Reviewerly/exHarmony huggingface dataset:
Mitosis Detection Using Image Segmentation and Object Detection

The World Health Organisation(WHO) identifies that in women, the second most cancer deaths are caused by Breast cancer[1]. This paper presents various approaches for Mitosis detection on publicly available MITOS data set and DSB (Data Science Bowl). The process involves using a U-Net architecture consisting of convolution and deconvolution layers to perform the image segmentation. On the segmented image, YOLO algorithm is used to perform the object detection, thus forming bounding boxes around the nuclei. The next task involves the classification of nuclei into either mitotic or amitotic which is achieved with help of one class SVM. The results achieved on the data sets were able to prove that the process followed got good results for mitosis detection on histology images.
Aspect context aware sentiment classification of online consumer reviews

Purpose Aspect based sentiment classification is valuable for providing deeper insight into online consumer reviews (OCR). However, the majority of the previous studies explicitly determine the orientation of aspect related sentiment bearing word and overlook the aspect-context. Therefore, this paper aims to propose an aspect-context aware sentiment classification of OCR for deeper and more accurate insights. Design/methodology/approach In the proposed methodology, first, aspect descriptions and sentiment bearing words are extracted. Then, the skip-gram model is used to extract the first set of features to capture contextual information. For the second category of features, cosine similarity is used between a pre-defined seed word list and aspects, to capture aspect context sensitive sentiments. The third set of features includes weighted word vectors using term frequency-inverse document frequency. After concatenating features, ensemble classifier is used using three base classifiers. Findings Experimental results on two real-world data sets with variable lengths, acquired from Amazon.com and TripAdvisor.com, show that the advised ensemble approach significantly outperforms sentiment classification accuracy of state-of-the-art and baseline methods. Originality/value This method is capable of capturing the correct sentiment of ambiguous words and other special words by extracting aspect-context using word vector similarity instead of expensive lexical resources, and hence, shows superior performance in terms of accuracy as compared to other methods.
Segmentation Neural Network Incorporating Scale-Space in the Application of Cardiac MRI

In this work, we present an image segmentation algorithm based on the convolutional neural network framework where the scale space theory is incorporated in the course of training procedure. The construction of data augmentation is designed to apply the scale space to the training data in order to effectively deal with the variability of regions of interest in geometry and appearance such as shape and contrast. The proposed data augmentation algorithm via scale space is aimed to improve invariant features with respect to both geometry and appearance by taking into consideration of their diffusion process. We develop a segmentation algorithm based on the convolutional neural network framework where the network architecture consists of encoding and decoding substructures in combination with the data augmentation scheme via the scale space induced by the heat equation. The quantitative analysis using the cardiac MRI dataset indicates that the proposed algorithm achieves better accuracy in the delineation of the left ventricles, which demonstrates the potential of the algorithm in the application of the whole heart segmentation as a compute-aided diagnosis system for the cardiac diseases.
A robust two quadrant sparse classifier for partially occluded face image recognition

In the last decade, Sparse representation method of classification has proved its efficiency for face recognition applications. The major challenge for face recognition algorithms is partial occlusion and expression changes in face images. In case of upper or lower face occlusion, a large portion of discriminative information gets missing which results into lower classification accuracy. In this paper a novel two quadrant sparse classifier is proposed which implements sparse representation in two different quadrants. Proposed classifier uses visual face information present in upper and lower quadrants independently to classify the test face image which provides correct classification even if one quadrant is occluded. The enhanced accuracy of proposed technique is proved using extensive simulations carried out on two standard databases (ORL and YALE). The experimental results for all number of training images and training sets are compared with simple sparse method in terms of mean classification accuracy. The performance of proposed technique is analysed with a maximum improvement of 3.51 % in terms of classification accuracy.
Compact Deeplearning Convolutional Neural Network based Hand Gesture Classifier Application for Smart Mobile Edge Computing

