Reviewerly/exHarmony huggingface dataset:
Integrating Blockchain Technology into Healthcare

One of the major challenges in the healthcare industry is the information sharing and patient's privacy. The implementation of the Health Insurance Portability and Accountability Act (HIPPA) [30] in 1996 made difficult for software vendors to manage the documents and records of the patients. Blockchain based technology, which uses cryptography to facilitate exchange and transaction of values and goods over a network, has been proposed to resolve this issue. The decentralized ledge of blockchain would provide immutability, confidentiality, accountability and transparency for information sharing between healthcare stakeholders. In this paper, we implemented the Ethereum Smart Contract, an extended part of the blockchain in healthcare industry with objectives of keeping all the records and transactions safe and secure, and at the same time, reducing the number of user data breaches. We carried out a number of experiments in order to demonstrate the usability and efficiency of our proposed approach.
Navigation Based Application with Augmented Reality and Accessibility

The implementation of adaptative applications for navigation and ubiquitous interfaces for accessibility defines a new era on spatial computing; especially on solutions that understand reacting to the environment, delivering navigation capabilities with high accuracy and utility for users. Real-time key information and awareness is presented in augmented reality (AR) by using interactive visual elements and auditive instructions. This paper describes a solution running on smartphones by using spatial computing capabilities for positional determination, tracking the user movements over the environment space. Besides the user-friendly minimalistic visual interface, the system is also able to instruct, guiding a completely blind person from a start position into a certain destination, avoiding obstacles. The communication is completely independent and uses voice recognition for accessibility capabilities.
UbiPoint

Throughout the past decade, numerous interaction techniques have been designed for mobile and wearable devices. Among these devices, smartglasses mostly rely on hardware interfaces such as touchpad and buttons, which are often cumbersome and counterintuitive to use. Furthermore, smartglasses feature cheap and low-power hardware preventing the use of advanced pointing techniques. To overcome these issues, we introduce UbiPoint, a freehand mid-air interaction technique. UbiPoint uses the monocular camera embedded in smartglasses to detect the user's hand without relying on gloves, markers, or sensors, enabling intuitive and non-intrusive interaction. We introduce a computationally fast and light-weight algorithm for fingertip detection, which is especially suited for the limited hardware specifications and the short battery life of smartglasses. UbiPoint processes pictures at a rate of 20 frames per second with high detection accuracy - no more than 6 pixels deviation. Our evaluation shows that UbiPoint, as a mid-air non-intrusive interface, delivers a better experience for users and smart glasses interactions, with users completing typical tasks 1.82 times faster than when using the original hardware.
Multiple strong and balanced cluster-based ensemble of deep learners

Convolutional Neural Networks (CNNs), also known as deep learners have seen much success in the last few years due to the availability of large amounts of data and high-performance computational resources. A CNN can be trained effectively if large amounts of data are available as it enables a CNN to find the optimal set of features and weights that can achieve the highest generalization performance. However, due to the requirement of large data size, CNNs require a lot of resources for example running time and computational resources to achieve a reasonable performance. Additionally, unbalanced data makes it difficult to train a CNN effectively that can achieve good generalization performance. In order to alleviate these limitations, in this paper, we propose a novel ensemble of deep learners that learns by combining multiple deep learners trained on small strongly class associated input data effectively. We propose a novel methodology of generating random subspace through clustering input data and propose a measure which can classify each cluster as a strong data cluster and a balanced data cluster. A methodology is also proposed that balances all strong data clusters in the pool so that an architecturally simple CNN can be trained on all balanced data clusters simultaneously. Classification decisions on all trained CNNs are then fused through majority voting to generate class decisions of the ensemble. The performance of the proposed ensemble approach is evaluated on UCI benchmark datasets, and results are compared with existing state-of-the-art ensemble approaches. Significance testing was conducted to further validate the efficacy of the results and a significance test analysis is presented.
Federation of <scp>5G</scp> services using distributed ledger technologies<sup>†</sup>

Federation of services, as a 5G networks concept, aims to provide orchestration of services across multiple administrative domains. In this paper, we are exploring a solution of applying distributed ledger technologies, precisely the combination of blockchain and smart contracts, to enable highly secure, private, fast and distributed interaction between administrative domains in the federation process. Along with the designed solution, we developed an experimental prototype that requires simple one‐time setup and fast simultaneous registration time for multiple administrative domains. Obtained results show single service federation times (without considering the deployment time) of around 5 seconds.
Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations (Short Version)

