Reviewerly/exHarmony huggingface dataset:
Secure mobile agents for patient status telemonitoring using blockchain

Many healthcare applications deployed using Mobile Agents. Despite the Mobile Agents' benefits, security is still the primary concern. Vulnerabilities in Mobile Agents are due to their mobility and sociability and dependence on the central point to control. A secure technology that acts as an immutable, traceable, decentralized ledger called Blockchain has appeared. Blockchain establishes a trusted network using peer-to-peer(P2P) networks that store data in transactions in a series of blocks. This paper proposes an approach that aims to secure the Mobile Agent during communication with the Stationary Agent by eliminating a malicious Mobile Agent using Blockchain. The proposed approach is integrated into a healthcare application based Mobile Agent that monitors the patient's status to improve the security of the Mobile Agent in healthcare application. The paper also shows how the proposed approach fulfills the security requirements of Mobile Agent.
Piecewise Multi-Parameter Tangent/Cotangent Function and Its Application in Chaotic Pseudorandom Bit Generator

Abstract To increase the number of parameters of 1-dimensional discrete chaotic mappings, employing translation &amp; scaling, this letter creatively designs a piecewise 5-parameters Tangent/Cotangent Function. Both bifurcation diagram and Lyapunov exponent spectrum illustrate that the proposed function seems to be inappropriate for cryptographic applications, due to its narrow chaotic area. However, after applying it to devising chaotic pseudorandom bit generator, it outperforms skew tent mapping, whose chaotic area is full. It might be applied to some other fields as well, such as cryptographic hash functions, which will possibly be paid attention to in the future.
Consistent Lock-free Parallel Stochastic Gradient Descent for Fast and Stable Convergence

Stochastic Gradient Descent (SGD) is an essential element in Machine Learning (ML) algorithms. Asynchronous shared-memory parallel SGD (AsyncSGD), including synchronization-free algorithms, e.g. HOGWILD!, have received interest in certain contexts, due to reduced overhead compared to synchronous parallelization. Despite that they induce staleness and inconsistency, they have shown speedup for problems satisfying smooth, strongly convex targets, and gradient sparsity. Recent works take important steps towards understanding the potential of parallel SGD for problems not conforming to these strong assumptions, in particular for deep learning (DL). There is however a gap in current literature in understanding when AsyncSGD algorithms are useful in practice, and in particular how mechanisms for synchronization and consistency play a role. We contribute with answering questions in this gap by studying a spectrum of parallel algorithmic implementations ofAsyncSGD, aiming to understand how shared-data synchronization influences the convergence properties in fundamental DL applications. We focus on the impact of consistency-preserving non-blocking synchronization in SGD convergence, and in sensitivity to hyper-parameter tuning. We propose Leashed-SGD, an extensible algorithmic framework of consistency-preserving implementations of AsyncSGD, employing lock-free synchronization, effectively balancing throughput and latency. Leashed-SGD features a natural contention-regulating mechanism, as well as dynamic memory management, allocating space only when needed. We argue analytically about the dynamics of the algorithms, memory consumption, the threads' progress over time, and the expected contention. We provide a comprehensive empirical evaluation, validating the analytical claims, benchmarking the proposed Leashed-SGD framework, and comparing to baselines for two prominent deep learning (DL) applications: multilayer perceptrons (MLP) and convolutional neural networks (CNN). We observe the crucial impact of contention, staleness and consistency and show how, thanks to the aforementioned properties, Leashed-SGD provides significant improvements in stability as well as wall-clock time to convergence (from 20-80% up to 4 x improvements) compared to the standard lock-based AsyncSGD algorithm and HOGWILD!, while reducing the overall memory footprint.
Storage Optimization for Certificates in Blockchain Based PKI System

In recent years, many researches on decentralized PKI (Public Key Infrastructure) by using block technology have emerged to prevent problems faced by tradition PKI, such as single point of failure. Certificates are recorded into the decentralized blockchain in many of the researches. With the increased number of certificates, the blockchain based PKI system will occupy a considerable amount of storage space. In this paper, the framework of blockchain based PKI system is introduced, and optimizations for certificate storage are provided. The certificates have expiry dates, and the optimization based on the invalid certificates is proposed. In many applications and scenarios, the target certificates are explicit, and then the optimization based on the target certificates is proposed. These optimization methods could improve the storage efficiency of specific nodes in blockchain based PKI system.
Flexible estimation of the state dwell-time distribution in hidden semi-Markov models

