a logit or probit functional form of the state transition distribution. With simulated data, we show that the estimation of parameters under a probit functional form is more efficient. However, a probit functional form renders estimation extremely slow if more than two states drive the time series process. Finally, various applications illustrate how to obtain informative switching in Markov switching models with time-invariant and time-varying transition distributions.
A College-Industry Partnership for Enterprise Computing

Abstract Abstract:Growth at Old Dominion Freight Lines, Inc. (ODFL) necessitated the relocation of the presentDisaster Recovery (DR) center in Greensboro, NC to a newly constructed center located inThomasville, NC. The old DR site was functional and equipped with modern equipmentcomparable to most other corporate DR sites. The relocation provided a unique opportunityfor ODFL and NCA&T SU. ODFL is a third-generation Triad business with an establishedhistory of giving back to their community. ODFL made a grant of the existing DR center toNCA&T SU and to Davidson County Community College (DCCC) for the purpose ofestablishing state-of-the-art IT data center research and experience-based learning labs.This project aims to improve data center management techniques for better informationavailability, reliability, security, and cost efficiency. The broader impacts of this project includedevelopment of advanced IT skills in the Triad workforce. This project will enhance learningoutcomes of enterprise computing technology students at NCA&T SU, area communitycolleges, and high schools by engaging students in applied research and providing hands-onexperienced-based learning. Additionally, availability of a skilled IT workforce will expand theTriad area economy by assisting the expansion of existing Triad enterprises and attractingnew information technology dependent enterprises to relocate to the Triad.
International Aspects Of Communication Technologies As A Tool For Learning

There is a belief in the education community that the traditional teaching-learning models do not scale to meet the new challenges created by emerging technologies in every nation.Educational institutions are looking for ways to reach students when faced with increased competitions, shifting demographics, and delivering education to isolated areas.One way this problem has been tackled is through communications technologies as a mean to enhance flexible delivery and student learning on-line.As Internet has become a way of life, web-based educational course management systems have become popular.WebCT, BlackBoard, and ANGEL are among few popular learning web-based tools.These tools enable faculty to post documents and files, securely post grades, track students' activities, interact with their students through e-mail and instant messaging.In addition, students can also use these tools to engage in group work and collaboration, online note-taking, take practice quizzes with immediate feedback, keep up progress tracking, and constant access to grade information at the instructor's discretion.The focus of these web-based tools is to aid instructors develop meaningful learning experiences and improve learning outcomes which will aid students in their course.The objective of this paper is to present the advantages and disadvantages of these three common types of computerized tools used on-line in reaching customers and institutions of higher learning around the world.Moreover, this paper will help individual faculty members and instructional designers who are considering course management systems.The results of this study indicated that using these web-based tools assisted both distance and on-campus students and their learning levels are comparable.
Grammatical construction of function words between old and modern written Arabic: A corpus-based analysis

Abstract This paper argues that Arabic function words (FWs) vary in usage between old and modern Arabic, thus prompting an experimental investigation into their changeability. This investigation is carried out by testing classical Arabic (CA) in Arabic heritage language (AHL) texts – those labeled as archistratum – and the modern standard Arabic (MSA) of Arabic newspaper texts (ANT), each group of which contains randomly collected 5 million (M) word texts. The linguistic theory of the grammar of Arabic FWs is explained through the differences between CA and MSA, despite Arabic FW changes and the unlearnability and/or unusability of some FW constructions between in these two eras of Arabic usage. The dispersion/distribution of the construction grammar (CxG) of FWs and the number ( n ) of word attractions/repulsions between the two distinct eras is explored using the very latest and most sophisticated Arabic corpus processing tools, and Sketch Engine’s SkeEn gramrels operators. The analysis of a 5 M word corpus from each era of Arabic serves to prove the non-existence of rigorous Arabic CxG. The approach in this study adopts a technique which, by contrasting AHL with ANT, relies on analyzing the frequency distributions of FWs, the co-occurrences of FWs in a span of 2n-grams collocational patterning, and some cases of FW usage changes in terms of lexical cognition (FW grammatical relationships). The results show that the frequencies of FWs, in addition to the case studies, are not the same, and this implies that FWs and their associations with the main part of speech class in a fusion language like Arabic have grammatically changed in MSA. Their constructional changes are neglected in Arabic grammar.