Reviewerly/exHarmony huggingface dataset:
Amalgamations are valid in compositional data analysis, can be used in agglomerative clustering, and their logratios have an inverse transformation

Amalgamations (i.e. summing) of parts can be included as new parts in compositional data analysis, and logratios can then be formed using these amalgamations as well as any of the individual parts themselves. In the first contribution of this paper, a comparison is made of the performance of different logratio transformations in explaining the structure of a geochemical data set ​− ​some of the transformations include amalgamations. The second contribution shows how amalgamations suggest a natural way of clustering compositional data, leading to a new clustering algorithm for compositional data. The third contribution deals with the inverse transformation where amalgamations are involved in the logratios. In the case of a linearly independent set of logratios of parts and amalgamated parts, consisting of one less logratio than the number of compositional parts and where each part is present in at least one logratio, it is possible to back-transform this set of logratios to the original parts. The solution is defined by a set of linear equations. A special case is a set of linearly independent pairwise logratios of parts, which are also invertible back to the original parts.
SegEQA: Video Segmentation Based Visual Attention for Embodied Question Answering

Embodied Question Answering (EQA) is a newly defined research area where an agent is required to answer the user's questions by exploring the real world environment. It has attracted increasing research interests due to its broad applications in automatic driving system, in-home robots, and personal assistants. Most of the existing methods perform poorly in terms of answering and navigation accuracy due to the absence of local details and vulnerability to the ambiguity caused by complicated vision conditions. To tackle these problems, we propose a segmentation based visual attention mechanism for Embodied Question Answering. Firstly, We extract the local semantic features by introducing a novel high-speed video segmentation framework. Then by the guide of extracted semantic features, a bottom-up visual attention mechanism is proposed for the Visual Question Answering (VQA) sub-task. Further, a feature fusion strategy is proposed to guide the training of the navigator without much additional computational cost. The ablation experiments show that our method boosts the performance of VQA module by 4.2% (68.99% vs 64.73%) and leads to 3.6% (48.59% vs 44.98%) overall improvement in EQA accuracy.
An Efficient Implementation of the NewHope Key Exchange on FPGAs

Lattice based cryptography plays an important role in the construction of post-quantum cryptography, including key exchange, public key encryption as well as digital signature schemes. Dating back to 2016, a new key exchange scheme called NewHope was proposed by Alkim, Ducas, Pöppelmann, and Schwabe that based security on the quantum hardness of Ring Learning with Errors (RLWE) problem, and was later submitted to the NIST public competition of standard post-quantum cryptography. The new scheme is attractive as it is designed to achieve high performance. In this work, we concentrate on its performance on hardware platforms and propose an efficient implementation of NewHope on Xilinx Artix-7 7020 FPGA platform that consumes 3158 slices, 10285 LUTs, 6623 registers in the server side, and 3042 slices, 10345 LUTs, 6704 registers in the client side, achieving a 45% reduction in LUT, 30% reduction in FF while the total time utilization also decreases by 16% compared with the up to date and directly related work. Specifically, we can accomplish three phases of the key exchange in 39.8/61.7/$21.4~\mu \text{s}$ respectively, operating in 153/152 MHz in server/client side.
Recurrent Temporal Aggregation Framework for Deep Video Inpainting

Video inpainting aims to fill in spatio-temporal holes in videos with plausible content. Despite tremendous progress on deep learning-based inpainting of a single image, it is still challenging to extend these methods to video domain due to the additional time dimension. In this paper, we propose a recurrent temporal aggregation framework for fast deep video inpainting. In particular, we construct an encoder-decoder model, where the encoder takes multiple reference frames which can provide visible pixels revealed from the scene dynamics. These hints are aggregated and fed into the decoder. We apply a recurrent feedback in an auto-regressive manner to enforce temporal consistency in the video results. We propose two architectural designs based on this framework. Our first model is a blind video decaptioning network (BVDNet) that is designed to automatically remove and inpaint text overlays in videos without any mask information. Our BVDNet wins the first place in the ECCV Chalearn 2018 LAP Inpainting Competition Track 2: Video Decaptioning. Second, we propose a network for more general video inpainting (VINet) to deal with more arbitrary and larger holes. Video results demonstrate the advantage of our framework compared to state-of-the-art methods both qualitatively and quantitatively. The codes are available at https://github.com/mcahny/Deep-Video-Inpainting, and https://github.com/shwoo93/video_decaptioning.
Tile-based Adaptive Streaming for Virtual Reality Video

