Reviewerly/exHarmony huggingface dataset:
A Combination of Moment Descriptors, Fourier Transform and Matching Measures for Action Recognition Based on Shape

This paper presents an approach for human action recognition based on shape analysis. The purpose of the approach is to classify simple actions by applying shape descriptors to sequences of binary silhouettes. The recognition process consists of several main stages: shape representation, action sequence representation and action sequence classification. Firstly, each shape is represented using a selected shape descriptor. Secondly, shape descriptors of each sequence are matched, matching values are put into a vector and transformed into final action representation—we employ Fourier transform-based methods to obtain action representations equal in size. A classification into eight classes is performed using leave-one-out cross-validation and template matching approaches. We present results of the experiments on classification accuracy using moment-based shape descriptors (Zernike Moments, Moment Invariants and Contour Sequence Moments) and three matching measures (Euclidean distance, correlation coefficient and C1 correlation). Different combinations of the above-mentioned algorithms are examined in order to indicate the most effective one. The experiments show that satisfactory results are obtained when low-order Zernike Moments are used for shape representation and absolute values of Fourier transform are applied to represent action sequences. Moreover, the selection of matching technique strongly influences final classification results.
ModuleNet: A Convolutional Neural Network for Stereo Vision

Convolutional Neural Networks (CNN) has gained much attention for the solution of numerous vision problems including disparities calculation in stereo vision systems. In this paper, we present a CNN based solution for disparities estimation that builds upon a basic module (BM) with limited range of disparities that can be extended using various BM in parallel. Our BM can be understood as a segmentation by disparity and produces an output channel with the memberships for each disparity candidate, additionally the BM computes a channel with the out–of–range disparity regions. This extra channel allows us to parallelize several BM and dealing with their respective responsibilities. We train our model with the MPI Sintel dataset. The results show that ModuleNet, our modular CNN model, outperforms the baseline algorithm Efficient Large-scale Stereo Matching (ELAS) and FlowNetC achieving about a 80% of improvement.
Ringer: Systematic Mining of Malicious Domains by Dynamic Graph Convolutional Network

Malicious domains are critical resources in network security, behind which attackers hide malware to launch the malicious attacks. Therefore, blocking malicious domains is the most effective and practical way to combat and reduce hostile activities. There are three limitations in previous methods over domain classification: (1) solely based on local domain features which tend to be not robust enough; (2) lack of a large number of ground truth for model-training to get high accuracy; (3) statically learning on graph which is not scalable. In this paper, we present Ringer, a scalable method to detect malicious domains by dynamic Graph Convolutional Network (GCN). Ringer first uses querying behaviors or domain-IP resolutions to construct domain graphs, on which the dynamic GCN is leveraged to learn the node representations that integrate both information about node features and graph structure. And then, these high-quality representations are further fed to the full-connected neural network for domain classification. Notably, instead of global statically learning, we adopt time-based hash to cut graphs to small ones and inductively learn the embedding of nodes according to selectively sampling neighbors. We construct a series of experiments on a large ISP over two days and compare it with state of the arts. The results demonstrate that Ringer achieves excellent performance with a high accuracy of 96.8% on average. Additionally, we find thousands of potential malicious domains by semi-supervised learning.
Experiencer Detection and Automated Extraction of a Family Disease Tree from Medical Texts in Russian Language

Text descriptions in natural language are an essential part of electronic health records (EHRs). Such descriptions usually contain facts about patient’s life, events, diseases and other relevant information. Sometimes it may also include facts about their family members. In order to find the facts about the right person (experiencer) and convert the unstructured medical text into structured information, we developed a module of experiencer detection. We compared different vector representations and machine learning models to get the highest quality of 0.96 f-score for binary classification and 0.93 f-score for multi-classification. Additionally, we present the results plotting the family disease tree.
iffDetector: Inference-aware Feature Filtering for Object Detection

Modern CNN-based object detectors focus on feature configuration during training but often ignore feature optimization during inference. In this paper, we propose a new feature optimization approach to enhance features and suppress background noise in both the training and inference stages. We introduce a generic Inference-aware Feature Filtering (IFF) module that can easily be combined with modern detectors, resulting in our iffDetector. Unlike conventional open-loop feature calculation approaches without feedback, the IFF module performs closed-loop optimization by leveraging high-level semantics to enhance the convolutional features. By applying Fourier transform analysis, we demonstrate that the IFF module acts as a negative feedback that theoretically guarantees the stability of feature learning. IFF can be fused with CNN-based object detectors in a plug-and-play manner with negligible computational cost overhead. Experiments on the PASCAL VOC and MS COCO datasets demonstrate that our iffDetector consistently outperforms state-of-the-art methods by significant margins\footnote{The test code and model are anonymously available in https://github.com/anonymous2020new/iffDetector }.
An Adaptive Response Matching Network for Ranking Multi-turn Chatbot Responses

