Reviewerly/exHarmony huggingface dataset:
Language Discrimination and Transfer Learning for Similar Languages: Experiments with Feature Combinations and Adaptation

This paper describes the work done by team tearsofjoy participating in the VarDial 2019 Evaluation Campaign. We developed two systems based on Support Vector Machines: SVM with a flat combination of features and SVM ensembles. We participated in all language/dialect identification tasks, as well as the Moldavian vs. Romanian cross-dialect topic identification (MRC) task. Our team achieved first place in German Dialect identification (GDI) and MRC subtasks 2 and 3, second place in the simplified variant of Discriminating between Mainland and Taiwan variation of Mandarin Chinese (DMT) as well as Cuneiform Language Identification (CLI), and third and fifth place in DMT traditional and MRC subtask 1 respectively. In most cases, the SVM with a flat combination of features performed better than SVM ensembles. Besides describing the systems and the results obtained by them, we provide a tentative comparison between the feature combination methods, and present additional experiments with a method of adaptation to the test set, which may indicate potential pitfalls with some of the data sets.
Learning to Compose Topic-Aware Mixture of Experts for Zero-Shot Video Captioning

Although promising results have been achieved in video captioning, existing models are limited to the fixed inventory of activities in the training corpus, and do not generalize to open vocabulary scenarios. Here we introduce a novel task, zeroshot video captioning, that aims at describing out-of-domain videos of unseen activities. Videos of different activities usually require different captioning strategies in many aspects, i.e. word selection, semantic construction, and style expression etc, which poses a great challenge to depict novel activities without paired training data. But meanwhile, similar activities share some of those aspects in common. Therefore, we propose a principled Topic-Aware Mixture of Experts (TAMoE) model for zero-shot video captioning, which learns to compose different experts based on different topic embeddings, implicitly transferring the knowledge learned from seen activities to unseen ones. Besides, we leverage external topic-related text corpus to construct the topic embedding for each activity, which embodies the most relevant semantic vectors within the topic. Empirical results not only validate the effectiveness of our method in utilizing semantic knowledge for video captioning, but also show its strong generalization ability when describing novel activities.
Phase-shifting profilometry for the robust 3-D shape measurement of moving objects

Arbitrary two-dimensional (2-D) motion introduces coordinate errors and phase errors to three-dimensional (3-D) shape measurement of objects in phase-shifting profilometry (PSP). This paper presents a new robust 3-D reconstruction method for arbitrary 2-D moving objects by introducing an adaptive reference phase map and the motion estimation based on fence image. First, a composite fence image is used to track object motion. Second, to obtain the transformation matrixes and remove the coordinate errors among object images, the angle extraction technique and the 1-D hybrid phase correlation method (1-D HPCM) are integrated to automatically estimate the sub-pixel motion of objects. Third, the phase errors are compensated to obtain the rough absolute phase map of objects by combining the transformation matrixes with the reference phase map. Finally, the absolute phase map is refined to reconstruct the 3-D surfaces of moving objects with adaptive reference phase map. The proposed computational framework can accurately and automatically realize 3-D shape measurement of arbitrary objects with 2-D movement. The results of experiment verify the effectiveness of our computational framework.
Adversarial Reprogramming of Text Classification Neural Networks

Paarth Neekhara, Shehzeen Hussain, Shlomo Dubnov, Farinaz Koushanfar. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Reinforcement learning with convex constraints

In standard reinforcement learning (RL), a learning agent seeks to optimize the overall reward. However, many key aspects of a desired behavior are more naturally expressed as constraints. For instance, the designer may want to limit the use of unsafe actions, increase the diversity of trajectories to enable exploration, or approximate expert trajectories when rewards are sparse. In this paper, we propose an algorithmic scheme that can handle a wide class of constraints in RL tasks: specifically, any constraints that require expected values of some vector measurements (such as the use of an action) to lie in a convex set. This captures previously studied constraints (such as safety and proximity to an expert), but also enables new classes of constraints (such as diversity). Our approach comes with rigorous theoretical guarantees and only relies on the ability to approximately solve standard RL tasks. As a result, it can be easily adapted to work with any model-free or model-based RL. In our experiments, we show that it matches previous algorithms that enforce safety via constraints, but can also enforce new properties that these algorithms do not incorporate, such as diversity.
Guided Meta-Policy Search

