Reviewerly/exHarmony huggingface dataset:
Combination of ELMo Representation and CNN Approaches to Enhance Service Discovery

With the rapid growth of Web services, the demand for discovering the optimal services to satisfy the users' requirements is no longer an easy task. The critical issue in the process of service discovery is to conduct a similarity calculation. To solve such an issue, this study proposes an effective approach that combines the Embeddings from Language Models (ELMo) representation and Convolutional Neural Network (CNN) to obtain a more accurate similarity score for retrieving target Web services. More specifically, first, the study adopts the ELMo model to generate effective word representations for capturing the sufficient information from services and queries. Then, the word representations are used to compose a similarity matrix, which will be taken as the input for the CNN to learn the matching relationships. Finally, the combination of the ELMo representation and CNN is used to address the representation and interaction processes within the matching task to improve the service discovery performance. The results demonstrate the effectiveness of our proposed approach for retrieving better targeted Web services.
Sequential online prediction in the presence of outliers and change points: An instant temporal structure learning approach

In this paper, we consider sequential online prediction (SOP) for streaming data in the presence of outliers and change points. We propose an INstant TEmporal structure Learning (INTEL) algorithm to address this problem. Our INTEL algorithm is developed based on a full consideration of the duality between online prediction and anomaly detection. We first employ a mixture of weighted Gaussian process models (WGPs) to cover the expected possible temporal structures of the data. Then, based on the rich modeling capacity of this WGP mixture, we develop an efficient technique to instantly learn (capture) the temporal structure of the data that follows a regime shift. This instant learning is achieved only by adjusting one hyper-parameter of the mixture model. A weighted generalization of the product of experts (POE) model is used for fusing predictions yielded from multiple GP models. An outlier is declared once a real observation seriously deviates from the fused prediction. If a certain number of outliers are consecutively declared, then a change point is declared. Extensive experiments are performed using a diverse of real datasets. Results show that the proposed algorithm is significantly better than benchmark methods for SOP in the presence of outliers and change points.
Highly Efficient and Re-executable Private Function Evaluation with Linear Complexity

Private function evaluation aims to securely compute a function <inline-formula><tex-math notation="LaTeX">$f(x_1, \ldots, x_n)$</tex-math></inline-formula> without leaking any information other than what is revealed by the output, where <inline-formula><tex-math notation="LaTeX">$f$</tex-math></inline-formula> is a private input of one of the parties (say <inline-formula><tex-math notation="LaTeX">$\mathsf {Party}_1$</tex-math></inline-formula> ) and <inline-formula><tex-math notation="LaTeX">$x_i$</tex-math></inline-formula> is a private input of the <inline-formula><tex-math notation="LaTeX">$i$</tex-math></inline-formula> th party <inline-formula><tex-math notation="LaTeX">$\mathsf {Party}_i$</tex-math></inline-formula> . In this article, we propose a novel and secure <i>two-party private function evaluation</i> (2PFE) scheme based on the DDH assumption. Our scheme introduces a reusability feature that significantly improves the state-of-the-art. Accordingly, our scheme has two variants, one is utilized in the initial execution of the function <inline-formula><tex-math notation="LaTeX">$f$</tex-math></inline-formula> , and the other is utilized in its subsequent evaluations. To the best of our knowledge, this is the first and most efficient 2PFE scheme that enjoys a reusablity feature. Our protocols achieve linear communication and computation complexities and a constant number of rounds which is at most three.
Social Engineering and the Dangers of Phishing

Social Engineering and phishing technique are subjects that have been evolving as the years pass, mainly through email, which is one of the most used communication tools in the world. Phishing emails are usually related to Social Engineering and may be proposed through links and / or attachments in this type of email, both of which are malicious propagation, and may be hacked into personal / confidential information or even complete control of the computer / email without the users noticing. Several studies have already been carried out showing that there have been more and more attacks of this type and increasingly impacting the population. The research described in this article aims to review prevention methods for this type of computer crime. The research included an exploratory study with a qualitative methodology, through interviews with professionals in the area of Computer Security and later a study with a quantitative methodology, through an online questionnaire.
Fast Clustering by Affinity Propagation Based on Density Peaks

