Reviewerly/exHarmony huggingface dataset:
Topic-BERT: Detecting harmful information from social media

Harmful information identification is a critical research topic in natural language processing. Existing approaches have been focused either on rule-based methods or harmful text identification of normal documents. In this paper, we propose a BERT-based model to identify harmful information from social media, called Topic-BERT. Firstly, Topic-BERT utilizes BERT to take additional information as input to alleviate the sparseness of short texts. The GPU-DMM topic model is used to capture hidden topics of short texts for attention weight calculation. Secondly, the proposed model divides harmful short text identification into two stages, and different granularity labels are identified by two similar sub-models. Finally, we conduct extensive experiments on a real-world social media dataset to evaluate our model. Experimental results demonstrate that our model can significantly improve the classification performance compared with baseline methods.
One for All: An End-to-End Compact Solution for Hand Gesture Recognition

The HGR is a quite challenging task as its performance is influenced by various aspects such as illumination variations, cluttered backgrounds, spontaneous capture, etc. The conventional CNN networks for HGR are following two stage pipeline to deal with the various challenges: complex signs, illumination variations, complex and cluttered backgrounds. The existing approaches needs expert expertise as well as auxiliary computation at stage 1 to remove the complexities from the input images. Therefore, in this paper, we proposes an novel end-to-end compact CNN framework: fine grained feature attentive network for hand gesture recognition (Fit-Hand) to solve the challenges as discussed above. The pipeline of the proposed architecture consists of two main units: FineFeat module and dilated convolutional (Conv) layer. The FineFeat module extracts fine grained feature maps by employing attention mechanism over multiscale receptive fields. The attention mechanism is introduced to capture effective features by enlarging the average behaviour of multi-scale responses. Moreover, dilated convolution provides global features of hand gestures through a larger receptive field. In addition, integrated layer is also utilized to combine the features of FineFeat module and dilated layer which enhances the discriminability of the network by capturing complementary context information of hand postures. The effectiveness of Fit-Hand is evaluated by using subject dependent (SD) and subject independent (SI) validation setup over seven benchmark datasets: MUGD-I, MUGD-II, MUGD-III, MUGD-IV, MUGD-V, Finger Spelling and OUHANDS, respectively. Furthermore, to investigate the deep insights of the proposed Fit-Hand framework, we performed ten ablation study
Augmenting Open-Domain Event Detection with Synthetic Data from GPT-2

Open-domain event detection (ODED) aims to identify event mentions of all possible types in text. A challenge for ODED research is the lack of large training datasets. In this work, we explore a novel method to overcome this challenge by fine-tuning the powerful pre-trained language model GPT-2 on existing datasets to automatically generate new training data for ODED. To address the noises presented in the generated data, we propose a novel teacher-student architecture where the teacher model is used to capture anchor knowledge on sentence representations and data type difference. The student model is then trained on the combination of the original and generated data and regularized to be consistent with the anchor knowledge from the teacher. We introduce novel regularization mechanism based on mutual information and optimal transport to achieve the knowledge consistency between the student and the teacher. Moreover, we propose a dynamic sample weighting technique for the generated examples based on optimal transport and data clustering. Our experiments on three benchmark datasets demonstrate the effectiveness of the propped model, yielding state-of-the-art performance for such datasets.
MUTECO: A Framework for Collaborative Allocation in CPU-FPGA Multi-tenant Environments

CPU-FPGA collaborative environments are progressively being adopted by Cloud Warehouses. In this environment, multiple clients share the same infrastructure to maximize resource utilization with energy efficiency and scalability. However, such a provisioning of resources is challenging, since kernels may be concurrently assigned to both CPU and FPGA in a scenario where available resources and workload characteristics drastically vary. To make the best use of resources in this complex environment, we propose MUTECO: A MUlti-TEnant COllaborative resource provisioning framework. MUTECO optimizes considering both multitenancy and CPU-FPGA collaborative execution, in contrast to existing approaches that focus on collaborative single-tenant or non-collaborative multi-tenant workloads. MUTECO is highly configurable and integrated to the Hypervisor layer, so it can be tuned to optimize convergence time, performance, and energy, according to different scenarios that comprise number of tenant requests, the incoming kernels' behavior, and the available resources. Over a varied set of scenarios, MUTECO outperforms in up to 2.91x and 2.39x the current non-collaborative and single-tenant approaches.
Optimizing Data Mining Efficiency in Professional Farmer Simulation Training System with Cloud-Edge Collaboration

