Reviewerly/exHarmony huggingface dataset:
MoveWithMe: Location Privacy Preservation for Smartphone Users

With the prevalence of smartphones, mobile websites have been more and more popular. However, many mobile websites collect the location information which greatly increases users' risks of being tracked unexpectedly. The current location access control setting is not sufficient since it cannot prevent the service providers which have been granted location-access permissions from tracking the users. In this paper, we propose a novel location privacy preservation mobile app, called MoveWithMe, which automatically generates decoy queries to hide the real users' locations and intentions when they are using location-based mobile services. Unlike the existing works on dummy trajectories which may be easily discovered by attackers through data analysis, the uniqueness of the MoveWithMe app is that our generated decoys closely behave like real humans. Each decoy in our system has its own moving patterns, daily schedules, and social behaviors, which ensures its movements to be semantically different from the real user's trace and satisfying geographic constraints. Thus, our decoys can hardly be distinguished even by advanced data mining techniques. Another advantage of the MoveWithMe app is that it guarantees the same level of user experience without affecting the response time or introducing extra control burdens. Decoys move independently in the back end and automatically submit queries to the same service provider whenever the user does so. Our proposed MoveWithMe app has both iOS and Android versions and has been tested on different brands of smartphones against various location-based services, such as Yelp and TripAdvisor. Experimental results demonstrate its practicality, effectiveness, and efficiency.
Engineering World-Wide Multi-Agent Systems with Hypermedia

A well studied problem in the engineering of open MASs is to enable uniform interaction among heterogeneous agents. However, AOSE as a field has grown to recognize that a MAS consists of more than only agents and thus should be designed on multiple dimensions (including the environment, organization etc.). The problem of enabling interaction among heterogeneous entities across dimensions is either not considered, or it is addressed in an ad hoc and non-uniform manner. In this chapter, we introduce a novel approach to use hypermedia as a general mechanism to support uniform interaction in MASs. The core idea is that agents use hypermedia to discover at runtime (i) other entities in a MAS (e.g., other agents, tools, organizations) and (ii) the means to interact with those entities (e.g., interaction protocols, APIs). This reduces coupling and enhances the scalability and evolvability of the MAS. We present a demonstrator that supports these claims. We believe that a hypermedia-based mechanism for uniform interaction in MASs could provide a foundation for engineering world-wide MASs.
SmartChain: A Smart and Scalable Blockchain Consortium for Smart Grid Systems

Smart grid (SG) provides a peer-to-peer energy trading mechanism wherein the electric vehicles (EVs) can trade for energy with their peers using the information and communication technologies. However, the dependence on third party for coordinating the energy trading decisions leads to a bottleneck for any distributed environment. Therefore, blockchain technology can provide a privacy-preserving and effective consensus mechanism without the control of trusted third party. Although blockchain provides inherent secure framework for transactional process, but the this security is because of computational complexity enforced. In SG environment, the conventional blockchain process could not be employed due to limited computational resources with EVs, which makes it difficult to solve the tough computational puzzles to validate the transactions. On the other hand, any compromise on the computation difficulty makes it more vulnerable to various types of attacks. Therefore, in this paper, SmartChain: a blockchain inspired smart and scalable ledger framework which does not require much computational complexity is designed for secure peer-to-peer energy trading in SG ecosystem. The proposed framework is evaluated using the parameters such as execution and validation time. The results obtained depict the superiority of SmartChain in contrast to the conventional blockchain process.
A Learning-based Framework for Line-spectra Super-resolution

We propose a learning-based approach for estimating the spectrum of a multisinusoidal signal from a finite number of samples. A neural-network is trained to approximate the spectra of such signals on simulated data. The proposed methodology is very flexible: adapting to different signal and noise models only requires modifying the training data accordingly. Numerical experiments show that the approach performs competitively with classical methods designed for additive Gaussian noise at a range of noise levels, and is also effective in the presence of impulsive noise.
Usability, security and trust in password managers: A quest for user-centric properties and features

A password manager stores and handles users' passwords from different services. This relieves the users from constantly remembering and recalling many different login credentials. However, because of the poor usability and limited user experience of password managers, users find it difficult to perform basic actions, such as a safe login. Unavoidably, the password manager holds the login credentials of many online services; as a result, it becomes a desired target for online attacks. This results in compromised security, which users often consider as an inevitable condition that must be accepted. Many studies analysed the usability and security of various password managers. Their research findings, though important, are rather incomprehensible to designers of password managers, because they are limited to particular properties or specific applications and they, often, are contradictory. Hence, we focus on investigating properties and features that can elevate the usability, security, and trustworthiness of password managers, aiming at providing practical, simple, and useful guidelines for building a useable password manager. We performed a systematic literature review, in which we selected thirty-two articles with coherent outcomes associated with usability and security. From these outcomes, we deduced and present meaningful suggestions for realising a useable, secure and trustworthy password manager.
Hybrid Methods of GMDH-Neural Networks Synthesis and Training for Solving Problems of Time Series Forecasting