Clustering is an important technique in data mining and knowledge discovery. Affinity propagation clustering (AP) and density peaks and distance-based clustering (DDC) are two significant clustering algorithms proposed in 2007 and 2014 respectively. The two clustering algorithms have simple and clear design ideas, and are effective in finding meaningful clustering solutions. They have been widely used in various applications successfully. However, a key disadvantage of AP is its high time complexity, which has become a bottleneck when applying AP for large-scale problems. The core idea of DDC is to construct the decision graph based on the local density and the distance of each data point, and then select the cluster centers, but the selection of the cluster centers is relatively subjective, and sometimes it is difficult to determine a suitable number of cluster centers. Here, we propose a two-stage clustering algorithm, called DDAP, to overcome these shortcomings. First, we select a small number of potential exemplars based on the two quantities of each data point in DDC to greatly compress the scale of the similarity matrix. Then we implement message-passing on the incomplete similarity matrix. In experiments, two synthetic datasets, nine publicly available datasets, and a real-world electronic medical records (EMRs) dataset are used to evaluate the proposed method. The results demonstrate that DDAP can achieve comparable clustering performance with the original AP algorithm, while the computational efficiency improves observably.
Uses of ICT Tools from the Perspective of Chilean University Teachers

The use of the ICT in university learning processes is linked to their use by the educator. This aspect will determine the use of some resources over others, and consequently, the prevalence of some methodologies over others. The main objective of this work is to determine the use of the ICT by university educators, and from this, to determine which elements will define the design of a training program on the use of the ICT. For this, and with the use of an ex post facto method with a sample of n = 1113 subjects, the main result obtained was that the male professors utilized the digital tools more often for their teaching tasks. Furthermore, those with less professional experience preferred to use tools found in the cloud. Thus, it is concluded that the model of training observed from the demonstrated use of digital resources is linked to the preoccupation of education professionals for caring for the digital environment, and this model should be designed as a function of the methodologies and the system of evaluations utilized by the professors.
A Novel Framework for Synchrophasor Based Online Recognition and Efficient Post-Mortem Analysis of Disturbances in Power Systems

Synchrophasor based applications become more and more popular in today’s control centers to monitor and control transient system events. This can ensure secure system operation when dealing with bidirectional power flows, diminishing reserves and an increased number of active grid components. Today’s synchrophasor applications provide a lot of additional information about the dynamic system behavior but without significant improvement of the system operation due to the lack of interpretable and condensed results as well as missing integration into existing decision-making processes. This study presents a holistic framework for novel machine learning based applications analyzing both historical as well as online synchrophasor data streams. Different methods from dimension reduction, anomaly detection as well as time series classification are used to automatically detect disturbances combined with a web-based online visualization tool. This enables automated decision-making processes in control centers to mitigate critical system states and to ensure secure system operations (e.g., by activating curate actions). Measurement and simulation-based results are presented to evaluate the proposed synchrophasor application modules for different use cases at the transmission and distribution level.
Hybrid IT and Multi Cloud an Emerging Trend and Improved Performance in Cloud Computing

In the present day scenario cloud computing is an attractive subject for IT and non IT personnel. It is a service-oriented pay per use computational model. Cloud has working models with service-oriented delivery mechanism as well as deployment-oriented infrastructure mechanism. Data centers are the backbone of cloud computing. The massive participation of public has also increased the load on the cloud servers. Proper scheduling of resources is always needed. Quality of service is to be provided as per the service level agreement. Virtualization technique is the main reason behind the huge success of cloud. Multi-cloud exchanges to optimize connectivity today, multi-cloud exchanges offer the next level in direct connectivity, allowing organizations to safely and easily expand multi-cloud capabilities. Exchanges eliminate the added worries that an open Internet can bring as well as the tedious provisioning and configuring that comes with connecting to the public Internet. Importantly, multi-cloud exchanges allow organizations to establish a single connection to multiple cloud providers at the same time through an Ethernet switching platform, rather than wrestling with multiple individual connections to cloud providers.
Forcing and Calculi for Hybrid Logics

