Reviewerly/exHarmony huggingface dataset:
A Multiple Granularity Co-Reasoning Model for Multi-choice Reading Comprehension

We propose a multi-granularity co-reasoning model for multi-choice reading comprehension task, which aims to select the correct option based on the interaction between passage, question and candidate options. Firstly, we introduce a multiple granularity text matching module to interact passage with question and each option. We take advantage of information extracted from diverse semantic spaces to conduct more extensive matching between text sequences. With this help, we could better match the passage against the question and each option to gather relevant information. Furthermore, we employ a multi-sentence co-reasoning module for sentence inference across multiple sentences. Specifically, we utilize 1D Convolutional Neural Network (1D-CNN) with different kernel sizes and self-attentive Recurrent Neural Network (RNN) to model the relationships of relevant sentences. This module could better synthesize and aggregate sentence-level evidence to make decisions. Experimental results demonstrate that our proposed model achieves state-of-the-art performance for single models on the RACE dataset.
Scalable k-means for large-scale clustering

The k-means clustering is arguably the most popular clustering technique, which has been applied to a wide range of applications. Lloyd’s algorithm is the most popular algorithm for the k-means problem due to its simplicity, geometric intuition and effectiveness. However, in a naive implementation of Lloyd’s algorithm, we need to compute the Euclidean distances between all data points and all cluster centers in each iteration. This prevents the algorithm from being scalable to large datasets and becomes the main bottleneck. To overcome the problem, this paper proposes two scalable k-means algorithms, Scalable Lloyd’s k-means and Scalable Mini-Batch k-means. They are distributed extensions of Lloyd’s algorithm and the mini-batch k-means, respectively. The two algorithms are all use the data-parallel technique to scale beyond computational and memory limits of a single machine. Meanwhile, they are all based on the parameter server abstraction that facilitates the data-parallel computation. The first algorithm can find better quality of solutions, while the second one converges to a modest solution faster. They both have good scalability and totally do in-memory computation. In addition, we propose a new aggregation method for Scalable Mini-Batch k-means. Extensive experiments conducted on four large-scale datasets show that our proposed algorithms have good convergence performance and achieve almost ideal speedup.
Visual tracking based on a unified tracking-and-detection framework with spatial-temporal consistency filtering

Exploring the advantages of combining convolutional features and discriminative correlation filters has recently attracted a great deal of attention in visual tracking fields. In this paper, we propose a spatial-temporal consistency filtering (STCF) tracker in a unified tracking-and-detection framework. First, we apply a continuous correlation filter that seamlessly embeds multi-domain multi-scale feature maps to exploit richer appearance representation. We then introduce a novel domain-aware detector for generating fine-grained deep features and highly-likely target candidates. To handle target drift issues, we designed spatial-temporal consistency filtering as a recovery mechanism for target re-identification and scale re-estimation. We additionally designed a model reliability indicator to avoid potential model degeneration and contamination. Compared with existing state-of-the-art trackers, our STCF tracker can achieve comparable accuracy and robust performance, and we demonstrate that with comprehensive experiments on Online Tracking Benchmark (OTB-2015), Visual Object Tracking challenge (VOT-2016 and VOT-2017) benchmarks.
MOOCs, Artificial Intelligence Systems, and the Dilemma of Tertiary Education in the 21st Century

Recent research on the application of artificial intelligence (AI) technology in the education industry for teaching and learning has stirred up a revolution via the use of platforms like the massive open online courses (MOOC) the likes of which the world have never seen before. Millions through this platform can now enroll online to get one form of education or the other. Many scholars, however, doubt the quality of education transmitted and acquired via these platforms; hence, some scholars describe the education gotten through this medium as artificial education. A situation that has resulted in a kind of revolution in the education industry described as education tsunami. The Marxian theory of alienation offers an appropriate theoretical platform for the analysis conducted in the paper. The ex-post factor method of analysis and Deidra's critical analytic method was adopted for attaining the objectives of the paper. The dilemmas eroding the quality of education were identified. Blended learning approaches, as against present methods, were recommended.
Holacracy In Software Development Teams: A Multivocal Literature Review

Holacracy can be seen as a strategy for decentralized management and organizational governance in which authority and decision-making are delivered by means of autonomous and self-organizing groups or teams. Although the topic is not new, its application in software development has not a long history behind it. This study is meant to give an overview on holacracy for software development teams, its features, benefits gained, as well as its challenges. To do so, in this paper, authors present a multivocal literature review in which a panoply of sources including research and grey literature is reviewed. Our study shows that adapting holacracy towards self-organizing software development teams can result in increased product transparency, improved decision making, more efficient progress, and a higher sense of individual responsibility. However, there are reported challenges such as implementation difficulty and increase in employee outflows.
Improving the Performance of MongoDB with RDMA

