Reviewerly/exHarmony huggingface dataset:
Towards Determining and Delivering the Most Suitable Form of Diagrammatic Representation by Compressing Lengthy Texts

Visualization of content is an essential component for thorough understanding, interpretation and quicker and effective processing by the human brain, considering the abundance of data to which one is exposed daily. Determining the most appropriate form of a visual presentation can be time-consuming and challenging. There is a need to reduce this manual effort in various domains and industries like marketing, education, travel and a business where presenting data is one of the crucial tasks. However, such textual content is absent to visual content converters that can be fed with structured, unstructured textual content and semi-textual content of various forms and recommend different suitable layouts and forms of visual representation. However, certain converters available in the market limit themselves in capacity to only structured content. Hence, developing a visual content generator that takes exhaustive textual content of various forms and recommends, a set of suitable forms of diagrammatic representation to deliver the content is aimed at, thus saving time and enhancing the speed of processing data by the human brain by 60,000 times and expand the delivery of content by 4 times.
Randomized Key Exchange Protocol Implementation for Internet of Things Application

Recently, the security of public key security algorithms have been research extensively; besides, the utilization of public key cryptography requires key exchange protocol. The security of key exchange protocol is significant as it transmits the key and therefore, the security of public key algorithms depend on the security of key exchange protocol. In addition, the concept of Perfect Forward Secrecy (PFS) is evolving with the number of security threats. Traditionally, the key exchange protocols use asymmetric and symmetric to exchange session keys, which consumes more hardware resources and time for implementation. Moreover, these encryption based key exchange protocols requires several messages to exchange before sending the key, which cause delay in key exchange and consume bendwidth ineffciently. This work provides a new key exchange protocol with randomized initialization vector and advanced features for PFS. The proposed algorithm is simple and faster as compared to previous algorithms, the security of proposed algorithm is comparable to One Time Pad (OTP) algorithm.
The Correlation of Students’ Perception About Online Learning and Students’ Grade Point at IAIN Bukittinggi

Abstract Abstract . This research is aimed to find correlation between students’ perception about online learning and students’ grade point average at Tarbiyah and Teacher Training Faculty IAIN Bukittinggi. Research population was all of Tarbiyah and Teacher Training Faculty students who registered active on even semester 2019/2020 that amounted to 3.335 students. Sample was chosen 10 % of the population, 333 students, using accidental sampling technique. Research instrument used questionnaire and distributed via Google form by using media like whatsapp group and email. Based on the finding, it was founded students’ perception about online learning at Tarbiyah and Teacher Training Faculty IAIN Bukittinggi classified into good and score gotten is 64. If it is reviewed from indicators which consisted of infrastructure, education management and human resource, the score of each indicators gotten is infrastructure aspect 63 classified into good, education management aspect 61 categorized into good and human resources aspect 68 classified into good. If it is reviewed from correlation of students’ perception about online learning and students’ grade point average, correlation coefficient (r) is 0,13 which means low or weak correlation. If it is compared between count correlation coefficient and table, it can conclude r count is bigger than r table means there is correlation between students’ perception about online learning and students’ grade point average.
Quality and Reliability of YouTube Videos on Sickle Cell Disease

*Emory University School of Medicine Atlanta, GA †Georgetown University School of Medicine, Washington, DC The authors declare no conflict of interest.
Indoor intelligent driving scene-based traffic signal data set

Abstract In recent years, with the rapid development of intelligent driving technology, vision based traffic signal target detection is one of the urgent problems to be solved; especially with the extensive application of deep learning technology, the traffic signal data set is more and more important. Due to the variety of traffic signals, there is no data set that can cover all categories at home and abroad. This paper presents a traffic signal data set based on indoor traffic scene. The currently disclosed data category includes 22 categories, including traffic signs and traffic lights. Simultaneously, this paper also uses a two-stage deep learning method to conduct experiments. In order to facilitate other researchers to conduct further research on the disclosed data set in this paper.
Active Class Incremental Learning for Imbalanced Datasets

Incremental Learning (IL) allows AI systems to adapt to streamed data. Most existing algorithms make two strong hypotheses which reduce the realism of the incremental scenario: (1) new data are assumed to be readily annotated when streamed and (2) tests are run with balanced datasets while most real-life datasets are imbalanced. These hypotheses are discarded and the resulting challenges are tackled with a combination of active and imbalanced learning. We introduce sample acquisition functions which tackle imbalance and are compatible with IL constraints. We also consider IL as an imbalanced learning problem instead of the established usage of knowledge distillation against catastrophic forgetting. Here, imbalance effects are reduced during inference through class prediction scaling. Evaluation is done with four visual datasets and compares existing and proposed sample acquisition functions. Results indicate that the proposed contributions have a positive effect and reduce the gap between active and standard IL performance.
Object Detection in RGB-D Image Based on Two-stream SSD

