Reviewerly/exHarmony huggingface dataset:
Design and Verification of Secure Cache Wrapper Against Access-Driven Side-Channel Attacks

While caches are shared resources used to speedup the execution of applications, including the execution of cryptographic applications, their use can expose the system to attacks. Access-driven is one of the most popular cache attacks. They have been demonstrated in different hardware platforms, from servers to smart phones, which even were operating in virtualized environments. Designing hardware solutions to protect against access-driven attacks is still a challenge. Moreover, the security verification of such solutions still needs further exploration. This paper presents two main contributions. First, we propose a generic hardware wrapper able to protect caches against accessdriven cache attacks, based on an address translation policy to obfuscate the cache accesses. Second, we use an extended version of a previously proposed formal method to verify the security of cache against such attacks, by means of properties. Experimental results show the effectiveness of our hardware wrapper against access-driven cache attacks along with formal proof, while incurring an average area overhead below 2% and a negligible critical path overhead.
Worst-case Quantum Hypothesis Testing with Separable Measurements

For any pair of quantum states (the hypotheses), the task of binary quantum hypotheses testing is to derive the tradeoff relation between the probability $p_{01}$ of rejecting the null hypothesis and $p_{10}$ of accepting the alternative hypothesis. The case when both hypotheses are explicitly given was solved in the pioneering work by Helstrom. Here, instead, for any given null hypothesis as a pure state, we consider the worst-case alternative hypothesis that maximizes $p_{10}$ under a constraint on the distinguishability of such hypotheses. Additionally, we restrict the optimization to separable measurements, in order to describe tests that are performed locally. The case $p_{01}=0$ has been recently studied under the name of "quantum state verification". We show that the problem can be cast as a semi-definite program (SDP). Then we study in detail the two-qubit case. A comprehensive study in parameter space is done by solving the SDP numerically. We also obtain analytical solutions in the case of commuting hypotheses, and in the case where the two hypotheses can be orthogonal (in the latter case, we prove that the restriction to separable measurements generically prevents perfect distinguishability). In regards to quantum state verification, our work shows the existence of more efficient strategies for noisy measurement scenarios.
Flexible service consolidation with nested virtualization and library operating systems

Summary In Infrastructure‐as‐a‐service (IaaS) clouds, users can reduce costs by scale‐in or scale‐down when running services are underutilized. Since these optimizations of instance deployment require at least one minimum instance even for running an underutilized service, cost reduction is limited. For further optimization, multiple services can be consolidated into one instance. However, services have to be stopped temporarily at the consolidation time, and isolation between services becomes weaker after the consolidation. To solve these problems, this paper proposes FlexCapsule , which enables seamless and secure service consolidation in existing IaaS clouds. FlexCapsule runs each service in a lightweight virtual machine (VM) called an app VM , using a library operating system . An app VM runs inside an instance using a technique called nested virtualization . FlexCapsule can optimize instance deployment with negligible downtime by flexibly migrating app VMs. Due to strong isolation provided by app VMs, it can guarantee security between consolidated services. In addition, FlexCapsule provides multiprocess support using app VMs by emulating process fork and process pools. We have implemented FlexCapsule in Xen using both fully virtualized OS v and paravirtualized MiniOS. Then, we examined the effectiveness of FlexCapsule using several applications. Due to the premature implementation of nested virtualization in Xen, the performance of app VMs largely degraded, but we believe that the performance could be improved using several existing optimizations.
Online QoE Prediction Model Based on Stacked Multiclass Incremental Support Vector Machine

The first stage to ameliorate and optimize the provision of mobile streaming services is calculating and estimating the Quality of Experience (QoE) of a multimedia stream. Machine learning models offer a solution to obtain the complicated relationships between various influencing factors and QoE. This article presents a novel online QoE prediction algorithm, namely, Stacked Incremental Support Vector Machine model (SISVM). This model relies on a combination of two main procedures: Ensemble learning model, more specifically, Stacking model and Incremental Support Vector Machine (ISVM). The principal contribution of this work consists of the use of separate Multiclass Incremental SVM, in order to estimate different parts of the problem and to combine the outputs of each model using probabilistic techniques. In fact, using ISVM in real-time applications is best fit for the manipulation non-stationary data. Experimental results have proven clearly that the proposed model has outperformed the existing state-of-the-art models, in terms of classification accuracy and computational complexity.
A Novel Approach towards Pattern and Speed Invariant Holistic Analysis of Dynamic Gesture Recognition System

