Reviewerly/exHarmony huggingface dataset:
The drivers of Bitcoin trading volume in selected emerging countries

While most of the debates about cryptocurrencies are centered on the global Bitcoin market, in this article, we focus on local Bitcoin trading volume in 21 emerging countries. In particular, we attempt to determine the drivers of Bitcoin trading volume in these countries over the period August 1st, 2015 – June 2nd, 2018. Based on VECM and ARDL models, we find evidence of significant relationship between the local Bitcoin trading volume in each country and the associated banking system access, especially in the short-run. Moreover, altcoins (Ethereum, Ripple) prices are shown to affect positively and significantly the local Bitcoin trading volume for most countries in the long-run (VECM results) and the short-run (ARDL results).
Learning from Longitudinal Face Demonstration—Where Tractable Deep Modeling Meets Inverse Reinforcement Learning

Modeling the face aging process is a challenging task due to large and non-linear variations present in different stages of face development. This paper presents a deep model approach for face age progression that can efficiently capture the non-linear aging process and automatically synthesize a series of age-progressed faces in various age ranges. In this approach, we first decompose the long-term age progress into a sequence of short-term changes and model it as a face sequence. The Temporal Deep Restricted Boltzmann Machines based age progression model together with the prototype faces are then constructed to learn the aging transformation between faces in the sequence. In addition, to enhance the wrinkles of faces in the later age ranges, the wrinkle models are further constructed using Restricted Boltzmann Machines to capture their variations in different facial regions. The geometry constraints are also taken into account in the last step for more consistent age-progressed results. The proposed approach is evaluated using various face aging databases, i.e. FG-NET, Cross-Age Celebrity Dataset (CACD) and MORPH, and our collected large-scale aging database named AginG Faces in the Wild (AGFW). In addition, when ground-truth age is not available for input image, our proposed system is able to automatically estimate the age of the input face before aging process is employed.
Blind Quality Assessment for Cartoon Images

Current blind image quality assessment (BIQA) algorithms are mainly designed for natural images. Unfortunately, cartoon and cartoon-like images are quite different from natural images. Hence, recent BIQA methods are not very robust to cartoon images. In this paper, we propose a specific BIQA algorithm designed for cartoon images, which consists of the following terms. First, a cartoon image is divided into edge areas and nonedge areas via a Tchebichef moment (TM)-based process. Second, a multiorder sharpness statistic term is used to measure the quality of the edges, and a sharpness statistic prior model of high-quality (HQ) cartoon images is built. Finally, a local encoding statistic term is adopted to describe the textural complexity in the nonedge areas, and a texture statistic prior model is also established. The experimental results on the cartoon image datasets demonstrate that the proposed method can accurately evaluate the visual quality of cartoon images and is more suitable for cartoon scenarios than some traditional BIQA algorithms.
A reconstruction strategy based on CSC registration for turbine blades repairing

Model reconstruction of damaged parts plays a key role in turbine blade repair. In this research, a new turbine blade model reconstruction strategy is proposed to deal with turbine blade with multiple damage factors simultaneously, such as wear, crack, material missing and deformation. To handle the blade deformation, the blade model is sectioned by a series of planes to generate cross section curves (CSCs). Measured CSCs in undamaged area of blade body are generated. The rigid and deformation transformations between model and measured CSCs are calculated, which is called registration. Using the registration results, the damaged or missing portions of the measured CSCs are reconstructed using interpolation or extrapolation. Finally, the complete model is reconstructed through these measured CSCs. The whole reconstruction process of a virtual damaged turbine blade with more than 1.0 mm deformation is simulated to verify the effectiveness. After blade reconstruction, using a set of sample points, the deviation between the damaged model and reconstructed blade model is below 0.011 mm, which is within 1% of deformation compared with that between the original and the damaged model. We conclude that the proposed reconstruction method can effectively handle multiple damage factors simultaneously, and the reconstructed model can be used as the base for the subsequent blade repair.
Learning Relational Representations with Auto-encoding Logic Programs

Deep learning methods capable of handling relational data have proliferated over the past years. In contrast to traditional relational learning methods that leverage first-order logic for representing such data, these methods aim at re-representing symbolic relational data in Euclidean space. They offer better scalability, but can only approximate rich relational structures and are less flexible in terms of reasoning. This paper introduces a novel framework for relational representation learning that combines the best of both worlds. This framework, inspired by the auto-encoding principle, uses first-order logic as a data representation language, and the mapping between the the original and latent representation is done by means of logic programs instead of neural networks. We show how learning can be cast as a constraint optimisation problem for which existing solvers can be used. The use of logic as a representation language makes the proposed framework more accurate (as the representation is exact, rather than approximate), more flexible, and more interpretable than deep learning methods. We experimentally show that these latent representations are indeed beneficial in relational learning tasks.
Hardening networks against strategic attackers using attack graph games

