Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Rendering Devices for Safety-Critical Applications in Operative Control Rooms

Recent advancements in augmented reality have helped bridging the gap between real world and virtual environments. In this context, augmented reality can be used to assist operators in control rooms by providing extra information in the decision making process and to improve the user attention. In this paper we focus on application of augmented reality in industrial safety applications in a control room environment. Our work is divided into three modules. First, we develop a safety-critical application for emergency fire scenarios. Then, we make it available on two rendering devices: a flat 2D laptop screen and on Microsoft HoloLens. Finally, to check the usability and the effectiveness of the application, subjective tests are performed.
Translation-based Embedding Model for Rating Conversion in Recommender Systems

Ratings, which are explicit feedback, are the most popular form that is often used in Recommender System (RSs). However, using the actual ratings from neighbors to predict ratings of target user toward target item often leads to low accuracy prediction due to the improper rating range problem. Rating conversion methods are proposed to solve this problem over the past few years. To propose rating conversion method, each user’s preference or rating pattern is needed. Some studies adopt the idea from translation-based embedding model and represent user’s preference in graph form. Although some studies represent users, items, and relations in embedding vector form, their representation may be improper and inaccurate if the rating pattern of each user is not in the same range. These vectors still suffer from the improper rating range as well. In this work, we propose a translation-based embedding model with rating conversion in RSs. We aim to solve the improper rating range problem in translation-based embedding model. Our challenges are 1) representing the relation (rating) between a pair of user and item in vector form, instead of scalar form and 2) dealing with rating conversion of user’s rating in vector form. The FilmTrust and MovieLens dataset are used in experiments comparing the proposed method with the existing methods. The evaluation showed that the proposed rating conversion method provides better accuracy results in term of both rating prediction and ranking recommendation.
Between informative and esthetic: A user study of content and visualization on government data dashboards

ABSTRACT This preliminary study aims to explore users' perceptions of and experiences with using government dashboards, accomplished by conducting experiments and interviews with eight participants. The results reveal that participants prefer information related to their lives and the government's performance on data dashboards. Confusing jargon on data dashboards is an obstacle that prevents users from understanding data visualizations. We also found that effective conveying of information does not always correlate with participants' preference of a data dashboard design. This study also suggests that participants are unclear about the role of government dashboards.
Global Smooth Path Planning for Mobile Robots Using a Novel Adaptive Particle Swarm Optimization

In this paper, a novel adaptive particle swarm optimization (PSO) algorithm is proposed for the smooth path planning of mobile robots when there exist obstacles in the workspace. By computing the success rate of the swarm in the current iteration, the situation of particles is determined in the search space. In the proposed PSO algorithm, the inertia weight and acceleration coefficient can be adaptively adjusted according to the situation of the swarm. Compared with some existing PSO algorithms, the adaptive PSO can enhance the search capability, which is verified by simulations on a standard collection of benchmark functions. Finally, the adaptive PSO algorithm is applied to the smooth path planning of mobile robots with constraints in the workspace, and the results show the effectiveness of the presented algorithm.
Research on Entity Mention Recognition based on LSTM

This paper proposes an improved neural network structure based on LSTM for recognizing entities in sentences. The deep learning model of LSTM has a label offset problem when performing sequence labeling tasks, because it does not utilize the information of the model's output layer. Therefore, this paper adds a layer of neurons on the output layer of the model to simulate the hidden state in the CRF to make full use of the label information contained in the output layer. On the other hand, the LSTM-based sequence labeling model cannot ensure that the recognized entities exist in the knowledge base. Besides, it has difficulty in word boundary recognition, so this paper introduces the entity dictionary in the model. Finally, part of speech is the core feature of the entity. The char vector in the LSTM-based sequence labeling model lacks part of speech information, so this paper modified the character vector incorporating the part of speech feature information. Experiments show that the improved LSTM-based model has achieved good results in entity mention recognition.
Particle size measurement based on quality assessment of crystal images

A synthetic image analysis method is proposed for in-situ particle size measurement used for monitoring crystallization processes, based on using a non-invasive imaging system. To facilitate crystal size measurement, a no-reference image quality assessment (NR-IQA) algorithm is proposed for crystal image analysis, based on image transformation, feature extraction, and a pre-trained support vector regression (SVR) model. Meanwhile, an adaptive thresholding algorithm is used to sieve out effective crystal images along the time evolution. Consequently, particle size measurement is conducted by computing the projection distances of primary axes identified from each crystal image. Experimental results on the cooling crystallization of $\beta$ form L-glutamic acid well demonstrate the effectiveness of the proposed image analysis method.
Deep learning-based single frame phase retrieval

