Reviewerly/exHarmony huggingface dataset:
Scalable Fuzzy Clustering-based Regression to Predict the Isoelectric Points of the Plant Protein Sequences using Apache Spark

Learning in non-stationary environments require modern tools and algorithms to quickly adapt to the new pattern because concept drift can change the underlying distribution. So, the existing assumption that the data is independent and identically distributed may be invalid in data stream scenarios. Given the massive volume of high-speed data streams and the concept drift, traditional machine learning algorithms must be self-adapting. One of the difficulties in handling regression tasks is the complexities of equations for the regression models when combined with drift handling techniques. The high dimensional protein data is a major challenge for bioinformatics researchers to analyse the dynamics of the sequences. This paper proposes a Scalable Fuzzy Clustering induced Regression (SFC-R) algorithm to predict the isoelectric point of the plant protein sequences using Apache Spark clusters. The SFC-R algorithm uses the input features extracted from the plant protein sequences and validates performance in terms of mean squared error (MAE) and root-mean-square error (RMSE). Experiments on plant protein datasets are carried out to validate the high accuracy and robustness of our approach.
Research on the Authorship of Dream of the Red Chamber Based on Link Prediction

Dream of the Red Chamber (DRC), written in Qing dynasty, is a prestigious classical novel in Chinese literature. There exists a disputation over the authorship of DRC for the last 40 chapters. This research makes an effort to explore the DRC's authorship from the perspective of link prediction. At first, segmentation, part-of-speech tagging and named entity recognition are performed on the Chinese text of the DRC novel. A social network representing the relationship of characters is constructed based on the co-word analysis. Link is weighted according to the co-occurrence of two characters in a sentence or a paragraph. Furthermore, link prediction is completed on two groups of datasets: first 80 chapters and the whole 120 chapters. Two link prediction approaches are utilized in this research, including the similarity-based method and the classification-based method. Finally, the experiments lead to a conclusion that the author of the last 40 chapters is different from the first 80 chapters with a high probability.
Covariance Pattern Search with Eigenvalue-determined Radii

Effective implementations of Memetic Algorithms often integrate, within their design, problem-based pieces of information. When no information is known, an efficient MA can still be designed after a preliminary analysis of the problem. This approach is usually referred to as Fitness Landscape Analysis (FLA). This paper proposes a FLA technique to analyse the epistasis of continuous optimisation problems and estimate those directions, within a multi-dimensional space, associated with maximum and minimum directional derivatives. This estimation is achieved by making use of the covariance matrix associated with a distribution of points whose objective function value is below (in case of minimisation) a threshold. The eigenvectors and eigenvalues of the covariance matrix provide important pieces of information about the geometry of the problem and are then used to design a memetic operator that is a local search belonging to the family of generalised Pattern Search. A restarting mechanism enables a progressive characterisation of the fitness landscape. Numerical results show that the proposed approach successfully explore ill-conditioned basins of attractions and outperforms the standard pattern search as well as a pattern search recently proposed in the literature and partially based on a similar design logic. The proposed local search based on FLA also displays a performance competitive with that of other types of local search.
Graph convolution network deep reinforcement learning approach based on manifold regularization in cognitive radio network

In this paper, we propose GCNMR-ELM policy model for deep reinforcement learning approach in cognitive radio network and applications this policy model in cognitive radio on steelworks scene. This policy model combines the advantage of GCNMR framework and ELM algorithm. The aim is to enhance the data rate of spectrum sharing in cognitive?radio of steelworks, and Reduced policy model training time. The proposed policy model has a higher data rates in CR network can be provided; the convergence rate GCNMR-ELM policy model are faster than other policy model in the same number of iterations and GCNMR-ELM no increase in algorithm complexity. We provides extensive experiments on three different policy model in order to evaluate the performance of the proposed policy model. Experimental results show that our strategy model can effectively reduce the training time and provide higher data rate.
Authentication-based access control approach for enhancing security in pervasive computing environments

