Reviewerly/exHarmony huggingface dataset:
Context-aware System for Dynamic Privacy Risk Inference

With the rapid expansion of smart cyber–physical systems and environments, users become more and more concerned about their privacy, and ask for more involvement in the protection of their data. However, users may not be necessarily aware of the direct and indirect privacy risks they take to properly protect their privacy. In this paper, we propose a context-aware semantic reasoning system, denoted as the Privacy Oracle, capable of providing users with a dynamic overview of the privacy risks taken as their context evolves. To do so, the system continuously models, according to a proposed Semantic User Environment Modeling (SUEM) ontology, the knowledge (received by the system) about the user of interest and his surrounding cyber–physical environment. In parallel, it performs continuous reasoning over modeled information, by relying on set of privacy rules, in order to dynamically infer the privacy risks taken by the user. To validate our approach, we developed a prototype based on the semantic web tools such as OWL API, SWRL API and the inference engine Pellet. We evaluated the system performance by considering multiple use cases. Our experimental results show that the Privacy Oracle can assist users by dynamically detecting their incurred privacy risks, and by tracking, in real-time, the evolution of those risks as user context changes.
What can you learn from an IP?

The Internet was not designed with security in mind. A number of recent protocols such as Encrypted DNS, HTTPS, etc. target encrypting critical parts of the web architecture, which can otherwise be exploited by eavesdroppers to infer users' data. But encryption may not necessarily guarantee privacy, especially when it comes to metadata. Emerging standards can protect the contents of both DNS queries and the TLS SNI extensions; however, it might still be possible to determine which websites users are visiting by simply looking at the destination IP addresses on the traffic originating from users' devices. We perform a measurement study to determine the anonymity provided by IP addresses resulting from the multiple sub-queries that are made as a consequence of accessing a particular web page. We show that, in most cases, an adversary can use the IP addresses during a page load as a form of a fingerprint to infer the original site identity.
Towards modelling heritage speakers' sound systems

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Robust Analysis of T-S Fuzzy Controller for Nonlinear System Using H-Infinity

Conventional controllers are not able to work well under the influence of delay and disturbance. Hence, it becomes necessary to design a robust fuzzy controller, which guarantees high stability, provides high disturbance rejection for any operating conditions and deals with model uncertainties. Here, the system response for the conventional PID controller, fuzzy logic controller and robust fuzzy controller is compared based on Sugeno model. Robust analysis of the system is carried out to guarantee the stability of the system. For given gamma values and weight values, we check the stability of the system. The results of robust fuzzy controller show that it works well under the influence of model uncertainty, delay and large disturbances.
Delegatable Order-Revealing Encryption

Order-revealing encryption (ORE) is a basic cryptographic primitive for ciphertext comparisons based on the order relationship of plaintexts while maintaining the privacy of them. In the data era we are experiencing, cross-dataset transactions become ubiquitous in practice. However, almost all the previous ORE schemes can only support comparisons on ciphertexts from the same user, which does not meet the requirement for the multi-user environment. In this work, we introduce and design ORE schemes with delegation functionality, which is referred to as delegatable ORE (DORE). The "delegation" here is an authorization that allows for efficient ciphertext comparisons among different users. To the best of our knowledge, it is the first ORE that allows an user to delegate the comparison privilege for his ciphertexts, which also opens the door for future explorations. At the heart of the construction and analysis of DORE is a new building tool proposed in this work, named delegatable equality-revealing encoding (DERE), which might be of independent interest.
QoE-Oriented Resource Optimization for Mobile Cloud Gaming: A Potential Game Approach

Cloud gaming is a novel service provisioning paradigm, which hosts video games in the cloud and transmits interactive game streaming to players via the Internet. In this model, the cloud is required to consume tremendous resources for video rendering and streaming, especially when the number of concurrent players reaches a certain level. On the other hand, different players may have distinct requirements on Quality-of-Experience, such as high video quality, low delay, etc. Under this circumstance, to ensure an overall satisfaction for all players with finite cloud resources becomes a major challenge to existing cloud services. This paper employs game theory to the cloud gaming scenario and proposes a model to meet players' overall requirements with low cost. This game is proved to be a potential game with determining a devised potential function. Our experiment has shown that, with our algorithm, players can achieve a mutually satisfactory steady state, and the system will reduce the overhead up to 50% within the time complexity of O(Mlog M), where M is the number of physical servers.
Graph-Based Image Matching for Indoor Localization

