Reviewerly/exHarmony huggingface dataset:
Location Privacy Protection in Smart Health Care System

In a smart health system, patients' location information is periodically sent to hospitals and this information helps hospitals to provide improved health care services. The location information together with time stamp alone can reveal a patient's private information, such as person's life style, places frequently visited by the person, and personal interests. Thus, it is important to protect the location privacy of a patient. In the existing privacy protection mechanisms, trusted third party (TTP) and location perturbation techniques are used. However, in TTP-based mechanism, an adversary who illegally gets access to TTP server will have access to the private location information. On the other hand, in location perturbation technique, utility of the location information is significantly compromised. In this paper, we propose a location privacy protection mechanism in which location privacy is protected while maintaining the utility of the location data. In the proposed mechanism, a main processing unit attached to a patient's body generates the perturbed location by considering the distance between the patient's location and the preidentified patient's sensitive locations. This adaptive generation of perturbed location, removes the necessity to trust other parties while preserving the privacy and utility of the location data. The validity of the proposed mechanism is demonstrated by simulation results.
Deep Wide-Activated Residual Network Based Joint Blocking and Color Bleeding Artifacts Reduction for 4:2:0 JPEG-Compressed Images

Blocking and color bleeding are two well-known artifacts for 4:2:0 JPEG-compressed images. Blocking mainly results from the block-level quantization of the luma component, while color bleeding is mainly caused by the subsampling and quantization of chroma components. Restoring luma can reduce blocking distortion, but with little influence on color bleeding. On the contrary, color bleeding can be removed via chroma components restoration. This letter proposes a deep wide-activated residual network for reducing blocking and color bleeding artifacts simultaneously, in which the luma and chroma components are jointly restored. Chroma components usually suffer from more severe distortion than the luma component due to subsampling and coarse quantization. Thus, we use the luma component to guide the restoration of chroma components. Moreover, we reduce blocking and color bleeding artifacts in low-resolution space via pixel shuffle-based decimation and assembling, which allows to obtain high restoration speed. Experimental results show that the proposed approach achieves state-of-the-art performance on joint blocking and color bleeding artifacts reduction.
Bitcoin fluctuations and the frequency of price overreactions

This paper investigates the role of the frequency of price overreactions in the cryptocurrency market in the case of BitCoin over the period 2013–2018. Specifically, it uses a static approach to detect overreactions and then carries out hypothesis testing by means of a variety of statistical methods (both parametric and non-parametric) including ADF tests, Granger causality tests, correlation analysis, regression analysis with dummy variables, ARIMA and ARMAX models, neural net models, and VAR models. Specifically, the hypotheses tested are whether or not the frequency of overreactions (i) is informative about Bitcoin price movements (H1) and (ii) exhibits no seasonality (H2). On the whole, the results suggest that it can provide useful information to predict price dynamics in the cryptocurrency market and for designing trading strategies (H1 cannot be rejected), whilst there is no evidence of seasonality (H2 cannot be rejected).
Restricted Sensitive Attributes-based Sequential Anonymization (RSA-SA) approach for privacy-preserving data stream publishing

Data streams have become a widely-adopted data representation format in many real-world applications. This data streaming may be published for different scientific research, mining, or analysis purposes. However, such streams may contain personal-specific data that could be considered as sensitive about individuals. This makes the privacy preserving of data streams against privacy disclosure attacks, while maintaining their utilization, is a real challenge. Some studies have considered privacy-preserving publishing over data streams with only Single Sensitive Attribute, in which they do not protect the published streams from all possible privacy attacks. In this paper, we propose a novel Restricted Sensitive Attributes-based Sequential Anonymization (RSA-SA) approach for privacy-preserving data stream publishing. Besides, two new privacy restrictions are introduced to restrict the published Sensitive Attributes values: Semantic-diversity and Sensitivity-diversity. RSA-SA can protect the sensitive values of the published data streams against the related privacy attacks, including the attribute disclosure, skewness, similarity, and sensitivity attacks. In addition, RSA-SA handles data streams that have either single or multiple sensitive attributes with minimum information loss and delay time. Thus, the data utility of the published data streams is efficiently maintained to provide more accurate mining and analytical results, where robust invulnerability to privacy attacks is sustained.
Digital Forensics for IoT and WSNs