With the increasing popularity of personal assistant systems, it is crucial to build a chatbot that can communicate with humans and assist them to complete different tasks. A fundamental problem that any chatbots need to address is how to rank candidate responses based on previous utterances in a multi-turn conversation. A previous utterance could be either a past input from the user or a past response from the chatbot. Intuitively, a correct response needs to match well with both past responses and past inputs, but in a different way. Moreover, the matching process should depend on not only the content of the utterances but also domain knowledge. Although various models have been proposed for response matching, few of them studied how to adapt the matching mechanism to utterance types and domain knowledge. To address this limitation, this paper proposes an adaptive response matching network (ARM) to better model the matching relationship in multi-turn conversations. Specifically, the ARM model has separate response matching encoders to adapt to different matching patterns required by different utterance types. It also has a knowledge embedding component to inject domain-specific knowledge in the matching process. Experiments over two public data sets show that the proposed ARM model can significantly outperform the state of the art methods with much fewer parameters.
On Counterfactual Explanations under Predictive Multiplicity

Counterfactual explanations are usually obtained by identifying the smallest change made to an input to change a prediction made by a fixed model (hereafter called sparse methods). Recent work, however, has revitalized an old insight: there often does not exist one superior solution to a prediction problem with respect to commonly used measures of interest (e.g. error rate). In fact, often multiple different classifiers give almost equal solutions. This phenomenon is known as predictive multiplicity (Breiman, 2001; Marx et al., 2019). In this work, we derive a general upper bound for the costs of counterfactual explanations under predictive multiplicity. Most notably, it depends on a discrepancy notion between two classifiers, which describes how differently they treat negatively predicted individuals. We then compare sparse and data support approaches empirically on real-world data. The results show that data support methods are more robust to multiplicity of different models. At the same time, we show that those methods have provably higher cost of generating counterfactual explanations under one fixed model. In summary, our theoretical and empiricaln results challenge the commonly held view that counterfactual recommendations should be sparse in general.
Reward Tweaking: Maximizing the Total Reward While Planning for Short Horizons

In reinforcement learning, the discount factor $γ$ controls the agent's effective planning horizon. Traditionally, this parameter was considered part of the MDP; however, as deep reinforcement learning algorithms tend to become unstable when the effective planning horizon is long, recent works refer to $γ$ as a hyper-parameter -- thus changing the underlying MDP and potentially leading the agent towards sub-optimal behavior on the original task. In this work, we introduce \emph{reward tweaking}. Reward tweaking learns a surrogate reward function $\tilde r$ for the discounted setting that induces optimal behavior on the original finite-horizon total reward task. Theoretically, we show that there exists a surrogate reward that leads to optimality in the original task and discuss the robustness of our approach. Additionally, we perform experiments in high-dimensional continuous control tasks and show that reward tweaking guides the agent towards better long-horizon returns although it plans for short horizons.
What is Stablecoin?: A Survey on Its Mechanism and Potential as Decentralized Payment Systems

Our study provides a survey on how existing stablecoins-- cryptocurrencies aiming at price stabilization-- peg their value to other assets, from the perspective of Decentralized Payment Systems (DPSs). This attempt is important because there has been no preceding surveys focusing on the stablecoin as DPSs, i.e., the one aiming at not only price stabilization but also decentralization. Specifically, we first classified existing stablecoins into four types according to their collaterals (fiat, commodity, crypto, and non-collateralized) and pointed out the high potential of non-collateralized stablecoins as DPSs; then, we further classified existing non-collateralized stablecoins into two types according to their intervention layers (protocol, application) and confirmed details of their representative mechanisms. Utilizing concepts such as Quantity Theory of Money (QTM), Tobin tax, and speculative attack, our survey revealed the status quo where, despite the high potential of non-collateralized stablecoins, they have no standard mechanism to achieve the stablecoin for practical DPSs.
RESEARCH ON MOTION PREDICTION IN A HOUSE USING A MARKERLESS AND NON-CONTACT SENSOR