Modern Deeplearning Convolutional Neural Network-based image classifier are now becoming extremely high performing with acute accuracy. Although this provides a reliable classification system the sheer computational power for this type of classification makes it very difficult to run in remote environments lower computational power. In smart next- generation mobile edge computing platforms, the applications need to be lightweight and very robust and faster to provide classification results. In the context of the hand-operated smart factory, the classification of a given hand gesture is vital for its accurate operation. In this research, we have designed a hand gesture image collection and label model and later train a deeplearning convolutional neural network to classify this model with the highest 98% accuracy. Later we introduced the node pruning system improvement which compresses the model while retraining the same accuracy rate of 98%. This type of low weight pruned deeplearning convolutional neural network will help build a lightweight Mobile edge-based server with high accuracy and lower computation overheard based application that is suitable for smart factory based remote applications.
Parameter Estimation and Covariance Matrix of the Linear Functional Relationship Model for Circular Variables with Unequal Error Concentrations

Functional relationship model is used to study the data that are subjected to errors. In this paper, we consider the linear functional relationship model with bivariate circular data where the pair of errors is with unequal concentration parameters. The parameter estimation of the model for circular data is different from linear data due to its wrapped around nature. We propose some improvements on the parameter estimation where some iterative procedures are considered. The concentration parameters are estimated based on the Bessel function. Also, we derive the corresponding covariance matrix of the model based on the Fisher Information matrix. Monte Carlo simulation studies were performed to study the suitability of the estimation method. It is found that the biasness of the estimates is small. Practical application of the method is illustrated by using real data set. Keywords: circular data; covariance matrix; Von Mises distribution; simulation study
Automatic Emotion Recognition Using Facial Expression by Python

Automated Detection of human emotions has done a great wild as well as daring issue in Artificial intelligence, Human-computer interaction, PC organised area. Facial appearance is significant factor to detect the people expression in day to day life. Facial recognition(FE) has got much importance in analysts and PC researchers for the uses of medicinal services evaluation, emotion analysis and human computer interaction. People express their feelings and emotions in several ways through the gestures, speech, words, facial expressions. Expressions are the important source to spread information to others because face can determine the human emotion and feelings. This paper helps to enquire about current work which is related to this automation concept. This comparative study of this research helps in finding the facial related datasets, drawingout features, Comparitive and further scope of this facial expression.
PREDIKSI STATUS PENGIRIMAN BARANG MENGGUNAKAN METODE MACHINE LEARNING

One of the key performance indicators for the logistics industry, especially freight forwarder company (cargo), is the delivery time. This is still a challenge in this industry in terms of ensuring the customer service level and reducing transportation costs. On the other hand, the development of information technology now allows an organization or company to collect large amounts of data automatically. A decent method that can be used to analyze the data for prediction purposes is machine learning, which is a method of extracting data into a certain pattern of information. This research aims to apply three machine learning methods to estimate the status of shipping goods. The method used in this study follows the machine learning process published by the Cross Industry Standard Process for Data Mining (CRISP-DM), namely; business processes understanding, data understanding, data preparation, model development, evaluation, and implementation. Based on the results of the study, the random forest method produces better accuracy than the logistic regression and artificial neural network (ANN) methods, which is 76.6%, while the results of ANN and logistic regression methods are 73.81% and 72.84% respectively.&#x0D;
How to quantify and visualise motivation hierarchy of social group: semantic analysis

The so-called Maslow's pyramid reflects the hierarchy of motivation priorities in one person. Neuroevolutionary approach as well as studies of collective intelligence focuses the attention to the group as a minimal information processing unit. Here we analyse semantics of the relation of youth groups to three levels of 'the most important' - for the past week, for professional development, and for the whole life. The group consensus is more for more periods of evaluated topic. Only four words (sememes) represent about 30% of all associations mentioned by participants in relation to life. The same share of profession-related associations is covered by 12 sememes. Twenty-two sememes cover 30% of week-related associations. Pyramid-like hierarchy has been visualised. Applied techniques can be developed based on this approach for designing of new group intelligence creative systems.
Maintaining Reusable Software Components

A software component is a piece of code or an executable that encapsulates a set of related functions and can be included as it is in another software or application. A software component can be something as small as a function, or as large as an entire package or application executable. Reusability is another major software feature, the ability to reuse is essential for being able to build larger things from smaller parts. Reusability allows saving of time so that existing software components can be used in other applications as well. In a language like JavaScript where a large number of reusable components are written there is an exceptionally large requirement for maintainable and clean code because of the number of applications that can be affected by a single component. JavaScript is a very flexible language, it allows developers to override built in library functions with prototypes and is dynamically typed. This allows developers to engineer applications faster but also makes it harder to write maintainable and clean code. This paper aims to review the problems faced by JavaScripts developers when trying to write clean and maintainable code for reusable components.
An intrinsic-stabilization polarization encoder for quantum key distribution