MongoDB is a NoSQL database which stores the data in form of key-value pairs. It is an open-source, document database which is being used in many data-center applications (e.g. Google, Facebook, etc.) because of its high performance, high availability and automatic scaling. For this kind of data intensive applications, low latency and high throughput are extremely important. However, the existing MongoDB is built upon Boost. Asio, which is a cross-platform C++ library for network and low-level I/O. It can provide a great degree of portability, but at the price of performance due to the limitation of Ethernet network and TCP/IP protocol. This makes MongoDB hard to meet the performance requirements of data intensive applications. The High Performance Computing(HPC) domain has developed high performance networks such as InfiniBand for many years, which provides higher bandwidth and lower latency than Ethernet. These kind of networks also provide advanced features, such as Remote Direct Memory Access(RDMA), to achieve better performance. In this paper, we present a modern RDMA capable design of MongoDB. The performance evaluation on QDR (32 Gbps) shows that our RDMA design achieves 2.84X and 1.93X peak speedup over 1 Gigabit Ethernet (1 GigE) and IP-over-InfiniBand (IPoIB) in all experiments. To the best of our knowledge, this is first MongoDB design utilizing high performance RDMA capable interconnects.
Vertical and Horizontal Partitioning in Data Stream Regression Ensembles

Data stream mining is an emerging topic in machine learning that targets the creation and update of predictive models over time as new data becomes available. Regarding existing works, classification is the most widely tackled task, which leaves regression nearly untouched. In this paper, the focus relies on ensemble learning for data stream regression, more specifically on vertical and horizontal data partitioning techniques. The goal is to determine whether and under which conditions partitioning can lessen the error rates of different types of learners in the data stream regression task. The proposed method combines vertical and horizontal partitioning, and it is compared with and against different types of learners and existing ensembles.
Robustness analysis of the interval-valued fuzzy inference algorithms1

A new distance metric between interval-valued fuzzy sets is proposed. The four special logic metric spaces based on the distance are structured. we analysis and compare the structures of the four logic metric spaces. It is shown that the Łukasiewicz logic metric space and Goguen logic metric space are more suitable for interval-valued fuzzy reasoning. Moreover, the robustness of interval-valued fuzzy reasoning triple I methods are studied in the two logic metric spaces. We prove that fuzzy reasoning triple I methods based on the interval-valued Łukasiewicz residuated implication and interval-valued Goguen residuated implication are robust.
evoRF: An Evolutionary Approach to Random Forests

Machine Learning is a field in which significant steps forward have been taken in the last years, resulting in a wide variety of available algorithms, for many different problems. Nonetheless, most of these algorithms focus on the training of static models, in the sense that the model stops evolving after the training phase. This is increasingly becoming a limitation, especially in an era in which datasets are increasingly larger and may even arrive as sequential streams of data. Frequently retraining a model, in these scenarios, is not realistic. In this paper we propose evoRF: a combination of a Random Forest with an evolutionary approach. Its key innovative aspect is the evolution of the weights of the Random Forest over time, as new data arrives, thus making the forest’s voting scheme adapt to the new data. Older trees can also be replaced by newly trained ones, according to their accuracy, ensuring that the ensemble remains up to date without requiring a whole retraining.
Image Plagiarism System for Forgery Detection in Maps Design

In an educational environment, plagiarism is a crucial task that needs to be identified, in recent years all known journals and conferences, as well as universities, request a plagiarism report from students and researchers to prove the originality of published text or scientific paper. Plagiarism detection usually checks the text content via many of the platforms which areavailable for productive use reliably identifying copied text or near-copies of text and these systems usually fail to detect the images, designs and maps plagiarism since it is originally built for text mainly. In this paper, we suggest an adaptive, scalable, and extensible, robust method for image plagiarism which is tested in designs collect from department of architecture University of Technology, this method mainly compare the data (designs images) entered to the system with data sets saved in the database mainly these designs are saved as feature which is constructed via the Scale Invariant Feature Transform (SIFT) algorithm, which is one of the artificial intelligence algorithms and match by using k-mean clustering and brute force L1 matcher, feature matching is done by Fast Approximate Nearest-Neighbor (FANN), and the similarity check is done with threshold used 40% which can be changed to an accepted levels when needed. Using the k-mean algorithm in clustering, whichis a robust artificial intelligence clustering algorithm giving us a strong system that is not discarding any feature extracted from the image. In this paper, data sets consist of 45 samples as training images saved and used in the system as the system database and using 48 samples as testing images which consist of original and forgery designs. These testing images were evaluated with 90% matching rate and 81% matching accuracy rating.
A Multivariate Fuzzy Kohonen Clustering Network

