Reviewerly/exHarmony huggingface dataset:
Safe spaces in participatory design with young forced migrants

'Safe spaces' have been at the core of participatory design (PD) in HCI. However, their characteristics, their potential impact and what enables them are not yet fully understood. The present article presents a series of insights derived from a six-week long study with 19 young forced migrants (YFMs). We investigated factors which contributed to enable the creation of 'safe spaces' while developing digital services with them. We based our case study on four main components of such spaces: (i) ethics, (ii) reflective processes, (iii) content, (iv) and spatial layout and dynamics. Our findings pointed to a promotion of 'safe spaces' through the combination of these components. Participants showed a high degree of engagement, seemed to be comfortable while interacting socially, and manifested signs of developing a trust relationship with the facilitators. The work presented in this article can benefit designers engaging in participatory activities in HCI with communities with highly complex and sensitive backgrounds or who are underrepresented.
Speckle denoising by variant nonlocal means methods

This study aims to demonstrate the performances of nonlocal means (NLM) and their variant denoising methods, mainly focusing on NLM-shaped adaptive patches and several NLM-reprojection schemes for speckle noise reduction in amplitude and phase images of the digital coherent imaging systems. In the digital coherent imaging systems such as digital speckle pattern interferometry, digital holographic interferometry, etc., the image quality is severely degraded by additive uncorrelated speckle noise, due to the coherent nature of the light source, and therefore limits the development of several applications of these imaging systems in many fields. NLM and its variant denoising methods are employed to denoise the intensity/phase images obtained from these imaging systems, and their effectiveness is evaluated by considering various parameters. The performance comparison of these methods with other existing speckle denoising methods is also presented. The performance of these methods for speckle noise reduction is quantified on the basis of two criteria matrices, namely, the peak-to-signal noise ratio and the image quality index. Based on these criteria matrices, it is observed that these denoising methods have the ability to improve the intensity and phase images favorably in comparison to other speckle denoising techniques, and these methods are more effective and feasible in speckle-noise reduction.
Seafloor massive sulfides from mid-ocean ridges: Exploring the causes of their geochemical variability with multivariate analysis

The neovolcanic zones of mid-ocean ridges are host to seawater-derived hydrothermal systems forming seafloor massive sulfide (SMS) deposits. These deposits have high concentrations of base metals and potentially economic enrichment of a wide range of trace elements. The factors controlling this enrichment are currently poorly understood. We have investigated the main factors controlling SMS compositional variability through robust principal component analysis and robust factor analysis of published and newly obtained bulk geochemical data for samples collected from SMS deposits worldwide. We found that a large part of the observed variability is produced by a combination of three independent factors, which are interpreted to reflect (in order of importance): (1) the temperature of deposition, (2) the ridge spreading rate, and (3) zone refining. The first and the third factors are mostly related to processes operating near the seafloor, such as conductive cooling, mixing of the hydrothermal fluids with seawater and metal remobilization, and determine the relative proportions of the main minerals and, thus, of Cu and Zn (Co, Se, Sb, Pb). The ridge spreading rate influences the structure of the oceanic lithosphere, which exerts a major control on the length and depth of the hydrothermal convection cell and on the rock-to-water ratios in the reaction zone, which in turn control the behavior of the precious metals Au and Ag and elements including Ni (Mo, Se). Despite the obvious role of substrate rocks as metal sources, their composition (specifically mafic vs. ultramafic) does not emerge as a statistically significant independent factor.
An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning

The rise of social media has led to an increasing online cyber-war via hate and violent comments or speeches, and even slick videos that lead to the promotion of extremism and radicalization. An analysis to sense cyber-extreme content from microblogging sites, specifically Twitter, is a challenging, and an evolving research area since it poses several challenges owing short, noisy, context-dependent, and dynamic nature content. The related tweets were crawled using query words and then carefully labelled into two classes: Extreme (having two sub-classes: pro-Afghanistan government and pro-Taliban) and Neutral. An Exploratory Data Analysis (EDA) using Principal Component Analysis (PCA), was performed for tweets data (having Term Frequency—Inverse Document Frequency (TF-IDF) features) to reduce a high-dimensional data space into a low-dimensional (usually 2-D or 3-D) space. PCA-based visualization has shown better cluster separation between two classes (extreme and neutral), whereas cluster separation, within sub-classes of extreme class, was not clear. The paper also discusses the pros and cons of applying PCA as an EDA in the context of textual data that is usually represented by a high-dimensional feature set. Furthermore, the classification algorithms like naïve Bayes’, K Nearest Neighbors (KNN), random forest, Support Vector Machine (SVM) and ensemble classification methods (with bagging and boosting), etc., were applied with PCA-based reduced features and with a complete set of features (TF-IDF features extracted from n-gram terms in the tweets). The analysis has shown that an SVM demonstrated an average accuracy of 84% compared with other classification models. It is pertinent to mention that this is the novel reported research work in the context of Afghanistan war zone for Twitter content analysis using machine learning methods.
Automated classification of metamorphosed coal from geophysical log data using supervised machine learning techniques