Biofied building is a building system that provides safe, secure and comfortable space for residents using a robot. The robot acquires the motions and positions of residents. However, it is difficult to control the robot not to disturb the residents’ activities. The purpose of this research is to incorporate motion prediction in addition to motion recognition. Many of the conventional methods use RNN (Recurrent Neural Networks), which is difficult to implement due to the necessity of big data and large calculation cost. Thus, we aim to propose a simple motion prediction method using a human body model based on dynamics.
PDPNN: Modeling User Personal Dynamic Preference for Next Point-of-Interest Recommendation

Next Point of Interest (POI) recommendation is an important aspect of information feeds for Location Based Social Networks (LSBNs). The boom in LSBN platforms such as Foursquare, Twitter, and Yelp has motivated a considerable amount of research focused on POI recommendations within the last decade. Inspired by the success of deep neural networks in many fields, researchers are increasingly interested in using neural networks such as Recurrent Neural Network (RNN) to make POI recommendation. Compared to traditional methods like Factorizing Personalized Markov Chain (FPMC) and Tensor Factorization (TF), neural network methods show great improvement in general sequences prediction. However, the user’s personal preference, which is crucial for personalized POI recommendation, is not addressed well in existing works. Moreover, the user’s personal preference is dynamic rather than static, which can guide predictions in different temporal and spatial contexts. To this end, we propose a new deep neural network model called Personal Dynamic Preference Neural Network(PDPNN). The core of the PDPNN model includes two parts: one part learns the user’s personal long-term preferences from the historical trajectories, and the other part learns the user’s short-term preferences from the current trajectory. By introducing a similarity function that evaluates the similarity between spatiotemporal contexts of user’s current trajectory and historical trajectories, PDPNN learns the user’s personal dynamic preference from user’s long-term and short-term preferences. We conducted experiments on three real-world datasets, and the results show that our model outperforms current well-known methods.
Analysis of Enterprise Management Information System Based on Cloud Computing

Abstract With the rapid development of Internet technology, many cement companies have gradually realized that the massive amount of information brought by the Internet can open up ideas for enterprise development and improve their economic benefits. This article decomposes the existing cement enterprise management information system hierarchically relying on cloud computing technology, and then discusses the idea of building an enterprise cloud management based on the cloud computing system, which provides a reference direction for achieving efficient and low-cost rapid development of cement enterprises.
Mixed and Augmented Reality Tools in the Medical Anatomy Curriculum.

The use of augmented reality (AR) in the medical field has grown since 2007-2013 and was first introduced in surgical departments. AR and mixed reality (MR) allow us to explore complex structures, observe phenomena that are difficult or impossible to see otherwise, and interact with the virtual structures they display. Recently, they are beginning to be adopted for education. This work examines whether new AR and MR technological tools, when used in the context of anatomical teaching, can allow for strengthening of teaching quality - for example by overcoming new or existing constraints such as the limited availability of dissection specimen. This work also considers how these technologies are to be applied efficiently and practically in teaching. An attempt is made to answer these questions in three stages: i) a non-systematic review of the literature ii) a review of augmented reality solutions for anatomy available on four different platforms iii) a 30-person study of the usability of augmented reality. The results show that there is potential for AR and MR to supplement anatomical teaching, but that traditional methods remain indispensable.
Learning to Optimize Autonomy in Competence-Aware Systems

Interest in semi-autonomous systems (SAS) is growing rapidly as a paradigm to deploy autonomous systems in domains that require occasional reliance on humans. This paradigm allows service robots or autonomous vehicles to operate at varying levels of autonomy and offer safety in situations that require human judgment. We propose an introspective model of autonomy that is learned and updated online through experience and dictates the extent to which the agent can act autonomously in any given situation. We define a competence-aware system (CAS) that explicitly models its own proficiency at different levels of autonomy and the available human feedback. A CAS learns to adjust its level of autonomy based on experience to maximize overall efficiency, factoring in the cost of human assistance. We analyze the convergence properties of CAS and provide experimental results for robot delivery and autonomous driving domains that demonstrate the benefits of the approach.
Building a Japanese Typo Dataset from Wikipedia’s Revision History

User generated texts contain many typos for which correction is necessary for NLP systems to work. Although a large number of typo–correction pairs are needed to develop a data-driven typo correction system, no such dataset is available for Japanese. In this paper, we extract over half a million Japanese typo–correction pairs from Wikipedia’s revision history. Unlike other languages, Japanese poses unique challenges: (1) Japanese texts are unsegmented so that we cannot simply apply a spelling checker, and (2) the way people inputting kanji logographs results in typos with drastically different surface forms from correct ones. We address them by combining character-based extraction rules, morphological analyzers to guess readings, and various filtering methods. We evaluate the dataset using crowdsourcing and run a baseline seq2seq model for typo correction.
Solovay–Kitaev Approximations of Special Orthogonal Matrices

