Reviewerly/exHarmony huggingface dataset:
No two gangs are alike: The digital divide in street gangs’ differential adaptations to social media

Social media provide novel opportunities for street gangs to operate beyond their traditional borders to sell drugs, recruit members and control their territory, virtually and physically. Although social media have contributed to the means available to street gangs today, it does not mean that every gang agrees on their use. Drawing on different perspectives (ex-gang members, law enforcement) on gangs using a multi-method design in a London borough, the current study shows that social media have polarized gangs, resulting in two distinct types of digital adaptation. The proposed division of ‘digitalist’ and ‘traditionalist’ gangs is rooted in Thrasher’s (1927) dictum that no two gangs are alike and explains how some gangs prefer to keep a low profile, thus, avoiding social media use. ‘Digitalists’, by contrast, prefer to use social media as a way to gain reputation and territorial expansion. They use it to brand themselves and to appear attractive for recruits and customers alike. These differences can be theoretically explained firstly as a generational gap, meaning that younger gang members prefer the use of social media; and secondly, by how well established a gang already is, as newer gangs need more attention to establish themselves.
Quantifying the Effect of Machine Translation in a High-Quality Human Translation Production Process

This paper studies the impact of machine translation (MT) on the translation workflow at the Directorate-General for Translation (DGT), focusing on two language pairs and two MT paradigms: English-into-French with statistical MT and English-into-Finnish with neural MT. We collected data from 20 professional translators at DGT while they carried out real translation tasks in normal working conditions. The participants enabled/disabled MT for half of the segments in each document. They filled in a survey at the end of the logging period. We measured the productivity gains (or losses) resulting from the use of MT and examined the relationship between technical effort and temporal effort. The results show that while the usage of MT leads to productivity gains on average, this is not the case for all translators. Moreover, the two technical effort indicators used in this study show weak correlations with post-editing time. The translators’ perception of their speed gains was more or less in line with the actual results. Reduction of typing effort is the most frequently mentioned reason why participants preferred working with MT, but also the psychological benefits of not having to start from scratch were often mentioned.
Learning spatial hierarchies of high-level features in deep neural network

This paper addresses a new approach to learn perceptual grouping of the extracted features of the convolutional neural network (CNN) to represent the structure contained in the image. In CNN, the spatial hierarchies between the high-level features are not taken into account. To do so, the perceptual grouping of features is utilized. To consider the intra-relationship between feature maps, modified Guided Co-occurrence Block (mGCoB) is proposed. This block preserves the joint co-occurrence of two features in the spatial domain and it prevents the co-adaptation. Also, to preserve the interrelationship in each feature map, the principle of common region grouping is utilized which states that the features which are located in the same feature map tend to be grouped together. To consider it, an MFC block is proposed. To evaluate the proposed approach, it is applied to some known semantic segmentation and image classification datasets that achieve superior performance.
Performance of continuous variable quantum key distribution system at different detector bandwidth

We evaluate the performance of Gaussian Modulated Coherent State (GMCS) Continuous-Variable Quantum Key Distribution (CV-QKD), in terms of secure key rate, at different detector bandwidths. We illustrate the advantages and limitations of high-speed CV-QKD systems using a general noise model in which we consider various noise sources and their dependence upon detection bandwidth. Experimentally, the feasibility of high speed CV-QKD is demonstrated by using a GHz bandwidth balanced homodyne detector (BHD) established with commercially available components. We provide secure key rates of transmitted and local local oscillator schemes under various clock rates.
Nonconvex Low-Rank Kernel Sparse Subspace Learning for Keyframe Extraction and Motion Segmentation

By exploiting the kernel trick, the sparse subspace model is extended to the nonlinear version with one or a combination of predefined kernels, but the high-dimensional space induced by predefined kernels is not guaranteed to be able to capture the features of the nonlinear data in theory. In this article, we propose a nonconvex low-rank learning framework in an unsupervised way to learn a kernel to replace the predefined kernel in the sparse subspace model. The learned kernel by a nonconvex relaxation of rank can better exploiting the low-rank property of nonlinear data to induce a high-dimensional Hilbert space that more closely approaches the true feature space. Furthermore, we give a global closed-form optimal solution of the nonconvex rank minimization and prove it. Considering the low-rank and sparseness characteristics of motion capture data in its feature space, we use them to verify the better representation of nonlinear data with the learned kernel via two tasks: keyframe extraction and motion segmentation. The performances on both tasks demonstrate the advantage of our model over the sparse subspace model with predefined kernels and some other related state-of-art methods.
Efficient Keypoint based Copy Move Forgery Detection Method using Hybrid Feature Extraction

