Reviewerly/exHarmony huggingface dataset:
Prior Guided Segmentation and Nuclei Feature Based Abnormality Detection in Cervical Cells

Computer-assisted techniques for cytological analysis and abnormality detection, can help to early diagnose anomalies in cervical smear images. Cell nuclei carry substantial evidence of pre-cancerous changes, thus morphological properties of nuclei are important for accurate diagnostic decision. A novel nucleus feature-based cervical cell classification framework is proposed in this study. Prior guided segmentation algorithms are employed to accurately detect and segment nucleus. Fuzzy entropy based feature selection technique is used to select most discriminatory features, extracted from segmented nucleus. Five classifiers: k-nearest neighbor (KNN), linear discriminant analysis (LDA), Ensemble, and support vector machine with linear kernel (SVM-linear) and radial basis function kernel (SVM-RBF), are used to detect abnormality in cervical cells. The proposed framework is evaluated using Herlev dataset of 917 cervical cell images and compared with state-of-the-art methods. Results indicate that the proposed framework matches the performance of recent techniques, while segmenting nucleus and classifying Pap smear images using only 10 nucleus features. Therefore, the proposed abnormality detection framework can assist cytologists in computerized cervical cell analysis, and help with early discovery of any anomaly that may lead to cervical cancer.
Interactive Remote Museum Visits for Older Adults: An Evaluation of Feelings of Presence, Social Closeness, Engagement, and Enjoyment in an Social Visit

This paper explores whether older adults can remotely participate in museum visits with the help of virtual environments. We design and build a system that supports shared museum co-visits between onsite visitors inside the museum and older adults from a care home. We make the experience more engaging by providing a meaningful story, connecting the objects in the museum. The aim of the study is to understand whether older adults are able to use such technology and to study the mediated sense of spatial presence, the experienced social closeness, and the level of participants' engagement and enjoyment in the visit. We discuss the relationship between these aspects and factors leading to a better remote experience for older adults. The results show that older adults enjoy and engage in remote visits, and that there is a positive correlation between enjoyment, engagement and social closeness.
Towards Privacy-Preserving Ego-Motion Estimation Using an Extremely Low-Resolution Camera

Ego-motion estimation is a core task in robotic systems as well as in augmented and virtual reality applications. It is often solved using visual-inertial odometry, which involves using one or more always-on cameras on mobile robots and wearable devices. As consumers increasingly use such devices in their homes and workplaces, which are filled with sensitive details, the role of privacy in such camera-based approaches is of ever increasing importance. In this letter, we introduce the first solution to perform privacy-preserving ego-motion estimation. We recover camera egomotion from an extremely low-resolution monocular camera by estimating dense optical flow at a higher spatial resolution (i.e., 4x super resolution). We propose SRFNet for directly estimating Super-Resolved Flow, a novel convolutional neural network model that is trained in a supervised setting using ground-truth optical flow. We also present a weakly supervised approach for training a variant of SRFNet on real videos where ground truth flow is unavailable. On image pairs with known relative camera orientations, we use SRFNet to predict the auto-epipolar flow that arises from pure camera translation, from which we robustly estimate the camera translation direction. We evaluate our super-resolved optical flow estimates and camera translation direction estimates on the Sintel and KITTI odometry datasets, where our methods outperform several baselines. Our results indicate that robust ego-motion recovery from extremely low-resolution images can be viable when camera orientations and metric scale is recovered from inertial sensors and fused with the estimated translations.
Development of Hardware Setup of an Autonomous Robotic Vehicle Based on Computer Vision Using Raspberry Pi

In this paper, the hardware setup of an autonomous robotic vehicle is developed. The controller of the vehicle is developed based on the computer vision technology which became more smart due to the application of popular tool open CV. An algorithm based on color detection is developed to navigate the vehicle in different directions. Raspberry Pi camera is used to access images which are the indications of the directions of the movements such as stop, left turn, right turn. The efficacy of the proposed algorithm is verified experimentally.
A quantum solution for efficient use of symmetries in the simulation of many-body systems

Abstract A many-body Hamiltonian can be block-diagonalized by expressing it in terms of symmetry-adapted basis states. Finding the group orbit representatives of these basis states and their corresponding symmetries is currently a memory/computational bottleneck on classical computers during exact diagonalization. We apply Grover’s search in the form of a minimization procedure to solve this problem. Our quantum solution provides an exponential reduction in memory, and a quadratic speedup in time over classical methods. We discuss explicitly the full circuit implementation of Grover minimization as applied to this problem, finding that the oracle only scales as polylog in the size of the group, which acts as the search space. Further, we design an error mitigation scheme that, with no additional qubits, reduces the impact of bit-flip errors on the computation, with the magnitude of mitigation directly correlated with the error rate, improving the utility of the algorithm in the Noisy Intermediate Scale Quantum era.
Mining High Utility Itemset for Online Ad Placement Using Particle Swarm Optimization Algorithm

