Reviewerly/exHarmony huggingface dataset:
That Really Escalated Quickly—Online Learning Moves Into the Mainstream: Introduction to the Special Issue

Early in 2020, it became clear the COVID-19 pandemic was on its way toward disrupting the status quo in education in a substantial manner. As schools reacted by moving teaching and learning online, teachers, staff, parents, students, and other stakeholders were thrust into a world of learning previously discussed in this journal, but unknown to most in the field of special education. In this introduction to the special issue on online learning, we highlight key themes across the six articles, as well as lay out a vision for additional research and development that is required in our field.
Semi-Supervised Extreme Learning Machine Channel Estimator and Equalizer for Vehicle to Vehicle Communications

Wireless vehicular communications are a promising technology. Most applications related to vehicular communications aim to improve road safety and have special requirements concerning latency and reliability. The traditional channel estimation techniques used in the IEEE 802.11 standard do not properly perform over vehicular channels. This is because vehicular communications are subject to non-stationary, time-varying, frequency-selective wireless channels. Therefore, the main goal of this work is the introduction of a new channel estimation and equalization technique based on a Semi-supervised Extreme Learning Machine (SS-ELM) in order to address the harsh characteristics of the vehicular channel and improve the performance of the communication link. The performance of the proposed technique is compared with traditional estimators, as well as state-of-the-art machine-learning-based algorithms over an urban scenario setup in terms of bit error rate. The proposed SS-ELM scheme outperformed the extreme learning machine and the fully complex extreme learning machine algorithms for the evaluated scenarios. Compared to traditional techniques, the proposed SS-ELM scheme has a very similar performance. It is also observed that, although the SS-ELM scheme requires the largest operation time among the evaluated techniques, its execution time is still far away from the latency requirements specified by the standard for safety applications.
IFSpard: An Information Fusion-based Framework for Spam Review Detection

Online reviews, which contain the quality information and user experience about products, always affect the consumption decisions of customers. Unfortunately, quite a number of spammers attempt to mislead consumers by writing fake reviews for some intents. Existing methods for detecting spam reviews mainly focus on constructing discriminative features, which heavily depend on experts and may miss some complex but effective features. Recently, some models attempt to learn the latent representations of reviews, users, and items. However, the learned embeddings usually lack interpretability. Moreover, most of existing methods are based on single classification model while ignoring the complementarity of different classification models.
BlockAIM: A Neural Network-Based Intelligent Middleware For Large-Scale IoT Data Placement Decisions

Current Internet of Things (IoT) infrastructures rely on cloud storage however, relying on a single cloud provider puts limitations on the IoT applications and Service Level Agreement (SLA) requirements. Recently, multiple decentralized storage solutions (e.g., based on blockchains) have entered the market with distinct architecture, Quality of Service (QoS) parameters and at lower price compared to the cloud storage. In this work, we introduce BAM: a neural network-based middleware designed for intelligent selection of storage technology for IoT applications. We first propose a blockchain-based data placement protocol and theoretically model a decision optimization problem, which jointly considers cloud, multi-cloud and decentralized storage technologies to select the appropriate medium to store large-scale IoT data, while ensuring data integrity, traceability, auditability and decision verifiability. We then propose a neural network-based maintenance reconfiguration, which aims to optimize the computational complexity of the middleware design along with the blockchain transaction and storage overhead by learning and predicting the applications parameters. We also propose the aggregation rate feedback functionality in our design and model it as a linear optimization problem to improve data quality and precision. Finally, we provide a reference implementation and perform extensive experiments, which demonstrate the effectiveness of the proposed design.
Data Hiding Method for Color AMBTC Compressed Images Using Color Difference

Image compression technology and copyright protection are certainly the important technologies for free exchange of multimedia. For compression of an image, we propose a color Absolute Moment Block Trucation Coding (AMBTC) method using a common bit-plane created by k-means. In addition, a data hiding method based on a color AMBTC using Optimal Pixel Adjustment Process (OPAP) was proposed for copyright protection and confidential secret communication. The number of quantization levels of the proposed color AMBTC is nine per block. Therefore, the edge of the compressed color image can be expressed more delicately. As a result of the simulation, it can be seen that the edge of the image of the color AMBTC is close to the original image. Moreover, the data hiding performance of the proposed method also obtained excellent results. For the experiment, we measured the quality of the image using the Color Difference (CD) we proposed, and the measurement result was very satisfactory.
XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction

