Reviewerly/exHarmony huggingface dataset:
Idiom-Based Features in Sentiment Analysis: Cutting the Gordian Knot

In this paper we describe an automated approach to enriching sentiment analysis with idiom-based features. Specifically, we automated the development of the supporting lexico-semantic resources, which include (1) a set of rules used to identify idioms in text and (2) their sentiment polarity classifications. Our method demonstrates how idiom dictionaries, which are readily available general pedagogical resources, can be adapted into purpose-specific computational resources automatically. These resources were then used to replace the manually engineered counterparts in an existing system, which originally outperformed the baseline sentiment analysis approaches by 17 percentage points on average, taking the F-measure from 40s into 60s. The new fully automated approach outperformed the baselines by 8 percentage points on average taking the F-measure from 40s into 50s. Although the latter improvement is not as high as the one achieved with the manually engineered features, it has got the advantage of being more general in a sense that it can readily utilize an arbitrary list of idioms without the knowledge acquisition overhead previously associated with this task, thereby fully automating the original approach.
A robust zero-watermarking scheme for tele-ophthalmological applications

Medical images are watermarked with patient details to impose patient identification in tele-medicine applications. Fundus images comprise of fine features like blood vessels, macula and optic disks. Conventional watermarking methods may tamper crucial information in this case. Even minutest change can distort fine features and affect final decision. ROI (Region of Interest) selection approach also fails here as fine features of fundus images are evenly distributed and it's almost impossible to properly define Region of Interest (ROI) and Region of Non-Interest (RONI) for medical practitioner. Hence arises an imperative necessity of a watermarking system which does not tamper the fundus images and results in no loss of medical information. This paper proposes a zero-watermarking system which uses singular value coefficients in Wavelet domain for generation of unique identification code from fundus images which is strategically combined with patient ID to generate watermark. Proposed technique suits to the specific requirements of fundus images and optimizes the conflicting requirements of traditional watermarking. The technique aims at unique identification, authentication and integrity verification of medical images. Experimental results reveal the robustness of the system against some common image processing attacks and indicate its suitability for secure exchange of medical images for tele-ophthalmological applications and for storage in large distributed medical databases.
Accurate Detection of Important Events in WSNs

Security applications of wireless sensor networks (such as surveillance systems) require highly accurate detection systems for distinguishing the events of vital importance from simultaneously occurring random events. In order to improve the accuracy of the detection of such events of critical importance, a distributed in-network inference scheme is proposed in this paper. The problem of detection of such events is formulated as a sparse event detection problem and the awake cycle of every node is partitioned into smaller time blocks. Such a division allows the distributed inference algorithm to precisely identify the events of importance and their exact location in the physical sensor field. In order to further minimize the false alarm rate (FAR), spatially correlated data, from targeted regions, are obtained via a compressed sensing based data gathering scheme. The proposed scheme is able to achieve a significantly high accuracy rate of 0.9875 and exceptionally low FAR of 0.01. The prodigious performance of the proposed scheme continues in the experiments performed in outdoor environment and simulations.
Fusion-based methods for result diversification in web search

Search result diversification of text documents is especially necessary when a user issues a faceted or ambiguous query to the search engine. A variety of approaches have been proposed to deal with this issue in recent years. In this article, we propose a group of fusion-based result diversification methods with the aim to improve performance that considers both relevance and diversity. They are linear combinations of scores that are obtained from different component search systems. The weight of each search system is determined by considering three factors: performance, dissimilarity, and complementarity. There are two major contributions. Firstly, we find that all the three factors of performance and complementarity and dissimilarity are useful for effective weighting of linear combination. Secondly, we present the logarithmic function-based model for converting ranking information into scores. Experiments are carried out with four groups of results submitted to the TREC web diversity task. Experimental results show that some of the fusion methods that use the aforementioned techniques perform more effectively than the state-of-the-art fusion methods for result diversification.
Improving Image Classification Accuracy With ELM and CSIFT

We present a new image classification system by integrating color information and the extreme learning machine algorithm based on the localized soft-assignment coding classification framework. Our system is original with its unique structure. It improves the image classification accuracy significantly compared to the existing image classification systems. Specifically, on classification accuracy, our system can improve more than 4% on the Caltech-101 dataset and achieve up to 3% improvement on the Caltech-256 dataset compared to existing systems.
A fuzzy image clustering method based on an improved backtracking search optimization algorithm with an inertia weight parameter