Hidden semi-Markov models generalise hidden Markov models by explicitly modelling the time spent in a given state, the so-called dwell time, using some distribution defined on the natural numbers. While the (shifted) Poisson and negative binomial distribution provide natural choices for such distributions, in practice, parametric distributions can lack the flexibility to adequately model the dwell times. To overcome this problem, a penalised maximum likelihood approach is proposed that allows for a flexible and data-driven estimation of the dwell-time distributions without the need to make any distributional assumption. This approach is suitable for direct modelling purposes or as an exploratory tool to investigate the latent state dynamics. The feasibility and potential of the suggested approach is illustrated in a simulation study and by modelling muskox movements in northeast Greenland using GPS tracking data. The proposed method is implemented in the R-package PHSMM which is available on CRAN.
LossPass: Absorbing Microbursts by Packet Eviction for Data Center Networks

A bursty traffic pattern, called the microburst, is a key hurdle to achieve low latency for user-facing applications because it causes excessive packet losses in shallow buffered switches. Explicit Congestion Notification (ECN) can absorb microbursts by reserving buffer headroom, but the existence of headroom results in a fundamental trade-off between latency and throughput. To this end, we present LossPass, a buffer sharing mechanism that absorbs microbursts as many as possible while maintaining line-rate throughput. Specifically, LossPass evicts buffered large flow packets to make free buffer space on demand for arriving small flow packets. Our solution is inexpensive to implement on hardware. We implement a LossPass prototype and evaluate its performance through extensive testbed experiments and large-scale simulations. Our evaluation results show that LossPass reduces the FCT of small flows while maintaining line-rate throughput. For example, in testbed experiments, LossPass outperforms ECN by up to <inline-formula><tex-math notation="LaTeX">$3.20\times$</tex-math></inline-formula> in the 99th percentile FCT of small flows.
Control System based on Affine T-S Fuzzy Model with Uncertainty

A class of affine T-S fuzzy models with uncertainty is considered in the paper. It partitions the system state space into two regions where one is away from the origin, which provides an opportunity to use the S-procedure in the corresponding LMIs for controller design. In order to deal with the uncertainty, an observer is proposed, capable of catching the uncertainty's trajectory which, in turn, is used in controller design to counteract the influence from the uncertainty. In addition, adaptive law is introduced in an effort to compensate the difference between the real uncertainty and the estimated trajectory.
Chest Pathology Detection in X-Ray Scans Using Social Spider Optimization Algorithm with Generalization Deep Learning

Nowadays, medical diagnosis field using artificial intelligence is state of the art technology, which is growing and covering a lot of technical approaches to build pathology detection models. In our work, we used a new optimizer using Social Spider Optimization (SSO) algorithm with convolutional layer in deep neural network for chest X-ray pathology detection. SSO is one of the newest optimization algorithms. It is related to simulate the behavior of social spiders living in groups. Three main phases are used in this work to overcome the limited resources, the first phase is to build Convolutional Neural Network (CNN) with the benefits of transfer learning to reuse two blocks from VGG16 model (Visual Geometry Group), while the second one is to train CNN using gradient-based optimizer to extract feature vectors from the input images, and the third phase is to feed these vectors to two-fully connected layers and train it using SSO. These three phases reduce the number of trainable parameters, and get results that is much smaller and easy to handle network. We used SSO with deep neural network to achieve an accuracy of 89% and recall 98%, that leads to success of SSO optimizer to detect pathology of chest X-ray.
A hierarchical mixture cure model with unobserved heterogeneity for credit risk

The specific nature of credit loan data requires the use of mixture cure models within the class of survival analysis tools. The constructed models allow for competing risks such as early repayment and default, and for incorporating maturity, expressed as an unsusceptible part of the population. A novel further extension of such models incorporates unobserved heterogeneity within the risk groups. A hierarchical expectation-maximization algorithm is derived to fit the models and standard errors are obtained. Simulations and a data analysis illustrate the applicability and benefits of these models, and in particular an improved event time estimation.
Intelligent Computation to build a Novel Recommender of Products through (PageRank-Clustering and DgSpan- FBR)

Data is considered as one of the worthiest existent resources in the world. The fast development of technology provides us with huge/big data that needs deep analysis to understand and extraction useful information\knowledge from it. Thus, analysing such kind of data resembles a great challenge for researchers in the field of intelligent computation. The proposed model consists of three stages: the first stage is the collecting and preparing data (i.e., includes handle missing values and data convert into graphs). The second stage generates communities from graphs by developing one of the graph mining algorithms called (PageRank Clustering & Verification). Third stage includes building association patterns to the optimal clusters (subgraphs) and validating from it through developing one of the association pattern algorithms called Develop gSpan-Forward\ Backward Rules (DgSpan-FBR). The design model called Novel Recommender of products based on Graph Mining (NRGM) is distinguished by compressing the lost time of searching the best products that users need as well as increasing the accuracy of selecting of products. NRGM model is applied using on one of huge/big database have six parts different in number of samples, NRGM processed database to generated a set of communities through PageRank-Clustering. Then verification from these communities by apply two measures called (i.e., Modularity and Silhouette) to find optimal community. We test different number of communities (i.e., 2, 4, 6, 8,10 and 12) Then found two communities are the optimal where the Modularity of it is 0.679 while, silhouette is 0.699. Finally, a set of association patterns is found from the optimal community through both traditional association graph mining method called (gSpan) and developed method (DgSpan-FBR), where, the traditional method generates 3000 pattens for each community. While DgSpan-FBR generates 262 patterns. As a result, NRGM is consider as pragmatic model to deal with hug/big dataset, at the same time, it reduces the searching time and increase the accuracy of results.
Τwo to Tango: Entrepreneurs and Robots’ Users in Hospitality Service Innovation

