Reviewerly/exHarmony huggingface dataset:
A Framework for Multimedia Event Classification With Convoluted Texture Feature

Background: The proposed work uses two approaches as its background. They are (i) LBP approach (ii) Kirsch compass mask. : Texture classification plays a vital role in object discrimination from the original image. LBP is majorly used for classifying texture. Many filtering based methods co-occurrence matrix method, etc., were used, but due to the computational efficiency and invariance to monotonic grey level changes, LBP is adopted majorly. Second, as Edge plays a vital role in discriminating the object visually, Kirsch compass mask was applied to obtain maximum edge strength in 8 compass directions which has the advantage of changing the mask according to users own requirement than any other compass mask. Objective: The objective of our work was to extract better features and model a classifier for the Multimedia Event Detection task. Methods: The proposed work consists of two steps for feature extraction. Initially, an LBP based approach is used for object discrimination, later, convolution is used for object magnitude determination using Kirsch Compass mask. Eigenvalue decomposition is adopted for feature representation. Finally, a classifier is modelled using a chi-square kernel for the event classification task. Results: The proposed event detection work is experimented using Columbia Consumer Video (CCV) dataset. It contains 20 event based videos. The proposed work is evaluated with other existing works using mean Average Precision (mAP). Several experiments have been carried out to evaluate our work, they are LBP vs. non-LBP approach, Kirsch vs. Robinson compass mask, Kirsch masks angle wise analysis, comparison of above approaches are performed in a modeled classifier. Two approaches are used to compare the proposed work with other existing works.They are (i) Non Clustered Events (events were considered individually and one versus one strategy was followed) (ii) Clustered Events (some events were clustered and followed one vs. all strategy and remaining events were non-clustered). Conclusion: In the proposed work, a method for event detection is described. Feature extraction is performed using LBP based approach and Kirsch compass mask for convolution. For event detection, a classifier model is generated using the chi-square kernel. The accuracy of event classification is further increased using clustered events approach. The proposed work is compared with various state- of- the- art methods and proved that the proposed work obtained outstanding performance.
Slave Voyages: Reflections on Data Sculptures

This pictorial presents the development of a data sculpture, followed by our reflections inspired by Research through Design (RtD) and Dahlstedt's process-based model of artistic creativity. We use the notion of negotiation between concept and material representation to reflect on the ideation, design process, production, and the exhibition of “Slave Voyages” - a set of data sculptures that depicts slave traffic from Africa to the American continent. The work was initially produced as an assignment on physicalization for the Design course at the Federal University of Rio de Janeiro. Our aim is to open discussion on material representation and negotiation in the creative process of data physicalization.
Isolating cost drivers in interstitial lung disease treatment using nonparametric Bayesian methods

Mixture modeling is a popular approach to accommodate overdispersion, skewness, and multimodality features that are very common for health care utilization data. However, mixture modeling tends to rely on subjective judgment regarding the appropriate number of mixture components or some hypothesis about how to cluster the data. In this work, we adopt a nonparametric, variational Bayesian approach to allow the model to select the number of components while estimating their parameters. Our model allows for a probabilistic classification of observations into clusters and simultaneous estimation of a Gaussian regression model within each cluster. When we apply this approach to data on patients with interstitial lung disease, we find distinct subgroups of patients with differences in means and variances of health care costs, health and treatment covariates, and relationships between covariates and costs. The subgroups identified are readily interpretable, suggesting that this nonparametric variational approach to inference can discover valid insights into the factors driving treatment costs. Moreover, the learning algorithm we employed is very fast and scalable, which should make the technique accessible for a broad range of applications.
Efficient Verification of Cryptographic Protocols with Dynamic Epistemic Logic

The security of cryptographic protocols has always been an important issue. Although there are various verification schemes of protocols in the literature, efficiently and accurately verifying cryptographic protocols is still a challenging research task. In this work, we develop a formal method based on dynamic epistemic logic to analyze and describe cryptographic protocols. In particular, we adopt the action model to depict the execution process of the protocol. To verify the security, the intruder’s actions are analyzed. We model exactly the protocol applying our formal language and give the verification models according to the security requirements of this cryptographic protocol. With analysis and proof on a selected example, we show the usefulness of our method. The result indicates that the selected protocol meets the security requirements.
Automatic Jamming Modulation Classification Exploiting Convolutional Neural Network for Cognitive Radar

