Reviewerly/exHarmony huggingface dataset:
An Expression-Reinforced Sparse Subspace Clustering By Orthogonal Matching Pursuit

Sparse Subspace Clustering (SSC) is an efficient method for clustering high-dimensional data. Traditional SSC calculates the sparse representation of each point separately to obtain coefficient matrix, might undermine the clustering results due to the potential of representation for interaction being neglected. In this paper, based on orthogonal matching pursuit (OMP), a new module for SSC is developed to release potential of interaction among representations of similar data points for expression reinforcement, and an Expression-Reinforced Max Chain for Interaction (ER-MCI) algorithm is proposed to strengthen the effectiveness of affinity matrix. By finding a specific atom of each point and tracking these atoms to build the chain iteratively on which all atoms would lie in the same subspace with high probability. Experimental results show that our approach achieves better clustering performances compared with other SSC algorithms in terms of clustering accuracy, anti-noise ability and subspace-preserving property, and keeps time efficiency.
Explaining Object Motion Using Answer Set Programming

Identifying objects and their motion from sequences of digital images is of growing interest due to the increasing application of autonomous mobile systems like autonomous cars or mobile robots. Although the reliability of object recognition has been increased significantly, there are often cases arising where objects are not classified correctly. There might be objects detected in almost all images of a sequence but not all. Hence, there is a need for finding such situations and taking appropriate measures to improve the overall detection performance. In this paper, we contribute to this research direction and discuss the use of logic for identifying motion in sequences of images that can be used for this purpose. In particular, we introduce the application of diagnosis and show an implementation using answer set programming.
Why Not? Tell us the Reason for Writer Dissimilarity

Writer verification has drawn significant attention over the past few decades due to its extensive applications in forensics and biometrics. In traditional writer verification, handwriting similarity/dissimilarity analysis is mostly performed by extracting two feature vectors from two respective handwritten samples, followed by comparing them in relation to their similarity. In the state-of-the-art writer verification approaches, a distance metric is usually employed in terms of the similarity between two handwritten samples. If the distance between two handwritten samples is greater than a given threshold, then the samples are assumed to be written by two different writers, otherwise, they are considered to be due to the same writer. In this paper, for the very first time, we propose a model that generates English sentences to explain reasons for writer dissimilarity/similarity. First, our proposed model obtains features from handwritten images by employing a convolutional neural network, verifies the writer using a Siamese architecture, and generates English words using a recurrent neural network. Finally, these two networks are merged using an affine transformation to produce an explanatory sentence in support of writer similarity/dissimilarity. We evaluated our model on a handwritten numeral database of 100 writers and obtained promising results.
Optimize FPGA-Based Neural Network Accelerator with Bit-Shift Quantization

Well-programmed Field Programmable Gate Arrays (FPGAs) can accelerate Deep Neural Network (DNN) with high power efficiency. The dominant workloads of DNNs are Multiply Accumulates (MACs), which can be directly mapped to Digital Signal Processors (DSPs) in the FPGA. A DNN accelerator pursuing high performance can consume almost all the DSPs, but with a considerable amount of Look-up Tables (LUTs) in the FPGA unused or performing MACs inefficiently. To solve this problem, we present a Bit-Shift method for FPGA-based DNN accelerator to fully utilize the resources in the FPGA. The MAC is converted to a limited number of shift-and-add operations, which can be implemented by LUTs with significant improvement of efficiency. A quantization method based on Minimum Mean Absolute Error (MMAE) is proposed to preserve the accuracy of the DNN inference in the conversion of DNN parameters without re-training. The quantized parameters can be compressed to a fixed and fewer number of bits to reduce the memory bandwidth. Accordingly, a Bit-Shift architecture is designed to load the compressed parameters and perform the converted MAC calculations without extra decompression module. A large scale DNN accelerator with the proposed Bit-Shift architecture is implemented in a Xilinx VU095 FPGA. Experimental results show that the proposed method can boost the processing speed by 32% and reach 331 GOPS at 190MHz clock frequency for ResNet-34.
Adaptive Level Set with region analysis via Mask R-CNN: A comparison against classical methods

The World Health Organization (WHO) registered around 3 million deaths caused by Chronic Obstructive Pulmonary Disease (COPD), representing 5% of all deaths registered in 2015. Computed tomography (CT) is among the main exam for clinical diagnosis of lung diseases. However, the first challenge experienced by the radiology specialist is to define the region of interest. Thus, the identification of diseases using systems of computer-aided diagnosis (CAD) medical via image processing techniques offers more accuracy and agility for diagnosis. In this paper, we propose a new automatic segmentation of lungs in CT images. Our method uses a deep learning technique called Mask Region-Based Convolutional Neural Network (Mask R-CNN), combined with an adaptive active contour method called Fast Morphological Geodesic Active Contour (FGAC). The proposed method was evaluated with 72 lung images, consisting of 24 images of healthy volunteers and 48 of unhealthy patients. Our approach achieved promising results with Accuracy of 98.93%, Matthews Correlation Coefficient of 95.84%, Hausdorff Distance of 5.48, DICE of 96.47%, and Jaccard of 93.24%. Thus, our method surpasses a recent classic approach that also uses FGAC as a segmentation method.
A Feature Pair Fusion And Hierarchical Learning Framework For Video Re-Localization