Identification of the extent of heat altered coals is important for coal mining and resource estimation because alteration directly affects key economic properties such as ash content, volatile matter, specific energy, sulphur, free swell index and beneficiation characteristics. The most reliable method to identify altered coal is through geochemical analysis of drill core samples; however this method is time consuming and costly. Cheaper alternative methods to identify altered coal is by macroscopic observation of non-core drill cuttings, but this is less reliable than core. Geophysical wireline log data is also used to identify altered coal, but with variable success. Over the past decades, Machine Learning methods have proven popular to automatically classify a variety of lithotypes from geophysical log data. However rarely has there been focus on predicting altered coal. In this paper we apply the most recent machine learning methods which include gradient boosted machines, random forests and artificial neural networks to automatically predict altered and non-altered lithotypes using geophysical log data. We use a massive data set comprising of 1230 samples from 263 boreholes from a highly intruded deposit in the Bowen Basin, Eastern Australia. To do this, we calculate each sample's distance from intrusion and predict their relative density from geophysical log inputs including gamma ray, caliper and compensated density. We then train our machine learning methods on 80% of the data to predict alteration class using the calculated distance to intrusion, predicted relative density, and geophysical log gamma ray, as primary inputs. We evaluated each of these machine learning methods on the remaining 20% of the data to determine the best performing model. Finally, using the best performing model we further split the altered and non-altered classification into lithotypes using a decision tree based on geological knowledge of the case study area. The results indicate that of the machine learning algorithms the random forest produced the best results with only 11 misclassifications across the entire data set of 1230 samples which represents <1% error.
EyePointing

Interacting with objects from a distance is not only challenging in the real world but also a common problem in virtual reality (VR). One issue concerns the distinction between attention for exploration and attention for selection -- also known as the Midas-touch problem. Researchers proposed numerous approaches to overcome that challenge using additional devices, gaze input cascaded pointing, and using eye blinks to select the remote object. While techniques such as MAGIC pointing still require additional input for confirming a selection using eye gaze and, thus, forces the user to perform unnatural behavior, there is still no solution enabling a truly natural and unobtrusive device free interaction for selection. In this paper, we propose EyePointing: a technique which combines the MAGIC pointing technique and the referential mid-air pointing gesture to selecting objects in a distance. While the eye gaze is used for referencing the object, the pointing gesture is used as a trigger.
Group public key encryption with equality test against offline message recovery attack

Public key encryption with equality test (PKEET) allows a tester to check whether two ciphertexts encrypted under different public keys contain the same message without decrypting them. In this paper, we first introduce group mechanism into PKEET and propose a new primitive, namely group public key encryption with equality test (G-PKEET). G-PKEET can resist the attack that the tester can recover the message from a given ciphertext by exhaustively guessing the message offline. Furthermore, the group mechanism makes PKEET supporting group granularity authorization, which could authorize a tester to perform the equality test only on ciphertexts of group users, and could greatly reduce not only the storage cost of trapdoors but also the cost of computation and communication. We define security models for G-PKEET, present its concrete construction in bilinear pairings and prove its security in the random oracle model.
Electromagnetic Fault Injection : How Faults Occur

Electromagnetic Fault Injection (EMFI) has recently gained popularity as a mean to induce faults because of its inherent advantages. Among them, the most interesting is probably its ability to generate faults in Systems on Chips without removing the package, and this even if only the frontside is exposed to the EM field. Despite this popularity, there is only little information on how EMFI generates faults. Within this context, this paper first aims at filling this lack by proposing a complete modeling of EM induction fault mechanism. In a second step, the introduced model is confronted to experimental data in order to demonstrate its soundness.
Ship Detection in Optical Satellite Images via Directional Bounding Boxes Based on Ship Center and Orientation Prediction