We consider the problem faced by a network administrator (defender) when deploying limited security resources to protect a network against a strategic attacker. To evaluate the effectiveness of a defense strategy, one must consider possible counterattacks that an attacker can choose. We use game theory to model the interaction between the defender and the attacker. Game theory provides relevant concepts and algorithms for computing optimal strategies in environments with multiple decision makers. To model the space of attacker’s possible actions, we use attack graphs, that compactly represent all known sequences of attacker’s action that may lead to successful attack for a given network. We demonstrate our approach on a specific type of defense actions, where the defender deploys deceptive hosts and services (honeypots) to detect and mitigate attacks. We assume the worst-case attacker who has a complete knowledge of the (typically randomized) defense strategy. We seek the optimal defense strategy against this attacker in the form of a Stackelberg equilibrium. Computing this solution exactly using standard techniques has limited scalability, so we investigate several approaches for increasing scalability to realistic problems. We introduce optimization methods for finding exact solutions for these games and then propose a variety of polynomial heuristic algorithms that scale to significantly larger games. We analyze the scalability and the quality of these heuristic solutions on realistic network topologies. We show that the strategies found by the heuristics are often near-optimal and that they outperform non-game-theoretic baselines. Finally, we show how attack graph games can be used to answer various research questions relevant to network security administrators.
A subgraph-representation-based method for answering complex questions over knowledge bases

Knowledge-based question answering has attracted a lot of attention in the research communities of natural language processing and information retrieval. However, existing studies do not adequately address the problem of answering complex questions which involve multiple entities and require extraction of facts from multiple relations. To address this issue, we propose a novel approach which learns the distributional representations of questions and candidate answers in a unified deep-learning framework based on directed-acyclic-graph-structured long short-term memory and memory networks. Specifically, the questions are encoded to match candidate directed acyclic subgraphs of the knowledge base, which are able to include information related to multiple entities and relations in the complex questions. The experimental results show that the proposed approach outperforms other methods on the widely used dataset SPADES, especially when dealing with complex questions with multiple entities.
EWGAN: Entropy-Based Wasserstein GAN for Imbalanced Learning

In this paper, we propose a novel oversampling strategy dubbed Entropy-based Wasserstein Generative Adversarial Network (EWGAN) to generate data samples for minority classes in imbalanced learning. First, we construct an entropyweighted label vector for each class to characterize the data imbalance in different classes. Then we concatenate this entropyweighted label vector with the original feature vector of each data sample, and feed it into the WGAN model to train the generator. After the generator is trained, we concatenate the entropy-weighted label vector with random noise feature vectors, and feed them into the generator to generate data samples for minority classes. Experimental results on two benchmark datasets show that the samples generated by the proposed oversampling strategy can help to improve the classification performance when the data are highly imbalanced. Furthermore, the proposed strategy outperforms other state-of-the-art oversampling algorithms in terms of the classification accuracy.
Explainable, Normative, and Justified Agency

In this paper, we pose a new challenge for AI researchers – to develop intelligent systems that support justified agency. We illustrate this ability with examples and relate it to two more basic topics that are receiving increased attention – agents that explain their decisions and ones that follow societal norms. In each case, we describe the target abilities, consider design alternatives, note some open questions, and review prior research. After this, we return to justified agency, offering a hypothesis about its relation to explanatory and normative behavior. We conclude by proposing testbeds and experiments to evaluate this empirical claim and encouraging other researchers to contribute to this crucial area.
Ad Hoc Teamwork With Behavior Switching Agents

As autonomous AI agents proliferate in the real world, they will increasingly need to cooperate with each other to achieve complex goals without always being able to coordinate in advance. This kind of cooperation, in which agents have to learn to cooperate on the fly, is called ad hoc teamwork. Many previous works investigating this setting assumed that teammates behave according to one of many predefined types that is fixed throughout the task. This assumption of stationarity in behaviors, is a strong assumption which cannot be guaranteed in many real-world settings. In this work, we relax this assumption and investigate settings in which teammates can change their types during the course of the task. This adds complexity to the planning problem as now an agent needs to recognize that a change has occurred in addition to figuring out what is the new type of the teammate it is interacting with. In this paper, we present a novel Convolutional-Neural-Network-based Change point Detection (CPD) algorithm for ad hoc teamwork. When evaluating our algorithm on the modified predator prey domain, we find that it outperforms existing Bayesian CPD algorithms.
Multi-target tracking of surveillance video with differential YOLO and DeepSort

