Reviewerly/exHarmony huggingface dataset:
Modeling of a 3D Temperature Field by Integrating a Physics-Specific Model and Spatiotemporal Stochastic Processes

Engineering thermal management (ETM) is one of the critical tasks for quality control and system surveillance in many industries, and acquiring the temperature field and its evolution is a prerequisite for efficient thermal management. By harnessing the sensing data from sensor networks, an unprecedented opportunity has emerged for an accurate estimation of the temperature field. However, limited resources of sensor deployment and computation capacity pose a great challenge while modeling the spatiotemporal dynamics of the temperature field. This paper presents a novel temperature field estimation approach to describe the dynamics of a temperature field by combining a physics-specific model and a spatiotemporal Gaussian process. To reduce the computational burden while dealing with a large set of spatiotemporal data, we employ a tapering covariance function and develop an associated parameter estimation procedure. We introduce a case study of grain storage to show the effectiveness and efficiency of the proposed approach.
A Machine Learning Based Approach to Predict Power Efficiency of S-Boxes

In the era of lightweight cryptography, designing cryptographically good and power efficient 4 × 4 S-boxes is a widely discussed problem. While the optimal cryptographic properties are easy to verify, it is not very straightforward to verify whether a S-box is power efficient or not. The traditional approach is to explicitly determine the dynamic power consumption using commercially available CAD tools and report accordingly based on a pre-defined threshold value. However, this procedure is highly time consuming, and the overhead becomes formidable while dealing with a set of S-boxes from a large space. This mandates development of an automation tool which should be able to quickly characterize the power efficiency from the Boolean function representation of an S-box. In this paper, we present a supervised machine learning (ML) assisted automated framework to resolve the problem for 4 × 4 S-boxes, which turns out to be approximately 14 times faster (using AND-OR-NOT gates) than the traditional approach. The key idea is to extrapolate the knowledge of the literal counts of various functional forms, AND-OR-NOT gate counts in the simplified SOP form of the underlying Boolean functions corresponding to the S-box to predict the dynamic power efficiency. We demonstrate the effectiveness of our framework by reporting a set of power efficient S-boxes from a large set of 4 × 4 optimal S-boxes. The experimental results and performance of our novel technique depicts its superiority with high efficiency and low time overhead.
A Robust Speech Encryption System Based on DNA Addition and Chaotic Maps

Encryption is one of the most important methods for providing data security mainly for an end to end protection of data transmitted across the networks. In this paper, we proposed a speech encryption algorithm based on DNA addition and chaotic maps for secure speech communication. The algorithm deals with sampled speech signal that is given as input which is divided into four segments of equal duration. During successive levels of iteration, the speech segment is permuted and substituted with different chaotic mapping techniques such as sine mapping, henon mapping, logistic mapping and tent mapping. After performing the permutation and substitution all the segments are combined and encoded as a sequence of DNA’s denoted by X. The DNA sequence X and DNA sequence Y obtained using henon mapping are summed up together using DNA addition operation. Finally, the speech encryption is obtained as the result using DNA decoding. The correlation co-efficient test, Signal to noise ratio test and Peak Signal to Noise Ratio test provides better analysis of the proposed system. The algorithm also provides a better simulation result and also helps in resisting the brute force attacks and differential attacks.
Video Retrieval Based on Motion Vector Key Frame Extraction and Spatial Pyramid Matching

Video retrieval technique aims at retrieving relevant video from a large video dataset. Generally, it consists of heterogeneous videos and retrieval of relevant videos with high accuracy is a tedious work. In this paper, we proposed a content-based video retrieval system that correspondingly retrieves the most relevant videos from the database based on the visual content. In this context, motion vector based key frame extraction is computed as video summarization technique to recapitulate the video content and spatial pyramid matching scheme is used for the key frame matching. Computational mechanism for key frame selection is based on the concept of shot selection using outliers detection followed by sub-shot detection for each shot using motion vector. Finally, in each sub-shot undulant and stable sub section. Spatial pyramid matching partitions the key frames into increasingly fine sub-regions and computes features from each sub-region. The proposed method has been implemented and tested on real datasets. The performance has been compared with other standard methods. Experimental results provide an insight of satisfactory results in video retrieval, in terms of both subjective visual perception and objective evaluation metrics like fidelity, video sapling error, precision and recall.
Similarity metrics analysis for principal concepts detection in ontology creation

