Reviewerly/exHarmony huggingface dataset:
EGO-CH: Dataset and fundamental tasks for visitors behavioral understanding using egocentric vision

Equipping visitors of a cultural site with a wearable device allows to easily collect information about their preferences which can be exploited to improve the fruition of cultural goods with augmented reality. Moreover, egocentric video can be processed using computer vision and machine learning to enable an automated analysis of visitors’ behavior. The inferred information can be used both online to assist the visitor and offline to support the manager of the site. Despite the positive impact such technologies can have in cultural heritage, the topic is currently understudied due to the limited number of public datasets suitable to study the considered problems. To address this issue, in this paper we propose EGOcentric-Cultural Heritage (EGO-CH), the first dataset of egocentric videos for visitors’ behavior understanding in cultural sites. The dataset has been collected in two cultural sites and includes more than 27hours of video acquired by 70 subjects, with labels for 26 environments and over 200 different Points of Interest. A large subset of the dataset, consisting of 60 videos, is associated with surveys filled out by real visitors. To encourage research on the topic, we propose 4 challenging tasks (room-based localization, point of interest/object recognition, object retrieval and survey prediction) useful to understand visitors’ behavior and report baseline results on the dataset.
An Evaluation of Uncle Block Mechanism Effect on Ethereum Selfish and Stubborn Mining Combined With an Eclipse Attack

Ethereum accelerates the transaction process through a quicker block creation design.Since the time interval between the generation of blocks is very short (about 15s), block propagation time in an inefficient network is not negligible compared with the block time interval.This lead to the production of a large number of orphan blocks.In order to solve the security problems that may be caused by the orphan block and improve the transaction processing efficiency, Ethereum introduces the uncle block mechanism, i.e., an orphan block may get part of minted reward if it gets a reference by a regular block.In this paper, we show the weakness of the uncle block mechanism.Firstly, we describe the specific differences of Ethereum selfish and stubborn mining in every state from the ones in Bitcoin.Secondly, we simulate possible attacks, and the results show that the Ethereum selfish and stubborn mining strategies not only increase the reward of an attacker but also decrease the security threshold.The security threshold refers to the proportion of the attacker's computational power that needs to be achieved in order to obtain a higher reward than he should.In a practical network congestion rate, the security threshold are weakened to 0.129 and 0.216 against the Lead stubborn mining strategy and the original selfish mining strategy, respectively.When the congestion rate is rising, the reward is increasing and the threshold is decreasing.Thirdly, possible strategies are evaluated to find out the optimal one in different settings.Fourthly, we also extend the evaluation by combining three eclipse attack strategies with selfish or stubborn mining.Most of combinations bring more advantages to an attacker than a single strategy.
Snap-Snap T-Shirt

In this pictorial we present a prototype of a novel personalized garment that provides rich haptic feedback for posture awareness in the context of repetitive strain injury (RSI). Unlike prior work concerned with posture correction, our aim was to design a garment that would allow the user to gain an awareness of his posture with a help of sensorial experiences. Collaboratively engaging the user as a co-designer in movement enactment, movement analysis and embodied co-design sessions enabled us to design a garment that offers posture awareness through playful and somaesthetic experience. We offer a reflective analysis of how our co-design approach enabled us to design for personalization, somaesthetics and playfulness in posture awareness.
Achieving Searchable Encryption Scheme With Search Pattern Hidden

Searchable Encryption (SE) enables a data owner to outsource encrypted data to an untrusted server while preserving the keyword search functionality. Typically, the server learns whether or not a query has been performed more than once, which is usually called the search pattern. However, such kind of information leakage might be leveraged to break query privacy. To further reduce such type of leakage and provide strong privacy guarantee, Wang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> proposed a novel SE scheme based on the Paillier encryption scheme in INFOCOM’15. Unfortunately, their scheme cannot perform keyword search successfully, because the additive homomorphic property is not sufficient for their construction. In this article, we first show that why their scheme fails to return the correct search result, and then propose a new SE scheme by adopting a special additive homomorphic encryption scheme to achieve the multiplicative homomorphic property efficiently. Furthermore, we enhance the security on the user side. Specifically, we use random polynomials with an appropriate degree to guarantee that the user cannot learn anything other than the desired search result. Finally, we present a formal security analysis and implement our scheme on a real-world database, which demonstrates that our construction can achieve the desired security properties with good performance.
Adversarial Attacks and Defenses on Graphs: A Review and Empirical Study

