Reviewerly/exHarmony huggingface dataset:
Mitigating Power Supply Glitch based Fault Attacks with Fast All-Digital Clock Modulation Circuit

This paper experimentally demonstrates that an on-chip integrated fast all-digital clock modulation (F-ADCM) circuit can be used as a countermeasure against supply glitch and temperature variations-based fault injection attacks (FIA). The F-ADCM circuit modulates clock edges in presence of DC/transient supply glitches and temperature variations to ensure correct operation of the underlying cryptographic circuit. With a testchip manufactured in 130nm CMOS process, we first demonstrate an inexpensive methodology to conduct a fault attack on hardware implementation of a 128-bit advanced encryption standard (AES) engine using externally controlled supply glitches. Next, we show that with F-ADCM circuit, it is no longer possible to inject supply/temperature glitch-based faults even after 10 million encryptions across varying operating conditions. Moreover, in extreme operating conditions, the F-ADCM circuit doesn't generate any clock edges, leading to complete failure of the AES encryption, indicating no exploitable faults are present.
A robust design of the translator’s skill set: evidence for transfer of metacognitive skills to intralingual paraphrasing

In recent years translators have experienced a new variation imposed by market dynamics and new technologies with many translation-like activities (e.g., transediting, post-editing, intralingual work, etc.). The question which arises is whether these new practices require the translator’s skill set to be redesigned. In this article, we argue that the modern translator’s skill set can accommodate the variation in new translation tasks because of its robust design. The translator’s skill set has a range of skills from simple to complex which can potentially be transferred to other translation-like activities. We focus on the higher-order metacognitive skills: planning, self-monitoring and self-revision, and we hypothesize that they are the locus of the skill set robust design because they are generic and transferable. First, we review the approaches to the modern translator’s skill set and the conditions under which skills become transferable. Next, we present the results of an experimental study which tested whether professional translators and trainees transfer their planning, self-monitoring and self-revision skills to a task of intralingual paraphrasing. We conclude that the translator’s skill set does have a robust design but its robustness is demonstrated only once a certain threshold level in the development of expertise has been achieved.
A Survey on Visualization Techniques Used for Big Data Analytics

According to the recent developments and efficient technology trends, Big Data has become a vital asset for all industries and organizations in modern times. Big Data is trending due to few of the main reasons such as cloud migration initiated by companies, aggregation of digital unstructured and machine data, strong administration of data security permission, and many others. As we all know, analytics is the process of drawing conclusions and finding insights from a big pile. Big Data analytics is defined as the process of querying, simplifying, obtaining insights from the huge set of data integrated in the file systems of Big Data framework. The insights obtained as the outcome of analytics should reach the end users operating on the application platform in the form of visual representation techniques such as reports, line graphs, and bar charts for better understanding and exploration about the data. We propose a case study consisting of comparison of all the existing data visualization tools and techniques available and suitable with Big Data. The paper outlines all the advantages and disadvantages of Data Visualization tools and recommends to use the one which outclasses the comparison test. The later part of paper explains about the methodology proposed using Big Data Hadoop and CDH’s Apache Impala, Hue, and Hive. The dataset chosen is imputed and fed to the Cloudera Engine for query processing and report generation. Further, the generated 2D output is given as input to Unity 3D engine for generating QR codes and 3D visualization using Marker-based technique of augmented reality.
Monotonicity of quantumness of ensembles under commutativity-preserving channels

A quantum channel (operation) is commutativity-preserving if it maps commutating states to commutating states. We show that commutativity-preserving channels cannot increase quantumness of ensembles, as quantified in terms of the commutator of square roots of constituent states in a quantum ensemble recently introduced [Phys. Rev. A 96, 022132 (2017)].
Using the Semantic Web as a Source of Training Data

Deep neural networks are increasingly used for tasks such as entity resolution, sentiment analysis, and information extraction. As the methods are rather training data hungry, it is necessary to use large training sets in order to enable the methods to play their strengths. Millions of websites have started to annotate structured data within HTML pages using the schema.org vocabulary. Popular types of entities that are annotated are products, reviews, events, people, hotels, and other local businesses [12]. These semantic annotations are used by all major search engines to display rich snippets in search results. This is also the main driver behind the wide-scale adoption of the annotation techniques. This article explores the potential of using semantic annotations from large numbers of websites as training data for supervised entity resolution, sentiment analysis, and information extraction methods. After giving an overview of the types of structured data that are available on the Semantic Web, we focus on the task of product matching in e‑commerce and explain how semantic annotations can be used to gather a large training dataset for product matching. The dataset consists of more than 20 million pairs of offers referring to the same products. The offers were extracted from 43 thousand e‑shops, that provide schema.org annotations including some form of product identifiers, such as manufacturer part numbers (MPNs), global trade item numbers (GTINs), or stock keeping units (SKUs). The dataset, which we offer for public download, is orders of magnitude larger than the Walmart-Amazon [7], Amazon-Google [10], and Abt-Buy [10] datasets that are widely used to evaluate product matching methods. We verify the utility of the dataset as training data by using it to replicate the recent result of Mudgal et al. [15] stating that embeddings and RNNs outperform traditional symbolic matching methods on tasks involving less structured data. After the case study on product data matching, we focus on sentiment analysis and information extraction and discuss how semantic annotations from the Web can be used as training data within both tasks.
Inside the Insider

