Reviewerly/exHarmony huggingface dataset:
Complete characterization of qubit masking

We study the problem of information masking through nonzero linear operators that distribute information encoded in single qubits to the correlations between two qubits. It is shown that a nonzero linear operator cannot mask any nonzero measure set of qubit states. We prove that the maximal maskable set of states on the Bloch sphere with respect to any masker is the ones on a spherical circle. Any states on a spherical circle on the Bloch sphere are maskable, which also proves the conjecture on maskable qubit states given by Modi et al. [Phys. Rev. Lett. 120, 230501 (2018)]. we provide explicitly operational unitary maskers for all maskable sets. As applications, different protocols for secret sharing are introduced.
Consumer Demand for Blockchain-Enabled Peer-to-Peer Electricity Trading in the United Kingdom: An Online Survey Experiment

Peer-to-peer (P2P) energy trading could help address grid management challenges in a decentralizing electricity system, as well as provide other social and environmental benefits. Many existing and proposed trading schemes are enabled by blockchain, a distributed ledger technology (DLT) relying on cryptographic proof of ownership rather than human intermediaries to establish energy transactions. This study used an online survey experiment (n = 2064) to investigate how consumer demand for blockchain-enabled peer-to-peer energy trading schemes in the United Kingdom varies depending on how the consumer proposition is designed and communicated. The analysis provides some evidence of a preference for schemes offering to meet a higher proportion of participants’ energy needs and for those operating at the city/region (as compared to national or neighbourhood) level. People were more likely to say they would participate when the scheme was framed as being run by their local council, followed by an energy supplier, community energy organization, and social media company. Anonymity was the most valued DLT characteristic and mentioning blockchain’s association with Bitcoin led to a substantial decrease in intended uptake. We highlight a range of important questions and implications suggested by these findings for the introduction and operation of P2P trading schemes.
Hierarchical Coding of Convolutional Features for Scene Recognition

Convolutional neural networks (CNNs) have achieved great success in visual recognition because of the availability of large-scale image datasets, such as the ImageNet. The transfer of convolutional features to challenging scene recognition remains an open problem. Multiple non-linear transforms endow the convolutional features with abundant information. On the other side, CNNs are adept at capturing the holistic appearances of scenes, whereas the lack of some critical local details may reduce the recognition accuracy. To address these problems, we propose a novel hierarchical coding algorithm to learn effective representations. To adapt the scale variations, many useful patches with various scales sampled from the whole image are considered to provide the sufficient details. Non-negative sparse decomposition model (NNSD) based on convolutional features is proposed to learn the sharable components for each scale and further produce global signatures. Based on the global signatures, inter-class linear coding (ICLC) is proposed to learn the discriminative components and ultimate image representations. Experimental results indicate that our approach significantly improves the recognition accuracy compared with general CNN models and achieves excellent performance on five standard benchmarks.
DPDT: A Differentially Private Crowd-Sensed Data Trading Mechanism

Along with the generation of Internet of Things (IoT), the values of tremendous volumes of sensing data will be slowly unlocked. Thus, crowd-sensed data trading as a new business paradigm has recently attracted increasing attention. A typical data trading system contains a platform, data consumers, and crowd workers. The platform recruits crowd workers to collect data and then sells the data to consumers. In this article, we design a differentially private crowd-sensed data trading mechanism, called DPDT, to preserve the identity privacy of consumers and the task privacy against crowd workers during the data collection process, simultaneously. DPDT consists of a differentially private auction-based data pricing algorithm and a differentially private data collection algorithm. The data pricing algorithm achieves a good approximation to the maximum revenue. Meanwhile, it guarantees (e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> - 1)ϵ-truthfulness and 2ϵ-differential privacy, where ϵ > 0 is a small constant. The data collection algorithm is able to effectively protect the data collection task privacy against crowd workers. We prove that this data collection algorithm achieves δ-approximate ϵ-differential privacy, where δ <; 1/e is a small constant, and meanwhile guarantees a tight bound of the expected approximation ratio. At last, extensive simulations are conducted to verify the significant performance of DPDT.
Mining Pool Manipulation in Blockchain Network Over Evolutionary Block Withholding Attack

In the current blockchain network, many participants rationally migrate the pool to receive a better compensation according to their contribution in situations where the pools they engage encounter undesirable attacks.The Nash equilibria of attacked pool has been widely analyzed, but the analysis of practical methodology for obtaining it is still inadequate.In this paper, we propose an evolutionary game theoretic analysis of Proof-of-Work (PoW) based blockchain network in order to investigate the mining pool dynamics affected by malicious infiltrators and the feasibility of autonomous migration among individual miners.We formulate a revenue model for mining pools which are implicitly allowed to launch a block withholding attack.Under our mining game, we analyze the evolutionary stability of Nash equilibrium with replicator dynamics, which can explain the population change with time between participated pools.Further, we explore the statistical approximation of successful mining events to show the necessity of artificial manipulation for migrating.Finally, we construct a better response learning based on the required block size which can lead to our evolutionarily stable strategy (ESS) with numerical results that support our theoretical discoveries.
Using virtual reality to promote the university brand: When do telepresence and system immersion matter?