Deep neural networks (DNNs) have achieved significant performance in varioustasks. However, recent studies have shown that DNNs can be easily fooled bysmall perturbation on the input, called adversarial attacks. As the extensionsof DNNs to graphs, Graph Neural Networks (GNNs) have been demonstrated toinherit this vulnerability. Adversary can mislead GNNs to give wrongpredictions by modifying the graph structure such as manipulating a few edges.This vulnerability has arisen tremendous concerns for adapting GNNs insafety-critical applications and has attracted increasing research attention inrecent years. Thus, it is necessary and timely to provide a comprehensiveoverview of existing graph adversarial attacks and the countermeasures. In thissurvey, we categorize existing attacks and defenses, and review thecorresponding state-of-the-art methods. Furthermore, we have developed arepository with representative algorithms(https://github.com/DSE-MSU/DeepRobust/tree/master/deeprobust/graph). Therepository enables us to conduct empirical studies to deepen our understandingson attacks and defenses on graphs.
A Code for Unscented Kalman Filtering on Manifolds (UKF-M)

The present paper introduces a novel methodology for Unscented Kalman Filtering (UKF) on manifolds that extends previous work by the authors on UKF on Lie groups. Beyond filtering performance, the main interests of the approach are its versatility, as the method applies to numerous state estimation problems, and its simplicity of implementation for practitioners not being necessarily familiar with manifolds and Lie groups. We have developed the method on two independent open-source Python and Matlab frameworks we call UKF-M, for quickly implementing and testing the approach. The online repositories contain tutorials, documentation, and various relevant robotics examples that the user can readily reproduce and then adapt, for fast prototyping and benchmarking. The code is available at https://github.com/CAOR-MINES-ParisTech/ukfm.
Sentiment Analysis With Comparison Enhanced Deep Neural Network

Sentiment analysis is a significant task in Natural Language Processing.It refers to classification based on the emotional tendency in text by extracting text features.The existing results show that models based on RNN and CNN have good performance.In order to improve the performance of text sentiment analysis, we reformulate the classification task as a comparing problem, and propose Comparison Enhanced Bi-LSTM with Multi-Head Attention (CE-B-MHA).In fact, it is efficient to classify by comparison mechanism instead of doing complex calculation.In this model, bidirectional LSTM is used for initial feature extraction, and valuable information is extracted from different dimensions and representation subspaces by Multi-Head Attention.The comparison mechanism aims to score the feature vectors by comparing with the labeled vectors.The experimental results show that CE-B-MHA has better performance than many existing models on three sentiment analysis datasets.
UnifiedQA: Crossing Format Boundaries With a Single QA System

Question answering (QA) tasks have been posed using a variety of formats, such as extractive span selection, multiple choice, etc. This has led to format-specialized models, and even to an implicit division in the QA community. We argue that such boundaries are artificial and perhaps unnecessary, given the reasoning abilities we seek to teach are not governed by the format. As evidence, we use the latest advances in language modeling to build a single pre-trained QA model, UnifiedQA, that performs surprisingly well across 17 QA datasets spanning 4 diverse formats. UnifiedQA performs on par with 9 different models that were trained on individual datasets themselves. Even when faced with 12 unseen datasets of observed formats, UnifiedQA performs surprisingly well, showing strong generalization from its out-of-format training data. Finally, simply fine-tuning this pre-trained QA model into specialized models results in a new state of the art on 6 datasets, establishing UnifiedQA as a strong starting point for building QA systems.
PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking

We propose the task of outline-conditioned story generation: given an outline as a set of phrases that describe key characters and events to appear in a story, the task is to generate a coherent narrative that is consistent with the provided outline. This task is challenging as the input only provides a rough sketch of the plot, and thus, models need to generate a story by interweaving the key points provided in the outline. This requires the model to keep track of the dynamic states of the latent plot, conditioning on the input outline while generating the full story. We present PlotMachines, a neural narrative model that learns to transform an outline into a coherent story by tracking the dynamic plot states. In addition, we enrich PlotMachines with high-level discourse structure so that the model can learn different writing styles corresponding to different parts of the narrative. Comprehensive experiments over three fiction and non-fiction datasets demonstrate that large-scale language models, such as GPT-2 and Grover, despite their impressive generation performance, are not sufficient in generating coherent narratives for the given outline, and dynamic plot state tracking is important for composing narratives with tighter, more consistent plots.
Topological Sort for Sentence Ordering

