Reviewerly/exHarmony huggingface dataset:
Body-Boundary-Refined Human Parsing Network based on Fully Convolutional Network and Conditional Random Fields

Abstract Human parsing is still a big challenge in computer vision task to accurately label every body part in an image. Current methods of sematic segmentation mainly focus on dividing each independent part and ignore the structural priors of human body. However, the position relationships of different parts, especially which around the area of boundary, indicate their associations and also contribute to the semantic segmentation. In our work, we propose a body-boundary-refined part to refine the segmentation result of human part edge by simply utilizing the structure priors around the body boundary. It puts a penalty mechanism on wrong marginal pixels to improve segmentation performance around the area of body boundary. The network achieves competitive performance on the PASCAL-Parts-dataset and especially the area around the body boundary has been refined.
An Efficient Hand Gesture Recognition System Using Deep Learning

Most of the people perform various tasks by using a computer keyboard/mouse leading to repetitive wrist and hand motions, resulting in Carpal Tunnel Syndrome. This paper is geared towards developing a computer management system using hand gestures accomplishing virtual keyboard/mouse operations/commands to effectively eliminate the Carpel Tunnel Syndrome. Gesture Recognition provides an accurate estimation of hand gestures using deep learning algorithm. The complexity of hand structure in obtaining gestures and the rapidness of the movements of the hand or fingers are the problems of tracking algorithms. Thus, deep learning provides a rapid and precise estimate of hand gestures using Convolutional Neural Network (CNN) algorithm. This paper uses articulated CNN algorithm capturing possible gestures, accomplishing various keyboard/mouse operations/commands, thereby avoiding the syndrome. Compared to the conventional algorithm, the proposed work produces high accuracy, a good estimation of hand gestures and cost-effective.
Research on Spatial Arc Fitting Method Based on RANSAN algorithm

Aiming at the problem that the traditional spatial arc fitting has low robustness and low fitting accuracy, a new method of space circle fitting is proposed in this paper. Firstly, based on the Lagrangian multiplier method, the objective function is established based on the constraint of the plane condition, and then the scheme of the fitting equation directly based on the spatial arc is derived. Secondly, the error tracking point is eliminated by using the RANSAC (random sample consensus) method, thereby improving the fitting accuracy. In this paper, three different algorithms are used to carry out experiments. The theoretical basis of the three algorithms is respectively based on least square method, based on Lagrange multiplier method and based on Lagrange multiplier method added RANSAC algorithm. Experimental results show that the method of spatial circle fitting based on Lagrange multiplier method is greatly superior to the traditional method based on least square method, and adding RANSAC algorithm on the basis of Lagrange multiplier method can further improve the accuracy of spatial circle fitting.
Image Encryption and Decryption using Enigma Algorithm

The main objective of this paper is to present a more secured and computationally efficient procedureof encrypting and decrypting images using the enigma algorithm in comparison to the existing methods. Available literature on image encryptions and descriptionsare not highly secured in every case.To achieve more secured image processing for highly advanced technologies, a proposed algorithm can be theprocess used in enigma machine for image encryption and decryption. Enigma machine is piece of spook hardware that was used frequently during the World War II by the Germans. This paper describes the detailed algorithm along with proper demonstration of several essential components present in an enigma machine that is required for image security. Each pixel in a colorful picture can be represented by RGB (Red, Green, Blue) value. The range of RGB values is 0 to 255 that states the red, green and blue intensity of a particular picture.These RGB values are accessed one by one and changed into another by various steps and hence it is not possible to track the original RGB value. In order to retrieve the original image, the receiver needs to know the setting of the enigma. To compare the decrypted image with the original one,these two images are subtracted and their results are also discussed in this paper.
A Steganographic Scheme based on Perceptual Loss and Feature Fusion

The purpose of steganography is to convey information in public that no one else pays attention to. With the development of communication technique, the communication in the public channels also causes security problems.This is why the study of steganography has been concerned. Traditional steganographic algorithms usually extract the embedded information from the image itself. In this paper, a steganographic algorithm is proposed, which uses the fusion of feature maps in the higher layers of convolutional neural network (CNN) to embed information and form the corresponding stego image through network back propagation. The stego image is visually closer to the cover image through the designed mixed loss function. Compared with the pixel-wise loss function one, this algorithm is not easy to detect visually, and can resist certain disturbances in information reconstruction, which means it has good robustness.
Users perception of using CBMT for informationsecurity training

