Reviewerly/exHarmony huggingface dataset:
Identifying Data Exposure Across Distributed High-Dimensional Health Data Silos through Bayesian Networks Optimised by Multigrid and Manifold

We present a novel, and use case agnostic method of identifying and circumventing private data exposure across distributed and high-dimensional data repositories. Examples of distributed high-dimensional data repositories include medical research and treatment data, where oftentimes more than 300 describing attributes appear. As such, providing strong guarantees of data anonymity in these repositories is a hard constraint in adhering to privacy legislation. Yet, when applied to distributed high-dimensional data, existing anonymisation algorithms incur high levels of information loss and do not guarantee privacy defeating the purpose of anonymisation. In this paper, we address this issue by using Bayesian networks to handle data transformation for anonymisation. By evaluating every attribute combination to determine the privacy exposure risk, the conditional probability linking attribute pairs is computed. Pairs with a high conditional probability expose the risk of deanonymisation similar to quasi-identifiers and can be separated instead of deleted, as in previous algorithms. Attribute separation removes the risk of privacy exposure, and deletion avoidance results in a significant reduction in information loss. In other words, assimilating the conditional probability of outliers directly in the adjacency matrix in a greedy fashion is quick and thwarts de-anonymisation. Since identifying every privacy violating attribute combination is a W[2]-complete problem, we optimise the procedure with a multigrid solver method by evaluating the conditional probabilities between attribute pairs, and aggregating state space explosion of attribute pairs through manifold learning. Finally, incremental processing of new data is achieved through inexpensive, continuous (delta) learning.
From Data Collection to Data Analytics: How to Successfully Extract Useful Information from Big Data in the Oil &amp; Gas Industry?

Abstract Big data has become a major topic in many industries. Most recently, the oil and gas industry adopted a special interest in data science as a result of the increasing availability of public domains and commercial databases. Utilizing and processing such data can help in making better future decisions. The aim of this work is to provide an example and demonstrate methodologies on how to collect and utilize big data to help in making better future decisions in the oils and gas industry. After reading a good number of papers and books about the applications of data analysis in the oil and gas industry, in addition to other industries, and given that data analysis is the area of expertise of the authors, this paper was written to demonstrate real examples of data processing and validation workflows. This work is intended to cover the gaps in the literature were many of the publications only discuss the importance of data-driven analytics. This paper provides an overview of the diverse and bulk data generating sources in the oil and gas industry, starting from the exploration phase to the end of the lifecycle of the well. It provides an example of utilizing a public domain database (FracFocus) and demonstrates a step by step workflow on how to collect and process the data based on the objective of the analytics. Two real examples of descriptive and predictive analytics are also demonstrated in this paper to show the power of having a diverse and multiple resources databases. A framework of data validation and preparation is also shown to illustrate data quality checks combined with best practices of data cleansing and outlier detection methodologies. This paper provides a clear methodology on how to successfully apply data analysis which can serve as a guide for some future data analysis applications in the oil and gas industry.
Design of a Lightweight Reconfigurable PRNG Using Three Transistor Chaotic Map

In this paper, we implemented a lightweight reconfigurable pseudo-random number generator (PRNG). The map generates discrete-time chaotic signals. The chaotic oscillator requires two map circuits to generate chaotic signals. The design is reconfigurable because two map circuits have two bifurcation parameters which can be leveraged to generate multiple random sequences. The design of the PRNG utilizes two chaotic oscillators, analog mux, 10 bit ADC, 2 bit shift register and an XOR gate. The circuit has been implemented in 65 nm CMOS technology with a supply voltage of 1.2 V. The estimated power consumption of the circuit is 2.12 mW and the area overhead is 0.132 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The throughput of the proposed PRNG is 100 MS/s. The proposed PRNG can be used for security applications in IoT devices which requires circuits with less area and power overhead. The PRNG design passes all the tests in NIST SP 800-22 test suite.
Beyond QoE: Diversity Adaption in Video Streaming at the Edge

Adaptive bitrate (ABR) algorithms have been critical techniques for high quality-of-experience (QoE) Internet video delivery. Prior work designs ABR algorithms by conducting the overall QoE function of fixed parameters. However, the QoE of end users are diverse and video bitrate may be chosen in a misleading way when leaving out the diversity. State-of-the-art ABR algorithms like MPC, Pensieve utilize off-line modeling techniques and result in performance degradation for online QoE diversity adaption. To address this issue, we propose Elephanta, an online flexible ABR algorithm for edge users which incorporates (1) user QoE perception interface and (2) adaption algorithm with flexible parameters. To avoid overheads for updating parameters online, we model video streaming as a renewal system and formulate specific QoE function into flexible formats by setting constraints on corresponding QoE metrics. To validate parameter setting, we emulate Elephanta under 5 thousand throughput traces including FCC broadband, 3G HSDPA data set from the Internet and 4G/LTE data set collected by ourselves. Accordingly, we implement Elephanta in dash.js at client side for user test. Evaluation results show that Elephanta achieves QoE improvement by 21.1% over MPC, in part for its superior adaptability to QoE diversity.
Face Recognition Using Local Binary Pattern Histogram for Visually Impaired People