The definition of institution formalizes the intuitive notion of logic in a category-based setting. Similarly, the concept of stratified institution provides an abstract approach to Kripke semantics. This includes hybrid logics, a type of modal logics expressive enough to allow references to the nodes/states/worlds of the models regarded as relational structures, or multi-graphs. Applications of hybrid logics involve many areas of research, such as computational linguistics, transition systems, knowledge representation, artificial intelligence, biomedical informatics, semantic networks, and ontologies. The present contribution sets a unified foundation for developing formal verification methodologies to reason about Kripke structures by defining proof calculi for a multitude of hybrid logics in the framework of stratified institutions . To prove completeness, the article introduces a forcing technique for stratified institutions with nominal and frame extraction and studies a forcing property based on syntactic consistency. The proof calculus is shown to be complete and the significance of the general results is exhibited on a couple of benchmark examples of hybrid logical systems.
ProCAVIAR: Hybrid Data-Driven and Probabilistic Knowledge-Based Activity Recognition

The recognition of physical activities using sensors on mobile devices has been mainly addressed with supervised and semi-supervised learning. The state-of-the-art methods are mainly based on the analysis of the user's movement patterns that emerge from inertial sensors data. While the literature on this topic is quite mature, existing approaches are still not adequate to discriminate activities characterized by similar physical movements. The context that surrounds the user (e.g., semantic location) could be used as additional information to significantly extend the set of recognizable activities. Since collecting a comprehensive training set with activities performed in every possible context condition is too costly, if possible at all, existing works proposed knowledge-based reasoning over ontological representation of context data to refine the predictions obtained from machine learning. A problem with this approach is the rigidity of the underlying logic formalism that cannot capture the intrinsic uncertainty of the relationships between activities and context. In this work, we propose a novel activity recognition method that combines semi-supervised learning and probabilistic ontological reasoning. We model the relationships between activities and context as a combination of soft and hard ontological axioms. For each activity, we use a probabilistic ontology to compute its compatibility with the current context conditions. The output of probabilistic semantic reasoning is combined with the output of a machine learning classifier based on inertial sensor data to obtain the most likely activity performed by the user. The evaluation of our system on a dataset with 13 types of activities performed by 26 subjects shows that our probabilistic framework outperforms both a pure machine learning approach and previous hybrid approaches based on classic ontological reasoning.
Reproducible kernel Hilbert space based global and local image segmentation

Image segmentation is the task of partitioning an image into individual objects, and has many important applications in a wide range of fields. The majority of segmentation methods rely on image intensity gradient to define edges between objects. However, intensity gradient fails to identify edges when the contrast between two objects is low. In this paper we aim to introduce methods to make such weak edges more prominent in order to improve segmentation results of objects of low contrast. This is done for two kinds of segmentation models: global and local. We use a combination of a reproducing kernel Hilbert space and approximated Heaviside functions to decompose an image and then show how this decomposition can be applied to a segmentation model. We show some results and robustness to noise, as well as demonstrating that we can combine the reconstruction and segmentation model together, allowing us to obtain both the decomposition and segmentation simultaneously.
Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model

Bird swarm algorithm is one of the swarm intelligence algorithms proposed recently. However, the original bird swarm algorithm has some drawbacks, such as easy to fall into local optimum and slow convergence speed. To overcome these short-comings, a dynamic multi-swarm differential learning quantum bird swarm algorithm which combines three hybrid strategies was established. First, establishing a dynamic multi-swarm bird swarm algorithm and the differential evolution strategy was adopted to enhance the randomness of the foraging behavior’s movement, which can make the bird swarm algorithm have a stronger global exploration capability. Next, quantum behavior was introduced into the bird swarm algorithm for more efficient search solution space. Then, the improved bird swarm algorithm is used to optimize the number of decision trees and the number of predictor variables on the random forest classification model. In the experiment, the 18 benchmark functions, 30 CEC2014 functions, and the 8 UCI datasets are tested to show that the improved algorithm and model are very competitive and outperform the other algorithms and models. Finally, the effective random forest classification model was applied to actual oil logging prediction. As the experimental results show, the three strategies can significantly boost the performance of the bird swarm algorithm and the proposed learning scheme can guarantee a more stable random forest classification model with higher accuracy and efficiency compared to others.
Multi-Stroke handwriting character recognition based on sEMG using convolutional-recurrent neural networks

Despite the increasing use of technology, handwriting has remained to date as an efficient means of communication. Certainly, handwriting is a critical motor skill for childrens cognitive development and academic success. This article presents a new methodology based on electromyographic signals to recognize multi-user free-style multi-stroke handwriting characters. The approach proposes using powerful Deep Learning (DL) architectures for feature extraction and sequence recognition, such as convolutional and recurrent neural networks. This framework was thoroughly evaluated, obtaining an accuracy of 94.85%. The development of handwriting devices can be potentially applied in the creation of artificial intelligence applications to enhance communication and assist people with disabilities.
Formation of Students’ Competence of Tertiary Educational Institutions by Practical Training Aids