We present an overview of two major research projects on the role of monetary incentives and psychological traits in attracting individuals to hacking behavior. In the first study, scenarios were developed for five situations to determine if monetary incentives could be used to influence subjects to obtain healthcare information and to release that information. Approximately 35% to 46% of the 523 survey participants indicated that there is a price, ranging from $1,000 to over $10 million, acceptable for violating HIPAA laws. In the second study, 439 subjects completed a survey that identified the psychological traits that contribute to an individual's propensity to participate in White Hat, Grey Hat, or Black Hat hacking. Preliminary results suggest that individuals that are White Hat, Grey Hat and Black Hat hackers score high on the Machiavellian and Psychopathy scales. We also found evidence that Gray Hatters oppose authority, Black Hatters score high on the thrill-seeking dimension, and White Hatters, the good guys, tend to be Narcissists. Our focus on both studies is malicious insider attacks because insiders have the ability to do substantial monetary and reputational damage to the organization. Several suggestions have been made on addressing insider threats.
Enhancing User Navigation Experience, Object identification and Surface Depth Detection for "Low Vision" with Proposed Electronic Cane

According to the World health organization, approximately 1.3 billion people have the problem of vision impairment. This also includes those people, who are under the category of near or distance vision and mild vision. Our paper is an attempt to understand the technology available to support for navigation to low vision category and also to present a conceptual model which will be very helpful in case of mild vision or near vision problem. Our attempt is to present a model which is easy to implement and affordable. In this attempt, our methodology is based on conceptual mobile application based navigation will process object identification with Google Image finder and location information based on the Differential Global Positioning System (DGPS). Object identification will provide the details of the nearby object and Differential Global Positioning System (DGPS) will help to navigate in the proper direction. This methodology also talks about surface depth identification, with the help of Kinect Sensors. And the result of both will be processed and submitted to a mobile application which will be communicated to the user. Combine these all three results will be given as an instruction by the mobile application which will help for smooth navigation and movement.
A Novel Eye Movement Data Transformation Technique that Preserves Temporal Information: A Demonstration in a Face Processing Task

Existing research has shown that human eye-movement data conveys rich information about underlying mental processes, and that the latter may be inferred from the former. However, most related studies rely on spatial information about which different areas of visual stimuli were looked at, without considering the order in which this occurred. Although powerful algorithms for making pairwise comparisons between eye-movement sequences (scanpaths) exist, the problem is how to compare two groups of scanpaths, e.g., those registered with vs. without an experimental manipulation in place, rather than individual scanpaths. Here, we propose that the problem might be solved by projecting a scanpath similarity matrix, obtained via a pairwise comparison algorithm, to a lower-dimensional space (the comparison and dimensionality-reduction techniques we use are ScanMatch and t-SNE). The resulting distributions of low-dimensional vectors representing individual scanpaths can be statistically compared. To assess if the differences result from temporal scanpath features, we propose to statistically compare the cross-validated accuracies of two classifiers predicting group membership: (1) based exclusively on spatial metrics; (2) based additionally on the obtained scanpath representation vectors. To illustrate, we compare autistic vs. typically-developing individuals looking at human faces during a lab experiment and find significant differences in temporal scanpath features.
Employing Auto-Annotated Data for Government Document Classification

In China, the government documents are documents with legal effect and of standard forms formulated in the process of government administration. With the continuous development of e-government in China, government database size increases hugely. To fully utilize the potential of the database, many applications based on natural language processing (NLP) are developed. Classification is a fundamental task for many NLP applications such as automatic document archive, intelligent search, and personalized recommendation. Presently, in China, the government document classification method which based on issuing departments has very low accuracy. Traditional text classifiers based on machine learning or deep learning models rely heavily on human-labeled training data. While there are no open data sets on the government documents, we propose a method to automatically constructing large-scale annotated data set for government document classification based on the information retrieval method. Experiment results show that the supervised classification model trained on our automatically constructed data set outperforms the baseline method 15% on F1-score.
Online Extremism Discovering through Social Network Structure Analysis

