Reviewerly/exHarmony huggingface dataset:
Towards Explainable Representation of Time-Evolving Graphs via Spatial-Temporal Graph Attention Networks

Many complex systems with relational data can be naturally represented as dynamic processes on graphs, with the addition/deletion of nodes and edges over time. For such graphs, network embedding provides an important class of tools for leveraging the node proximity to learn a low-dimensional representation before using the off-the-shelf machine learning models. However, for dynamic graphs, most, if not all, embedding approaches rely on various hyper-parameters to extract spatial and temporal context information, which differ from task to task and from data to data. Besides, many regulated industries (e.g., finance, health care) require the learning models to be interpretable and the output results to meet compliance. Therefore, a natural research question is how we can jointly model the spatial and temporal context information and learn a unique network representation, while being able to provide interpretable inference over the observed data. To address this question, we propose a generic graph attention neural mechanism named STANE, which guides the context sampling process to focus on the crucial part of the data. Moreover, to interpret the network embedding results, STANE enables the end users to investigate the graph context distributions along three dimensions (i.e., nodes, training window length, and time). We perform extensive experiments regarding quantitative evaluation and case studies, which demonstrate the effectiveness and interpretability of STANE.
An improved CNN framework for detecting and tracking human body in unconstraint environment

Human tracking and localization play a crucial role in many applications like accident avoidance, action recognition, safety and security, surveillance and crowd analysis. Inspired by its use and scope, we introduced a novel method for human tracking (one or many) and re-localization in a complex environment with large displacement. The model can handle complex background, variations in illumination, changes in target pose, the presence of similar target and appearance (pose and clothes), the motion of target and camera, occlusion of the target, background variation, and massive displacement of the target. Our model uses three convolutional neural network based deep architecture and cascades their learning such that it improves the overall efficiency of the model. The first network learns the pixel level representation of small regions. The second architecture uses these features and learns the displacement of a region with its category between moved, not-moved, and occluded classes. Whereas, the third network improves the displacement result of the second network by utilizing the previous two learning. We also create a semi-synthetic dataset for training purpose. The model is trained on this dataset first and tested on a subset of CamNeT, VOT2015, LITIV-tracking and Visual Tracker Benchmark database without training with real data. The proposed model yield comparative results with respect to current state-of-the-art methods based on evaluation criteria described in Object Tracking Benchmark, TPAMI 2015, CVPR 2013 and ICCV 2017.
Multi-scale sequential network for semantic text segmentation and localization

We present a novel method for semantic text document analysis which in addition to localizing text it labels the text in user-defined semantic categories. More precisely, it consists of a fully-convolutional and sequential network that we apply to the particular case of slide analysis to detect title, bullets and standard text. Our contributions are twofold: (1) A multi-scale network consisting of a series of stages that sequentially refine the prediction of text and semantic labels (text, title, bullet); (2) A synthetic database of slide images with text and semantic annotation that is used to train the network with abundant data and wide variability in text appearance, slide layouts, and noise such as compression artifacts. We evaluate our method on a collection of real slide images collected from multiple conferences, and show that it is able to localize text with an accuracy of 95%, and to classify titles and bullets with accuracies of 94% and 85% respectively. In addition, we show that our method is competitive on scene and born-digital image datasets, such as ICDAR 2011, where it achieves an accuracy of 91.1%.
Investigation of the efficiency of continuous evaluation‐based interactive evolutionary computation for composing melody

Interactive Evolutionary Computation (IEC) is known as a method reflecting user's feelings on media contents. However, user's fatigue caused by repetitive evaluation tasks remains as a problem. The authors proposed continuous evaluation‐based IEC (CEIEC) that divides user's evaluation tasks into multiple days, while users finish the task in 1 day in conventional IECs. The efficiency of CEIEC was shown by the 2‐day experiment: the highest fitness was obtained in the final generation of the second day. The purpose of this study is to show further efficiencies of CEIEC by comparing experiment. The target of search was designing a 'bright' sign sound. Forty‐two subjects were divided into two groups: half of them performed the 2‐day task of CEIEC, and the other half performed the 1‐day task of IEC. After that, they evaluated representative three sounds. A gradual decrease in update of the solution candidates and a shrink of the search space were observed in both conditions. Furthermore, the same trend of increases in fitness was observed. These results showed the efficiencies of CEIEC in terms of having the same searching ability as the ordinal IEC. Additionally, elapsed time for completing the task was significantly shorter in CEIEC. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.
The Impact of Reported Cybersecurity Breaches on Firm Innovation