Digital images are considered as one of most sorted mediums as an evidence or proof in various areas. For example, in forensic analysis, military intelligence, medical imaging, journalism, crime scene investigation and so on. With increased use of smart phones and cheap availability of internet has led to increased circulation of digital image. Along with, availability of wide range of digital image editing tools has made tampering image much easier. Such tampered image generates an improper message and could be used to influence events especially when the society based important decisions are carried out using these tampered image. Copy move tampering method has been widely applied passive image tampering method. Extensive work has been carried out in recent time for detecting such copy move tampering attack using key-point and block based method. However, these methods does not extract enough feature points considering small and smoothed region. For overcoming research challenges, this paper presents an efficient copy move forgery detection(CMFD) technique using key-points employing hybrid feature extraction, detection and hierarchical clustering method. Experiment result shows the proposed method attain significant performance when compared with other forgery detection methods.
Cyberbullying Lexicon for Social Media

The presence of cyberbullying lexicon can be used to prevent cyberbullying, since user can know which words represent the cyberbullying word in social media. However, there is less research been done for developing the cyberbullying lexicon. Many researches are focusing on cyberbullying detection rather than cyberbullying instances. In order to fill the gaps, this paper aims to propose a cyberbullying lexicon for social media. This paper is focusing on exclusion cyberbullying and proposing an exclusion cyberbullying lexicon by using an ontological approach. This approach consists of several phases, namely: understanding the concepts of exclusion cyberbullying, word list selection, keyword identification, classes and subclasses identification, and lastly cyberbullying ontology and lexicon development. The classes, subclasses and the documentation have been included in the development of exclusion cyberbullying ontology. Classes are based on the semantic frame, subclasses are based on the lexical entry, while documentation of classes and subclasses are based on the definition of the semantic frame and lexical entry from FrameNet. The proposed exclusion cyberbullying lexicon is arranged in alphabetical order and it contains 13 exclusion cyberbullying keywords with its definition and category. In the proposed exclusion cyberbullying lexicon, keyword, definition, and category represent the subclasses, documentation, and classes from the exclusion cyberbullying ontology respectively. The presence of exclusion cyberbullying lexicon can provide a reference for the social media users as well as for cyberbullying detection.
CACNet: Salient object detection via context aggregation and contrast embedding

Recently, how to adaptively exploring the most useful context information in Convolutional Neural Networks (CNNs) has been one of the most pressing problems facing the saliency detection task. In this paper, we propose a novel Context Feature Aggregation Network with Boundary Contrast Embedding (CACNet) to flexibly integrate context information without being affected by the fixed geometric structures of convolution filters. We adopt a Detail Enhancement Module (DEM) to make the network pay greater attention to the changes of image structure. A Context Adaptive Aggregation Module (CA2M) is also employed to selectively integrate the context information of the feature map. Moreover, a Boundary Contrast Loss function (BCL) enhances the discriminability of learned features by maximizing feature differences between boundary pixels. Extensive experiments on five benchmark datasets demonstrate that the proposed method outperforms other state-of-the-art methods under different evaluation metrics. During the testing stage, the network can run at about 36 FPS and does not need any post-processing.
Large Scale Video Representation Learning via Relational Graph Clustering

Representation learning is widely applied for various tasks on multimedia data, e.g., retrieval and search. One approach for learning useful representation is by utilizing the relationships or similarities between examples. In this work, we explore two promising scalable representation learning approaches on video domain. With hierarchical graph clusters built upon video-to-video similarities, we propose: 1) smart negative sampling strategy that significantly boosts training efficiency with triplet loss, and 2) a pseudo-classification approach using the clusters as pseudo-labels. The embeddings trained with the proposed methods are competitive on multiple video understanding tasks, including related video retrieval and video annotation. Both of these proposed methods are highly scalable, as verified by experiments on large-scale datasets.
Local differential privacy: Elbow effect in optimal density estimation and adaptation over Besov ellipsoids

We address the problem of non-parametric density estimation under the additional constraint that only privatised data are allowed to be published and available for inference.For this purpose, we adopt a recent generalisation of classical minimax theory to the framework of local α-differential privacy and provide a lower bound on the rate of convergence over Besov spaces B s pq under mean integrated L r -risk.This lower bound is deteriorated compared to the standard setup without privacy, and reveals a twofold elbow effect.In order to fulfill the privacy requirement, we suggest adding suitably scaled Laplace noise to empirical wavelet coefficients.Upper bounds within (at most) a logarithmic factor are derived under the assumption that α stays bounded as n increases: A linear but non-adaptive wavelet estimator is shown to attain the lower bound whenever p ≥ r but provides a slower rate of convergence otherwise.An adaptive non-linear wavelet estimator with appropriately chosen smoothing parameters and thresholding is shown to attain the lower bound within a logarithmic factor for all cases.
Covert Timing Channel Analysis Either as Cyber Attacks or Confidential Applications