Sentence ordering is the task of arranging the sentences of a given text in the correct order. Recent work using deep neural networks for this task has framed it as a sequence prediction problem. In this paper, we propose a new framing of this task as a constraint solving problem and introduce a new technique to solve it. Additionally, we propose a human evaluation for this task. The results on both automatic and human metrics across four different datasets show that this new technique is better at capturing coherence in documents.
A Finite Time Analysis of Two Time-Scale Actor Critic Methods

Actor-critic (AC) methods have exhibited great empirical success compared with other reinforcement learning algorithms, where the actor uses the policy gradient to improve the learning policy and the critic uses temporal difference learning to estimate the policy gradient. Under the two time-scale learning rate schedule, the asymptotic convergence of AC has been well studied in the literature. However, the non-asymptotic convergence and finite sample complexity of actor-critic methods are largely open. In this work, we provide a non-asymptotic analysis for two time-scale actor-critic methods under non-i.i.d. setting. We prove that the actor-critic method is guaranteed to find a first-order stationary point (i.e., $\|\nabla J(\boldsymbol{\theta})\|_2^2 \le \epsilon$) of the non-concave performance function $J(\boldsymbol{\theta})$, with $\mathcal{\tilde{O}}(\epsilon^{-2.5})$ sample complexity. To the best of our knowledge, this is the first work providing finite-time analysis and sample complexity bound for two time-scale actor-critic methods.
A reinforcement learning approach for quantum state engineering

Machine learning (ML) has become an attractive tool for solving various problems in different fields of physics, including the quantum domain. Here, we show how classical reinforcement learning (RL) could be used as a tool for quantum state engineering (QSE). We employ a measurement based control for QSE where the action sequences are determined by the choice of the measurement basis and the reward through the fidelity of obtaining the target state. Our analysis clearly displays a learning feature in QSE, for example in preparing arbitrary two-qubit entangled states and delivers successful action sequences that generalise previously found human solutions from exact quantum dynamics. We provide a systematic algorithmic approach for using RL for quantum protocols that deal with a non-trivial continuous state space, and discuss the scaling of these approaches for the preparation of larger entangled (cluster) states.
Resource requirements for efficient quantum communication using all-photonic graph states generated from a few matter qubits

Quantum communication technologies show great promise for applications ranging from the secure transmission of secret messages to distributed quantum computing. Due to fiber losses, long-distance quantum communication requires the use of quantum repeaters, for which there exist quantum memory-based schemes and all-photonic schemes. While all-photonic approaches based on graph states generated from linear optics avoid coherence time issues associated with memories, they outperform repeater-less protocols only at the expense of a prohibitively large overhead in resources. Here, we consider using matter qubits to produce the photonic graph states and analyze in detail the trade-off between resources and performance, as characterized by the achievable secret key rate per matter qubit. We show that fast two-qubit entangling gates between matter qubits and high photon collection and detection efficiencies are the main ingredients needed for the all-photonic protocol to outperform both repeater-less and memory-based schemes.
Average Jane, Where Art Thou? – Recent Avenues in Efficient Machine Learning Under Subjectivity Uncertainty

In machine learning tasks an actual ‘ground truth’ may not be available. Then, machines often have to rely on human labelling of data. This becomes challenging the more subjective the learning task is, as human agreement can be low. To cope with the resulting high uncertainty, one could train individual models reflecting a single human’s opinion. However, this is not viable, if one aims at mirroring the general opinion of a hypothetical ‘completely average person’ – the ‘average Jane’. Here, I summarise approaches to optimally learn efficiently in such a case. First, different strategies of reaching a single learning target from several labellers will be discussed. This includes varying labeller trustability and the case of time-continuous labels with potential dynamics. As human labelling is a labour-intensive endeavour, active and cooperative learning strategies can help reduce the number of labels needed. Next, sample informativeness can be exploited in teacher-based algorithms to additionally weigh data by certainty. In addition, multi-target learning of different labeller tracks in parallel and/or of the uncertainty can help improve the model robustness and provide an additional uncertainty measure. Cross-modal strategies to reduce uncertainty offer another view. From these and further recent strategies, I distil a number of future avenues to handle subjective uncertainty in machine learning. These comprise bigger, yet weakly labelled data processing basing amongst other on reinforcement learning, lifelong learning, and self-learning. Illustrative examples stem from the fields of Affective Computing and Digital Health – both notoriously marked by subjectivity uncertainty.
A secure and extensible blockchain-based data provenance framework for the Internet of Things

