Reviewerly/exHarmony huggingface dataset:
A Framework for Privacy-Preserving Multi-Party Skyline Query Based on Homomorphic Encryption

Nowadays, the management and analyses of `big data' are becoming indispensable for numerous organizations all over the world. In many cases, multiple organizations want to perform data analyses on their combined databases. Skyline query is one of the popular operations for selecting representative objects from a large database, where any other object within the database does not dominate each of the representative objects, called `skyline'. Like other data analytics operations, the multi-party skyline query can provide benefits to the participating organizations by retrieving the skyline objects from their combined databases. Such a multi-party skyline query demands the disclosure of individual parties' objects to others during the computation. But, owing to the data privacy and security concern of the present IT era, such disclosure of the individual parties' databases is strictly prohibited. Considering this issue, we are proposing a new framework for the privacy-preserving multi-party skyline query, exploiting additive homomorphic encryption along with data anonymization, perturbation, and randomization techniques. The underlying protocols within our proposed framework ensure that every participating party can identify its multi-party skyline objects without revealing the objects to others during the multi-party skyline query. The detailed privacy and security analyses show that the proposed framework can achieve the desired computation goal without privacy leakage. Besides, the performance evaluation through complexity analyses, extensive simulations, and comprehensive comparison also demonstrate the utility and the efficiency of the proposed framework.
Discovery and Analysis About the Evolutionof Service Composition Patterns

Service ecosystems, consisting of various kinds of services and mashups, usually keep evolving over time. Existing works on the evolution of service ecosystems focus on either evaluating the impacts of single services’ changes on the usage of services and the stability of the whole ecosystem, or discovering co-occurrence relationship between services, but fail to disclose any knowledge from the aspect of the evolution of service composition patterns. Based on our previous work, this paper moves one step further, revealing the latent service composition trends in a service ecosystem and providing more distinct explanation of different topic evolution patterns. A novel methodology, named Extended Dependency-Compensated Service Co-occurrence LDA (EDC-SeCo-LDA), is developed to calculate the directed dependencies between different topics and build topic evolution graph. The evolution trend of service composition could be disclosed by the graph intuitively. What’s more, EDC-SeCo-LDA proposes five different ways to adopt dependency compensation to improve the performance when making service recommendation. Experiments on ProgrammableWeb.com show that EDC-SeCo-LDA can reveal significant topic dependencies, and recommend service composition more effectively, i.e., 6% better in terms of Mean Average Precision compared with baseline approaches.
Impact on Administrative Processes by Cyberattacks in a Public Organization of Ecuador

The problems of information security regarding the impact on administrative processes due to cyber-attacks in public and private organizations in Ecuador and globally persist despite having first-class technological infrastructure. It defines a group of cybernetic vulnerabilities that are evident in public organizations. The objective is to identify the impact of cyber-attacks in various administrative processes in public organizations in Ecuador. The deductive method was used to analyze the information of the different articles related to the topic and the exploratory research. It resulted the definition strategies to prevent the infiltration of external users to obtain the information for their benefit; these strategies can be used as alternatives to lower the cyber-attack. It was concluded that in the public organizations of Ecuador there are problems because of cyber-attacks since they have an adequate technological infrastructure for information security, they do not have defined a strategic approach to protect cyber-attacks.
ESTIMATING KNOTS IN BILINEAR SPLINE GROWTH MODELS WITH TIME-INVARIANT COVARIATES IN THE FRAMEWORK OF INDIVIDUAL MEASUREMENT OCCASIONS

The linear spline growth mixture model (LSGMM), which incorporates the linearspline change patterns in the growth mixture model framework, is an appropriatetool for analyzing longitudinal data sets that come from a mixture of at leasttwo latent classes when the underlying growth trajectories within each classare nonlinear. For each latent class, it approximates complex patterns byattaching at least two linear pieces. Besides examining within-person changesand between-person differences of trajectories simultaneously, it posesinteresting statistical challenges, such as estimating the location of a changepoint (or knot), grouping individuals into at least two unobserved classes,examining factors that may be associated with those latent groups, andanalyzing data with individually-varying measurement occasions. We developed atwo-step bilinear spline growth mixture model (BLSGMM) to cluster these linearpiecewise individual trajectories as well as to associate time-invariantcovariates (TICs) to the latent classes. We present the proposed model bysimulation studies and a real-world data analysis. Our simulation studiesdemonstrate that the proposed BLSGMM-TICs is capable of clustering thenonlinear change patterns well. More importantly, they generally estimate theparameters of interest unbiasedly, precisely, and exhibit appropriateconfidence interval coverage. An empirical example using longitudinal mathscores shows that the model helps identify latent groups of nonlineartrajectories with different knot locations and associate the classes withbaseline characteristics. The corresponding computer code for the proposedmodel is provided.
Science Education and Mobile Learning: A Content Analysis Review of the Web of Science Database

