Reviewerly/exHarmony huggingface dataset:
FPGA Implementation of MobileNetV2 CNN Model Using Semi-Streaming Architecture for Low Power Inference Applications

The recent research advances in deep learning have led to the development of small and powerful Convolutional Neural Network (CNN) architectures. Meanwhile Field Programmable Gate Arrays (FPGAs) has become a popular hardware target choice for their deployment, splitting into two main implementation categories: streaming hardware architectures and single computation engine design approaches. The streaming hardware architectures generally require implementing every layer as a discrete processing unit, and are suitable for smaller software models that could fit in their unfolded versions into resource-constrained targets. On the other hand, single computation engines can be scaled to fit into a device to execute CNN models of different sizes and complexities, however, the achievable performance of one-size-fits-all implementations may vary across CNNs with different workload attributes leading to inefficient utilization of hardware resources. By combing the advantages of both of the above methods, this work proposes a new design paradigm called semi-streaming architecture, where layer-specialized configurable engines are used for network realization. As a proof of concept this paper presents a set of five layer-specialized configurable processing engines for implementing 8-bit quantized MobilenevV2 CNN model. The engines are chained to partially preserve data streaming and tuned individually to efficiently process specific types of layers: normalized addition of residuals, depthwise, pointwise (expansion and projection), and standard 2D convolution layers capable of delivering 5.4GOp/s, 16GOp/s, 27.2GOp/s, 27.2GOp/s and 89.6GOp/s, respectively, with the overall energy efficiency of 5.32GOp/s/W at a 100MHz system clock, requiring total power of 6.2W on a XCZU7EV SoC FPGA.
A General Method for mining high-Utility itemsets with correlated measures

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Unsupervised Learning to Subphenotype Heart Failure Patients from Electronic Health Records

Heart failure (HF) is a deadly disease and its prevalence is slowly increasing. The sub-types of HF are currently mostly determined by the so-called ejection fraction (EF). In this work, we try to find novel subgroups of heart failure following a complete data-driven approach of clustering patients based on their electronic health records (EHRs). Using a validated phenotyping algorithm we were able to identify 14,334 adult patients with heart failure in our database. We derived embeddings of patients using two different strategies, one processing aggregated clinical features using principal component analysis (PCA) and uniform manifold approximation and projection (UMAP), and one where we learn embeddings from the sequence of medical events using a long short-term memory (LSTM) autoencoder. Then we evaluated different clustering strategies like k-means and agglomerative hierarchical to derive the most informative subtypes. The results were compared based on different metrics such as silhouette coefficient and so on and also based on comparing outcomes such as hospitalization, EF etc. between the clusters. In the most promising result, we were able to identify 3 subclusters using the aggregated data approach in combination with UMAP as dimension reduction method and k-means as cluster method. Patients in cluster 1 had the lowest number of hospital days and comorbidities, while patients in cluster 3 had a significantly higher number of hospital days together with a higher prevalence of comorbidities such as chronic kidney disease and atrial fibrillation. Patients in cluster 2 had a high prevalence of drug allergies in their medical history.
Low Light Image Enhancement Algorithm Based on Retinex and Dehazing Model

Low light images often have low visibility, which not only affects the visual effect, but also reduces the performance of algorithms that require high-quality input. Aiming at the problem of low light image enhancement, this paper proposes a composite enhancement algorithm. Firstly, the dark channel prior model and retinex model are combined by two adjustable parameters to obtain a new enhancement model DeRetinex. Then, according to the duality of the dehazing model and retinex theory, the image of the previous step is inverted, and the DeRetinex model is used for the second enhancement, which can eliminate the haze caused by enhancement. Compared with the existing mainstream algorithms, the proposed algorithm has the advantages of avoiding over exposure, rich texture details, low noise and high color recovery.
The Ecosystem of Training the Future Leaders of the Digital World: Results of the Inclusion of “Internet Entrepreneurship” in the Educational Program of the University of Economics

The article discusses the features of teaching the course "Internet entrepreneurship", supported by the Foundation for development of Internet initiatives in universities in Russia, Belarus, Kazakhstan. The Department of "Business Informatics" of the Financial University has been teaching students and postgraduates the basics of entrepreneurship on the Internet since 2015. The authors highlight the problems of teaching students technological entrepreneurship. The key participants in the process of training specialists whose competencies can be in demand in the digital world are identified. Recommendations for improving the effectiveness of the learning process are given. An important advantage of the approach to teaching the course" Internet entrepreneurship" is the integration of Lean Startup methodology and Design thinking. The authors developed cases and practical recommendations for the creation of digital services based on the Human-Centered Design. The article also describes the developed business game "Your profession in the field of IT". The tasks of the business game describe necessary personal qualities and competencies of 8 popular IT professions: Consultant, Presales, Product Manager, Software Developer, Support Engineer, Tester, Project Manager, System Administrator. A portrait of a typical representative of the profession, the type of behaviour in a conflict situation, behavioural characteristics is constructed. The game uses psychological tests, checks subject area knowledge, identification of communication skills, logic. The result of the game is a set of competencies inherent to the IT profession competency, as well as a roadmap for learning and the pattern of career development.
Qualification of holistic and generic camera-system calibration by fringe projection