Video re-localization has become an emerging research topic nowadays but existing methods still have many deficiencies. The existing deficiencies mainly lie in the interference caused by the irrelevant information in the input reference video and the ignorance of the correlation between query and reference video features. Therefore, we present a novel framework named Semantic Relevance Learning Network to address these shortcomings. First, we extract effective proposals from reference video as new inputs to reduce interference from irrelevant video frames. Second, two key components of our proposed model, the Attention-based Fusion Tensor and Semantic Relevance Measurement, jointly explore the intrinsic correlation between video feature pairs and finally get a score as measurement. To better evaluate our proposed model, we reorganize Thumos14 to obtain another new dataset for the video re-localization task. For both ActivityNet and Thumos14, our model achieves the best performance reported so far.
Building Vector Representations for Candidates and Projects in a CV Recommender System

We describe a CV recommender system built for the purpose of connecting candidates with projects that are relevant to their skills. Each candidate and each project is described by a textual document (CV or a project description) from which we extract a set of skills and convert this set to a numeric representation using two known models: Latent Semantic Indexing (LSI) and Global Vectors for Word Representation (GloVe) model. Indexes built from these representations enable fast search of similar entities for a given candidate/project and the empirical results demonstrate that the obtained l2 distances correlate with the number of common skills and Jaccard similarity.
Secure grid‐based density peaks clustering on hybrid cloud for industrial IoT

Summary Cloud computing gives clients the convenience of outsourcing data calculations. However, it also brings the risk of privacy leakage, and datasets that process industrial IoT information have a high computational cost for clients. To address these problems, this paper proposes a secure grid‐based density peaks clustering algorithm for a hybrid cloud environment. First, the client utilizes the homomorphic encryption algorithm to construct encrypted objects with client dataset. Second, the client uploads the encrypted data to the cloud servers to implement our security protocol. Finally, the cloud servers return the clustering results with the disturbance to the client. The experimental results on the UCI datasets and the smart power grid dataset reveal that the secure algorithm presented in this paper can improve upon the precision and efficiency of other clustering algorithms while also preserving user privacy. Moreover, it only performs encryption and removes the disturbance operation on the client, so that the client has lower computational complexity. Therefore, the secure clustering scheme proposed in this paper is applicable to industrial IoT big data and has high security and scalability.
From Planetscope To Worldview: Micro-Satellite Image Super-Resolution With Optimal Transport Distance

The vast majority of prior work on satellite image super-resolution (SR) assumes the availability of paired training data, and then uses low-resolution (LR) images artificially generated by simple blurring and/or down-sampling. These methods often fail to produce convincing results in real-world data since the actual degradation is much more complex than manually designed. This paper presents a deep learning framework to model the degradation process of microsatellite image. To this end, we first introduce remote sensing dataset consisting of WorldView (0.4m) and PlanetScope (3m) satellite images. They are aligned to the same coordinate, but are collected at different days/times. Using such data, we design Degradation Network (DegNet), generating realistic micro-satellite images from its high-resolution (HR) counterpart. A degradation loss using an optimal transport distance is proposed which makes the empirical distribution, i.e., histogram of outputs to be similar to that of real microsatellite images. It faithfully reflects the degradation characteristic of micro-satellite while preserving the content of an input. Finally, a SR network is trained with the generated LR-HR pairs. Extensive experiments show that the proposed method greatly improves the SR performance on real-world data.
A Feature Ensemble-based Approach to Malicious Domain Name Identification from Valid DNS Responses

