Reviewerly/exHarmony huggingface dataset:
ARCHITECTURE AND DESIGN: SUBSTITUTION IN ART TRANING OF DECORATIVE DISCIPLINES IN PROJECT FIELD

The purpose of our exploration is to investigate the computer technology impact on the development of art component in the modern design and engineering education. This is a case in the first instance of traditional arts such as Drawing, Painting and Sculpture. The thematic justification there is to analyse transformation trends of art image creation in a changing development context in the design area. The evolving methodological modern concept requires preservation and development the evolution way of things from its realistic perception (image) to its usage in the capacity of artistic tools and modern information technology tools. It would be instructive to examine the painting methodology of such authors as Nikolai Beschastnov, Vadim Kulakov, Irina Stor, Yuriy Avdeev, Gusein Guseinov, Victor Dyminskiy, Aleksey Sheboldaev (Painting), Yuriy Kirtser (Drawing and painting) as well as the legacy of classic and modern painters. In process of training painting and decorative transformations, information technology adaptation and appliance aim to educate high-level designers and architects. Careful study of nature combine with information technology art tools – this is a key stone of student training of Fine Arts departure of Institute of Architecture and Design Altay State Technical University. Perfect art skills together with technical achievements provides an opportunity to expand and enrich creative potential in carrying out tasks of any kind of complexity importing an art component in the modern design culture. Interest in information technology in a class-room environment Drawing-Painting-Sculpture is no doubt. It’s fascinatingly for students. New methodologies of Institute of Architecture and Design test and endorse in several subjects: Painting and Coloristics, Drawing, Colour transformations in design culture. Based on methodological experiments developed in Fine Arts departure of Institute of Architecture and Design Altay State Technical University and other Russian universities authors suppose that the modern information technology impact on new art architect training methodologies, student abilities for abstract creative reasoning, preserving art component legacy in architect education.
CodeFace: A Deep Learning Printer-Proof Steganography for Face Portraits

Identity Documents (IDs) containing a facial portrait constitute a prominent form of personal identification.Photograph substitution in official documents (a genuine photo replaced by a nongenuine photo) or originally fraudulent documents with an arbitrary photograph are well known attacks, but unfortunately still efficient ways of misleading the national authorities in in-person identification processes.Therefore, in order to confirm that the identity document holds a validated photo, a novel face image steganography technique to encode secret messages in facial portraits and then decode these hidden messages from physically printed facial photos of Identity Documents (IDs) and Machine-Readable Travel Documents (MRTDs), is addressed in this paper.The encoded face image looks like the original image to a naked eye.Our architecture is called CodeFace.CodeFace comprises a deep neural network that learns an encoding and decoding algorithm to robustly include several types of image perturbations caused by image compression, digital transfer, printer devices, environmental lighting and digital cameras.The appearance of the encoded facial photo is preserved by minimizing the distance of the facial features between the encoded and original facial image and also through a new network architecture to improve the data restoration for small images.Extensive experiments were performed with real printed documents and smartphone cameras.The results obtained demonstrate high robustness in the decoding of hidden messages in physical polycarbonate and PVC cards, as well as the stability of the method for encoding messages up to a size of 120 bits.
Interpreting Decision Patterns in Financial Applications

Decisions in financial applications that directly impact citizens are often based on black-box intelligent methods. Given the growing interest in making these decisions more transparent, and the emergent legislation on interpretability and privacy, new solutions to give some insight on such black-boxes, presenting explanations on the decision patterns are being sought. In this paper we propose a method that transfers knowledge from black-box models to more interpretable models to understand the decision patterns in financial applications. Results on credit risk and stock market data show that it is possible to use white-box methods that work on black-box results to show the potential interpretation of the decision patterns.
Performance Analysis of Underwater Acoustic Automatic Modulation Classification System with Machine Learning

Underwater acoustic (UWA) channel is a triple-selective fading channel in time, space and frequency as a result of its lower sound speed and its complicated boundaries in the ocean. It is very difficult to realize a successful classification of the communication signal modulation within a single feature in such harsh channel. The feature extraction is much more sensitive to the environmental parameters and signals. In this paper, an automatic modulation classification (AMC) system based on machine learning is established in order to improve the correction rate of AMC for the several common modulation types. In the case of extracting the feature, the machine learning algorithms, including Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Classification and Regression Tree (CART) are adopted. The performance comparison of these three algorithms is implemented through the simulation data analysis, which would denote how the UWA multipath channel, the signal noise ratio (SNR) affects the performance of the proposed system.
FWUA : A Flexible Winograd-Based Uniform Accelerator for 1D/2D/3D CNNs