Vision Ray Calibration provides a description of imaging properties of cameras by identification of an independent vision ray for each sensor pixel. Due to this approach, no model parameters of any cameras are determined in the non-linear optimization procedure of the calibration. Therefore, a setup of multiple cameras can be considered as one imaging system. This enables simultaneous and holistic calibration of an arbitrary number of cameras. Vision Ray Calibration utilizes Liquid Crystal Displays as calibration targets since these can provide the required continuous spatial coding of their surfaces by means of Phase Shifting Technique. However, displays employed as calibration targets exhibit some unfavorable properties such as flatness deviations of the surface. It is known that extending the Vision Ray Calibration by a polynomial parameterization of the display surface increases the calibration accuracy. This work investigates the influence of the order of polynomial terms employed for display surface parameterization. The stereoscopic Fringe Projection enables the evaluation of the calibration accuracy since Vision Ray Calibration of a stereo camera setup provides full system calibration for this technique. Our results confirm the significant improvement of calibration accuracy due to parameterization of flatness deviations of the display. Best calibration results were obtained by limiting the maximum order of polynomial terms to three. Our results indicate that terms of higher orders do not contribute true surface shape features.
Academic achievement analysis of Universitas Negeri Semarang students using the naïve bayes classifier algorithm

Abstract This study aims to evaluate the academic data of students in third year and classified in the category of students who can graduate on time or not. This system uses students master data and student academic data as input data. Data of students who have graduated will be used as training and testing data while the data of students who have passed will be used as target data. Input data will be processed using the Naïve Bayes Classifier (NBC) data mining algorithm to form a probability table as the basis for the student graduation classification process. The output of this system is in the form of a classification of student academic performance that is predicted to pass and provides recommendations for the graduation process on time or graduates in the most appropriate time with optimal grades.
The Relationship of Electronic Human Resource Management Systems on Artificial Intelligence and Digital Economy in the Organization

The study aimed to investigate the relationship of Electronic human resource management systems on artificial intelligence and digital economy in Organizations. An inductive research method was followed in the completion of this research paper, by reviewing previous literature on the topic, in addition to reviewing recent documents, articles, and seminars in this field. The main conclusion of this study is that even although the application of AI in E-HRM is relatively new, it is an increasing area HR field. To do that, the implications for the digital economy in organizations are important to study. Moreover, the study represents the There are major trends that are emerging due to the quick changes in HR technology, containing privatization, as there is a real opportunity to move far from the way HR programs were offered in the past. It is considered to understand the importance of digital transformation the digital technologies play a gradually strong role in both the employed lives of workers and electronic human resource management that is to be affected in many ways.
Helicopter Track Identification with Autoencoder

Computing power, big data, and advancement of algorithms have led to a renewed interest in artificial intelligence (AI), especially in deep learning (DL). The success of DL largely lies on data representation because different representations can indicate to a degree the different explanatory factors of variation behind the data. In the last few year, the most successful story in DL is supervised learning. However, to apply supervised learning, one challenge is that data labels are expensive to get, noisy, or only partially available. With consideration that we human beings learn in an unsupervised way; self-supervised learning methods have garnered a lot of attention recently. A dominant force in self-supervised learning is the autoencoder, which has multiple uses (e.g., data representation, anomaly detection, denoise). This research explored the application of an autoencoder to learn effective data representation of helicopter flight track data, and then to support helicopter track identification. Our testing results are promising. For example, at Phoenix Deer Valley (DVT) airport, where 70% of recorded flight tracks have missing aircraft types, the autoencoder can help to identify twenty-two times more helicopters than otherwise detectable using rule-based methods; for Grand Canyon West Airport (1G4) airport, the autoencoder can identify thirteen times more helicopters than a current rule-based approach. Our approach can also identify mislabeled aircraft types in the flight track data and find true types for records with pseudo aircraft type labels such as HELO. With improved labelling, studies using these data sets can produce more reliable results.
Blockchain Based Approach for tackling Deepfake videos

