Reviewerly/exHarmony huggingface dataset:
Lossless Data Hiding Based on Homomorphic Cryptosystem

With the development of cloud server, reversible data hiding in encrypted domain has received widespread attention for management of encrypted media. Most of existing reversible data hiding methods are based on stream ciphers, which are mainly concerned with data storage security. While homomorphic ciphers focus on data processing security, which is popular in cloud and other third-party platforms. Reversible data hiding with homomorphic cryptography has been an active topic in the research filed. In this paper, we propose a new lossless data hiding method based on homomorphic cryptosystem. After a content owner generates encrypted media with homomorphic ciphers, a data hider embeds secret data by establishing a mapping between secret bits and losslessly modifying encrypted media. At the receiver side, the embedded data is extracted from encrypted domain with a little auxiliary message. And original media is recovered by directly decrypting marked encrypted media. During the embedding process, the modification of encrypted media does not change the corresponding original media owing to homomorphic and probabilistic properties. Thus, no distortion is introduced in the whole procedures of data embedding. And directly decrypted media containing embedded data is the same as the original media. Meanwhile, the proposed method achieves a high embedding rate through efficient mapping and skillful use of expanded pixel values. The experimental results show that compared with state-of-the-art methods, the proposed method has higher embedding rate without distortion.
PeerLens

Online question pools like LeetCode provide hands-on exercises of skills and knowledge. However, due to the large volume of questions and the intent of hiding the tested knowledge behind them, many users find it hard to decide where to start or how to proceed based on their goals and performance. To overcome these limitations, we present PeerLens, an interactive visual analysis system that enables peer-inspired learning path planning. PeerLens can recommend a customized, adaptable sequence of practice questions to individual learners, based on the exercise history of other users in a similar learning scenario. We propose a new way to model the learning path by submission types and a novel visual design to facilitate the understanding and planning of the learning path. We conducted a within-subject experiment to assess the efficacy and usefulness of PeerLens in comparison with two baseline systems. Experiment results show that users are more confident in arranging their learning path via PeerLens and find it more informative and intuitive.
Bayesian DeNet: Monocular Depth Prediction and Frame-Wise Fusion With Synchronized Uncertainty

Using deep convolutional neural networks (CNN) to predict the depth from a single image has received considerable attention in recent years due to its impressive performance. However, existing methods process each single image independently without leveraging the multiview information of video sequences in practical scenarios. Properly taking into account multiview information in video sequences beyond individual frames could offer considerable benefits in terms of depth prediction accuracy and robustness. In addition, a meaningful measure of prediction uncertainty is essential for decision making, which is not provided in existing methods. This paper presents a novel video-based depth prediction system based on a monocular camera, named Bayesian DeNet. Specifically, Bayesian DeNet consists of a 59-layer CNN that can concurrently output a depth map and an uncertainty map for each video frame. Each pixel in an uncertainty map indicates the error variance of the corresponding depth estimate. Depth estimates and uncertainties of previous frames are propagated to the current frame based on the tracked camera pose, yielding multiple depth/uncertainty hypotheses for the current frame which are then fused in a Bayesian inference framework for greater accuracy and robustness. Extensive exper-iments on three public datasets demonstrate that our Bayesian DeNet outperforms the state-of-the-art methods for monocular depth prediction. A demo video and code are publicly available. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Informative sample generation using class aware generative adversarial networks for classification of chest Xrays

Training robust deep learning (DL) systems for disease detection from medical images is challenging due to limited images covering different disease types and severity. The problem is especially acute, where there is a severe class imbalance. We propose an active learning (AL) framework to select most informative samples for training our model using a Bayesian neural network. Informative samples are then used within a novel class aware generative adversarial network (CAGAN) to generate realistic chest xray images for data augmentation by transferring characteristics from one class label to another. Experiments show our proposed AL framework is able to achieve state-of-the-art performance by using about 35% of the full dataset, thus saving significant time and effort over conventional methods.
WhatFutures

WhatsApp, as the world's most popular messaging application, offers significant opportunities for improving the reach and effectiveness of engagement projects. In collaboration with the International Federation of Red Cross and Red Crescent Societies (IFRC) we designed WhatFutures, a collaborative future forecasting engagement for global youth using WhatsApp. WhatFutures was successfully deployed with 487 players across 5 countries (Kenya, Bulgaria, Finland, Australia and Hong Kong) to inform strategic change within the IFRC. Based on our analysis of the activity - including 16,100 messages, 95 multimedia artifacts, and a post-engagement survey - we present a reflection upon the design decisions underpinning WhatFutures and identify how decisions made around group structures, processes and externalization of outputs influenced engagement and data quality. We conclude with the wider implications of our findings for the design of engagements that best utilize the affordances of existing messaging applications.
Niching particle swarm optimization with equilibrium factor for multi-modal optimization