The process of obtaining relevant content from the raw datasets, present in huge databases is called data mining. It consists of four stages: Data Sources, Data Gathering, Modeling, and Deployment. These databases are searched thoroughly using various data mining techniques. Association rule mining, also known as ARM is the significant method of mining the data that tells us about the relationships of the items that are available in the proceedings. Traditionally, the mining of data focuses on the association between the Frequent Itemset that are present in large numbers in the databases. Mining High Utility Itemset (HUI) is an advancement of the problem of Frequent Itemset Mining (FIM). In the mining of HUI, the aim is to find itemsets that have more utility use than that of the threshold utility value. In this paper, Particle Swarm Optimization (PSO) algorithm is used to mine high utility itemsets.
Geographic Hints for Passphrase Authentication

We propose and study the use of geographic hints to aid memorability of passphrase-style authentication secrets. Geographic hints are map locations that are selected by the user at the time of passphrase creation, and shown to the user as a hint at the time of passphrase login. We implement the GeoHints system and analyze how geographic hints impact the usability and security of passphrase-style secrets in a multi-session user study (n=38). The study involved testing for multiple passphrase interference-each participant was asked to recall 4 distinct passphrases. Our study indicates that while geographic hints showed promise for reducing memory interference, GeoHints (as implemented) does not produce a viable authentication system, as the login success rate was 25% 7-11 days after passphrase selection. We analyze the root causes of login errors, finding that most were due to inexact recall of free-form text input. This finding points towards opportunities to improve the system design, and we suggest improvements that we believe will lead to viable systems that employ geographic hints.
Unsupervised Clustering Algorithm as Region of Interest Proposals for Cancer Detection Using CNN

Deep learning methods are now getting lot of attention due to their success in many fields. Computer-aided bio-medical image analysis systems act as a tool to assist medical practitioners in correct decision making. Use of Deep Learning algorithms to predict Cancer at early stage was highly promoted by Kaggle Data Science Bowl 2017 competition and Cancer Moonshot Initiative. In this article, we have proposed a novel combination of unsupervised machine learning tool which is modularity optimization based graph clustering method and Convolutional Neural Networks (CNN) based architectures for lung cancer detection. The unsupervised clustering method helps in reducing the complexity of CNN by providing Region of Interest (ROI) proposals. Our CNN model has been trained and tested on LUNA 2016 dataset which contains Computed Tomography (CT) scans of lung region. This method provides an approximate pixel-wise segmentation mask along with the class label of the ROI proposals.
Comparative Analysis for Arabic Sentiment Classification

Sentiment analysis categorizes human opinions, emotions and reactions extracted from text into positive or negative polarity. However, mining sentiments from the Arabic text is challenging due to the scarcity of Arabic datasets for training the context. To address this gap, this study builds an Arabic sentiment dataset sourced from tweets, product reviews, hotel reviews, movie reviews, product attraction, and restaurant reviews from different websites; manually labeled for training the sentiment analysis model. The dataset is then used in a comparative experiment with three machine learning algorithms, which are Support Vector Machine (SVM), Naïve Bayes (NB), and Decision Tree (DT) via a classification methodology. The best results for polarity prediction in sentiment analysis models was achieved by SVM with product attraction dataset, with the accuracy of 0.96, precision of 0.99, recall of 0.99, and F-measure of 0.98. This is followed by the average performance from NB and DT. It can be concluded that the ML classifiers need the right morphological features to enhance the classification accuracy when dealing with different words that play different roles in the sentence with the same letters.
An attention-guided and prior-embedded approach with multi-task learning for shadow detection

Shadow detection is a fundamental and challenging task, requiring understanding accurately the visual semantic context of the shadow region and backgrounds. In this paper, we propose an attention-guided and prior-embedded approach with multi-task learning for shadow detection task. Different from most existing works, we introduce the effective multi-task learning into this target detection task to add the high-level prior into the detection process, instead of using the pertained weighting network as the front-end module and complex recurrent network. Especially, we also employ a channel attention-guided module to complement the high-level feature and low-level feature. Moreover, for the proposed approach with multi-task learning, we design the weighted loss function for effective training. Experimental results on two public available benchmarks demonstrate our approach achieves competitive results than the existing typical shadow detection approaches.
Improved Multi-view Multi-label Learning with Incomplete Views and Labels