In the field of precise 3D reconstruction, fringe pattern profilometry (FPP) is always regarded as the preferred method for it provides relatively higher accuracy. However, the phase acquisition process generally requires a sequence of images with different phase shift, which is rather time-consuming. Thus the application scenario of FPP is greatly limited and this has long been a bottleneck in practice. Although single-frame based phase retrieval algorithms like Fourier transform profilometry (FTP) has been proposed and extensively studied, they still suffer from relatively unbearable loss of accuracy. In response to this problem, we take advantage of the deep learning techniques and present a deep-learning based phase acquisition system in which the phase can be acquired by a single frame of fringe pattern image. The network is constructed according to the procedure of phase retrieval, which is trained by thousands of fringe pattern images with the phase data being known in advance. And it can predict more preciously the phase of a new fringe pattern map. Experiments illustrate the effect of our method which will be promising for practical use.
Group Pruning with Group Sparse Regularization for Deep Neural Network Compression

Network pruning is important for the deployment of deep neural network on hardware platforms. However, most pruning methods focus on coarse-grained pruning with high precision loss. In addition, many fine-grained pruning methods focus the pruning on the fully connected layer rather than the convolution layer. The group pruning technique is proposed, focusing only on CONV-layer that enables to keep the weight reduction rate consistent within each weight group. This helps solve some inefficiency problems including internal buffer misalignment and load imbalance after fine-grained pruning. In the pre-training, the strategy of group sparse regularization (GSR) and standardizing the weight distribution is added to alleviate the loss of precision under high sparsity. Finally, we used the MNIST and CIFAR-10 data sets to test the LeNet and VGG-16 for experiments. The reduction rates of convolution layer are 87.5% and 62.5% respectively in the range of 0.14% accuracy loss.
Experimental observation of the effect of global phase on optimal times of SU(2) quantum operations

We study the role that the global phase plays in quantum operations. Previous theoretical works suggested that the optimal (minimum) times to realize $\mathrm{SU}(n)$ quantum operations with different global phases are generally different. Here, we experimentally constructed two SU(2) operations with different global phases, i.e., $U$ and $\ensuremath{-}U$, in optimal times. Then, utilizing nuclear magnetic resonance interferometry, we measured these phases and observed the global phase's effect on the optimal times of these two operations. Our result further clarifies that the effect that the global phase has on unitary operations is not only mathematical, but also physical. In addition, this work has potential applications in many areas, such as designing time-optimal controls in quantum systems.
Aesthetics of Art and Life Sciences: Collaborations and Resistance

In this paper, the author pays attention to the actual phenomenon of art and life science collaborative projects. She discusses the orientation of this project towards the world. In the course of modernity, the fields of art and science have been established as relatively autonomous fields with canonized methods and objectives. The author will compare scientific and artistic activities and address the question of their objectives. If art and science strive for different objectives, are these art and science projects about harmonizing them, or what is the objective that art follows and perhaps differs much from science? The author emphasizes a certain role of art, which art inherited from Romanticism. Comprehension of art as an avant-garde was extremely important for 19th-century art, particularly in France, where artists considered themselves the avant-garde of the society and also used militant rhetoric. Mallarmé, for instance, said that the modern poet is “at strike against the society”. This romantic attitude of the artists that position themselves rebelliously against the norms and cannons of the majority of population, insisted in the art throughout modernism and expressed particularly strongly in the historical avant-gardes. The author claims that exactly this heritage is crucial for the art that enters the field of science and is engaged with its socially-relevant aspects. The contemporary art projects entering the field of life sciences inherit the tradition of the avant-garde. The modes of collaborations and resistance will be addressed in the paper. Particular relevance will be given to the orientation of art towards the future. That is the comprehension of art as a political agent. Article received: June 12, 2019; Article accepted: July 6, 2019; Published online: October 15, 2019; Review article
Application of Cognitive Protocols in Transformative Computing

Transformative computing is a new computational paradigm, which join wireless technologies with artificial intelligence, and edge computing. In this paper we describe possible applications of different cognitive protocols for such computational areas, especially focused on security technologies, and considering personal characteristics, and environmental features. Also will be presented the impact of cognitive approaches and cryptographic algorithms for securing distributed systems and distributed infrastructures.
Smart Health Care Monitoring System By Seven Segment Image Processing Technique