In the construction of agriculture modernization, the production need professional farmer. It is important that the professional farmer training in simulation production surrounding supported by agriculture internet of things. The application of the Internet of things generates massive data. It need optimize the smart mining method, which edge computing and cloud computing constitute an organic interaction system , with excellent functions in data intelligent mining. This paper based on the self-developed professional farmer training simulation system, aiming at the massive data in the system, a distributed deep learning algorithm based on the edge cloud collaboration be proposed, furthermore, optimized the algorithm by constructing the edge cloud collaboration network architecture of deep learning and the distributed parallel training be described, so as to solve the poor efficiency of intelligent mining of cloud alliance data in he simulation training system.
Wasserstein Distance-Based Domain Adaptation and Its Application to Road Segmentation

Domain adaptation is used in applying a classifier acquired in one data domain to another data domain. A classifier obtained by supervised training with labeled data in an original source domain can also be used for classification in a target domain in which the labeled data are difficult to collect with the help of domain adaptation. The most recently proposed domain adaptation methods focus on data distribution in the feature space of a classifier and bring the data distribution of both domains closer through learning. The present work is based on an existing unsupervised domain adaptation method, in which both distributions become closer through adversarial training between a target data encoder to the feature space and a domain discriminator. We propose to use the Wasserstein distance to measure the distance between two distributions, rather than the well-known Jensen-Shannon divergence. Wasserstein distance, or earth mover's distance, measures the length of the shortest path among all possible pairs between a corresponding pair of variables in two distributions. Therefore, minimization of the distance leads to overlap of the corresponding data pair in source and target domain. Thus, the classifier trained in the source domain becomes also effective in the target domain. The proposed method using Wasserstein distance shows higher accuracies in the target domains compared with an original distance in computer experiments on semantic segmentation of map images.
Relevance-Aware Q-matrix Calibration for Knowledge Tracing

Knowledge tracing (KT) lies at the core of intelligent education, which aims to diagnose students’ changing knowledge level over time based on their historical performance. Most of the existing KT models either ignore the significance of Q-matrix associated exercises with knowledge concepts (KCs) or fail to eliminate the subjective tendency of experts within the Q-matrix, thus it is insufficient for capturing complex interaction between students and exercises. In this paper, we propose a novel Relevance-Aware Q-matrix Calibration method for knowledge tracing (RAQC), which incorporates the calibrated Q-matrix into Long Short-Term Memory (LSTM) network to model the complex students’ learning process, for getting both accurate and interpretable diagnosis results. Specifically, we first leverage the message passing mechanism in Graph Convolution Network (GCN) to fully exploit the high-order connectivity between exercises and KCs for obtaining a potential KC list. Then, we propose a Q-matrix calibration method by using relevance scores between exercises and KCs to mitigate the problem of subjective bias existed in human-labeled Q-matrix. After that, the embedding of each exercise aggregated the calibrated Q-matrix with the corresponding response log is fed into the LSTM to tracing students’ knowledge states (KS). Extensive experimental results on two real-world datasets show the effectiveness of the proposed method.
In-air Signature Authentication Using Smartwatch Motion Sensors

Handwriting signature, as an important behavioral biometric trait, has been broadly adopted for authorization and identity verification. Nowadays, the emergence of consumer-level devices and the development of deep neural networks have vastly facilitated this field. In this paper, we present a practical recurrent neural network-based in-air signature authentication system using smartwatch. The signature is represented by the readings of gyroscope and accelerometer compensated by device attitude readings. The system can extract features from in-air signing motions to verify whether a signature is from an imposter or the genuine user. Moreover, an in-air signature dataset, consisting of 3190 signature pairs from 22 participants, is built for validation. Experimental results demonstrate that our proposed approach achieves an equal error rate(EER) of 0.83%. Besides, we investigate the impact of properties of motion sensory data on in-air signature authentication.
Operational state assessment of cross-linked polyethylene power cable on the basis of an optimized cloud matter-element theory