With the shortcomings of traditional algorithm in video surveillance on low accuracy, poor robustness and unable achieved real-time tracking for multi-targets, this paper presents a Multi-target tracking algorithm, DeepSort, on the base of deep neural network to achieve the end-to-end surveillance video multi-personal target real-time detection and tracking. The high accuracy of target detection by YOLO algorithm provides DeepSort with weaker dependence on detection results, lower interference of occlusion and illumination and improved tracking robustness. Moreover, due to the high redundancy of the surveillance video itself, the difference filter is used to screen the video frames with no foreground targets and small changes, so as to reduce the detection cost and improve the detection and tracking speed. The experimental evaluation of the video surveillance dataset NPLR, the average MOTA of this algorithm is 68.7, the highest value is 86.8; the average speed is 81.6Hz, the highest value is 140Hz. It shows that the end-to-end algorithm is feasible and effective.
The impact of knowledge sharing on innovative work behavior of Vietnam telecommunications enterprises employees

Innovative work behavior can stem from the sharing of knowledge between individuals and colleagues.Based on sample size survey of 396 Vietnam telecommunication employees with Cronbach's Alpha analysis, exploratory factor analysis (EFA), correlation analysis, regression analysis, the study shows that the impact of knowledge sharing with two central processes (knowledge donation and knowledge collection) on innovative work behavior was meaningful.Compared with the process of knowledge donation, the process of knowledge collection has a stronger impact on innovative work behavior (β = 0.213 and 0.204 respectively).Besides, the authors propose some suggestions for telecommunications enterprise managers to influence the employee's behavioral innovation through the impact of knowledge sharing.
Low-Light Image Enhancement with Attention and Multi-level Feature Fusion

Low-light image enhancement has made impressive progress with convolutional neural networks (CNNs). However, most existing CNNs-based networks ignore the importance of feature channels and multi-level features. To address these issues, we propose a novel low-light image enhancement network. First, we establish Feature Extraction Block (FEB) to extract features and Feature Fusion Block (FFB) to fuse multi-level features. Then, we adopt a compact channel attention module to re-define the channel importance of input features at the beginning of each Feature Extraction Block (FEB) and Feature Fusion Block (FFB). Besides, we adopt two types of low-light image datasets for training, concluding synthetic images and real-world images. Experiments show that our new network makes competitive progress for low-light image enhancement compared with the state-of-the-art methods.
Deep Learning Based Pedestrian Detection at Distance in Smart Cities

Generative adversarial networks (GANs) have been promising for many computer vision problems due to their powerful capabilities to enhance the data for training and test. In this paper, we leveraged GANs and proposed a new architecture with a cascaded Single Shot Detector (SSD) for pedestrian detection at distance, which is yet a challenge due to the varied sizes of pedestrians in videos at distance. To overcome the low-resolution issues in pedestrian detection at distance, DCGAN is employed to improve the resolution first to reconstruct more discriminative features for a SSD to detect objects in images or videos. A crucial advantage of our method is that it learns a multi-scale metric to distinguish multiple objects at different distances under one image, while DCGAN serves as an encoder-decoder platform to generate parts of an image that contain better discriminative information. To measure the effectiveness of our proposed method, experiments were carried out on the Canadian Institute for Advanced Research (CIFAR) dataset, and it was demonstrated that the proposed new architecture achieved a much better detection rate, particularly on vehicles and pedestrians at distance, making it highly suitable for smart cities applications that need to discover key objects or pedestrians at distance.
An Energy-Efficient Method for Human Activity Recognition with Segment-Level Change Detection and Deep Learning

Human activity recognition (HAR), which is important in context awareness services, needs to occur continuously in daily life, owing to which an energy-efficient method is needed. However, because human activities have a longer cycle than HAR methods, which have analysis cycles of a few seconds, continuous classification of human activities using these methods is computationally and energy inefficient. Therefore, we propose segment-level change detection to identify activity change with very low computational complexity. Additionally, a fully convolutional network (FCN) with a high recognition rate is used to classify the activity only when activity change occurs. We compared the accuracy and energy consumption of the proposed method with that of a method based on a convolutional neural network (CNN) by using a public dataset on different embedded platforms. The experimental results showed that, although the recognition rate of the proposed FCN model is similar to that of the CNN model, the former requires only 10% of the network parameters of the CNN model. In addition, our experiments to measure the energy consumption on the embedded platforms showed that the proposed method uses as much as 6.5 times less energy than the CNN-based method when only HAR energy consumption is compared.
Towards Participatory Design of Social Robots