Multi-view multi-label learning has attracted the attention of many scholars and widely used in multiple fields. While in real-world applications, due to the lack of manpower and equipment failure, the data sets to be processed maybe loss some labels or views. Moreover, most multi-view multi-label learning methods neglect the global and local label correlations of both the whole data set and each view and the complementary information coming from different views sometimes. Furthermore, some methods ignore such a phenomenon that each class label might be determined by some specific features of its own. In order to improve the performance of such methods, in this paper, we develop an improved multi-view multi-label learning with incomplete views and labels (IMVL-IV). In framework of IMVL-IV, the usage of label-specific features makes the decision of label be determined by some specific features rather than all features so that we can pay more attention to portion specific features and save time; the introduction of label correlation matrix offsets the defect of missing labels; the adoption of low-rank assumption matrix restores missing views; global and local label correlations are taken into consideration with clustering technology; a consensus multi-view representation is put to use to encode the complementary information from different views. Different from traditional learning methods, this is the first attempt to design a multi-view multi-label learning method with incomplete views and labels by the learning of label-specific features, label correlation matrix, low-rank assumption matrix, global and local label correlations, and complementary information. Experimental results validate that IMVL-IV achieves a better performance and it is superior to the classical multi-view learning methods and multi-label learning methods.
Deep Neural Network based on Genetic Algorithm and Ensemble Methods for Regional Solar Power Forecasting in Thailand

This article presents Deep Neural Network (DNN) based on Genetic Algorithms (GA) and Ensemble Methods for Regional Solar Power Forecasting in Thailand for one day in advance by using forecast weather data, real measured data and Solar power data and applying DNN with structure (window size, number of neurons in hidden layer, activation function, loss function, optimizer, numbers of epoch) designed with GA as well as using Ensemble Methods to blend the forecast values from each model together with Neural Network to improve predictive accuracy which is the solar power forecast. This article uses a total of 7 power plants with total power generation capacity of 436 MW. The strength of the proposed method is 1) Using inputs from multiple models to improve accuracy. 2) Weights are provided for weather forecast station surrounding power plants. 3) Models are structurally designed with GA. 4) Combining the advantages and decreasing the disadvantages of other models, by applying all these techniques for forecasting the total power generation capacity of all 7 solar power plants as a case study and found that the Root Mean Square Error was 5.61%, which decreased by 1.71% compared to the single model that provides the best forecasting results.
Book Spine Recognition Based on OpenCV and Tesseract

In a traditional library, the inventory management of books is a time-consuming and laborious work. Due to such constraint as cost and technical complexity, some technologies, e.g., RFID, are infeasible to be used in such large-scale application scenarios as book inventory in library. We design a prototype system called Book Spine Recognition Based on OpenCV and Tesseract, or BSRBOT for short, which is based on image processing mechanism, and can be further integrated into existing book inventory management systems. Firstly, according to the characteristics of book arrangement on bookshelf, a book spine image segmentation algorithm is designed. Secondly, the recognition and retrieval of the index number at the lower part of the spine is carried out based on character recognition. Finally, each segmented spine image can be matched with the image database to overcome the occasional unsuccessful identification of the call numbers. The experiment shows BSRBOT achieves accurate recognition of the book spines, thus providing a solution for fast, feasible whilst low-cost book inventory management in modern library.
Image-Based Dedicated Methods of Night Traffic Visibility Estimation

Traffic visibility is an essential reference for safe driving. Nighttime conditions add to the difficulty of estimating traffic visibility. To estimate the visibility in nighttime traffic images, we propose a Traffic Sensibility Visibility Estimation (TSVE) algorithm that combines laser transmission and image processing and needs no reference to the corresponding fog-free images and camera calibration. The information required is first obtained via the roadside equipment which collects environmental data and captures road images and then analyzed locally or remotely. The proposed analysis includes calculating the current atmospheric transmissivity with the laser atmospheric transmission theory and acquiring image features by using the cameras and the adjustable brightness target. Image analysis is performed using two image processing algorithms, namely, dark channel prior (DCP) and image brightness contrast. Finally, to improve the accuracy of visibility estimation, multiple nonlinear regression (MNLR) is performed on the various visibility indicators obtained by the two methods. Extensive on-site measurements analysis confirms the advantages of TSVE. Compared with other visibility estimation methods, such as the laser atmospheric transmission theory and image analysis method, TSVE significantly decreases the estimation errors.
Action-Triggering Recommenders: Uplift Optimization and Persuasive Explanation