Graphs are a very useful framework for representing information. In general, these data structures are used in different application domains where data of interest are described in terms of local and spatial relations. In this context, the aim is to propose an alternative graph-based image representation. An image is encoded by a Region Adjacency Graph (RAG), based on Multicolored Neighborhood (MCN) clustering. This representation is integrated into a Content-Based Image Retrieval (CBIR) system, designed for the vision-based positioning task. The image matching phase, in the CBIR system, is managed with an approach of attributed graph matching, named the extended-VF algorithm. Evaluated in a context of indoor localization, the proposed system reports remarkable performance.
Why do people produce pronouns? Pragmatic selection vs. rational models

Models of language production must account for how speakers select referential expressions, a process that is complicated by the range of options available to speakers (e.g. he, Pim, Dr. Levelt). This paper reviews models of reference production, and suggests that they fall into two classes: (1) Pragmatic selection models, and (2) Rational models. We review evidence for and against each class of model, focusing on the use of pronouns. We argue that pronoun selection is not primarily driven by considerations of efficiency, and current models of accessibility do not explain the range of variation in production choices. We identify several challenges for future work in this area.
Vitiden: Transforming a policy-orienting scenario to a practice-oriented energy fiction

There is a lack of futures studies addressing consumption and lifestyles at the level of everyday life. This article reports on the transformation of the policy-orienting scenario "Legato", developed by the Swedish Energy Agency in 2016, to a practice-oriented design speculation. The article describes the process of transformation and the resulting energy fiction "Vitiden". The transformation involved three acts of translation. First, the scope of the transition was explored in-depth, both quantitatively and qualitatively, providing a more detailed understanding of the gap between the 'sustainable' 2050 and today. Second, the scenario Legato was analysed for practices and elements of practices that could be elaborated to descriptions of how everyday life could play out in this future. The third step involved re-presenting the practice-oriented scenario as a design speculation. The design speculation was given the form of a book named "Vitiden - an energy fiction" in which the re-formulated version of Legato is presented through text and images, combining a forward-looking manifesto and a backward-looking future archaeology. Besides the written content and the pictures and illustrations of Vitiden, the design of the book is also part of the speculation as it embodies an exploration of how publications, including form, graphic design and choice of materials, could look like in a future such as Legato.
Segmentifier: Interactive Refinement of Clickstream Data

Abstract Clickstream data has the potential to provide insights into e‐commerce consumer behavior, but previous techniques fall short of handling the scale and complexity of real‐world datasets because they require relatively clean and small input. We present Segmentifier, a novel visual analytics interface that supports an iterative process of refining collections of action sequences into meaningful segments. We present task and data abstractions for clickstream data analysis, leading to a high‐level model built around an iterative view‐refine‐record loop with outcomes of conclude with an answer, export segment for further analysis in downstream tools, or abandon the question for a more fruitful analysis path. Segmentifier supports fast and fluid refinement of segments through tightly coupled visual encoding and interaction with a rich set of views that show evocative derived attributes for segments, sequences, and actions in addition to underlying raw sequences. These views support fast and fluid refinement of segments through filtering and partitioning attribute ranges. Interactive visual queries on custom action sequences are aggregated according to a three‐level hierarchy. Segmentifier features a detailed glyph‐based visual history of the automatically recorded analysis process showing the provenance of each segment as an analysis path of attribute constraints. We demonstrate the effectiveness of our approach through a usage scenario with real‐world data and a case study documenting the insights gained by a corporate e‐commerce analyst.
Real-Time Stereo Vision Implementation on Nvidia Jetson TX2

We present a real-time stereo vision system implemented on a low power-consumption Nvidia Jetson TX2 embedded platform with a GPU accelerator. We use a local matching method with Normalized Cross Correlation as the cost function, perform a set of optimisations to reduce computational cost and improve output quality. We use the KITTI dataset to evaluate our system. With image resolution of 1242x375, our system achieves an average of 155 FPS with 90 disparity levels on the TX2 platform.
High-Level Abstractions for Simplifying Extended String Constraints in SMT

Satisfiability Modulo Theories (SMT) solvers with support for the theory of strings have recently emerged as powerful tools for reasoning about string-manipulating programs. However, due to the complex semantics of extended string functions, it is challenging to develop scalable solvers for the string constraints produced by program analysis tools. We identify several classes of simplification techniques that are critical for the efficient processing of string constraints in SMT solvers. These techniques can reduce the size and complexity of input constraints by reasoning about arithmetic entailment, multisets, and string containment relationships over input terms. We provide experimental evidence that implementing them results in significant improvements over the performance of state-of-the-art SMT solvers for extended string constraints.
Defending Adversarial Attacks on Cloud-aided Automatic Speech Recognition Systems