Reinforcement learning (RL) algorithms have demonstrated promising results on complex tasks, yet often require impractical numbers of samples because they learn from scratch. Meta-RL aims to address this challenge by leveraging experience from previous tasks so as to more quickly solve new tasks. However, in practice, these algorithms generally also require large amounts of on-policy experience during the \emph{meta-training} process, making them impractical for use in many problems. To this end, we propose to learn a reinforcement learning procedure in a federated way, where individual off-policy learners can solve the individual meta-training tasks, and then consolidate these solutions into a single meta-learner. Since the central meta-learner learns by imitating the solutions to the individual tasks, it can accommodate either the standard meta-RL problem setting, or a hybrid setting where some or all tasks are provided with example demonstrations. The former results in an approach that can leverage policies learned for previous tasks without significant amounts of on-policy data during meta-training, whereas the latter is particularly useful in cases where demonstrations are easy for a person to provide. Across a number of continuous control meta-RL problems, we demonstrate significant improvements in meta-RL sample efficiency in comparison to prior work as well as the ability to scale to domains with visual observations.
Everything Happens for a Reason: Discovering the Purpose of Actions in Procedural Text

Our goal is to better comprehend procedural text, e.g., a paragraph about photosynthesis, by not only predicting what happens, but why some actions need to happen before others.Our approach builds on a prior process comprehension framework for predicting actions' effects, to also identify subsequent steps that those effects enable.We present our new model (XPAD) that biases effect predictions towards those that (1) explain more of the actions in the paragraph and ( 2) are more plausible with respect to background knowledge.We also extend an existing benchmark dataset for procedural text comprehension, ProPara, by adding the new task of explaining actions by predicting their dependencies.We find that XPAD significantly outperforms prior systems on this task, while maintaining the performance on the original task in ProPara.
Comparison between SQL and NoSQL Databases and Their Relationship with Big Data Analytics

A key ingredient in the world of cloud computing is a database that can be used by a great number of users. Distributed storage mechanisms become the de-facto method for data storage used by companies for the new generation of web applications. In the world of data storage, NoSQL (usually interpreted as "not only SQL" by developers) database is a growing trend. It is said that NoSQL alternates with the most widely used relational databases for the data storage, but, as the name implies, it does not fully replace the SQL. In this paper we will discuss about SQL and NoSQL databases, comparison of traditional SQL with NoSQL databases for Big Data analytics, NoSQL data models, types of NoSQL data stores, characteristics and features of each data store, advantages and disadvantages of NoSQL and RDBMS.
Iterative Greedy Matching for 3D Human Pose Tracking from Multiple Views

In this work we propose an approach for estimating 3D human poses of multiple people from a set of calibrated cameras. Estimating 3D human poses from multiple views has several compelling properties: human poses are estimated within a global coordinate space and multiple cameras provide an extended field of view which helps in resolving ambiguities, occlusions and motion blur. Our approach builds upon a real-time 2D multi-person pose estimation system and greedily solves the association problem between multiple views. We utilize bipartite matching to track multiple people over multiple frames. This proofs to be especially efficient as problems associated with greedy matching such as occlusion can be easily resolved in 3D. Our approach achieves state-of-the-art results on popular benchmarks and may serve as a baseline for future work.
Pyramid Multi‐View Stereo with Local Consistency

Abstract In this paper, we propose a PatchMatch‐based Multi‐View Stereo (MVS) algorithm which can efficiently estimate geometry for the textureless area. Conventional PatchMatch‐based MVS algorithms estimate depth and normal hypotheses mainly by optimizing photometric consistency metrics between patch in the reference image and its projection on other images. The photometric consistency works well in textured regions but can not discriminate textureless regions, which makes geometry estimation for textureless regions hard work. To address this issue, we introduce the local consistency. Based on the assumption that neighboring pixels with similar colors likely belong to the same surface and share approximate depth‐normal values, local consistency guides the depth and normal estimation with geometry from neighboring pixels with similar colors. To fasten the convergence of pixelwise local consistency across the image, we further introduce a pyramid architecture similar to previous work which can also provide coarse estimation at upper levels. We validate the effectiveness of our method on the ETH3D benchmark and Tanks and Temples benchmark. Results show that our method outperforms the state‐of‐the‐art.
Computational Argumentation Synthesis as a Language Modeling Task