In the last decade, wireless sensor networks (WSNs) and Internet-of-Things (IoT) devices are proliferated in many domains including critical infrastructures such as energy, transportation and manufacturing. Consequently, most of the daily operations now rely on the data coming from wireless sensors or IoT devices and their actions. In addition, personal IoT devices are heavily used for social media applications, which connect people as well as all critical infrastructures to each other under the cyber domain. However, this connectedness also comes with the risk of increasing number of cyber attacks through WSNs and/or IoT. While a significant research has been dedicated to secure WSN/IoT, this still indicates that there needs to be forensics mechanisms to be able to conduct investigations and analysis. In particular, understanding what has happened after a failure or an attack is crucial to many businesses, which rely on WSN/IoT applications. Therefore, there is a great interest and need for understanding digital forensics applications in WSN and IoT realms. This chapter fills this gap by providing an overview and classification of digital forensics research and applications in these emerging domains in a comprehensive manner. In addition to analyzing the technical challenges, the chapter provides a survey of the existing efforts from the device level to network level while also pointing out future research opportunities.
A system for generating storyline visualizations using hierarchical task network planning

Existing storyline visualization techniques present narratives as a node-link graph where a sequence of links shows the evolution of causal and temporal relationships between characters in the narrative. These techniques make a number of simplifying assumptions about the narrative structure, however. They assume that all narratives progress linearly in time, with a well-defined beginning, middle, and end. They assume that the narrative is complete prior to visualization. They also assume that at least two participants interact at every event. Finally, they assume that all events in the narrative occur along a single timeline. Thus, while existing techniques are suitable for visualizing linear narratives, they are not well suited for visualizing narratives with multiple timelines, non-linear narratives such as those with flashbacks, or for narratives that contain events with only one participant. In our previous work we presented Yarn, a system for automatic construction and visualization of narratives with multiple timelines. Yarn employs hierarchical task network planning to generate all possible narrative timelines and visualize them in a web-based interface. In this work, we extend Yarn to support non-linear narratives with flashbacks and flash-forwards, and non-linear point-of-view narratives. Our technique supports both single-participant as well as multi-participant events in the narrative, and constructs both linear as well as non-linear narratives. Additionally, it enables pairwise comparison within a group of multiple narrative timelines.
Differentiating between static and complex problems: A theoretical framework and its empirical validation

Ever since its first conception, the concept of complex problem solving (CPS) has been debated regarding its role within the conception of human intelligence. The aim of the current paper was to theoretically introduce and empirically test a multifaceted framework of CPS that subsumes different positions and provides testable predictions on the nature of CPS. Following a review of the existing literature on complex problem solving, we conclude that it is necessary to differentiate between the dimensions of connectivity and dynamics. These dimensions are further distinguishable for both phases of CPS, knowledge acquisition and knowledge application, resulting in four facets. Static problems, as used in conventional measures of intelligence, on the other hand, do not include dynamics as much as complex problems. We argue that the differences in CPS and static problem solving proposed by various researchers result from the dimension of dynamics that is highly relevant for CPS but less so for static problem solving. An empirical analysis based on two independent samples supported the assumptions made by the framework. This brings substantial implications for the understanding of CPS as well as the interpretation of previous research, which we discuss.
An Efficient Multi-Cloud Service Composition Using a Distributed Multiagent-Based, Memory-Driven Approach

Cloud services are often distributed across several data centers requiring new scalable approaches to efficiently perform searching to reduce the energy and price cost of fulfilling requests.Multiagent-based systems have arisen as a powerful technique for improving distributed processing on a wide scale, which can operate in environments where partial observability is the norm and the cost of prolonged search can be exponential.In this paper, we present a multiagent-based service composition approach, using agent-matchmakers and agent-representatives, for the efficient retrieval of distributed services and propagation of information within the agent network to reduce the amount of brute-force search.Our extensive simulation results indicate that by introducing localised agent-based memory searches, the amount of actions (with their associated energy costs) can be reduced by over 50% which results in a lower energy cost per composition request.
ASIC and FPGA Comparative Study for IoT Lightweight Hardware Security Algorithms

Data security, privacy and authenticity are crucial in wireless data transmission. Low power consumption is the main requirement for any chip design targeting the Internet of Things (IoT) applications. In this research paper, a comparative study of eight authenticated encryption and decryption algorithms, selected from the “Competition for Authenticated Encryption: Security, Applicability and Robustness” (CAESAR), namely, ACORN, ASCON, CLOC, JOLTIK, MORUS, PRIMATEs, SCREAM and SILC, is presented. The FPGA and ASIC implementations of these eight algorithms are synthesized, placed and routed. Power, area, latency and throughput are measured for all algorithms. All results are analyzed to determine the most suitable algorithm for IoT applications. These results show that ACORN algorithm exhibits the lowest power consumption of the eight studied at the expense of lower throughput and higher latency. MORUS algorithm gives the highest throughput among the eight selected algorithms at the expense of large area utilization.
Introducing a new method for the fusion of fraud evidence in banking transactions with regards to uncertainty