ABSTRACT Many firms cite cyber risk as a primary risk factor due to the increase in cybersecurity breach (CSB) incidents. Existing research focuses on the negative, short-term impacts from CSBs, but the longer-term impact is still unknown. Our study investigates firms' levels of innovation following a CSB as an important determinant of firm growth and profitability. Examining reported breaches from 2005–2014, we find a 10 percent decline in research and development spending in the year following a CSB. Further investigation indicates that firms for which R&amp;D is not the primary business model drive the results. We also provide evidence of a decrease in patents two years after a breach, an increase in cash holdings in the year after the breach, and a decrease in investment efficiency four years following the breach. Our aggregate results suggest that CSBs are associated with future strategic decisions involving firm-level innovation and investment decisions. Data Availability: Data are available from the public sources cited in the text.
Impact of mobile technology‐based physics curriculum on preservice elementary teachers' technology self‐efficacy

Abstract The growing popularity of mobile technologies in educational settings, from grade schools through college, has prompted science educators to prepare preservice teachers to successfully integrate technology into science teaching. This mixed‐methods study explores the effectiveness of a mobile technology‐based physics curriculum, Exploring Physics, on preservice elementary teachers' technology self‐efficacy. Participants included 67 preservice elementary teachers enrolled in a specialized physics content course at a large public university in the United States. The experimental group ( N = 34) used the Exploring Physics curriculum on iPads, and the comparison group ( N = 33) used a hard‐copy version of a similar curriculum. Data sources included a technology self‐efficacy survey administered as pre‐ and posttests, focus group and individual interviews with 24 participants at two time points, weekly classroom observations, and artifacts. Data analyses included repeated measures analysis of variance and posthoc t tests with Bonferroni adjustments and grounded theory techniques. The results showed significant positive changes in the experimental group participants' technology self‐efficacy. In contrast, there was a significant decrease in the comparison group participants' technology self‐efficacy. Several affordances of the curriculum assisted the experimental group participants in developing an appreciation for using mobile technologies in science teaching. Implications for preservice teacher preparation for technology integration in science teaching are discussed.
Learning Compositional, Visual and Relational Representations for CTR Prediction in Sponsored Search

As the main revenue source for search engines, sponsored search system retrieves and allocates ads to display on the search result pages. Click-through rate (CTR) prediction is a crucial task for search ads, due to it plays a key role in ranking and pricing of candidate ads. Commercial search engines typically model it as a classification problem and use machine learning models for CTR prediction. Recently deep learning based models have been proposed to learn latent representations of query-ad relevance and historical information to improve the accuracy, which follow the Embedding&MLP paradigm. As the learning feasible embeddings requires sufficient samples and these models rely mainly and heavily on textual features and sparse ID features, representations learning for new ads is inadequate and models are confronted with the cold-start problem. Meanwhile, as search ad offerings become increasingly complex, rich ads with various sizes, decorations and formats are growing rapidly. Due to the diverse ad extensions and layouts, rich ads pose new challenges for CTR prediction. To tackle these problems, in this paper, we propose an approach to improve the accuracy of CTR prediction by learning supplementary representations from three new aspects: the compositional components, the visual appearance and the relational structure of ads. This method can utilize straightforward and auxiliary information for new and rich ads, which can improve the expressive ability and the generalization of model greatly. We demonstrate the performance of this method on datasets obtained from a real sponsored search system in the offline environment. Experimental results show that our approach can improve the accuracy of CTR prediction and achieve superior performance compared to the baseline method, especially for the rich and new ads.
Context-Interactive CNN for Person Re-Identification

Despite growing progresses in recent years, cross-scenario person re-identification remains challenging, mainly due to the pedestrians commonly surrounded by highly-complex environment contexts. In reality, the human perception mechanism could adaptively find proper contextualized spatial-temporal clues towards pedestrian recognition. However, conventional methods fall short in adaptively leveraging the long-term spatial-temporal information due to ever-increasing computational cost. Moreover, CNN-based deep learning methods are hard to conduct optimization due to the non-differentiable property of the built-in context search operation. To ameliorate, this paper proposes a novel Context-Interactive CNN (CI-CNN) to dynamically find both spatial and temporal contexts by embedding multi-task Reinforcement Learning (MTRL). The CI-CNN streamlines the multi-task reinforcement learning by using an actor-critic agent to capture the temporal-spatial context simultaneously, which comprises a context-policy network and a context-critic network. The former network learns policies to determine the optimal spatial context region and temporal sequence range. Based on the inferred temporal-spatial cues, the latter one focuses on the identification task and provides feedback for the policy network. Thus, CI-CNN can simultaneously zoom in/out the perception field in spatial and temporal domain for the context interaction with the environment. By fostering the collaborative interaction between the person and context, our method could achieve outstanding performance on various public benchmarks, which confirms the rationality of our hypothesis, and verifies the effectiveness of our CI-CNN framework.
New Code-Based Privacy-Preserving Cryptographic Constructions