Usually, in a fuzzy clustering, the memberships are the same for all the variables (features), i.e., the variables are considered equally important for the definition of the memberships. Fuzzy Kohonen Clustering network (FKCN) is a self-organizing fuzzy neural network that uses fuzzy membership values from the popular Fuzzy c-Means as learning rates. The replacement of the arbitrary learning rate by a fuzzy membership function can produce better clustering results. This paper introduces a new variant of the FKCN algorithm that finds a set of weights and a multivariate fuzzy partition minimizing an objective function. Here, the multivariate memberships allow to take account the intra-class and inter-class dispersion structures of the input data. Experiments with different configurations of synthetic data sets and applications with real data sets demonstrate the usefulness of this fuzzy clustering network model.
Exploring Machine Learning and Deep Learning Frameworks for Task-Oriented Dialogue Act Classification

Dialogue Act (DA) Classification plays a signifi-cant role in the understanding of an utterance in a dialogue. Components of Spoken Dialogue System (SDS) such as Natural Language Understanding (NLU) and Dialogue Management (DM) modules can significantly exploit the output of the DA classification. In this paper, we propose a task-oriented DA classifier based on both traditional supervised Machine Learning (ML) as well as Deep Learning (DL) techniques. The type and nature of dialogues basically depend on the domain and the DA itself. So, in order to make the model task-oriented, a new tag-set has been designed by studying the properties of the target domain. On the benchmark SwitchBoard (SWBD) and TRAINS corpus, our proposed models have performed exceptionally well with the new tag-set. Experimental results indicate that our proposed models have achieved good accuracy on both the datasets and outperformed several state of the art approaches and the new tag-set is well suited for task-oriented applications.
Detection and Distance Estimation against Motorcycles as Navigation Aids for Visually-impaired People

Visually impaired people are limited to detecting obstacles when walking in an outdoor environment. This research aims to detect, estimate distance and relative position of the visually impaired people to the obstacle, particularly parked motorcycle. The proposed system uses the Single Shot Multibox Detector (SSD) Mobilenet for parked motorcycle detection by testing several different learning rates. The Pinhole Camera Model method is used to estimate the distances from 2 to 5 meters by comparing the actual motorcycle with the motorcycle in the image based on the similar triangle principle. The last stage is estimating the relative normal horizontal position of the visually impaired people to the obstacle to determine whether the position of the visually impaired people is in line with the right, the middle, or the left side of the parked motorcycle. This research uses 500 images, 80% as training data, and 20% as testing data. There are four scenarios based on the user's height with six parked motorcycles positions, i.e., the front, rear, right, left, right oblique, and left oblique. In addition, the tilting position of the smartphone camera uses two different angles, depending on the user's height. The highest parked motorcycle detection results obtained at the learning rate 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> with an accuracy of 95.04%, the average RMSE of the estimated distance is 0.11, and the highest accuracy of the estimated relative position is 83.26% at 2 meters distance.
Unsupervised Phoneme and Word Discovery From Multiple Speakers Using Double Articulation Analyzer and Neural Network With Parametric Bias

This paper describes a new unsupervised machine-learning method for simultaneous phoneme and word discovery from multiple speakers. Phoneme and word discovery from multiple speakers is a more challenging problem than that from one speaker, because the speech signals from different speakers exhibit different acoustic features. The existing method, a nonparametric Bayesian double articulation analyzer (NPB-DAA) with deep sparse autoencoder (DSAE) only performed phoneme and word discovery from a single speaker. Extending NPB-DAA with DSAE to a multi-speaker scenario is, therefore, the research problem of this paper. This paper proposes the employment of a DSAE with parametric bias in the hidden layer (DSAE-PBHL) as a feature extractor for unsupervised phoneme and word discovery. DSAE-PBHL is designed to subtract speaker-dependent acoustic features and speaker-independent features by introducing parametric bias input to the DSAE hidden layer. An experiment demonstrated that DSAE-PBHL could subtract distributed representations of acoustic signals, enabling extraction based on the types of phonemes rather than the speakers. Another experiment demonstrated that a combination of NPB-DAA and DSAE-PBHL outperformed other available methods accomplishing phoneme and word discovery tasks involving speech signals with Japanese vowel sequences from multiple speakers.
COMC: A Framework for Online Cross-domain Multistream Classification