Detection of fraudulent transactions is a vital factor for financial institutions, and finding more effective and accurate methods is of tremendous importance. The use of supervised data mining techniques is not feasible in many cases due to the lack of access to labeled data. Fraud detection is a complex task, and unsupervised methods like clustering and outlier detection techniques employed alone do not yield satisfactory results. Another issue is epistemic uncertainty due to the absence of sufficient information on the behavioral aspects of different customers, which also leads to poorer results for fraud detection and makes the fraud detection system inapplicable in real world environment. In this paper, using multi criteria decision method, intuitionistic fuzzy set, and evidential reasoning, a new method for detection of fraud was introduced, which infuses several behavioral evidence of a transaction concerning the effect of uncertainty for them. Transactional behavior was modeled by considering the trends of different main and aggregated variables at different periods and the extent of deviation of the new arrived transaction from each of these trends were considered as behavioral evidence. The final belief, which is the result of the combination of much evidence using the proposed method, will determine the originality of a newly arrived transaction. Finally, using a real world dataset, the results of the new method were compared with the results of Dempster–Shafer method in terms of the number of frauds discovered and the number of erroneous alerts they issued. The findings showed that the method introduced in this study has higher accuracy and lower false alarms compared to Dempster–Shafer method while the computational complexity of this method makes its implementation time longer.
RGB-D Action Recognition Using Multimodal Correlative Representation Learning Model

The launching of low-cost depth sensors opens up new potentials for RGB-D-based human action recognition. However, most of current RGB-D-based methods simply fuse multimodal features in a holistic manner and ignore the latent connections among different modalities. In this paper, we propose a multimodal correlative representation learning (MCRL) model for human action recognition from RGB-D videos. Specifically, we propose a spatio-temporal pyramid Fourier HOG feature (STPF-HOG) to capture local dynamic patterns around each human joint, which integrates both spatial arrangement and temporal structures. The proposed MCRL model utilizes multimodal data (skeleton, depth, and RGB), and learns shared structures among different modalities. The original low-level features are effectively compressed and projected to a latent subspace. Then, the discriminative shared features are learned by a supervised fashion. We formulate both subspace learning and shared features mining in a modified multi-task learning framework and solve the formulation using an iterative optimization algorithm. To perform computationally efficient action recognition, a robust collaborative representation classifier is presented by introducing a weight regularization matrix. Experimental results on three action datasets demonstrate that the proposed method leads to a more favorable performance compared with the state-of-the-art methods.
Integrating sequential indicator simulation and singularity analysis to analyze uncertainty of geochemical anomaly for exploration targeting of tungsten polymetallic mineralization, Nanling belt, South China

The Nanling belt (South China) is one of the most important metallogenic region in the world in regard to tungsten polymetallic resources. In this belt and surrounding area, the most important elements accompanying tungsten deposits are Sn, Bi, and Mo. Modelling the spatial variability and uncertainty of W, Sn, Bi, and Mo anomalies is critical for tungsten polymetallic exploration risk assessment. However, traditional interpolation methods (e.g., kriging, polynomial trend surface and inverse distance weighted method) for modelling continuous geochemical fields based on sparse sampling data provide smoothed representations of element concentrations, which often result in unreliable decisions. Here, uncertainty analysis of geochemical anomaly for tungsten polymetallic exploration targeting was investigated through combined sequential indicator simulation and local singularity analysis. Anomalous thresholds of E-type singularity indices for the selected elements (W, Sn, Mo and Bi) were determined by singularity-quantile plot analysis. The distribution pattern of probability of not exceeding a threshold singularity index was obtained from a series of equally probable representations of singularity indices of individual elements (W, Sn, Mo and Bi) based on local and spatial uncertainty algorithms. Based on the four probabilistic models, one per element, a synthetic probability map was produced for uncertainty assessment of geochemical anomaly for exploration targeting. The results indicate that zones with very high probabilities are significantly correlated with known tungsten polymetallic deposits and are closely associated with lithostratigraphic contacts. These results provide for important decision-making for risk evaluation of tungsten polymetallic exploration targeting based on spare stream sediment geochemical data in the Nanling belt.
High-capacity encryption system based on single-shot-ptychography encoding and QR code

