Reviewerly/exHarmony huggingface dataset:
Study on User-Centered Usability Elements of User Interface Designs in an Augmented Reality Environment

In order to complete the augmented reality (AR) user interface (UI) design simply and quickly, the usable factors were studied in this work. The main focus of interface design is to increase usability. Various factors should be considered together when evaluating usability. An ideal usable model is usually user-centered, with the aim of perceiving the interests of users and easily completing targets. In order to cover all types of usable factors, the literature survey method had been used and a total of 85 usable factors had been collected by this survey. To make the usable factors adapt the augmented reality, the concept of factors is redefined. We extract the items which are adaptable and user-centered, combine or delete the items that have the same meanings and finally select 25 usable evaluative factors. The Human Computer Interaction professional is set as the target, and the related data is collected by Heuristic Evaluation. We are able to systematize the usable factors by principal component analysis, observe the correlation between the usable factors, and classify those with high correlation.
On Minrank and Forbidden Subgraphs

The minrank over a field F of a graph G on the vertex set { 1,2,… , n } is the minimum possible rank of a matrix M ∈ F n × n such that M i, i ≠ 0 for every i , and M i, j =0 for every distinct non-adjacent vertices i and j in G . For an integer n , a graph H , and a field F, let g ( n , H , F) denote the maximum possible minrank over F of an n -vertex graph whose complement contains no copy of H . In this article, we study this quantity for various graphs H and fields F. For finite fields, we prove by a probabilistic argument a general lower bound on g ( n , H ,F), which yields a nearly tight bound of Ω (√ n / log n ) for the triangle H = K 3 . For the real field, we prove by an explicit construction that for every non-bipartite graph H , g ( n , H , R) ≥ n δ for some δ = δ ( H )&gt; 0. As a by-product of this construction, we disprove a conjecture of Codenotti et al. [11]. The results are motivated by questions in information theory, circuit complexity, and geometry.
Energy consumption of virtual machine migration within varied DCN architectures

Virtual machine (VM) migration mechanism in data centers significantly improves the utilization of the server resource. While the recent work focuses on how to use VM migration to stability physical machine (PM) utilization or improve energy consumption, little attention has been agreed to network performance issues, such as architecture, link, load and inter-traffic between VMs in the data centers network (DCN) architectures. Network-aware Virtual Machine (NVM) placement and migration is developing as a very favorable technique for the optimization of compute network resource utilization, energy consumption, and network traffic minimization. Thus, NVM operation guarantees a fair share allocation of network resources, leading to a seamless VM mobility though decreasing degradation of network performance.The focus of this paper is to present the impact of several network architectures and virtualisation on how to reduce the energy consumed by virtual machine migration. We introduce a simulation setting for energy aware employing Green-Cloud simulator. The simulator is designed to detect the details of the consumed energy by datacenter components such us VMs, PMs, servers, switches, and links. The simulation results gotten for three-tier, Debug and High speed architectures show the value of each system in employing power management schema through DNS and DVFS by applying voltage scaling and dynamic shutdown technics.
Facial Recognition for Car Security System Using Fisherface Method

Abstract The increasing crime of car theft proves that there is still a frail security system installed on the car. Apart from the expertise possessed by the car thieves, the crime of car theft can also occur due to the negligence of the car owner in leaving his car. One of the examples is leaving the car when the engine is on. This is the main reason for car owners to have an advanced security system. The application of biometrics technology is expected to be a solution for improving the car security system. One of the biometrics technologies to be applied in car security systems is facial recognition technology. In this study, facial recognition is in real-time using the fisherface method to improve the security system of a car when the engine is still on. The results show that the fisherface method performs well with an accuracy of 83.04%. The results indicate that the facial recognition application using the fisherface method is one of the most effective solutions in improving car security systems.
Fully homomorphic encryption based on the ring learning with rounding problem

Almost all existing well-known fully homomorphic encryption (FHE) schemes, which are based on either the learning with errors (LWE) or the ring LWE problem, require expensive Gaussian noise sampling. In this study, the authors propose an FHE scheme based on the ring learning with rounding (RLWR) problem. The learning with rounding (LWR) problem was proposed as a deterministic variant of LWE, while the RLWR is a variant of LWR. Sampling an LWR instance does not require Gaussian noise sampling process, and neither does an RLWR instance. Thus, our FHE scheme can be instantiated without the need for Gaussian noise sampling. To implement homomorphic operations, we devise a specific relinearisation method. Furthermore, we also prove that our RLWR-based FHE scheme is IND-CPA secure under RLWR assumption.
Planar bipartite biregular degree sequences