In this paper, we introduced a novel image clustering method based on combination of the classical Fuzzy C-Means (FCM) algorithm and Backtracking Search optimization Algorithm (BSA). The image clustering was achieved by minimizing the objective function of FCM with BSA. In order to improve the local search ability of the new algorithm, an inertia weight parameter (w) was proposed for BSA. The improvement was accomplished by using w in the steps of the determination of the search-direction matrix of BSA and the new algorithm was named as w-BSAFCM. In order to show the effectiveness of the new algorithm, FCM was also combined with the general forms of BSA in the same manner and three benchmark images were clustered by utilizing these algorithms. The obtained results were analyzed according to the objective function and Davies-Bouldin index values to compare the performances of the algorithms. According to the results, it was shown that w-BSAFCM can be effectively be used for solving image clustering problem.
A novel segmentation technique for online handwritten Bangla words

In the present work, we have proposed a novel Bangla word segmentation technique that is based on stroke-level busy zone formation procedure. In an unconstrained domain, people often write text where strokes may be poorly aligned (due to multi-directional skewness) and varied combination of strokes with various types of joining between them are possible while forming the words. Hence, a segmentation approach for stroke extraction is pertinent for any stroke-based word recognition system. The presence of a large volume of symbols set (58 basic symbols with more than 280 compound characters) in Bangla script makes the task more challenging. In the current experiment, our stroke-level segmentation approach effectively handles such type of Bangla words. A sub-zoning scheme within busy zone followed by a modified Down->Up->Down (DUD) concept within these sub-zones has been used to find valid segmentation points. This scheme avoids over and under-segmentation issues caused by either inherent writing pattern or due to writing style variations up to certain extent. The proposed segmentation approach has been tested on 6500 online handwritten Bangla word samples with 98.45% correct segmentation accuracy (compared with manually generated ground truth of the same database).
SPOOK - A comprehensive Space Surveillance and Tracking analysis tool

Space Surveillance and Tracking (SST) is the ability to detect and predict the movement of space debris in orbit around the Earth. SPOOK (SPace Object Observations and Kalman filtering) is a versatile and highly configurable software tool developed by Airbus, for the detection, cataloguing and orbit prediction analysis of Earth orbiting objects. It provides an integrated framework for all the activities related with SST sensor architecture simulation. Throughout this paper, the functionalities of the tool are described and examples of different simulation scenarios are provided.
Kernel Methods: Support Vector Machines

Support Vector Machines (SVMs) and Kernel methods have found a natural and effective coexistence since their introduction in the early 90s. In this article, we will describe the main concepts that motivate the importance of this relationship. In fact SVMs use kernels for learning linear predictors in high dimensional feature spaces. First, we will describe intuitively how this mechanism is realized, introducing the main concepts and definitions, i.e., maximum margin hyperplane, kernels and non-linearly separable problems. Then the main mathematical issues for linear and nonlinear SVM-based classification will be detailed. We will also introduce some important extensions of the SVMs ideas, by considering the Soft Margin Classification, SVM multi-class classification, SVM clustering, and SVM regression.
Robust Spatio-Temporal Clustering and Reconstruction of Multiple Deformable Bodies

In this paper we present an approach to reconstruct the 3D shape of multiple deforming objects from a collection of sparse, noisy and possibly incomplete 2D point tracks acquired by a single monocular camera. Additionally, the proposed solution estimates the camera motion and reasons about the spatial segmentation (i.e., identifies each of the deforming objects in every frame) and temporal clustering (i.e., splits the sequence into motion primitive actions). This advances competing work, which mainly tackled the problem for one single object and non-occluded tracks. In order to handle several objects at a time from partial observations, we model point trajectories as a union of spatial and temporal subspaces, and optimize the parameters of both modalities, the non-observed point tracks, the camera motion, and the time-varying 3D shape via augmented Lagrange multipliers. The algorithm is fully unsupervised and does not require any training data at all. We thoroughly validate the method on challenging scenarios with several human subjects performing different activities which involve complex motions and close interaction. We show our approach achieves state-of-the-art 3D reconstruction results, while it also provides spatial and temporal segmentation.
A novel framework for measuring software quality-in-use based on semantic similarity and sentiment analysis of software reviews