The circuit-gate framework of quantum computing relies on the fact that an arbitrary quantum gate in the form of a unitary matrix of unit determinant can be approximated to a desired accuracy by a fairly short sequence of basic gates, of which the exact bounds are provided by the Solovay–Kitaev theorem. In this work, we show that a version of this theorem is applicable to orthogonal matrices with unit determinant as well, indicating the possibility of using orthogonal matrices for efficient computation. We further develop a version of the Solovay–Kitaev algorithm and discuss the computational experience.
Investigating the New Criminal Neighborhood: The Need for Dark Web Education for Law Enforcement Personnel

Responses in investigations of drug and sex traffic related crimes often originate in the Dark Web. Internet and cybercrimes are increasingly becoming reliant on criminal justice practitioners to help solve them. However, while the demand for criminal justice participation in cyber investigations increases daily, most law enforcement agencies or state law enforcement accrediting bodies are lagging in their educational and training opportunities for new police officers entering the law enforcement fields. This article discusses the need incorporate education courses covering the Dark and Deep Web in the law enforcement curriculum. A review of existing cyber-criminal justice programs in Texas (N=28) and nationally suggests that most colleges, with a cyber program, have yet to develop courses/programs in understanding and investigating the dark web on the internet. The federal government and a few companies have limited training on the Dark Web for law enforcement officers. The Dark Web serves as the new “Criminal Neighborhood” for illegal activity and needs to be better understood so it can be investigated. This research outlines the need for police officers and police investigators to learn the geographies of the Dark Web.
Label Noise in Context

Label noise-incorrectly or ambiguously labeled training examples-can negatively impact model performance.Although noise detection techniques have been around for decades, practitioners rarely apply them, as manual noise remediation is a tedious process.Examples incorrectly flagged as noise waste reviewers' time, and correcting label noise without guidance can be difficult.We propose LNIC, a noise-detection method that uses an example's neighborhood within the training set to (a) reduce false positives and (b) provide an explanation as to why the example was flagged as noise.We demonstrate on several short-text classification datasets that LNIC outperforms the state of the art on measures of precision and F 0.5 -score.We also show how LNIC's training set context helps a reviewer to understand and correct label noise in a dataset.The LNIC tool lowers the barriers to label noise remediation, increasing its utility for NLP practitioners.
AN ANALYSIS ON THE SELF-INITIATION SELF REPAIR STRATEGIES OF THE THIRD SEMESTER STUDENTS OF ENGLISH STUDY PROGRAM IN THE ORAL INTERACTION WITH THEIR LECTURER AT WIDYA MANDIRA CATHOLIC UNIVERSITY KUPANG IN ACADEMIC

This article studies self-initiation self-repair employed by the third semester students of English study program of Widya Mandira catholic university Kupang on speaking class, aims at finding out whether or not the third semester students of English study program do the self-repair initiation toward the trouble source they produce on the interaction they are engaged. The main purposes are to discover the types of trouble source that trigger the students’ self-initiation self-repair, the self-repair strategies performed by the students on the interaction with their lecturer, and identify the way how the students produce the self-initiation self-repair. The qualitative method and CA approach were employed as a theoretical framework. The results indicated that; types of trouble sources that trigger students’ self-initiation self-repair in the interaction with their lecturer on speaking class were vocabulary, pronunciation, and grammar. The self-initiation self-repair strategies performed by the students in their interaction with their lecturer on the speaking class were replacement, partial repetition of pronunciation, completion, correction, repetition by modifying intonation, modification, and rearranging. The self-initiation self-repair strategies are done in six ways namely repeating, replacing, modifying, correcting, completing, and rearranging.
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms.

We present a distributional approach to theoretical analyses of reinforcement learning algorithms for constant step-sizes. We demonstrate its effectiveness by presenting simple and unified proofs of convergence for a variety of commonly-used methods. We show that value-based methods such as TD($\lambda$) and $Q$-Learning have update rules which are contractive in the space of distributions of functions, thus establishing their exponentially fast convergence to a stationary distribution. We demonstrate that the stationary distribution obtained by any algorithm whose target is an expected Bellman update has a mean which is equal to the true value function. Furthermore, we establish that the distributions concentrate around their mean as the step-size shrinks. We further analyse the optimistic policy iteration algorithm, for which the contraction property does not hold, and formulate a probabilistic policy improvement property which entails the convergence of the algorithm.