The increasing popularity of head-mounted devices and 360° video cameras allows content providers to provide virtual reality (VR) video streaming over the Internet, using a two-dimensional representation of the immersive content combined with traditional HTTP adaptive streaming (HAS) techniques. However, since only a limited part of the video (i.e., the viewport) is watched by the user, the available bandwidth is not optimally used. Recent studies have shown the benefits of adaptive tile-based video streaming; rather than sending the whole 360° video at once, the video is cut into temporal segments and spatial tiles, each of which can be requested at a different quality level. This allows prioritization of viewable video content and thus results in an increased bandwidth utilization. Given the early stages of research, there are still a number of open challenges to unlock the full potential of adaptive tile-based VR streaming. The aim of this work is to provide an answer to several of these open research questions. Among others, we propose two tile-based rate adaptation heuristics for equirectangular VR video, which use the great-circle distance between the viewport center and the center of each of the tiles to decide upon the most appropriate quality representation. We also introduce a feedback loop in the quality decision process, which allows the client to revise prior decisions based on more recent information on the viewport location. Furthermore, we investigate the benefits of parallel TCP connections and the use of HTTP/2 as an application layer optimization. Through an extensive evaluation, we show that the proposed optimizations result in a significant improvement in terms of video quality (more than twice the time spent on the highest quality layer), compared to non-tiled HAS solutions.
Economic and technical assessment of rooftop solar photovoltaic potential in Brownsville, Texas, U.S.A

Localized assessment of solar energy economic feasibility will benefit the structuring of residential solar energy deployment globally. In the U.S. growing interest in rooftop residential solar among city managers has spurred the development of photovoltaic (PV) feasibility maps of the technical and economic solar potential within cities. The City of Brownsville, Texas was interested in evaluating solar feasibility for their city but lacked information to make informed policy decisions on PV development. This paper presents novel and systems approaches for determining the technical and economic feasibility of solar development for homes in the Brownsville using LiDAR and local information. Residential technical and economic potential was assessed by optimizing the internal rate of return (IRR) and an average residential building demand profile to determine ideal size and placement of solar arrays. Results showed that residential structures in Brownsville have the technical potential to generate approximately 11% of the total energy provided by the local utility; however, average IRR was only 2.9% with a payback period of over 15 years. Five neighborhoods in the City of Brownsville were identified with spatially clustered homes that had relatively higher IRRs compared with other areas in the city. Despite the high technical potential, modeled results indicate that perspective home owners interested in solar development may require additional incentives to improve the economic feasibility of PV in Brownsville. This study provides a demonstration of an interdisciplinary systems approach and methodology that can be adopted internationally to evaluate the feasibility of solar development in other areas.
Review of Blockchain Technology Vulnerabilities and Blockchain-System Attacks

Blockchain is a relatively young technology. In recent years, blockchain has gained popularity, which keeps growing. Such interest is mainly due to cryptocurrencies, such as Bitcoin and Ethereum. This technology has presented promising prospects of application. With the increasing interest in blockchain from cryptocurrency to smart contracts, there are precedents of attacks on the blockchain platform. This, in turn, encourages consideration of blockchain in terms of security, identifying vulnerabilities and predicting the emergence of new vulnerabilities. That further would allow to find new methods and solutions for blockchain security. In this paper, the known vulnerabilities were considered, as well as the known attacks on blockchain and their successful implementations in recent times.
Planning the application of blockchain technology in identification of counterfeit products: sectorial prioritization

This paper Decision making at the top level is very complex. One important responsibility of the policy makers and the governance system is to plan the safety and security to its citizen and consumers for the firms. This research address of the main concern of counterfeiting goods in today’s time. This research identify the potential and needs of the distributed ledger technologies like blockchain and their application potential to combat the problem of counterfeiting in Indian economy. The research uses SWARA (Step-wise Weight Assessment Ratio Analysis)-WASPAS (Weighted Aggregated Sum Product Assessment) methodology after expert opinion gathered to understand the problem better and it results into the prioritization of application of blockchain in different industries. SWARA is applied to evaluate the weights of the criteria and WASPAS to evaluate and prioritize the alternatives.
Sparse Generative Adversarial Network