Multi-modal optimization is an active research topic that has attracted increasing attention from evolutionary computation community. Particle swarm optimization (PSO) with niching technique is one of the most effective approaches for multi-modal optimization. However, in existing PSO with niching methods, the number of particles around different niches varies distinctly from each other, which makes it difficult for the algorithm to find high-quality solutions in all niches. To address this issue, this paper proposes a new niching PSO with equilibrium factor named E-SPSO. Different from the existing niching PSOs, the numbers of particles in different niches have been kept in balance in E-SPSO. The velocity of each particle is influenced by not only the personal best particle and the global best particle, but also an equilibrium factor (EF). By using the equilibrium factor to update the velocities of particles, the particles can be allocated uniformly among the niches. In this way, the computation resources can be assigned to the niches in a more balanced manner, so that the algorithm can gain more population diversity and find high-quality solutions in all niches. Experimental results on eleven benchmark problems show that the proposed mechanism not only increases the number of optima found, but also improves the search efficiency.
Accessing a New Land

This paper presents a study of mobile phone use by people settling in a new land to access state provided digital services. It shows that digital literacy and access to technology are not the only resources and capabilities needed to successfully access digital services and do not guarantee a straightforward resettlement process. Using creative engagement methods, the research involved 132 "newcomers" seeking to settle in Sweden. Ribot and Peluso's theory of access (2003) was employed to examine the complex web of access experienced by our participants. We uncover that when communities are dealing with high levels of precarity, their primary concerns are related to accessing the benefits of a service, rather than controlling access. Broadening the HCI framework, the paper concludes that a sociotechnical model of access needs to connect access control and access benefit to facilitate the design of an effective digital service.
Speeding up k-Nearest Neighbors classifier for large-scale multi-label learning on GPUs

Multi-label classification is one of the most dynamically growing fields of machine learning, due to its numerous real-life applications in solving problems that can be described by multiple labels at the same time. While most of works in this field focus on proposing novel and accurate classification algorithms, the issue of the computational complexity on growing dataset sizes is somehow marginalized. Owning to the ever-increasing capabilities of data capturing, we are faced with the problem of large-scale data mining that forces learners to be not only highly accurate, but also fast and scalable on high-dimensional spaces of instances, features, and labels. In this paper, we propose a highly efficient parallel approach for computing the multi-label k-Nearest Neighbor classifier on GPUs. While this method is highly effective due to its accuracy and simplicity, its computational complexity makes it prohibitive for large-scale data. We propose a four-step implementation that takes an advantage of the GPU architecture, allowing for an efficient execution of the multi-label k-Nearest Neighbors classifier without any loss of accuracy. Experiments carried out on a number of real and artificial benchmarks show that we are able to achieve speedups up to 200 times when compared to a sequential CPU execution, while efficiently scaling up to varying number of instances and features.
PrivHome: Privacy-Preserving Authenticated Communication in Smart Home Environment

A smart home enables users to access devices such as lighting, HVAC, temperature sensors, and surveillance camera. It provides a more convenient and safe living environment for users. Security and privacy, however, is a key concern since information collected from these devices are normally communicated to the user through an open network (i.e. Internet) or system provided by the service provider. The service provider may store and have access to these information. Emerging smart home hubs such as Samsung SmartThings and Google Home are also capable of collecting and storing these information. Leakage and unauthorized access to the information can have serious consequences. For example, the mere timing of switching on/off of an HVAC unit may reveal the presence or absence of the home owner. Similarly, leakage or tampering of critical medical information collected from wearable body sensors can have serious consequences. Encrypting these information will address the issues, but it also reduces utility since queries is no longer straightforward. Therefore, we propose a privacy-preserving scheme, \sf PrivHomePrivHome. It supports authentication, secure data storage and query for smart home systems. PrivHome provides data confidentiality as well as entity and data authentication to prevent an outsider from learning or modifying the data communicated between the devices, service provider, gateway, and the user. It further provides privacy-preserving queries in such a way that the service provider, and the gateway does not learn content of the data. To the best of our knowledge, privacy-preserving queries for smart home systems has not been considered before. Under our scheme is a new, lightweight entity and key-exchange protocol, and an efficient searchable encryption protocol. Our scheme is practical as both protocols are based solely on symmetric cryptographic techniques. We demonstrate efficiency and effectiveness of our scheme based on experimental and simulation results, as well as comparisons to existing smart home security protocols.
Blockchain Redefining: The Halal Food Sector