In this paper an analysis, based on similarity metrics, was carried out in order to detect main concepts related to the superclasses in a pedagogical domain ontology. A semi-automatic corpus containing articles in Spanish was built. Afterward, the corpus was lemmatized and three representations wer e extracted. Four textual similarity metrics based on terms and Pointwise Mutual Information were implemented. A list of words, which was evaluated using a gold standard built by an expert in the domain, was retrieved from each experiment according to establish thresholds for the metrics. Precision and recall were used for evaluation step, where a detailed discussion by representation and class was presented. Results showed a higher precision in types of intelligences class and 5-grams representation.
Runtime Revision of Norms and Sanctions based on Agent Preferences

To fulfill the overall objectives of a multiagent system, the behavior of individual agents should be controlled and coordinated. Runtime norm enforcement is one way to do so without over-constraining the agents' autonomy. Due to the dynamicity and uncertainty of the environment, however, it is hard to specify norms that, when enforced, will fulfill the system-level objectives in every operating context. In this paper, we propose a mechanism for the automated revision of norms by altering their sanctions, based on the data monitored during the system execution and on some knowledge about the agents' preferences. We use a Bayesian Network to learn at runtime the relationship between the obedience/violation of a norm and the achievement of the system objectives. We propose two heuristic strategies that explore the updated Bayesian Network and automatically revise the sanction of an enforced norm. An evaluation of our heuristics using a traffic simulator shows that our mechanisms outperform uninformed heuristics in terms of convergence speed.
Mapping Data Associations in Enterprise Systems

The use of visualizations to access and process enterprise data has been proposed as a boost to the usability of enterprise systems. Yet, novel user interfaces do not always yield the expected gains in performance. To realize the potential benefits from using a visual approach, it is important to examine the effectiveness of visualizations in a specific task context and to follow an iterative, evaluation-driven process for identifying and addressing shortcomings in their design and execution. In this paper, we present the results of a study examining the effectiveness of a visual interface called Association Map (AM) for exploring ternary associations of data versus a typical tabular interface from a popular enterprise system. This latest version of AM evolved from earlier versions that underwent similar evaluations and we discuss the design choices that were important for achieving user performance gains in accuracy and time-on-task compared to earlier versions and to table-based interfaces. Findings presented here help build the case for incorporating task-driven visual interfaces into traditional table-based representations used in enterprise systems to improve both the user experience and task-related outcomes. Furthermore, they provide insights for investigating the theoretical foundations behind what makes a visual interface work.
Overview of Opinion Detection Approaches in Arabic

Opinion mining or sentiment analysis has drawn the interest of several researchers around the world and it is becoming nowadays an ongoing field of research. Besides, with the growth of available Arabic electronic documents and the use of this morphologically-Rich Language, especially by youth, in social networks and by companies in shopping or commercial websites (e-commerce), opinion mining in Arabic texts has become essential. Individuals and organizations are more and more interested in other people's opinion available on the web, for their decision making. Moreover, governments resort to public citizen's opinions to improve the quality of services and information offered to them. Recently, many researchers proposed approaches to detect opinions in Arabic texts, identify existing ones, extract, quantify and study their influence. So this paper provides an overview of the last update in this field and present different approaches of opinion detection in Arabic texts, so as to contribute to finding the optimal approach and discuss some of their main limitations.
Efficient Multispectral Face Recognition using Random Feature Selection and PSO-SVM

Biometric technologies have been widely used in recent years for authentication and security purposes. Face recognition is one of the important techniques for its simple way to obtain the subject samples without being intrusive. However, this technology has been employed mostly using the visible spectrum only, which suffers from some limitations such as illumination change, facial expression and pose variations. The infrared spectrum offers some advantages over the visible spectrum, mainly the robustness to light change. In this paper, we propose a new multispectral framework that uses both infrared and visible spectra with an optimization based on a new proposed feature selection algorithm and the PSO-SVM. Experimental tests were conducted on IRIS OTCBVS Thermal/Visible and CSIST Lab 2 Databases. The obtained results clearly demonstrate the effectiveness of our new framework compared to a mono-spectral face recognition system.
Machine Learning Quorum Decider (MLQD) for Large Scale IoT Deployments

The usage of sensors and actuators in industrial applications has increased widely. Cyber Physical Systems (CPS) has improved the usage of closed loop control systems using sensors and actuators. However the health of sensors & actuators and their impact on the efficiency of the industry is still a challenging area. The other important aspect is the network related issues that rise due to the large scale of sensors & actuators. There need an efficient mechanism that manages the network delay and packet drops associated with the large scale sensor deployments. There are machine learning techniques which are used to predict the sensor values when it is absent due to various reasons. Similarly, there are anomaly detection techniques that are used to detect faulty sensors. Both the prediction approaches fail to address the properties related to lifetime and service needs of sensors & actuators. This paper proposes a machine learning framework that combines the results of individual learning modules for achieving collective decisions. This proposed method provides the solution to address the delays and failures occurring in large scale deployments.
Detecting double compression for JPEG images of low quality factor