In this paper, for solving the problem of forecasting non-stationary time series, hybrid learning methods for GMDH-neural networks are proposed. Training methods combine artificial immune systems with members of the evolutionary algorithm family, in particular, gene expression programming systems. The following hybrid computational methods for the synthesis and training of GMDH-neural networks have been developed: a method in which candidate models are represented as gene expression, and training is performed by clonal selection; the method of two-phase structural-parametric synthesis, in which the structural component is formed by programming the expression of genes, and the parameterization is performed by clonal selection; a method based on cooperative-competitive processes of interaction of the elements of the immune system, in which the structure and parameters of the GMDH-neural network are represented by the entire population element-wise. Comparative experimental studies of the quality of the proposed computational methods for solving forecasting problems were carried out.
Cost-sensitive convolutional neural networks for imbalanced time series classification

Time series classification and class imbalance problem are two common issues in a multitude of real-life scenarios. This paper simultaneously explores both issues with deep convolution neural networks (CNNs). Because standard networks treat the major
CBinfer: Exploiting Frame-to-Frame Locality for Faster Convolutional Network Inference on Video Streams

The last few years have brought advances in computer vision at an amazing pace, grounded on new findings in deep neural network construction and training as well as the availability of large labeled datasets. Applying these networks to images demands a high computational effort and pushes the use of state-of-the-art networks on real-time video data out of reach of embedded platforms. Many recent works focus on reducing network complexity for real-time inference on embedded computing platforms. We adopt an orthogonal viewpoint and propose a novel algorithm exploiting the spatio-temporal sparsity of pixel changes. This optimized inference procedure resulted in an average speed-up of 9.1X over cuDNN on the Tegra X2 platform at a negligible accuracy loss of <; 0.1% and no retraining of the network for a semantic segmentation application. Similarly, an average speed-up of 7.0X has been achieved for a pose detection DNN and a reduction of 5X of the number of arithmetic operations to be performed for object detection on static camera video surveillance data. These throughput gains combined with a lower power consumption result in an energy efficiency of 511GOp/s/W compared to 70GOp/s/W for the baseline.
Generalized RBF kernel for incomplete data

We construct genRBF kernel, which generalizes standard Gaussian RBF kernel to the case of incomplete data. Instead of using typical imputation techniques, which fill missing attributes by single values, we model possible outcomes at missing coordinates using data distribution. This allows to derive analytical formula for the expected value of RBF kernel taken over all possible imputations, which is a basic idea behind our method. In particular, for complete observations genRBF reduces to standard RBF kernel. Experiments show that introduced kernel applied to SVM classifier and regressor gives better results than state-of-the-art methods, especially in the case when large number of features is missing. Moreover, genRBF is easy to implement and can be used together with any kernel approach without any additional modifications.
Repeater-enhanced distributed quantum sensing based on continuous-variable multipartite entanglement

Entanglement is a unique resource for quantum-enhanced applications. When employed in sensing, shared entanglement between distributed quantum sensors enables a substantial gain in the measurement sensitivity in estimating global parameters of the quantum sensor network. Loss incurred in the distribution of entanglement, however, quickly dissipates the measurement-sensitivity advantage enjoyed by the entangled quantum sensors over sensors supplied with local quantum resources. Here, we present a viable approach to overcome the entanglement-distribution loss and show that the measurement sensitivity enabled by entangled quantum sensors beat that afforded by the optimum local resource. Our approach relies on noiseless linear amplifiers (NLAs) to serve as quantum repeaters. We show that unlike the outstanding challenge of building quantum repeaters to suppress the repeaterless bound for quantum key distribution, NLA-based quantum repeaters for distributed quantum sensing are realizable by available technology. As such, distributed quantum sensing would become the first application instance that benefits from quantum repeaters.
On an open problem about a class of optimal ternary cyclic codes

Cyclic codes are a subclass of linear codes and have applications in consumer electronics, data storage systems and communication systems as they have efficient encoding and decoding algorithms. In this paper, we settle an open problem about a class of optimal ternary cyclic codes which was proposed by Ding and Helleseth [5]. Let C(1,e) be a cyclic code of length 3m−1 over GF(3) with two nonzeros α and αe, where m and e are given integers, and α is a generator of GF(3m)⁎. It is shown that C(1,e) is optimal with parameters [3m−1,3m−1−2m,4] if one of the following conditions is met. 1) m≡0(mod4), m≥4, and e=3m2+5. 2) m≡2(mod4), m≥6, and e=3m+22+5.
Moving into a technology land