Technological advances are modifying the processing of human needs, as well as our perception of needs, satisfaction, and service requirements. A human-centered approach has become necessary when examining hotel human–robot interaction in customer service, an emerging field of study. This chapter employs thematic analysis of papers written in 2015–2019 in order to examine social research methodological tools employed to study robot use in hospitality services. The chapter (a) adds to the ongoing discussion on the technology-driven design of hotel robots and automated services; (b) presents the use of social science methodological tools employed in technology-oriented research. It reinforces various previous studies on robots by incorporating technology competence or high level of novelty acceptance as a prerequisite for research participants, which can bring informed results to designers and startups. Thematic analysis identifies research that employed real-time measurements of humans’ experience of automated services and hotel robots’ design. These results can inform entrepreneurial strategic decisions on hotel and general-service robots’ design and development. Multidisciplinary and cross-cultural research teams should also should include a human–robot interface in a synergistic framework that (re)positions the human factor in the center.
Contemporary Perspectives on Entrepreneurial Challenges and Innovation in Education

COVID-19 has changed the way the whole world used to operate, and education is no exception. Worldwide, a massive transition has been observed in the education sector. During the pandemic situation, the world has experienced the mode of education shifting to a digital platform. This chapter investigates the impact of COVID-19 on entrepreneurs, their challenges, and adoption strategies that have created a pathway for innovation in the private education sector in Bangladesh. A qualitative investigation on 57 observations along with 18 detailed case studies was included in this study. The study sheds focus on primary, secondary, and tertiary levels of education in Bangladesh. The outcome of the study emphasized the strategies taken by the entrepreneurs that focus on the bright and dark sides of the educational sector. Private institutions face technological, financial, and operational challenges during the pandemic time. Urgent initiatives need to be taken by the government or other agencies for addressing these issues for the sustainability of this sector.
Native Advertising in a Mobile Era: Effects of Ability and Motivation on Recognition in Digital News Contexts

Digital news is progressively blurring with commercial content at the same time that mobile technology is increasingly being used to access news. To understand if these trends affect ability to distinguish news from covert advertising, two experiments were conducted among U.S. adults examining whether viewing content on a mobile device versus computer interacts with motivation levels in affecting recognition of native advertising. Consistent with tenets of the Covert Advertising Recognition and Effects model, results show that although people with greater motivation to process media content were more likely to recognize native advertising, it was not enough to compensate for the greater cognitive effort required when using a mobile device.
Synthesis Method of Robust Neural Network Models of Systems and Processes

The work deals with the study of some problems of reconstruction multidimensional statistical dependences on the basis of empirical data by means of artificial neural networks. To provide stability (robustness) of systems and processes statistical model parameters on the basis of trained artificial neural networks (ANN) at the a priori input data uncertainty as well as practically sufficient accuracy of data approximation, it is appropriate to use stable (robust) methods of deep ANN training methods. The work uses the cricking model for statistical data since we couldn’t get precise parameter values; therefore, to achieve the required accuracy, some probability was introduced. The synthesis method of scalar convolution of selection functions for mathematical model identification, based on the law of requisite variety (Ashby law), Kolmogorov power overage concentration and the maximum likelihood principle, where Student and Romanovski statistics, are used as the proximity measure of true multidimensional samples. It makes it possible to structure preference systems of a person who makes decisions for multi-criterial problems to identify mathematical models in determinate and stochastic formulations (MV-, MH- problem). Neural network identification was made by the stochastic approximation method on the basis of ravine conjugate gradient method. The method of effective robust estimation of statistical model systems parameters was worked out by employing a regularizing sequential (adaption) algorithm for synthesis of solutions with deferred correction. Samples of Rosenbrock function data and corresponding parameters of aerodynamic characteristics of the jet engine multistage axial compressor were taken as examples.
Hardware Implementation of Random Forest Algorithm Based on Classification and Regression Tree

