Reviewerly/exHarmony huggingface dataset:
Semiparametric Bayesian networks for continuous data

The Bayesian network is crucial for computer technology and artificial intelligence when dealing with probabilities. In this paper, we extended a new semiparametric model for Bayesian networks which is more flexible and robust than the parametric or linear one, providing a further generalization of the Gaussian Bayesian network. In the classical Gaussian Bayesian networks, the regression function between nodes has always been assumed to be linear. Actually, this is not necessary because the links between nodes may be more complex than simply linear relationships. Learning the structure of the semiparametric Bayesian network, by adding the nonlinear structures, was an important issue discussed in this work. We have illustrated the problem of estimating and testing both parameters and regression functions of the proposed model. We, then, introduced a new algorithm for constructing the proposed semiparametric Bayesian network. Some sensitivity analyses have been explained in order to validate the correctness of the network. Simulation studies and a real application for the energy field were used to examine the fitted model.
Modeling solar extinction using artificial neural networks. Application to solar tower plants

The extinction of solar radiation is considered an important variable to take into account in the design and operation of commercial concentrated solar power (CSP) tower plants, where the distances between the concentrating heliostats and the receiver on top of the tower are, in many cases, around 1 km. Aerosol particles and water vapor in the path traveled by solar radiation are the main causes of its extinction due to the phenomena of scattering and absorption. Since June 2017, solar extinction has been reliably measured daily at Plataforma Solar de Almería, which has allowed analyzing the dependence of this parameter with other meteorological variables. It has been observed that during high turbidity events there is a clear linear dependence of the solar extinction with the particle concentration and humidity. This work shows that, although this linear character is diluted under normal conditions, artificial neural networks (ANN) allow modeling and predicting extinction as a function of these two magnitudes.
Development of a “Smart Materials” Master’s Degree Module for Chemical Engineering Students

This paper describes development of an interdisciplinary “Smart Materials” module in English for Master’s students in a Russian chemical engineering university. The module is intended for promoting English proficiency of engineering students and internationalizing education by attracting foreign students within the framework of short-term academic exchange. The research stage of project implementation included comparative analysis of international engineering education in Germany and Russia in a form of survey included 30 faculty members and 100 students. Germany was selected because it offers excellent engineering education in English and is originally non-English speaking. The visits to universities in four different regions of Germany allowed for planning the structure of the module and the content of lectures and practicums. The survey results contributed to the implementation of the second stage of the project that was focused on developing three disciplines for the module: “Introduction to smart Materials”, “Research Methods for Smart Materials” as lecture courses and “Practical Course in Smart Materials” as the laboratory practicum. The module is ready for introduction into the education process of a Russian chemical engineering university starting from the 2019–2020 academic year and is selected as the core for a new Master’s degree program “Molecular Materials Engineering”.
A meta-analysis of field experiments on phishing susceptibility

Phishing is a serious threat to any organization allowing their employees to use messaging systems and computers connected to the internet. Consequently, researchers have undertaken a large number of studies to identify the variables that determine this threat, i.e. variables that influence users' susceptibility to phishing emails. This paper presents a meta-analysis of the findings in 48 papers describing field experiments. The mean susceptibility rate to phishing emails across all studies and measurements was 21 percent. A majority (116 of 140) of the association tests reported, concerned variables related to the recipient. Most of these reported insignificant results. Both relative risks and association tests showed that technical warning systems, email personalization, training, and the use of established deceptive tactics influence the susceptibility rate. The type of scam as such also appears to be important, with some types of scams being orders of magnitude more successful than other types. Many of the results had limitations in control and sampling, which may explain unexpected and contradictory results.
Interactive hybrid approach to combine machine and human intelligence for personalized rehabilitation assessment

Automated assessment of rehabilitation exercises using machine learning has a potential to improve current rehabilitation practices. However, it is challenging to completely replicate therapist's decision making on the assessment of patients with various physical conditions. This paper describes an interactive machine learning approach that iteratively integrates a data-driven model with expert's knowledge to assess the quality of rehabilitation exercises. Among a large set of kinematic features of the exercise motions, our approach identifies the most salient features for assessment using reinforcement learning and generates a user-specific analysis to elicit feature relevance from a therapist for personalized rehabilitation assessment. While accommodating therapist's feedback on feature relevance, our approach can tune a generic assessment model into a personalized model. Specifically, our approach improves performance to predict assessment from 0.8279 to 0.9116 average F1-scores of three upper-limb rehabilitation exercises (p < 0.01). Our work demonstrates that machine learning models with feature selection can generate kinematic feature-based analysis as explanations on predictions of a model to elicit expert's knowledge of assessment, and how machine learning models can augment with expert's knowledge for personalized rehabilitation assessment.
Clustering and Constructing User Coresets to Accelerate Large-scale Top-K Recommender Systems