Purpose Pervasive computing environment allows the users to access the services anywhere and anytime. Due to the dynamicity, mobility, security, heterogeneity, and openness have become a major challenging task in the Pervasive computing environment. To solve the security issues and to increase the communication reliability, an authentication-based access control approach is developed in this research to ensure the level of security in the Pervasive computing environment. Design/methodology/approach This paper aims to propose authentication-based access control approach performs the authentication mechanism using the hashing, encryption, and decryption function. The proposed approach effectively achieves the conditional traceability of user credentials to enhance security. Moreover, the performance of the proposed authentication-based access control approach is estimated using the experimental analysis, and performance improvement is proved using the evaluation metrics. It inherent the tradeoff between authentication and access control in the Pervasive computing environment. Here, the service provider requires authorization and authentication for the provision of service, whereas the end-users require unlinkability and untraceability for data transactions. Findings The proposed authentication-based access control obtained 0.76, 22.836 GB, and 3.35 sec for detection rate, memory, and time by considering password attack, and 22.772GB and 4.51 sec for memory and time by considering without attack scenario. Originality/value The communication between the user and the service provider is progressed using the user public key in such a way that the private key of the user can be generated through the encryption function.
A Note on the Primitive Roots and the Golomb Conjecture

In this paper, we use the elementary methods and the estimates for character sums to prove the following conclusion. Let <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>p</mi> </math> be a prime large enough. Then, for any positive integer <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>n</mi> </math> with <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <msup> <mrow> <mi>p</mi> </mrow> <mrow> <mfenced open="(" close=")" separators="|"> <mrow> <mrow> <mn>1</mn> <mo>/</mo> <mn>2</mn> </mrow> </mrow> </mfenced> <mo>+</mo> <mi>ɛ</mi> </mrow> </msup> <mo>≤</mo> <mi>n</mi> <mo>&lt;</mo> <mi>p</mi> </math> , there must exist two primitive roots <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>α</mi> </math> and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mi>β</mi> </math> modulo <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mi>p</mi> </math> with <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <mn>1</mn> <mo>&lt;</mo> <mi>α</mi> <mo>,</mo> <mi>β</mi> <mo>≤</mo> <mi>n</mi> <mo>−</mo> <mn>1</mn> </math> such that the equation <math xmlns="http://www.w3.org/1998/Math/MathML" id="M8"> <mi>n</mi> <mo>=</mo> <mi>α</mi> <mo>+</mo> <mi>β</mi> </math> holds, where <math xmlns="http://www.w3.org/1998/Math/MathML" id="M9"> <mn>0</mn> <mo>&lt;</mo> <mi>ɛ</mi> <mo>&lt;</mo> <mfenced open="(" close=")" separators="|"> <mrow> <mrow> <mn>1</mn> <mo>/</mo> <mn>2</mn> </mrow> </mrow> </mfenced> </math> is a fixed positive number. In other words, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M10"> <mi>n</mi> </math> can be expressed as the exact sum of two primitive roots modulo <math xmlns="http://www.w3.org/1998/Math/MathML" id="M11"> <mi>p</mi> </math> .
Digital photoelasticity studies for analyzing fractal stress concentrators into birefringent bodies

Identifying the state of stress around a concentrator is essential in a loaded structure. However, most studies are based on circular geometries, leaving aside complex ones such as fractals. In this paper, the effect of fractal concentrators are evaluated by means of digital photoelasticity by considering a circular disc of epoxy resin, a Canon color camera and a Baumer VCXU-50MP polarized camera. Additionally, a phase map was obtained with phase shifting, and phase wavelengths stepping algorithms. The digital photoelasticity executed detection of stress fields related to the fractal concentrator.
Personalizing TTS Voices for Progressive Dysarthria

Amyotrophic lateral sclerosis (ALS) patients experience progressive speech deterioration due to muscle paralysis, leading to eventual loss of verbal communication capability. Text-to-speech synthesis (TTS) is an important technology for speech generating devices, enabling users to communicate using generic electronic voices, but often without the vocal identity of the users. Our work is aimed at personalizing TTS voices for people with ALS induced dysarthria by integrating machine learning and speech processing techniques of voice conversion (VC) and TTS. This is challenging as only small quantities of dysarthric speech are available from individual patients. Our system includes both timbre and prosody conversion for VC, neural TTS to generate TTS speech, and neural feature converter to interface VC and TTS. We collected speech data from 4 ALS target speakers with mild to severe dysarthria. Subjective listening tests showed that on average, our approach improved speech intelligibility by about 72% over the target speakers’ speech, the converted voice was 2 to 3 times more similar to ALS targets than to TTS sources, and the converted speech quality was in the MOS scale of fair to good.
An Improved Imperialist Competitive Algorithm For Solving Traveling Salesman Problems