An intrinsic-stabilization polarization encoder based on a PBS-Sagnac loop with a polarization-maintain-fiber arm for quantum key distribution is proposed, which is completely immune to polarization disturbance and can be applied for quantum satellite communications.
Design of Controller and PWM-enabled DC Motor Simulation using Proteus 8 for Flipper Track Robot

The developed tracked mobile robot such as flipper track robot increases its ability and capability in overcoming more challenges in urban environment context and rough terrains. In addition, flippers are its support in dealing with this circumstances. The configuration of flipper tracked robots came from the extended version of conventional two-tracked mobile robot such as two and four-tracked robots. Then, the study aims to create a dedicated controller for the modified flipper track robot. Correspondingly, the target instruments, display and analog control are identified for adept monitoring of the robot status while doing its intended function. Afterwards, using Proteus 8 Professional simulation software, the Arduino UNO controller as main MCU, 16x2 LCD, analog joysticks in terms of analog resistors, and virtual terminal for serial print monitoring are attached and wired accurately. The nine speed level is established and paralleled to the required PWM output for the fine movement of flipper track robot and also the map function of Arduino IDE for degree manipulation of servo motor of two flipper arms. Finally, the results are shown in LCD which matches the established logical conditions of nine speed level as well as the status movement of the flipper track robot. The functionality and feasibility of the controller is verified and exhibited.
Using Machine Learning Algorithms for Automatic Cyber Bullying Detection in Arabic Social Media

Social media allows people interact to express their thoughts or feelings about different subjects. However, some of users may write offensive to other via social media which known as cyber bullying. Successful prevention depends on automatically detecting malicious messages. Automatic detection of bullying in the text of social media by analyzing the text twits via one of the machine learning algorithms. In this paper, we have reviewed algorithms for automatic cyberbullying detection in Arabic of machine learning, and after comparing the highest accuracy of these classifications we will propose the techniques Ridge Regression (RR) and Logistic Regression (LR), which achieved the highest accuracy between the various techniques applied in the automatic cyberbullying detection in English and between the techniques that was used in the sentiment analysis in Arabic text, The purpose of this work is applying these techniques for detecting cyberbullying in Arabic.
Depressions caused by localized subsidence in the Netherlands, Belgium and Germany: a link with coal mining?

Abstract. In the coal mining districts of the Netherlands, Belgium and Germany, we identified 662 previously unidentified depressions at the land surface using LIDAR data. Their density decreases westwards along with deepening of the Carboniferous coal layers, while not changing in dimensions. The timing of their formation based on historical maps and landowner reports, suggests that they mostly formed during the period 1920–1970, the peak of mining activity. Based on their position, density and age, we link the formation of depressions to the coal-mining activities in South Limburg, Germany and Belgium. Our working hypothesis tentatively explains the origin, mechanism of formation and timing of these local subsidence features.
Deep Electric Pole Anomaly Detection and Unsupervised Description Generation

In the past few years, image recognition and computer vision with deep learning have evolved rapidly to become a commercial application that assists traditional human decision making with the engineering of data professionals. However, there are a number of problems with existing deep learning. Deep learning is a black box method that suffers from over-fitting problems more seriously than traditional learning methods and cannot be used to understand the decision-making process in the middle layer. It is used skeptically for applications that require reliability and transparency such as safety. The recent generation model gives a user understanding of the data manifold and is emerging as a new unsupervised learning method. In particular, we focus on the unsupervised learning of these generation models and propose experiments that can be used to detect abnormalities in power distribution facilities. We propose techniques to mitigate dataset shortages by learning and analyzing dataset in power environments with extreme shortages of abnormal dataset, and providing an assessment of the abnormal dataset. The module is still experimental and has no results. But in the future, it is expected to be used as a convergence technology of the power industry and artificial intelligence.
A Spatiotemporal Agent for Robust Multimodal Registration