With an increase in research and development of social robotics and commercial robots entering the market, there is a need for design tools that enable non-experts to design, build and use customized social robots. Human Computer Interaction researchers have a rich evolving tool-set when it comes to user-centered design. To encourage the use of user-centered design techniques in early design iterations of social robots, we propose the use of Opsoro. We present a case study which exhibits participatory design sessions using a Do-It-Yourself platform to enable the creation of social robots with non-experts.
Decreasing motion sickness by mixing different techniques

We investigated the effectiveness of galvanic cutaneous stimulation (GCS) and auditory stimulation (AS) together and separately in mitigating motion sickness (MS). Forty-eight drivers (twenty-two men; mean age = 21.58 years) participated in a driving simulation experiment. We compared the total scores of the Simulator Sickness Questionnaire (SSQ) across four different stimulation conditions (GCS, AS, Mixed GCS-AS and no stimulation as a baseline condition). We provided evidence that mixing techniques mitigates MS owing to an improvement in body balance; furthermore, mixing techniques improves driving behavior more effectively than GCS and AS in isolation. We encourage the use of the two techniques together to decrease MS.
Hierarchical Document Encoder for Parallel Corpus Mining

We explore using multilingual document embeddings for nearest neighbor mining of parallel data.Three document-level representations are investigated: (i) document embeddings generated by simply averaging multilingual sentence embeddings; (ii) a neural bagof-words (BoW) document encoding model; (iii) a hierarchical multilingual document encoder (HiDE) that builds on our sentence-level model.The results show document embeddings derived from sentence-level averaging are surprisingly effective for clean datasets, but suggest models trained hierarchically at the document-level are more effective on noisy data.Analysis experiments demonstrate our hierarchical models are very robust to variations in the underlying sentence embedding quality.Using document embeddings trained with HiDE achieves state-of-the-art performance on United Nations (UN) parallel document mining, 94.9% P@1 1 for en-fr and 97.3% P@1 for en-es.
Learning Hierarchical Priors in VAEs

We propose to learn a hierarchical prior in the context of variational autoencoders to avoid the over-regularisation resulting from a standard normal prior distribution. To incentivise an informative latent representation of the data, we formulate the learning problem as a constrained optimisation problem by extending the Taming VAEs framework to two-level hierarchical models. We introduce a graph-based interpolation method, which shows that the topology of the learned latent representation corresponds to the topology of the data manifold---and present several examples, where desired properties of latent representation such as smoothness and simple explanatory factors are learned by the prior.
Multilabel reductions: what is my loss optimising?

Multilabel classification is a challenging problem arising in applications ranging from information retrieval to image tagging. A popular approach to this problem is to employ a reduction to a suitable series of binary or multiclass problems (e.g., computing a softmax based cross-entropy over the relevant labels). While such methods have seen empirical success, less is understood about how well they approximate two fundamental performance measures: precision@$k$ and recall@$k$. In this paper, we study five commonly used reductions, including the one-versus-all reduction, a reduction to multiclass classification, and normalised versions of the same, wherein the contribution of each instance is normalised by the number of relevant labels. Our main result is a formal justification of each reduction: we explicate their underlying risks, and show they are each consistent with respect to either precision or recall. Further, we show that in general no reduction can be optimal for both measures. We empirically validate our results, demonstrating scenarios where normalised reductions yield recall gains over unnormalised counterparts.
Transferring Knowledge from Discourse to Arguments: A Case Study with Scientific Abstracts

In this work we propose to leverage resources available with discourse-level annotations to facilitate the identification of argumentative components and relations in scientific texts, which has been recognized as a particularly challenging task.In particular, we implement and evaluate a transfer learning approach in which contextualized representations learned from discourse parsing tasks are used as input of argument mining models.As a pilot application, we explore the feasibility of using automatically identified argumentative components and relations to predict the acceptance of papers in computer science venues.In order to conduct our experiments, we propose an annotation scheme for argumentative units and relations and use it to enrich an existing corpus with an argumentation layer. 1