Code-based cryptography has a long history but did suffer from periods of slow development. The field has recently attracted a lot of attention as one of the major branches of post-quantum cryptography. However, its subfield of privacy-preserving cryptographic constructions is still rather underdeveloped, e.g., important building blocks such as zero-knowledge range proofs and set membership proofs, and even proofs of knowledge of a hash preimage, have not been known under code-based assumptions. Moreover, almost no substantial technical development has been introduced in the last several years. This work introduces several new code-based privacy-preserving cryptographic constructions that considerably advance the state-of-the-art in code-based cryptography. Specifically, we present 3 major contributions, each of which potentially yields various other applications. Our first contribution is a code-based statistically hiding and computationally binding commitment scheme with companion zero-knowledge (ZK) argument of knowledge of a valid opening that can be easily extended to prove that the committed bits satisfy other relations. Our second contribution is the first code-based zero-knowledge range argument for committed values, with communication cost logarithmic in the size of the range. A special feature of our range argument is that, while previous works on range proofs/arguments (in all branches of cryptography) only address ranges of non-negative integers, our protocol can handle signed fractional numbers, and hence, can potentially find a larger scope of applications. Our third contribution is the first code-based Merkle-tree accumulator supported by ZK argument of membership, which has been known to enable various interesting applications. In particular, it allows us to obtain the first code-based ring signatures and group signatures with logarithmic signature sizes.
Fast Anytime Motion Planning in Point Clouds by Interleaving Sampling and Interior Point Optimization

Robotic manipulators operating in unstructured environments such as homes and offices need to plan their motions quickly while relying on real-world sensors, which typically produce point clouds. To enable intuitive, interactive, and reactive user interfaces, the motion plan computation should provide high-quality solutions quickly and in an anytime manner, meaning the algorithm progressively improves its solution and can be interrupted at any time and return a valid solution. To address these challenges, we combine two paradigms: (1) asymptotically-optimal sampling-based motion planning, which is effective at providing anytime solutions but can struggle to quickly converge to high quality solutions in high dimensional configuration spaces, and (2) optimization, which locally refines paths quickly. We propose the use of interior point optimization for its ability to perform in an anytime manner that guarantees obstacle avoidance in each iteration, and we provide a novel lazy formulation that efficiently operates directly on point cloud data. Our method iteratively alternates between anytime sampling-based motion planning and anytime, lazy interior point optimization to compute high quality motion plans quickly, converging to a globally optimal solution.
A verifiable hidden policy CP‐ABE with decryption testing scheme and its application in VANET

Abstract As a new kind of cryptographic primitive, attribute‐based encryption (ABE) is widely used in various complex scenarios because it has the characteristic of access control while encrypting messages. However, the existing ciphertext policy ABE (CP‐ABE) encryption schemes have some inherent defects, such as the lack of privacy‐preserving and inefficient decryption. These weak points make them difficult to play a role in scenarios with high real‐time and high data confidentiality requirements, for example, vehicle ad hoc network (VANET). Therefore, we proposed a verifiable hidden policy CP‐ABE with decryption testing scheme (or VHPDT, for short). It has the following features: hidden policy to privacy‐preserving and outsourced decryption testing can verify the correctness of the decrypted result. Furthermore, we apply it into VANET.
ChromaCode: A Fully Imperceptible Screen-Camera Communication System

Hidden screen-camera communication techniques emerge as a new paradigm that embeds data imperceptibly into regular videos while remaining unobtrusive to human viewers. Three key goals on imperceptible, high rate, and reliable communication are desirable but conflicting, and existing solutions usually made a trade-off among them. In this paper, we present the design and implementation of CHROMACODE, a screen-camera communication system that achieves all three goals simultaneously. In our design, we consider for the first time color space for perceptually uniform lightness modifications. On this basis, we design an outcome-based adaptive embedding scheme, which adapts to both pixel lightness and regional texture. Last, we propose a concatenated code scheme for robust coding and devise multiple techniques to overcome various screen-camera channel errors. Our prototype and experiments demonstrate that CHROMACODE achieves remarkable raw throughputs of >700 kbps, data goodputs of 120 kbps with BER of 0.05, and with fully imperceptible flicker for viewing proved by user study, which significantly outperforms previous works.
"Forgot your password again?"

