Reviewerly/exHarmony huggingface dataset:
Guided-spatio-temporal Filtering for Extracting Sound from Optically Measured Images Containing Occluding Objects

Recent development of optical interferometry enables us to measure sound without placing any device inside the sound field. In particular, parallel phase-shifting interferometry (PPSI) has realized advanced measurement of refractive index of air. Its novel application investigated very recently is simultaneous visualization of flow and sound, which had been difficult until PPSI enabled high-speed and accurate measurement several years ago. However, for understanding aerodynamic sound, separation of air flow and sound is necessary since they are mixed up in the observed video. In this paper, guided-spatio-temporal filtering is proposed to separate sound from the optically measured images. Guided filtering is combined with a physical-model-based spatio-temporal filterbank for extracting sound-related information without the undesired effect caused by the image boundary or occluding objects. Such image boundary and occluding objects are typical difficulty arose in signal processing of an optically measured sound filed.
Use of Social Media Tools by the Academic Libraries in Northern India: A Study

The use of various social media tools in libraries is not a new topic and is in vogue for more than a decade now. Academic libraries around the globe are widely using social media tools to reach to their users and promote their products and services. The present study was carried out to determine the use of Social Media tools used for promoting the library resources and services in the academic libraries in Northern India. The library websites were analyzed for identifying social media tools being used. The study reveals that the majority of the academic libraries do not use any Social Media tool. However, their respective parent organizations use a few Social Media tools like Facebook, Twitter, Ask a Librarian and RSS feeds. The study further reveals that Email service is being used by many libraries followed by YouTube and Blog services.
A Time-Efficient Co-Operative Path Planning Model Combined with Task Assignment for Multi-Agent Systems

Dealing with uncertainties along with high-efficiency planning for task assignment problem is still challenging, especially for multi-agent systems. In this paper, two frameworks—Compromise View model and the Nearest-Neighbour Search model—are analyzed and compared for co-operative path planning combined with task assignment of a multi-agent system in dynamic environments. Both frameworks are capable of dynamically controlling a number of autonomous agents to accomplish multiple tasks at different locations. Furthermore, these two models are capable of dealing with dynamically changing environments. In both approaches, the Particle Swarm Optimization-based method is applied for path planning. The path planning approach combined with the obstacle avoidance strategy is integrated with the task assignment problem. In one framework, the Compromise View model is used for completing the tasks and a combination of clustering method with the Nearest-Neighbour Search model is used to assign tasks to the other framework. The frameworks are compared in terms of computational time and the resulting path length. Results indicate that the Nearest-Neighbour Search model is much faster than the Compromise View model. However, the Nearest-Neighbour Search model generates longer paths to accomplish the mission. By following the Nearest-Neighbour Search approach, agents can successfully accomplish their mission, even under uncertainties such as malfunction of individual agents. The Nearest-Neighbour Search framework is highly effective due to its reactive structure. As per requirements, to save time, after completing its own tasks, one agent can complete the remaining tasks of other agents. The simulation results show that the Nearest-Neighbour Search model is an effective and robust way of solving co-operative path planning combined with task assignment problems.
Medical gesture recognition using dynamic arc length warping

Hand gesture recognition is a promising research area often used in applications of human–computer interactions in the medical field. In this paper, we present a novel approach to differentiate gestures based on an arc-length parametrization and a curvature analysis of 3D trajectories. This new method called dynamic arc length warping (DALW) can outperform classic multi dimensional-dynamic time warping (MD-DTW) algorithm as it is invariant to sensor location and more tolerant to temporal distortions. Experimental validation of the algorithm is presented using different gestures and sensors in biomedical applications: an exoskeleton apparatus, surgical gestures captured by an instrumented laparoscopic device and finally, a birth simulator with an instrumented forceps. A basic perceptron multilayer neural network was implemented in order to perform the classification. Results involve an average increase of 7.14% in the classification rates by using DALW distance, compared to the classical MD-DTW.
OrigamiSpeaker