Illegal wildlife poaching threatens ecosystems and drives endangered species toward extinction. However, efforts for wildlife protection are constrained by the limited resources of law enforcement agencies. To help combat poaching, the Protection Assistant for Wildlife Security (PAWS) is a machine learning pipeline that has been developed as a data-driven approach to identify areas at high risk of poaching throughout protected areas and compute optimal patrol routes. In this paper, we take an end-to-end approach to the data-to-deployment pipeline for anti-poaching. In doing so, we address challenges including extreme class imbalance (up to 1:200), bias, and uncertainty in wildlife poaching data to enhance PAWS, and we apply our methodology to three national parks with diverse characteristics. (i) We use Gaussian processes to quantify predictive uncertainty, which we exploit to improve robustness of our prescribed patrols and increase detection of snares by an average of 30%. We evaluate our approach on real-world historical poaching data from Murchison Falls and Queen Elizabeth National Parks in Uganda and, for the first time, Srepok Wildlife Sanctuary in Cambodia. (ii) We present the results of large-scale field tests conducted in Murchison Falls and Srepok Wildlife Sanctuary which confirm that the predictive power of PAWS extends promisingly to multiple parks. This paper is part of an effort to expand PAWS to 800 parks around the world through integration with SMART conservation software.
Hydrogel-Textile Composites

The current work examines interactions that are enabled when depositing a human-safe hydrogel onto textile substrates. These hydrogel-textile composites are water-responsive, supporting reversible actuation. To enable these interactions, we describe a fabrication process using a consumer-grade 3D printer. We show how different combinations of printed hydrogel patterns and textiles create a rich actuator design space. Finally, we show an application of this approach and discuss opportunities for future work.
The QoS and privacy trade-off of adversarial deep learning: An evolutionary game approach

Deep learning-based service has received great success in many fields and changed our daily lives profoundly. To support such service, the provider needs to continually collect data from users and protect users' privacy at the same time. Adversarial deep learning is of widespread interest to service providers because of its ability to automatically select privacy-preserving features that have less impact on the Quality of Service (QoS). However, choosing an appropriate threshold to adjust the weight of the QoS and privacy-preserving becomes a significant issue for both the provider and users. In this paper, we model the contradicting incentives between the QoS and privacy-preserving as an evolutionary game, and achieve an Evolutionary Stable Strategy (ESS) to help users decide whether to submit high-quality data or not. First, we define the individual contribution to the QoS and the privacy cost of submitting high-quality data. Then, we propose an incentive mechanism to deal with the problems that the users are bounded rational and do not own the complete knowledge about other users' choices. Moreover, we propose an ESS-based algorithm of balancing the QoS and privacy risk, which reaches a stable state of maintaining long-term service by multiple iterations. Finally, we conduct the simulation experiments to demonstrate that our strategy can efficiently incentivize users to make a trade-off between the long-term benefits of the QoS and the current cost of privacy.
Reversible data hiding in JPEG bitstream using optimal VLC mapping

The traditional RDH method for JPEG bitstream is conducted by building the mapping between the variable length codes (VLC). However, the capacity is limited, and the file size may not be well preserved as the capacity is increased. This is because that the trade-off between the capacity and the file size has not been deeply investigated, neither explicitly formulated nor appropriately optimized. In this paper, we propose to take the file size preservation into consideration and minimize the file size increase for a given capacity. We use the value transfer matrix to simulate a theoretical model and then design some optimization rules to reach the reversible solution. Consequently, a better reversible VLC mapping can be obtained in terms of both the capacity and the file size preservation. The experimental results show that the proposed method can increase the capacity with a relatively low cost of file size increase.
Assessing the Accuracy of an Outdoor Augmented Reality Solution for Mapping Underground Utilities

Augmented reality (AR) has been identified as a technique that can enhance information extraction from the virtual world to the real world and improve the access and utilization of information. The main challenge in creating a reliable and adaptive outdoor AR system is the accurate registration of virtual objects in the real world. There is currently limited research using outdoor AR for collecting underground utility inventory and evaluating the accuracy. Because of the limited accuracy of smartphones, this study used external GPS devices to reduce positional error. The main objective of this research was to evaluate the horizontal accuracy of the captured data used by the AR phone application XR-GIS, developed by the authors. This study used root-mean-square error (RMSE), average Euclidean error (AEE), and central error (CE) in the evaluation of two smartphones and two popular GPS devices on 16 survey location points. Leveraging a Trimble S5 robotic total station, a geodetic base point and the location points used in the study were authoritatively measured. The results show that the smartphone accuracies were significantly different compared with smartphones linked to external GPS devices. Generally, the results suggest that current smartphones cannot be adopted in the underground construction industry without higher-accuracy integrated GPS devices.
Sensitizing Scenarios