Click-Through Rate (CTR) prediction is a core task in nowadays commercial recommender systems. Feature crossing, as the mainline of research on CTR prediction, has shown a promising way to enhance predictive performance. Even though various models are able to learn feature interactions without manual feature engineering, they rarely attempt to individually learn representations for different feature structures. In particular, they mainly focus on the modeling of cross sparse features but neglect to specifically represent cross dense features. Motivated by this, we propose a novel Extreme Cross Network, abbreviated XCrossNet, which aims at learning dense and sparse feature interactions in an explicit manner. XCrossNet as a feature structure-oriented model leads to a more expressive representation and a more precise CTR prediction, which is not only explicit and interpretable, but also time-efficient and easy to implement. Experimental studies on Criteo Kaggle dataset show significant improvement of XCrossNet over state-of-the-art models on both effectiveness and efficiency.
OpenMatch: An Open Source Library for Neu-IR Research

OpenMatch is a Python-based library that serves for Neural Information Retrieval (Neu-IR) research. It provides self-contained neural and traditional IR modules, making it easy to build customized and higher-capacity IR systems. In order to develop the advantages of Neu-IR models for users, OpenMatch provides implementations of recent neural IR models, complicated experiment instructions, and advanced few-shot training methods. OpenMatch reproduces corresponding ranking results of previous work on widely-used IR benchmarks, liberating users from surplus labor in baseline reimplementation. Our OpenMatch-based solutions conduct top-ranked empirical results on various ranking tasks, such as ad hoc retrieval and conversational retrieval, illustrating the convenience of OpenMatch to facilitate building an effective IR system. The library, experimental methodologies and results of OpenMatch are all publicly available at https://github.com/thunlp/OpenMatch.
WITHDRAWN: Implementation of Grafana as open source visualization and query processing platform for data scientists and researchers

With the huge usage of smart gadgets and Internet connected devices, the need arise to evaluate these datasets. These datasets collected from multiple channels are required so that effective and high performance knowledge discovery and predictive analytics can be done. This domain is directly associated with the data science in which assorted techniques are used for data analytics with the use of statistics, computational engineering, machine learning and many others.
Quantization-Aware Pruning Criterion for Industrial Applications

In this study, we propose a simple and effective quantization-aware neural network pruning method to prune and quantize deep neural networks and then run them on lightweight and cost-effective embedded industrial devices. The proposed pruning method provides a suitable pruning strategy for a given bit precision before performing the subsequent quantization. In particular, the dropout rate is set to be bounded below by the maximum number of quantization levels, which enables the regulation of the tradeoff between sparsity and performance loss. The Kullback–Leibler divergence term of the evidence lower bound is also weighted for adjusting the performance loss. From learned dropout rates, efficient bit allocation is provided to maintain randomness even through the quantization step. In supervised and reinforcement learning applications, the proposed quantization-aware pruning method was proven to be successful in obtaining sparse and lightweight neural networks while maintaining their performance even after quantization. In particular, a real inverted pendulum system was used to determine whether the proposed method works well for actual physical control systems.
Measurement transformation algorithm for extended target tracking

Extended target tracking algorithms require the estimation of the target's shape in addition to its kinematic properties. The star-convex model can describe the details of the target's shape compared with the traditional elliptical model. The shape estimation based on the star-convex model is a complex nonlinear problem, which also contains the uncertainty of the measurement distribution on the surface of the target. Therefore, we propose a novel nonlinear measurement transformation (MT) to map measurements into a high dimensional space, so that the transformed measurements have a more linear relationship with the states. The MT also extracts the uncorrelated distance and angle information of the measurement in the local polar coordinates to improve the stability and accuracy of the shape estimation. Then a linear minimum mean square error estimator is constructed using the nonlinear uncorrelated MT, and a Bayesian filter under Gaussian assumptions is derived. Moreover, we also propose a novel mathematical metric based on the star-convex model that measures the distance between the reference shape parameters and their estimated values. Simulation results demonstrate the effectiveness of the proposed algorithm in the case of high sensor noise, low measurement rate, and dense sensor clutter.
Graph Learning Regularization and Transfer Learning for Few-Shot Event Detection