This study aims to build a real-time system that can help visually impaired to recognize people present in their surroundings. Visually impaired people experience many difficulties in daily activities, especially to recognize people. The proposed system applies the concept of a mobile personal assistant that runs in Android smartphone. The input data is a real-time video taken using 16-megapixel smartphone camera with a resolution of 1280×720. The system built uses Gamma Correction and Difference of Gaussian methods to improve the accuracy of the Local Binary Pattern Histogram (LBPH) algorithm. Based on the experiment with 148 cm and 172 cm user heights and 8 respondents, the system was able to increase the accuracy by 8.32% for 148 cm height, while the accuracy for 172 cm height increased by 6.22%. The output of this system is the sound of the identification result.
PandaSync: Network and Workload Aware Hybrid Cloud Sync Optimization

With the widespread use and increasing popularity of cloud storage, more and more data are moved to the cloud, making cloud storage a platform for both data sharing among users, devices, and data backup for data reliability. Thus, it is critically important to ensure data consistency through efficient cloud synchronization (sync). The existing cloud synchronization schemes are either delta sync, which sends only the updated portion of a file but incurs high compute overhead of data deduplication for small files, or full sync, which avoids data deduplication by sending the full file but wastes network bandwidth and lengthens sync time by transferring significant amount of redundant data over the networks for large files. In this paper, we propose a hybrid cloud sync scheme, PandaSync, that combines full sync and delta sync dynamically based on file size and network conditions. To further improve small-file sync performance, we propose an optimization, Full2Sync, that merges the sync request with the file-sending request to reduce the number of network round-trips between the client and the cloud servers. The experiments conducted on our lightweight prototype implementation of PandaSync show that PandaSync reduces the sync time by an average of 85.1% and 74.6% from the delta sync scheme and full sync scheme, respectively.
Design and Prototyping of Neural Network Compression for Non-Orthogonal IoT Signals

The non-orthogonal IoT signal, following the bandwidth compression spectrally efficient frequency division multiplexing (SEFDM) characteristics, can bring benefits in enhanced massive device connections, signal coverage extension and data rate increase, but at the cost of computational complexity. Resource-constrained IoT devices have limited memory storage and complex signal processing is not allowed. Machine learning can simplify signal detection by training a general data-driven signal detection model. However, fully connected neural networks would introduce processing latency and extra power consumption. Therefore, the motivation of this work is to investigate different neural network compression schemes for system simplification. Three compression strategies are studied including topology compression, weight compression and quantization compression. These methods show efficient neural network compression with trade-offs between computational complexity and bit error rate (BER) performance. Practical neural network prototyping is evaluated as well on a software defined radio (SDR) platform. Results show that the practical weight compression neural network can achieve similar performance as the fully connected neural network but with great resource saving.
Knowledge Graph Embedding With Interactive Guidance From Entity Descriptions

Knowledge Graph (KG) embedding aims to represent both entities and relations into a continuous low-dimensional vector space. Most previous attempts perform the embedding task using only knowledge triples to indicate relations between entities. Entity descriptions, although containing rich background information, have not been well utilized in these methods. In this paper, we propose Entity Descriptions-Guided Embedding (EDGE), a novel method for learning the knowledge graph representations with semantic guidance from entity descriptions. EDGE enables an embedding model to learn simultaneously from 1) knowledge triples that have been directly observed in a given KG, and 2) entity descriptions which have rich semantic information about these entities. In the learning process, EDGE encodes the semantics of entity descriptions to enhance the learning of knowledge graph embedding, and integrates such learned KG embedding to constraint their corresponding word embeddings in entity descriptions. Through this interactive procedure, semantics of entity descriptions may be better transferred into the learned KG embedding. We evaluate EDGE in link prediction and entity classification on Freebase and WordNet. Experimental results show that: 1) with entity descriptions injected, EDGE achieves significant and consistent improvements over state-of-the-art baselines; and 2) compared to those one-time injection schemes studied before, the interactive guidance strategy maximizes the utility of entity descriptions for KG embedding, and indeed achieves substantially better performance.
Residual Attention-Based Fusion for Video Classification