While virtual reality (VR) has been shown to increase a sense of being there (i.e., telepresence) and enhance audience perceptions of the content, few studies have examined the impact of the varying degrees of 'system immersion' on persuasion outcomes under different VR viewing conditions. To fill this gap, our research investigates consumer perceptions of a branded VR experience of a university campus tour under different viewing conditions across two studies. In Study 1, we interviewed 17 participants who experienced the VR tour on Oculus Rift CV1. Emerging themes from the data revealed a sense of telepresence, enjoyment, and appreciation for going places not available to participants in real life. Study 2 employed an experimental design where participants viewed the same content using one of the three VR devices that varied in system immersion (smartphone, Google Cardboard, Oculus Rift DK2). Telepresence and its impact on recall and persuasion were measured. Results show that all three viewing conditions had equal capacity to create a sense of telepresence. Perceived telepresence negatively impacted recall of targeted locations in the virtual tour, whereas telepresence positively correlated with intentions to recommend the campus to others.
Sentiment Analysis on Customer Feedback Data: Amazon Product Reviews

Sentiment analysis is one of the fastest spreading research areas in computer science, making it challenging to keep track of all the activities in the area. We present a customer feedback reviews on product, where we utilize opinion mining, text mining and sentiments, which has affected the surrounded world by changing their opinion on a specific product. Data used in this study are online product reviews collected from http://Amazon.com. We performed a comparative sentiment analysis of retrieved reviews. This research paper provides you with sentimental analysis of various smart phone opinions on smart phones dividing them Positive, Negative and Neutral Behaviour.
Comparison of end-to-end and hybrid deep reinforcement learning strategies for controlling cable-driven parallel robots

Deep reinforcement learning (DRL) has been proven effective in learning policies of high-dimensional states and actions. Recently, a variety of robot manipulation tasks have been accomplished using end-to-end DRL strategies. An end-to-end DRL strategy accomplishes a robot manipulation task as a black box. On the other hand, a robot manipulation task can be divided into multiple subtasks and accomplished by non-learning-based approaches. A hybrid DRL strategy integrates DRL with non-learning-based approaches. The hybrid DRL strategy accomplishes some subtasks of a robot manipulation task by DRL and the rest subtasks by non-learning-based approaches. However, the effects of integrating DRL with non-learning-based approaches on the learning speed and the robustness of DRL to model uncertainties have not been discussed. In this study, an end-to-end DRL strategy and a hybrid DRL strategy are developed and compared in controlling a cable-driven parallel robot. This study shows that, by integrating DRL with non-learning-based approaches, the hybrid DRL strategy learns faster and is more robust to model uncertainties than the end-to-end DRL strategy. This study demonstrates that, by taking advantages of both learning and non-learning-based approaches, the hybrid DRL strategy provides an alternative to accomplish a robot manipulation task.
Controller-based Text-input Techniques for Virtual Reality: An Empirical Comparison

Existing consumer VR systems support text input using handheld controllers in combination with virtual keyboards and many designers have attempted to build on these widely used techniques. However, information on current and well-established VR text-input techniques is lacking. In this work, we conduct a comparative empirical evaluation of four controller-based VR text-input techniques, namely, raycasting, drum-like keyboard, head-directed input, and split keyboard. We focus on their text-entry rate and accuracy, usability, and user experience. Twenty-two participants evaluated the techniques by completing a typing session, answering usability and user-experience questionnaires, and participating in a semi-structured interview. The drum-like keyboard and the raycasting techniques stood out, achieving good usability scores, positive experiential feedback, satisfactory text-entry rates, and moderate error rates that can be reduced in future studies. The specific documented usability and experiential characteristics of the techniques are presented and discussed herein.
Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features