The activity of extremist organizations on the Internet is continuously growing with the increase of Web's usage for means of communication. Therefore analysis of radical members in social networks provides important information on how to prevent them propagate ideology and recruiting new members in the future. But nowadays terrorists often use confidential chats and private threads to communicate, thus it's quite hard to detect them using only the public messages they generate. In fact, it is usually known that some users of social networks are dangerous, another are innocent, and no information is available about the remaining users. In this paper, we propose an approach for detecting radical users of social network among unknown ones by analyzing their relationships and features as of vertices of social graph without usage of any information about text content they generate. We find that the proposed method is very promising and may be efficiently used for real-time monitoring systems and future terrorism and extremism research.
A note on the lattice structure for subalgebras of the algebra of truth values of type-2 fuzzy sets

The algebra of truth values of type-2 fuzzy sets is not a lattice, but some of its subalgebras may be lattices. In this paper, we give a necessary and sufficient condition under which subalgebras of the truth value algebra of type-2 fuzzy sets form lattices. Further, we prove that if a certain subalgebra is a lattice, then it is isomorphic to an appropriate subalgebra whose all elements are convex functions with the same height. Based on these results, if a certain subalgebra is a lattice, the equivalent characterizations of the partial order in it are given.
Estimating Relative Tag Locations based on Time-Differences in Read Events

Over the course of recent years, Radio Frequency Identification (RFID) technology has been applied in many different business domains to solve, for example, the problem of monitoring stock. However, keeping track of the exact (geo-) locations of items in stock is still an open problem. This is particularly problematic for logistics, retail as well as warehouses, where information about relative locations of items can drastically increase staff efficiency and business process efficacy. In this paper we set out to tackle the problem of determining relative distances between RFID tags based on time-differences in read events, without the introduction of additional hardware. To that end, we first present a novel approach, which leverages time-based distances for inferring relative RFID tag distances. We then qualitatively and quantitatively evaluate our proposed approach by (i) inferring tag locations for different experimental setups and (ii) comparing our results to ground truth data. Our results show that time-differences in read events are suitable for calculating and approximating the relative position of RFID tags in several setups. We strongly believe that the results presented in this paper represent a novel and important step towards new approaches, which leverage time-differences in read events for inferring relative tag distances.
Efficient and Adaptive Tone Mapping Algorithm Based on Guided Image Filter

The recognition rate of computer vision algorithms is highly dependent on the image quality. To enhance the visual quality of the images captured under high-dynamic range (HDR) scenes, we propose an efficient and adaptive tone mapping algorithm based on guided image filter (GIF). The HDR image is compressed adaptively according to its average luminance. Then we decompose it into a base layer and a detail layer using the guided image filter. We improve the base layer and enhance the detail layer simultaneously, and combine the two layers to get the final low-dynamic range (LDR) image. Since the parameters are linked with image statistics, they adaptively fit to various kinds of images. The objective evaluation results on HDR image sets demonstrate the superiority of our proposed algorithm. Meanwhile, the result of our algorithm can reduce the halo artifacts and preserve more detail by subjective observation.
MLPrivacyGuard

As services based on Machine Learning (ML) applications find increasing use, there is a growing risk of attack against such systems. Recently, adversarial machine learning has received a lot of attention, where an adversary is able to craft an input or manipulate an input to cause an ML system to misclassify. Another attack of concern is when an adversary with access to a ML model can reverse engineer attributes of a target class, creating a privacy concern, which is the subject of this paper. Such attacks use non-sensitive data obtainable by the adversary and the confidence levels returned by the ML model to infer sensitive attributes of the target user. Model Inversion attacks may be classified as white-box, where the ML model is known to the attacker, or black-box, where the adversary does not know the internals of the model. If the attacker has access to non-sensitive data of a target user, they can infer sensitive data by applying gradient ascent on the confidence returned by the model. Therefore, a black-box attack can be mounted by numerical approximations of the gradient to perform the gradient ascent. In this work, we present MLPrivacyGuard, a countermeasure against black-box model inversion attack is presented. This countermeasure consists of adding controlled noise to the output of the confidence function. It is important to preserve the accuracy of prediction/classification for the real users of the model while preventing attackers to infer sensitive data. This involves a trade-off between misclassification error and the effectiveness of defense. Based on experimental results, we demonstrate that when noise is injected with a long-tailed distribution, the objectives of low misclassification error with a strong defense can be attained as model inversion attacks are neutralized because numerical approximation of gradient ascent is unable to converge.
An Improved and Secure Visual Secret Sharing (VSS) scheme for Medical Images