Top-K recommender systems aim to generate few but satisfactory personalized recommendations for various practical applications, such as item recommendation for e-commerce and link prediction for social networks. However, the numbers of users and items can be enormous, thereby leading to myriad potential recommendations as well as the bottleneck in evaluating and ranking all possibilities. Existing Maximum Inner Product Search (MIPS) based methods treat the item ranking problem for each user independently and the relationship between users has not been explored. In this paper, we propose a novel model for clustering and navigating for top-K recommenders (CANTOR) to expedite the computation of top-K recommendations based on latent factor models. A clustering-based framework is first presented to leverage user relationships to partition users into affinity groups, each of which contains users with similar preferences. CANTOR then derives a coreset of representative vectors for each affinity group by constructing a set cover with a theoretically guaranteed difference to user latent vectors. Using these representative vectors in the coreset, approximate nearest neighbor search is then applied to obtain a small set of candidate items for each affinity group to be used when computing recommendations for each user in the affinity group. This approach can significantly reduce the computation without compromising the quality of the recommendations. Extensive experiments are conducted on six publicly available large-scale real-world datasets for item recommendation and personalized link prediction. The experimental results demonstrate that CANTOR significantly speeds up matrix factorization models with high precision. For instance, CANTOR can achieve 355.1x speedup for inferring recommendations in a million-user network with 99.5% [email protected] to the original system while the state-of-the-art method can only obtain 93.7x speedup with 99.0% [email protected]
Stable Model Semantics for Recursive SHACL

SHACL (SHape Constraint Language) is a W3C recommendation for validating graph-based data against a set of constraints (called shapes). Importantly, SHACL allows to define recursive shapes, i.e. a shape may refer to itself, directly of indirectly. The recommendation left open the semantics of recursive shapes, but proposals have emerged recently to extend the official semantics to support recursion. These proposals are based on the principle of possibility (or non-contradiction): a graph is considered valid against a schema if one can assign shapes to nodes in such a way that all constraints are satisfied. This semantics is not constructive, as it does not provide guidelines about how to obtain such an assignment, and it may lead to unfounded assignments, where the only reason to assign a shape to a node is that it allows validating the graph.
RETRACTED: Efficient fuzzy based K-nearest neighbour technique for web services classification

Web services playing a vital role in the World Wide Web and generates huge amount of information across various domains of internet. Due to this evolution data in the form of articles, reports, digital galleries and web data of companies were increased everyday. To handle the huge volume of data each and every day, automatic query classification based on internet is more significant method. Research and development community has developed various techniques for the web services discovery, where it offers the mandated data for the improvement method. With respect to the literature survey, most of the researchers are concentrating to provide the efficient web service discovery. The amount of data that is available in the web is keeps on increasing and also it is used to differentiate the services, explanation and work of art. In order to achieve this method, machine learning algorithm is applied extensively for domain categorization. Various machine learning algorithm like KNN is applied for web service discovery. The systems are effectively learning the input and evaluate the performance accuracy with the given datasets. This paper, proposes an improved fuzzy with KNN algorithm for effective web service classification. This is used to increase an outcome in the form of accuracy and performance measures.
Deep reinforcement one-shot learning for artificially intelligent classification in expert aided systems

In recent years there has been a sharp rise in applications, in which significant events need to be classified but only a few training instances are available. These are known as cases of one-shot learning. To handle this challenging task, organizations often use human analysts to classify events under high uncertainty. Existing algorithms use a threshold-based mechanism to decide whether to classify an object automatically or send it to an analyst for deeper inspection. However, this approach leads to a significant waste of resources since it does not take the practical temporal constraints of system resources into account. By contrast, the focus in this paper is on rigorously optimizing the resource consumption in the system which applies to broad application domains, and is of a significant interest for academic research, industrial developments, as well as society and citizens benefit. The contribution of this paper is threefold. First, a novel Deep Reinforcement One-shot Learning (DeROL) framework is developed to address this challenge. The basic idea of the DeROL algorithm is to train a deep-Q network to obtain a policy which is oblivious to the unseen classes in the testing data. Then, in real-time, DeROL maps the current state of the one-shot learning process to operational actions based on the trained deep-Q network, to maximize the objective function. Second, the first open-source software for practical artificially intelligent one-shot classification systems with limited resources is developed for the benefit of researchers and developers in related fields. Third, an extensive experimental study is presented using the OMNIGLOT dataset for computer vision tasks, the UNSW-NB15 dataset for intrusion detection tasks, and the Cleveland Heart Disease Dataset for medical monitoring tasks that demonstrates the versatility and efficiency of the DeROL framework.
Anonymous Tokens with Private Metadata Bit