Sensing the external complex electromagnetic environment is an important function for cognitive radar, and the concept of cognition has attracted wide attention in the field of radar since it was proposed. In this paper, a novel method based on an idea of multidimensional feature map and convolutional neural network (CNN) is proposed to realize the automatic modulation classification of jamming entering the cognitive radar system. The multidimensional feature map consists of two envelope maps before and after the pulse compression processing and a time-frequency map of the receiving beam signal. Drawing the one-dimensional envelope in a 2-dimensional plane and quantizing the time-frequency data to a 2-dimensional plane, we treat the combination of the three planes (multidimensional feature map) as one picture. A CNN-based algorithm with linear kernel sensing the three planes simultaneously is selected to accomplish jamming classification. The classification of jamming, such as noise frequency modulation jamming, noise amplitude modulation jamming, slice jamming, and dense repeat jamming, is validated by computer simulation. A performance comparison study on convolutional kernels in different size demonstrates the advantage of selecting the linear kernel.
Poisson Vector Graphics (PVG)-Guided Face Color Transfer in Videos

This article presents a simple yet effective algorithm for automatically transferring face colors in portrait videos. We extract the facial features and vectorize the faces in the input video using Poisson vector graphics, which encodes the low-frequency colors as the boundary colors of diffusion curves, and the high-frequency colors as Poisson regions. Then, we transfer the face color of a reference image/video to the first frame of the input video by applying optimal mass transport between the boundary colors of diffusion curves. Next the boundary color of the first frame is transferred to the subsequent frames by matching the curves. Finally, with the original or modified Poisson regions, we render the video using an efficient random-access Poisson solver. Thanks to our efficient diffusion curve matching algorithm, transferring colors for the vectorized video takes less than 1 millisecond per frame. Our method is particularly desired for frequent transfer from multiple references due to its information reuse nature. The simple diffusion curve matching also greatly improves the performance of video vectorization, since we only need to solve an optimization problem for the first frame. Since our method does not require correspondence between the reference image/video and the input video, it is flexible and robust to handle faces with significantly different geometries and postures, which often pose challenges to the existing methods. Moreover, by manipulating Poisson regions, we can enhance or reduce the highlight and contrast so that the reference color can fit into the input video naturally. We demonstrate the efficacy of our method on image-to-video transfer and color swap in videos.
Uncertain Distribution-Based Similarity Measure of Concepts

The similarity of concepts is a basic task in the field of artificial intelligence, e.g., image retrieval, collaborative filtering, and public opinion guidance. As a powerful tool to express the uncertain concepts, similarity measure based on cloud model (SMCM) is always utilized to measure the similarity between two concepts. However, current studies on SMCM have two main limitations: (1) the similarity measures based on conceptual intension lack interpretability for merging the numerical characteristics and cannot discriminate some different concepts. (2) The similarity measures based on conceptual extension are always instable and inefficient. To address the above problems, an uncertain distribution-based similarity measure of cloud model (UDCM) is proposed in this paper. By analyzing the definition of the CM, we propose a new complete uncertainty including first-order and second-order uncertainty to calculate the uncertainty more accurately. Then, based on the difference between the complete uncertainty of two concepts, the computing process of UDCM and its some properties are introduced. Finally, we exhibit its advantages by comparing with other methods and verify its validity by experiments.
Remote Sensing in Geology

Remote sensing is the science of obtaining information about objects or areas from a distance, without being in direct contact with the target that's being investigated. Remote sensing methods make use of emitted and reflected radiation, captured from sensors on airplanes, satellites and drones, or from ground-based systems. Remote sensing exploits the fact that the Earth's atmosphere features three main atmospheric windows, each allowing the penetration of radiation. Active and passive methods are the two main types of remote sensing, utilizing naturally sourced radiation, or radiation actively produced by instruments, respectively. For decades, geologists have employed visual observations, photographs and, subsequently, aerial images to map and understand the Earths’ surface. Today, satellite imagery is used in addition to traditional methods or, sometimes, as a replacement. Recent advances in remote-sensing technologies, especially the rapid increase in the number of satellites, and consequently, in the image acquisition frequency, enable geoscientists to explore and analyze the land surface, acquire data and generate maps and evaluate the geo-potential of any specific area on the globe, at unprecedented scales and speeds. Today, remote sensing has become an irreplaceable tool in the geosciences at large, and specifically also in geology, the latter with many applications, such as geologic and geo-structural mapping, mineral and water exploration, hydrocarbon exploration, natural hazards analysis, and geomorphology.
Shared Node and Its Improvement to the Theory Analysis and Solving Algorithm for the Loop Cutset