The use of mobile technologies in education has appealed considerable interest and attention from educators and researchers in the last decade. Nonetheless, the effects of mobile learning on students’ learning outcome in science education are still ambiguous. The time and place flexibility, ubiquity and easy accessibility of mobile devices are among the features that make them valuable in m-learning, especially in science education, at present times. Despite the large number of researches conducted, no bibliometrics study has been carried out in this topic so far. The aim of this review is to assess the progress of international publications in the area of science education and mobile learning. This content analysis review shows the trends in publications regarding mobile learning and science education researches indexed in Web of Science database between years 2004-2019. Out of 57 documents related to the topic of interest found in the database of Web of Science, the results demonstrate that the publication frequency has increased after 2012 where the most productive year was 2016. Most of the published documents were proceedings papers and original articles. Taiwan and United States are the countries with most work published where Pakistan, Taiwan and China are the countries that funded the most scientific research. The top journal involving the publications associated with this topic is Computers &amp; Education journal. A more comprehensive criteria has been used in this content analysis which has interpreted in detail. This study can provide a much clear picture of the field to researchers, educational leaders and funders to incorporate new understandings into their research.
Development of Countenance Application Oriented on Combining ANEKA-Tri Hita Karana as A Mobile Web to Evaluate the Computer Knowledge and Morality

The principal objective of this study was to introduce the existence on development of &lt;em&gt;Countenance&lt;/em&gt; evaluation application that can be used to evaluate the moral and quality of students’ computer knowledge by using evaluation indicators which oriented to combining between &lt;em&gt;ANEKA&lt;/em&gt; values with the &lt;em&gt;Tri Hita Karana&lt;/em&gt; concept. This research was developed by using the &lt;em&gt;Borg&lt;/em&gt; &amp;amp; &lt;em&gt;Gall&lt;/em&gt; method, which focuses on the stages of creating a mobile web-based evaluation application, field trial stage, and revision stage of field trial results. The subjects involved in creating the application as much as three peoples, subjects involved in the field trial as much as 94 peoples, and subjects involved in the revision of field trial as much as three peoples. The instruments that were used in conducting the assessment on the field trial was in questionnaires form. The technique that was used to analyze the collected data from field trials results using quantitative descriptive techniques by determining the average of percentage from application quality that was compared to a standard score of application quality that refers to eleven’s scale. The study results showed that the quality of the evaluation application belongs to a very good category, so it was ready to be implemented, and it is no need to be done the major revision.
FPGA Implementation and Comparison of Protections Against SCAs for RLWE

We present various FPGA implementations of protections against SCAs for RLWE-based PKE. We implemented the main solutions from the state of the art with improved variants. We also propose a new protection based on a redundant representation of the ring elements to randomize computations. We compare the implementation results of all these solutions.
Assessing Emotion by Mouse-cursor Tracking: Theoretical and Empirical Rationales

A handful of research has demonstrated the potential of mouse tracking for emotion assessment. Yet, theoretical and empirical bases for this approach remain opaque. If emotion influences motor control (e.g., controlling the movement of the computer mouse), how does that happen? What experimental situations constrain or promote the purported connection? To address these questions, we examined how prior emotional experience (viewing emotional photos) would influence participants' motor activity, as measured by the movement of the computer cursor. Results from two experiments indicate that emotional experience impacts both temporal (peak velocity) and spatial characteristics (deviation of the trajectory) of the cursor motion. But there are clear gender differences; for male participants, emotion influenced temporal features (peak velocity) but this impact was absent in female participants. It is suggested that emotion intervenes motor vigor and decision-making processes differently in male and female participants. Theoretical rationales and design factors that are essential for trajectory-based emotion assessment are also discussed.
Security Chaos Engineering for Cloud Services: Work In Progress

The majority of security breaches in cloud infrastructure in recent years are caused by human errors and misconfigured resources. Novel security models are imperative to overcome these issues. Such models must be customer-centric, continuous, not focused on traditional security paradigms like intrusion detection and adopt proactive techniques. Thus, this paper proposes CloudStrike, a cloud security system that implements the principles of Chaos Engineering to enable the aforementioned properties. Chaos Engineering is an emerging discipline employed to prevent non-security failures in cloud infrastructure via Fault Injection Testing techniques. CloudStrike employs similar techniques with a focus on injecting failures that impact security i.e. integrity, confidentiality and availability. Essentially, CloudStrike leverages the relationship between dependability and security models. Preliminary experiments provide insightful and prospective results.
On the Global Convergence of Training Deep Linear ResNets