Convolutional neural networks (CNNs) have proven to be promising in various applications such as audio recognition, image classification, and video understanding. Different dimensions of CNNs (e.g., 1D, 2D, and 3D CNNs) are proposed to adapt to these applications. To accelerate different dimensional convolution, a uniform accelerator is necessary. Nevertheless, the implementation poses a significant challenge due to several observations. Firstly, computational complexity, network mapping methods, and data reuse strategies vary greatly among different dimensional convolutional neural networks. Secondly, various efficient algorithms such as Winograd have been proposed to accelerate CNNs, but their implementations lack flexible support for different network types. Typically, the Winograd-base accelerator is designed for 1-stride and the non-1-stride methods haven’t been implemented on 3D CNNs. To address these challenges, we propose a flexible Winograd-based uniform accelerator (FWUA) for 1D/2D/3D CNNs. With adaptive support for different dimensions, strides, and filter sizes, FWUA is runtime-reconfigurable for different dimensions of CNNs applications, i.e., audio, image, and video. The FWUA is verified on the Xilinx ZCU102 evaluation board FPGA. Our design achieves 1.51/1.13/0.66 (GOPS/DSP) DSP-efficiency and 242/181/105 (GOPS/W) energy-efficiency in C3D, VGG-16, and HAR-CNNs, which are up to 2x comparing to state-of-the-art FPGA works.
An Enhanced Entity Model for Converting Relational to Non-Relational Documents in Hospital Management System Based on Cloud Computing

In the past few years, several cloud services such as IBM cloud services, Microsoft service, Amazon web services as well as Google cloud platform services related databases are rising rapidly all over the world. In the case of a Relational database, it is easy to handle only a small amount of data. So to overcome the shortcomings, this paper proposes a Hospital Management System (HMS) using Mongo DB as a database. The Mongo DB is the classification of a document-related database that comes under the category of NoSQL database which is referred to as a non-relational database. Mongo DB aims in reducing the gap among two different types of scalable key-value databases such as fast and high scalable key-value databases. Also, this database reduces the time delay during the working of four operational modes such as selection, insertion, deletion, and updation. Furthermore, the Enhanced Entity-Relationship Model (EERM) for the HMS that is designed to manage the entire section of the hospital which includes the reception section, casualty section, details regarding the medical treatment, and employees. The HMS stores three million tuple entities and is loaded into the Mongo DB database. Also, the operation based on selection, insertion, deletion, and updation mode is evaluated. Therefore, the experimental analysis reveals that the proposed HMS using Mongo DB provides is highly efficient with less time delay, thus obtaining an effective system.
[Implementation of a WeChat small program assisted process assessment system in "Experiment of Inorganic Chemistry" for Biological Engineering undergraduates].

The convenience of "no installation, available at your fingertips" of the WeChat small program makes it unique in the application of mobile terminal auxiliary experimental teaching. In order to optimize the assessment system and improve the quality and outcomes of experimental teaching, a self-designed WeChat small program was used to assist the development of the process assessment system. This system was applied to the teaching practice of "Experiment of Inorganic Chemistry" course for the first-year undergraduates majored in Biological Engineering, with the aim to promote teaching and learning by assessment. The results showed that course scores of the students who used this small program were superior to the control group and the correlation between the process assessment and final examination results was significant. These results indicated the WeChat small program assisted process assessment could effectively improve the learning outcomes of students, enable them to grasp the knowledge of Experiment of Inorganic Chemistry efficiently. The results of the questionnaire for the teachers and students also showed a high recognition of the WeChat small program assisted teaching.
AutoML for Log File Analysis (ALFA) in a Production Line System of Systems pointed towards Predictive Maintenance

Automated machine learning and predictive maintenance have both become prominent terms in recent years. Combining these two fields of research by conducting log analysis using automated machine learning techniques to fuel predictive maintenance algorithms holds multiple advantages, especially when applied in a production line setting. This approach can be used for multiple applications in the industry, e.g., in semiconductor, automotive, metal, and many other industrial applications to improve the maintenance and production costs and quality. In this paper, we investigate the possibility to create a predictive maintenance framework using only easily available log data based on a neural network framework for predictive maintenance tasks. We outline the advantages of the ALFA (AutoML for Log File Analysis) approach, which are high efficiency in combination with a low entry border for novices, among others. In a production line setting, one would also be able to cope with concept drift and even with data of a new quality in a gradual manner. In the presented production line context, we also show the superior performance of multiple neural networks over a comprehensive neural network in practice. The proposed software architecture allows not only for the automated adaption to concept drift and even data of new quality but also gives access to the current performance of the used neural networks.
A Survey on Depth Based Hand Pose Estimation