This study focused to develop a user-friendly gesture recognition system with multiple speed and gesturing pattern. The differences in speed and gesturing pattern by different users immensely affect the accuracy of the gesture system if the features are spatiotemporal in nature. Existing trajectory features in literature are spatiotemporal in nature. The trajectory data represents the gesture as an ordered sequence of direction in 2D retaining its temporal information. Such systems are not user-convenient and restricted to limited applications. Therefore, we proposed a new approach of feature extraction from the dynamic gestures which are not trajectory-based and yet pattern and speed invariant with minimum computational complexity. In the proposed system, after the gesture is smoothened, it is represented as a binary image unlike a 2D sequence of trajectory points in the Euclidean space. The image is sub-divided into local instances to extract magnitude of Zernike moment and histogram features from each instance. The study is carried out on 16 keyboard characters from literature which are highly likely to have different patterns from different users. The proposed approach improved the accuracy by 8.65% (histogram + ANN model) and 2.05% (Zernike moment + ELM model) than existing spatial trajectory based system for pattern variation.
Curation Technologies for Cultural Heritage Archives

We present a platform that enables the semantic analysis, enrichment, visualisation and presentation of a document collection in a way that enables human users to intuitively interact and explore the collection, in short, a curation platform or workbench. The data set used is the result of a research project, carried out by scholars from South Korea, in which official German government documents on the German re-unification were collected, intellectually curated, analysed, interpreted and published in multiple volumes. The documents we worked with are mostly in German, a small subset, mostly summaries, is in Korean. This paper describes the original research project that generated the data set and focuses upon a description of the platform and Natural Language Processing (NLP) pipeline adapted and extended for this project (e. g., OCR was added). Our key objective is to develop an interactive curation workbench that enables users to interact with the data set in several different ways that go beyond the current version of the published document collection as a set of PDF documents that are available online. The paper concludes with suggestions regarding the improvement of the platform and future work.
Examining the Boundary Effect of Information Systems Security Behavior Under Different Usage Purposes

The purpose of information systems (IS) security behavior, represented by the difference between work and personal purpose, has been neglected in the research of user's IS security behavior. This is especially important for the organizations that allow or inevitably allow their employees to do personal tasks during work hours, because it may bring big threats to an organization's IS. Unfortunately, few empirical studies have examined if different behavioral purpose, typically, work/personal purpose, affects users' IS security behavior decision. Based on boundary theory and the idea of multidimensional rationality, we argue that people demarcate a psychological boundary for work and personal usages of IS and thus apply different rationalities to make IS security-related decisions. Our empirical investigation indicates that in a work usage context, people apply rule-based rationality and therefore are more influenced by mandatoriness and facilitating conditions. Whereas in a personal usage context, people apply outcome-based rationality and therefore are more concerned with task benefits and costs. The findings imply that organizations and vendors may devise different measures to ensure users' IS security behavior in different contexts.
A Novel Cloud Monitoring Framework with Enhanced QoS Supporting

In Cloud environments, performance monitoring service is to obtain a full knowledge of underlying resources. However, it is still a challenging task to manage the information of heterogeneous resources in an efficient way, which is especially true when user-specific quality-of-service (QoS) should be concerned. Although many Cloud monitoring solutions have been proposed in recent years, most of them only passively raise an alert event when a QoS violation occurs. In this article, the authors present a novel Cloud monitoring framework, in which enhanced-QoS is supported through three mechanisms: proactive service-layer agreement (SLA) violation prediction, SLA ranking service, and multi-tenant resource monitoring mechanism. Extensive experiments are conducted in a realistic cloud platform, and the results indicate the proposed framework is capable of providing better QoS supporting comparing with existing monitoring solutions. Also, it exhibits desirable scalability and adaptiveness in a wide range of experimental scenarios.
A Simple but Powerful Word Polarity Classification Model

Polarity lexicons have largely been used for opinion mining, also known as sentiment analysis. Their high relevance has made them invaluable when it comes to classifying the opinions originated by digital platforms users. However, there is a great dependence on resources developed in other languages, in special English, to create a lexicon in Spanish. In this work we develop a polarity lexicon for Mexican Spanish from a thesaurus and two seed words sets inside it that we use to assign polarity values to words, from a frequentist treatment, and taking into account the assumption that synonymous words have similar polarities.
Meta-learning of Text Classification Tasks