The purpose of the scientific article is aimed at studying the features of students’ competenceformation at tertiary educational institutions by practical training aids. To reveal the purpose of a scientific article, methods of theoretical analysis and synthesis have been used (to study the theoretical framework of students’ competence formation at tertiary educational institutions by practical training aids) and methods of comparison, grouping and concretization (to analyze and assess the practical results of students’ competence formation at tertiary educational institutions by practical training aids). The practical results of the study are presented through: the results of assessing students’ knowledge of Mathematics, Reading and Science, according to the PISA program; dynamics and structure of the number of students enrolled in tertiary education; the proportion of undergraduate students in% of the population at the age of 20-24 years old. According to the results of the PISA program, developed by the Organization for Economic Cooperation and Development (OECD), it has been found that Austria, Belgium and Germany have the highest average scores in Mathematics, Reading and Science, compared to the average scores in OECD countries. It has been established that in Ukraine the average students’score in Mathematics in 2018 is lower than the average score in OECD countries by 21 points, in Reading - by 36 points, and in Science - by 20 points. In the course of the study it hasbeenestablished that currentlyeducators use the following practical training aids for the formation of students’ competence in the learning process, namely: introduction of a modular academic program in the educational process, providing the necessary level of theoretical basis, implementation of introductory,educational, training, undergraduate practices, work experience internship in the educational process of students’ training, application of information, innovation and interactive technologies in the educational process, teaching and training of students in accordance with the requirements of the labor market and employers, ensuring cooperation between tertiary educational institutionsin the framework of student exchange programs.
Model-Protected Multi-Task Learning

Multi-task learning (MTL) refers to the paradigm of learning multiple related tasks together. In contrast, in single-task learning (STL) each individual task is learned independently. MTL often leads to better trained models because they can leverage the commonalities among related tasks. However, because MTL algorithms can "leak" information from different models across different tasks, MTL poses a potential security risk. Specifically, an adversary may participate in the MTL process through one task and thereby acquire the model information for another task. The previously proposed privacy-preserving MTL methods protect data instances rather than models, and some of them may underperform in comparison with STL methods. In this paper, we propose a privacy-preserving MTL framework to prevent information from each model leaking to other models based on a perturbation of the covariance matrix of the model matrix. We study two popular MTL approaches for instantiation, namely, learning the low-rank and group-sparse patterns of the model matrix. Our algorithms can be guaranteed not to underperform compared with STL methods. We build our methods based upon tools for differential privacy, and privacy guarantees, utility bounds are provided, and heterogeneous privacy budgets are considered. The experiments demonstrate that our algorithms outperform the baseline methods constructed by existing privacy-preserving MTL methods on the proposed model-protection problem.
Recent Advances on Graph Analytics and Its Applications in Healthcare

Graph is a natural representation encoding both the features of the data samples and relationships among them. Analysis with graphs is a classic topic in data mining and many techniques have been proposed in the past. In recent years, because of the rapid development of data mining and knowledge discovery, many novel graph analytics algorithms have been proposed and successfully applied in a variety of areas. The goal of this tutorial is to summarize the graph analytics algorithms developed recently and how they have been applied in healthcare. In particular, our tutorial will cover both the technical advances and the application in healthcare. On the technical aspect, we will introduce deep network embedding techniques, graph neural networks, knowledge graph construction and inference, graph generative models and graph neural ordinary differential equation models. On the healthcare side, we will introduce how these methods can be applied in predictive modeling of clinical risks (e.g., chronic disease onset, in-hospital mortality, condition exacerbation, etc.) and disease subtyping with multi-modal patient data (e.g., electronic health records, medical image and multi-omics), knowledge discovery from biomedical literature and integration with data-driven models, as well as pharmaceutical research and development (e.g., de-novo chemical compound design and optimization, patient similarity for clinical trial recruitment and pharmacovigilance). We will conclude the whole tutorial with a set of potential issues and challenges such as interpretability, fairness and security. In particular, considering the global pandemic of COVID-19, we will also summarize the existing research that have already leveraged graph analytics to help with the understanding the mechanism, transmission, treatment and prevention of COVID-19, as well as point out the available resources and potential opportunities for future research.