Covert timing channels are an important alternative for transmitting information in the world of the Internet of Things (IoT). In covert timing channels data are encoded in inter-arrival times between consecutive packets based on modifying the transmission time of legitimate traffic. Typically, the modification of time takes place by delaying the transmitted packets on the sender side. A key aspect in covert timing channels is to find the threshold of packet delay that can accurately distinguish covert traffic from legitimate traffic. Based on that we can assess the level of dangerous of security threats or the quality of transferred sensitive information secretly. In this paper, we study the inter-arrival time behavior of covert timing channels in two different network configurations based on statistical metrics, in addition we investigate the packet delaying threshold value. Our experiments show that the threshold is approximately equal to or greater than double the mean of legitimate inter-arrival times. In this case covert timing channels become detectable as strong anomalies.
Deep Learning Side-Channel Attack Resilient AES-256 using Current Domain Signature Attenuation in 65nm CMOS

This article, for the first time, demonstrates an efficient circuit-level countermeasure to prevent deep-learning based side-channel analysis (DLSCA) attacks on encryption devices. Machine learning (ML) SCA, particularly DLSCA attacks have been shown to be extremely effective as it can potentially reveal the secret key of the cryptographic device with as low as a single trace, by offloading the heavy-lifting on the profiling phase where the model learns the correlated leakage patterns of the key. This work presents a current-domain signature attenuation (CDSA) hardware embedding an AES256 engine fabricated in 65nm CMOS technology to suppress the current signature by >350× before it reaches the power supply pin accessible to an attacker. Measurement results show that a 256-class deep neural network (DNN) model for DLSCA attack can be fully trained (>99.9% test accuracy) using only <; 5K power traces from the unprotected AES256, while the DNN model for the protected CDSA-AES256 cannot be trained even with 10M traces.
A sentiment reporting framework for major city events: Case study on the China-United States trade war

Smart cities are conceptualized as a vehicle for sustainable urban development and a means to deliver high quality of life for residents. One of the core functions of a smart city consists in the continuous monitoring of events, assets and people and the use of this information and intelligence for the streamlining of the city’s operations. Public opinion represents one type of intelligence of particular importance and value. By monitoring public opinion, governments seek to understand prevalent views about the current events and policies, as well as identify extreme views and trends that may represent problematic situations or precursors to violent actions. Ultimately, maintaining a constant awareness of public opinion means that authorities can better assess and predict public reactions in relation to ongoing events, and thus take appropriate actions to maintain public safety. Due to the popular use of social media to express sentiments and emotions about current events, social media content analysis has been contemplated as a promising solution to capture public opinion. However, existing approaches take a coarse-grained retrospective approach to social media content analysis. Furthermore, those approaches suffer from the lack of scalability and efficiency, as they necessitate the collection and analysis of large volumes of social media content (often millions of posts), to come up with relevant conclusions. In this work, we address those limitations by proposing a novel framework for the real-time monitoring of public opinion. To ensure efficiency and scalability, our framework focuses on the analysis of high impact social media content generated by opinion leaders and their followers as means to offer in-depth insights and sentiment intelligence reports about events, as they are occurring in real time. The proposed framework was implemented and tested on data harvested from 52 economic opinion leaders, with a focus on the China-US trade war as case study. The results show that the convolutional neural network (CNN) classifier used for sentiment analysis yielded a classification accuracy of 86% when differentiating between four sentiment categories: Support, strong support, dissent, and strong dissent. The Support Vector Machine (SVM) classifier employed to perform in-depth emotional analysis attained an accuracy of 82% when differentiating between five emotions: Angry, depressed, excited, happy, and worried. Unlike existing retrospective social media analysis approaches that require the analysis of millions of posts, our approach focuses on the analysis of high-impact social media content in real-time, thus constituting an efficient, sustainable, and timely solution to public opinion monitoring.
Harnessing semantic segmentation masks for accurate facial attribute editing