In this study, we present an OrigamiSpeaker which can be handcrafted with silver nano-particle ink on a paper substrate. The OrigamiSpeaker is based on the electrostatic loudspeaker technique. The audio signal is amplified to a high voltage and applied to an electrode that vibrates to generate sound. By using Origami techniques, users are able to design various shapes of OrigamiSpeaker.
Applying Statistical Methods to Library Data Analysis

This article describes two projects at The College of New Jersey Library that used statistical methods and the programming language R to analyze electronic usage data. Project one utilized simple linear regression to model the monthly database search and full text usage data of eight years from 2010 to 2017. In project two, Pearson correlation was applied to analyzing the relationship between the journal’s impact factor and the journal’s full text downloads. It was found that there is a moderate positive relationship between these two variables.
Russian Anti-Extremism Legislation and Internet Censorship

This article is focused on the enforcement of Russian anti-extremist legislation through web regulation. Russian law enforcement has shifted from a focus on preventing terrorist violence to a focus on prosecuting online hate speech and anti-authority rhetoric under current anti-extremist and anti-terrorist laws. The vague definition of extremist activity in federal law has been broadly interpreted by law enforcement agencies. Through the idea of public security, Russian law enforcement agencies have overestimated the threat of online content without regard for the context, audience, and impact of online statements. The result has been a steady increase in blocked access to web pages and sanctions on web users and providers. The article analyzes the legal norms pertaining to web content control, and details law enforcement practice and major trends. It also identifies the most common publications, people, and organizations targeted by Russian law enforcement for sanctions based on online statements.
Content based Document Classification using Soft Cosine Measure

Document classification is a deep-rooted issue in information retrieval and assumed to be an imperative part of an assortment of applications for effective management of text documents and substantial volumes of unstructured data.Automatic document classification can be defined as a contentbased arrangement of documents to some predefined categories which is for sure, less demanding for fetching the relevant data at the right time as well as filtering and steering documents directly to users.For recovering data effortlessly at the minimum time, scientists around the globe are trying to make content-based classifiers and as a consequence, an assortment of classification frameworks has been developed.Unfortunately, because of using conventional algorithms, almost all of these frameworks fail to classify documents into the proper categories.However, this paper proposes the Soft Cosine Measure as a document classification method for classifying text documents based on its contents.This classification method considers the similarity of the features of the texts rather than making their physical compatibility.For example, the traditional systems consider 'emperor' and 'king' as two different words where the proposed method extracts the same meaning for both of these words.For feature extraction capability and content-based similarity measure technique, the proposed system scores the classification accuracy up to 98.60%, better than any other existing systems.
Blockchain and the GDPR: A Data Protection Authority Point of View

Since the publication, in 2009, of the blockchain founding article by Nakamoto [3] more and more solutions rely on this architecture. In the process an increasing number of solution process personal data stored on this type of decentralized database. In this context the property of undeniability (i.e. once data is recorded, it cannot be altered or removed) of such solutions raise questions regarding how to assure compliance to GDPR. The French Data protection authority, the CNIL, received numerous requests from both the public and the private sector regarding blockchain projects and GDPR [1]. She thus addressed the matter in November 2018 through a publication on its website [2].
Digital image correlation with reduced bias error based on digital signal upsampling theory

Based on digital signal upsampling theory, a new computing strategy has been proposed to reduce the bias error in digital image correlation (DIC) caused by intensity interpolation. For each subset, before subpixel image matching, the subimage around the target subset was processed by increasing the sampling rate with an integer factor. The increase of the sampling rate is realized by resampling in the digital domain. The combination of digital signal upsampling processing with DIC can greatly reduce the interpolation bias error. The measurement accuracy of the proposed computing strategy was investigated in this study. Both numerical experiments and real-world experiments have been conducted in order to verify the effectiveness of the proposed computing strategy. The results indicate that the bias error can be significantly reduced without sacrificing the standard deviation error. With the proposed computing strategy, high-accuracy DIC measurement with near-negligible bias error is expected.
Context-Driven Tour Planning Service: An Approach Based on Synthetic Coordinates Recommendation