Aiming at an unsatisfactory detection performance due to the limitation of the object detection method using color image or depth image alone, a two-stream SSD detection network combining color image and depth image is proposed, the depth map channel network is added on the basis of the original SSD detection network, the color information and the depth information are merged in different ways on the feature layer of the two-stream network, and the different features of the color map and the depth map are fully utilized, thereby effectively overcame the disadvantages of detection by single image, improved the accuracy of the object detection and the robustness of the environment. The experimental results on the SUN RGB-D dataset show that compared to the SSD detection network using color image alone and the SSD detection network using depth image alone, the accuracy of the proposed network is improved by 3.0 percentage points and 8.2 percentage points, respectively, and the detection rate on the GTX 1080 Ti is about 17FPS.
Fast 3D Hand Pose Estimation for Real-time System

In this paper, we propose a novel system that estimates 3D coordinates of the hand joints from a single depth image using a convolutional neural network (CNN). The proposed system consists of the CNN-based feature extraction, and a regression neural network that estimates 3D joint coordinates. The regression network is composed of two branches by dividing a palm and fingers. In the experimental results, the proposed system was compared with other state-of-the-art methods using a public NYU dataset. The proposed system had an average 3D distance error of 9.62mm and inference time of 172 fps. It had the best performance in terms of both accuracy and speed than other recent methods.
Variable-Length Image Compression Based on Generative Adversarial Model

Lossy image compression algorithms based on deep neural networks have received tremendous attention in recent years, and they can achieve much higher compression ratio than traditional information-theoretic methods. However, almost all the existing algorithms based on neural networks are fixed-length compressions, which are hard to deal with images with different sizes or different amount of information. In this paper, we explore image compression algorithms based on deep neural networks with fixed input-length and variable output-length, which can adaptively select the output length based on the quality of the compressed images. Our proposed deep neural network consists of an auto-encoder and a generative adversarial network for obtaining a decompressed image that looks `real' and close to the original one in L2 distance. The main idea of variable-length compression is to train the neural network such that the nodes of feature representation layer (compressed layer) have different importance in feature expression to recover the original image, and then to discard those less-important feature nodes depending on the resulting distortion. Experiments show that our method can not only achieve variable-length compression, but also can recover high-quality compressed images at extremely low bit rates.
A Dictionary-learning-based Denoising Algorithm with log-regularizer for MR Image

With the wide application of magnetic resonance imaging (MRI) technology in the medical field, MR image denoising draws a lot of research attention. The dictionary learning methods based on sparsity representation analysis models have been developed for image denoising recently. Despite these methods solve a class of challenging nonconvex and nonsmooth, it remains an hot topic to find a dictionary learning method that is not only empirically fast, but also has mathematically guaranteed strong convergence. In this paper, we employ log-regularizer instead of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm sparsity regularizer, and than we propose a novel dictionary-learning-based denoising algorithm with log-regularizer for MR image. To solve the optimization problem efficiently, we divide MR image into local image patches. And then, we decompose the overall problem in matrix form into subproblems in vector form. In the final, we adopt alternating optimization scheme to update sparse vectors, dictionary atoms and the reconstructed MR image alternately. In addition, proximal operator algorithm and the least square method are employed to obtain the solutions of sparse vectors and dictionary atoms, leading to an efficient and fast algorithm. More importantly, a theoretical analysis shows the proposed algorithm satisfies the global convergence property. Experiments also show that the proposed algorithm achieves better results compared with other dictionary-learning-based denoising algorithms.
The Science of Statistics in Victim-hood Culture: Dignity and Dishonor

Recent renaming of the R. A. Fisher Award and Lectureship without official discussion among members of American Statistical Association (ASA), along with other events of similar type, was an important step in creating a new atmosphere in science in general. On the one hand, this is tightly related to such critical issues as freedom of speech, research and opinion; while on the other hand, it is related to conformity, yielding to irrelevant demand, and to denial of evidence as a first basic principle in science. The paper shows that a) all accusations against R. Fisher are factually groundless; b) the decision was motivated not by the facts, but by political and moral pressure, connected with allegedly “institutional racism” of the American society and the US police specifically; c) the decision was a natural consequence of the victim-hood culture, which penetrates more and more into academia; d) this culture, in turn, is counter-scientific in a sense that it does not need any evidence; e) particularly, the most popular current thesis about “police bias against black people”, - which seems a direct real trigger of the lecture renaming -, cannot be confirmed by available data. Showing all that, the paper could be considered a warning against dangerous social tendencies in modern science e in general and statistics in particular.Published in reduced version in: Model Assisted Statistics and Applications 15 (2020) 181–195
Translating RDA Reference in Greek: Reflections on the Project