Summary In recent years, with the rapid development of adversarial learning technology, facial attribute editing has made great success in a number of areas. Realistic visual effect, invariant identity information, and accurate editing area are the three key issues of facial attribute editing. Unfortunately, most researches focus on the former two problems. However, lack of awareness of the accurate editing area in the task is the main reason for damaging attribute‐irrelevant details. To address this issue, this article proposes a novel facial attribute editing algorithm—a generative adversarial network (GAN) with semantic masks—from the perspective of editing location accuracy. By generating the mask with respect to attribute‐related areas, the semantic segmentation network can only constrain the manipulation in the target region while not harming any attribute‐irrelevant details. The GAN is then combined with the semantic segmentation network to formulate the entire framework, which is referred to as SM‐GAN. Extensive experiments on the public datasets CelebA and LFWA prove that the presented method can not only ensure that the attribute manipulation is realistic, but also allow attribute‐irrelevant regions to remain unchanged. Moreover, it can also simultaneously edit multiple facial attributes.
Fixation durations in natural scene viewing are guided by peripheral scene content

Fixation durations provide insights into processing demands. We investigated factors controlling fixation durations during scene viewing in two experiments. In Experiment 1, we tested the degree to which fixation durations adapt to global scene processing difficulty by manipulating the contrast (from original contrast to isoluminant) and saturation (original vs. grayscale) of the entire scene. We observed longer fixation durations for lower levels of contrast, and longer fixation durations for grayscale than for color scenes. Thus fixation durations were globally slowed as visual information became more and more degraded, making scene processing increasingly more difficult. In Experiment 2, we investigated two possible sources for this slow-down. We used “checkerboard” stimuli in which unmodified patches alternated with patches from which luminance information had been removed (isoluminant patches). Fixation durations showed an inverted immediacy effect (longer, rather than shorter, fixation durations on unmodified patches) along with a parafoveal-on-foveal effect (shorter fixation durations, when an unmodified patch was fixated next). This effect was stronger when the currently fixated patch was isoluminant as opposed to unmodified. Our results suggest that peripheral scene information substantially affects fixation durations and are consistent with the notion of competition among the current and potential future fixation locations.
Identification and authentication of user voice using DNN features and <i>i</i>-vector

Currently, computerized systems, such as language learning, telephone advertising, criminal cases, computerized health care and education systems are rapidly spreading and creating an urgent need for improved productivity. Speech recordings are a rich source of personal, confidential data that can be used to support a wide variety of applications, from health profiling to biometric recognition. Therefore, it is important that the speech recordings are properly protected, so that they cannot be misused. The leakage of encrypted biometric information is irreversible and biometric links are renewable. The article proposes a block diagram of the identification of the users of the systems by individual voice characteristics, based on the joint use of the Deep Neural Network (DNN) method and i -vector in the model of the elementary speech units, distinguished by increased security from various types of attacks on the biometric identification system, which allowed identifying the users with probability of first and second errors genus 0.025 and 0.005. The analysis of the vulnerability of the modules of the biometric voice identification system was performed and a structural scheme of the voice identification system of the user identification by voice with enhanced the protection against attacks was proposed. The use of elementary speech units in the developed identification systems makes it possible to improve computational indicators, reduce subjective decisions in biometric systems, and increase the security against attacks on the voice biometric identification systems.
The controlling factors of lacustrine shale lithofacies in the Upper Yangtze Platform (South China) using artificial neural networks

The influence of paleoenvironments on the lithofacies of lacustrine mudstone is important for both paleoenvironment reconstruction and hydrocarbon exploration. Compared with the machine learning approaches, the methods to find coupling relationships are too qualitative to be convincing. This study used the artificial neural network (ANN) approach to better understand the primary factors that control lacustrine shale lithofacies development and how paleoenvironments influence depositional processes. The neural network was trained with the back propagation training algorithm based on identified shale lithofacies (desired output) and geochemical indicators (input). The lithofacies deposited under different paleoenvironments were identified from an 80-m long core based on observations of the sediment texture and structure and biota and analyses of the mineralogy and trace elements. To confirm the primary factors of lithofacies development in J1d of the Upper Yangtze Platform, five groups of elements were selected as geochemical indicators (Sr/Ba, Zr/Rb, Sr/V, V/V + Ni, and Ba/Ca) to monitor the changes in paleosalinity, paleohydrodynamic condition, paleoclimate, paleoredox, and paleoproductivity, respectively. The element groups were selected and parameterized as inputs to constrain the lithofacies (output) boundaries using artificial neural networks (ANNs). The results of the ANN models indicate that the lacustrine lithofacies development of J1d was controlled by paleosalinity, paleohydrodynamic condition, paleoclimate, and paleoredox but not by paleoproductivity. The results of this study indicate that the primary factors of shale lithofacies development can be effectively assessed via geochemical, sedimentological, and ANN analyses.