Polyethylene power cables are likely to encounter high failure rates due to adverse operation conditions. In order to ensure a safe and reliable operation of the Polyethylene power cables, a state assessment approach should be conducted to evaluate the actual operation state of the Polyethylene power cables and determine the maintenance schedule. Several techniques which are used to preempt cable failure and schedule efficient maintenance in the past work have been reviewed, each of which is found with limitation. Taking a 10 kV – Polyethylene power cable as an example and considering the uncertainty of cloud model as well as the qualitative characteristics of matter-element theory, a cable operation state assessment method based on improved cloud matter-element theory with optimal entropy was proposed. Firstly, aiming at the randomness and fuzziness of operation condition level boundary, cloud matter-element theory was proposed to construct the index system of state evaluation. Secondly, based on a comprehensive consideration on the characteristics of clarity and fuzziness of grade division, the optimized cloud entropy calculation method suitable for fuzzy indices is proposed, and the traditional extension cloud theory is improved. Finally, according to the cloud matter-element model correlation degree assessment matrix and comprehensive weight, the operation condition grade of Polyethylene power cable is comprehensively evaluated. The method could effectively deal with the uncertainties in the state assessment according to the changes of the power cable operation condition, and the assessment results were more objective and reasonable. The proposed method in this paper conquers the drawbacks of the past work and can effectively deal with the uncertainty factors in transformer state assessment. The evaluation results are closer to the real state of the equipment and have wider application scenarios.
Emotion Detection from Text Using LSTM

Emotion can be expressed in several forms, which can be facial expressions, voice and text. This paper implements a methodology based on the LSTM (long-short-term memory) theory that includes principles to identify the text emotions from NLP (Natural Language Processing) domain. It proposes an approach based on LSTM, which is defined to identify feelings in text using pre-trained words from Glove Word Embeddings.
A two-layer aggregation model with effective consistency for large-scale Gaussian process regression

To scale full Gaussian process (GP) to large-scale data sets, aggregation models divide the dataset into independent subsets for factorized training, and then aggregate predictions from distributed experts. Some aggregation models have been able to produce consistent predictions which converge to the latent function when data size approaches infinity. However, these consistent predictions will become ineffective due to the limited subset size of experts. Oriented by the transition from theory to practice, the key idea is using Generalized Robust Bayesian Committee Machine (GRBCM) with corrective function to replace experts of Generalized Product of Experts (GPoE) which focuses on global information, in order to get rid of the limitation of the experts’ size. Such a nested two-layer structure enables the proposed Generalized Product of Generalized Robust Bayesian Committee Machine (GPoGRBCM) to provide effective predictions on large-scale datasets and to inherit virtues of aggregations, e.g., a slightly flawed Bayesian inference framework, distributed/parallel computing. Furthermore, we perform comparisons of GPoGRBCM against the state-of-the-art aggregation models on one toy example and six real-world datasets with up to more than 3 million training points, showing dramatic performance improvement on scalability, capability, controllability, and robustness.
A Lagrangian-based score for assessing the quality of pairwise constraints in semi-supervised clustering

Clustering algorithms help identify homogeneous subgroups from data. In some cases, additional information about the relationship among some subsets of the data exists. When using a semi-supervised clustering algorithm, an expert may provide additional information to constrain the solution based on that knowledge and, in doing so, guide the algorithm to a more useful and meaningful solution. Such additional information often takes the form of a cannot-link constraint (i.e., two data points cannot be part of the same cluster) or a must-link constraint (i.e., two data points must be part of the same cluster). A key challenge for users of such constraints in semi-supervised learning algorithms, however, is that the addition of inaccurate or conflicting constraints can decrease accuracy and little is known about how to detect whether expert-imposed constraints are likely incorrect. In the present work, we introduce a method to score each must-link and cannot-link pairwise constraint as likely incorrect. Using synthetic experimental examples and real data, we show that the resulting impact score can successfully identify individual constraints that should be removed or revised.
Feedback-based object detection for multi-person pose estimation

In this paper, a novel method is proposed for increasing the performance through coupling of top-down models adjusting the object detector based on a new loss function. Generally, object detectors and keypoint estimators are sequentially used in real-time multi-person pose estimations; however, these two models are separately trained. Therefore, the results of the object detector are not optimized for the keypoint estimator. To solve this problem, we analyze the relationship between the two models and propose a feedback-based loss optimization in the object detector, based on the estimation results of the keypoint estimator. In addition, the resulting bounding box of the object detector is readjusted to improve the accuracy of the keypoint estimation model. The experimental results demonstrate that the proposed approach can perform real-time operations with a high frame rate similar to that of the baseline model. Moreover, it achieved an accuracy of 74.2 average precision (AP), which is higher than the state-of-the-arts model including the human detector used in the experiment.
The Effects of Mobile Phone Technology, Knowledge Creation and Diffusion on Inclusive Human Development in Sub-Saharan Africa