Health care monitoring (HCM) is an innovation to empower observing of patients outside of traditional clinical settings, which may build access to mind and reduction medicinal services conveyance costs. In order to proper recovery, monitoring a patient’s medication and health condition is mandatory. In daily life, it is very common that patients forget about taking their antibiotics on time and for that they have to start their medication course again. Moreover, in the hospital nurses sometimes cannot give the patient, medicine on time and do other checkups because of work load. In terms of emergency patients, we believe a single minute matter. We are proposing a model where patients will be able to set up their medication time. On time they will be alerted to take medicine. Moreover, we are using pulse sensor and body temperature sensor. These sensors will take reading automatically from the patient on time. After that, all these records will be saved in a text file and doctor will be able to check from his chamber anytime he wants through remote wireless connectivity with our system.
Ultrasonic Images Denoising Based on Calculus of Variations

The presence of speckle noise in medical ultrasound images not only reduces the image quality and affects the diagnosis results, but also brings difficulties to the subsequent processing such as image edge detection and feature extraction. Therefore, how to effectively remove speckle noise is of great significance to the research on enhancing the quality of medical ultrasound images and clinical application. In this paper, we propose a new method to remove speckle noise from ultrasonic images based on calculus of variations. The innovation of this method lies in the introduction of anisotropic diffusion equation of partial differential equation. Therefore, this method not only considers the effect of removing speckle noise, but also can well preserve the edge features of the image. Firstly, logarithmic transformation is carried out on ultrasonic images to convert multiplicative noise into additive noise. Then, calculus of variations is applied to the image after logarithmic transformation. Finally, the ultrasonic image is reconstructed by exponential transformation. In the denoising experiment, calculus of variations is compared with mean filtering, median filtering, wiener filtering and wavelet transform. The experimental results show that the denoising performance of the calculus of variations is obviously better than other methods.
An Approach to Testability Evaluation Based on Improved D-S Evidence Theory

To address the conflict of prior information in traditional D-S evidence theory, an approach to testability assessment based on improved D-S evidence theory is proposed by integrating testability virtual test information, testability expert experience information and testability prediction information. On the basis of such information, density distribution function is developed for equipment testability indicator fault detection rate (FDR), and then mass functions are further constructed. Eventually, testability assessment results are obtained by introducing Lance and Williams distance for information fusion of improved D-S evidence theory.
Incomplete data fuzzy C-means method based on spatial distance of sample

In this paper, an incomplete data fuzzy C-means method based on spatial distance of sample(FCM-SDS) is proposed. The method uses the nearest neighbor rule to utilize the spatial distribution information of the sample to obtain the missing attribute information of the incomplete data to interpolate the incomplete data set. Combining the nearest neighbor samples, the spatial distance with the adjustment factor $\alpha$ is constructed to replace the original Euclidean distance, the nearest neighbor density is added to the cluster objective function in a weighted manner, and the clustering effect is improved. The method is validated on the Iris and Wine datasets of the standard database UCI. When the attribute missing ratio is increased from 0% to 20%, the method has the smallest misclassification compared with other methods.
WITHDRAWN: Human Action Recognition via Learning Joint Points Information toward Big AI system

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal
Clay-Based Brick Porosity Estimation Using Image Processing Techniques

Abstract This work intends to apprehend and emphasize the contribution of image-processing techniques and computer vision in the treatment of clay-based material known in Meknes region. One of the various characteristics used to describe clay in a qualitative manner is porosity, as it is considered one of the properties that with “kill or cure” effectiveness. For this purpose, we use scanning electron microscopy images, as they are considered the most powerful tool for characterising the quality of the microscopic pore structure of porous materials. We present various existing methods of segmentation, as we are interested only in pore regions. The results show good matching between physical estimation and Voronoi diagram-based porosity estimation.
Reconstruction of underwater images with distortion using robust image registration

When imaging through an undulating clear water surface, a complex series of refractions and reflections occur throughout the imaging path, and light rays are bent by unknown amounts. So the captured images usually contain severe geometric distortions. In this paper, an iterative robust registration algorithm is employed to remove the distortions in frames by registering each frame to a reference image. As the traditional image registration algorithm is impeded by the severely blur mean, we decide to reconstruct a high quality reference as the surrogate of the mean. We first select the image patches with higher quality from the distorted sequence to reconstruct a single image. In the patches selection process, the image quality of patches is evaluated from sharpness and geometric distortion. Then the blind deconvolution technique is employed to deblur the image, which will be used as a reference of the next registration process. Experiment shows that the proposed algorithm performs well in restoring the distorted underwater images and has less computational time than the state-of-the-art method.
Face Identification With Top-Push Constrained Generalized Low-Rank Approximation of Matrices