It is well established that user behavior is a crucial aspect of information security and archivingsecure behavior through awareness and security training is the go-to solution proposed bypractitio ...
Semantic Graph-Based Recommender System. Application in Cultural Heritage

Research on visitor support systems for museums and cultural sites has been particularly important in recent years. Many research projects have been created to assist the visitor before and during his visit. The cultural heritage area is affected by the problem of information overload. With the advent of the social web, a large number of available resources have emerged coming from the social information systems SocIS. Therefore, visitors are swamped with enormous choices in their visited cities. Even though, SocIS platforms use the features of collaborative tagging, named folksonomy, to commonly contribute to the management of the shared resources. Collaborative tagging lacks semantic which reduces the effectiveness of organizing resources. It decreases their findability and discoverability, thereby their recommendation. In this paper, we aim to personalize the cultural heritage visit, i.e., to suggest semantically related places that are most likely to interest a visitor. Our proposed approach represents a semantic graph-based recommender system of cultural heritage places by (1) constructing an emergent semantic description that semantically augments the place and (2) effectively modeling the emerging graphs representing the semantic relatedness of similar cultural heritage places and their related tags. The experimental evaluation shows relevant results attesting the efficiency of our proposal applied to recommend cultural heritage of Marrakesh city. Future perspectives will focus on creating a real-world application using augmented reality. It will include a semantic-based context-aware recommender system that rises in value the cultural heritage of the touristic city by suggesting historical places that suit the visitor’s interests.
Discriminative Competitive Representation for Image Classification

For collaborative representation based image classification methods, it is very important to represent the image well. In this paper, we propose a novel representation method called discriminative competitive representation (DCR) for image classification. It divides the set of all training samples into two competitive subsets to represent a test sample, and then uses the representation result to perform classification. This method can obtain an approximate representation of a test sample and enable representation components to be discriminative and competitive, which is beneficial to correct classification of the test sample. Specifically, the main idea of the DCR method is that it makes test set and training set have low correlation. One notable advantage of the DCR method is that it exploits a mathematically tractable way to obtain discrimination of classes. The image classification experiments show that the DCR method can obtain a higher accuracy than conventional sparse representation methods.
Retrieval system enhanced by fine‐grained knowledge entities

ABSTRACT Along with the massive increase of academic full texts, users now have a growing need for fine‐grained academic literature retrieval. However, few studies have attempted to build domain knowledge retrieval system for academic literature in information science. Compared with other disciplines, information science pays more attention to the domain knowledge entities sequence including data resources, models and software. To fill the gap, this paper use academic full texts from JASIST. Using SVM, Text‐CNN, Bi‐LSTM and BERT to extract the knowledge entities sequence including data resource, model, software. The highest Precision, Recall and F1 are 92.45%, 93.86% and 93.14%, respectively. And the knowledge entity sequence indexing system is built based on the extracted knowledge entities sequences. It was found that the MRR score of the knowledge entities sequence retrieval system was higher by 13% as compared with the full text retrieval system.
Systems Become Experts

Knowledge-based expert systems are computer programs that store and accumulate knowledge about a specific area, from which knowledge automatically draws conclusions in order to offer solutions to concrete problems in that area. In contrast to the human expert, however, the knowledge of an expert system is limited to a specialized information base without general and structural knowledge about the world.In order to build an expert system, the knowledge of the expert must be laid down in rules, translated into a program language and processed with a problem-solving strategy. The architecture of an expert system therefore consists of the following components: Knowledge base, problem-solving component (derivation system), explanatory component, knowledge acquisition, dialogue component. The coordination of these components is shown in Fig. 4.1.Knowledge is the key factor in the representation of an expert system. There are two types of knowledge. One kind of knowledge concerns the facts of the field of application, which are recorded in textbooks and journals. Equally important is the practice in the respective area of application as knowledge of the second kind. It is heuristic knowledge on which judgement and any successful problem-solving practice in the field of application are based. It is knowledge of experience, the art of successful presumption, which a human expert acquires only in many years of professional work.
Image Significance Region Detection Based on Global Color Clustering and Contrast

In this paper, an image significance region detection algorithm combining global color clustering and color contrast is proposed. Firstly, by clustering the image color, the color of each pixel in the image is replaced by the color of the closest clustering center. Then, the clustering color contrast is used to calculate the significance region of the image, which enhances the significance region and reduces the influence of nonsignificant objects. Experimental results show that this method has higher precision and better recall rate, and significantly reduces the influence of complex texture on the calculation of significance region.
Universal Multipartite D-Level Entanglement Witnesses for Realistic Measurement Settings