This paper presents and reports on the ongoing project of the Greek translation of the Resource Description and Access (RDA) Reference as pursued by its author. It discusses the first outcomes of the effort during the four-year period from 2016-2020. It focuses on the steps followed; the procedures and various technical issues; as well as the knowledge, expertise, and skills gained by the liaison-translator as the project evolved. It aims to contribute to RDA-related documentation as implemented in the case of the Greek translation of the RDA vocabularies and to encourage greater involvement by and within the Greek library and information science sector.
Lexicographic Product of Digraphs and Related Boundary-Type Sets

Let $$D=(V,E)$$ be a digraph and $$u ,v\in V(D)$$ . The metric, maximum distance is defined by $$md(u,v)=\max \{\overrightarrow{d}(u,v), \overrightarrow{d}(v,u)\}$$ where $$\overrightarrow{d}(u,v)$$ denote the length of a shortest directed $$u-v$$ path in D. The relationship between the boundary-type sets of the lexicographic product of two digraphs and its factor graphs have been studied in this article.
MoNet: Impressionism As A Defense Against Adversarial Examples

While image classifiers based on Convolutional Neural Networks (CNN) are extremely successful, they often suffer from adversarial examples. To address this problem, we explore the combination of using secret randomness and Floyd-Steinberg dithering. More specifically, each CNN is trained with a secret, random key. Input images are first processed using Floyd-Steinberg dithering to reduce the color depth, and then each pixel is encrypted using the AES block cipher. Processed images are then fed into a standard CNN. We call our approach MoNet, because the processed images are reminiscent of impressionistic paintings. Classifiers trained with MoNet showed significant improvements in model stability and robustness against adversarial inputs. MoNet significantly improves the resilience against transferability of adversarial examples, at the cost of a small drop in prediction accuracy.
Keywords Extraction in Vertical Domain Based on Global Knowledge and Local Semantic Relations

Because most keywords extraction algorithms do not combine domain expertise and semantic information effectively, a vertical domain keywords extraction method is proposed and verified in some fields. Based on the construction of domain dictionary database, the algorithm uses the Label Latent Dirichlet Allocation (Label-LDA) model to learn the global knowledge to obtain the importance evaluation of each words under vertical domain, and uses the Bidirectional Encoder Representation from Transformers (BERT) model to obtain the local semantic relationship evaluation at the same time. The two models are weighted by the gradient descent method to construct a new word graph. Finally, an improved K-shell algorithm is proposed for ranking the importance of words and extract keywords. Experiments show that, compared with several commonly used domain wide keywords extraction algorithms, the effect of this algorithm on military field is improved by 18% to 52%. At the same time, the algorithm can be effectively generalized to other different vertical fields such as economy, sports, science and technology.
On Shortest Path Problem via a Novel Neurodynamic Model: A Case Study

Our aim is investigating the Shortest Path Problem (SPP) by employing the Recurrent Neural Networks (RNNs). The Karush–Kuhn–Tucker (KKT) optimality conditions play an essential role to present the RNN model. In fact, the KKT conditions are reformulated as a Nonlinear Complementarity Problem (NCP). Indeed, the stability theorem of the RNN is provided. By testing some examples, we show the performance of the model. Also, we apply the approach to solve a real-world problem as a case study.
Semi-supervised Learning in Computer-aided Diagnosis

Computer-aided diagnosis techniques have significant potential in assisting pathologists in diagnosis, especially in the field of medical image processing. Many supervised learning-based approaches have been successfully used on computerised tomography, ultrasound, or magnetic resonance imaging images recently. Meanwhile, since some pathology disciplines cannot pro-vide the amount of labelled data required by these conventional methods for training, semi-supervised learning (SSL) methods have recently attracted attention. This paper provides the basic introduction of SSL and computer-aided-diagnosis (CAD) and reviews the current experiments and prospects of the application of SSL in CAD systems, including data acquisition, image pre-processing, feature extraction, classification and validation, etc. This paper also reports and highlights the strategy and performance of SSL combined with CAD according to some findings of researchers to date and provides some approaches for model validation.
Location Based Communication Privacy in Internet of Vehicles Using Fog Computing

