Reviewerly/exHarmony huggingface dataset:
KeyListener: Inferring Keystrokes on QWERTY Keyboard of Touch Screen through Acoustic Signals

This paper demonstrates the feasibility of a side-channel attack to infer keystrokes on touch screen leveraging an off-the-shelf smartphone. Although there exist some studies on keystroke eavesdropping attacks on touch screen, they are mainly direct eavesdropping attacks, i.e., require the device of victims compromised to provide side-channel information for the adversary, which are hardly launched in practical scenarios. In this work, we show the practicability of an indirect eavesdropping attack, KeyListener, which infers keystrokes on QWERTY keyboards of touch screen leveraging audio devices on a smartphone. We investigate the attenuation of acoustic signals, and find that a user's keystroke fingers can be localized through the attenuation of acoustic signals received by the microphones in the smartphone. We then utilize the attenuation of acoustic signals to localize each keystroke, and further analyze errors induced by ambient noises. To improve the accuracy of keystroke localization, KeyListener further tracks finger movements during inputs through phase change and Doppler effect to reduce errors of acoustic signal attenuation-based keystroke localization. In addition, a binary tree-based search approach is employed to infer keystrokes in a context-aware manner. The proposed keystroke eavesdropping attack is robust to various environments without the assistance of additional infrastructures. Extensive experiments demonstrate that the accuracy of keystroke inference in top-5 candidates can approach 90% with a top-5 error rate of around 6%, which is a strong indication of the possible user privacy leakage of inputs on QWERTY keyboard.
A Pseudo-Blind Convolutional Neural Network for the Reduction of Compression Artifacts

This paper presents methods based on convolutional neural networks (CNNs) for removing compression artifacts. We modify the Inception module for the image restoration problem and use it as a building block for constructing blind and non-blind artifact removal networks. It is known that a CNN trained in a non-blind scenario (known compression quality factor) performs better than the one trained in a blind scenario (unknown factor), and our network is not an exception. However, the blind system is more practical because the compression quality factor is not always available or does not reflect the actual quality when the image is a transcoded or requantized image. Hence, in this paper, we also propose a pseudo-blind system that estimates the quality factor for a given compressed image and then applies a network that is trained with a similar quality factor. For this purpose, we propose a CNN that estimates the compression quality factor and prepare several non-blind artifact removal networks that are trained for some specific compression quality factors. We train the networks and conduct experiments on widely used compression standards, such as JPEG, MPEG-2, H.264, and HEVC. In addition, we conduct experiments for dynamically changing and transcoded videos to demonstrate the effectiveness of the quality estimation method. The experimental results show that the proposed pseudo-blind network performs better than the blind one for the various cases stated above and requires fewer computations.
TransLink: User Identity Linkage across Heterogeneous Social Networks via Translating Embeddings

Nowadays people tend to create accounts with multiple social networks (SNs) to enjoy a variety of social network services. User identity linkage (UIL) aims to identify those multiple accounts belonging to a same person. UIL is of great importance to user behavior understanding and prediction, information dissemination, viral marketing, wellness diagnosis, etc. Most of existing solutions typically rely on the embedding of either user's attributes or behaviors into a latent vector space, and establish anchor link based on vector distance. However, these efforts still face challenges originated from the heterogeneity of SNs, incompleteness of user information and lack of enough known anchor links. In this paper, we investigate the UIL problem by presenting a translation-modeling approach-TransLink. It jointly embeds both users and interactive behaviors of various SNs into a unified low-dimensional representation space according to a set of known anchor links. More specifically, we primarily study three typical SNs, e.g, Twitter, Foursquare and Instagram. Before embedding, we abstract schemas of three SNs and extract interaction metapaths for each SN. By doing this we can efficiently address the first two challenges. Furthermore, iterative linkage can ensure linkage performance by using a very small set of known anchor links. Experiment results on two real-world datasets demonstrate the superiority of TransLink over the state-of-the-art approaches.
Multi-Channel Decomposition in Tandem With Free-Energy Principle for Reduced-Reference Image Quality Assessment