Entanglement is an essential resource in quantum information science [1] and its presence in any quantum system can be experimentally detected through entanglement witness operators [2]. In particular, measuring a negative expectation value of a witness with high statistical confidence provides a necessary and sufficient condition to confirm the generation of a genuine multipartite [3] and/or d-level entangled state [4]. In recent years, the experimental generation of complex quantum states has intensified the need for witnesses that are capable of detecting such systems and are experimentally optimal at the same time. This means that the witness should require the least measurement effort (in terms of number and complexity of the measurement settings), include only projections on single qudits, while at the same time possessing a high noise tolerance (Fig. 1a). However, "experimentally-friendly" witnesses capable of accomplishing these tasks have not been derived yet.
Solar energy estimation and generation in Ibadan Nigeria

Abstract Energy challenge in Nigeria is really disturbing due to many factors that borders on the inability of the national grid to meet-up with the huge energy demands in the most populated country in Africa. About 85% industries and domestic users patronize fossil fuel generators. This development had increased the air pollution load over the country. In this research, fourteen years dataset from the Nigeria Metrological Agency (NIMET) was used to simulate the energy accruable from solar energy in Nigeria. The study is poised to enhance higher patronage of solar technology in the research site.
ORCID, RRID’s and Research Objects, Oh My!

To support superior ways of sharing scientific, medical and engineering knowledge, Elsevier has been an active participant in a plethora of research projects, multi-stakeholder communities, alliances and data standards organisations. As an example, we will discuss how Elsevier has contributed to organisations that advocate and govern the use of unique identifiers throughout the scholarly lifecycle.&#x0D; The shared use of unique identifiers for finding and linking to papers, authors, and institutions has been an area where Elsevier has invested in, from the beginning. After the resounding success of the Digital Object Identifier project, which lead to the foundation of CrossRef, a multi-stakeholder organisation that manages DOIs across the publishing landscape, Elsevier was one of the founders of ORCID, intended to assign unique identifiers to scientists.&#x0D; Further cross-stakeholder initiatives for identifying objects to improve findability were pursued through Force11, a multi-stakeholder organisation to improve scholarly communication, which Elsevier staff helped co-found. The RRID project advocates and governs the use of identifiers for key research resources (such as antibodies and model animals) and the Data Citation Principles advocate a common model of sharing, citing and attributing datasets, based on DOIs for Datasets. These principles have been brought to bear inside the open Mendeley Data platform, where unique identifiers are assigned to datasets, and even versions of datasets, so that distinct iterations of a collection of research data can be cited and found.&#x0D; Currently, Elsevier staff are collaborating with the University of Manchester to build a portal to generate identifiers for bioinformatics workflows, based on Manchester’s ‘Research Object’ specification. This will allow bioinformatics researchers to link their software, data inputs, outputs, and publications together in a single ‘Research Object’, which can be found, cited, and linked to publications.
Document Similarity Approach Using Grammatical Linkages with Graph Databases

Document similarity had become essential in many applications such as document retrieval, recommendation systems, and plagiarism checker. Many similarity evaluation approaches rely on word-based document representation, because it is very fast. But these approaches are not accurate when documents with different language and vocabulary are used. When graph representation is used for documents, they use some relational knowledge which is not feasible in many applications because of expensive graph operations. In this work a novel approach for document similarity computation which utilizes verbal intent has been developed. This improves the similarity and graph databases were also used for faster performance. The performance of the system is evaluated using various datasets and verbal intent-based approach has registered promising results.
Performance-based Expressive Character Animation

For decades, animation has been a popular storytelling technique. Traditional tools for creating animations are labor-intensive requiring animators to painstakingly draw frames and motion curves by hand. An alternative workflow is to equip animators with direct real-time control over digital characters via performance, which offers a more immediate and efficient way to create animation. Even when using these existing expression transfer and lip sync methods, producing convincing facial animation in real-time is a challenging task. In this position paper, I describe my past and proposed future research in developing interactive systems for perceptually-valid expression retargeting from humans to stylized characters, real-time lip sync for 2D animation, and building an expressive style aligned embodied conversational agent.
Impact of Curvature on Intensity-Based Non-rigid Medical Image Registration