We present a novel top-push constrained feature learning (TFL) method for face identification. The idea is to learn low-rank approximations from raw intensity face images such that the squared distance between all faces of the same identity should be smaller than that of different identities by a margin. To this end, we formulate the learning process under the framework of generalized low-rank approximation of matrices (GLRAM) supervised with a top-push constraint. GLRAM operates on the matrix representation of images to seek low-dimensional and compact features, which improves the discriminative power of the features while reducing the time and space costs. The top-push constraint aims to optimize the face identification accuracy of the top-rank matching list, which makes the learned features more discriminative and compact, thus reducing the variability and ambiguity. Once the optimization is finished, the features are vectorized and used for similarity measurement. The effectiveness of TFL is verified on four publicly available face datasets.
Active Sensor Radiation Control Based on Posterior Cramer-Rao Lower Bound with Passive Measurement

In this paper, the issue of active radar radiation control for target tracking is studied to improve the concealment of aircraft. Since too much emission will lead to our aircraft be locked in by enemy electronic support measures (ESM), radar emission should be as low as possible. To reduce the radar radiation, firstly, the problem is formulated and a multi-sensor fusion algorithm based on particle filter (PF) for synergy tracking is given. Then, a novel method combined posterior Cramer-Rao lower bound (PCRLB) with angle is proposed. It compares the angle measurement with the predicted value to determine whether the active sensor needs to be turned on, and then selects the sensor with PCRLB. Finally, simulation results show that the radiation times have obvious decrease, which demonstrates the validation of our proposed algorithm.
Algorithmic modifications in procedural generation systems

AlGorIthmIC moDIFICAtIons In proCeDurAl GenerAtIon systemsThe Modified Diamond Square algorithm is presented, as a result of which a 3-dimensional map of the fractal surface is obtained.This method of visualization using voxels allows to generate the relief structures (caves, canyons, quarries) that cannot be generated using a regular elevation map.The result of using the modified algorithm is shown on the landscape construction.
DETERMINATION AND RESEARCH OF THE MENTAL HEALTH DYNAMICS AMONG TEACHERS OF HIGHER EDUCATION WITH DIFFERENT PLACES OF PROFESSIONAL ACTIVITY

Purpose of the study: The article deals with the problem of teacher burnout of higher education institutions in the process of carrying out his professional activity. Over rapid pace of life and neglecting own needs, people often make people who, in addition to endless challenges and obsessive ideas, do everything quickly and thoroughly, leading to the emergence of a syndrome of emotional (professional) burnout.&#x0D; Methodology: The data of this research particularly are Sharabi’s works published during 1980s as crucial period of his intellectual journey.&#x0D; Results: The article reveals the results of an empirical study aimed at identifying the dependence of mental burnout on the length of professional experience. The research was conducted using questionnaire O. Rukavishnikov's "Definition of mental burnout" and questionnaires for specialists (author M. Kokun). The indexes of mental burnout among teachers with different pedagogical experiences are determined. It was found that the symptoms of mental burnout take place both at the beginning of and during the professional activity, and thus the tendency of reducing the tendency to mental burnout after 20 years of work in the specialty is traced. The high indicators of the professional motivation phase have been received, which may indicate higher expectations of teachers about the profession in general and the work in progress, in particular.&#x0D; Applications of this study: This research can be used for the universities, teachers, and students.&#x0D; Novelty/Originality of this study: The urgency of the choice of this problem is conditioned by the fact that frequent changes in the field of higher education in recent years have raised demands both on the professional competence of the teacher and on his personal component. Those or other forms of personally-professional deformations of the teacher reflected in his interaction with students and colleagues and may have a destructive tone in communication, attitude to the subject, assessment of their own results.
Comparison of Token-Based Code Clone Method with Pattern Mining Technique and Traditional String Matching Algorithms In- terms of Software Reuse

Code clones have a great impact on software. Detecting them has become a challenge among software industries. Code clones are mostly found in large-scale applications. There are many techniques available to detect them. Most of them are sophisticated, expensive and also difficult to implement. The detected clones are eliminated without further investigation. Careful analysis of code clones will aid in software reuse. In this paper, a generic architecture of token-based code clone reuse method which assists software reuse is proposed. It detects type-1 i.e. exact clones and type-4 i.e. functional clones and also helps in software reuse. Its performance is compared with traditional string matching technique and FP-Growth technique. Evaluation of the three types of algorithms is done based on six open source software. The results show that the proposed method can find more number of functional clones when compared to other techniques. This analysis will pave way for introducing new concepts based on fundamental logics.