The visual quality of perceptions is highly correlated with the mechanisms of the human brain and visual system. Recently, the free-energy principle, which has been widely researched in brain theory and neuroscience, is introduced to quantize the perception, action, and learning in human brain. In the field of image quality assessment (IQA), on one hand, the free-energy principle can resort to the internal generative model to simulate the visual stimulus of the human beings. On the other hand, abundant psychological and neurobiological studies reveal that different frequency and orientation components of one visual stimulus arouse different neurons in the striate cortex, and the striate cortex processes visual information in the cerebral cortex. Motivated by these two aspects, a novel reduce-reference IQA metric called the multi-channel free-energy based reduced-reference quality metric is proposed in this paper. First, a two-level discrete Haar wavelet transform is used to decompose the input reference and distorted images. Next, to simulate the generative model in the human brain, the sparse representation is leveraged to extract the free-energy-based features in subband images. Finally, the overall quality metric is obtained through the support vector regressor. Extensive experimental comparisons on four benchmark image quality databases (LIVE, CSIQ, TID2008, and TID2013) demonstrate that the proposed method is highly competitive with the representative reduced-reference and classical full-reference models.
Confusion and information triggered by photos in persona profiles

We investigate whether additional photos beyond a single headshot makes a persona profile more informative without confusing the end user. We conduct an eye-tracking experiment and qualitative interviews with digital content creators after varying the persona in photos via a single headshot, a headshot and photo of the persona in different contexts, and a headshot with photos of different people with key persona attributes the gender and age. Findings show that contextual photos provide significantly more persona information to end users; however, showing photos of multiple people engenders confusion and lowers informativeness. Also, as anticipated, viewing additional photos requires more cognitive focus, which is measured by eye-tracking metrics; these metrics are correlated with levels of informativeness and confusion. Furthermore, various interpretations of the persona based on the choice of photos are biased by the end users’ experiences and preconceptions. Concerning persona design, findings indicate that persona creators need to consider the intended persona use objectives when selecting photos and when producing persona profiles. Using contextual photos can improve informativeness, but this demands more cognitive focus from end users. Thus, adding contextual photos increases the perceived informativeness of the persona profile without being obfuscating, but multiple photos of different people do evoke confusion about the targeted persona.
A Privacy-Preserving Lightweight Biometric System for Internet of Things Security

Constant expansion of Internet of Things (IoT) devices creates considerable convenience for peoples' daily lives; however, it also brings about some challenges. Advanced modern techniques equip IoT devices with additional sensory and communication modules, enabling them to perform more functions than just sensing, but it also means that these devices may consume more energy. Many IoT devices are powered by batteries of limited life and composed of electronic parts of limited capacity. Therefore, energy efficiency is an essential requirement for any IoT device with limited resources in terms of storage space, power, and computing capability. Moreover, possible attack vectors and attack sources for vulnerabilities in the IoT environment present another great challenge for secure user authentication. To address the above issues, in this article we propose a privacy- preserving lightweight biometric system, specifically designed for resource-limited IoT devices to save memory and computational cost. The proposed system employs a block logic operation based algorithm to reduce the biometric feature size in a simple and efficient way. Experimental results demonstrate that with a much reduced feature size, the proposed lightweight biometric system still maintains high recognition accuracy. It is worth noting that any sizable reduction in memory and computing cost is beneficial for resource-constrained IoT devices.
Block withholding game among bitcoin mining pools

Although mining pools smooth out Bitcoin rewards and make it more predictable for an individual miner, they concentrate power to the pool’s operator. There are multiple huge mining pools, and each of them may possess up to 30% of the total computation power of the Bitcoin network (the same applies to some other altcoins). Putting such enormous computation power in the hands of pool operators provides the necessary incentive for them to misuse their power over the network. One way to misuse this power is to launch a block withholding attack against other mining pools. Indeed, this ability starts a block withholding game among the pool operators. Some researchers have analyzed such a game. However, their analyses were limited because they considered simple scenarios, e.g., a single-shot game between only two mining pools. In this paper, we first demonstrate that the block withholding game is a stochastic game with finitely many states and actions. Then, we use a reinforcement learning method to analyze this game. Our simulation results show that in the recent four years, by launching a block withholding attack some pools had the potential to reach to the majority (51%) of the total computation power of the network with much lower initial computation power (even with less than 25% of the total computation power of the network).
Reformulating Queries for Duplicate Bug Report Detection