<p indent=0mm>Depth-based hand pose estimation has received increasing attention in the fields of human-computer interaction and virtual reality. A comprehensive survey and analysis of depth-based hand pose estimation of recent works are conducted. First, the definition and difficulties of this problem are explained, the widely used sensor and public datasets are also introduced. Then, the works of this field are divided into three categories, model-driven, data-driven, and hybrid method. The model-driven methods perform a model fitting between the model and the depth points. The data-driven methods learn a function, which maps the depth image to pose. The hybrid methods combine model-driven and data-driven to recovery the hand pose. In the course of narration, we focus on the solved problems and shortcomings to be solved. In the final, the works are compared in terms of accuracy, suitability, and robustness. The future research in this direction is also discussed.
A Systematic Review on Background Subtraction Model for Data Detection

The background subtraction is a simplest form of an effective method for object detection that is in motion. It is used as a preprocessing technique in various vision fields such as action recognition and visual tracking the main difficulty in background subtraction is to set up a self-adaptive background modeling to describe accurately the information of background. The main goal of this survey is to describe the main research methods in background subtraction for the identification of objects and to provide the applicable learning and references for the relevant researchers. Further, this survey explains the main problems associated with the existing methods and provides better guidance for further research work. In addition, the taxonomy of how the existing background subtraction methods were presented is explained and the problems arisen from the existing method are described. The automatic object detection from background subtraction by using the input sequences can be understood. This review can lay the foundation for better analysis, planning, and accurate quantitative evaluation of background subtraction for object detection.
Virtual Police Station System with Chat Bot Using FSM

We can notice that innovation has contacted numerous circles of our lives in India. There is innovation in business, in instruction, in mingling and keeping up human relations, in buying, in horticulture, in banking, correspondence, and pretty much all aspects of our lives. This interruption of innovation has supported the work in every one of these areas, and has demonstrated valuable, and time. The solitary significant piece of our general public that actually remains significantly without this extravagance is the Indian Police System. The authorities have been receiving the essential thing techniques for completing the procedures with the customary “pen and paper” strategy being exceptionally common. In the present India, when the insidious components of the public are in a blast and such countless cases being enlisted each day, it has become a monotonous assignment to deal with the case and all its connected records, physically. The conventional strategy for visiting a police headquarters for enlisting a police officer grumbling and persuading refreshes should be supplanted with an online cycle. The means of this paper is to develop an E-police reporting and a framework with the help of a chat bot which is based on finite state machine (FSM), and NodeJS development which is effortlessly available to people in general and the police division. In this paper, we concentrate on the establishment of an e-police framework and moreover its means, difficulties of usage and its need.
MSC virtual e-abstract (OCL-01)

&#x0D; Stroke rehabilitation in Hospital Raja Perempuan Zainab II: A cross sectional study.&#x0D; Factors affecting arrival time of acute ischaemic stroke patients in Pusat Perubatan Universiti Kebangsaan Malaysia.&#x0D; Outcomes of antiplatelet therapy for secondary prevention in patients with ischaemic stroke or transient ischaemic attack.&#x0D; Use smartphone application to save life in patients of acute ischemic stroke.&#x0D; Frailty and its association with characteristics and outcomes of older persons with acute stroke.&#x0D; Factors contributing to mortality and prolonged length of stay among older persons admitted with acute stroke.&#x0D; Assessment of effect of age on glasgow coma scale (gcs) and national institutes of health stroke scale (nihss) in stroke patients.&#x0D;
Partial Image Encryption of Medical Images Based on Various Permutation Techniques

Medical image security becomes more and more important. Full image encryption is not necessary in the field of medical because partial amount of encryption is enough to provide the security. Here Proposed is a partial image encryption of medical images, which uses different permutation techniques. Proposed technique mainly consists of permutation and diffusion process. Original medical image divided into nonoverlapping blocks with the help of block size table. Then position of each pixel in every blocks are shuffled according to chaotic sequence generated from the chaotic map system and predefined block size table. In the diffusion process, based on basic intensity image (BII) and different permutation technique, the mapping operation apply to get partially encrypted medical 224images. Experiential results show that proposed method provide more security with less complexity and computational time.
Automatic HTML Code Generation Using Image Processing