This paper aims to address the various challenges of the Halal Food Industry and how far the emerging Distributed Ledger Technology, Blockchain, can play a role in redefining the current system. Blockchain technology allows users to assess all transactions simultaneously and in real-time, enforcing transparency, safety, authenticity, and auditability. Blockchain along with other disruptive technologies, like IoT and AI, will allow stakeholders to retrieve the high-quality information required to make more informed choices. The Islamic teachings followed in preparation of halal food are principles not only followed by Muslims but every person soughing to have hygienic, healthy food. With the rise of the Halal Food sector, organizations must find ways to increase their supply chain efficiency and Blockchain promises to fill that gap with higher transparency and assurance of Halal compliance. A demonstration is provided to have an insight on how the Blockchain technology would shape the Halal Food Supply Chain and an experimental study is also carried out on Hyperledger Fabric Composer - Playground - to analyze how ownership of halal product may be tracked.
High-Speed Implementation of ECC Scalar Multiplication in GF(p) for Generic Montgomery Curves

Elliptic curve-based cryptography (ECC) has become the automatic choice for public key cryptography due to its lightweightness compared to Rivest-Shamir-Adleman (RSA). The most important operation in ECC is elliptic curve scalar multiplication, and its efficient implementation has gathered significant attention in the research community. Fast implementation of ECC scalar multiplication is often desired for speed-critical applications such as runtime authentication in automated cars, web server certification, and so on. Such fast architectures are achieved by implementing ECC scalar multiplication in fields with pseudo-Mersenne prime or Solinas prime. In this paper, we aim to implement a fast implementation of ECC scalar multiplication for any generic Montgomery curve in Galois Field in p [GF(p)] without having the constraint of using any specialized modulus. We will show that the proposed ECC scalar multiplication architecture is as fast as scalar multiplication in special curves like Curve25519, albeit with little area overhead. The proposed architecture can be modified to support ECC scalar multiplication in both Montgomery and short Weierstrass curves.
Mobile marketing recommendation method based on user location feedback

Abstract Location-based mobile marketing recommendation has become one of the hot spots in e-commerce. The current mobile marketing recommendation system only treats location information as a recommended attribute, which weakens the role of users and shopping location information in the recommendation. This paper focuses on location feedback data of user and proposes a location-based mobile marketing recommendation model by convolutional neural network (LBCNN). First, the users’ location-based behaviors are divided into different time windows. For each window, the extractor achieves users’ timing preference characteristics from different dimensions. Next, we use the convolutional model in the convolutional neural network model to train a classifier. The experimental results show that the model proposed in this paper is better than the traditional recommendation models in the terms of accuracy rate and recall rate, both of which increase nearly 10%.
Graphene Smart Textile-Based Wearable Eye Movement Sensor for Electro-Ocular Control and Interaction with Objects

Wearable graphene textile embedded smart headband and its feasibility in electrooculography (EOG) applications is demonstrated by benchmarking against clinical Ag/AgCl wet electrodes; where the recorded biopotentials displayed excellent correlation of 91.3% over durations up to hundred seconds. Automatic eye movement (EM) detection is implemented and performance of the graphene-embedded "all-textile" eye movement sensor and its application as a control element toward human-computer interaction (HCI) and human-machine interfaces (HMI) is experimentally demonstrated by: 1) generating digital clock transitions directly from eye blinks for facilitating switching requirements in HCI/HMI applications, 2) controlling and sequentially lighting up a single LED in a 5 × 5 LED array in four directions to draw a pattern of "8", 3) evaluating the limits of the entire system in an hour-long EOG recording session which includes several activities like checking a phone, watching a video, reading, and performing several EMs including blinks, saccades, and fixations. The excellent success rate ranging from 85% up to 100% for eleven different EM patterns demonstrates the applicability of the proposed algorithm in wearable EOG-based sensing and HCI/HMI applications with graphene textiles. The system-level integration and the holistic design approach presented herein which starts from fundamental materials level up to the architecture and algorithm stage is highlighted and will be instrumental to advance the state-of-the-art in wearable electronic devices.
Personal Data Privacy Challenges of the Fourth Industrial Revolution