We propose a new approach to Generative Adversarial Networks (GANs) to achieve an improved performance with additional robustness to its so-called and well-recognized mode collapse. We first proceed by mapping the desired data onto a frame-based space for a sparse representation to lift any limitation of small support features prior to learning the structure. To that end, we start by dividing an image into multiple patches and modifying the role of the generative network from producing an entire image, at once, to creating a sparse representation vector for each image patch. We synthesize an entire image by multiplying generated sparse representations to a pre-trained dictionary and assembling the resulting patches. This approach restricts the output of the generator to a particular structure, obtained by imposing a Union of Subspaces (UoS) model to the original training data, leading to more realistic images, while maintaining a desired diversity. To further regularize GANs in generating high-quality images and to avoid the notorious mode-collapse problem, we introduce a third player in GANs, called reconstructor. This player utilizes an auto-encoding scheme to ensure that first, the input-output relation in the generator is injective and second each real image corresponds to some input noise. We present a number of experiments, where the proposed algorithm shows a remarkably higher inception score compared to the equivalent conventional GANs.
Gaussian process models—I. A framework for probabilistic continuous inverse theory

SUMMARY We develop a theoretical framework for framing and solving probabilistic linear(ized) inverse problems in function spaces. This is built on the statistical theory of Gaussian Processes, and allows results to be obtained independent of any basis, avoiding any difficulties associated with the fidelity of representation that can be achieved. We show that the results of Backus–Gilbert theory can be fully understood within our framework, although there is not an exact equivalence due to fundamental differences of philosophy between the two approaches. Nevertheless, our work can be seen to unify several strands of linear inverse theory, and connects it to a large body of work in machine learning. We illustrate the application of our theory using a simple example, involving determination of Earth’s radial density structure.
Easing Power Consumption of Wearable Activity Monitoring with Change Point Detection

Continuous monitoring of complex activities is valuable for understanding human behavior and providing activity-aware services. At the same time, recognizing these activities requires both movement and location information that can quickly drain batteries on wearable devices. In this paper, we introduce Change Point-based Activity Monitoring (CPAM), an energy-efficient strategy for recognizing and monitoring a range of simple and complex activities in real time. CPAM employs unsupervised change point detection to detect likely activity transition times. By adapting the sampling rate at each change point, CPAM reduces energy consumption by 74.64% while retaining the activity recognition performance of continuous sampling. We validate our approach using smartwatch data collected and labeled by 66 subjects. Results indicate that change point detection techniques can be effective for reducing the energy footprint of sensor-based mobile applications and that automated activity labels can be used to estimate sensor values between sampling periods.
Anti-compression JPEG steganography over repetitive compression networks

Existing work on steganography mostly assumes that an image remains unchanged during transmission from the sender to the receiver. This assumption, however, may not hold in the era of the Internet due to unknown compression from the network service providers. As a result, the hidden information cannot be correctly recovered at the receiver. To address the problem, we design a new JPEG steganographic method that can resist repetitive compression during network transmission, without even knowing the compression process controlled by the network service providers. Our method uses a simulated repetitive compression network, and based on its feedback performs adaptive dither adjustment to dynamically modify the DCT coefficients disturbed by the compression process. Stego images generated with our method can be used to successfully extract the original secret messages, even after the stego images pass through multiple unknown compression processes during network transmission. Extensive experiments demonstrate that compared with existing JPEG steganographic methods, our method can effectively resist repetitive compression, while maintaining a lower bit error rate and strong anti-steganalysis capability.
Land Record Management using Hyperledger Fabric and IPFS

Status quo, the process of maintaining land ownership records employed by many countries is archaic, providing scope for fraudulence, tampering and/or misrepresentation of data, with little to no accountability for ensuring the authenticity of the managed records. This inefficiency and lack of transparency in the system can heavily exploited by the malicious middlemen with questionable intent, entrusted with the task of maintaining the records. The paper aims at introducing transparency, especially in the realm of land acquisition and ownership record management, by ensuring that each transaction is validated by all involved parties. It removes the scope for fraudulence by creating an immutable history of records, which is permanently linked to the system, thereby streamlining documentation and record-keeping. A peer-to-peer tamper-proof and forge-proof network is leveraged for this purpose, using a permissioned Blockchain such as Hyperledger Fabric. The system is interfaced with Interplanetary File System for secure documentation record management.
Single-Image Dehazing via Compositional Adversarial Network

Single-image dehazing has been an important topic given the commonly occurred image degradation caused by adverse atmosphere aerosols. The key to haze removal relies on an accurate estimation of global air-light and the transmission map. Most existing methods estimate these two parameters using separate pipelines which reduces the efficiency and accumulates errors, thus leading to a suboptimal approximation, hurting the model interpretability, and degrading the performance. To address these issues, this article introduces a novel generative adversarial network (GAN) for single-image dehazing. The network consists of a novel compositional generator and a novel deeply supervised discriminator. The compositional generator is a densely connected network, which combines fine-scale and coarse-scale information. Benefiting from the new generator, our method can directly learn the physical parameters from data and recover clean images from hazy ones in an end-to-end manner. The proposed discriminator is deeply supervised, which enforces that the output of the generator to look similar to the clean images from low-level details to high-level structures. To the best of our knowledge, this is the first end-to-end generative adversarial model for image dehazing, which simultaneously outputs clean images, transmission maps, and air-lights. Extensive experiments show that our method remarkably outperforms the state-of-the-art methods. Furthermore, to facilitate future research, we create the HazeCOCO dataset which is currently the largest dataset for single-image dehazing.
New mixed-coding PSO algorithm for a self-adaptive and automatic learning of Mamdani fuzzy rules