With the advancement of deep learning based speech recognition technology, an increasing number of cloud-aided automatic voice assistant applications, such as Google Home, Amazon Echo, and cloud AI services, such as IBM Watson, are emerging in our daily life. In a typical usage scenario, after keyword activation, the user's voice will be recorded and submitted to the cloud for automatic speech recognition (ASR) and then further action(s) might be triggered depending on the user's command(s). However, recent researches show that the deep learning based systems could be easily attacked by adversarial examples. Subsequently, the ASR systems are found being vulnerable to audio adversarial examples. Unfortunately, very few works about defending audio adversarial attack are known in the literature. Constructing a generic and robust defense mechanism to resolve this issue remains an open problem. In this work, we propose several proactive defense mechanisms against targeted audio adversarial examples in the ASR systems via code modulation and audio compression. We then show the effectiveness of the proposed strategies through extensive evaluation on natural dataset.
Noise-Tolerant, Deep-Learning-Based Radio Identification with Logarithmic Power Spectrum

Wireless devices have trivial individual differences due to hardware imperfections. Therefore, extracting unique features from the physical waveforms of wireless signals enables us to identify transmitter devices. It has often been assumed that high signal-to-noise ratio (SNR) waveforms can be obtained for identification in previous studies. However, there have been a small number of evaluation examples considering lower SNR environments. Classification accuracy could decrease when the feature is extracted from signals in a low SNR. In this paper, we propose a radio identification method using the logarithmic power spectral density (PSD) with a convolutional neural network (CNN) to improve identification performance in a low SNR environment. The simulation results based on indoor experiments reveals that the proposed method achieves 90 percent accuracy even at a low SNR condition of 7 dB, which is 11 dB lower than the conventional one. Additionally, the outdoor experiment results show that the proposed method achieves 99.4 percent accuracy, which is 6.5 points higher than the conventional one.
Identifying topic relevant hashtags in Twitter streams

Hashtags have become a crucial social media tool. The categorization of posts in a simple and informal way helps to spread the content through the web. At the same time, it enables users to easily find messages within a specific topic. However, the flexibility provided to use and create a hashtag carries some problems. Equivalent expressions, like synonyms, are handled like entirely different words. On the other hand, the same hashtag may refer to different topics. In this paper, we present TORHID (Topic Relevant Hashtag Identification), a method that employs topic modeling with the purpose of retrieving and identifying hashtags relevant to a specific topic in Twitter streams, starting from a seed hashtag and resorting to a classifier to remove non relevant hashtags. The result is a network of hashtags related to the seed, that we can use to deepen the initial search.
On scaling functions of non-uniform multiresolution analysis in L2(ℝ)

The main purpose of this paper is to provide a characterization of scaling functions for non-uniform multiresolution analysis (NUMRA, in short). Some necessary and sufficient conditions for scaling functions of wavelet NUMRA in the frequency domain are also obtained.
A cost-effective strategy for software vulnerability prediction based on bellwether analysis

Vulnerability Prediction Models (VPMs) aims to identify vulnerable and non-vulnerable components in large software systems. Consequently, VPMs presents three major drawbacks (i) finding an effective method to identify a representative set of features from which to construct an effective model. (ii) the way the features are utilized in the machine learning setup (iii) making an implicit assumption that parameter optimization would not change the outcome of VPMs. To address these limitations, we investigate the significant effect of the Bellwether analysis on VPMs. Specifically, we first develop a Bellwether algorithm to identify and select an exemplary subset of data to be considered as the Bellwether to yield improved prediction accuracy against the growing portfolio benchmark. Next, we build a machine learning approach with different parameter settings to show the improvement of performance of VPMs. The prediction results of the suggested models were assessed in terms of precision, recall, F-measure, and other statistical measures. The preliminary result shows the Bellwether approach outperforms the benchmark technique across the applications studied with F-measure values ranging from 51.1%-98.5%.
Learning a multi-level guided residual network for single image deraining

Rainy images severely degrade visibility and make many computer vision algorithms invalid. Hence, it is necessary to remove rain streaks from a single image. In this paper, we propose a novel end-to-end deep learning based deraining method. Previous methods neglect the correlation between different layers with different receptive fields that loss a lot of important information. To better solve the problem, we develop a multi-level guided residual block that is the basic unit of our network. In this block, we utilize multi-level dilation convolutions to obtain different receptive fields and the layer with smaller receptive fields to guide the learning of larger receptive fields. Moreover, in order to reduce the model sizes, the parameters are shared among all multi-level guided residual blocks. Experiments illustrate that guided learning improves the deraining performance and the shared parameters strategy is also feasible. Quantitative and qualitative experimental results demonstrate the superiority of the proposed method compared with several state-of-the-art deraining methods.
Stopping the Cyberattack in the Early Stage: Assessing the Security Risks of Social Network Users