The non-rigid registration problem has gained much prominence in the area of medical image analysis. Graph cut-based solution to such a non-rigid registration problem is shown to improve the accuracy of the registration results over other existing approaches. Incorporation of curvature information in graph cut-based non-rigid bio-medical image registration has not been explored yet. In this work, a novel approach based on curvature is proposed to improve the performance of the Graph-Cut solution. In this method, first of its kind, the impact of curvature in an intensity-based non-rigid registration is well demonstrated. At first, the extraction of mean and Gaussian curvatures from the brain MRI are outlined and shown appropriate results. Based on the mismatch pattern in curvature distribution, a local displacement map (LDM) is generated for every such unmatched pixel in source image. The map essentially indicates the local displacement such as source pixel should undergo within a local neighborhood to achieve best curvature-wise matching with the corresponding target pixel. Experimental results clearly corroborate to the fact that intensity-based registration along with curvature can yield better registration accuracy.
Performance Analysis of Parallel and Scalable GPU Based Convolutional Neural Network

Convolutional Neural networks (CNN) have succeeded great impact in various tasks of machine learning. Training CNN model is a computationally intensive task. Scalability and performance of CNN with GPU is demonstrated in this study using CUDA (Compute Unified Device Architecture) framework. We evaluated performance characteristics using our own designed CNN model. The model is configured for Facial expression Recognition classification task. The novelty of this effort is to demonstrate performance acceleration in scalable CNN. The parallel tasks using the hardware feature of general-purpose- computing on graphics processing unit (GPGPU) has been shown to be appropriate to be applied to CNN. We implemented at multi-node distributed training; which allows us to efficiently parallelize deep networks across multiple servers, in order to minimize the time to train. The experiment result shows that the proposed experiment in this study gained over 7 times speedup.
IT Crisisology: The New Discipline for Managing Software Development in Crisis

This paper discusses crises in software product development. It defines a development crisis and presents the new discipline of IT Crisisology, which systematically studies and addresses crises in software production. Essential ingredients of this IT Crisisology discipline include models, methods, tools, patterns and practices. The IT Crisisology framework developed addresses technology, business and human factors; for each kind of factors, it uses a dedicated set of the above-mentioned ingredients. These include tradeoff optimization, agility matrix, models for knowledge transfer and data lifecycle, to name a few. For enterprise scale development, these ingredients are further enhanced and improved. In case of systematic application, the suggested IT Crisisology framework allows for better disciplined, predictable, and manageable software product development, even in the event of a crisis.
Manipulation Planning with Multimodal End Point Optimization for Obtaining Multiple Solutions

Motion planning for robotics manipulation is an essential component for automating various tasks. In this study we discuss optimization-based motion planning methods for robotic manipulation. The optimization-based method can compute smooth and collision-free trajectories with relatively short computational cost. Although existing methods are often designed to output a single solution, the objective function is often multimodal and there exist multiple solutions to achieve a given task. On such a task, obtaining multiple solutions gives a user an opportunity to choose one of the solutions based on factors which are not encoded in the objective function. To address this issue, we propose a motion planning framework that finds multiple solutions. The proposed method is validated in simulated environments with a four-link manipulator in 2D space and a 6 DoFs manipualtor in 3D space.
Enhanced Beam-Search Restructuring Restraint for Phrase-Based Statistical Machine Translation

Within "Phrase Based Statistical Machine Translation (SMT)", the hypothesis translation generation is computationally costly. On the off chance that the legitamate subjective reorganization made, then the exploration issue becomes nondeterministic polynomial time (NP)-hard. Then again, on the off chance that the researcher confine the conceivable reordering in a proper way, to get a polynomial-time look calculation. This paper explores diverse reordering limitations for expression-based SMT, specifically the ITG requirements, present ing effective unique programming calculations for the two requirements. the researcher assess the constraints regarding interpretation quality on the Telugu– English undertakings. This paper demonstrates the reordering limitations which enhance the interpretation quality contrasted with an unrestricted exploration with the aim of licenses subjective expression reorganizations. The Inversion Transduction Grammer (ITG) requirements achieve most excellent lying on the two errands and this yield factually huge upgrades contrasted with the unconstrained inquiry.
A Web Application to Optimization of Transport in Military Operations

Transport is an operation necessary to carry out any logistical mission, especially in times of war, peace or natural disasters. The distribution of the necessary demanded resources is done from a military unit, to the different locations or military bases. However, operational efficiency depends on the planners. In more than 60% of trips, shipping and return isn’t efficient, even between the same units. The cause is the non-consolidation of trips and the lack of return load, coming from perimeter units. Planning is done without consolidating trips and in many cases on demand. It’s presented a web application, a parametric framework to any geographical area, given the integration with applications such as Google Maps®. Computational times are reasonable, given a to hardiness to the problem. The software architecture is scalable and extensible, complying with software quality practices present in ISO 25000.