Concepts and theories that emerge within the social sciences tend to be nuanced, dealing with complex social phenomena. While their relevance to design could be high, it is difficult to make sense of them in design projects, especially when participants have a variety of backgrounds. We report on our experiences using role-play scenarios as a way to sensitize heterogeneous designer teams to complex theoretical concepts related to museology as social and cultural phenomena. We discuss design requirements on such scenarios, and the importance of connecting their execution closely to the context of the design and the current stage of the design process.
Heatmaps, Shadows, Bubbles, Rays: Comparing Mid-Air Pen Position Visualizations in Handheld AR

In Handheld Augmented Reality, users look at AR scenes through the smartphone held in their hand. In this setting, having a mid-air pointing device like a pen in the other hand greatly expands the interaction possibilities. For example, it lets users create 3D sketches and models while on the go. However, perceptual issues in Handheld AR make it difficult to judge the distance of a virtual object, making it hard to align a pen to it. To address this, we designed and compared different visualizations of the pen's position in its virtual environment, measuring pointing precision, task time, activation patterns, and subjective ratings of helpfulness, confidence, and comprehensibility of each visualization. While all visualizations resulted in only minor differences in precision and task time, subjective ratings of perceived helpfulness and confidence favor a 'heatmap' technique that colors the objects in the scene based on their distance to the pen.
A code-based signature scheme from the Lyubashevsky framework

We propose a new method to construct code-based signature scheme following the Lyubashevsky's lattice-based framework. Our technique ensures that the Hamming weight of each row of the private key matrix is below the GV bound instead of fixed weight. Our scheme can generate signatures whose maximum Hamming weight is below the GV bound of random linear codes with the public key matrix as parity-check matrix. We argue that our scheme can resist existing attacks on code-based signatures. We provide a detailed security analysis and prove that our scheme is existentially unforgeable under adaptive chosen-message attacks (EUF-CMA) in the random oracle model through exploiting and expanding code-based complex problems. Our scheme enjoys the shorter signature size than the Durandal signature scheme (EUROCRYPT 2019) and the Wave signature scheme (ASIACRYPT 2019) for security level of 128 bits. We also show that there is a generic method to construct Weight Restricted Hash (WRH) functions which can produce hash value with a given Hamming weight.
Contrast Enhancement Using Optimum Threshold Selection

In the recent era, a boom was observed in the field of information retrieval from images. Digital images with high contrast are sources of abundant information. The gathered information is useful in the precise detection of an object, event, or anomaly captured in an image scene. Existing systems do uniform distribution of intensities and apply intensity histogram equalization. These improve the characteristics of an image in terms of visual appearance. The problem of over enhancement and the increase in noise level produces undesirable visual artefacts. The use of Otsu's single threshold method in existing systems is inefficient for segmenting an image with multiple objects and complex background. Additionally, these are incapable to improve the yield of the maximum entropy and brightness preservation. The aforementioned limitations motivate us to propose an efficient statistical pipelined approach, the Range Limited Double Threshold Weighted Histogram Equalization (RLDTWHE). This approach is an integration of Otsu's double threshold, dynamic range stretching, weighted distribution, adaptive gamma correction, and homomorphic filtering. It provides optimum contrast enhancement by selecting the best appropriate threshold value for image segmentation. The proposed approach is efficient in the enhancement of low contrast medical MRI images and digital natural scene images. It effectively preserves all essential information recorded in an image. Experimental results prove its efficacy in terms of maximum entropy preservation, brightness preservation, contrast enhancement, and retaining the natural appearance of an image.
Online Pricing with Reserve Price Constraint for Personal Data Markets

The society's insatiable appetites for personal data are driving the emergency of data markets, allowing data consumers to launch customized queries over the datasets collected by a data broker from data owners. In this paper, we study how the data broker can maximize her cumulative revenue by posting reasonable prices for sequential queries. We thus propose a contextual dynamic pricing mechanism with the reserve price constraint, which features the properties of ellipsoid for efficient online optimization, and can support linear and non-linear market value models with uncertainty. In particular, under low uncertainty, our pricing mechanism provides a worst-case regret logarithmic in the number of queries. We further extend to other similar application scenarios, including hospitality service and online advertising, and extensively evaluate all three application instances over MovieLens 20M dataset, Airbnb listings in U.S. major cities, and Avazu mobile ad click dataset, respectively. The analysis and evaluation results reveal that our proposed pricing mechanism incurs low practical regret, online latency, and memory overhead, and also demonstrate that the existence of reserve price can mitigate the cold-start problem in a posted price mechanism, and thus can reduce the cumulative regret.
Global Learnable Pooling With Enhancing Distinctive Feature for Image Classification