Double JPEG compression is often used to conceal some illegal operations on the source image. Since there exist some traces when an image undergoes double JPEG compression, the detection of double JPEG compression can be used as a tool of digital forensics. In the past decade, researchers have focused on detecting double compression with the same quality factor (QF) but cannot provide reliable results when the QF is approximately below 70. To remedy this, we propose to analyze the low-frequency components and the error image and extract two types of features to improve the detection accuracy. Since the high-frequency components are easily lost in the JPEG compression, the low-frequency components are utilized to extract the reliable features. With this in mind, our method first analyzes the low-frequency discrete cosine transform coefficients and the lost information. Then, the features are extracted to characterize the difference between the two sequential compressions. Finally, the feature set is fed to a support vector machine for classification. Experimental results on two standard image databases verify that our method could improve the detection accuracy of the low QF double compressed JPEG images with the same QF. The average classification accuracy of the different QFs (the range of QF is usually set from 70 to 20) was obtained as 87.75%.
A Game Theoretic Approach for Virtual Machine Allocation Security in Cloud Computing

The Virtualization technologies make cloud computing desirable but they also introduce serious security risks like co-resident attack and interdependence between users on the same hypervisor. The previous works on the subject of virtualization security have some limitations: either we can not implement them, no performance evaluation or it increases the workload at servers or haven't quantitative analysis.
Data generation approaches for topic classification in multilingual spoken dialog systems

The conception of spoken-dialog systems (SDS) usually faces the problem of extending or adapting the system to multiple languages. This implies the creation of modules specifically for the new languages, which is a time consuming process. In this paper, we propose two methods to reduce the time needed to extend the SDS to other languages. Our methods are particularly oriented to the topic classification and semantic tagging tasks and we evaluate their effectiveness on topic classification for three languages: English, Spanish, French.
Secure Image Watermarking Approach Using HT-AT

A new technique of image watermarking for ownership protection using homomorphic transform (HT), singular value decomposition (SVD) along with Arnold transform (AT) is proposed in this paper. In the developed method host image is splitted into illumination and reflectance components using HT and reflectance component is chosen for embedding of watermark as it offers better immunity towards attacks. For enhancing security of algorithm, watermark is scrambled through AT. Scrambled watermark is directly embedded with singular values (SVs) of reflectance component resulting in a blind scheme. Proposed method is examined under various attacks such as rotation, scaling and results clearly demonstrates effectiveness of proposed algorithm. Comparative study of proposed technique with other schemes clearly reflects its outstanding performance.
Shape analysis of 3D nanoscale reconstructions of brain cell nuclear envelopes by implicit and explicit parametric representations

Shape analysis of cell nuclei is becoming increasingly important in biology and medicine. Recent results have identified that large variability in shape and size of nuclei has an important impact on many biological processes. Current analysis techniques involve automatic methods for detection and segmentation of histology and microscopy images, but are mostly performed in 2D. Methods for 3D shape analysis, made possible by emerging acquisition methods capable to provide nanometric-scale 3D reconstructions, are still at an early stage, and often assume a simple spherical shape. We introduce here a framework for analyzing 3D nanoscale reconstructions of nuclei of brain cells (mostly neurons), obtained by semiautomatic segmentation of electron micrographs. Our method considers two parametric representations: the first one customizes the implicit hyperquadricsformulation and it is particularly suited for convex shapes, while the latter considers a spherical harmonics decomposition of the explicit radial representation. Point clouds of nuclear envelopes, extracted from image data, are fitted to the parameterized models which are then used for performing statistical analysis and shape comparisons. We report on the analysis of a collection of 121 nuclei of brain cells obtained from the somatosensory cortex of a juvenile rat.
A Novel Covert Channel Attack Using Memory Encryption Engine Cache

Microarchitectural covert channel attack is a threat when multiple tenants share hardware resources such as last-level cache. In this work, we propose a novel covert channel attack that exploits new microarchitecture that have been introduced to support memory encryption -- in particular, the memory encryption engine (MEE) cache. The MEE cache is a shared resource but only utilized when accessing the integrity tree data and provides opportunity for a stealthy covert channel attack. However, there are challenges since MEE cache organization is not publicly known and the access behavior differs from a conventional cache. We demonstrate how the MEE cache can be exploited to establish a covert channel communication.
Forensic Gender Speaker Recognition under Clean and Noisy Environments