We proposed a high-capacity optical encryption system based on single-shot-ptychography encoding (SPE) technique and quick response (QR) code. Two QR codes are utilized as date containers, one of which is encoded into random binary images (one is encoded image, another one is key) through visual cryptography (VC). Finally, the encoded images are encrypted by single-shot-ptychography encoding technology. It increases the variety of system-keys and overcomes the disadvantages of encrypting only binary images in SPE encoding systems. The security of the proposed system is mainly supported by VC extension encoding. The result of recovery does not have any distortion compared to the original plaintext. Simulation results show that this system has large-capacity information storage, powerful error correction capability, higher security and robustness.
The Optimal Sub-Packetization of Linear Capacity-Achieving PIR Schemes With Colluding Servers

Suppose M records are replicated in N servers (each storing all M records), a user wants to privately retrieve one record by accessing the servers such that the identity of the retrieved record is secret against any up to T servers. A scheme designed for this purpose is called a T-private information retrieval (PIR) scheme. In practice, capacity-achieving and small sub-packetization are both desired for PIR schemes, because the former implies the highest download rate and the latter means simple realization. Meanwhile, sub-packetization is the key technique for achieving capacity. In this paper, we characterize the optimal sub-packetization for linear capacity-achieving T-PIR schemes. First, a lower bound on the sub-packetization L for linear capacity-achieving T-PIR schemes is proved, i.e., L ≥ dnM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> , where d = gcd(N, T) and n = N/d. Then, for general values of M and N > T ≥ 1, a linear capacity-achieving T-PIR scheme with sub-packetization dnM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> is designed. Comparing with the first capacity-achieving T-PIR scheme given by Sun and Jafar in 2016, our scheme reduces the sub-packetization from N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sup> to the optimal and further reduces the field size by a factor of NdM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> .
Multi-Grained Deep Feature Learning for Robust Pedestrian Detection

In this paper, we address the challenging problem of detecting pedestrians, which are heavily occluded and/or far from cameras. Unlike most existing pedestrian detection methods which only use coarse-resolution feature maps with fixed receptive fields, our approach exploits multi-grained deep features to make the detector robust to visible parts of occluded pedestrians and small-size targets. Specifically, we jointly train a multi-scale network and a human parsing network in a weakly supervised manner with only bounding box annotations. We carefully design the multi-scale network to predict pedestrians of particular scales with the most appropriate feature maps, by matching their receptive fields with the target sizes. The human parsing network generates a fine-grained attention map, which helps guide the detector to focus on the visible parts of occluded pedestrians and small-size instances. Both networks are computed in parallel and form a unified single stage pedestrian detector, which assures a suitable tradeoff between accuracy and speed. Moreover, we introduce an adversarial hiding network to make our detector more robust to occlusion situations, which generates occlusions on pedestrians with the goal to fool the detector that in turn adapts itself to learn to localize these adversarial instances. Experiments on three challenging pedestrian detection benchmarks show that our proposed method achieves a state-of-the-art performance and executes 2× faster than the competitive methods.
S3DRGF: Spatial 3-D Relational Geometric Features for 3-D Sign Language Representation and Recognition

Locations, angles, edges, and surfaces are spatial joint features that were predominantly used for characterizing three-dimensional (3-D) skeletal data in human action recognition. Despite their demonstrated success, features described earlier find difficulty in representing a relational change among joint movements in 3-D space for classifying human actions. To characterize a relation between joints on 3-D skeleton, we propose spatial 3-D relational geometric features (S3DRGFs). S3DRGFs are calculated on a subset of four joints in a chronological order covering all joints on the skeleton. Each of these four joints shape into a polygon, that reshapes spatially and temporally with respect to the sign (action) in the 3-D video. Consequently, we construct the spatio-temporal features (S3DRGF) by computing the area and perimeter of these polygons. Accordingly, query 3-D sign (action) recognition process transforms the joint area and perimeter features (JAF and JPF) into global alignment kernels based on the computed similarity scores with the dataset features. The similarity scores from JAF and JPF kernels are averaged for recognition. The proposed framework has been tested on our own 3-D sign language dataset (BVC3DSL) and three other publicly available datasets: HDM05, CMU, and NTU RGBD skeletal data. The results show higher levels of accuracy in decoding 3-D sign language into text for building a 3-D model based sign language translator.
The Labeled Multi-Bernoulli Filter for Multitarget Tracking With Glint Noise

A labeled multi-Bernoulli (LMB) filter is presented to perform multitarget tracking (MTT) for the glint noise. The measurement noise is modeled as a multivariate Student-t process. The variational Bayesian method is applied in the LMB framework with the augmented state. The predictive likelihood is calculated via minimizing the Kullback-Leibler divergence by the variational lower bound. Simulation results show that our approach is effective in MTT with the glint noise.