Online social networks have become an essential part of our daily life. While we are enjoying the benefits from the social networks, we are inevitably exposed to the security threats, especially the serious Advanced Persistent Threat (APT) attack. The attackers can launch targeted cyberattacks on a user by analyzing its personal information and social behaviors. Due to the wide variety of social engineering techniques and undetectable zero-day exploits being used by attackers, the detection techniques of intrusion are increasingly difficult. Motivated by the fact that the attackers usually penetrate the social network to either propagate malwares or collect sensitive information, we propose a method to assess the security risk of the user being attacked so that we can take defensive measures such as security education, training, and awareness before users are attacked. In this paper, we propose a novel user analysis model to find potential victims by analyzing a large number of users’ personal information and social behaviors in social networks. For each user, we extract three kinds of features, i.e., statistical features, social-graph features, and semantic features. These features will become the input of our user analysis model, and the security risk score will be calculated. The users with high security risk score will be alarmed so that the risk of being attacked can be reduced. We have implemented an effective user analysis model and evaluated it on a real-world dataset collected from a social network, namely, Sina Weibo (Weibo). The results show that our model can effectively assess the risk of users’ activities in social networks with a high area under the ROC curve of 0.9607.
A Comprehensive Measurement Study of Domain-Squatting Abuse

Domain-squatting abuse refers to the premeditated attempt by an attacker to register perceptively confusing domain names thereby tricking visitors into querying them. There are totally five squatting types have been investigated so far, namely typo-squatting, bit-squatting, homograph-squatting, sound-squatting, and combo-squatting. Existing researches only focus on one specific squatting type and never explore the relationship among them. In this paper, we perform the first comprehensive measurement study of domain-squatting abuse. We select 786 the most queried domains, and hunt for squatting abuses against them in ISP-level DNS traffic. We find that although typo-squatting accounts for most of squatting domains, combo-squatting are able to attract more traffic. Our further case studies show that parking ads is still the most important way for attackers to make profits. The only exception is combo-squatting, in which squatters tend to leverage the reputation of squatted domains to develop their own business. It is worth noting that some squatting domains are even used to deliver malware. Moreover, the Alexa ranks of certain squatting domains have already surpassed the original domains. These results clearly call for the need to better protect the intellectual property of domain names.
Optimal Viewport-Adaptive 360-Degree Video Streaming Against Random Head Movement

Recently, a significant interest in 360-degree virtual reality (VR) video has been formed. However, a key problem is how to design a robust adaptive streaming approach and implement a practical system. The traditional streaming methods which are not sensitive to user's viewport could cause huge bandwidth budget with low video quality, while the viewport-adaptive schemes may be not accurate enough especially under random head movement. In this paper, we have designed an optimal viewport-adaptive 360-degree video streaming scheme, which is to maximize Quality of Experience (QoE) by predicting user's viewport with a probabilistic model, prefetching video segments into the buffer and replacing some unbefitting segments. In this way, continuous and smooth playback, high bandwidth utilization, low viewport prediction error and high peak signal-to-noise ratio in the viewport (V-PSNR) can be obtained. In order to deal with user's head movement, we have reduced prediction error rate by employing a probabilistic viewport prediction model, and a replacement strategy has been applied to update the downloaded segments when the user's viewport suddenly changes. To reduce smoothness loss, the segments' oscillation during playback has been considered. We also developed a prototype system with our method. The well-designed experiments provided numerous results which proved the better performance of our scheme.
Birds of a Feather Gel Together: Impact of Team Homogeneity on Software Quality and Team Productivity

Even though a lot of projects fail due to social issues or personality conflicts, only a small number of empirical studies have been conducted to quantitatively assess the impact of individual personality attributes on the software being developed and the team developing that software.The goal of this paper is to quantify the abstract notion of team homogeneity and to measure its impact on software quality and team productivity.A metric called team homogeneity index (THI) is proposed for this purpose.The six-step process of calculating the THI of a software development team is described and illustrated with the help of an example.Finally, the utility of THI is assessed by conducting a controlled experiment in two different phases of the software development life cycle (SDLC), i.e., implementation and testing.The results reveal that, during the implementation phase, teams with greater THI values were noticeably more productive and produced better quality code.Similarly, during the testing phase, teams with higher values of THI tested more features and wrote better quality test cases.Therefore, the evidence obtained so far suggests that the newly proposed metric, THI, appears to be useful in predicting the quality of software and the productivity of software development teams.Future work includes determining the weights of the five traits using input from the software industry and replication of this empirical study on different phases of SDLC with software practitioners to validate our findings.