Identifying malicious domain names in Internet activities has become an effective method to protect Internet users. Previous works have achieved great identification results, but they highly rely on historical Domain Name System (DNS) responses and external intelligence sources. Thus, they may fail to identify unknown domain name without any prior knowledge. In this paper, we propose Glacier, a feature ensemble-based approach to identifying malicious domain names from valid DNS responses. Glacier addresses the aforementioned problem by utilizing two types of features in domain name strings: the linguistical features and the statistical features. (1) Linguistical features are vector representations generated from the character sequences of domain names by a bidirectional long short-term memory (BiLSTM) neural network. It is worthy to notice that we modify the last BiLSTM layer to enhance the expressiveness of the linguistical features. (2) Statistical features are six manually designed statistics that represent the structural information of a domain name. Structural information can hardly be learnt by a BiLSTM neural network directly. Thus, combining statistical features with linguistical features can improve the effectiveness of malicious domain name identification. We evaluate the identification ability of Glacier on a real-world domain name data set. The best metrics of Glacier are an average accuracy of 90.86% and an average F1-score of 84.37%. Our experimental results show that Glacier can accurately identify resolvable malicious domain names without any DNS traffic data or prior knowledge about unknown domain names.
GuideSQL: Utilizing Tables to Guide the Prediction of Columns for Text-to-SQL Generation

Text-to-SQL is a task of synthesizing SQL queries from utterances. Most existing approaches of text-to-SQL rarely utilize tables to guide the prediction of SQL query. We present a novel approach called GuideSQL which predicts tables first and uses a pruning algorithm for removing the columns which don't belong to the predicted tables to avoid errors caused by misprediction of table-column dependencies. For reducing the prediction errors of tables, we use the top-K predicted tables to generate SQL queries and employ a string-matching algorithm to get the most reasonable one. Furthermore, a type linking mechanism is utilized to augment the relevance between utterances and schemas. On the challenging text-to-SQL benchmark SParC, we use previous query attention to get context-dependent information of SQL queries. GuideSQL obtains 36.3% question matching accuracy and 19.5% interaction matching accuracy on the dev set. With BERT augmentation, GuideSQL achieves 49.2% question matching accuracy and 31.6% interaction matching accuracy on the dev set, outperforms the previous state-of-the-art model by 2% question matching accuracy and 2.1% interaction matching accuracy.
Migration of Artificial Neural Networks to Smartphones

The paper explains the process of migration of an artificial neural network (ANN) to a smartphone device. It focuses on a situation when the ANN is already deployed on a desktop computer. Our goal is to describe the process of the migration of the network to a mobile environment. In the current system we have, images have to be scanned and fed to a computer that is applying the ANN. However, every smartphone has a camera that can be used instead of a scanner. Migration to such a device should save the overall processing time. ANNs in the field of computer vision have a long history. Despite that, mobile phones were not used as a target platform for ANNs because they did not have enough processing power. In the past years, smartphones have developed dramatically, and they have the processing power necessary for deploying ANNs now. Also, major mobile operating systems, Android and iOS, have included the support for the deployment.
Immersion in Desktop-Based Web Browsing

In this article, we report on findings from an empirical study designed to assess factors determining the experience of immersion in desktop-based web-browsing, which is identified as occurring when a user reports a deep sense of cognitive and perceptual absorption in the content and/or the interaction with a website. A within-subjects study was designed and administered to participants (N = 29) who interacted with three different websites. Results of the self-report measure have been factor analyzed by Principal Component Analysis (PCA). The PCA yielded three factors (media form and content; control and continuity; consistency), which appear to influence the determinants of an immersive experience, namely vividness and interactivity, in different ways. Directions for further research are briefly discussed.
Gradient Methods With Dynamic Inexact Oracles

We present a framework for generalizing the primal-dual gradient method, also known as the gradient descent ascent method, for solving convex-concave minimax problems. The framework is based on the observation that the primal-dual gradient method can be viewed as an inexact gradient method applied to the primal problem. Unlike the setting of traditional inexact gradient methods, the inexact gradient is computed by a dynamic inexact oracle, which is a discrete-time dynamical system whose output asymptotically approaches the exact gradient. For minimax problems, dynamic inexact oracles are capable of modeling a range of first-order methods for computing the gradient of the primal objective, which relies on solving the inner maximization problem. We provide a unified convergence analysis of gradient methods with dynamic inexact oracles and demonstrate its use in creating new accelerated primal-dual algorithms.
Multiple Timescale and Gated Mechanisms for Action and Language Learning in Robotics

Recurrent Neural Network (RNN) have been used for sequence-related learning tasks, such as language and action, in the field of cognitive robotics. Gated mechanisms used in LSTM and GRU perform well in remembering long-term dependency. But to better mimic the neural dynamics in cognitive processes, the Multiple Time-scales (MT) RNN uses a hierarchical organization of memory updates which is similar to human cognition. Since the MT feature is typically used with a vanilla RNN or different gated mechanisms, its effect on the updates and training is still not fully uncovered. Therefore, we conduct a comparative experiment on two MT recurrent neural network models, i.e. the Multiple Time-Scale Recurrent Neural Network (MTRNN) and the Multiple Time-Scale Gated Recurrent Unit (MTGRU), for action sequence learning in robotics. The experiment shows that the MTRNN model can be used in learning tasks with low requirements for learning of long-term dependency due to its low computation. On the other hand, the MTGRU model is appropriate for learning the longterm dependency. Furthermore, because of the duplicated feature of the MT and the GRU feature, we also propose a simplified MTGRU model, named Multiple Time-scale SingleGate Recurrent Unit (MTSRU) which could reduce computational cost while it achieves the similar performance as the original version.
Integration of Web-Scraped Data in CPM Tools: The Case of Project Sibilla