Bayesian Network is one of the famous network models, and the loop cutset is one of the crucial structures for Bayesian Inference. In the Bayesian Network and its inference, how to measure the relationship between nodes is very important, because the relationship between different nodes has significant influence on the node-probability of the loop cutset. To analyse the relationship between two nodes in a graph, we define the shared node, prove the upper and lower bounds of the shared nodes number, and affirm that the shared node influences the node-probability of the loop cutset according to the theorems and experiments. These results can explain the problems that we found in studying on the statistical node-probability belonging to the loop cutset. The shared nodes are performed not only to improve the theoretical analysis on the loop cutset, but also to the loop cutset solving algorithms, especially the heuristic algorithms, in which the heuristic strategy can be optimized by a shared node. Our results provide a new tool to gauge the relationship between different nodes, a new perspective to estimate the loop cutset, and it is helpful to the loop cutset algorithm and network analysis.
LTV-MPC Based Trajectory Planning Considering Uncertain Object Prediction Through Adaptive Potential Fields

In this paper a combined longitudinal and lateral trajectory planning approach is presented using an linear-time-varying model predictive control (LTV-MPC) scheme. To consider uncertain moving object state and prediction information, an adaptive potential field is modelled generating a time-varying obstacle cost-term. Approximating the nonlinear planning problem along the previous planned control and state sequence results in an LTV-MPC planning problem and ensures small approximation errors of the original nonlinear formulation. The proposed LTV-MPC planning approach is analyzed in a collision avoidance scenario with a pedestrian. The results show a more defensive maneuver integrating the uncertain object prediction information in the planning problem and the proposed approximation scheme delivers a difference of 492,18 % in the relative approximation error of the cost function in the presented scenario compared to the widely used approach, approximating the planning problem by using the current vehicle state.
High dimensional data reconstruction based on L2,1 norm

In recent years, high dimensional data reconstruction based on sparse tensor has been one promising research field, especially for color images reconstruction and denoising magnetic resonance images. This paper aims at developing a new high dimensional data reconstruction model based on L2,1-norm. A fast algorithm is devised to solve the optimal problem. Additionally, error bound analysis of proposed scheme is discussed. Experiments show that the proposed algorithm is efficient, and the reconstruction results are better than two state-of-the-art methods. The proposed method has higher efficiency in terms of computational time and iterations.
An Automatic Text Summarization Method with the Concern of Covering Complete Formation

Background: Text summarization is the process of generating a short description of the entire document which is more difficult to read. This method provides a convenient way of extracting the most useful information and a short summary of the documents. In the existing research work, this is focused by introducing the Fuzzy Rule-based Automated Summarization Method (FRASM). Existing work tends to have various limitations which might limit its applicability to the various real-world applications. The existing method is only suitable for the single document summarization where various applications such as research industries tend to summarize information from multiple documents. Methods: This paper proposed Multi-document Automated Summarization Method (MDASM) to introduce the summarization framework which would result in the accurate summarized outcome from the multiple documents. In this work, multi-document summarization is performed whereas in the existing system only single document summarization was performed. Initially document clustering is performed using modified k means cluster algorithm to group the similar kind of documents that provides the same meaning. This is identified by measuring the frequent term measurement. After clustering, pre-processing is performed by introducing the Hybrid TF-IDF and Singular value decomposition technique which would eliminate the irrelevant content and would result in the required content. Then sentence measurement is one by introducing the additional metrics namely Title measurement in addition to the existing work metrics to accurately retrieve the sentences with more similarity. Finally, a fuzzy rule system is applied to perform text summarization. Results: The overall evaluation of the research work is conducted in the MatLab simulation environment from which it is proved that the proposed research method ensures the optimal outcome than the existing research method in terms of accurate summarization. MDASM produces 89.28% increased accuracy, 89.28% increased precision, 89.36% increased recall value and 70% increased the f-measure value which performs better than FRASM. Conclusion: The summarization processes carried out in this work provides the accurate summarized outcome.
Augmenting Sheet Music with Rhythmic Fingerprints

In this paper, we bridge the gap between visualization and musicology by focusing on rhythm analysis tasks, which are tedious due to the complex visual encoding of the well-established Common Music Notation (CMN). Instead of replacing the CMN, we augment sheet music with rhythmic fingerprints to mitigate the complexity originating from the simultaneous encoding of musical features. The proposed visual design exploits music theory concepts such as the rhythm tree to facilitate the understanding of rhythmic information. Juxtaposing sheet music and the rhythmic fingerprints maintains the connection to the familiar representation. To investigate the usefulness of the rhythmic fingerprint design for identifying and comparing rhythmic patterns, we conducted a controlled user study with four experts and four novices. The results show that the rhythmic fingerprints enable novice users to recognize rhythmic patterns that only experts can identify using non-augmented sheet music.
Fine-Tuning the Fuzziness of Strong Fuzzy Partitions through PSO

