Reviewerly/exHarmony huggingface dataset:
A Conceptual Model for Virtual Queuing System to Support Social Distancing

In the early December 2019, Coronavirus Disease 2019 (COVID-19) epidemic outbreak emerged from Wuhan City, Hubei Province, China and spread rapidly to the rest of the world. Globally, IT Professionals were forced to adapt and innovate rapidly in response to the pandemic and have devised a variety of IT solutions to ease peoples’ transitions into the new normal. There are 4 major categories of IT-based COVID-19 solutions, namely contact tracing, quarantine management, symptom monitoring and information provision. However, most of these applications only focus on contact tracing as opposed to preventing the spread of the virus through the enforcement of social distancing. This paper presents a conceptual model for virtual queuing system, MyQueue which allows its users to enter virtual queues by scanning unique QR codes of various premises. It introduces proper process flows and approaches to eliminate waiting in a crowded queue and help support social distancing efforts to reduce the spread of COVID-19.
The HSPRec E-Commerce System Open Source Code Implementation

To promote big data application access, usage and deployment, this paper presents a downloadable open source code implementation for an E-Commerce Recommendation system, HSPRec (Historical Sequential Pattern Recommendation System), in JAVA. The HSPRec system is composed of six different modules for generating purchase/click sequential databases, mining sequential patterns, computing click purchase similarities, generating purchase sequential rules, computing weights for frequent purchase patterns through Weighted Frequent Purchase Pattern Miner, and normalization of the user-item ratings to predict level of interest. The source code of each module and the main runner are discussed under four possible headings of running environment, input data files and format, minimum support format, output data files and format. The overall goal of the HSPRec system is to improve E-commerce Recommendation accuracy by incorporating more complex sequential patterns of user purchase and click stream behavior learned through frequent sequential purchase patterns. HSPRec provides more accurate recommendations than the tested comparative systems.
Testing the Effectiveness of CNN and GNN and Exploring the Influence of Different Channels on Decoding Covert Speech from EEG Signals

In this paper, the effectiveness of two deep learning models was tested and the significance of 62 different electroencephalogram (EEG) channels were explored on covert speech classification tasks using time series EEG signals. Experiments were done on the classification between the words “in” and “cooperate” from the ASU dataset and the classification between 11 different prompts from the KaraOne dataset. The types of deep learning models used are the 1D convolutional neural network (CNN) and the graphical neural network (GNN). Overall, the CNN model showed decent performance with an accuracy of around 80% on the classification between “in” and “cooperate”, while the GNN seemed to be unsuitable for time series data. By examining the accuracy of the CNN model trained on different EEG channels, the prefrontal and frontal regions appeared to be the most relevant to the performance of the model. Although this finding is noticeably different from various previous works, it could provide possible insights into the cortical activities behind covert speech.
Instance Selection Based on Linkage Trees

Nowadays, the massive production of raw multivariate data implies an accumulation of redundant information. When machine learning approaches learn from unfiltered data, the training time might increase significantly. This problem can be overcome with instance selection (IS) to obtain a subset of patterns that keep the original underlying distribution. IS has been often addressed as an optimization problem solved by evolutionary algorithms, which usually adopt a binary representation to encode all the instances explicitly; hence, the larger the number of instances, the greater the search space. We propose an IS method based on linkage trees, where cut-off levels per class are encoded to reduce the search space. Thus, the codification length equals the number of classes, which is considerably smaller than the binary codification size. Clusters of instances are created from cut-off levels, and their medoids form the selected subset of instances. The linkage trees cut-off levels are optimized by simulated annealing to find the best subset of instances that preserves the original data distribution with a reduced number of instances. The experimental results on synthetic and realworld datasets show that the proposed approach outperforms IS methods from the literature regarding a tradeoff between classification accuracy, reduction rate, and density preservation.
Image-Based Joint State Estimation Pipeline for Sensorless Manipulators

Motion planning is a largely solved problem for robot arms with joint state feedback, but remains an area of research for sensorless manipulators such as toy robot arms and heavy equipment such as excavators and cranes. A promising approach to this problem is deep learning, which employs a pre-trained convolutional neural network to identify manipulator links and estimate joint states from a monocular camera video feed. Whereas manual labeling of training image sets is tedious and non-transferable, a simulation environment can automatically generate labeled training image sets of any size. The issue is the gap between simulated and real-world images. This paper solves this problem by implementing a Generative Adversarial Network. The complete joint state estimation pipeline is implemented and tested in hardware experiments to validate our proposed approach.
Investigating Protection of Deep Learning Visual Features on ECB Encrypted Images