Video data is inherently multimodal and sequential. Therefore, deep learning models need to aggregate all data modalities while capturing the most relevant spatio-temporal information from a given video. This paper presents a multimodal deep learning framework for video classification using a Residual Attention-based Fusion (RAF) method. Specifically, this framework extracts spatio-temporal features from each modality using residual attention-based bidirectional Long Short-Term Memory and fuses the information using a weighted Support Vector Machine to handle the imbalanced data. Experimental results on a natural disaster video dataset show that our approach improves upon the state-of-the-art by 5% and 8% regarding F1 and MAP metrics, respectively. Most remarkably, our proposed residual attention model reaches a 0.95 F1-score and 0.92 MAP for this dataset.
Towards Human-Robot Collaboration: An Industry 4.0 VR Platform with Clouds Under the Hood

Safe and efficient Human-Robot Collaboration (HRC) is an essential feature of future Industry 4.0 production systems which requires sophisticated collision avoidance mechanisms with intense computation need. Digital twins provide a novel way to test the impact of different control decisions in a simulated virtual environment even in parallel. In addition, Virtual/Augmented Reality (VR/AR) applications can revolutionize future industry environments. Each component requires extreme computational power which can be provided by cloud platforms but at the cost of higher delay and jitter. Moreover, clouds bring a versatile set of novel techniques easing the life of both developers and operators. Can these applications be realized and operated on today's systems? In this demonstration, we give answers to this question via real experiments.
A mixture of linear-linear regression models for a linear-circular regression

We introduce a new approach to a linear-circular regression problem that relates multiple linear predictors to a circular response. We follow a modelling approach of a wrapped normal distribution that describes angular variables and angular distributions and advances them for a linear-circular regression analysis. Some previous works model a circular variable as projection of a bivariate Gaussian random vector on the unit square, and the statistical inference of the resulting model involves complicated sampling steps. The proposed model treats circular responses as the result of the modulo operation on unobserved linear responses. The resulting model is a mixture of multiple linear-linear regression models. We present two EM algorithms for maximum likelihood estimation of the mixture model, one for a parametric model and another for a nonparametric model. The estimation algorithms provide a great trade-off between computation and estimation accuracy, which was numerically shown using five numerical examples. The proposed approach was applied to a problem of estimating wind directions that typically exhibit complex patterns with large variation and circularity.
Behavior Switch for DRL-based Robot Navigation

Recently, deep reinforcement learning (DRL) has shown great potential in mapless navigation of mobile robotics. However, without a map, most reported DRL-based controllers failed to drive robots out of local-minimum areas. In this paper, a hierarchical control framework is proposed to address this problem. In this framework, two low-level controllers, i.e., one for basic navigation and one for wall-following, are trained by DRL methods. The high-level switch is designed by some heuristic rules, which can adaptively choose the suitable low-level controller based on the current situation. Simulation-based testing results show that the proposed method is able to control the robot to navigate in challenging scenarios with local-minimum areas.
Memory Optimization for Energy-Efficient Differentially Private Deep Learning

With the advent of Internet of Things (IoT) technologies and availability of a large amount of data, deep learning has been applied in a variety of artificial intelligence (AI) applications. However, sharing personal data using IoT edge devices carries inherent risks to individual privacy. Meanwhile, the energy and memory resources needed during the inference process become a constraint to the resource-limited IoT edge devices. This article brings memory hardware optimization to meet the tight power budget in IoT edge devices by considering the privacy, accuracy, and power efficiency tradeoff in differentially efficient deep learning systems. Based on a detailed analysis on these characteristics, an integer linear programs (ILP) model is developed to minimize mean square error (MSE), thereby enabling optimal input data memory design. Our simulation results in 45-nm CMOS technology show that the proposed technique can enable near-threshold energy-efficient memory operation for different privacy requirements, with less than 1% degradation in classification accuracy.
Extended Sliding in Virtual Reality

Although precise 3D positioning is not always necessary in virtual environments, it is still an important task for current and future applications of Virtual Reality (VR), including 3D modelling, engineering, and scientific applications. We focus on 3D positioning techniques in immersive environments that use a 6DOF controller as input device and present a new technique that improves 3D positioning performance in VR, in both speed and accuracy. Towards this goal, we adapted an extended sliding technique to VR systems with a controller as input device and compared it with previously presented 3DOF positioning techniques. The results showed that our new Extended VR Sliding technique significantly improved the accuracy for 3D positioning tasks, especially for targets in contact with the scene.
Toward real-time object detection on heterogeneous embedded systems