We study the influence of fuzziness of trapezoidal fuzzy sets in the strong fuzzy partitions (SFPs) that constitute the database of a fuzzy rule-based classifier.To this end, we develop a particular representation of the trapezoidal fuzzy sets that is based on the concept of cuts, which are the cross-points of fuzzy sets in a SFP and fix the position of the fuzzy sets in the Universe of Discourse.In this way, it is possible to isolate the parameters that characterize the fuzziness of the fuzzy sets, which are subject to fine-tuning through particle swarm optimization (PSO).In this paper, we propose a formulation of the parameter space that enables the exploration of all possible levels of fuzziness in a SFP.The experimental results show that the impact of fuzziness is strongly dependent on the defuzzification procedure used in fuzzy rule-based classifiers.Fuzziness has little influence in the case of winner-takes-all defuzzification, while it is more influential in weighted sum defuzzification, which however may pose some interpretation problems.
Blockchain, Cybersecurity, and Industry 4.0

The world is going digital, and the wave of automation is sweeping across all facets of our corporate and personal lives. Industry 4.0 is all about leveraging IoT (internet of things) devices to facilitate further the process of automation that helps all organisations to rapidly scale by leveraging technology. The amount of data and information generated by the connected things is being harnessed with the help of advanced algorithm empowered analytics to induce intelligence into all the actions undertaken for the functioning of these connected devices. This chapter is geared towards giving a representative outlook on the concepts of blockchain that see a base in the concepts of cybersecurity. Further to that, this chapter explores the very imminent use cases of what we call the Industry 4.0. This includes use cases from remmitance, insurance, governance, internet of things (IoT), and supply chain, including the kinds of challenges we currently face.
Blockchain in Clinical Trials

Clinical research comprises participation from patients. Often there are concerns of enrolment from patients. Hence, it has to face various challenges related to personal data, such as data sharing, privacy and reproducibility, etc. Patients and researchers need to track a set plan called study protocol. This protocol spans through various stages such as registration, collection and analysis of data, report generation, and finally, results in publication of findings. The Blockchain technology has emerged as one of the possible solutions to these challenges. It has a potential to address all the problem associated with clinical research. It provides the comfort for building transparent, secure services relying on trusted third party. This technology enables one to share the control of the data, security, and the parameters with a single patient or a group of patients or any other stakeholders of clinical trial. This chapter addresses the use of blockchain in execution of secure and trusted clinical trials.
Joint Topic-Semantic-aware Social Matrix Factorization for online voting recommendation

Social voting is an emerging new feature in online social platforms, through which users can express their attitudes and opinions towards various interested subjects. Since both social relations and textual content decide the votes propagation, the diverse sources present opportunities and challenges for recommender systems. In this paper, we jointly consider these two factors for the online voting recommendation. First, we conduct feature learning on the vote content. Note that the vote questions are usually short and contain informal expressions, existing text mining methods cannot handle it well. We propose a novel topic-enhanced word embedding (TEWE) method, which learns the word vectors by considering both token-level semantics and document-level mixture topics. Second, we propose two Joint Topic-Semantic-aware Social Matrix Factorization (JTS-MF) models, which fuse social relations and textual content for the vote recommendation. Specifically, JTS-MF1 directly identifies the interaction strength to calculate the similarity among users and votes, while JTS-MF2 aims to preserve inter-user and inter-vote similarities during matrix factorization. Extensive experimental results on real online voting dataset show the effectiveness of our approaches against several state-of-the-art baselines. JTS-MF1 and JTS-MF2 models surpass the matrix factorization based method, with 25.4% and 57.1% improvements in the top-1 recall, and 59.12% and 25.1% improvements in the top-10 recall.
Grouping Mixed Documents: Mexico Job Offers Case Study

A mixed dataset is composed of structured and unstructured documents whose heterogeneous data formats complicate not only their processing but also their content analysis. Finding the semantic correspondence among documents stored in a mixed dataset requires identifying, combining, and assembling diverse techniques from many knowledge fields to analyze and reveal possible patterns among them. In the case of text documents, it has been addressed by processing semantic properties and linguistic relationships between words through vector representations and word embedding models. In this paper, we present a methodology to calculate the content proximity in mixed documents, using three techniques: similarity measure, doc2vec embedding model, cosine similarity, and K-means algorithm. The study is centered on the Mexican job market documents to find relationships among mixed documents of job offers. The results show that creating groupings of mixed documents, based on their semantic and cosine similarity, allows them to reveal patterns.