In this paper, we demonstrate that images encrypted with Advanced Encryption Standard (AES) in Electronic Code Book (ECB) mode retain some local properties of the original images that Deep Neural Networks (DNNs) can detect these properties and perform classification tasks on this encrypted data. The experiment with the ECB encrypted MNIST handwritten digit dataset revealed that models trained on this dataset have an accuracy of around 80%. It also demonstrated that the model trained using one secret key does not work with other secret keys or the original dataset; the prediction accuracy plummeted to less than 10%. As a result, malicious users who do not know the secret keys will find the model inefficient, and it may be difficult to manipulate or change the prediction results.
Face Transferring on Webcam images using StyleGAN

Abstract In image processing technology, face transfer is broadly used for privacy protection, picture enhancement, and entertainment applications. Face transfer is the domain that maps one image into another image and extracts several features of the face from one person to morph that face to another person. This face transfer will carry the facial expressions also. This is also called face morph, face swap, etc. Here we propose StyleGAN technology using face transfer with the image to get high quality. In this StyleGAN contribute the bilinear interpolation and affine transformation. Bilinear interpolation is to remove the noise and increase the quality of images. Affine transformation is to supply the images with 2d warping to improve the image quantity. To upgrade the quality of the images with face transfer is adopted to increase the accuracy of the image quality after image transfer.
Web and Mobile Application Based Missing Query Platform (Lost and Found BD)

Lost & Found BD is a web and mobile application based platform to store and share the data of missing people, pets, doc files etc. Users can have the full opportunity utilizing features of each section as the site is being developed in such way. Each section has its own credibility to help users finding missing people, pets and document files in a more efficient way. Sections like news, dynamic search engine, publishing reports directly to live web portals, getting notifications being a registered user etc. are one of those main features.
Coordinated Planning Research on Distributed Command and Control System based on UAV

In modern warfare, the requirements on multi Unmanned Aerial Vehicle coordinated operations increase more and more, and the current processing system cannot meet these requirements. By connecting existing control stations via networking, a multi-unit processing system can be formed. This paper proposes to use multi-unit system to solve problems in the architecture and processing method of multi Unmanned Aerial Vehicle coordinated planning; quantify the planning restriction of Unmanned Aerial Vehicle, build up threat source model; and take the shortest route and the lowest threaten among all routes as basic restrictions. The genetic simulated annealing algorithm is used for route planning, which will combine the threatening values of each leg and the flying distance to build up the adaptability evaluation function based on threat quantum to design the algorithms, so as to solve the problem of low efficiency in Unmanned Aerial Vehicle coordinated planning.
Deep Learning based Transcribing and Summarizing Clinical Conversations

Doctor-Patient interaction plays a very vital role in patient care. The essence of the interaction is lost when a lot of time is consumed in writing or typing the required patient details. These administrative tasks happen at the expense of patient care. With Artificial Intelligence and Natural Language Processing, this research work creates different ways to reduce the time spent on such onerous, trivial administrative tasks at healthcare facilities and concentrate on serving the patients in a better way. This paper proposes an automation mechanism of noise suppression to eliminate environmental disturbance, transcription and sum- summarization of the recorded conversation taking place between the doctor and the patient(s) to focus only on the essential information, since abridging the entire conversation as a whole may be counterproductive. The tabular summary obtained at the end of the process can be used by the doctors and patients alike, to understand the patient history, prognoses and/or diagnoses. A supervised deep learning technique is used for noise suppression by using a convolutional network, the Google Speech- to-Text API for transcription of the conversation and a basic SVM module which categorizes text based on the given tags and relative frequency of occurrence of a word to create the tabular summary of the said doctor-patient verbal exchange.
Tonality analysis in Wolfram Mathematica 12 computer algebra system

Abstract Sentiment analysis is a class of methods that are used to automatically determine the tonality of statements in the text. There are many solutions and technologies to define of text tonality at the moment and the choice depends on task. With the release of Wolfram Mathematica 12 versions, it became possible to use included nlp methods for determination of text tonality. This article is about definition of text tonality in the wolfram Mathematica 12 using nlp and compare nlp methods with standard definition function.
Study on linear optimization of activation function of homomorphic encryption neural network