We study the convergence of gradient descent (GD) and stochastic gradient descent (SGD) for training $L$-hidden-layer linear residual networks (ResNets). We prove that for training deep residual networks with certain linear transformations at input and output layers, which are fixed throughout training, both GD and SGD with zero initialization on all hidden weights can converge to the global minimum of the training loss. Moreover, when specializing to appropriate Gaussian random linear transformations, GD and SGD provably optimize wide enough deep linear ResNets. Compared with the global convergence result of GD for training standard deep linear networks \citep{du2019width}, our condition on the neural network width is sharper by a factor of $O(\kappa L)$, where $\kappa$ denotes the condition number of the covariance matrix of the training data. In addition, for the first time we establish the global convergence of SGD for training deep linear ResNets and prove a linear convergence rate when the global minimum is $0$.
Research on Smart Mobile Classroom Design from the Perspective of Game Learning — Taking Preschool Education Major of Distance Education as an Example

At present, smart mobile classroom is becoming more and more popular, and the research progress of game based learning is constantly promoted, It provides favorable conditions for the combination of game learning and smart mobile classroom.On the basis of defining the concepts of game based learning and smart mobile classroom, the combination of the two is proposed.This paper proposes a framework of smart mobile classroom design in the view of game learning, and takes the English natural spelling course of distance education as an example to realize the design of the framework, and answers the questions of "how to combine the mobile terminal of smart classroom with the game learning classroom" and "how to establish the smart mobile classroom teaching mode based on game learning".
Novelty Detection Via Blurring

Conventional out-of-distribution (OOD) detection schemes based on variational autoencoder or Random Network Distillation (RND) are known to assign lower uncertainty to the OOD data than the target distribution. In this work, we discover that such conventional novelty detection schemes are also vulnerable to the blurred images. Based on the observation, we construct a novel RND-based OOD detector, SVD-RND, that utilizes blurred images during training. Our detector is simple, efficient in test time, and outperforms baseline OOD detectors in various domains. Further results show that SVD-RND learns a better target distribution representation than the baselines. Finally, SVD-RND combined with geometric transform achieves near-perfect detection accuracy in CelebA domain.
Robust training with ensemble consensus

Since deep neural networks are over-parametrized, they may memorize noisy examples. We address such memorizing issue under the existence of annotation noise. From the fact that deep neural networks cannot generalize neighborhoods of the features acquired via memorization, we find that noisy examples do not consistently incur small losses on the network in the presence of perturbation. Based on this, we propose a novel training method called Learning with Ensemble Consensus (LEC) whose goal is to prevent overfitting noisy examples by eliminating them identified via consensus of an ensemble of perturbed networks. One of the proposed LECs, LTEC outperforms the current state-of-the-art methods on MNIST, CIFAR-10, and CIFAR-100 despite its efficient memory usage.
A Delay-Aware Adaptation Framework for Cloud Gaming Under the Computation Constraint of User Devices

Cloud gaming has emerged as a new trend in the gaming industry, bringing a lot of benefits to both players and service providers. In cloud gaming, it is essential to ensure low end-to-end delay for good use experience. Hence, sufficient computational resources must be available at the client in order to process video in a timely manner. However, thin clients such as mobile devices generally have limited computation capabilities. Thus, the available computational resources may be insufficient to support the client, such as in case of low battery. In this paper, we propose a new adaptation framework for resource-constrained cloud gaming clients. The proposed framework combines frame skipping at the server and frame discarding at the client according to available computational resources of the client. Experiment results show that the proposed framework can significantly improve video quality given a delay constraint compared to conventional methods.
Least General Generalizations in Description Logic: Verification and Existence

We study two forms of least general generalizations in description logic, the least common subsumer (LCS) and most specific concept (MSC). While the LCS generalizes from examples that take the form of concepts, the MSC generalizes from individuals in data. Our focus is on the complexity of existence and verification, the latter meaning to decide whether a candidate concept is the LCS or MSC. We consider cases with and without a background TBox and a target signature. Our results range from coNP-complete for LCS and MSC verification in the description logic εℒ without TBoxes to undecidability of LCS and MSC verification and existence in εℒI with TBoxes. To obtain results in the presence of a TBox, we establish a close link between the problems studied in this paper and concept learning from positive and negative examples. We also give a way to regain decidability in εℒI with TBoxes and study single example MSC as a special case.
DGE: Deep Generative Network Embedding Based on Commonality and Individuality