Software quality in use (QinU) relates to human-software interactions when a software product is used in a particular context. Currently, QinU measurement models are bound to ineffective measurement formulation and many models are subjectively incoherent. This paper proposes a novel QinU framework (QinUF) to measure QinU competently consuming software reviews. The framework has three components: QinU prediction, polarity classification, and QinU scoring. The QinU prediction component computationally maps software review-sentences to its respective QinU characteristics (topics) of the ISO 25010 model based on a text similarity measure. The topic prediction problem is run as a text to text similarity; where the first text (test) is the actual unlabeled review-sentence and the second text is the set of selected features (keywords) from a benchmark dataset. The polarity classification component classifies each test sentence to its polarity orientation; the respective sentimental values are recorded. To score QinU, the sentimental values are grouped and summarized into their respective QinU topics. The QinUF evaluation over real-life scenarios showed that the QinUF automates software QinU measurement; therefore, users could compare and acquire software on the fly. The framework is consistent and superior to related compared works.
Evolution analysis of a UAV real-time operating system from a network perspective

With the flourishing development of Unmanned Aerial Vehicles (UAVs), the mission tasks of UAVs have become more and more complex. Consequently, a Real-Time Operating System (RTOS) that provides operating environments for various mission services on these UAVs has become crucial, which leads to the necessity of having a deep understanding of an RTOS. In this paper, an empirical study is conducted on FreeRTOS, a commonly used RTOS for UAVs, from a complex network perspective. A total of 85 releases of FreeRTOS, from V2.4.2 to V10.0.0, are modeled as directed networks, in which the nodes represent functions and the edges denote function calls. It is found that the size of the FreeRTOS network has grown almost linearly with the evolution of the versions, while its main core has evolved steadily. In addition, a k-core analysis-based metric is proposed to identify major functionality changes of FreeRTOS during its evolution. The result shows that the identified versions are consistent with the version change logs. Finally, it is found that the clustering coefficient of the Linux OS scheduler is larger than that of the FreeRTOS scheduler. In conclusion, the empirical results provide useful guidance for developers and users of UAV RTOSs.
Deep depth-based representations of graphs through deep learning networks

Graph-based representations are powerful tools in structural pattern recognition and machine learning. In this paper, we propose a framework of computing the deep depth-based representations for graph structures. Our work links the ideas of graph complexity measures and deep learning networks. Specifically, for a set of graphs, we commence by computing depth-based representations rooted at each vertex as vertex points. In order to identify an informative depth-based representation subset, we employ the well-known k-means method to identify M dominant centroids of the depth-based representation vectors as prototype representations. To overcome the burdensome computation of using depth-based representations for all graphs, we propose to use the prototype representations to train a deep autoencoder network, that is optimized using Stochastic Gradient Descent together with the Deep Belief Network for pretraining. By inputting the depth-based representations of vertices over all graphs to the trained deep network, we compute the deep representation for each vertex. The resulting deep depth-based representation of a graph is computed by averaging the deep representations of its complete set of vertices. We theoretically demonstrate that the deep depth-based representations of graphs not only reflect both the local and global characteristics of graphs through the depth-based representations, but also capture the main structural relationship and information content over all graphs under investigations. Experimental evaluations demonstrate the effectiveness of the proposed method.
Webpage Depth Viewability Prediction Using Deep Sequential Neural Networks

Display advertising is the most important revenue source for publishers in the online publishing industry. The ad pricing standards are shifting to a new model in which ads are paid only if they are viewed. Consequently, an important problem for publishers is to predict the probability that an ad at a given page depth will be shown on a user's screen for a certain dwell time. This paper proposes deep learning models based on Long Short-Term Memory (LSTM) to predict the viewability of any page depth for any given dwell time. The main novelty of our best model consists in the combination of bi-directional LSTM networks, encoder-decoder structure, and residual connections. The experimental results over a dataset collected from a large online publisher demonstrate that the proposed LSTM-based sequential neural networks outperform the comparison methods in terms of prediction performance.
Automatically Categorizing Software Technologies