Outline of a website starts with creating models for each web page either by hand or using graphic design as well as expert model creation tools. Then the model is converted into systematic HTML or equivalent code by software engineers. This process is usually done frequently until the desired template is created. Our system will detect the text, input box, check box, buttons, etc. from the input image and convert it into Html code. To implement the proposed system, some deep learning methods are used. Any non-developer can create web pages using our system in a smaller amount of time with efficient results. 96% method accuracy and 73% validation accuracy will be achieved by the proposed system.
Application of Computer Aided System in Basic Skill Training of Advanced Nursing

The main content of the computer-aided system of nursing basic skill training is divided into three parts: requirement part, teaching part, training part, test part, rhyme part and attachment part. This paper introduces the usage of each part of the system, which is not only helpful for the nursing course study of the students in the College of traditional Chinese medicine, but also can be used as the auxiliary courseware of the network teaching of nursing, which is helpful for the training of the basic skills of traditional Chinese medicine.
The Use of Gaussian Processes as Particles for Sequential Monte Carlo Estimation of Time-Varying Functions

We propose modeling of time-varying functions by Gaussian processes based on random features and relying on the sequential Monte Carlo methodology, also known as particle filtering. The models make use of time-varying random features and parameter variables to adapt to changes of the modeled functions with time. The Gaussian processes are treated as latent states and are estimated by using particle filtering, which altogether allows for learning functions at each time instant. The proposed models have the ability to search for optimal functions in the dynamic space over time. The experimental results show that the approach has better performance than existing state-of-the-art methods based on ensemble of Gaussian processes both in accuracy and stability.
Measuring Similarity of Opinion-bearing Sentences

For many NLP applications of online reviews, comparing two opinion-bearing sentences is the key.We argue that, while general purpose text similarity metrics have been applied for this purpose, there has been limited exploration of their applicability to opinion texts.We address this gap by studying: (1) how humans judge the similarity of pairs of opinionbearing sentences; and, (2) the degree to which existing text similarity metrics, particularly embedding-based ones, correspond to human judgments.We crowdsourced annotations for opinion sentence pairs and our main findings are: (1) annotators tend to agree on whether or not opinion sentences are similar or different; and (2) embedding-based metrics capture human judgments of "opinion similarity" but not "opinion difference".Based on our analysis, we identify areas where the current metrics should be improved.We further propose to learn a similarity metric for opinion similarity via fine-tuning the Sentence-BERT sentenceembedding network based on review text and weak supervision by review ratings.Experiments show that our learned metric outperforms existing text similarity metrics and especially show significantly higher correlations with human annotations for differing opinions.
Real-Time Anomaly Detection Surveillance System

Video surveillance plays an important role in today’s generation. Due to its relationship with image understanding and video analysis, it has been attracting a lot of research in recent times. With the advancement in the technologies when artificial intelligence, deep learning and machine learning are introduced into the system, more powerful tools which are able to extract high semantic features are introduced to deal with the traditional architecture problems. Majority of anomaly detection applications are concerned with indoor surveillance with monitoring activities which helps to identify anomalous behaviour by tracking live stream footage. The main one of them is human behaviour detection. It is the most unpredictable behaviour, and it is very difficult to decide whether it is suspicious or normal. This paper demonstrates a deep learning approach to detect anomalous behaviour in a campus of school or college. The monitoring is performed through consecutive frames of cameras which are extracted from a video. Our model will be implemented on the extracted camera frames which detect the activities as abnormal. The feed is saved from the time of detection of an anomaly which sends an alert message to the respective authority. So we only need to save that portion of the video at which the anomaly is happening rather than saving the entire feed. The entire system is divided into two parts. In the first part, features will be computed from the live camera feed, and in the second part, based on the features extracted, the classifier will predict the anomaly. Proposed system detects the anomalies with a loss of 4.795.
Visual Analytics of RNN for Thermal Power Control System Identification

<p indent="0mm">Due to the problems such as strong continuity and high complexity of the data generated by the thermal power control process, and the difficulty of establishing a semantic correlation between the model behavior of recurrent neural networks and the actual control process, the model debugging, optimization and semantic analysis cannot be carried out intuitively. Visual analytics in recurrent neural networks modeling for system identification is applied and a visual analysis system called iaRNN is proposed. Firstly, by visualizing the activation value distribution and coverage of hidden units, the combined view for model evaluation is designed to support the evaluation of model performance in many aspects including inside and outside. Then, visual views are designed from the perspectives of temporal evolution, sensitivity analysis, etc, to support the exploration of model response behavior to thermal power control parameters. Finally, based on the symbolic representation of time sequences and clustering analysis, a method for exploring association patterns between strong time-dependent real-valued time series and hidden units is proposed. A case study using real power plant data is conducted to verify the effectiveness of iaRNN in assisting users to understand the working mechanism of the model and diagnose model defects.