To accurately detect ships of arbitrary orientation in optical remote sensing images, we propose a two-stage CNN-based ship-detection method based on the ship center and orientation prediction. Center region prediction network and ship orientation classification network are constructed to generate rotated region proposals, and then we can predict rotated bounding boxes from rotated region proposals to locate arbitrary-oriented ships more accurately. The two networks share the same deconvolutional layers to perform semantic segmentation for the prediction of center regions and orientations of ships, respectively. They can provide the potential center points of the ships helping to determine the more confident locations of the region proposals, as well as the ship orientation information, which is beneficial to the more reliable predetermination of rotated region proposals. Classification and regression are then performed for the final ship localization. Compared with other typical object detection methods for natural images and ship-detection methods, our method can more accurately detect multiple ships in the high-resolution remote sensing image, irrespective of the ship orientations and a situation in which the ships are docked very closely. Experiments have demonstrated the promising improvement of ship-detection performance.
A Method of Speech Coding for Speech Recognition Using a Convolutional Neural Network

This work presents a new approach to speech recognition, based on the specific coding of time and frequency characteristics of speech. The research proposed the use of convolutional neural networks because, as we know, they show high resistance to cross-spectral distortions and differences in the length of the vocal tract. Until now, two layers of time convolution and frequency convolution were used. A novel idea is to weave three separate convolution layers: traditional time convolution and the introduction of two different frequency convolutions (mel-frequency cepstral coefficients (MFCC) convolution and spectrum convolution). This application takes into account more details contained in the tested signal. Our idea assumes creating patterns for sounds in the form of RGB (Red, Green, Blue) images. The work carried out research for isolated words and continuous speech, for neural network structure. A method for dividing continuous speech into syllables has been proposed. This method can be used for symmetrical stereo sound.
Privacy Preserving Image Queries for Camera Localization

Augmented/mixed reality and robotic applications are increasingly relying on cloud-based localization services, which require users to upload query images to perform camera pose estimation on a server. This raises significant privacy concerns when consumers use such services in their homes or in confidential industrial settings. Even if only image features are uploaded, the privacy concerns remain as the images can be reconstructed fairly well from feature locations and descriptors. We propose to conceal the content of the query images from an adversary on the server or a man-in-the-middle intruder. The key insight is to replace the 2D image feature points in the query image with randomly oriented 2D lines passing through their original 2D positions. It will be shown that this feature representation hides the image contents, and thereby protects user privacy, yet still provides sufficient geometric constraints to enable robust and accurate 6-DOF camera pose estimation from feature correspondences. Our proposed method can handle single- and multi-image queries as well as exploit additional information about known structure, gravity, and scale. Numerous experiments demonstrate the high practical relevance of our approach.
MS-Pointer Network: Abstractive Text Summary Based on Multi-Head Self-Attention

Abstractive text summarization plays an important role in the field of natural language processing.However, the abstractive text summary adopts deep learning research method to predict words often appears semantic inaccuracy and repetition and so on.at the present stage, in order to solve the problem that semantic inaccuracy, we propose an MS-Pointer Network that based on the multi-head self-attention mechanism, which a multi-head self-attention mechanism is introduced in the basic encoder-decoder model.Since multi-head self-attention can combine input words into the encoder-decoder arbitrarily, and given a higher weight of these words that combination of the semantics, thereby achieving the purpose of enhancing the semantic features of the text, so that the abstractive text summary is more semantically structured, And the multi-head self-attention mechanism add the position information of the input text, which can enhance the semantic representation of the text.At the same time, in order to solve the problem of out of vocabulary, a pointer network is introduced on the seqtoseq with a multi-head attention mechanism.The model is referred to as MS-Pointer Network.We used CNN/Daily Mail and Gigaword datasets to validate our model, and uses the ROUGE metric to measure model.Experiments have shown that abstractive text summaries generated using the multi-head self-attention mechanism outperforming current open state-of-the-art two points averagely. INDEX TERMS Abstractive text summarization, encoder-decoder model
Lemma Synthesis for Automating Induction over Algebraic Data Types

In this paper we introduce a new approach for proving quantified theorems over inductively defined data-types. We present an automated prover that searches for a sequence of simplifications and transformations to prove the validity of a given theorem, and in the absence of required lemmas, attempts to synthesize supporting lemmas based on terms and expressions witnessed during the search for a proof. The search for lemma candidates is guided by a user-specified template, along with many automated filtering mechanisms. Validity of generated lemmas is checked recursively by our prover, supported by an off-the-shelf SMT solver. We have implemented our prover called AdtInd and show that it is able to solve many problems on which a state-of-the-art prover fails.
Mitigation of Adversarial Examples in RF Deep Classifiers Utilizing AutoEncoder Pre-training