With the tremendous increase of the online data, training a single classifier may suffer because of the large variety of data domains. One solution could be to learn separate classifiers for each domain. However, this would arise a huge cost to gather annotated training data for a large number of domains and ignore similarity shared across domains. Hence, it leads to our problem setting: can labeled data from a related source domain help predict the unlabeled data in the target domain? In this paper, we consider two independent simultaneous data streams, which are referred to as the source and target streams. The target stream continuously generates data instances from one domain where the label is unknown, while the source stream continuously generates labeled data instances from another domain. Most likely, the two data streams would have different but related feature spaces and different data distributions. Moreover, these streams may have asynchronous concept drifts between them. Our problem setting, which is called Cross-domain Multistream Classification, is to predict the class labels of data instances in the target stream using a classifier trained on the labeled source stream. In this paper, we propose an efficient solution for cross-domain multistream classification by integrating change detection into online data stream adaptation. The class labels of data instances in the target stream are predicted using the sufficient amount of label information in the related source stream. And the concept drifts along the two independent streams are continuously being addressed at the same time. Experimental results on real-world data sets indicate significantly improved performance over baseline methods.
Type-Based Genetic Algorithms

This paper introduces a novel type-based genetic algorithm and its applications to two well-known problems: N-queen problem and finding the global minimum of the Rosenbrock function. The algorithm offers a new approach to internal structure of individuals in population of genetic algorithms.
Ethnicity Recognition System using Back Propagation Algorithm of an MLP

This paper includes a novel technique for “Ethnicity Detection System” via extracting geometric features from pictures, such as the nose width, lips width, forehead width and color. These aforementioned features can be easily extracted by means of the Viola Jones algorithm, followed by the execution of the artificial neural networking. This process is achieved by following three steps: face recognition, face segmentation and ethnicity classification. These steps can provide further input for trained neural network. Thus, the output of the neural network helps in determining the race of the individual. The results are obtained via a database of many people of different ethnicities.
User Perception of Situated Product Recommendations in Augmented Reality

Augmented reality (AR) interfaces increasingly utilize artificial intelligence systems to tailor content and experiences to the user. We explore the effects of one such system — a recommender system for online shopping — which allows customers to view personalized product recommendations in the physical spaces where they might be used. We describe results of a [Formula: see text] condition exploratory study in which recommendation quality was varied across three user interface types. Our results highlight potential differences in user perception of the recommended objects in an AR environment. Specifically, users rate product recommendations significantly higher in AR and in a 3D browser interface, and show a significant increase in trust in the recommender system, compared to a web interface with 2D product images. Through semi-structured interviews, we gather participant feedback which suggests AR interfaces perform better due to their ability to view products within the physical context where they will be used.
Incident-Based Methods for Studying Expertise

Abstract Incident-based interview methods are powerful tools for studying expertise in context. They provide insight into expert cognition that cannot be obtained with other methods. They complement performance measures that show what an expert can do, by providing insight into what is going on in the interviewee’s head. These methods facilitate description of expertise in the context of work and aid in developing testable hypotheses. They can be used to uncover cognitive requirements to support acquisition of skill via decision support design and training. Findings can also be used to inform the design of tools to support expertise and descriptive models to inform basic research. This chapter discusses four types of incident-based interview methods, examines analysis strategies, and provides examples of products resulting from this type of research and their influence on applications. Practical issues associated with the use of incident-based interview methods are considered, and future directions are discussed.
Fine-grained Adversarial Image Inpainting with Super Resolution

Image inpainting refers to synthesizing plausible contents for images with missing regions. However, current methods often create blurry textures, distorted structures and loss of details, especially when the image has complex scenes or large missing regions. We propose a fine-grained adversarial image inpainting model with super resolution. It performs a coarse-to-fine inpainting procedure in two stages. The proposed generator first synthesizes initial predictions of the missing regions with a novel encoder-decoder structure. Then it refines the predicted missing regions by generating high-frequency details via super resolution. We evaluate the proposed from both pixel level and semantic level. Experiments demonstrate that the proposed can generate higher quality inpainting results than the baseline models in both metrics.
The Conditional Boundary Equilibrium Generative Adversarial Network and its Application to Facial Attributes

We propose an extension of the Boundary Equilibrium GAN (BEGAN) neural network, named Conditional BEGAN (CBEGAN), as a general generative and transformational approach for data processing. As a novelty, the system is able of both data generation and transformation under conditional input. We evaluate our approach for conditional image generation and editing using five controllable attributes for images of faces from the CelebA dataset: age, smiling, cheekbones, eyeglasses and gender. We perform a set of objective quantitative experiments to evaluate the model's performance and a qualitative user study to evaluate how humans assess the generated and edited images. Both evaluations yield coinciding results which show that the generated facial attributes are recognizable in more than 80% of all new testing samples.