A text mining characterization is proposed consisting of a set of meta-features, unlike previous meta-learning approaches, some of them are extracted directly from raw text. Such novel description is useful for comparing text mining tasks and study their differences. The problem of determining the task associated to a text classification dataset is introduced and approached with our characterization. Experimental results on a set of 81 corpora show that the proposed meta-features indeed allow to recognize tasks with acceptable performance using only a few meta-features.
Task-GAN: Improving Generative Adversarial Network for Image Reconstruction

Generative Adversarial Network (GAN) has demonstrated great potentials in computer vision tasks such as image restoration. However, image restoration for specific scenarios, such as medical image enhancement is still facing challenge: How to ensure the visually plausible results while not containing hallucinated features that might jeopardize downstream tasks such as pathology identification? Here, we propose Task-GAN, a generalized model for medical reconstruction problem. A task-specific network that captures the diagnostic/pathology features, was added to couple the GAN based image reconstruction framework. Validated on multiple medical datasets, we demonstrated that the proposed method leads to improved deep learning based image reconstruction while preserving the detailed structure and diagnostic features.
HOG of Region of Interest for Improving Clothing Retrieval Performance

Due to spread of garment online store and the revenue from the online clothing market, clothing retrieval is becoming an active research topic in the last decade. The buyer commonly searches for shape, but the availability of various clothing designs and details on the Internet caused a growing interest to buy clothing with similar detail, at a particular position. The proposed method aims at enhancing the performance of garment image retrieval, in which the garments have similar details at a particular position. The method is developed based on histogram of oriented gradient (HOG), with a new approach for ROI partition mechanism prior employing the HOG. Simulations with four categories of clothing details confirmed that the proposed approach can improve the retrieval performance.
Classification of Attentional Focus Based on Head Pose in Multi-object Scenario

Recently determining of visual focus of attention has gained increased consciousness in computer vision to develop non-verbal communication based system. In this paper, we proposed a computer vision based approach to classify the focus of attention of human in multi-object scenario. In order to determine the current focus of attention head pose is used. To classify the different attentional direction the system is trained supervised machine learning and geometrical analysis techniques. The proposed system is trained with more than 7 h live videos with 9 head poses that contains 435000 frames. The proposed attention classification model achieved 97.00% accuracy on test set with 81000 video frames and visual focus of attention accuracy near to 95.00% with multi-object scenarios.
Chip-based Anti-collision System for Car Door Opening

Warning the rear approach of objects before opening the car door is essential for safety reasons. Particularly in Taiwan, the traffic accident caused by car door opening were about 20% in 2016 and resulted in a lot of motorcyclists and cyclists being suffered from severe injury. For this, we propose an anti-collision system by using single digital camera and a Field Programmable Gate Array (FPGA) chip. The proposed system first detects approaching objects by motion detection and then derives their speeds to raise an alarm. Here, the warning threshold is set at 16 km/hr to detect an object from 3.8 to 18.8 meters away. Our experimental results indicate that the proposed system is practical for smart car designs with embedded collision detection.
Generating Environmental Models for Testing Self-adaptive Systems

Self-adaptive systems (a.k.a. SASs) are useful but error-prone. This stems from the complexity of the interaction between a self-adaptive system and its running environment. Therefore, a testing approach of self-adaptive system has to consider the system's running environment. However, due to their poor controllability and observability, neither the real environment nor the environmental simulators could support SAS-testing effectively and efficiently. In this paper, we propose a novel approach AutoModel to generate environmental models for testing self-adaptive systems effectively. Our key insight is that a self-adaptive system's execution traces naturally encode the behavior of its running environment, especially for the logic of how the environment interacts with the system. Based on the collected execution traces, our AutoModel approach synthesizes an environmental model and learns the model's reaction logic. The derived environmental model is able to imitate the real environment's behavior in program-environment iteration. Our primitive evaluation on real-world self-adaptive systems validates the effectiveness of our AutoModel approach. The average predictive R-squared value of the generated environmental model's prediction results is 55.0%.
Dynamic Logic Reconfiguration Based Side-Channel Protection of AES and Serpent