Since the 20th century, the research on cipher neural network is more and more in-depth. Encryption scheme based on neural network is from layered homomorphic encryption scheme to fully homomorphic encryption scheme, the accuracy of neural network and the resource consumption in calculation are always the core of the research. This paper mainly studies the optimization of the excitation layer in the neural network. Since the convolution layer and pooling layer in the hidden layer are linear operations, they can support the secret state operation, and the excitation layer introduces nonlinear factors into the neural network in the form of nonlinear functions, so the secret state calculation cannot be directly applied to the layer. In order to solve this problem, this paper on the basis of CryptoNets model, only in the case of low degree to consider about the commonly used three kinds of activation function : Sigmoid, Tanh and ReLU function for the linear approximation, and proposed a new approximate method : An approximate function is constructed by using the structural characteristics of the derivative of the function, compared with the commonly used approximate method, a new method of approximate function replacement after activation function, the neural network prediction accuracy and operation time in both performance is more outstanding.
Modeling nuisance classifier towards class-incremental learning of crowd-sourced data

In this paper, we propose a novel framework for tackling class-incremental learning of crowd-sourced data. The hallmark of crowd-sourced data is the presence of multiple participants, with limited expertise contributing incrementally leading to varying amounts of data. Modern machine learning algorithms are plagued with catastrophic forgetting, where the model performance degrades as new classes are added progressively. Unlike methods that use standard datasets with a known number of image categories, incremental learning of crowd-sourced data is challenging, since the number of categories is not known a priori and the data itself is noisy. To solve this problem, we propose a novel training strategy that leads to a Modular Stacked Classifier architecture capable of adapting to incremental data. The elegance of this solution lies in its modularity, which allows for re-training the desired model for specific classes without affecting knowledge acquired hitherto. We achieve this through two steps - first we formulate a deep clustering method to represent the data in latent space and use it to automatically discover novel categories in given unlabeled data. The second step involves designing a novel Nuisance Classifier to learn newly added classes while retaining the knowledge acquired from previously learned classes. We introduce a crowd-sourced heritage dataset to demonstrate our proposed framework, and extensive experiments show consistent improvements over existing methods.
Light Diacritic Restoration to Disambiguate Homographs in Modern Arabic Texts

Diacritic restoration (also known as diacritization or vowelization) is the process of inserting the correct diacritical markings into a text. Modern Arabic is typically written without diacritics, e.g., newspapers. This lack of diacritical markings often causes ambiguity, and though natives are adept at resolving, there are times they may fail. Diacritic restoration is a classical problem in computer science. Still, as most of the works tackle the full (heavy) diacritization of text, we, however, are interested in diacritizing the text using a fewer number of diacritics. Studies have shown that a fully diacritized text is visually displeasing and slows down the reading. This article proposes a system to diacritize homographs using the least number of diacritics, thus the name “light.” There is a large class of words that fall under the homograph category, and we will be dealing with the class of words that share the spelling but not the meaning. With fewer diacritics, we do not expect any effect on reading speed, while eye strain is reduced. The system contains morphological analyzer and context similarities. The morphological analyzer is used to generate all word candidates for diacritics. Then, through a statistical approach and context similarities, we resolve the homographs. Experimentally, the system shows very promising results, and our best accuracy is 85.6%.
A Multi-Sensor Multi-Target Tracker Based on Labeled MS-CPHD Filter

The multi-sensor cardinalized probability hypothesis density (MS-CPHD) filter based on the random finite set (RFS) have been developed in the literature for multi-sensor multitarget tracking. However, this filter is not strictly a multi-target tracker as it cannot estimate identities of individual target states. To form the target tracks, a multiple target tracker based on the MS-CPHD filter is given in this paper. Specifically, in the Gaussian mixture recursion of the MS-CPHD filter, each Gaussian component is identify identified with a unique label for separating different targets. Then the target tracks can be determined from the calculation of the Gaussian component with a corresponding label. Furthermore, we also propose a track management mechanism to determine the creation, maintenance, and termination of tracks. Numerical results from simulations show that, our proposed method can obtain target tracks and has higher filtering accuracy compared with the original MS-CPHD filter, especially in scenarios with high clutter intensity.
Single object tracking algorithm in occlusion scene based on improved ECO

Abstract To alleviate the occlusion problem in a single object tracking scene, this paper proposes an ECO-MHDU object tracking algorithm with a more powerful anti-occlusion performance based on the ECO tracker. The algorithm first uses the pre-trained MobileNetV3 lightweight backbone network on the ImageNet dataset to replace the ResNet network in the ECO to increase the speed of the algorithm to obtain the shallow and deep feature information of the image, while effectively using the attention mechanism in the MobileNetV3 network to strengthen the algorithm’s ability to extract target features; secondly, use the DropBlock operation on the acquired feature map to generate a random continuous mask on the feature map channel to improve the algorithm’s learning of the global robust spatial structure information; finally, a confidence update strategy is introduced into the GMM sample generation space. To improve the quality of training samples, unreliable tracking states such as confidence detection and occlusion are designed to avoid updating the sample space with damaging information. Compared with the ECO algorithm, the ECO-MHDU algorithm proposed in this paper has a success rate of 68.0% on the occlusion attributes of the OTB100 dataset, which is 2.3% higher than the ECO algorithm, and the ECO-MHDU algorithm also showed the best performance on the entire dataset sequence, with a success rate of 69.3%.
Sparse coded spatial pyramid matching and multi-kernel integrated SVM for non-linear scene classification