In the Vehicular Ad-hoc Networks (VANETs), a vehicle or the vehicle driver could be perceived and followed by listening in its inquiries (e.g., reference points) by an enemy, since these quires contain individual data and information, for example, the questions and area of the vehicle. This attack prompts dangers on the vehicles area protection. The current arrangements, practices anonymizer, a Third Trusted Party (TTP) in the middle of the LBS and the vehicles. The addition of the TTP shifts the imperilled element from the LBS to the Anonymizer, with respect to security hazard, and with the endangered anonymizer, the vehicles or vehicles drivers related information will likewise be in danger. In this paper, we think of productive area based correspondence protection in VANETs communication. In which, we utilize the Fog Computing (FC) alongside TTP calculation and data perturbation. Before sending the inquiry to the TTP first we anonymizes the communication information by utilizing data perturbation. Then, we utilize the Adaptive Interval Cloaking Algorithm (AICA) as TTP to handle the ideal question from vehicles to LBS. In this paper, we provide the double communication privacy based on data perturbations and AICA. Subsequent to accepting the prepared inquiry from the LBS, the TTP sends the outcomes back to the drives, where the vehicle drivers finds their ideal results. The results shows that the proposed method save the security area dependent on the questions at the low correspondence and computational expense.
10k Logo Dataset for Machine Learning Logo Retrieval Purposes

Trademarks and logos are used worldwide to represent companies, malls, shops and restaurants. It is one of the first visual symbols a company creates in the early stages of planning for this business. The logo must be unique and captures the idea of that business to make it easier for the common people to figure out what this business is about from the logo. With logos and trademarks details are very important where every element in the logo would make a difference including the colours used, shapes and fonts. Many efforts were made in last few years to detect, recognize or match similarities between logos; Moreover one of the major challenges faced was finding the correct dataset that include different local and international clear logos to test on . Thus, this paper presents a new dataset that would help in these types of machine learning project which provides 10K logo images with 2000 unique logo.
Making Use of Virtual Reality for Artificial Intelligence Education

This project explored how virtual reality (VR) can be used in artificial intelligence (AI) education. A prototype VR application was developed to give students an introduction to deep learning using the Oculus Quest. The application applied escape room elements as an attempt to let students learn the curriculum in an engaging way by doing 3D-puzzles, calculations, and quizzes based on the course-material. The topics were split into separate rooms to let students progress through the curriculum intuitively. 15 people tested the application and responded to a questionnaire. 26 people evaluated the application’s concepts after watching a video. Based on the evaluation, we believe that using such a VR application in AI education can be a good supplementary tool to introduce students to new topics in an engaging way. The main advantage of using VR in this context is to use interactive 3D-visualizations and hands-on activities that are challenging to experience by other means. The questionnaire’s respondents were very positive to the concept, and it could potentially be beneficial in other types of STEM-education as well.
Explainable NLP: A Novel Methodology to Generate Human-Interpretable Explanation for Semantic Text Similarity

Text Similarity has significant application in many real-world problems. Text Similarity Estimation using NLP techniques can be leveraged for automating a variety of tasks that are relevant in business and social context. The outcomes given by AI-powered automated systems provide guidance for humans to take decisions. However, since the AI-powered system is a “black-box”, for the human to trust its outcome and to take the right decision or action based on the outcome, there needs to be an interface between the human and the machine which can explain the reason for the outcome and that interface is what we call “Explainable AI”. In this paper, we have made a twofold attempt, first, 1) to build a state-of-the-art Text Similarity Scoring System which would match two texts based on semantic similarity and then, 2) build an Explanation Generation Methodology to generate human-interpretable explanation for the text similarity match score.
On-Chip Intelligent Frequency Scaling using Artificial Neural Networks

Dynamic Voltage and Frequency Scaling (DVFS) is a popular method for reducing power consumption. Several DVFS techniques have been used for manually triggering a change in frequency to save power. This paper proposes a novel lightweight on-chip neural network (Kohonen Self-Organizing Maps) for user-behavior based frequency scaling to improve CPU performance, which senses and classifies the user-behavior based on the utilization of the processor and memory components and assigns appropriate frequency to achieve significant performance boost, while also obtaining mild power savings, and catering to multiple user-behaviors. Also, since the hardware of the ANN is implemented on-chip, no communication of data is required, thus, reducing the overhead of the implementation appreciably. The proposed technique has been evaluated on Intel i7-4720HQ Haswell processor and has shown performance boost by up to 47%, while saving up to 6% SoC power, simultaneously, as compared to the existing DVFS technique.
Spatial Pyramid Based 3D Hog Feature Extraction for Alzheimer’s Disease Identification

Image based machine learning is widely used for Alzheimer’s disease (AD) identification, however, traditional two dimensional (2D) image processing techniques may lead to spatial information lost. In this paper, a spatial pyramid based three dimensional (3D) histogram of oriented gradient (HOG) feature extraction method is proposed to increase spatial discrimination. Then a simple f-divergence based dimension reduction method was used to extract principal features in HOG, which solved the problem of high dimensionality of variables with relatively small number of subjects. Experimental results illustrated the effectiveness of proposed method.