&lt;p&gt;The technological advancements and a qualitative improvement in the field of artificial intelligence and deep learning leads to the creation of realistic-looking but phoney digital content known as deepfakes .These manipulated videos can quickly be shared via social media to spread fake news or disinformation which not only impacts those who are deceived it also harms social media sites by diminishing faith.These deepfake videos cannot be checked since there are no regulatory mechanisms in place .As a result these untrustworthy outlets will post whatever they wish causing confusion in society in some ways.Current solutions are unable to provide digital media history tracing and authentication it is essential to develop successful methods for detecting deepfake video as a result it is necessary to determine the source or origin of such deepfake footage.That’s why we are implementing blockchain techniques to trace back and determine the origin of digital media blockchain techniques helps in the effective recognition of deepfake video and calculating the trust factor of user.&lt;/p&gt;
Research on SCADA data preprocessing method of Wind Turbine

The installed capacity of wind turbine is gradually increasing, and the cost of operation and maintenance of wind turbine is also gradually increasing. In order to reduce the cost of wind turbine operation and maintenance, using SCADA data for fault early warning and condition monitoring has become one of the hot research directions in recent years. The SCADA data of wind turbine include abandoned wind data, fault data, shutdown data and so on, these data can not correctly reflect the operating status of the unit, in order to improve the accuracy of early warning and facilitate the follow-up research work, it is necessary to preprocess the data. For this reason, this paper proposes a method of combining dispersion analysis and bin algorithm to preprocess the operation data of wind turbine. First of all, the principles of bin algorithm and dispersion analysis method are introduced, and then the improved bin algorithm is used to fit the wind speed-power curve. Finally, based on the power curve, the data are preprocessed by the method of dispersion analysis. The experimental results show that the combination of dispersion analysis and bin algorithm can effectively remove the abnormal data in SCADA data.
Pedagogical Communication in the Conditions of Distance Education: Models, Methods, Effectiveness

The article is devoted to the problem of the effectiveness of distance education, considered from the point of view of the organization of pedagogical communication. The article analyzes the structure and methods of the sociocentric model of education, which is basic for secondary and vocational education in the Russian Federation, and its differences from the distance learning model; the characteristics of the structural and content features of the distance model are given. The article describes the main reasons that reduce the effectiveness of pedagogical communication in the conditions of mixing models of the educational process, which takes place in 2020-2021, and in general, the communicative effectiveness of distance education in comparison with the sociocentric traditional model.
Template Matching and Deep CNN-SVM for Online Characters Recognition

Facing the increasing fast pace that technological progress is witnessing with the proliferation of new technologies and the growing touch-based devices, research works on Online Handwriting Recognition (OHR) have vastly broadened. In this paper, the problem of online handwritten Arabic character recognition (OAHCR) is addressed with Support Vector Machine (SVM) through combining output of two methods: template matching and SVM. On the one hand, a template matching-based method is applied based on fuzzy graph strategies for modeling as well as tree search algorithm and weighted euclidean distance for matching. On the other hand, a convolution neural network (CNN) works as an automatic feature extractor and SVM performs as classifier are introduced. Experiments performed on MAYASTROUN data set demonstrate that the proposed approach fulfills promising results compared to those of existing Arabic recognition approaches.
Localization of Facial Images Manipulation in Digital Forensics via Convolutional Neural Networks

Throughout digital media forensics, the identification of manipulated images and videos is a key topic. Many methods of detection use a binary classification to assess the likelihood of manipulation of a message. Another significant subject is the position, mainly due to three standard attacks, of the exploited regions (i.e., segmentation): elimination, copy-move and splicing. In order to simultaneously detect manipulated images and videos and locate the region for every question, a convolutional neural network is built which uses the multi-work learning approach. The information gained during the execution of one task would be exchanged and both tasks strengthened. To improve network generation, a semi-supervised learning approach is used. A decoder and a Y-shaped decoder are part of the network. For binary classification, activation of the encoded features is used. The output from one decoder branch is used to segment the areas manipulated and from the other branch to reconstruct the input to boost overall efficiency. Experiments using FaceForensics and FaceForensics++ have shown the network's ability to deal with the flaws in the preceding attacks and to counter facial reenactment attacks. In addition, the network can deal with unknown attacks by fine-tuning just a limited amount of data.
An Improved Generative Adversarial Network for Micro-expressions based on Multi-label Learning from Action Units