Abstract Support vector machine (SVM) techniques and deep learning have been prevalent in object classification for many years. However, deep learning is computation-intensive and can require a long training time. SVM is significantly faster than Convolution Neural Network (CNN). However, the SVM has limited its applications in the mid-size dataset as it requires proper tuning. Recently the parameterization of multiple kernels has shown greater flexibility in the characterization of the dataset. Therefore, this paper proposes a sparse coded multi-scale approach to reduce training complexity and tuning of SVM using a non-linear fusion of kernels for large class natural scene classification. The optimum features are obtained by parameterizing the dictionary, Scale Invariant Feature Transform (SIFT) parameters, and fusion of multiple kernels. Experiments were conducted on a large dataset to examine the multi-kernel space capability to find distinct features for better classification. The proposed approach founds to be promising than the linear multi-kernel SVM approaches achieving 91.12 % maximum accuracy.
Segmentation of Nucleus in Histopathological Images Using Deep Learning Architectures

The aim of this study is to develop a image segmentation system for Histopathological images by using Deep Learning Methods. In today’s world cancer is a world wide problem for a lot of people from all around the world. Over several years in scientific studies showed us that scientist tried to find a way to cure cancer or reduce the damage or increase the chance of survival from cancer. As studies shows that early diagnosis of the cancer plays a huge part on the treatment process and highly increases the chance of survival of the cancer patients. Most of the cancer diagnosis system uses a medical image analyze. However most of the cancer types requires is a large amount of test and examination on medical cell images. It is a hard and time consuming process. As a result of this, there is a need has arisen for image segmentation. With the development on computer science, computers started to play a big role for making this process much more fast and accurate. Recent development on Machine Learning and Deep Learning methods can provide efficient image segmentation for large amount of data in a agreeable time line. There was a lot of studies which focused on traditional methods for image segmentation but in this study we aim on the recent development on Deep Learning methods which is used for image segmentation. First of all, an introduction about image segmentation for Histopathological images by Deep Learning Methods is given. After that, literature review about recent studies which is covered our main topic is given. Then, our feasibility studies and system analysis is addressed. Then, implementation and performance studies is given. Finally, an assessment of this study on image segmentation by Deep Learning methods into clinical images are given. The main topic as Deep Learning methods we targeted U-Net, Mask R-CNN, UNet++ and YOLO. We implemented those models to the medical histopathologial image data. We provided the process, result and analyzed outcome and the summary for all the models that we used in this paper.
Secondary effects converted to useful knowledge in e-Learning system

The purpose of the Distributed eLearning Center and its extension IntelliDeLC is to provide a personalized elearning environment with reactive and proactive behavior. This paper presents a very brief explanation of these systems, as the main accent is on analysis of the intelligent assistants’ work as part of IntelliDeLC and the secondary effects, resulting from data analysis. It turned out that these secondary effects could be converted to knowledge, showing what students fail to understand in particular academic subjects, and this led to various major updates in the course materials, seminars, and exams. An interesting trend in students’ behavior was revealed as well, and, as a result, a new assistant was planned for development. The presented results also emphasize how these assistants did their job and how they contributed to improve the entire education process – teaching methodologies, approaches to measure students’ knowledge, lectures updates, etc., and how digging into data could provide additional information not obvious at first sight.
Adaptive Saliency Aware Spatially-Regularized Correlation Filter for Object Tracking

Since correlation filter (CF) can achieve a relative tradeoff between accuracy and speed, it has become a mainstream object tracking algorithm in the past decade. However, CF extends the training sample by circular shifts of a given object patch which consequently produces inevitable boundary effects. Such effects will seriously affect the tracking results, especially in challenging situations, such as occlusion, scale variation and background clutters. In order to solve them, we propose an adaptive saliency aware spatially-regularized correlation filter (ASARCF). First, an adaptive spatial regularization component is incorporated to penalize the CFs values of the non-object regions. Second, a saliency feature map of the object is integrated into CF to enhance the ability of the tracker to extract a tracking object from complex backgrounds. Third, to adapt the filter to the rapid variation of the object, we modify the learning rate of filter template by adaptive adjustment during tracking. Experimental results demonstrate that our proposed ASARCF shows competitive performance among state-of-the-art object tracking methods.