Multimodal image registration is a crucial step for a variety of medical applications to provide complementary information from the combination of various data sources.Conventional image registration methods aim at finding a suited similarity metric as well as a descriptive image feature, which is quite challenging due to the high diversity of tissue appearance across modalities.In this paper, we present a novel approach to register images via an asynchronously trained reinforcement learning agent automatically.Within this approach, convolutional gated recurrent units (ConvGRU) is incorporated after stacked convolutional layers to extract both spatial and temporal features of the neighboring frames and implicitly learn the similarity metric.Moreover, we propose a customized reward function driven by fixed points error (FPE) to guide the agent to the correct registration direction.A Monte Carlo rollout strategy is also leveraged to perform a look-ahead inference to the elimination of jitter in the test stage.Evaluation is performed on paired CT and MR images from patients diagnosed as nasopharyngeal carcinoma.The results demonstrate that our method achieves state-of-the-art performance in medical image registration.
CHEER: Rich Model Helps Poor Model via Knowledge Infusion

There is a growing interest in applying deep learning (DL) to healthcare, driven by the availability of data with multiple feature channels in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rich-data</i> environments (e.g., intensive care units). However, in many other practical situations, we can only access data with much fewer feature channels in a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">poor-data</i> environments (e.g., at home), which often results in predictive models with poor performance. How can we boost the performance of models learned from such <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">poor-data</i> environment by leveraging knowledge extracted from existing models trained using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rich data</i> in a related environment? To address this question, we develop a knowledge infusion framework named <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CHEER</monospace> that can succinctly summarize such <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rich model</i> into transferable representations, which can be incorporated into the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">poor model</i> to improve its performance. The infused model is analyzed theoretically and evaluated empirically on several datasets. Our empirical results showed that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CHEER</monospace> outperformed baselines by 5.60 to 46.80 percent in terms of the macro-F1 score on multiple physiological datasets.
Extended systematic clustering: Microdata protection by distributing semsitive values

Anonymity data for multiple sensitive attributes in microdata publishing is a growing field at present. This field has several models for anonymizing such as k-anonymity and l-diversity. Generalization and suppression became a common technique in anonymize data. But, the real problem in multiple sensitive attributes is sensitive value distribution. If sensitive values do not distribute evenly to each quasi identifier group, it is potentially revealed to sensitive value holder. This research investigated on how the high-sensitive values are distributed evenly into each group. We proposed a novel method/algorithm for distributing high-sensitive values when it forms groups. This method distributes high-sensitive values evenly and varies high-sensitive values in a group. We called our method as extended systematic clustering since it is an extension of systematic clustering method. Diversity metrics was used for evaluating our method. Experiment result showed our method outperformed systematic clustering with average diversity value 0.9719 while systematic clustering 0.3316.
Data protection and provenance in cloud of things environment: research challenges

Internet of things are increasingly being deployed over the cloud (also referred to as cloud of things) to provide a broader range of services. However, there are serious challenges of CoT in the data protection and security provenance. This paper proposes a data privacy protection and provenance model (DPSPM) based on CoT. It can protect the privacy data of the users and trace the source of leaked data. In detail, security encryption and watermarking algorithms are proposed. Meanwhile, we use the improved k-anonymity data masking algorithm and pseudo-row watermarking algorithm in this scheme. Those algorithms can carry out security control over the whole process of data publishing, especially in data encryption, data masking and provenance verification. Finally, the experimental results show that our scheme has good efficiency. It is proved that the data masking time is proportional to the parameters k and L, the results also show good robustness to the common database watermarking attacks.
Cowbree: A novel dataset for fine-grained visual categorization

Fine-grained visual categorization (FGVC) dealt with objects belonging to one class with intra-class differences into subclasses. FGVC is challenging due to the fact that it is very difficult to collect enough training samples. This study presents a novel image dataset named Cowbreefor FGVC. Cowbree dataset contains 4000 images belongs to eight different cow breeds. Images are properly categorized under different breed names (labels) based on different texture and color features with the help of experts. While evidence shows that the existing dataset are of low quality, targeting few breeds with less number of images. To validate the dataset, three state of the art classifiers sequential minimal optimization (SMO), Multiclass classifier and J48 were used. Their results in term of accuracy are 68.81%, 55.81% and 57.45% respectively. Where results shows that SMO out performed with 68.81% accuracy, 68.4% precision and 68.8% recall.
Security Protection Mechanisms Must Be Embedded in Blockchain Applications

There are unknown and known attacks against the consensus algorithms used in blockchain. Detection and protection mechanisms must be embedded in blockchain applications for protecting vulnerabilities of known consensus algorithms.