The paper presents a hybrid context/model-based tour planning service aimed at recommendation generation by providing the tourists the sequence of attractions that are more interesting for him/her based on previous activity with the service. The service is developed based on SCoR recommender system that is aimed at recommendation generation based on calculating the synthetic coordinate between tourists of the service in according with their ratings. SCoR is a model-based collaborative filtering algorithm, constructing a model based on the user's personal ratings as well as exploiting collaborative information from the ratings of the rest of the users. One of the main advantages of SCoR's model is its ability to incorporate additional training information (new ratings) without having to perform the training process from the beginning. The prototype has been implemented for Android-based smartphone and has been evaluated for St. Petersburg city. For the evaluation the attraction database has been formed that includes attraction location information from OpenStreeMaps platform, location description and media from Wikipedia, and ratings from Google Place.
Casting a Wider (Neural) Net

A presentation introducing foundational concepts related to Data Science and Machine learning was given five times over a one year period to audiences with different backgrounds. This report summarizes the successes and failures in delivering this material. Insights about the topic and useful teaching resources are presented.
Optimising Worlds to Evaluate and Influence Reinforcement Learning Agents

Training reinforcement learning agents on a distribution of procedurally generated environments has become an increasingly common method for obtaining more generalisable agents. However, this makes evaluation challenging, as the space of possible environment settings is large; simply looking at the average performance is insufficient for understanding how well - or how poorly - the agents perform. To address this, we introduce a method for strategically evaluating and influencing the behaviour of reinforcement learning agents. Using deep generative modelling to encode the environment, we propose a World Agent which efficiently generates and optimises worlds (i.e. environment settings) relative to the performance of the agents. Through the use of our method on two distinct environments, we demonstrate the existence of worlds which minimise and maximise agent reward beyond the typically reported average reward. Additionally, we show how our method can also be used to modify the distribution of worlds that agents train on, influencing their emergent behaviour to be more desirable.
Image Segmentation by Fuzzy Edge Detection and Region Growing Technique

Image segmentation is one of the most important steps in computer vision and commonly used for applications like object detection, object recognition, etc. Some of the frequently used image segmentation techniques include region splitting and merging, k-means clustering, and region growing. Region growing can effectively segment the region of interest. However, due to lack of edge information, region growing process may terminate at the incorrect region boundary. To overcome the termination problem, edge detection and region growing techniques can be combined. In this paper, a new image segmentation technique is proposed. The proposed technique has two basic stages; the first stage detects edges and in the second stage, region growing technique is applied. We have used fuzzy logic to decide edge point in the image. The fuzzy membership function’s parameters are determined by genetic algorithm. In the second stage, seeded region growing technique is applied at the edge detected image, where the seed point and the threshold value for comparing neighboring pixels are computed by the algorithm. The performance of the proposed technique is compared with some of the commonly used existing segmentation techniques based on segmentation accuracy and sensitivity, and it is found that the result of the proposed technique yields better result.
An Integrated CRO and FLANN Based Classifier for a Non-Imputed and Inconsistent Dataset

This paper presents an integrated approach by considering chemical reaction optimization (CRO) and functional link artificial neural networks (FLANNs) for building a classifier from the dataset with missing value, inconsistent records, and noisy instances. Here, imputation is carried out based on the known value of two nearest neighbors to address dataset plagued with missing values. The probabilistic approach is used to remove the inconsistency from either of the datasets like original or imputed. The resulting dataset is then given as an input to boosted instance selection approach for selection of relevant instances to reduce the size of the dataset without loss of generality and compromising classification accuracy. Finally, the transformed dataset (i.e., from non-imputed and inconsistent dataset to imputed and consistent dataset) is used for developing a classifier based on CRO trained FLANN. The method is evaluated extensively through a few bench-mark datasets obtained from University of California, Irvine (UCI) repository. The experimental results confirm that our preprocessing tasks along with integrated approach can be a promising alternative tool for mitigating missing value, inconsistent records, and noisy instances.
Use of Smart Phones by Farmers as a Tool for Information Support in Agriculture