Nowadays, medical information is being shared over the communication networks due to ease of technology. The patient's medical information has to be securely communicated over the network for automatic diagnosis. Most of the communication networks are prone to attacks from an intruder thus compromising the security of patients data. Therefore, there is a need to transmit medical images securely over the network. Visual secret sharing scheme can be used to transmit the medical images over the network securely. Visual Secret Sharing (VSS) scheme generates multiple shares to share secret information among n participants. To recover the secret information, all shares should be stacked together. In our previous work [9], we proposed a VSS based technique to recover secret images with the contrast of 70-80% known as Randomized Visual Secret Sharing (RVSS) scheme. However, RVSS scheme suffers from problems like 1) Generation of blocking artifacts in the recovered images. 2) It recovers medical images with a maximum contrast of 30-40%, hence it is not suitable for medical images.In this paper, we propose a modified RVSS scheme to recover the medical images with improved contrast. The proposed scheme introduces the idea of using super-resolution concept to improve the contrast of reconstructed medical images. The reconstruction quality of the medical images is evaluated using Human Visual System (HVS) based parameters. Additionally, the performance of the proposed system is evaluated using the existing Computer Aided Diagnosis (CAD) systems. The experimental results showed that the proposed system is able to reconstruct the secret image with the contrast of almost 85-90% and similarity of almost 77%. AIso, the reconstructed images using the proposed system achieves the similar classification accuracy as that of existing CAD systems.
Deep adversarial attack on target detection systems

Target detection systems identify targets by localizing their coordinates on the input image of interest. This is ideally achieved by labeling each pixel in an image as a background or a potential target pixel. Deep Convolutional Neural Network (DCNN) classifiers have proven to be successful tools for computer vision applications. However, prior research confirms that even state of the art classifier models are susceptible to adversarial attacks. In this paper, we show how to generate adversarial infrared images by adding small perturbations to the targets region to deceive a DCNN-based target detector at remarkable levels. We demonstrate significant progress in developing visually imperceptible adversarial infrared images where the targets are visually recognizable by an expert but a DCNN-based target detector cannot detect the targets in the image.
Differentiating synthetic and optical zooming for passive video forgery detection: An anti-forensic perspective

A video can be manipulated using synthetic zooming without using the state-of-the-art video forgeries. Synthetic zooming is performed by upscaling individual frames of a video with varying scale factors followed by cropping them to the original frame size. These manipulated frames resemble genuine natural (optical) camera zoomed frames and hence may be misclassified as a pristine video by video forgery detection algorithms. Even if such a video is classified as forged, forensic investigators may ignore the results, believing it as part of an optical camera zooming activity. Hence, this can be used as an anti-forensic method which eliminates digital evidence. In this paper, we propose a method for differentiating optical camera zooming from synthetic zooming for video tampering detection. The features used for this method are pixel variance correlation and sensor pattern noise. Experimental results on a dataset containing 3200 videos show the effectiveness of the proposed method.
Towards Adversarial and Unintentional Collisions Detection Using Deep Learning

We introduce a set of techniques to achieve transfer learning from computer vision to RF spectrum analysis. In this paper, we demonstrate the usefulness of this approach to scale the learning, accuracy, and efficiency of detection of adversarial and unintentional communications collisions using VGG-16. We achieve high accuracy (94% collisions detected) on a DARPA Spectrum Collaboration Challenge (SC2) dataset.
LINDASearch: a faceted search system for linked open datasets

The importance of Linked Data lies on the fact that its practices and principles have been adopted by an increasing number of data providers, resulting in the creation of a data space on the Web containing billions of RDF Triples and accessible worldwide throughout the Internet. RDF datasets can be queried by tools and applications for searching and gathering information. However and due to the huge amount and types of dataset, the selection and reuse of data resources is not easy task. Therefore, a metasearch system for open Linked Data projects called LINDASearch (LINDASearch stands for Linked Data Search) is introduced. LINDASearch provides a middleware architecture in order to provide information about the most known Open Linked Data Projects such as DBpedia, The GeoNames geographical database, LinkedGeoData, FOAF profiles, Global Health Observatory, Linked Movie DataBase (LinkedMDB) and World Bank Linked Data. This paper describes the LINDASearch’s architecture as well as its functionality through one case study divided in two scenarios in order to show the architecture’s functionality and present the results obtained from each scenario.