Pooling layers appear widely in deep networks for its aggregating information in a local region and fast downsampling. Due to the reason that the closer to the output layer, the more the network learns is the high-level semantic information related to classification, the global average pooling would inhibit the contribution of local high magnitudes features in the global region. Besides, the gradient of the distinctive features is considerably attenuated due to the large region size of global average pooling. In this paper, we propose a global learnable pooling operation to enhance the distinctive high-level features in the global region, which is codenamed as GLPool. Because it is located before the classification layer, our GLPool is more sensitive to network performance. Besides, GLPool is not a hand-crafted pooling operation, which has the characteristic of adapting to any size of the input. With few parameters is added, GLPool is also a plug-and-play layer. The visualization via class activation map (CAM) on GoogLeNet and ShuffleNet-v2 also shows that GLPool can learn more concentrated and high-level distinctive features than global average pooling. The experiments on several classical deep models demonstrate the significant performance improvements on ImageNet32 and CIFAR100 datasets, which is exceeding obvious for lightweight networks.
DRprofiling: Deep Reinforcement User Profiling for Recommendations in Heterogenous Information Networks

Recommender systems are popular for personalization in online communities. Users, items, and other affiliated information such as tags, item genres, and user friends of an online community form a heterogenous information network. User profiling is the foundation of personalized recommender systems. It provides the basis to discover knowledge about an individual user’s interests to items. Typically, users are profiled with their direct explicit or implicit ratings, which ignored the inter-connections among users, items, and other entity nodes of the information network. This paper proposes a deep reinforcement user profiling approach for recommender systems. The user profiling process is framed as a sequential decision making problem which can be solved with a Reinforcement Learning (RL) agent. The RL agent interacts with the external heterogenous information network environment and learns a decision making policy network to decide whether there is an interest or preference path between a user and an unobserved item. To effectively train the RL agent, this paper proposes a multi-iteration training process to combine both expert and data-specific knowledge to profile users, generate meta-paths, and make recommendations. The effectiveness of the proposed approaches is demonstrated in experiments conducted on three datasets.
Mode-assisted unsupervised learning of restricted Boltzmann machines

Restricted Boltzmann machines (RBMs) are a powerful class of generative models, but their training requires computing a gradient that, unlike supervised backpropagation on typical loss functions, is notoriously difficult even to approximate. Here, we show that properly combining standard gradient updates with an off-gradient direction, constructed from samples of the RBM ground state (mode), improves their training dramatically over traditional gradient methods. This approach, which we call mode training, promotes faster training and stability, in addition to lower converged relative entropy (KL divergence). Along with the proofs of stability and convergence of this method, we also demonstrate its efficacy on synthetic datasets where we can compute KL divergences exactly, as well as on a larger machine learning standard, MNIST. The mode training we suggest is quite versatile, as it can be applied in conjunction with any given gradient method, and is easily extended to more general energy-based neural network structures such as deep, convolutional and unrestricted Boltzmann machines.
Automatic YouTube-Thumbnail Generation and Its Evaluation

YouTubers have recently become highly popular. Generating eye-catching thumbnails is an important factor in attracting viewers. In this study, we propose an automatic YouTube-video-thumbnail generation method that ensures the following: rich facial expression of the YouTuber in the frame, clear presentation of the subject of the video, and clear description of the content through a headline. We compared thumbnails generated by the proposed method with those generated by existing methods or those of actually posted videos (i.e., ground truth), and we evaluated the results.
Sentence-based and Noise-robust Cross-modal Retrieval on Cooking Recipes and Food Images

In recent years, people are facing with billions of food images, videos and recipes on social medias. An appropriate technology is highly desired to retrieve accurate contents across food images and cooking recipes, like cross-modal retrieval framework. Based on our observations, the order of sequential sentences in recipes and the noises in food images will affect retrieval results. We take into account the sentence-level sequential orders of instructions and ingredients in recipes, and noise portion in food images to propose a new framework for cross-retrieval. In our framework, we propose three new strategies to improve the retrieval accuracy. (1) We encode recipe titles, ingredients, instructions in sentence level, and adopt three attention networks on multi-layer hidden state features separately to capture more semantic information. (2) We apply attention mechanism to select effective features from food images incorporating with recipe embeddings, and adopt an adversarial learning strategy to enhance modality alignment. (3) We design a new triplet loss scheme with an effective sampling strategy to reduce the noise impact on retrieval results. The experimental results show that our framework clearly outperforms the state-of-art methods in terms of median rank and recall rate at top k on the Recipe 1M dataset.