Modern corporate performance management (CPM) systems are crucial tools for enterprises, but they typically lack a seamless integration with solutions in the Industry 4.0 domain for the exploitation of large amounts of data originated outside the enterprise boundaries. In this paper, we propose a solution to this problem, according to lessons learned in the development of project “Sibilla,” aimed at devising innovative tools in the business intelligence area. A proper software module is introduced with the purpose of enriching existing predictive analysis models with knowledge extracted from the Web and social networks. In particular, we describe how to support two functionalities: identification of planned real-world events and monitoring of public opinion on topics of interest to the company. The effectiveness of the proposed solution has been evaluated by means of a long-term experimental campaign.
Interval Evaluation of Stationary State Probabilities for Markov Set-Chain Models

Interval estimation problem for stationary state probability distribution of set-chain Markov model uncertainties of transition matrix parameters is considered. To obtain final probability vector interval estimates, an optimization approach is proposed using regularized Lagrange function. To solve the obtained regularized bilinear programming problem, computational gradient algorithms are used with ensures the stability of resulting estimates. An example of Markov model of a Bonus-Malus system with interval uncertainties of claim flow intensity is presented.
Mathematical Modelling of a DC-DC Boost Converter with Index Matrices

This paper aims to present a mathematical model of a DC-DC boost converter based on index matrices. The last ones represent electronic components, electrical nodes and loops, and also a switching topology as a result of summation of index matrices of different sizes on such entities. The proposed methodology is shown to be extendable to wide class of electronic devices. Arrangement of rows and columns in usual matrices is substituted by proper index sets. Index matrices with real number elements are used in matrix equations in order voltage and amperage values to be obtained in step-wise manner.
Challenge Training to Simulate Inference in Machine Translation

Despite much success has been achieved, neural machine translation (NMT) suffers from exposure bias and evaluation discrepancy. To be specific, the generation inconsistency between the training and inference process further causes error accumulation and distribution disparity. Furthermore, NMT models are generally optimized on word-level cross-entropy loss function but evaluated by sentence-level metrics. This evaluation-level mismatch may mislead the promotion of translation performance. To address these two drawbacks, we propose to challenge training to gradually simulate inference. Namely, the decoder is fed with inferred words rather than ground truth words during training with a dynamic probability. To ensure accuracy and integrity, we adopt alignment and tailoring on the inferred words. Therefore, these words can leverage inferred information to help improve the training process. As for the dynamic simulation, we define a novel loss-sensitive probability that can sense the converge of training and finetune itself in turn. Experimental results on IWSLT 2016 German-English and WMT 2019 English-Chinese datasets demonstrate that our methodology can significantly improve translation quality. The approach of alignment and tailoring outperforms previous works. Meanwhile, the proposed loss-sensitive sampling is also useful for other state-of-the-art scheduled sampling methods to achieve further promotion.
rScholar: An Interactive Contextual User Interface to Enhance UX of Scholarly Recommender Systems

Scholarly recommender systems attempt to reduce the number of research resources or papers presented to scholars and predict the utility of resources for their scholarly tasks. Industry practitioners and academic researchers agree that the interface of a recommender system may have as profound an effect on users' experience as the recommender's algorithmic performance. Despite this, little attention has been given to User Interface and Interaction Design of scholarly recommender systems. Scholarly recommender systems rarely use contextual data, such as personal and situational characteristics, that can dramatically affect the user experience (UX) and effectiveness of SRSs. This research presents rScholar, a scholarly recommender system interface that utilizes User Interface and Interaction Design adequacy indicators as well as the user contextual data to enhance the user experience. The evaluation of rScholar is performed by user studies and expert review of feedback by users and by comparison to the UI of the recommendation display of Google Scholar.
Bio-Inspired End-Stopped Neuron Model for the Curves Segmentation

This article is dedicated to modeling the end-stopped neuron. This type of neuron gives the maximum response at the end of the line and is used to refine the edge. The article provides an overview of different models of end-stopped neurons. I have proposed a simpler and more accurate model of an end-stopped neuron based on the use of Gabor filters in antiphase. For this purpose, the models of simple and complex cells whose output is used in the proposed model are also described. Simple cells are based on the use of a Gabor filter, the parameters of which are also described in this article. The proposed model has shown its effectiveness.