While a growing body of literature in HCI is focusing on the initial needs of the refugees soon after their migration, most challenges associated with the long-term process of their integration with the host communities using technology have still remained understudied. This work builds on a 3 year-long fieldwork with the refugees in Canada, extended observations, and interviews with 26 participants (19 refugees, 4 refugee sponsors, and 3 refugee workers) to illustrate how refugees encounter various challenges in accessing necessary services in Canada through its computerized infrastructures. This paper documents the intricacies and nuances of this problem extended over their struggles in obtaining information, getting social support, learning new technologies, securing their digital activities, and the gender dynamics associated with these activities. Our analysis generates several design implications to address these issues. Moreover, we discuss the challenges' entanglement with some of the broader concerns in HCI regarding infrastructure, inclusion, and mobility.
Blockchain for the Internet of Vehicles

Recent advancements in the Internet of Things and mobile internet helped the development of traditional Vehicular Adhoc-NETwork (VANET) based systems into the Internet of Vehicles (IoV). Vehicles in IoV make decisions based on the information from other vehicles and roadside infrastructure. This information helps them make critical decisions and alert the user about unpredictable situations. Security and privacy concerns in vehicles are increasing, hence it is important to address these issues, thus preventing malicious nodes transmitting falsified information or tampering critical communication data. In this paper, we propose RealTime Blockchain for the Internet of Vehicles for ensuring authentication and maintaining secure communication between the vehicles. The blockchain keeps track of communication information between vehicles to provide accountability. This paper also introduces smart contracts based vehicle services like automatic toll payment, vehicle servicing slot booking and payment, fuel payment, vehicle insurance renewal, etc. The proposed blockchain supports native cryptocurrency for the payments within the network.
Black-box Adversarial Machine Learning Attack on Network Traffic Classification

Deep machine learning techniques have shown promising results in network traffic classification, however, the robustness of these techniques under adversarial threats is still in question. Deep machine learning models are found vulnerable to small carefully crafted adversarial perturbations posing a major question on the performance of deep machine learning techniques. In this paper, we propose a black-box adversarial attack on network traffic classification. The proposed attack successfully evades deep machine learning-based classifiers which highlights the potential security threat of using deep machine learning techniques to realize autonomous networks.
6D-VNet: End-To-End 6DoF Vehicle Pose Estimation From Monocular RGB Images

We present a conceptually simple framework for 6DoF object pose estimation, especially for autonomous driving scenario. Our approach efficiently detects traffic participants in a monocular RGB image while simultaneously regressing their 3D translation and rotation vectors. The method, called 6D-VNet, extends Mask R-CNN by adding customised heads for predicting vehicle's finer class, rotation and translation. The proposed 6D-VNet is trained end-to-end compared to previous methods. Furthermore, we show that the inclusion of translational regression in the joint losses is crucial for the 6DoF pose estimation task, where object translation distance along longitudinal axis varies significantly, e.g., in autonomous driving scenarios. Additionally, we incorporate the mutual information between traffic participants via a modified non-local block. As opposed to the original non-local block implementation, the proposed weighting modification takes the spatial neighbouring information into consideration whilst counteracting the effect of extreme gradient values. Our 6D-VNet reaches the 1 st place in ApolloScape challenge 3D Car Instance task. Code has been made available at: https://github.com/stevenwudi/6DVNET .
LRCoin: Leakage-Resilient Cryptocurrency Based on Bitcoin for Data Trading in IoT

Currently, the number of Internet of Things (IoT) devices making up the IoT is more than 11 billion and this number has been continuously increasing. The prevalence of these devices leads to an emerging IoT business model called Device-as-a-service, which enables sensor devices to collect data disseminated to all interested devices. The devices sharing data with other devices could receive some financial reward, such as Bitcoin. However, side-channel attacks, which aim to exploit some information leaked from the IoT devices during data trade execution, are possible since most of the IoT devices are vulnerable to be hacked or compromised. Thus, it is challenging to securely realize data trading in IoT environment due to the information leakage, such as leaking the private key for signing a Bitcoin transaction in Bitcoin system. In this paper, we propose LRCoin, a kind of leakage-resilient cryptocurrency based on bitcoin in which the signature algorithm used for authenticating bitcoin transactions is leakage-resilient. LRCoin is suitable for the scenarios where information leakage is inevitable, such as IoT applications. Our core contribution is proposing an efficient bilinear-based continual-leakage-resilient ECDSA signature. We prove the proposed signature algorithm is unforgeable against adaptively chosen messages attack in the generic bilinear group model under the continual leakage setting. Both the theoretical analysis and the implementation demonstrate the practicability of the proposed scheme.
Motivating scholars’ responses in academic social networking sites: An empirical study on ResearchGate Q&amp;A behavior