Network embedding plays a crucial role in network analysis to provide effective representations for a variety of learning tasks. Existing attributed network embedding methods mainly focus on preserving the observed node attributes and network topology in the latent embedding space, with the assumption that nodes connected through edges will share similar attributes. However, our empirical analysis of real-world datasets shows that there exist both commonality and individuality between node attributes and network topology. On the one hand, similar nodes are expected to share similar attributes and have edges connecting them (commonality). On the other hand, each information source may maintain individual differences as well (individuality). Simultaneously capturing commonality and individuality is very challenging due to their exclusive nature and existing work fail to do so. In this paper, we propose a deep generative embedding (DGE) framework which simultaneously captures commonality and individuality between network topology and node attributes in a generative process. Stochastic gradient variational Bayesian (SGVB) optimization is employed to infer model parameters as well as the node embeddings. Extensive experiments on four real-world datasets show the superiority of our proposed DGE framework in various tasks including node classification and link prediction.
On the distribution of test smells in open source Android applications: an exploratory study

The impact of bad programming practices, such as code smells, in production code has been the focus of numerous studies in software engineering. Like production code, unit tests are also affected by bad programming practices which can have a negative impact on the quality and maintenance of a software system. While several studies addressed code and test smells in desktop applications, there is little knowledge of test smells in the context of mobile applications. In this study, we extend the existing catalog of test smells by identifying and defining new smells and survey over 40 developers who confirm that our proposed smells are bad programming practices in test suites. Additionally, we perform an empirical study on the occurrences and distribution of the proposed smells on 656 open-source Android applications (apps). Our findings show a widespread occurrence of test smells in apps. We also show that apps tend to exhibit test smells early in their lifetime with different degrees of co-occurrences on different smell types. This empirical study demonstrates that test smells can be used as an indicator for necessary preventive software maintenance for test suites.
Normalization of Abbreviation and Acronym on Microtext in Bahasa Indonesia by Using Dictionary-Based and Longest Common Subsequence (LCS)

The communication nowadays has reached a need to express the idea in short text. This kind of communication is delivered in various media such as short messages service (SMS), Facebook status, Twitter post, chat messages, comments, and any form of short text. These various kinds of short text are known as microtext. The microtext usually has one sentence or less, written informally, consists of abbreviations, acronyms, emoticons, hashtags, and others. These features of the microtext become a particular challenge to the text classification. These features cannot be processed directly as in the traditional text processing, because it may lead to inaccuracy. Therefore, it requires microtext normalization to transform these features into well-written texts before applying text processing. This research aims to normalize some of these features, which are abbreviations and acronyms. The normalization applied dictionary-based and longest common subsequence (LCS) approach to the microtext in Bahasa Indonesia. Dictionary-based has shown an excellenct performance instead of LCS. However, it is limited to pre-defined abbreviations and acronyms. Besides, the LCS offers dynamic microtext normalization. Nevertheless, the combination of both approaches increases normalization performance slightly.
LEAP nets for system identification and application to power systems

Using neural network modeling, we address the problem of system identification for continuous multivariate systems, whose structures vary around an operating point. Structural changes in the system are of combinatorial nature, and some of them may be very rare; they may be actionable for the purpose of controlling the system. Although our ultimate goal is both system identification and control, we only address the problem of identification in this paper. We propose and study a novel neural network architecture called LEAP net, for Latent Encoding of Atypical Perturbation. Our method maps system structure changes to neural net structure changes, using structural actionable variables. We demonstrate empirically that LEAP nets can be trained with a natural observational distribution, very concentrated around a “reference” operating point of the system, and yet generalize to rare (or unseen) structural changes. We validate the generalization properties of LEAP nets theoretically in particular cases. We apply our technique to power transmission grids, in which high voltage lines are disconnected and re-connected with one-another from time to time, either accidentally or willfully. We discuss extensions of our approach to actionable variables, which are continuous (instead of discrete, in the case of our application) and make connections between our problem setting, transfer learning, causal inference, and reinforcement learning.
Stochastic Approach for System Identification using Machine Learning

This paper gives a clear understanding of how a system can be modeled in a non-parametric and probabilistic way using machine learning based on historical data with taking into account uncertain noise added to the output of the system. We explained key concepts step by step and end up with a unique algorithm. Moreover, we demonstrated an illustrative example of the proposed algorithm in case of nonlinear dynamic system.
On complementary symmetry under Cumulative Prospect Theory

Complementary symmetry has been proved to be fulfilled in Cumulative Prospect Theory with random reference, regardless of the form of the utility function. Furthermore an alternative model, in which complementary symmetry generally does not hold, is discussed.