When bugs are reported, one important task is to check if they are new or if they were reported before. Many approaches have been proposed to partially automate duplicate bug report detection, and most of them rely on text retrieval techniques, using the bug reports as queries. Some of them include additional bug information and use complex retrieval- or learning-based methods. In the end, even the most sophisticated approaches fail to retrieve duplicate bug reports in many cases, leaving the bug triagers to their own devices. We argue that these duplicate bug retrieval tools should be used interactively, allowing the users to reformulate the queries to refine the retrieval. With that in mind, we are proposing three query reformulation strategies that require the users to simply select from the bug report the description of the software's observed behavior and/or the bug title, and combine them to issue a new query. The paper reports an empirical evaluation of the reformulation strategies, using a basic duplicate retrieval technique, on bug reports with duplicates from 20 open source projects. The duplicate detector failed to retrieve duplicates in top 5-30 for a significant number of the bug reports (between 34% and 50%). We reformulated the queries for a sample of these bug reports and compared the results against the initial query. We found that using the observed behavior description, together with the title, leads to the best retrieval performance. Using only the title or only the observed behavior for reformulation is also better than retrieval with the initial query. The reformulation strategies lead to 56.6%-78% average retrieval improvement, over using the initial query only.
Aspect-based sentiment analysis of mobile reviews

E-commerce websites provide an easy platform for users to put forth their viewpoints on different topics-ranging from a news item to any product in the market. Such online content encourages authors to express opinions on various aspects of an entity. Aspect based sentiment analysis deals with anal yzing this textual content to look for the aspect in question. After locating the aspects, corresponding sentiment bearing words are looked for. This paper describes an integrated system that generates the opinionated aspect based graphical and extractive summaries from a large set of mobile reviews. The system focuses on three tasks (a) identification of aspects in given field, (b) computation of sentiment polarity of each aspect, and (c) generates opinionated aspect based graphical and extractive summaries. The system has been evaluated on three mobile-reviews dataset and obtains better precision and recall than baseline approach. The system generates summaries from reviews without any training.
Modeling Side Information in Preference Relation based Restricted Boltzmann Machine for recommender systems

A majority of the collaborative filtering techniques exploit user-item rating information to generate recommendations of unseen items for a user. However, a user's preference also depends on some extra information like item features, user attributes and others, which is known as side information. Further, according to recent studies, using preference relation as an alternative to absolute ratings often produces quality recommendations. This study proposes a collaborative filtering technique using Preference Relation based Restricted Boltzmann Machine for recommender system. The proposed method takes the preference relations of items as input and generates a ranking of items for any user. Using Conditional Restricted Boltzmann Machine, the side information of items along with preference relations are integrated into the model. Besides side information, the proposed method is also able to capture second order and higher order user-item interactions. Experimental verification of the proposed model is done using three datasets: MovieLens-1M, MovieLens-20M, and Book-Crossing, which are the most widely used datasets for testing recommender systems. Results obtained at different positions using standard ranking measures like, NDCG and MAP, indicate that the performance of the proposed method is better compared to related state-of-the-art collaborative filtering models for Top-N recommendation task.
Personalised Reranking of Paper Recommendations Using Paper Content and User Behavior

Academic search engines have been widely used to access academic papers, where users’ information needs are explicitly represented as search queries. Some modern recommender systems have taken one step further by predicting users’ information needs without the presence of an explicit query. In this article, we examine an academic paper recommender that sends out paper recommendations in email newsletters, based on the users’ browsing history on the academic search engine. Specifically, we look at users who regularly browse papers on the search engine, and we sign up for the recommendation newsletters for the first time. We address the task of reranking the recommendation candidates that are generated by a production system for such users. We face the challenge that the users on whom we focus have not interacted with the recommender system before, which is a common scenario that every recommender system encounters when new users sign up. We propose an approach to reranking candidate recommendations that utilizes both paper content and user behavior. The approach is designed to suit the characteristics unique to our academic recommendation setting. For instance, content similarity measures can be used to find the closest match between candidate recommendations and the papers previously browsed by the user. To this end, we use a knowledge graph derived from paper metadata to compare entity similarities (papers, authors, and journals) in the embedding space. Since the users on whom we focus have no prior interactions with the recommender system, we propose a model to learn a mapping from users’ browsed articles to user clicks on the recommendations. We combine both content and behavior into a hybrid reranking model that outperforms the production baseline significantly, providing a relative 13% increase in Mean Average Precision and 28% in Precision@1. Moreover, we provide a detailed analysis of the model components, highlighting where the performance boost comes from. The obtained insights reveal useful components for the reranking process and can be generalized to other academic recommendation settings as well, such as the utility of graph embedding similarity. Also, recent papers browsed by users provide stronger evidence for recommendation than historical ones.
Denunciation and doxing: towards a conceptual model of digital vigilantism