We present a cryptographic construction for anonymous tokens with private metadata bit, called PMBTokens. This primitive enables an issuer to provide a user with a lightweight, single-use anonymous trust token that can embed a single private bit, which is accessible only to the party who holds the secret authority key and is private with respect to anyone else. Our construction generalizes and extends the functionality of Privacy Pass (PETS'18) with this private metadata bit capability. It provides unforgeability, unlinkability, and privacy for the metadata bit properties based on the DDH and CTDH assumptions in the random oracle model. Both Privacy Pass and PMBTokens rely on non-interactive zero-knowledge proofs (NIZKs). We present new techniques to remove the need for NIZKs, while still achieving unlinkability. We implement our constructions and we report their efficiency costs.
Real-Time Image Stitching with Convolutional Neural Networks

Traditional image stitching methods are designed manually and time-consuming, while Convolutional Neural Networks based methods can solve the problem end to end automatically and stitch images in real time after training. However, there is still no suitable dataset for image stitching. Now, we propose a novel approach to capture the images to be stitched and the corresponding ground truth, and make a dataset generated by the proposed method. We also propose a network to verify our dataset experimentally and propose the quantitative metric based on our dataset. It will make progresses for real time image stitching.
Generic-Group Delay Functions Require Hidden-Order Groups

Despite the fundamental importance of delay functions, underlying both the classic notion of a time-lock puzzle and the more recent notion of a verifiable delay function, the only known delay function that offers both sufficient structure for realizing these two notions and a realistic level of practicality is the "iterated squaring" construction of Rivest, Shamir and Wagner. This construction, however, is based on rather strong assumptions in groups of hidden orders, such as the RSA group (which requires a trusted setup) or the class group of an imaginary quadratic number field (which is still somewhat insufficiently explored from the cryptographic perspective). For more than two decades, the challenge of constructing delay functions in groups of known orders, admitting a variety of well-studied instantiations, has eluded the cryptography community. In this work we prove that there are no constructions of generic-group delay functions in cyclic groups of known orders: We show that for any delay function that does not exploit any particular property of the representation of the underlying group, there exists an attacker that completely breaks the function's sequentiality when given the group's order. As any time-lock puzzle and verifiable delay function give rise to a delay function, our result holds for these two notions we well, and explains the lack of success in resolving the above-mentioned long-standing challenge. Moreover, our result holds even if the underlying group is equipped with a d-linear map, for any constant $$d \ge 2$$ (and even for super-constant values of d under certain conditions).
A Novel Remote User Authentication Scheme by using Private Blockchain-Based Secure Access Control for Agriculture Monitoring

Pakistan is an agricultural country and its economy is mainly dependent on agriculture. Agriculture provides large economic opportunities along with the yield of food and other raw materials in our life. To increase the yield of products there is a need for the deployment of modern technology. To tackle this problem, a private blockchain-based secure access control for agriculture can be employed to monitor the different climatic parameters such as (light, humidity, temperature, and, soil moisture, etc) in the fields. The environmental and climatic parameters are very important for the quantity, quality, efficient growth of the crops. Also, any kind of unwanted modification, interception, and expunction on these parameters can leave a bad impact on fields and crops. Therefore, privacy and security are key challenges while deploying the monitoring systems in the fields. In this regard, we have designed a novel remote user authentication scheme by using private blockchain-based secure access control for agriculture monitoring. The proposed scheme is authorized through private blockchain access control (PBAC) algorithms and is applied using hyper ledger cello tools. We customarily analyze the privacy and security of the scheme by using a random oracle model. Furthermore, the proposed protocol is secure and resist different sorts of malicious attacks. The proposed scheme is also suitable in a real-life application to avoid all kinds of security attacks.
One-Bit Precoding Constellation Design via Autoencoder-Based Deep Learning