A principal purpose of recommender systems is to induce positive user actions such as clicks and purchases. The performance of a recommender is typically evaluated in terms of prediction accuracy; a recommender is considered to be better if a larger number of its recommended items are purchased. However, accurate prediction is not enough for increasing user actions. The items might have been purchased even without recommendations, that is, the purchases are not triggered by the recommendations. Conversely, a user might be reluctant to purchase any items and a mere recommendation alone cannot motivate the user to purchase an item even if the recommended item is the best among all the items. In this work, we pursue recommender systems for triggering user actions. For this purpose, we tackle two issues: 1) uplift optimization and 2) persuasive explanation. For the first issue, we propose uplift-based evaluation and optimization methods for recommenders. Uplift, which is defined as an increase in user actions caused by recommendations, cannot be observed directly. We apply a causal inference framework to estimate the average uplift for the offline evaluation of recommenders. For optimization, we derive positive and negative samples specific to uplift and construct pointwise and pairwise optimization methods. Through experiments with three public datasets, we demonstrate the effectiveness of our optimization methods in improving uplift. For the second issue, we propose a new explanation style using context. The context style explanation presents contexts suitable for consuming the recommended items. The exhibited contexts make users imagine situations of items' usage and motivate them to purchase the items. Via a crowdsourcing-based user study, we confirm that the persuasiveness of our explanation style is better than conventional styles. The hybridization of context style with other styles further improves the persuasiveness.
Improve the Accuracy of SenseComp in Thai Consumer’s Review Using Syntactic Analysis

Thai sentiment analysis can be analyzed in multiple dimensions: product, price, and shipping. The scores from sentiment analysis can be considered as multi-dimensional trust score of e-vendor which affects the purchase decision of the consumer. A consumer's review that has different sentiments describing the same dimension in the sentence leads to the incorrect scoring problem in SenseComp - the method to automatically analyze Thai sentiment in the consumer's review in multiple dimensions with sentiment compensation technique. In this paper, SenseComp using syntactic analysis method (SenseComp2) is proposed in order to improve the accuracy of the original SenseComp. Thai syntactic structure in the consumer's review is analyzed before the scores in multiple dimensions are given. The results show that SenseComp2 increases the accuracy of the original SenseComp up to 31.88% in the sentiment analysis of Thai consumer's review when there are multiple dimensions and various sentiments in the same sentence.
Risk Factors Identification for Heart Disease in Unstructured Dataset using Deep Learning Approach

An automatic identification of the heart disease status can give timely support to medical decision-making process. The identified key factors can expedite the prevention actions in right direction. Existing solutions to identify disease factor or current disease status is based on hybrid approach which requires significant amount of human efforts. In addition to that an information extraction and de-identification on clinical dataset performed manually is error prone, expensive, prohibitively and time consuming [1]. These drawbacks can be overcome by using deep learning approach, in this paper we have used LSTM, BiLSTM and Google's Sentence encoder for automatic disease status identification. We have used i2b2 dataset with the proposed deep learning models that have led to promising results.
Parallel Implementation and Performance Evaluation of some Supervised Clustering Algorithms for MRI Images Segmentation

Given the continuous increase of the data size to be processed, the need for the information manipulation' speed (acquisition, processing, and analysis) has become increasingly necessary in many areas. In brain imaging, diagnostic systems provide large amounts of data in 2D and 3D with different modalities. Nevertheless, in front of the technological limitation and the development of the microprocessors speed, in recent years, researchers have moved to parallelism as an alternative to design algorithms running on distributed systems, computing grids or massively parallel systems. With the advent of the GP-GPU concept (General Purpose - Graphical Processing Unit), which means the use of graphic cards (originally designed for graphic rendering) for general computing, several researchers are oriented towards this new use of GPUs to dispense the microprocessor CPUs expensive treatment portions of their sequential algorithms. In this paper a comparative study of two parallel implementation of bias correction FCM (BCFCM) and Spatial FCM (SFCM) has been done in term of robustness and efficiency
Adaptive Prediction, Context Modeling, and Entropy Coding Methods for CALIC Lossless Image Compression

The three main pillars for lossless image compression are prediction, prediction residual correction and entropy coding. In this work, we propose a lossless image compression algorithm which utilizes local information to dynamically switch between different prediction techniques. Moreover, we improve on traditional prediction techniques and modify them into diagonal GAP and dynamic window weighted linear prediction. Finally, since the coding efficiency is directly proportional to the context being chosen if context arithmetic coding is applied, we propose a robust context model which utilizes local information to get better coding efficiency.