A pair of sequences of natural numbers is called planar if there exists a simple, bipartite, planar graph for which the given sequences are the degree sequences of its parts. For a pair to be planar, the sums of the sequences have to be equal and Euler's inequality must be satisfied. Pairs that verify these two necessary conditions are called admissible. We prove that a pair of constant sequences is planar if and only if it is admissible (such pairs can be easily listed) and is different from (35|35) and (325|515).
Graph-based selective outlier ensembles

An ensemble technique is characterized by the mechanism that generates the components and by the mechanism that combines them. A common way to achieve the consensus is to enable each component to equally participate in the aggregation process. A problem with this approach is that poor components are likely to negatively affect the quality of the consensus result. To address this issue, alternatives have been explored in the literature to build selective classifier and cluster ensembles, where only a subset of the components contributes to the computation of the consensus. Of the family of ensemble methods, outlier ensembles are the least studied. Only recently, the selection problem for outlier ensembles has been discussed. In this work we define a new graph-based class of ranking selection methods. A method in this class is characterized by two main steps: (1) Mapping the rankings onto a graph structure; and (2) Mining the resulting graph to identify a subset of rankings. We define a specific instance of the graph-based ranking selection class. Specifically, we map the problem of selecting ensemble components onto a mining problem in a graph. An extensive evaluation was conducted on a variety of heterogeneous data and methods. Our empirical results show that our approach outperforms state-of-the-art selective outlier ensemble techniques.
Paracoherent Answer Set Semantics meets Argumentation Frameworks

In the last years, abstract argumentation has met with great success in AI, since it has served to capture several non-monotonic logics for AI. Relations between argumentation framework (AF) semantics and logic programming ones are investigating more and more. In particular, great attention has been given to the well-known stable extensions of an AF, that are closely related to the answer sets of a logic program. However, if a framework admits a small incoherent part, no stable extension can be provided. To overcome this shortcoming, two semantics generalizing stable extensions have been studied, namely semi-stable and stage. In this paper, we show that another perspective is possible on incoherent AFs, called paracoherent extensions, as they have a counterpart in paracoherent answer set semantics. We compare this perspective with semi-stable and stage semantics, by showing that computational costs remain unchanged, and moreover an interesting symmetric behaviour is maintained. Under consideration for acceptance in TPLP.
Bar recursion is not computable via iteration

We show that the bar recursion operators of Spector and Kohlenbach, considered as third-order functionals acting on total arguments, are not computable in Gödel's System T plus minimization, which we show to be equivalent to a programming language with a higher-order iteration construct. The main r esult is formulated so as to imply the non-definability of bar recursion in T+min within a variety of partial and total models, for instance the Kleene–Kreisel continuous functionals. The paper thus supplies proofs of some results stated in the book by Longley and Normann. The proof of the main theorem makes serious use of the theory of nested sequential procedures (also known as PCF Böhm trees), and proceeds by showing that bar recursion cannot be represented by any sequential procedure within which the tree of nested function applications is well-founded.
Mining Social Network Data for Predictive Personality Modelling by Employing Machine Learning Techniques

Facebook, Twitter, LinkedIn and Tumblr are online social networking platforms where the users send and receive messages on the topic of their choice and express their sentiments. The usage of these sites has exponentially increased over the last few years, thereby increasing the information posted on online social media sites. The quantity of information/tweets keeps increasing on a daily basis. Twitter has become a stable platform to identify personality-related indicators and encrypted in user profiles and pages related to a subject. In this proposed work, we present a scalable real-time system for sentiment analysis of Twitter data. This work will collect tweets of the users in real time and thus provide a basis to identify each tweet into either positive or negative based on the mind-set of the user, thereby providing a real-time analysis of the users regarding a certain topic. The system relies on feature extraction from the tweets generated in real time. A supervised learning approach based on ensemble learning is used to train various classifiers based on the features extracted. A design and implementation in Flask and Celery has been carried out which contains the feature extraction and classification tasks. The system is scalable with respect to the size of the input data and the rate of data arrival. The merits of the proposed system in terms of scalability, performance and classification accuracy was evaluated experimentally.
GP-SUM. Gaussian Process Filtering of non-Gaussian Beliefs