Dynamic logic reconfiguration is a concept which allows for efficient on-the-fly modifications of combinational circuit behaviour in both ASIC and FPGA devices. The reconfiguration of Boolean functions is achieved by modification of their generators (e.g. shift register-based look-up tables) and it can be controlled from within the chip, without the necessity of any external intervention. This hardware polymorphism can be utilized for the implementation of side-channel attack countermeasures, as demonstrated by Sasdrich et al. for the lightweight cipher PRESENT. In this work we adopt these countermeasures to two of the AES finalists, namely Rijndael and Serpent. Just like PRESENT, both Rijndael and Serpent are block ciphers based on a substitution-permutation network. We describe the countermeasures and adjustments necessary to protect these ciphers using the resources available in modern Xilinx FPGAs. We describe our VHDL implementations and evaluate the side-channel leakage and effectiveness of different countermeasure combinations using a methodology based on Welch's t-test. We did not detect any significant leakage from the fully protected versions of our implementations. We show that the countermeasures proposed by Sasdrich et al. are, with some modifications compared to the protected PRESENT implementation, successfully applicable to AES and Serpent.
Deblurring X-Ray Digital Image Using LRA Algorithm

Abstract Deblurring of X-rays digital images has always been a problem of crucial interest. A specific solution to the problem of image restoration is generally determined by the nature of degradation phenomena. So, it is highly dependent on the nature of the noise present there. In this work Lucy Richardson algorithm (LRA) is implemented on the X-ray image and deblurring is processes is observed. Deblurring is an important step of image processing especially when the diagnosis needs to be classified based on the result of X-ray image. LRA is an iterative procedure in which the pixels of the observed image are represented using the PSF. Image restoration is an emerging field of image processing in which the focus is on recovering an original image from a degraded image. The blurred image can be a result of a known degradation or unknown degradation. Hence image deblurring can be defined as a process of recovering a sharp image from a degraded image which is blurred by a degradation function, commonly by a Point Spread Function (PSF). The Point Spread Function describes the response of an imaging system to a point source or point object.
STEM for Public Safety in Cyber: Training for Local Law Enforcement and Cyber Security

Public safety and cybersecurity are integrated endeavors as pervasive computing puts interconnected devices everywhere. STEM training in computing and cyber security might occur within the traditional public safety framework, both of criminological study and practical implementation. We tested the responsiveness to cybersecurity training for law enforcement from this traditional perspective. Our findings suggest great potential within this group for expanding effective cyber security protections for communities through STEM computing and cyber security education. They further suggest a commensurate need for training and support to realize this potential.
Mobile Application for the Support in the Learning of the Alphabet, Verbs and Pronouns of the Mexican Sign Language Based on Augmented Reality

In this paper we present a mobile application for android operating system focused for children’s, for the support in the learning of the alphabet, verbs and pronouns in the Mexican Sign Language context. The parents or teacher will be able to download the markers for each Lesson or Game. Once the markers are available, the child will be able to carry out his activity with the support of the augmented reality.
Is Group-Awareness Context-Awareness?

This article discusses the interest of emerging a unified view for group awareness and context information on groupware and context-aware systems. Group awareness corresponds to an important concept on Groupware applications, allowing individual users to be kept aware of group's activities and status. Context is defined by ubiquitous computing as any relevant information that can be used to characterize the situation of an entity. We assume that group awareness information should be considered as context information and handled as such. Group awareness information is often employed for decision making, contributing to users' activities and decisions, but it gives also an important clue about user's context, characterizing individual's actions regarding the group. As such, group awareness may be used for adaptation purposes, adapting the system behavior, the supplied content or its services. Besides, architectural concerns adopted on context-aware system should also be considered when developing new groupware applications that are more and more designed as context-aware systems.
A class of doubly stochastic shift operators for random graph signals and their boundedness

A class of doubly stochastic graph shift operators (GSO) is proposed, which is shown to exhibit: (i) lower and upper L2-boundedness for locally stationary random graph signals, (ii) L2-isometry for i.i.d. random graph signals with the asymptotic increase in the incoming neighbourhood size of vertices, and (iii) preservation of the mean of any graph signal - all prerequisites for reliable graph neural networks. These properties are obtained through a statistical consistency analysis of the proposed graph shift operator, and by exploiting the dual role of the doubly stochastic GSO as a Markov (diffusion) matrix and as an unbiased expectation operator. For generality, we consider directed graphs which exhibit asymmetric connectivity matrices. The proposed approach is validated through an example on the estimation of a vector field.
Prediction Future Disaster Using Convex Hull &amp; K-Mean, An Approach

Data mining concept is used to find pattern of data from large set of data. In this proposed model firstly any kind of disaster information taken like time, place from all over the world have been collected. The complete database has been divided according to location and after that location wise it divided into two parts - natural disaster and man-made disaster then find out the outer region of the unstructured data by using convex hull. According to month of occurrence, previous structural data are passed through K-mean and the clustered data is passed through priority based protocol and then the resultant data can be used to predict future disaster by the analysis of the previous data.