Fourth industrial revolution (Industry 4.0) promises a connected and smart manufacturing system where internet, machine (physical system) and humans lumped together. Unlike other industrial revolutions, this industrial revolution deals more with information. Device to device (D2D) and Machine to Machine (M2M) communications often generate, preserve and share private information. Personal data has already turned out to be a new commodity and currently identified as a ‘new oil’ or ‘new domain of warfare’. The more information gets generated and accumulated, the more extensive and risky the personal information becomes. Although privacy and security are often bundled together, they are different. This study investigates the privacy attack surfaces of key Industry 4.0 components (i.e. Cyber-Physical System, Artificial Intelligence, additive manufacturing, autonomous vehicle, big data, cloud computing, internet of things, distributed ledger etc). Multi-dimensional privacy challenges, data breaching incidents, regulations and need of a contextual privacy awareness is discussed in this study. Finally, this work elaborates the risk of Personally Identifiable Information (PII) leaking in the era of industry 4.0.
Scratch Nodes ML

Children are growing up in a Machine Learning infused world and it's imperative to provide them with opportunities to develop an accurate understanding of basic Machine Learning concepts. Physical gesture recognition is a typical application of Machine Learning, and physical gestures are also an integral part of children's lives, including sports and play. We present Scratch Nodes ML, a system enabling children to create personalized gesture recognizers by: (1) Creating their own gesture classes; (2) Collecting gesture data for each class; (3) Evaluating the classifier they created with new gesture data; (4) Integrating their classifiers into the Scratch environment as new Scratch blocks, empowering other children to use these new blocks as gesture classifiers in their own Scratch creations.
2D Partial Unwinding—A Novel Non-Linear Phase Decomposition of Images

This paper aims at proposing a novel 2D non-linear phase decomposition of images, which performs the image processing tasks better than the traditional Fourier transformation (linear phase decomposition), but further, it has additional mathematical properties allowing more effective image analysis, including adaptive decomposition components and positive instantaneous phase derivatives. 1D unwinding Blaschke decomposition has recently been proposed and studied. Through factorization it expresses arbitrary 1D signal into an infinite linear combination of Blaschke products. It offers fast converging positive frequency decomposition in the form of rational approximation. However, in the multi-dimensional cases, the usual factorization mechanism does not work. As a consequence, there is no genuine unwinding decomposition for multi-dimensions. In this paper, a 2D partial unwinding decomposition based on algebraic transforms reducing multi-dimensions to the 1D case is proposed and analyzed. The result shows that the fast convergence offers efficient image reconstruction. The tensor type decomposing terms are mutually orthogonal, giving rise to 2D positive frequency decomposition. The comparison results show that the proposed method outperforms the standard greedy algorithm and the most commonly used methods in the Fourier category. An application in watermarking is presented to demonstrate its potential in applications.
Joint graph based embedding and feature weighting for image classification

Recently, several inductive and flexible nonlinear data projection methods for graph-based semi-supervised learning were proposed. These state-of-the art techniques have a good performance. However, they have not taken into account the relevance of the original features in their model estimation. In this paper, we propose a joint graph-based embedding and feature weighting for getting a flexible and inductive nonlinear data representation on manifolds. The proposed criterion explicitly estimates the feature weights together with the projected data and the linear transformation such that data smoothness and large margins are achieved in the projection space. Moreover, the paper introduces a kernel variant of the model in order to get an inductive nonlinear embedding that is close to a real nonlinear subspace for a good approximation of the embedded data. The proposed frameworks can be seamlessly used in semi-supervised and supervised settings. The resulting optimization problems can be solved efficiently. The proposed embedding methods are evaluated on six public scene and face datasets. Experiments on image classification, in a semi-supervised setting, show that our proposed methods can have a performance that is better than that of many state-of-the-art methods including linear and nonlinear methods.
A convolutional neural‐network‐based pedestrian counting model for various crowded scenes

Abstract Pedestrian counting from unconstrained images is an important task in various applications such as resource management, transportation engineering, urban design, and advertising, but it is greatly challenged by some factors such as interocclusion, cross‐scene, scale, and scene perspective distortion. Traditional image‐based methods suffer from them, and the performance of conventional sensor‐based methods such as Kinect and LASER degrades gradually with the increase in pedestrian count and distance from the device to pedestrians. Based on these challenges, this paper proposes a new network model making use of stacked multicolumn convolutional neural networks (CNNs) for pedestrian counting. The human's head features are used to replace the whole body for solving the problem of serious occlusion and choose multicolumn CNNs for dealing with scale and scene perspective distortion. Also, pretrained VGG‐16 is used to generate deeper detailed features and expand the receptive field of the model. Extensive analysis and experiments on current major pedestrian counting datasets show that the proposed network model has considerable advantages in pedestrian counting tasks compared to other state‐of‐the‐art models, and the proposed network model has an improvement effect for the training process. Moreover, the visual differences between the generated density map and ground‐truth density map are visualized and analyzed quantitatively to demonstrate the feasibility of the model.