This work studies the problem of stochastic dynamic filtering and state propagation with complex beliefs. The main contribution is GP-SUM, a filtering algorithm tailored to dynamic systems and observation models expressed as Gaussian Processes (GP), and to states represented as a weighted Sum of Gaussians. The key attribute of GP-SUM is that it does not rely on linearizations of the dynamic or observation models, or on unimodal Gaussian approximations of the belief, hence enables tracking complex state distributions.The algorithm can be seen as a combination of a sampling-based filter with a probabilistic Bayes filter. On the one hand, GP-SUM operates by sampling the state distribution and propagating each sample through the dynamic system and observation models. On the other hand, it achieves effective sampling and accurate probabilistic propagation by relying on the GP form of the system, and the sum-of-Gaussian form of the belief. We show that GP-SUM outperforms several GP-Bayes and Particle Filters on a standard benchmark. We also demonstrate its use in a pushing task, predicting with experimental accuracy the naturally occurring non-Gaussian distributions.
PowerMonitor: Aspect Mining and Sentiment Analysis on Online Reviews

Customer reviews on a product regard multi-aspect with emotional tendencies. Aspects in a review show what properties customers concern about and sentiment towards an aspect reveals how a customer evaluates it. The aspect mining and sentiment analysis provides a lot of valuable references and market feedback information to online commercial platforms. Due to the unpredictability of aspects appearing in a review, the method proposed in this paper is supposed to be dynamic and intelligent and to define the sentiment related to an aspect negative or positive polarity in semantic analysis. Based on the improved aspect dictionary and sentiment dictionary, this paper presents a framework for aspect mining and sentiment analysis for online customer reviews-PowerMonitor. The experimental results show that the framework performs well in aspect extraction and aspect emotion judgment. We evaluate the model using small, widely used sentiment and subjectivity corpora from JD.com and find it out-performs several previously introduced methods for sentiment classification. We also introduce future works to serve as a reference for efforts in this area.
Specific heat and thermal entanglement in an open quantum system

In this paper, the density matrix is obtained using the non-Markovian master equation method for a system consisting of two-qubit modeling with the Heisenberg XXZ chain, which involves the Dzyaloshinskii–Moriya interaction and is exposed to the bosonic baths. Also, using the proposed formula for calculating the specific heat through the density matrix of the open quantum system, the specific heat is calculated. The quantum entanglement behavior with time and coupling constant is investigated. It is observed that the specific heat at low temperatures becomes negative when the system is exposed to the environment. The time behavior of quantum entanglement for this model showed that the entanglement decreases to zero with increasing time.
Fast Fourier Transforms for Microgrid Climate Computing

Solar panels and wind turbines are key technologies for a sustainable low-carbon energy transition. The large diffusion of weather-dependent renewable energy generators face the challenge to fit the demand of uncertain loads with the most appropriate mix of distributed energy resources. The availability of accurate climate variables projections is essential to identify the best combination among distributed generators, energy storages and power loads in each geographical location. From the European Centre for Medium-Range Weather Forecasts (ECMWF) datasets of several climate variables are available for renewable energy resources yield forecasting. This paper presents different approaches to manipulate ECMWF datasets by combining Fast Fourier Transform with polynomial and forest tree regression models to predict climate variables over a one-year period, typical for microgrid simulations. An example of climate datasets forecasts related to the city of Bremen is presented with the evaluation of performances during test and training scenarios phases.
Impact three pass protocol modifications to key transmission performance

Abstract Key exchange is important to ensure cryptography works as expected. The three pass protocol (TPP) is the popular key exchange protocol. Modifications have been research interest for security improvement. This paper reports experiments to show modification impact to processing time. As modifications applied to 1, 2 and 3 links, the TPP processing time increases exponentially from 79 ms to 511 ms. The modification increases TPP processing time significantly. But modifications have minor impact to encryption and decryption times.
Rate of change analysis for interestingness measures

The use of association rule mining techniques in diverse contexts and domains has resulted in the creation of numerous interestingness measures. This, in turn, has motivated researchers to come up with various classification schemes for these measures. One popular approach to classify the objective measures is to assess the set of mathematical properties they satisfy in order to help practitioners select the right measure for a given problem. In this research, we discuss the insufficiency of the existing properties in the literature to capture certain behaviors of interestingness measures. This motivates us to adopt an approach where a measure is described by how it varies if there is a unit change in the frequency count $$(f_{11},f_{10},f_{01},f_{00})$$, at different preexisting states of the counts. This rate of change analysis is formally defined as the first partial derivative of the measure with respect to the various frequency counts. We use this analysis to define two novel properties, unit-null asymptotic invariance (UNAI) and unit-null zero rate (UNZR). UNAI looks at the asymptotic effect of adding frequency patterns, while UNZR looks at the initial effect of adding frequency patterns when they do not preexist in the dataset. We present a comprehensive analysis of 50 interestingness measures and classify them in accordance with the two properties. We also present multiple empirical studies, involving both synthetic and real-world datasets, which are used to cluster various measures according to the rule ranking patterns of the measures. The study concludes with the observation that classification of measures using the empirical clusters shares significant similarities to the classification of measures done through the properties presented in this research.
Adaptive Learning for Concept Drift in Application Performance Modeling