Adversarial examples in machine learning for images are widely publicized and explored. Illustrations of misclassifications caused by these slightly perturbed inputs are abundant and commonly known (e.g., a picture of panda imperceptibly perturbed to fool the classifier into incorrectly labeling it as a gibbon). Similar attacks on deep learning (DL) for radio frequency (RF) signals and their mitigation strategies are scarcely addressed in the published work. Yet, RF adversarial examples (AdExs) with minimal waveform perturbations can cause drastic, targeted misclassification results, particularly against spectrum sensing/survey applications (e.g. BPSK is mistaken for 8-PSK). Our research on deep learning AdExs and proposed defense mechanisms are RF-centric, and incorporate physical-world, over-the-air (OTA) effects. We herein present defense mechanisms based on pre-training the target classifier using an autoencoder. Our results validate this approach as a viable mitigation method to subvert adversarial attacks against deep learning-based communications and radar sensing systems.
PhishDump: A multi-model ensemble based technique for the detection of phishing sites in mobile devices

Phishing is a technique in which the attackers trick the online users to reveal the sensitive information by creating the phishing sites which look similar to that of legitimate sites. There exist many techniques to detect phishing sites in desktop computers. In recent years, the number of mobile users accessing the web has increased which lead to a rise in the number of attacks in mobile devices. Existing techniques designed for desktop computers may not be suitable for mobile devices due to their hardware limitations such as RAM, Screen size, low computational power etc. In this paper, we propose a mobile application named PhishDump to classify the legitimate and phishing websites in mobile devices. PhishDump is based on the multi-model ensemble of Long Short Term Memory (LSTM) and Support Vector Machine (SVM) classifier. As PhishDump focuses on the extraction of features from URL, it has several advantages over existing works such as fast computation, language independence and robust to accidental download of malwares. From the experimental analysis, we observed that our proposed multi-model ensemble outperformed traditional LSTM character and word-level models. PhishDump performed better than the existing baseline models with an accuracy of 97.30% on our dataset and 98.50% on the benchmark dataset.
Saliency guided faster-RCNN (SGFr-RCNN) model for object detection and recognition

Recently, the object detection and recognition based applications are widely adopted in various real-time and offline applications. The computer vision based automatic learning schemes have gained huge attraction from researchers due to their significant nature of learning that can significantly improve the detection performance. The advances in deep and convolutional neural networks have improved the efficiency of applications based on recognition and detection. However, enhancing precision, decreasing detection error, and detecting camouflaged items are still regarded as difficult problems. In this work, we concentrated on these problems and presented a model based on Faster-RCNN that utilizes saliency detection, proposal generation and bounding box regression, for better detection along with loss functions. The suggested method is referred to as the saliency driven Faster RCNN model for object detection and recognition using computer vision approach (SGFr-RCNN). The performance of the suggested strategy is assessed using the data sets (PASCAL VOC 2007, PASCAL VOC 2012 & CAMO_UOW) and contrasted with current methods in terms of mean average precision. The comparative research demonstrates the important improvement in the results of the suggested strategy relative to the current methods.
A Robust Distance Measure for Similarity-Based Classification on the SPD Manifold

The symmetric positive definite (SPD) matrices, forming a Riemannian manifold, are commonly used as visual representations. The non-Euclidean geometry of the manifold often makes developing learning algorithms (e.g., classifiers) difficult and complicated. The concept of similarity-based learning has been shown to be effective to address various problems on SPD manifolds. This is mainly because the similarity-based algorithms are agnostic to the geometry and purely work based on the notion of similarities/distances. However, existing similarity-based models on SPD manifolds opt for holistic representations, ignoring characteristics of information captured by SPD matrices. To circumvent this limitation, we propose a novel SPD distance measure for the similarity-based algorithm. Specifically, we introduce the concept of point-to-set transformation, which enables us to learn multiple lower dimensional and discriminative SPD manifolds from a higher dimensional one. For lower dimensional SPD manifolds obtained by the point-to-set transformation, we propose a tailored set-to-set distance measure by making use of the family of alpha-beta divergences. We further propose to learn the point-to-set transformation and the set-to-set distance measure jointly, yielding a powerful similarity-based algorithm on SPD manifolds. Our thorough evaluations on several visual recognition tasks (e.g., action classification and face recognition) suggest that our algorithm comfortably outperforms various state-of-the-art algorithms.
Diverse Agents for Ad-Hoc Cooperation in Hanabi

In complex scenarios where a model of other actors is necessary to predict and interpret their actions, it is often desirable that the model works well with a wide variety of previously unknown actors. Hanabi is a card game that brings the problem of modeling other players to the forefront, but there is no agreement on how to best generate a pool of agents to use as partners in ad-hoc cooperation evaluation. This paper proposes Quality Diversity algorithms as a promising class of algorithms to generate populations for this purpose and shows an initial implementation of an agent generator based on this idea. We also discuss what metrics can be used to compare such generators, and how the proposed generator could be leveraged to help build adaptive agents for the game.