We address the poor generalization of few-shot learning models for event detection (ED) using transfer learning and representation regularization. In particular, we propose to transfer knowledge from open-domain word sense disambiguation into few-shot learning models for ED to improve their generalization to new event types. We also propose a novel training signal derived from dependency graphs to regularize the representation learning for ED. Moreover, we evaluate few-shot learning models for ED with a large-scale human-annotated ED dataset to obtain more reliable insights for this problem. Our comprehensive experiments demonstrate that the proposed model outperforms state-of-the-art baseline models in the few-shot learning and supervised learning settings for ED. Code and data splits are available at https://github.com/laiviet/ed-fsl.
Automated Body Parts Estimation and Detection using Salient Maps and Gaussian Matrix Model

Estimation and detection different human body Portion from different scenes of videos and images is an important step for most model based systems. Human body-portion detection from a single still image estimate the layout of human body portion, the position of body portion (head, torso, arms, and legs), size and orientation within the scene to recognize the action. For the foreground segmentation technique; we have used salient-object detection via Structured Matrix Decomposition (SMD) and skin-tone detection. After extraction of silhouette; body-portion estimation is applied by using Gaussian Mixture Model (GMM). Five basic parts are determined by using classical expectation maximization (EM) algorithm. The minimum number of twelve ellipsoids represented on the image with the centroid of each ellipse. The estimated distance between the centroids of ellipses are compared. The experimental results over dataset as PAMI09_release has accuracies of 86.2%, respectively. Our proposed pose descriptors outperform other state-of-the-art body portion detection model.
Vehicle Counting Using Detecting-Tracking Combinations: A Comparative Analysis

In light of the rapid progress in building smart cities and smart traffic systems, the need for an accurate and real-time counting vehicles system has become a very urgent need. Finding a robust and accurate counting system is a challenge, as the system must detect, classify and track multi vehicles in complex and dynamic scene situations, different models and classes, and various traffic densities. Several hardware and software systems have emerged for this purpose and their results have varied. In recent years, and due to the great growth in computational capacities and deep learning techniques, deep learning based vehicle counting systems have delivered an impressive performance at low costs. In this study, several state-of-the-art detection and tracking algorithms are studied and combined with each other to render different models. These models are applied in automatic vehicle counting frameworks in traffic videos to assess how accurate are their results against the ground truth. Experiments on these models present the existing challenges that hinder their ability to extract the distinctive object features and thus undermine their efficiency such as problems of occlusion, large scale objects detection, illumination, and various weather conditions. The study revealed that the detectors coupled with the Deep Sort tracker, such as YOLOv4, Detectron2 and CenterNet, achieved the best results compared to the rest of the models.
Adopting Conditional Random Field (CRF) for Khasi Part-of-Speech Tagging (KPOST)

The main purpose of this paper is to develop part-of-speech (PoS) tagging for the Khasi language based on conditional random field (CRF) approaches. Khasi belongs to a Mon–Khmer language of the Austroasiatic language family that is spoken by the native people of the state Meghalaya, Northeastern Part of India. This work is the first instance of a CRF approach in Khasi language for PoS tagging. A grammatically tagged corpus is the main requirement to work on part-of-speech tagging. But, for Khasi language, we do not have a standard corpus for PoS tagging. Therefore, in this paper, Khasi lexicon or corpus is developed for training and testing the system. The designed Khasi corpus consists of around 41,000 tokens. Using the conditional random field (CRF) model for learning the created corpus, an F1-score of 0.92 is the yield for testing data and 0.95 is obtained on training data.
Catch Me If You Can — Detecting Fraudulent Online Reviews of Doctors Using Deep Learning

Fake online reviews are becoming more prevalent and are a significant concern for consumer protection groups and regulatory authorities. However, identifying fake reviews has been a challenge in IS, marketing, and computer science. In this study, we design a deep learning approach to capture the linguistic traits that differentiate between genuine and fake reviews. Our deep learning model is evaluated on a dataset of 181,951 doctor reviews, 8% of which are fake. Since a natural honeypot existed at one point on the platform that hosted these reviews, we are able to label the reviews that exploited the natural honeypot as fraudulent, thus overcoming the major challenge in constructing the ground truth for training the model. Our model shows a significant improvement in accuracy when compared to traditional machine learning algorithms such as logistic regression and random forest. Interestingly, we also find that human evaluators perform much worse than machine learning approaches. Compared to 200 human evaluators, our deep learning approach has a true positive rate (14.29% vs. 8.70%) that is twice as high, and it also achieves a much lower false positive rate (0.63% vs. 11.68%). We also observe that these evaluators are susceptible to human bias, as they are more likely to label fake reviews as genuine than they are to label genuine reviews as genuine. Our study offers further explanations for the advantages of deep learning and is the first to construct a deep learning model to detect fraudulent online reviews, an approach that can help curb fake reviews and increase information quality and market efficiency.
Self-consistent tomography of temporally correlated errors