Supervised learning is a promising approach for modeling the performance of applications running on large HPC systems. A key assumption in supervised learning is that the training and testing data are obtained under the same conditions. However, in production HPC systems these conditions might not hold because the conditions of the platform can change over time as a result of hardware degradation, hardware replacement, software upgrade, and configuration updates. These changes could alter the data distribution in a way that affects the accuracy of the predictive performance models and render them less useful; this phenomenon is referred to as concept drift. Ignoring concept drift can lead to suboptimal resource usage and decreased efficiency when those performance models are deployed for tuning and job scheduling in production systems. To address this issue, we propose a concept-drift-aware predictive modeling approach that comprises two components: (1) an online Bayesian changepoint detection method that can automatically identify the location of events that lead to concept drift in near-real time and (2) a moment-matching transformation inspired by transfer learning that converts the training data collected before the drift to be useful for retraining.
Population anomaly detection through deep gaussianization

We introduce an algorithmic method for population anomaly detection based on gaussianization through an adversarial autoencoder. This method is applicable to detection of 'soft' anomalies in arbitrarily distributed highly-dimensional data. A soft, or population, anomaly is characterized by a shift in the distribution of the data set, where certain elements appear with higher probability than anticipated. Such anomalies must be detected by considering a sufficiently large sample set rather than a single sample. Applications include, but not limited to, payment fraud trends, data exfiltration, disease clusters and epidemics, and social unrests. We evaluate the method on several domains and obtain both quantitative results and qualitative insights.
Controller synthesis for continuous T-S fuzzy systems with partly immeasurable premise variables

In this paper, the controller synthesis problem for continuous T-S fuzzy systems with partly immeasurable premise variables is studied. The objective is to synthesize an observer-based controller to bound the reachable set of the system by an ellipsoid set such that the problem of state bounding can be addressed. First, the measurable premise variables and the estimated immeasurable premise variables are simultaneously utilized to construct the controller. Then, the information of the measurable premise variables is employed for deriving the controller synthesis criterion. In the end, the effectiveness of the presented approach is confirmed by an example.
Robust Bayesian model selection for variable clustering with the Gaussian graphical model

Variable clustering is important for explanatory analysis. However, only few dedicated methods for variable clustering with the Gaussian graphical model have been proposed. Even more severe, small insignificant partial correlations due to noise can dramatically change the clustering result when evaluating for example with the Bayesian information criteria (BIC). In this work, we try to address this issue by proposing a Bayesian model that accounts for negligible small, but not necessarily zero, partial correlations. Based on our model, we propose to evaluate a variable clustering result using the marginal likelihood. To address the intractable calculation of the marginal likelihood, we propose two solutions: one based on a variational approximation and another based on MCMC. Experiments on simulated data show that the proposed method is similarly accurate as BIC in the no noise setting, but considerably more accurate when there are noisy partial correlations. Furthermore, on real data the proposed method provides clustering results that are intuitively sensible, which is not always the case when using BIC or its extensions.
Enhanced Bag-of-Features Method Using Grey Wolf Optimization for Automated Face Retrieval

As images are increasing exponentially over the Internet, the retrieval of such images using content-based approach becomes an important research area. Out of the various models of the image retrievals, recognition of facial images is highly used by many application areas. However, due to the different variations involved in the facial images, it is a challenging problem. Therefore, this work introduces an efficient face recognition method which uses the bag-of-features approach for the same. The proposed bag-of-features based face recognition approach uses Grey wolf optimization algorithm for obtaining the prominent visual words. The enhanced bag-of-features based face recognition approach has been analyzed on a face database of Oracle Research Laboratory against the classification accuracy. The experimental results show that the presented method identifies the faces more accurately than the other meta-heuristic based approaches.
Unseen Object Segmentation in Videos via Transferable Representations

In order to learn object segmentation models in videos, conventional methods require a large amount of pixel-wise ground truth annotations. However, collecting such supervised data is time-consuming and labor-intensive. In this paper, we exploit existing annotations in source images and transfer such visual information to segment videos with unseen object categories. Without using any annotations in the target video, we propose a method to jointly mine useful segments and learn feature representations that better adapt to the target frames. The entire process is decomposed into two tasks: (1) solving a submodular function for selecting object-like segments, and (2) learning a CNN model with a transferable module for adapting seen categories in the source domain to the unseen target video. We present an iterative update scheme between two tasks to self-learn the final solution for object segmentation. Experimental results on numerous benchmark datasets show that the proposed method performs favorably against the state-of-the-art algorithms.