Reviewerly/exHarmony huggingface dataset:
Decompositional Argument Mining: A General Purpose Approach for Argument Graph Construction

This work presents an approach decomposing propositions into four functional components and identify the patterns linking those components to determine argument structure.The entities addressed by a proposition are target concepts and the features selected to make a point about the target concepts are aspects.A line of reasoning is followed by providing evidence for the points made about the target concepts via aspects.Opinions on target concepts and opinions on aspects are used to support or attack the ideas expressed by target concepts and aspects.The relations between aspects, target concepts, opinions on target concepts and aspects are used to infer the argument relations.Propositions are connected iteratively to form a graph structure.The approach is generic in that it is not tuned for a specific corpus and evaluated on three different corpora from the literature: AAEC, AMT, US2016G1tv and achieved an F score of 0.79, 0.77 and 0.64, respectively.
One-shot coherence distillation with catalysts

The resource theory of quantum coherence is an important topic in quantum information science. Standard coherence distillation and dilution problems have been thoroughly studied. In this paper, we introduce and study the problem of one-shot coherence distillation with catalysts. In order to distill more coherence from a state of interest, a catalytic system can be involved and a jointly free operation is applied to both systems. The joint output state should be a maximally coherent state in tensor product with the unchanged catalysts, with some allowable fidelity error. We consider several different definitions of this problem. First, with a small fidelity error in both systems, we show that, even via the smallest free operation class (physically implementable incoherent operations), the distillable coherence of any state with no restriction on the catalysts is infinite, which is a ``coherence embezzling phenomenon.'' We then define and calculate a lower bound for the distillable coherence when the dimension of catalysts is restricted. Finally, in consideration of physical relevance, we define the ``perfect catalysts'' scenario where the catalysts are required to be pure and precisely unchanged. Interestingly, we show that in this setting catalysts basically provide no advantages in pure state distillation via incoherent operations and strictly incoherent operations under certain smoothing restriction. Our work enhances the understanding of catalytic effect in quantum resource theory.
Figure Eight at SemEval-2019 Task 3: Ensemble of Transfer Learning Methods for Contextual Emotion Detection

This paper describes our transfer learningbased approach to contextual emotion detection as part of SemEval-2019 Task 3. We experiment with transfer learning using pretrained language models (ULMFiT, OpenAI GPT, and BERT) and fine-tune them on this task.We also train a deep learning model from scratch using pre-trained word embeddings and BiLSTM architecture with attention mechanism.The ensembled model achieves competitive result, ranking ninth out of 165 teams.The result reveals that ULMFiT performs best due to its superior fine-tuning techniques.We propose improvements for future work.
Mention Recommendation in Twitter with Cooperative Multi-Agent Reinforcement Learning

In Twitter-like social networking services, the "@'' symbol can be used with the tweet to mention users whom the user wants to alert regarding the message. An automatic suggestion to the user of a small list of candidate names can improve communication efficiency. Previous work usually used several most recent tweets or randomly select historical tweets to make an inference about this preferred list of names. However, because there are too many historical tweets by users and a wide variety of content types, the use of several tweets cannot guarantee the desired results. In this work, we propose the use of a novel cooperative multi-agent approach to mention recommendation, which incorporates dozens of more historical tweets than earlier approaches. The proposed method can effectively select a small set of historical tweets and cooperatively extract relevant indicator tweets from both the user and mentioned users. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods.
SINAI at SemEval-2019 Task 6: Incorporating lexicon knowledge into SVM learning to identify and categorize offensive language in social media

Offensive language has an impact across society.The use of social media has aggravated this issue among online users, causing suicides in the worst cases.For this reason, it is important to develop systems capable of identifying and detecting offensive language in text automatically.In this paper, we developed a system to classify offensive tweets as part of our participation in SemEval-2019 Task 6: Offen-sEval.Our main contribution is the integration of lexical features in the classification using the SVM algorithm.
DDTMS: Dirichlet-Distribution-Based Trust Management Scheme in Internet of Things

Information security is important for the Internet of Things (IoT), the security of front-end information is especially critical. With this consideration, the integrity and authenticity of sensed information directly impacts the results of back-end big data and cloud computing. The front end of the IoT faces many security threats. In these security threats, internal attacks cannot be defended by traditional security schemes, such as encryption/decryption, authentication, and so on. Our contribution in this paper is that a DirichletDistribution-based Trust Management Scheme (DDTMS) in IoT is proposed to defend against the internal attacks. The novelty of our scheme can be summed up in two aspects. The first aspect considers the actual physical channel to extend the node behaviors from success and failure to success, failure, and uncertainty, meanwhile, the corresponding behaviors are weighted by using &lt;ws, wf, wu&gt;, in order to limit the measurement of each behavior by custom. In the second aspect, we introduce a third-party recommendation to calculate the trust value more acurrately. The simulated results demonstrate that DDTMS is better than the other two reputation models (Beta distribution and Gaussian distribution),and can more accurately describe the reputation changes to detect the malicious node quickly.
Optimized RRT-A* Path Planning Method for Mobile Robots in Partially Known Environment