This paper considers a multicasting system in which the base station has a large number of antennas with cost-effective one-bit digital-to-analog converters and aims to send a common symbol to multiple remote users. Unlike the existing literature which seeks to design the one-bit precoder for a given constellation, e.g., quadrature amplitude modulation (QAM) or phase shift keying (PSK), this paper aims to jointly design the transmit one-bit precoder and the receive constellation by leveraging the concept of autoencoder, wherein the end-to-end multicasting system is modeled using a deep neural network with the one-bit precoding constraint represented by a binary thresholding layer. To deal with the issue that such a binary layer always produces a gradient of zero, and thus prevents an effective end-to-end training when using the conventional back-propagation method, this paper uses a variant of straight-through estimator which approximates the thresholding function with a properly scaled sigmoid function in the back-propagation phase. Numerical results show that, for a fixed channel scenario, the proposed autoencoder-based constellation design is superior to the conventional QAM and PSK constellations. Using the insights obtained from fixed channel scenarios, we also propose a constellation design for varying channel scenarios and numerically show that the proposed design achieves a better performance as compared to the conventional constellations.
CPU–GPU Utilization Aware Energy-Efficient Scheduling Algorithm on Heterogeneous Computing Systems

Nowadays, heterogeneous computing systems have proven to be a good solution for processing computation intensive high-performance applications. The main challenges for such large-scale systems are energy consumption, computing node CPU-GPU utilization dynamic variability, and so on. In response to these challenges, this study first provides heterogeneous computing systems architecture and parallel application job model. Then, we build system computing node CPU-GPU utilization model and analyze job execution energy consumption. We also deduce the optimal CPU-GPU utilization and job deadline scheduling constraint. Third, we propose a systems CPU-GPU utilization aware and energy-efficient heuristic greedy strategy (UEJS) to solve this job scheduling problem. To improve the algorithm global optimization ability, we design a hybrid particle swarm optimization algorithm (H-PSO), which incorporates the heuristic greedy strategy into the bio-inspired search optimization technique. The rigorous experimental results clearly demonstrate that our proposed H-PSO outperforms heuristic greedy strategy, Max-EAMin, and Genetic Algorithm in terms of the average energy consumption of jobs and system job rejection rate. In particular, H-PSO is better than UEJS by 36.5%, Max-EAMin by 36.3%, and GA by 46.7% in term of the job average energy consumption for heterogeneous system with high workload.
Parallel Compact Differential Evolution for Optimization Applied to Image Segmentation

A parallel compact Differential Evolution (pcDE) algorithm is proposed in this paper. The population is separated into multiple groups and the individual is run by using the method of compact Differential Evolution. The communication is implemented after predefined iterations. Two communication strategies are proposed in this paper. The first one is to replace the local optimal solution by global optimal solution in all groups, which is called optimal elite strategy (oe); the second one is to replace the local optimal solution by mean value of the local optimal solution in all groups, which is called mean elite strategy (me). Considering that the pcDE algorithm does not need to store a large number of solutions, the algorithm can adapt to the environment with weak computing power. In order to prove the feasibility of pcDE, several groups of comparative experiments are carried out. Simulation results based on the 25 test functions demonstrate the efficacy of the proposed two communication strategies for the pcDE. Finally, the proposed pcDE is applied to image segmentation and experimental results also demonstrate the superior quality of the pcDE compared with some existing methods.
Human scanpath prediction based on deep convolutional saccadic model

Human scanpath represents the sequence of human eye fixations, revealing the dynamic process of saccadic eye movement when a natural scene is freely viewed by humans. It is valuable to have an in-depth understanding of the dynamic visual attention and visual search behavior. In this paper, a deep convolutional saccadic model (DCSM) is proposed to predict human scanpath. The model simultaneously predicts the foveal saliency maps and fixation durations with considering on modeling the inhibition of return, which is a well recognized physiological mechanism to mimic human saccadic behavior. Both the foveal saliency and fixation durations are predicted by convolutional neural networks, which associate the inhibition of return with image content from spatial and temporal aspects. With the proposed DCSM, fixations of a scanpath are sequentially predicted with only a single image as input. Our method is capable of handling the challenges of temporal dependency and spatial association with image content. Experimental results on MIT1003 and FIGRIM datasets demonstrate the effectiveness of our proposed method when compared with state-of-the-art methods.
New classes of optimal ternary cyclic codes with minimum distance four

Cyclic code is an interesting topic in coding theory and communication systems. In this paper, we investigate the ternary cyclic codes with parameters [3m−1,3m−1−2m,4] based on some results proposed by Ding and Helleseth in 2013. Six new classes of optimal ternary cyclic codes are presented by determining the solutions of certain equations over F3m.