Low power consumption and high efficiency of heterogeneous systems improves processing power and enables the implementation of real-time applications. Deep learning, as one of the hottest topics of today, plays an important role in solving difficult problems such as machine vision. The use of traditional methods for solving visual machine problems requires the engineering of features by humans, which makes it difficult to create a comprehensive model for a problem. The use of revolutionary deep learning in the machine vision, which along with the embedded systems can be useful in many today's issues. Convolutional neural networks have shown a high degree of efficiency in the task of categorizing images and detecting objects. An important feature in neural networks is the intrinsic parallelism of its structure, which results in the use of embedded heterogeneous systems that can provide excellent performance in the implementation of neural networks. Implementing real-time objects detection systems in enclosed environments with limited computing resources and memory is challenging. This paper presents a method for implementing the MobileNet-SSD object detection system on the Jetson TK1, which attempts to improve performance by changing the network's convoys and dividing tasks between the central and the graphics processor.
Cryptanalysis and Improvement of an Anonymous Batch Verification Scheme for Mobile Healthcare Crowd Sensing

As an emerging application of smart healthcare, mobile healthcare crowd sensing (MHCS) has become a research hotspot. However, how to ensure the confidentiality and integrity of data and protect the privacy of user is still a challenge for MHCS. To handle these issues, an effective and secure privacy protection scheme is indispensable. Recently, a large-scale concurrent data anonymous batch verification scheme for mobile healthcare crowd sensing was proposed by Liu et al. Unfortunately, we demonstrate that their scheme is insecure. This paper presents an improved anonymous scheme based on certificateless aggregate signature (CL-AS) for MHCS. First, considering the efficiency and the characteristics of the MHCS, the technique of aggregate signature is adopted, which can achieve batch verification and greatly save the bandwidth and computation resources. Second, anonymous communication is carried out in this scheme to realize privacy preservation. Third, based on certificateless cryptography, the proposed scheme can simplify the complicated certificate management and eliminate the key escrow problem. In addition, our scheme is provably secure against the existential forgery on adaptively chosen message attack in the Random Oracle Model assuming the computational Diffie-Hellman problem is intractable. Furthermore, security and efficiency analysis shows that our scheme is secure and efficient.
Virtual Reality Fostering Empathy: Meet the Enemy

“Ben Khalifa’s virtual reality installation confronts users with the perspectives of soldiers while forcing us to confront our perspectives of them.”The Enemy, a “virtual reality experience at the ...
Novel Three-Hierarchy Multiple-Tag-Recognition Technique for Next Generation RFID Systems

In this paper, we propose a novel hierarchical radio-frequency identification (RFID) tag-recognition method based on blind source separation (BSS), graph-based automatic modulation classification (AMC), and direct-sequence spread-spectrum (DSSS). In our proposed method, RFID tags can be modulated using different modulation schemes according to different scenarios (e.g., different users or different tag devices). For each modulation scheme, the direct-sequence spread-spectrum strategy is employed to allow simultaneous transmissions of multiple commands. In the signal separation phase, BSS is employed to separate different transmitted signals. Then in the first hierarchy of the recognition phase, different modulation types are adopted to distinguish different users, the graph-based AMC is built upon the periodicity of the modulated signals: the cyclic spectrum of the received signal is established; the graph representation is then constructed according to the cyclic spectrum. Ultimately, robust features are extracted from the graph representation. In the second hierarchy of the recognition phase, the DSSS scheme is utilized to differentiate the control or sensed data carried by individual tags; the signature sequence set with low cross-correlations can be generated from Kasami sequences. In the third hierarchy of the recognition phase, the information data are thus spread by these signature sequences. In our proposed new RFID framework, multiple tags can transmit signals simultaneously in the same frequency band where each tag signal can still be separated and identified and its carried information can be recovered. Monte Carlo simulation results demonstrate the promising performance of our proposed new RFID scheme.
A Deep Image Compression Framework for Face Recognition

Face recognition technology has advanced rapidly and has been widely used in various applications. Due to the huge amount of data of face images in large-scale face recognition tasks and the large computing resource cost required correspondingly, there is a requirement for a face image compression approach that is highly suitable for face recognition tasks. In this paper, we propose a deep convolutional autoencoder compression network for face recognition tasks. In compression process, deep features are extracted from the original image by a Compression Network (CompNet) to produce a compact representation of the original image, which is then encoded and saved by an existing codec PNG. In reconstruction process, this compact representation is utilized by a Reconstruction Network (RecNet) to generate a restored image of the original one. In order to improve the face recognition accuracy when the compression framework is used in a face recognition system, we combine the CompNet and RecNet with an existing face recognition network for joint optimization. We test the proposed scheme and find that after joint optimization, the Labeled Faces in the Wild (LFW) dataset compressed by our compression framework has higher face verification accuracy than that compressed by JPEG2000, and is much higher than that compressed by JPEG.