Informal language and the absence of a standard taxonomy for software technologies make it difficult to reliably analyze technology trends on discussion forums and other on-line venues. We propose an automated approach called Witt for the categorization of software technologies (an expanded version of the hypernym discovery problem). Witt takes as input a phrase describing a software technology or concept and returns a general category that describes it (e.g., integrated development environment), along with attributes that further qualify it (commercial, php, etc.). By extension, the approach enables the dynamic creation of lists of all technologies of a given type (e.g., web application frameworks). Our approach relies on Stack Overflow and Wikipedia, and involves numerous original domain adaptations and a new solution to the problem of normalizing automatically-detected hypernyms. We compared Witt with six independent taxonomy tools and found that, when applied to software terms, Witt demonstrated better coverage than all evaluated alternative solutions, without a corresponding degradation in false positive rate.
Combining Context and Knowledge Representations for Chemical-disease Relation Extraction

Automatically extracting the relationships between chemicals and diseases is significantly important to various areas of biomedical research and health care. Biomedical experts have built many large-scale knowledge bases (KBs) to advance the development of biomedical research. KBs contain huge amounts of structured information about entities and relationships, therefore plays a pivotal role in chemical-disease relation (CDR) extraction. However, previous researches pay less attention to the prior knowledge existing in KBs. This paper proposes a neural network-based attention model (NAM) for CDR extraction, which makes full use of context information in documents and prior knowledge in KBs. For a pair of entities in a document, an attention mechanism is employed to select important context words with respect to the relation representations learned from KBs. Experiments on the BioCreative V CDR dataset show that combining context and knowledge representations through the attention mechanism, could significantly improve the CDR extraction performance while achieve comparable results with state-of-the-art systems.
Asymptotic normality for wavelet deconvolution density estimators

This current paper shows the asymptotic normality for wavelet deconvolution density estimators, when a density function belongs to some Lp(R) (p>2) and the noises are moderately ill-posed with the index β. The estimators include both the linear and non-linear wavelet ones. It turns out that the situation for 0<β≤1 is more complicated than that for β>1.
Factors affecting intention to disclose patients’ health information

Inadvertent violation of health information security by healthcare providers is a serious concern. Prior study identifies nursing students as the weakest link in health information security. Despite its potential significance in the context of health information security, nursing students’ deviant behavior has been under-investigated. To better understand this, this study develops a research model of nursing students’ behavior toward disclosing health information. This study identifies health information security awareness (HISA), medical assessment, and self-efficacy as factors affecting the behavior of disclosing a patient's health information. Our study empirically tests the model with data collected from 105 nursing students. We find that HISA directly affects intentions to disclose, while medical assessment mediates the relationship between HISA and intentions to disclose. We also find that HISA affects self-efficacy that, in turn, affects intentions to disclose via medical assessment.
A Novel Segmentation Based Depth Map Up-Sampling

A novel color image segmentation-based depth map upsampling method is proposed in this paper. In this method, the color image is segmented into a certain number of connected regions first. Based on the segmentation result, the target pixels will be interpolated by the seed pixels <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> The seed pixels are directly from the low resolution depth maps, i.e., the ones that have depth values. The targets are those without depth and to be interpolated. regionally. In the segmentation part, simple linear iterative clustering is introduced to generate superpixels in the first place. Then, the obtained superpixels will be judged whether they are correct-clustered or not, and the incorrect-clustered ones will be subdivided with an adaptive region-growing strategy. Third, the regions that have no seed will be constantly merged into their nearest neighbors, until seed pixel can be found in each independent region. Finally, adjacent regions that have quite small depth gaps will be united as one. The proposed color image segmentation strictly follows the guidance of the depth; therefore, the segmented regions adhere to the depth boundary well. In the interpolation part, the targets will be interpolated with their surrounding seeds weighted by a joint trilateral filter (JTF). The JTF is constructed by three terms: the color term, the distance term, and the region term, which are driven by the previous segmentation result. Experimental results indicate that our method greatly reduces depth bleeding and depth confusion artifacts, and leads to clear depth boundary in the up-sampled image. Comparisons with the state of art verify the advantages of the proposed method in both visual experience and quantitative evaluations.