The advent of academic social networking sites (ASNS) has offered an unprecedented opportunity for scholars to obtain peer support online. However, little is known about the characteristics that make questions and answers popular among scholars on ASNS. Focused on the statements embedded in questions and answers, this study strives to explore the precursors that motivate scholars to respond, such as reading, following, or recommending a question or an answer. We collected empirical data from ResearchGate and coded the data via the act4teams coding scheme. Our analysis revealed a threshold effect—when the length of question description is over circa 150 words, scholars would quickly lose interest and thus not read the description. In addition, we found that questions, including positive action-oriented statements, are more likely to entice subsequent reads from other scholars. Furthermore, scholars prefer to recommend an answer with positive procedural statements or negative action-oriented statements.
Deadline-constrained workflow scheduling in IaaS clouds with multi-resource packing

Workflow is a common model to represent large computations composed of dependent tasks. Most existing workflow scheduling algorithms use computing resources in a non-multiprogrammed way, by which only one task can run on a service (machine) at a time. In this paper, we study a new workflow scheduling model on heterogeneous Infrastructure-as-a-Service (IaaS) platforms, which allows multiple tasks to run concurrently on a virtual machine (VM) according to their multi-resource demands. First, we propose a list-scheduling framework for the new multiprogrammed cloud resource model. In the order of a priority list, this framework gradually appoints tasks the best placements found on both existing and new VMs on the platform. Different task prioritization and placement comparison methods can be employed for different scheduling objectives. To fully exploit the heterogeneity of IaaS platforms, the VMs can be scaled up during the scheduling process. Then, we propose a deadline-constrained workflow scheduling algorithm (called DyDL) based on this framework to optimize the cost of workflow execution. This algorithm prioritizes tasks by their latest start times and appoints tasks the placements which can meet their latest start times and incur the minimal cost increases. Experimental results show that DyDL can achieve significantly better schedules in most test cases compared to several existing deadline-constrained workflow scheduling algorithms.
Using machine learning to estimate a key missing geochemical variable in mining exploration: Application of the Random Forest algorithm to multi-sensor core logging data

Mining exploration increasingly relies on large, multivariate databases storing data ranging from drill core geochemical analysis to geophysical data or geological descriptions. Utilizing these large datasets to their full potential implies the use of multivariate statistical analysis such as machine learning. The Random Forest algorithm has proved its efficiency in mining applications. In this study we use it to estimate a key geochemical element, sodium, using a multivariate chemo-physical dataset measured on drill cores in the Matagami mining district of Québec, Canada. Sodium is important to characterize hydrothermal alteration in volcanogenic massive sulfide settings, since Na depletion can be used to vector towards ore, but this element is not readily measured by portable X-ray fluorescence (pXRF). We first test the algorithm on a database of over 8000 traditional laboratory geochemistry analyses and find a correlation of 0.95 between estimated and measured Na. We then test the algorithm on the multi-sensor core logging data, including density, magnetic susceptibility, and 15 geochemical elements by pXRF, but borrowing Na from traditional geochemistry (n = 260). This yields correlations of 0.66 to 0.75 depending on the training and testing sets. Finally the algorithm is applied to the whole multiparameter database (n = 9675) to estimate Na downcore. There is a good general correspondence with the downcore Na patterns seen through traditional geochemistry, and the estimated Na which has much greater spatial resolution. Random Forest appears to be a very good estimation tool when using large amounts of data and variables, as it uses all variables and automatically prioritizes the most useful. This method also allows visualization of the weight of each variable in the estimation. Future studies should compare RF with other methods.
Modulating the Use of Multiple Memory Systems in Value-based Decisions with Contextual Novelty

With multiple learning and memory systems at its disposal, the human brain can represent the past in many ways, from extracting regularities across similar experiences (incremental learning) to storing rich, idiosyncratic details of individual events (episodic memory). The unique information carried by these neurologically distinct forms of memory can bias our behavior in different directions, raising crucial questions about how these memory systems interact to guide choice and the factors that cause one to dominate. Here, we devised a new approach to estimate how decisions are independently influenced by episodic memories and incremental learning. Furthermore, we identified a biologically motivated factor that biases the use of different memory types-the detection of novelty versus familiarity. Consistent with computational models of cholinergic memory modulation, we find that choices are more influenced by episodic memories following the recognition of an unrelated familiar image but more influenced by incrementally learned values after the detection of a novel image. Together this work provides a new behavioral tool enabling the disambiguation of key memory behaviors thought to be supported by distinct neural systems while also identifying a theoretically important and broadly applicable manipulation to bias the arbitration between these two sources of memories.