In order to better solve traveling salesman problems, we proposed an improved imperialist competitive algorithm (IICA). Firstly, the original continuous algorithm is discretized to be suitable for solving combinatorial optimization problems. Then, four improvements are proposed to enhance the overall performances of the proposed algorithm: introducing immune fitness based on immune mechanism, employing parallel computation strategy, improving the strengthening link of imperialist countries according to the idea of simulated annealing, as well as adopting adaptive mechanism to accelerate algorithm convergence speed. Finally, comparing simulation results by proposed algorithm and other algorithms on typical traveling salesman problems selected from the classic test library demonstrate that IICA can solve these problems on different scales more effectively, and it has better convergence accuracy and speed.
Demonstration of Invariant‐Based Shortcuts to Coherent Population Transfer in a Superconducting Qutrit

Invariant‐based shortcuts to adiabaticity (IB‐STA) approach is demonstrated to speed up the coherent population transfer of a three‐level quantum system on a superconducting circuit. Compared with the conventional stimulated Raman adiabatic passage (STIRAP), the scheme can achieve the same high transfer efficiency with evolution time reduced by 5.96 times. The final transfer efficiency of the scheme is measured to be , which is mainly decoherence limited. Further, the robustness of the scheme is experimentally characterized, which is superior to the time‐optimal resonant Rabi scheme under systematic pulse amplitude error. Therefore, the approach provides an alternative approach to realize high‐fidelity state manipulation and preparation for weakly coupled Λ‐ or Ξ‐type quantum systems.
Financial Sector Development and Investment in Selected ECOWAS Countries: Empirical Evidence using Heterogeneous Panel Data Method

This study investigated the impact of financial sector development on domestic investment in selected Economic Community of West African States (ECOWAS) countries for the years 1985 to 2017. The study employed the Augmented Mean Group procedure which accounts for country specific heterogeneity and cross sectional dependence, and the Granger non-causality test robust to cross sectional dependence. The result reveals that (1) the impact of financial sector development on domestic investment depends on the measure of financial sector development utilized, (2) domestic credit to the private sector has a positive but insignificant impact on domestic investment in ECOWAS while banking inter-mediation efficiency (i.e. ability of the banks to transform deposits into credit) and broad money supply negatively and significant influence domestic investment, (3) cross country differences exist on the impact of financial sector development on domestic investment in the selected ECOWAS countries, and (4) domestic credit to the private sector Granger causes domestic investment in ECOWAS. The study recommends cautiousness in terms of the measure of financial development which is being utilized as a policy instrument to foster domestic investment as well as the importance of employing country-specific domestic investment policies in order to avoid blanket policy measures. Also, domestic credit to the private sector should be given priority when forecasting domestic investment into the future.
k-relevance vectors: Considering relevancy beside nearness

This study combines two different learning paradigms, k-nearest neighbor (k-NN) rule, as memory-based learning paradigm and relevance vector machines (RVM), as statistical learning paradigm. The purpose is to improve the performance of k-NN rule through selection of important features with sparse Bayesian learning method. This combination is performed in kernel space and is called k-relevance vector (k-RV). The proposed model significantly prunes irrelevant features. Our combination of k-NN and RVM presents a new concept of similarity measurement for k-NN rule, we call it k-relevancy which aims to consider “relevancy” in the feature space beside “nearness” in the input space. We also introduce a new parameter, responsible for early stopping of iterations in RVM that is able to improve the classification accuracy. Intensive experiments are conducted on several classification datasets from University of California Irvine (UCI) repository and two real datasets from computer vision domain. The performance of k-RV is highly competitive compared to a few state-of-the-arts in terms of classification accuracy.
Systems Engineering Roles in Software Organizations Delivering Service Products

ABSTRACT The software industry has been experiencing several transformations. Development teams now often autonomously deliver business capabilities to software service systems. The Unicorn Project , a best‐seller, tells how a retail company transformed into an Agile and DevOps organization. This paper uses a model from the systems engineering toolset to understand those organizational changes and proposes an evolution of the systems engineering discipline to increase the value provided by this type of organization.
Exploration of general education work in universities based on the feature extraction algorithm

Abundant general education resources meet the needs of learners to independently choose learning content. However, the resources themselves and their platforms have problems such as uneven content quality, lack of high-quality educational resources, homogenization of resources, and single method of resource selection. It is difficult for learners to quickly and efficiently obtain information related to their own needs from the massive teaching resources and valuable resources. To allow learners to efficiently obtain personalized general education resources, when selecting resources, the study adopts deep learning methods to accurately identify the knowledge points in general education resources, by constructing a relationship between the knowledge points in the teaching resources and the needs of learners. The feature vector is used as the input of the support vector machine, and the support vector machine decides whether to recommend the content of general education to learners. The actual click rate of the learner's push results and the satisfaction of the learner's feedback are used as the performance evaluation index of the method. This push method pays attention to the combination of learners' interest needs and the characteristics of general education courses, which can better meet the requirements of learners and improve learning efficiency, and has greater application potential.
Argus: A Fully Transparent Incentive System for Anti-Piracy Campaigns