This paper presents optimized rapidly exploring random trees A* (ORRT-A*) method to improve the performance of RRT-A* method to compute safe and optimal path with low time complexity for autonomous mobile robots in partially known complex environments. ORRT-A* method combines morphological dilation, goal-biased RRT, A* and cubic spline algorithms. Goal-biased RRT is modified by introducing additional step-size to speed up the generation of the tree towards the goal after which A* is applied to obtain the shortest path. Morphological dilation technique is used to provide safety for the robots while cubic spline interpolation is used to smoothen the path for easy navigation. Results indicate that ORRT-A* method demonstrates improved path quality compared to goal-biased RRT and RRT-A* methods. ORRT-A* is therefore a promising method in achieving autonomous ground vehicle navigation in unknown environments
Introducing Multi Shippers Mechanism for Decentralized Cash on Delivery System

One of the major problems of e-commerce globally is the selling and buying of goods among the parties over the Internet in which the traders may not trust their partners.Cash on delivery allows customers to pay in cash when the product is delivered to their home or a location they choose.This is sometimes called a payment system because customers receive goods before making a payment.This paper investigates a critical verification process issue in the cash on delivery system.In particular, we propose a multi shippers mechanism, which consists of blockchain technology, smart contracts and hyperledger fabric platform to achieve distributed and trustworthy verification across participants in the decentralized markets.Our proposed mechanism is given to not only ensure the benefits of the seller but also prevent shipper's fraudulent.The solution leverages the consistency and robustness of decentralized markets where trust is flexible and effectively controlled.To demonstrate the application and implementation of the proposed framework, we conduct several case studies on real-world transaction datasets from a local computer retailer.We also provide our sources codes for further reproducibility and development.Our conclusion is that the continued integration of multi-shipper mechanism and blockchain technology in the decentralized markets will cause significant transformations across several disciplines.
Edmodo mobile: developing e-module biology cell for online learning community

Observation result in cell biology (CB) learning showed that most students assumed cell biology as a complex and abstract material. The level of conceptual understanding was still relatively low observable from inadequate test results. Misconception on cell structure as a dense and non-porous structure was reported; this can be seen from students’ drawing on cell membranes. One solution to overcome the above problem was to develop contemporary teaching materials in the form of e-module based on Edmodo mobile (based android) so that students can easily access them by their smartphone. The research objective was to develop an Edmodo android cell biology module. Research and development (R&amp;D) method were applied in this study. The development model used was Thiagarajan 4D, which is defined, design, and develop. The instruments used were the e-module validation questionnaire by experts (material and media experts, practitioners) and the feasibility test questionnaire on 24 students. The result showed that Edmodo mobile on CB developed through Thiagarajan 4D has valid criteria. Thus, it can be used in cell biology learning.
Photon statistics of a double quantum dot micromaser: Quantum treatment

A semiconductor single-atom micromaser consists of a microwave cavity coupled to a gain medium, a double quantum dot driven out of equilibrium by a bias voltage. The masing threshold of this system was recently probed by measuring photon statistics in the cavity [Y-Y. Liu \textit{et al}, Phys. Rev. Lett. 119, 097702 (2017)]. In this paper, we develop an in-depth, rigorous understanding of this experiment and related works. First, we use a semiclassical theory and study transmission spectroscopy. This approach allows us to derive the masing threshold condition for arbitrary temperature and voltage bias, and expose microscopic principles required for realizing photon gain and thereby a photon amplifier. Next, by employing the quantum master equation approach we extend the Scully-Lamb quantum theory of a laser to the present setup, and investigate the statistics of emitted photons below and above the masing threshold as a function of experimentally tunable parameters. Although our focus is primarily on hybrid quantum dot circuit - quantum electrodynamics systems, our approach is adaptable to other light-matter systems where the gain medium consists of a mesoscopic structure.
On Inductive Abilities of Latent Factor Models for Relational Learning