Abstract As data collected and provided by Internet of Things (IoT) devices power an ever-growing number of applications and services, it is crucial that this data can be trusted. Data provenance solutions combined with blockchain technology are one way to make data more trustworthy by providing tamper-proof information about the origin and history of data records. However, current blockchain-based solutions for data provenance fail to take the heterogeneous nature of IoT applications and their data into account. In this work, we identify functional and non-functional requirements for a secure and extensible IoT data provenance framework, and conceptualise the framework as a layered architecture. Evaluating the framework using a proof-of-concept implementation based on Ethereum smart contracts, we conclude that our framework can be used to realise data provenance concepts for a wide range of IoT use cases. While blockchain technology generally poses constraints on scalability and privacy, we discuss multiple solutions aiming to overcome these issues.
More-Than-Human Design and AI

This one-day workshop brings together HCI researchers, designers, and practitioners to explore how to study and design (with) AI agents from a more-than-human design perspective. We invite participants to experiment with thing ethnography and material speculations, as a starting point to map and possibly integrate emergent frameworks and methodologies for more-than-human design. By using conversational agents as a case, participants will discuss what a more-than-human approach can offer to the understanding and design of AI systems, and how this aligns with third-wave HCI concerns of networks, infrastructures, and ecologies.
A Combinator-Based Superposition Calculus for Higher-Order Logic

We present a refutationally complete superposition calculus for a version of higher-order logic based on the combinatory calculus. We also introduce a novel method of dealing with extensionality. The calculus was implemented in the Vampire theorem prover and we test its performance against other leading higher-order provers. The results suggest that the method is competitive.
Privacy Enforced Access Control Model for Secured Data Handling in Cloud-Based Pervasive Health Care System

Health care is a significant application of pervasive computing. In recent years, the pervasive health care applications are replaced by cloud-based health care applications thus solving the problem of scalability and computing cost. In a cloud-based pervasive health care data security and privacy becomes a greater concern. The key contribution of this paper is to provide high privacy, data confidentiality, availability against the health data and this is achieved using the proposed access control model which uses a PR based approach for providing access control to various users of the system. Privacy rating (PR) is calculated for both the user and the data to provide access to any data that is being requested by the user. The results achieved shows that the proposed model achieves high level of privacy and security for the data stored in the healthcare system.
Secure and Optimized Cloud-Based Cyber-Physical Systems with Memory-Aware Scheduling Scheme

Computer based algorithms are used for monitoring and controlling a computers where human, physical, digital and analog interaction occurs. This scheme termed as cyber-physical systems (CPS) is being deployed in a wide array of applications due to its easy deployment feature and connectivity. CPS based on cloud computing technology offers wider application range with enormous amount of storage and computing resources. However, despite the wide application and deployment of CPS in combining the key technologies like big data analytics, cloud computing and IoT, its energy consumption is large. Optimization of this energy is a major field of research and is of due importance. For this purpose, in cloud environment, virtual machines (VMs) are used for hosting the applications and the resources are managed thereby optimizing the energy consumption. The security issues and the quality of service (QoS) requirements of the system is also met by optimizing the system design. The scheduling issue of the system is addressed by implementation of efficient memory-aware scheduling strategy and algorithms. The proposed technique is tested for performance and the results of simulation is presented.
Manthan: A Data-Driven Approach for Boolean Function Synthesis

Boolean functional synthesis is a fundamental problem in computer science with wide-ranging applications and has witnessed a surge of interest resulting in progressively improved techniques over the past decade. Despite intense algorithmic development, a large number of problems remain beyond the reach of the state of the art techniques. Motivated by the progress in machine learning, we propose $$\textsf {Manthan}$$ , a novel data-driven approach to Boolean functional synthesis. $$\textsf {Manthan}$$ views functional synthesis as a classification problem, relying on advances in constrained sampling for data generation, and advances in automated reasoning for a novel proof-guided refinement and provable verification. On an extensive and rigorous evaluation over 609 benchmarks, we demonstrate that $$\textsf {Manthan}$$ significantly improves upon the current state of the art, solving 356 benchmarks in comparison to 280, which is the most solved by a state of the art technique; thereby, we demonstrate an increase of 76 benchmarks over the current state of the art. Furthermore, $$\textsf {Manthan}$$ solves 60 benchmarks that none of the current state of the art techniques could solve. The significant performance improvements, along with our detailed analysis, highlights several interesting avenues of future work at the intersection of machine learning, constrained sampling, and automated reasoning.