Relation classification (RC) plays a pivotal role in both natural language understanding and knowledge graph completion. It is generally formulated as a task to recognize the relationship between two entities of interest appearing in a free-text sentence. Conventional approaches on RC, regardless of feature engineering or deep learning based, can obtain promising performance on categorizing common types of relation leaving a large proportion of unrecognizable long-tail relations due to insufficient labeled instances for training. In this paper, we consider few-shot learning is of great practical significance to RC and thus improve a modern framework of metric learning for few-shot RC. Specifically, we adopt the large-margin ProtoNet with fine-grained features, expecting they can generalize well on long-tail relations. Extensive experiments were conducted by FewRel, a large-scale supervised few-shot RC dataset, to evaluate our framework: LM-ProtoNet (FGF). The results demonstrate that it can achieve substantial improvements over many baseline approaches.
GANE: A Generative Adversarial Network Embedding

Network embedding is capable of providing low-dimensional feature representations for various machine learning applications. Current work focuses on: 1) designing the embedding as an unsupervised learning task to explicitly preserve the structural connectivity in the network or 2) generating the embedding as a by-product during the supervised learning of a specific discriminative task in a deep neural network. In this paper, we aim to take advantage of these two lines of research in the view of multi-output learning. That is, we propose a generative adversarial network embedding (GANE) model to adapt the generative adversarial framework to achieve the network embedding learning during the specific machine learning tasks. GANE has a generator to generate link edges, and a discriminator to distinguish the generated link edges from real connections (edges) in the network. Wasserstein-1 distance is adopted to train the generator to gain better stability. GANE is further extended by utilizing the pairwise connectivity of vertices to preserve the structural information in the original network. Experiments with real-world network data sets demonstrate that our models constantly outperform state-of-the-art solutions with significant improvements for the tasks of link prediction, clustering, and network alignment.
Interactive Multimedia-Based Educational System for Children Using Interactive Book with Augmented Reality

This paper describes a system's model for Augmented Reality Learning in which a traditional book is converted to an interactive book using Glyphs (TAGs) and multimedia.The interactive book can be used by a child, parent, or by a teacher to make learning an enjoyable experience.As the child goes through the contents of the book, illustrations and images come to live, thus enforcing the learning and comprehension of concepts in an interactive and fun way.To make a printed book interactive, special TAGs (Glyphs) are inserted in the required places within the book, ready to be read by the webcam and then converted to video, 2-D or 3-D images, audio and explanation text.An actual example (Sandy Starfish) is presented to illustrate the architecture and the implementation of the Augmented Reality learning system and to explain the steps and procedure used to transform a textbook to an interactive one.
Augmented Reality Storytelling – Narrative Design and Reconstruction of a Historical Event in situ

&lt;p class="0abstract"&gt;&lt;span lang="EN-GB"&gt;How may we best utilize mobile augmented reality for storytelling when reconstructing historical events onlocation? In this article we present a series of narrative design considerations when developing an augmented reality application recreating the assault on Omaha Beach in the early morning on D-Day. To what extent may we select existing genre conventions from, for example, documentary film, and adapt them to a location–based audio–visual medium like AR? How can we best combine sequence and access, the narrative flow of an unfolding historical event with the availability of background information, in order to enrich the experience of the story, but without distorting its coherence? To what extent may we draw from existing and well known media representations of the Omaha Beach landing? How was the battle documented with contemporary means? We present the rich documentation of photos, films, drawings, paintings, maps, action reports, official reports, etc., and discuss how these have been employed to create the published AR situated simulation. We also describe and discuss the testing and evaluation of the application on location with visitors, as well as online tracking of its current use.&lt;/span&gt;&lt;/p&gt;
Lower dimensional kernels for video discriminators

This work presents an analysis of the discriminators used in Generative Adversarial Networks (GANs) for Video. We show that unconstrained video discriminator architectures induce a loss surface with high curvature which make optimization difficult. We also show that this curvature becomes more extreme as the maximal kernel dimension of video discriminators increases. With these observations in hand, we propose a methodology for the design of a family of efficient Lower-Dimensional Video Discriminators for GANs (LDVD-GANs). The proposed methodology improves the performance and efficiency of video GAN models it is applied to and demonstrates good performance on complex and diverse datasets such as UCF-101. In particular, we show that LDVDs can double the performance of Temporal-GANs and provide for state-of-the-art performance on a single GPU using the proposed methodology.
Program synthesis by type-guided abstraction refinement