&#x0D; &#x0D; &#x0D; Latent factor models are increasingly popular for modeling multi-relational knowledge graphs. By their vectorial nature, it is not only hard to interpret why this class of models works so well, but also to understand where they fail and how they might be improved. We conduct an experimental survey of state-of-the-art models, not towards a purely comparative end, but as a means to get insight about their inductive abilities. To assess the strengths and weaknesses of each model, we create simple tasks that exhibit first, atomic properties of binary relations, and then, common inter-relational inference through synthetic genealogies. Based on these experimental results, we propose new research directions to improve on existing models.&#x0D; &#x0D; &#x0D;
Agent Modeling as Auxiliary Task for Deep Reinforcement Learning

In this paper we explore how actor-critic methods in deep reinforcement learning, in particular Asynchronous Advantage Actor-Critic (A3C), can be extended with agent modeling. Inspired by recent works on representation learning and multiagent deep reinforcement learning, we propose two architectures to perform agent modeling: the first one based on parameter sharing, and the second one based on agent policy features. Both architectures aim to learn other agents’ policies as auxiliary tasks, besides the standard actor (policy) and critic (values). We performed experiments in both cooperative and competitive domains. The former is a problem of coordinated multiagent object transportation and the latter is a two-player mini version of the Pommerman game. Our results show that the proposed architectures stabilize learning and outperform the standard A3C architecture when learning a best response in terms of expected rewards.
Multi-task and multi-view training for end-to-end relation extraction

Transfer learning has shown promising results for transferring knowledge ofsource tasks to target tasks in natural language processing (NLP). In this paper, we investigate a multi-task and multi-view learning (MTMVL) framework for end-to-end neural relation extraction, using large noisy data as one auxiliary task for improving manually labeled data, and a dependency parsing objective as a second auxiliary task for leveraging syntax. Two views are taken for relation extraction, consisting of a joint tagging view and a novel context relation view. To capture sentence multi-level information, we explore a weighted average approach to represent shared deep bi-directional recurrent neural networks and encourage auxiliary tasks to accommodate relation extraction task via a time-related scheduled sampling strategy. We evaluate MTMVL framework on manual-labeled ACE2005 dataset, and experimental results show that our model outperforms the state-of-the-art methods, which indicates the effectiveness of multi-auxiliary information for knowledge transferring.
A Novel Quantum Visual Secret Sharing Scheme

Inspired by Naor et al.'s visual secret sharing (VSS) scheme, a novel n out of n quantum visual secret sharing (QVSS) scheme is proposed, which consists of two phases: sharing process and recovering process.In the first process, the color information of each pixel from the original secret image is encoded into an n-qubit superposition state by using the strategy of quantum expansion instead of classical pixel expansion, and then these n qubits are distributed as shares to n participants, respectively.During the recovering process, all participants cooperate to collect these n shares of each pixel together, then perform the corresponding measurement on them, and execute the n-qubit XOR operation to recover each pixel of the secret image.The proposed scheme has the advantage of single-pixel parallel processing that is not available in the existing analogous quantum schemes and perfectly solves the problem that in the classic VSS schemes the recovered image has the loss in resolution.Moreover, its experiment implementation with the IBM Q is conducted to demonstrate the practical feasibility.
Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals

Explanations in machine learning come in many forms, but a consensus regarding their desired properties is still emerging. In our work we collect and organise these explainability desiderata and discuss how they can be used to systematically evaluate properties and quality of an explainable system using the case of class-contrastive counterfactual statements. This leads us to propose a novel method for explaining predictions of a decision tree with counterfactuals. We show that our model-specific approach exploits all the theoretical advantages of counterfactual explanations, hence improves decision tree interpretability by decoupling the quality of the interpretation from the depth and width of the tree.
Leap-LSTM: Enhancing Long Short-Term Memory for Text Categorization

Recurrent Neural Networks (RNNs) are widely used in the field of natural language processing (NLP), ranging from text categorization to question answering and machine translation. However, RNNs generally read the whole text from beginning to end or vice versa sometimes, which makes it inefficient to process long texts. When reading a long document for a categorization task, such as topic categorization, large quantities of words are irrelevant and can be skipped. To this end, we propose Leap-LSTM, an LSTM-enhanced model which dynamically leaps between words while reading texts. At each step, we utilize several feature encoders to extract messages from preceding texts, following texts and the current word, and then determine whether to skip the current word. We evaluate Leap-LSTM on several text categorization tasks: sentiment analysis, news categorization, ontology classification and topic classification, with five benchmark data sets. The experimental results show that our model reads faster and predicts better than standard LSTM. Compared to previous models which can also skip words, our model achieves better trade-offs between performance and efficiency.
Densely Connected Attention Flow for Visual Question Answering