Thanks to its algorithmic performances, PSO algorithm becomes a popular tune tool for fuzzy systems in literature. However, it still encounters many complications, especially when dealing with Mamdani fuzzy system type because of its nature. The Mamdani fuzzy system is known as a linguistic model where the semantic meaning of the fuzzy rules is an intrinsic characteristic that must be retained during the learning process, while seeking for high accuracy. Therefore, to tune the Mamdani fuzzy system, it is very crucial to well represent each rule in a way that preserves this characteristic firstly, and to look for a search mechanism to optimize them throughout this topic secondly. In this paper, we introduce a new and promising approach to optimize the Mamdani fuzzy systems without a need of any prior knowledge. To the best of our knowledge, this approach is the first to optimize simultaneously the membership functions, the scaling factor parameters and the fuzzy rule conclusions with a mixed-coding PSO algorithm by combining a special monitoring function and a self-adaptive threshold. The proposed approach is validated by a comparative study with other design strategies taken from Box–Jenkinsgas furnace system literature and two theoretical examples in addition to a real-time control of the inverted pendulum Feedback 33-200. The obtained results proved the potential and the effectiveness of the proposed approach.
Elicitation study investigating hand and foot gesture interaction for immersive maps in augmented reality

Immersive maps in augmented reality (AR) are virtual maps that plausibly blend with the physical environment, such that the user perceives them as a part of the real world. While immersive maps can offer unprecedented engaging experiences, the way to perform panning, zooming and other basic map interaction is not obvious. This limitation may hamper widespread adoption of immersive maps. We therefore conducted an elicitation study to identify commonly suggested hand and foot gestures for interacting with large immersive AR maps placed on the floor. Study participants were shown simulations of interaction operations, and asked to design gestures to trigger these operations. We selected interaction with hand gestures because they are natural and familiar from interacting with touchscreens. Foot gestures were included because the users’ feet touch immersive maps placed on the floor. Eighteen participants designed hand and foot gestures for panning, rotating, zooming, changing the height of the map, creating a point marker, and selecting a point marker. The most agreed-on hand gesture was for zooming, consisting of grabbing with both hands, then separating or bringing the hands together. Our study participants suggested numerous other inspirational hand and foot gestures, which can guide the further development of interactive immersive maps. Because user preference does not necessarily align with performance, it is important to evaluate the efficiency, accuracy, ease of learning, and physical fatigue for these gestures in follow-up studies.
Design of Blockchain-based Precision Health-Care Using Soft Systems Methodology

Purpose The purpose of this paper is to describe the application of soft systems methodology (SSM) to address the problematic situation of low opt-in rates for Precision Health-Care (PHC). Design/methodology/approach The design logic is that when trust is enhanced and compliance is better assured, participants such as patients and their doctors would be more likely to share their medical data and diagnosis for the purpose of precision modeling. Findings The authors present the findings of an empirical study that confronts the design challenge of increasing participant opt-in to a PHC repository of Electronic Medical Records and genetic sequencing. Guided by SSM, the authors formulate design rules for the establishment of a trust-less platform for PHC which incorporates key principles of transparency, traceability and immutability. Research limitations/implications The SSM approach has been criticized for its lack of “rigour” and “replicability”. This is a fallacy in understanding its purpose – theory exploration rather than theory confirmation. Moreover, it is unlikely that quantitative modeling yields any clearer an understanding of complex, socio-technical systems. Practical implications The application of Blockchain, a platform for distributed ledgers, and associated technologies present a feasible approach for resolving the problematic situation of low opt-in rates. Social implications A consequence of low participation is the weak recall and precision of descriptive, predictive and prescriptive analytic models. Factors such as cyber-crime, data violation and the potential for misuse of genetic and medical records have led to a lack of trust from key stakeholders – accessors, participants, miners and regulators – to varying degrees. Originality/value The application of Blockchain as a trust-enabling platform in the domain of an emerging eco-system such as precision health is novel and pioneering.