The Forensic Gender Speaker Recognition (FGSR) deals with finding out the gender of a person from his or her voice. This task has been implemented in several Automatic Speaker Recognition systems and has proved to be of great significance. The use of gender recognition eases for user authentication and identification in high security systems. In this paper, we have used the gender recognition for speakers using Mel Frequency Cepstral Coefficients (MFCCs), a Gaussian Mixture Model-Universal Background Model (GMM-UBM) and Likelihood Report (LR) were used for modelization of features extracted from the speech signal of speakers under clean and noisy conditions. The performance of FGSR system is evaluated in terms of Equal Proportion Probability (EPP). Three sets of experiments were performed - the first experiment; corpus contains males’ voices. The second; contains females’ voices and third; contains both voices of both sexes. In clean conditions, the average accuracy of the first experiment was slightly higher for EPP=3.1579% than the second and the third experiments. Therefore, Performance evaluation results are encouraging in clean conditions compared to in noisy condition for EPP=27%.
A modified analytical framework for describing m-learning (as applied to early grade Mathematics)

Background: There has been little Southern African research attention on the potentials of m-learning to support quality mathematics learning for young children and their caring adults. This article argues that m-learning research has shifted from claims of being promising to claims of effect in educational settings of both classrooms and homes. This is particularly the case in mathematics, where there is increasing evidence of positive (although modest) improvement in learning outcomes. Aim: This article modifies an analytical framework for initial descriptions of m-learning interventions. Comparison between interventions in the Southern African Development Community (SADC) context is then possible. Setting: Three large-scale m-learning interventions focused on early grade mathematics in the SADC countries. Methods: Targeting the early grades and building on an existing framework for describing m-learning interventions, three large-scale m-learning interventions from within the SADC were purposively selected. The three interventions exemplify a possible way to describe the learning theory and pedagogical emphasis underlying the design of their mathematics programmes. Results: The cases themselves contribute to understanding the m-learning landscape and approaches to early grade mathematics in the SADC in more detail. Conclusion: A modified analytical framework is offered as a means of describing m-learning in ways that attend to children’s and caregivers’ use of mobile devices, as well as the underlying learning theories.
A Fast Density Peak Clustering Algorithm Optimized by Uncertain Number Neighbors for Breast MR Image

Abstract Aiming at the characteristics of abundant Magnetic Resonance (MR) image information, uneven gray scale, fuzzy boundary, and a fine structure that is difficult to distinguish and segment, this paper proposes an algorithm for segmenting MR images by an improved density clustering algorithm. First, the superpixel clustering algorithm (SLIC) is used to divide the image into a certain number of ultra-pixel regions and subsequently search the neighborhood of each superpixel according to a pre-specified threshold. Then, the KNN-DPC algorithm, in which the value of K is adaptively determined, is used to obtain the distance information of K nearest neighbors (density) and adjacent superpixels, and image segmentation is completed for each superpixel cluster. Two sets of natural image experiments show that this algorithm has high segmentation accuracy. Experiments on clinical breast MR images showed that this algorithm achieved good results for clinical MR image segmentation.
Non decomposable connectives of linear logic

This paper studies the so-called generalized multiplicative connectives of linear logic, focusing on the question of finding the "non-decomposable" ones, i.e., those that cannot be expressed as combinations of the default binary connectives of multiplicative linear logic, ⊗ (times) and ⅋ (par). In particular, we concentrate on generalized connectives of a surprisingly simple form, called "entangled connectives", and prove a characterization theorem giving a criterion for identifying the undecomposable entangled ones.
Continuous A/B Testing in Containers

Software version ranking plays an important role in improved user experience and software quality. A/B testing is a technique to distinguish between the popularity and usability of two quite similar versions (A and B) of a product, marketing strategy, search ad, etc. It is a kind of two-sample hypothesis testing, used in the field of statistics. This controlled experiment can evaluate user engagement or satisfaction with a new service, feature, or product. A/B testing is typically used in evaluation of user-experience design in software technology. DevOps is an emerging software methodology in which the development and operations are not independent processes, they affect each other. DevOps emphasizes the usage of virtualization technologies (e.g. containers). Docker is widely-used technology for containerization. In this paper, we deal with a new approach for regular A/B testing via Docker containers. Our solution provides an API that can be available from many DevOps tools. This approach is DevOps-style A/B testing because after the evaluation the better version remains in production.