Individuals rely on digital media to denounce and shame other individuals. This may serve to seek justice in response to perceived offences, while often reproducing categorical forms of discrimination. Both offence taking and its response are expressed online by gathering and distributing information about targeted individuals. By seeking their own form of social and/or criminal justice, participants may supersede institutions and formal procedures. Yet digital vigilantism includes shaming and other forms of cultural violence that are not as clearly regulated. They may feed from state or press-led initiatives to shame targets, or simply to gather information about them. Digital vigilantism remains a contested practice: Terms of appropriate use are unclear, and public discourse may vary based on the severity of the offence, the severity of response, and on participants' identities and affiliations. This paper advances a conceptually informed model of digital vigilantism, in recognition of its coordinated, moral and communicative components. Drawing upon literature on embodied vigilantism as well as concurrent forms of online coordination and harassment, it considers recent cases in a global context in order to direct subsequent analysis of how digital vigilantism is rendered meaningful.
A Survey on Code Analysis Tools for Software Maintenance Prediction

Software maintenance is a widely studied area of software engineering that it is particularly important in safety-critical and mission-critical applications where defects may have huge impact and code needs to be checked carefully through the analysis of data collected using a number of tools developed to investigate specific aspects. However, such tools are often not available to practitioners preventing them from applying the most recent and advanced approaches to industrial projects. This paper is an initial investigation about code analysis tools used to perform research studies on software maintenance prediction. We focus on the identification of tools that are available and can be used by practitioners to apply the same maintenance approaches described in published academic papers.
Selecting a representative decision tree from an ensemble of decision-tree models for fast big data classification

The goal of this paper is to reduce the classification (inference) complexity of tree ensembles by choosing a single representative model out of ensemble of multiple decision-tree models. We compute the similarity between different models in the ensemble and choose the model, which is most similar to others as the best representative of the entire dataset. The similarity-based approach is implemented with three different similarity metrics: a syntactic, a semantic, and a linear combination of the two. We compare this tree selection methodology to a popular ensemble algorithm (majority voting) and to the baseline of randomly choosing one of the local models. In addition, we evaluate two alternative tree selection strategies: choosing the tree having the highest validation accuracy and reducing the original ensemble to five most representative trees. The comparative evaluation experiments are performed on six big datasets using two popular decision-tree algorithms (J48 and CART) and splitting each dataset horizontally into six different amounts of equal-size slices (from 32 to 1024). In most experiments, the syntactic similarity approach, named SySM—Syntactic Similarity Method, provides a significantly higher testing accuracy than the semantic and the combined ones. The mean accuracy of SySM over all datasets is $${0.835} \pm {0.065}$$ for CART and $${0.769} \pm {0.066}$$ for J48. On the other hand, we find no statistically significant difference between the testing accuracy of the trees selected by SySM and the trees having the highest validation accuracy. Comparing to ensemble algorithms, the representative models selected by the proposed methods provide a higher speed for big data classification along with being more compact and interpretable.
Multi-view learning for visual violence recognition with maximum entropy discrimination and deep features

Harmful information full of violence, whose transmission is difficult to control, is one of the negative effects that the Internet brings to us today. Such corrosive information in the network environment infiltrates the mental health of adolescents. Many young people who lack judgment tend to imitate violent behaviors that are propagated on the network. This study applies multi-view learning to violent behavior recognition of still images, and can be useful for the research of network image or video information monitoring and filtering. In this paper, to start with, we construct a violence image recognition database, which contains 5974 violence images and 11,516 non-violence images. From this database, different datasets can be constructed for model and algorithm evaluations. Then we propose a multi-view maximum entropy discriminant (MVMED+) model for learning with different numbers of views. It can combine various features of the images and thus classify the images with the complementary information between the views. For efficient optimization, we further derive a sequential minimal optimization algorithm to train the model. In the experiments, we use handcrafted features and deep learning features to validate the effectiveness of the proposed model, respectively. As a useful byproduct, large performance improvements on visual violence recognition are further observed with deep learning features.
Dragonfly Algorithm: Theory, Literature Review, and Application in Feature Selection

In this chapter, a wrapper-based feature selection algorithm is designed and substantiated based on the binary variant of Dragonfly Algorithm (BDA). DA is a successful, well-established metaheuristic that revealed superior efficacy in dealing with various optimization problems including feature selection. In this chapter we are going first present the inspirations and methamatical modeds of DA in details. Then, the performance of this algorithm is tested on a special type of datasets that contain a huge number of features with low number of samples. This type of datasets makes the optimization process harder, because of the large search space, and the lack of adequate samples to train the model. The experimental results showed the ability of DA to deal with this type of datasets better than other optimizers in the literature. Moreover, an extensive literature review for the DA is provided in this chapter.