Intensity of information needed in agriculture is at an increasing pace and access to information has become the fundamental issue to be addressed. However, smart phones have the potential to narrow down the information gaps in agriculture. Therefore, the present study was conducted to assess the information utility of smart phones. The study was carried out among paddy growing farmers of Erode district of Tamil Nadu. Data on access to smart phones, period of smart phone adoption, its use for information support in agriculture and utility against other agricultural information systems used by farmers were collected using face-to-face interview method. Results revealed that smart phones were widespread among the farmers. Smart phones ranked fifth among the ten identified components of farmers'agricultural information systems regarding the information utility with improved information score due to its high frequency of utilization.
Multi-Tasking Neural Networks for Anatomy Segmentation in Prostate Brachytherapy MRI

To automate the prostate brachytherapy MRI workflow with machine learning, we investigated fully convolutional networks (FCNs) for semantic segmentation of prostate brachytherapy magnetic resonance (MR) images of different image contrasts acquired with different pulse sequences using a single neural network. Both 2D and 3D FCN approaches were investigated.
Semi-supervised Classification Using Multiple Clustering and Low-Rank Matrix Operations

This paper proposes a semi-supervised classification method which combines machine learning regularization framework and cluster ensemble approach. We use the low-rank decomposition of the co-association matrix of the ensemble to significantly speed up calculations and save memory. Numerical experiments using Monte Carlo approach demonstrate the efficiency of the proposed method.
Inter-Intra Information Preserving Attributed Network Embedding

To alleviate the problem caused by the sparsity of network structure which is often the case in large-scale network, attributed network embedding has attracted an increasing amount of attention. Some existing attributed network embedding models integrate linkage structure and node attribute by adding a consistency criterion on the structure representation and attribute representation, whereby the similarity between them can be ensured. However, to enforce the structure and attribute representations to be similar may cause the information distortion due to the inherent difference between these two kinds of information. Additionally, the existing models mainly focus on learning the inter-relation between structure and attribute information, while the intra-characteristic of each information is ignored, leading to the information loss. To address the above two problems, we propose a novel model named <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b> nter- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b> ntra Information Preserving Attributed <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> etwork <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> mbedding (IINE) to effectively learn the node representations of attributed network, which can <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">not only</i> capture the inter-relation between structure and attribute information with less information distortion <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">but also</i> effectively preserve the intra-characteristic of each information. The proposed model is composed of a primary model named <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coupled autoencoder</b> and two auxiliary models named <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">structure miner</b> and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">attribute miner</b> . The coupled autoencoder trains the node representation by smoothly combining both structure and attribute information, while the structure miner and attribute miner are utilized to further mine the intra-characteristic from the corresponding information so as to assist the primary model. The Extensive experiments are conducted on seven real-world datasets, and the results confirm the superior performance of IINE over several state-of-the-art models.
Extracting Statistical Mentions from Textual Claims to Provide Trusted Content

Claims on statistic (numerical) data, e.g., immigrant populations, are often fact-checked. We present a novel approach to extract from text documents, e.g., online media articles, mentions of statistic entities from a reference source. A claim states that an entity has certain value, at a certain time. This completes a fact-checking pipeline from text, to the reference data closest to the claim. We evaluated our method on the INSEE dataset and show that it is efficient and effective.
Saliency detection in deep learning era: trends of development

Introduction: Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection. Purpose: A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing. Results: A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation. Practical relevance: This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.