Synthesis approaches in computational argumentation so far are restricted to generating claim-like argument units or short summaries of debates.Ultimately, however, we expect computers to generate whole new arguments for a given stance towards some topic, backing up claims following argumentative and rhetorical considerations.In this paper, we approach such an argumentation synthesis as a language modeling task.In our language model, argumentative discourse units are the "words", and arguments represent the "sentences".Given a pool of units for any unseen topic-stance pair, the model selects a set of unit types according to a basic rhetorical strategy (logos vs. pathos), arranges the structure of the types based on the units' argumentative roles, and finally "phrases" an argument by instantiating the structure with semantically coherent units from the pool.Our evaluation suggests that the model can, to some extent, mimic the human synthesis of strategy-specific arguments.
AtomNAS: Fine-Grained End-to-End Neural Architecture Search

Designing of search space is a critical problem for neural architecture search (NAS) algorithms. We propose a fine-grained search space comprised of atomic blocks, a minimal search unit much smaller than the ones used in recent NAS algorithms. This search space facilitates direct selection of channel numbers and kernel sizes in convolutions. In addition, we propose a resource-aware architecture search algorithm which dynamically selects atomic blocks during training. The algorithm is further accelerated by a dynamic network shrinkage technique. Instead of a search-and-retrain two-stage paradigm, our method can simultaneously search and train the target architecture in an end-to-end manner. Our method achieves state-of-the-art performance under several FLOPS configurations on ImageNet with a negligible searching cost. We open our entire codebase at: https://github.com/meijieru/AtomNAS.
A Framework for Software Defect Prediction Using Feature Selection and Ensemble Learning Techniques

Persistent and quality graduation rates of students are increasingly important indicators of progressive and effective educational institutions.Timely analysis of students' data to guide instructors in the provision of academic interventions to students who are at risk of performing poorly in their courses or dropout is vital for academic achievement.In addition there is need for performance attributes relationship mining for the generation of comprehensible patterns.However, there is dearth in pieces of knowledge relating to predicting students' performance from patterns.This therefore paper adopts hierarchical cluster analysis (HCA) to analyze students' performance dataset for the discovery of optimal number of fail courses clusters and partitioning of the courses into groups, and association rule mining for the extraction of interesting course-status association.Agglomerative HCA with Ward's linkage method produced the best clustering structure (five clusters) with a coefficient of 92% and silhouette width 0.57.Apriori algorithm with support (0.5%), confidence (80%) and lift (1) thresholds were used in the extraction of rules with student's status as consequent.Out of the twenty one courses offered by students in the first year, seven courses frequently occur together as failed courses, and their impact on the respective students' performance status were assessed in the rules.It is conjectured that early intervention by the instructors and management of educational activities on these seven courses will increase the students' learning outcomes leading to increased graduation rate at minimum course duration, which is the overarching objective of higher educational institutions.As further work, the integration of other machine learning and nature inspired tools for the adaptive learning and optimization of rules respectively would be performed.
A Reproducible Data Analysis Workflow

<p xmlns="http://www.ncbi.nlm.nih.gov/JATS1">In this tutorial, we describe a workflow to ensure long-term reproducibility of R-based data analyses. The workflow leverages established tools and practices from software engineering. It combines the benefits of various open-source software tools including R Markdown, Git, Make, and Docker, whose interplay ensures seamless integration of version management, dynamic report generation conforming to various journal styles, and full cross-platform and long-term computational reproducibility. The workflow ensures meeting the primary goals that 1) the reporting of statistical results is consistent with the actual statistical results (dynamic report generation), 2) the analysis exactly reproduces at a later point in time even if the computing platform or software is changed (computational reproducibility), and 3) changes at any time (during development and post-publication) are tracked, tagged, and documented while earlier versions of both data and code remain accessible. While the research community increasingly recognizes dynamic document generation and version management as tools to ensure reproducibility, we demonstrate with practical examples that these alone are not sufficient to ensure long-term computational reproducibility. Combining containerization, dependence management, version management, and dynamic document generation, the proposed workflow increases scientific productivity by facilitating later reproducibility and reuse of code and data.
Nature-Inspired Algorithms: A Comprehensive Review