Decision tree is a popular method for various machine learning tasks. Tree learning structure can best meet the requirements of data mining. The current applications of decision tree usually aims for a specific case, In addition, the number of features extracted from these decision trees is relatively low, usually less than 10. Although this reduces the use of resources and time, it is also easy to reduce the accuracy of the algorithm. This paper mainly studies the method of building decision tree based on CART algorithm and the hardware design and implementation of the random forest algorithm. In this paper, a random forest algorithm is introduced to solve the problem of over fitting, the SystemC language is used to model the random forest algorithm, including the training part and the inference part of the algorithm, when the input data exceeds the bank depth, it can be stored 2-4 times by cutting the bank address bit in half, and constructed a system model of the data storage method, the design completes the hardware implementation of the random forest algorithm, the training part includes the data storage module, the top-level control module, and the algorithm execution module, the inference part includes the data storage module and the top-level control module. Finally, the functional correctness of the hardware implementation is verified on FPGA. The decision tree data storage scheme proposed in this paper has high practical value, and improves storage efficiency through bank folding.
Extraction of Useful Information from Crude Job Descriptions

As we know, one of the essential social problems is unemployment. Hence, many job advertising agencies tend to consolidate various job offerings to assist the job seekers. As these job descriptions carry unstructured text, therefore, it makes the process of finding and grouping relevant jobs difficult. Hence, the aim of this paper is to automatically extract information from job descriptions. This involves application of text mining and information extraction approaches. To carry out this study, first various features are extracted and later Gradient Boosting classification algorithm is used to perform information extraction. Fields such as salary, required degree, required experience etc. are being extracted automatically. Results show that various features affect the overall result. This proposed model is currently evaluated with the help of precision, recall, receiver operating characteristics curve, and area under the curve (AUC). Experiments show that boosting classification tends to provide reasonable result with 77% of precision.
Prophetic Philosophy of Mykola Berdyaev as the Art of Understanding in the Context of Global Crisis

This article is an in-depth study of the prophetic philosophy of Berdyaev as it related to the threat of totalitarianism as a response to the current global pandemic. The author considers Berdyaev’s conceptual interpretation of prophetic philosophy as a special way of philosophical and religious understanding of the world and humanty, based on the religious studies and philosophy of art for the first time. The defining theoretical and methodological provisions of this study are the conceptual orientation toward the complementarity of ideographic and biographical methods, specific to humanitarian knowledge, which made it possible to reveal the genesis of the formation of the theoretical foundations of Berdyaev’s concept of prophetic philosophy and implementation of these principles in Berdyaev’s creative thinking.
Fast and Flexible Elliptic Curve Cryptography for Dining Cryptographers Networks

A Dining Cryptographers network (DCnet for short) allows anonymous communication with sender and receiver untraceability even if an adversary has unlimited access to the connection metadata of the network. Originally introduced by David Chaum in the 1980s, DCnets were for a long time considered not practical for real-world applications because of the tremendous communication and computation overhead they introduce. However, technological innovations such as 5G networks and extremely powerful 64-bit processors make a good case to reassess the practicality of DCnets. In addition, recent advances in elliptic-curve based commitment schemes and Zero-Knowledge Proofs (ZKPs) provide a great opportunity to reduce the computational cost of modern DCnets that are able to detect malicious behavior of communicating parties. In this paper we introduce X64ECC, a self-contained library for Elliptic Curve Cryptography (ECC) developed from scratch to support all the public-key operations needed by modern DCnets: key exchange, digital signatures, Pedersen commitments, and ZKPs. X64ECC is written in C and uses compiler intrinsics to speed up performance-critical arithmetic operations. It is highly scalable and works with Montgomery curves and twisted Edwards curves of different cryptographic strength. Despite its high scalability and portability, X64ECC is able to compute a fixed-base scalar multiplication on a twisted Edwards curve over a 255-bit prime field in about 145,000 clock cycles on a modern Intel X64 processor. All cryptosystems can be adapted on-the-fly (i.e. without recompilation) to implement DCnets with arbitrary message sizes, and tradeoffs between the cryptographic strength and throughput of a DCnet are possible.
Overview of Machine Translation Development

Access to information is increasingly global, which brings with it the growth in a non-English speaking public and, as such, a demand for tools that allow users to access this information. Faced with this scenario, the development of new tools and technologies is essential. This is the case of machine translation (MT), one of the most popular and used tools to overcome linguistic barriers. As a result, linguistic companies are increasingly offering services related to MT. In addition, MT systems can be integrated with other tools and resources such as Translation Memories or multilingual terminology databases in order to improve the efficacy of the translation process. In this chapter an overview of MT development since its early beginnings in the late 1950s until the present day is proposed. The evolution of MT techniques will be reviewed starting from the traditional approaches—statistical and rule-based MT—and finishing with the latest trends, which include hybrid MT systems, adaptive and neural MT.