This paper examines the joint effects of mobile phone technology, knowledge creation and diffusion on inclusive human development in 49 sub-Saharan African (SSA) countries. The empirical evidence is based on Tobit regressions for the period 2000-2012. The net effects of interactions between the mobile phone, knowledge creation and diffusion variables are positive indicating that the combined effects of these variables improve inclusive human development in SSA countries. Further analysis dividing the data set into a number of fundamental characteristics based on economic, legal, religion and political stability associated with African economies show that mobile phone penetration and associated innovation in SSA improve inclusive human development irrespective of the country’s level of income, legal origins, religious orientation and the state of the nation. The pupil-teacher ratio exerts a negative influence on the outcome variable which is favorable for inclusive human development because higher ratios denote lower education quality since more pupils are accommodated by fewer teachers. The study contributes to innovation diffusion theory and economic development literature.
A framework for benchmarking uncertainty in deep regression

Abstract We propose a framework for the assessment of uncertainty quantification in deep regression. The framework is based on regression problems where the regression function is a linear combination of nonlinear functions. Basically, any level of complexity can be realized through the choice of the nonlinear functions and the dimensionality of their domain. Results of an uncertainty quantification for deep regression are compared against those obtained by a statistical reference method. The reference method utilizes knowledge about the underlying nonlinear functions and is based on Bayesian linear regression using a prior reference. The flexibility, together with the availability of a reference solution, makes the framework suitable for defining benchmark sets for uncertainty quantification. Reliability of uncertainty quantification is assessed in terms of coverage probabilities, and accuracy through the size of calculated uncertainties. We illustrate the proposed framework by applying it to current approaches for uncertainty quantification in deep regression. In addition, results for three real-world regression tasks are presented.
The Multi-Domain International Search on Speech 2020 ALBAYZIN Evaluation: Overview, Systems, Results, Discussion and Post-Evaluation Analyses

The large amount of information stored in audio and video repositories makes search on speech (SoS) a challenging area that is continuously receiving much interest. Within SoS, spoken term detection (STD) aims to retrieve speech data given a text-based representation of a search query (which can include one or more words). On the other hand, query-by-example spoken term detection (QbE STD) aims to retrieve speech data given an acoustic representation of a search query. This is the first paper that presents an internationally open multi-domain evaluation for SoS in Spanish that includes both STD and QbE STD tasks. The evaluation was carefully designed so that several post-evaluation analyses of the main results could be carried out. The evaluation tasks aim to retrieve the speech files that contain the queries, providing their start and end times and a score that reflects how likely the detection within the given time intervals and speech file is. Three different speech databases in Spanish that comprise different domains were employed in the evaluation: the MAVIR database, which comprises a set of talks from workshops; the RTVE database, which includes broadcast news programs; and the SPARL20 database, which contains Spanish parliament sessions. We present the evaluation itself, the three databases, the evaluation metric, the systems submitted to the evaluation, the evaluation results and some detailed post-evaluation analyses based on specific query properties (in-vocabulary/out-of-vocabulary queries, single-word/multi-word queries and native/foreign queries). The most novel features of the submitted systems are a data augmentation technique for the STD task and an end-to-end system for the QbE STD task. The obtained results suggest that there is clearly room for improvement in the SoS task and that performance is highly sensitive to changes in the data domain.
Hierarchical Summarization for Longform Spoken Dialog

Every day we are surrounded by spoken dialog. This medium delivers rich diverse streams of information auditorily; however, systematically understanding dialog can often be non-trivial. Despite the pervasiveness of spoken dialog, automated speech understanding and quality information extraction remains markedly poor, especially when compared to written prose. Furthermore, compared to understanding text, auditory communication poses many additional challenges such as speaker disfluencies, informal prose styles, and lack of structure. These concerns all demonstrate the need for a distinctly speech tailored interactive system to help users understand and navigate the spoken language domain. While individual automatic speech recognition (ASR) and text summarization methods already exist, they are imperfect technologies; neither consider user purpose and intent nor address spoken language induced complications. Consequently, we design a two stage ASR and text summarization pipeline and propose a set of semantic segmentation and merging algorithms to resolve these speech modeling challenges. Our system enables users to easily browse and navigate content as well as recover from errors in these underlying technologies. Finally, we present an evaluation of the system which highlights user preference for hierarchical summarization as a tool to quickly skim audio and identify content of interest to the user.
The Model of Curriculum Constructor

The curriculum constructor model based on ontology that allows establishing connection between curriculum objectives, learning outcomes, expertise and disciplines is introduced in this paper. Thus, it makes possible to consider the compliance of the Education Programme with the learning outcomes and expertise, as well as, it helps check the curriculum goal achievements, the learning outcome development and mastering the competences. The constructor lets create different subsets with different properties and set various individual trajectories.