Learning effective interactions between multi-modal features is at the heart of visual question answering (VQA). A common defect of the existing VQA approaches is that they only consider a very limited amount of interactions, which may be not enough to model latent complex image-question relations that are necessary for accurately answering questions. Therefore, in this paper, we propose a novel DCAF (Densely Connected Attention Flow) framework for modeling dense interactions. It densely connects all pairwise layers of the network via Attention Connectors, capturing fine-grained interplay between image and question across all hierarchical levels. The proposed Attention Connector efficiently connects the multi-modal features at any two layers with symmetric co-attention, and produces interaction-aware attention features. Experimental results on three publicly available datasets show that the proposed method achieves state-of-the-art performance.
Sybil-Resilient Reality-Aware Social Choice

Sybil attacks, in which fake or duplicate identities (a.k.a., Sybils) infiltrate an online community, pose a serious threat to such communities, as they might tilt community-wide decisions in their favor. While the extensive research on sybil identification may help keep the fraction of sybils in such communities low, it cannot however ensure their complete eradication. Thus, our goal here is to enhance social choice theory with effective group decision mechanisms for communities with bounded sybil penetration. Inspired by Reality-Aware Social Choice, we use the status quo as the anchor of Sybil Resilience, characterized by Sybil Safety -- the inability of sybils to change the status quo against the will of the genuine agents, and Sybil Liveness -- the ability of the genuine agents to change the status quo against the will of the sybils. We consider the social choice settings of deciding on a single proposal, on multiple proposals, and on updating a parameter. For each, we present social choice rules that are sybil-safe and, under certain conditions, satisfy sybil-liveness.
Cross-Validated Locally Polynomial Modeling for 2-D/3-D Gaze Tracking With Head-Worn Devices

In the context of wearable gaze tracking techniques, the problems of two-dimensional (2-D) and three-dimensional (3-D) gaze estimation can be viewed as inferring 2-D epipolar lines and 3-D visual axes from eye monitoring cameras. To this end, in this article, a simple local polynomial model is proposed to back-project a pupil center onto its corresponding visual axis. Based on this approximation, a homographylike relation is derived in a local manner, and via the Leave-One-Out cross-validation criterion, training gaze samples at one certain depth is leveraged to partition entire input space into multiple overlapping subregions. Then, the gaze data at another depth are utilized to recover the epipolar point, i.e., the image eyeball center. Thus, given a pupil image, the corresponding epipolar line can be determined by the resolved homographylike mapping and the epipolar point. By using the same partition structure, 3-D gaze prediction model can be inferred by solving a nonlinear optimization problem, which aims to minimize the angular disparities between training visual directions and prediction ones. Meanwhile, it is necessary to form a good starting point and suitable constraints for the optimization problem. Otherwise, it may end up with trivial solutions, i.e., faraway eye positions. To facilitate the practical implementation of our proposed method, we also analyze how the spatial distribution of calibration points impacts the model learning accuracy. The experiment results justify the effectiveness of our proposed gaze estimation method for both the normal vision and eyewear users.
On Principled Entropy Exploration in Policy Optimization

In this paper, we investigate Exploratory Conservative Policy Optimization (ECPO), a policy optimization strategy that improves exploration behavior while assuring monotonic progress in a principled objective. ECPO conducts maximum entropy exploration within a mirror descent framework, but updates policies using reversed KL projection. This formulation bypasses undesirable mode seeking behavior and avoids premature convergence to sub-optimal policies, while still supporting strong theoretical properties such as guaranteed policy improvement. Experimental evaluations demonstrate that the proposed method significantly improves practical exploration and surpasses the empirical performance of state-of-the art policy optimization methods in a set of benchmark tasks.
Learning Shared Knowledge for Deep Lifelong Learning using Deconvolutional Networks

Current mechanisms for knowledge transfer in deep networks tend to either share the lower layers between tasks, or build upon representations trained on other tasks. However, existing work in non-deep multi-task and lifelong learning has shown success with using factorized representations of the model parameter space for transfer, permitting more flexible construction of task models. Inspired by this idea, we introduce a novel architecture for sharing latent factorized representations in convolutional neural networks (CNNs). The proposed approach, called a deconvolutional factorized CNN, uses a combination of deconvolutional factorization and tensor contraction to perform flexible transfer between tasks. Experiments on two computer vision data sets show that the DF-CNN achieves superior performance in challenging lifelong learning settings, resists catastrophic forgetting, and exhibits reverse transfer to improve previously learned tasks from subsequent experience without retraining.