This chapter focuses the light on new research trends. It discusses classifications of the nature-inspired algorithms and presents a brief list of algorithms followed by several different variants nature-inspired algorithms. To a certain extent, new optimization algorithms are developed and modified based on the nature as a main source of inspiration. Sometimes nature-inspired algorithms produce inaccurate solutions for some real-time optimization problems. Imitating the best feature in nature to solve optimization problems is an ongoing work. The chapter discusses the most nature-inspired algorithms clarifying their main inspiration source, motivation scenario, mathematical model, and heuristics. Algorithms could be categorized into eight main categories depending on problem type: continuous, discrete, parallel, distributed, binary, chaotic, multi-objective and hybrid algorithms. Instead, optimization algorithms are classified according to a high level set of selected criteria like main inspiration sources, e.g., biology, physics or chemistry. To a certain extent, new optimization algorithms are developed and modified based on the nature as a main source of inspiration.
End-to-End Argumentation Knowledge Graph Construction

This paper studies the end-to-end construction of an argumentation knowledge graph that is intended to support argument synthesis, argumentative question answering, or fake news detection, among others. The study is motivated by the proven effectiveness of knowledge graphs for interpretable and controllable text generation and exploratory search. Original in our work is that we propose a model of the knowledge encapsulated in arguments. Based on this model, we build a new corpus that comprises about 16k manual annotations of 4740 claims with instances of the model's elements, and we develop an end-to-end framework that automatically identifies all modeled types of instances. The results of experiments show the potential of the framework for building a web-based argumentation graph that is of high quality and large scale.
Deep orientation: Fast and Robust Upper Body orientation Estimation for Mobile Robotic Applications

An essential feature for navigating socially with a mobile robot is the upper body orientation of persons in its vicinity. For example, in a supermarket orientation indicates whether a person is looking at goods on the shelves or where a person is likely to go. However, given limited computing and battery capabilities, it is not possible to rely on high-performance graphics cards to run large, computationally expensive deep neural networks for orientation estimation in real time. Nevertheless, deep learning performs quite well for regression problems. Therefore, we tackle the problem of upper body orientation estimation with small yet efficient deep neural networks on a mobile robot in this paper. We employ a fast person detection approach as preprocessing that outputs fixed size person images before the actual estimation of the orientation is done. The combination with lightweight networks allows us to estimate a continuous angle in real time, even using a CPU only. We experimentally evaluate the performance of our system on a new, self-recorded data set consisting of more than 100,000 RGB-D samples from 37 persons, which is made publicly available. We also do an extensive comparison of different network architectures and output encodings for their applicability in estimating orientations. Furthermore, we show that depth images are more suitable for the task of orientation estimation than RGB images or the combination of both.
Security vulnerabilities in Information communication technology: Blockchain to the rescue (A survey on Blockchain Technology)

With the increasing concerns about the security of data, Blockchain a decentralised public ledger, linked block structure which verifies and stores data and follows trusted consensus algorithms which ensure synchronisation of data in a distributed peer to peer network acquired a lot of attention as well as discussions recently. As it makes sure a tamper-proof digital platform for decision-making processes to store and share data among different individuals, authorities and organisations who do not trust each other. This technology is considered as the future of data security and integrity and is expected to serve in a lot of new or existing potential application, so the motive of this paper is to present insights of blockchain's architecture, it's security background, and it elaborate the two versions of blockchain: permissioned and permission-less. and some of its applications. Moreover, the first decentralized cryptocurrency and a public ledger named bitcoin are also elaborated in this paper. Furthermore, we summarize some typical implementations in blockchain, the lifecycle of bitcoin and explore future research challenges that still need to be addressed in order to preserve privacy when blockchain is used.
Parametric Deformable Exponential Linear Units for deep neural networks

Rectified activation units make an important contribution to the success of deep neural networks in many computer vision tasks. In this paper, we propose a Parametric Deformable Exponential Linear Unit (PDELU) and theoretically verify its effectiveness for improving the convergence speed of learning procedure. By means of flexible map shape, the proposed PDELU could push the mean value of activation responses closer to zero, which ensures the steepest descent in training a deep neural network. We verify the effectiveness of the proposed method in the image classification task. Extensive experiments on three classical databases (i.e., CIFAR-10, CIFAR-100, and ImageNet-2015) indicate that the proposed method leads to higher convergence speed and better accuracy when it is embedded into different CNN architectures (i.e., NIN, ResNet, WRN, and DenseNet). Meanwhile, the proposed PDELU outperforms many existing shape-specific activation functions (i.e., Maxout, ReLU, LeakyReLU, ELU, SELU, SoftPlus, Swish) and the shape-adaptive activation functions (i.e., APL, PReLU, MPELU, FReLU).