Anti-piracy is fundamentally a procedure that relies on collecting data from the open anonymous population, so how to incentivize credible reporting is a question at the center of the problem. Industrial alliances and companies are running anti-piracy incentive campaigns, but their effectiveness is publicly questioned due to the lack of transparency. We believe that full transparency of a campaign is necessary to truly incentivize people. It means that every role, e.g., content owner, licensee of the content, or every person in the open population, can understand the mechanism and be assured about its execution without trusting any single role. We see this as a distributed system problem. In this paper, we present Argus, a fully transparent incentive system for anti-piracy campaigns. The groundwork of Argus is to formulate the objectives for fully transparent incentive mechanisms, which securely and comprehensively consolidate the different interests of all roles. These objectives form the core of the Argus design, highlighted by our innovations about a Sybil-proof incentive function, a commit-and-reveal scheme, and an oblivious transfer scheme. In the implementation, we overcome a set of unavoidable obstacles to ensure security despite full transparency. Moreover, we effectively optimize several cryptographic operations so that the cost for a piracy reporting is reduced to an equivalent cost of sending about 14 ETH-transfer transactions to run on the public Ethereum network, which would otherwise correspond to thousands of transactions. With the security and practicality of Argus, we hope real-world anti-piracy campaigns will be truly effective by shifting to a fully transparent incentive mechanism.
Deep AutoEncoders in Recommender Systems: An Application about Internet of Things Service Recommendation

The Internet of Things (IoT) is a system that includes small interconnected devices sharing information through the Internet. Future expects an increasing number of services in IoT devices. Therefore, recommending IoT services will be a vital task for the future of IoT and the convenience of the users. This study aims to find the best methodology to provide IoT service recommendation. With this aim, this study proposes deep autoencoders methodology to recommend services and applications to users based on the devices they own. Deep autoencoders utilize neural networks to predict user service preference matrix. The data used in this study is constructed from a real-world Social IoT dataset. The results showed that deep autoencoders outperformed the state-of-the-art recommendation methods. According to the results, Deep autoencoders improved performance indicators varying between 13.5% and 69.5% compared to other methods. Findings also indicate that the performance of the deep autoencoders can be enhanced by using ELU (exponential linear units), a specific type of activation function. This study contributes to the IoT service recommendation literature by proposing a superior approach when compared to the traditional recommendation techniques.
NoR-VDPNet++: Efficient Training and Architecture for Deep No-Reference Image Quality Metrics

Efficiency and efficacy are two desirable properties of the utmost importance for any evaluation metric having to do with Standard Dynamic Range (SDR) imaging or High Dynamic Range (HDR) imaging. However, these properties are hard to achieve simultaneously. On the one side, metrics like HDR-VDP2.2 are known to mimic the human visual system (HVS) very accurately, but its high computational cost prevents its widespread use in large evaluation campaigns. On the other side, computationally cheaper alternatives like PSNR or MSE fail to capture many of the crucial aspects of the HVS. In this work, we try to get the best of the two worlds: we present NoR-VDPNet++, an improved variant of a previous deep learning-based metric for distilling HDR-VDP2.2 into a convolutional neural network (CNN). In this work, we try to get the best of the two worlds: we present NoR-VDPNet++, an improved version of a deep learning-based metric for distilling HDR-VDP2.2 into a convolutional neural network (CNN).
Blockchain-based Smart Contracts in Procurement: A Technology Readiness Level Analysis

The use of blockchain-based smart contracts in procurement is an emerging research area with potentially significant implications in both practical and scientific terms. The new technology seems to have the potential to alter the way cross-company procurement processes are designed and executed. In theory, the blockchain can enable a pure peer-to-peer network without a need for third parties, while smart contracts can automatically execute predefined actions based on contractual clauses. This could increase the trustworthiness between supply chain partners and provide secure and traceable audit trails of all transactions. However, these potential benefits face a variety of challenges. These include, in particular, technological shortcomings, legal issues, and functional requirements. This paper analyzes the current state of the art of smart contracts in the context of procurement based on a systematic literature review and evaluates the technology’s maturity using the Technology Readiness Level model. For this purpose, critical characteristics, application areas, as well as technological, legal, and functional aspects of smart contracts are examined. In this way, this paper lays a foundation for future research, provides valuable insight for procurement practitioners, and offers approaches for the further development of smart contracts.