Abstract The error model of a quantum computer is essential for optimizing quantum algorithms to minimize the impact of errors using quantum error correction or error mitigation. Noise with temporal correlations, e.g. low-frequency noise and context-dependent noise, is common in quantum computation devices and sometimes even significant. However, conventional tomography methods have not been developed for obtaining an error model describing temporal correlations. In this paper, we propose self-consistent tomography protocols to obtain a model of temporally correlated errors, and we demonstrate that our protocols are efficient for low-frequency noise and context-dependent noise.
Machine learning based success prediction for crowdsourcing software projects

Competitive Crowdsourcing Software Development is an online software development paradigm, promises the innovative, cost effective and high quality solutions on time. However, the paradigm is still in infancy and does not address the key challenges such as low rate of submissions and high risk of project failure. A significant number of software projects fail to receive a satisfactory solution and end up wasting the time and efforts of stakeholders. Therefore, the success prediction of a new software project may help stakeholders in the project crowdsourcing decision, saving their time and efforts. To this end, this study proposes a novel approach based on machine learning to predict the success of a software project for crowdsourcing platforms in terms of whether the given project will reach its completion or otherwise. First, the textual description and important attributes of software projects from TopCoder is extracted. Next, the description is preprocessed using natural language processing technologies. Then, keywords are identified using a modified keyword ranking algorithm and each software project is awarded a ranking score. Every software project is modeled as a vector that is based on the extracted attributes, its identified keywords and ranking scores. Using these vectors with their associated solution status, a support vector machine classifier is trained to predict the success of a given software project. Different machine learning classifiers are applied and it turns out that support vector machine yields the highest performance on the given dataset. Finally, the proposed approach is evaluated with history data of real software projects. The results of hold-out validation suggest that the average precision, recall, and f-measure are up to 94.53%, 99.30% and 96.85%, respectively.
Pathfinder Discovery Networks for Neural Message Passing

In this work we propose Pathfinder Discovery Networks (PDNs), a method for jointly learning a message passing graph over a multiplex network with a downstream semi-supervised model. PDNs inductively learn an aggregated weight for each edge, optimized to produce the best outcome for the downstream learning task. PDNs are a generalization of attention mechanisms on graphs which allow flexible construction of similarity functions between nodes. They also support edge convolutions and cheap multiscale mixing layers. We show that PDNs overcome weaknesses of existing methods for graph attention (e.g. Graph Attention Networks), such as the diminishing weight problem.
Adaptive Feature Fusion and Spatio-Temporal Background Modeling in KDE Framework for Object Detection and Shadow Removal

Moving cast shadow identification and extraction are the commonly encountered problems in visual surveillance applications. In the video object detection problem, a shadow is a category of the dynamic entity which needs to be removed for accurate detection of moving objects. In this paper, we have proposed a new scheme to detect the moving object in video frames while removing the shadow. The proposed scheme is based on the notion of background modeling and model learning. The background is modeled by the proposed Spatio-Temporal Kernel Density Estimation (ST-KDE) based model, but the model learning takes place in online mode in the fused feature space. In the fusion process, the Local Binary Pattern (LBP) features of the ST-KDE model of a frame are determined and are fused probabilistically with the Gabor features of the corresponding original frame. To achieve accurate background modeling, weights in the probabilistic fusion are adaptively determined from the scene dynamics. Background model learning is pixel-based where model histograms learn the background with every temporal frame. Learning and classification of the fused feature frames happen simultaneously with frame by frame. The classified frame thus obtained contains the object with some residual shadow which is eliminated by entropy map and thresholding. The main novelties of our paper are: (i) ST-KDE based background modeling, (ii) Determination of the weights of the fusion process in an adaptive framework and, (iii) online background model learning in feature space. The proposed modeling approach has been tested successfully with different video sequences considered from CDnet, Lasiesta, ATON-CVRR, SRD, and ISTD databases. The performance metrics of the proposed scheme have been compared with many existing methods and it has been found that the proposed method exhibits improved performance.