Micro-expression is a spontaneous facial expression with short duration, low intensity and facial partial action units. Micro-expression recognition plays an important role in psychological diagnosis, lie detection, and security systems. However, even the state-of-the-art recognition models suffer from the lack of micro-expression samples. In order to augment the training data, we propose a new method — an improved Generative adversarial network (GAN) for micro-expressions based on multi-label learning from action units. In the proposed model, action units (AUs) are added to GAN in the form of multi-labels. The designed loss function ensures to generate high quality images. The designed loss function for video can obtain a smooth action video with a trajectory of the AU. The designed loss function of optical flow guarantees low intensity for micro-expression generation and high similarity with the original training micro-expression samples. Moreover, the micro-expression recognition accuracy can be improved via adding the generated samples to the training data set. The experimental results on the two benchmark databases including CASME II and MMEW demonstrate the superiority of the proposed method over other state-of-the-art micro-expression recognition methods.
Phase Unwrapping with Multiple Wavelengths on the Flat Torus

We consider estimation of range from phase measurements at different wavelengths and argue that the flat torus is the natural mathematical entity to reason about this problem when we consider the circular nature of phase noise. Inspired by work in analog coding theory, we derive a new phase unwrapping algorithm and compare its performance to a state- of-the-art lattice decoder. Monte Carlo simulations of the new range estimation algorithm show a decrease of 2 dB in the SNR value where where the threshold effect of the MSE appears.
Graph Convolutional Hourglass Networks for Skeleton-Based Action Recognition

Graph convolution networks (GCNs) have become the mainstream framework for the skeleton-based action recognition task, since the skeleton representation of human action can be naturally modeled by the graph structure. Generally, most of the existing GCN based models extract and aggregate skeleton features by exploiting single-scale joint information, while neglecting the valuable multi-scale information such as part and body features in the skeleton. To address this issue, we propose a novel Graph Convolutional Hourglass Network (GCHN) model, which is scalable by stacking several basic modules of Graph Convolutional Hourglass Block (GCHB). Each GCHB module consists of the sequential operations of graph convolution, graph pooling and graph unpooling, which can promote the interaction of multi-scale information in the skeleton and effectively improve the recognition performance. Extensive experiments on the challenging NTU-RGB+D and Kinetics-Skeleton datasets demonstrate that the proposed GCHN model achieves state-of-the-art performance.
Risk assessment of SQL injection: An experimental study

The Web applications often handle confidential data such as Internet account passwords, credit card numbers, and so on. These sensitive data are generally transmitted over the Internet and therefore, exposed to the public. Given that, there is a very high risk of unlawfully accessing these data by hackers and others, especially since web applications are becoming notoriously vulnerable and are the target of a majority of Internet-based attacks. This paper proposes an assessment of web scanners that used for detecting web applications security flaws. In particular, we're focusing on SQL injection vulnerability in web applications.
Collection, organization, analysis, and application of usage data to inform an academic library's annual electronic resource renewal decisions

Providence College is a Catholic, Dominican, liberal arts institution of higher education and a community committed to academic excellence in pursuit of the truth, growth in virtue, and service of ...
A Software Pipeline Based on Sentiment Analysis to Analyze Narrative Medicine Texts

By using social media people can exchange sentiments and emotions, allowing to understand public opinion on specific issues. Sentiment Analysis (SA) is a novel text-mining (TM) and natural language processing (NLP) methodology to extract sentiment, opinions and emotions from written texts, usually provided through social media or questionnaires. Sharing medical and clinical experiences of patients through social media, is the target of the so-called Narrative Medicine (NM). Here we report some research experiences in applying SA techniques to analyze NM texts. A problem to be faced in NM is the automatic analysis of a potentially large set of documents. Application of SA is useful for having immediate analysis and extracting information from medical literature quickly. Here we present a software pipeline based on SA and TM which allows to effectively analyze NM texts. First experimental results allow to discover topics related to diseases.
Character Recognition using Adjustment Convolutional Network with Dropout Layer

Abstract Character Recognition is the machine’s ability to receive and translate certain character input. It can be handwriting, computer-fonts, and images. In this article, character recognition is done using the Adjustment Convolution Network (ACN). The ACN architecture used consists of 28 layers (input, 5 convolutional, 5 Batch Normalization, 5 Rectified Linear Units, 4 max-pooling, 5 dropouts, Fully Connected Layer, Softmax, Output). Dropout is located in each convolution block. The values used are 0.25, 0.3 and 0.4. Dropout keeps away from the overfitting incident. The network is carried out with the optimization of Adaptive Moment Estimation (Adam) which is a combination of momentum and adaptive sub-gradient variable. Data contain digits 0-9, letters A-Z, and letters a-z that amounted to 171,562. Data is taken from MNIST, notMNIST and Char74K public data. A comparison between training vs. testing data is 70:30. The results showed accuracy of up to 99.04% (MNIST), 94.90% (notMNIST), 98.9% (font-Char74K), 79.4% (Handwritten-Char74K), and 86.80% (Image-Char74K) respectively.