Over the last few years, chatbots have become a common channel for customer service interactions. In contrast, their usage for in-company applications and respective scientific knowledge about so-called virtual enterprise assistants from a user perspective are still scarce. In this paper, we studied the acceptance and user experience of a chatbot for in-company IT support. In a user study, 12 employees of a bank and a hospital evaluated and assessed a respective chatbot prototype supporting three typical use cases. Our results indicate that such an in-company chatbot is well-suited for structured use cases, such as resetting a password and releasing an email attachment from quarantine. Participants appreciated the simplicity, the pro-active guidance and immediate feedback. The participants assessed a chatbot and the phone as preferred channels to the IT help desk.
Blockchain-based Mechanisms for Local Energy Trading in Smart Grids

Managing the energy demand and supply during peak hours is a challenging prospect, especially in areas where the energy demand grows rapidly. To alleviate the shortage in energy supply, an increasing number of renewable energy sources (RES) are being integrated into the main grid. However, the unpredictability and intermittency of energy generation in power systems may lead to unforeseen demand peaks. To address this issue, we propose two blockchain-based Local energy market (LEM) mechanisms that assist prosumers (i.e., users with RESs that can either produce or absorb energy) and consumers to trade energy securely, balance demand and supply in a decentralized approach, and sell surplus energy to the main grid. Specifically, this paper presents the concept of Proof of Energy Generation (PoEG) to increase energy production by rewarding a prosumer as a validator to add a block in the blockchain. Also, we propose the concept of Proof of Energy Consumption (PoEC) to incentivize a user to reduce energy consumption at peak hours. To validate the proposed mechanisms, we implement a blockchain-based transaction system that consists of five nodes, a wallet, and real price and consumption data from Ontario Electric Board. The results of our experiments show that prosumers could increase their probability to win as a validator with increased energy productions and decreased consumption. We simulate two case studies, and the results show that an increase of 20% generation increases wallet balance by 10% followed by PoEG algorithm while a reduction of 20% consumption increments the balance by 9% employing PoEC algorithm.
Shorter QA-NIZK and SPS with Tighter Security

Quasi-adaptive non-interactive zero-knowledge proof (QA-NIZK) systems and structure-preserving signature (SPS) schemes are two powerful tools for constructing practical pairing-based cryptographic schemes. Their efficiency directly affects the efficiency of the derived advanced protocols. We construct more efficient QA-NIZK and SPS schemes with tight security reductions. Our QA-NIZK scheme is the first one that achieves both tight simulation soundness and constant proof size (in terms of number of group elements) at the same time, while the recent scheme from Abe et al. (ASIACRYPT 2018) achieved tight security with proof size linearly depending on the size of the language and the witness. Assuming the hardness of the Symmetric eXternal Diffie-Hellman (SXDH) problem, our scheme contains only 14 elements in the proof and remains independent of the size of the language and the witness. Moreover, our scheme has tighter simulation soundness than the previous schemes. Technically, we refine and extend a partitioning technique from a recent SPS scheme (Gay et al., EUROCRYPT 2018). Furthermore, we improve the efficiency of the tightly secure SPS schemes by using a relaxation of NIZK proof system for OR languages, called designated-prover NIZK system. Under the SXDH assumption, our SPS scheme contains 11 group elements in the signature, which is shortest among the tight schemes and is the same as an early non-tight scheme (Abe et al., ASIACRYPT 2012). Compared to the shortest known non-tight scheme (Jutla and Roy, PKC 2017), our scheme achieves tight security at the cost of 5 additional elements. All the schemes in this paper are proven secure based on the Matrix Diffie-Hellman assumptions (Escala et al., CRYPTO 2013). These are a class of assumptions which include the well-known SXDH and DLIN assumptions and provide clean algebraic insights to our constructions. To the best of our knowledge, our schemes achieve the best efficiency among schemes with the same functionality and security properties. This naturally leads to improvement of the efficiency of cryptosystems based on simulation-sound QA-NIZK and SPS.
Dynamic imposter based online instance matching for person search

Person search aims to locate the target person matching a given query from a list of unconstrained whole images. It is a challenging task due to the unavailable bounding boxes of pedestrians, limited samples for each labeled identity and large amount of unlabeled persons in existing datasets. To address these issues, we propose a novel end-to-end learning framework for person search. The proposed framework settles pedestrian detection and person re-identification concurrently. To achieve the goal of co-learning and utilize the information of unlabeled persons, a novel yet extremely efficient Dynamic Imposter based Online Instance Matching (DI-OIM) loss is formulated. The DI-OIM loss is inspired by the observation that pedestrians appearing in the same image obviously have different identities. Thus we assign the unlabeled persons with dynamic pseudo-labels. The pseudo-labeled persons along with the labeled persons can be used to learn powerful feature representations. Experiments on CUHK-SYSU and PRW datasets demonstrate that our method outperforms other state-of-the-art algorithms. Moreover, it is superior and efficient in terms of memory capacity comparing with existing methods.