We consider the problem of type-directed component-based synthesis where, given a set of (typed) components and a query type , the goal is to synthesize a term that inhabits the query. Classical approaches based on proof search in intuitionistic logics do not scale up to the standard libraries of modern languages, which span hundreds or thousands of components. Recent graph reachability based methods proposed for Java do scale, but only apply to monomorphic data and components: polymorphic data and components infinitely explode the size of the graph that must be searched, rendering synthesis intractable. We introduce type-guided abstraction refinement (TYGAR), a new approach for scalable type-directed synthesis over polymorphic datatypes and components. Our key insight is that we can overcome the explosion by building a graph over abstract types which represent a potentially unbounded set of concrete types. We show how to use graph reachability to search for candidate terms over abstract types, and introduce a new algorithm that uses proofs of untypeability of ill-typed candidates to iteratively refine the abstraction until a well-typed result is found. We have implemented TYGAR in H+, a tool that takes as input a set of Haskell libraries and a query type, and returns a Haskell term that uses functions from the provided libraries to implement the query type. Our support for polymorphism allows H+ to work with higher-order functions and type classes, and enables more precise queries due to parametricity. We have evaluated H+ on 44 queries using a set of popular Haskell libraries with a total of 291 components. H+ returns an interesting solution within the first five results for 32 out of 44 queries. Our results show that TYGAR allows H+ to rapidly return well-typed terms, with the median time to first solution of just 1.4 seconds. Moreover, we observe that gains from iterative refinement over exhaustive enumeration are more pronounced on harder queries.
The next 700 relational program logics

We propose the first framework for defining relational program logics for arbitrary monadic effects. The framework is embedded within a relational dependent type theory and is highly expressive. At the semantic level, we provide an algebraic presentation of relational specifications as a class of relative monads, and link computations and specifications by introducing relational effect observations, which map pairs of monadic computations to relational specifications in a way that respects the algebraic structure. For an arbitrary relational effect observation, we generically define the core of a sound relational program logic, and explain how to complete it to a full-fledged logic for the monadic effect at hand. We show that this generic framework can be used to define relational program logics for effects as diverse as state, input-output, nondeterminism, and discrete probabilities. We, moreover, show that by instantiating our framework with state and unbounded iteration we can embed a variant of Benton's Relational Hoare Logic, and also sketch how to reconstruct Relational Hoare Type Theory. Finally, we identify and overcome conceptual challenges that prevented previous relational program logics from properly dealing with control effects, and are the first to provide a relational program logic for exceptions.
Secure Architectures Implementing Trusted Coalitions for Blockchained Distributed Learning (TCLearn)

Distributed learning across a coalition of organizations allows the members of the coalition to train and share a model without sharing the data used to optimize this model. In this paper, we propose new secure architectures that guarantee preservation of data privacy, trustworthy sequence of iterative learning and equitable sharing of the learned model among each member of the coalition by using adequate encryption and blockchain mechanisms. We exemplify its deployment in the case of the distributed optimization of a deep learning convolutional neural network trained on medical images.
Age Progression and Regression with Spatial Attention Modules

Age progression and regression refers to aesthetically rendering a given face image to present effects of face aging and rejuvenation, respectively. Although numerous studies have been conducted in this topic, there are two major problems: 1) multiple models are usually trained to simulate different age mappings, and 2) the photo-realism of generated face images is heavily influenced by the variation of training images in terms of pose, illumination, and background. To address these issues, in this paper, we propose a framework based on conditional Generative Adversarial Networks (cGANs) to achieve age progression and regression simultaneously. Particularly, since face aging and rejuvenation are largely different in terms of image translation patterns, we model these two processes using two separate generators, each dedicated to one age changing process. In addition, we exploit spatial attention mechanisms to limit image modifications to regions closely related to age changes, so that images with high visual fidelity could be synthesized for in-the-wild cases. Experiments on multiple datasets demonstrate the ability of our model in synthesizing lifelike face images at desired ages with personalized features well preserved, and keeping age-irrelevant regions unchanged.
Relation Extraction with Convolutional Network over Learnable Syntax-Transport Graph

A large majority of approaches have been proposed to leverage the dependency tree in the relation classification task. Recent works have focused on pruning irrelevant information from the dependency tree. The state-of-the-art Attention Guided Graph Convolutional Networks (AGGCNs) transforms the dependency tree into a weighted-graph to distinguish the relevance of nodes and edges for relation classification. However, in their approach, the graph is fully connected, which destroys the structure information of the original dependency tree. How to effectively make use of relevant information while ignoring irrelevant information from the dependency trees remains a challenge in the relation classification task. In this work, we learn to transform the dependency tree into a weighted graph by considering the syntax dependencies of the connected nodes and persisting the structure of the original dependency tree. We refer to this graph as a syntax-transport graph. We further propose a learnable syntax-transport attention graph convolutional network (LST-AGCN) which operates on the syntax-transport graph directly to distill the final representation which is sufficient for classification. Experiments on Semeval-2010 Task 8 and Tacred show our approach outperforms previous methods.