Reviewerly/exHarmony huggingface dataset:
Group-Oriented Cryptosystem for Personal Health Records Exchange and Sharing

Personal health records (PHRs) have been developed into a type of patient-centered health information exchange model in recent years. It provides users powerful saving, reading, and sharing of medical data. Considering the fullness of current Cloud construction, complicated combination of hospital staff, differences of prioritization between hospital staff and patients, and varied levels of privacy regulation of people in groups or individuals, the difficulty of security exchange and information sharing will increase. Therefore, there is necessity of existence for one flexible and efficient group-oriented cryptosystem. We proposed a bilinear pairing-based group-oriented cryptosystem to overcame above situations. This proposal owns the following advantages: (I) The cryptosystem can simultaneously realize four decryption strategies, enabling receivers to designate appropriate decryptors according to the content of plaintext. (II) All group members need only one private key, which can be used for decryption regardless of the decryption modes. Therefore, errors resulting from the misuse of keys can be avoided, and the difficulty of key management can be reduced. (III) The system is required to disclose only six parameters, thus decreasing spatial complexity. (IV) Regardless of the encryption and decryption modes, receivers must perform encryption only one time, and the length of the ciphertext comprises only four parameters. Thus, the proposed cryptosystem computing (including environment setting and the processes of encryption and decryption) is highly efficient, with easy key management, low spatial complexity, and small amount of ciphertext being transmitted.
Discriminative Features for Texture Retrieval Using Wavelet Packets

Wavelet Packets (WPs) bases are explored seeking new discriminative features for texture indexing. The task of WP feature design is formulated as a learning decision problem by selecting the filter-bank structure of a basis (within a WPs family) that offers an optimal balance between estimation and approximation errors. To address this problem, a computationally efficient algorithm is adopted that uses the tree-structure of the WPs collection and the Kullback-Leibler divergence as a discrimination criterion. The adaptive nature of the proposed solution is demonstrated in synthetic and real data scenarios. With synthetic data, we demonstrate that the proposed features can identify discriminative bands, which is not possible with standard wavelet decomposition. With data with real textures, we show performance improvements with respect to the conventional Wavelet-based decomposition used under the same conditions and model assumptions.
Classification of Criminal Crimes From Data Twitter Using Class Association Rules Mining

The police obtain criminal crime data from the field based on reports from a person or group, from the data the police can evaluate the crimes that occurred.The police have no reports of crimes from other parties such as from social media.One such social networking media is Twitter.The information conveyed by Twitter users in a tweet usually contains something related to himself or his environment, including the occurrence of a crime.Such information will serve as data for classification as well as to know the trends of criminal crime.This research uses classification data mining technique that is Class Association Rules (CARs).CARs will find all frequent ruleitems through a series of stages and build rules using frequent ruleitems obtained, then rules will be obtained.The resulting rule will be evaluated to determine the strength of the rule using the Laplace Accuracy equation, which will produce the best rule.These rules will serve as models for the new data classification.The result of accuracy test of this method by using 100 test data is 96%.
MSAFusionNet: Multiple Subspace Attention Based Deep Multi-modal Fusion Network

It is common for doctors to simultaneously consider multi-modal information in diagnosis. However, how to use multi-modal medical images effectively has not been fully studied in the field of deep learning within such a context. In this paper, we address the task of end-to-end segmentation based on multi-modal data and propose a novel deep learning framework, multiple subspace attention-based deep multi-modal fusion network (referred to as MSAFusionNet hereon-forth). More specifically, MSAFusionNet consists of three main components: (1) a multiple subspace attention model that contains inter-attention modules and generalized squeeze-and-excitation modules, (2) a multi-modal fusion network which leverages CNN-LSTM layers to integrate sequential multi-modal input images, and (3) a densely-dilated U-Net as the encoder-decoder backbone for image segmentation. Experiments on ISLES 2018 data set have shown that MSAFusionNet achieves the state-of-the-art segmentation accuracy.
Outlier Detection in Health Record Free-Text using Deep Learning

In recent years, machine learning approaches have been successfully applied to analysis of patient symptom data in the context of disease diagnosis, at least where such data is well codified. However, much of the data present in Electronic Health Records (EHR) is unlikely to prove suitable for classic machine learning approaches. In particular, the use of free (or unstructured) text for clinical notes presents significant analytical opportunities, but also unique difficulties. Furthermore, the wide dispersal of health data relating to individuals necessitates the development of decentralized solutions. We provide, in this paper, an overview of our approach to develop a neural network framework for patient classification in the environment of EHRs where data may be heterogeneous, incomplete (containing missing values), and noisy. In this paper we describe our system which provides prediction of outlier cases which are likely to relate to frequent attender patients, which acheives an Area-Under-the-Curve score of up to 0.92.
Multi-view image coding using disparity-compensated and weighted binary patterns

We proposed an efficient coding scheme for a dense light field, i.e., a set of multi-view images taken with very small viewpoint intervals. The key idea behind our proposal is that a light field is represented only using some weighted binary images. This coding scheme is completely different from modern video codecs, but it has some advantages. For example, the decoding process is extremely simple, which leads to a faster and less power-hungry decoder. Moreover, we found that our scheme can achieve comparable rate-distortion performances to that of modern video codecs for datasets with small disparities. However, it is difficult to express regions with larger disparities using only a limited number of common binary images. Therefore, in this paper, we extend our light field coding scheme to the one with disparity-compensation and show its effectiveness.
From Explainability to Explanation: Using a Dialogue Setting to Elicit Annotations with Justifications

Despite recent attempts in the field of explainable AI to go beyond black box prediction models, typically already the training data for supervised machine learning is collected in a manner that treats the annotator as a "black box", the internal workings of which remains unobserved.We present an annotation method where a task is given to a pair of annotators who collaborate on finding the best response.With this we want to shed light on the questions if the collaboration increases the quality of the responses and if this "thinking together" provides useful information in itself, as it at least partially reveals their reasoning steps.Furthermore, we expect that this setting puts the focus on explanation as a linguistic act, vs. explainability as a property of models.In a crowd-sourcing experiment, we investigated three different annotation tasks, each in a collaborative dialogical (two annotators) and monological (one annotator) setting.Our results indicate that our experiment elicits collaboration and that this collaboration increases the response accuracy.We see large differences in the annotators' behavior depending on the task.Similarly, we also observe that the dialog patterns emerging from the collaboration vary significantly with the task.
Scalable Parallel SVM on Cloud Clusters for Large Datasets Classification

This paper proposes a new parallel support vector machine (PSVM) that is efficient in terms of time complexity. Support vector machine is one of the popular classifiers for analysis of data and classification of patterns. However, SVM requires a large memory (in the range of 100 GB or more) in order to process big-data (i.e., in the range of 1 TB data or more). This paper proposes to execute SVMs in parallel on several clusters to analyze and classify big-data. In this approach, the data are divided to n equal partitions. Each partitioned data is used by an individual cluster to train an SVM. The outcomes of each of the SVMs executed on several clusters are then combined by another SVM referred as final SVM. The inputs to this final SVM are the support vectors (SVs) of the SVMs that were executed on different clusters, while the desired output is the corresponding output of the respective SV. We evaluated our proposed method on high performance computing (HPC) clusters and amazon cloud clusters (ACC) using different benchmark datasets. Experimental results show that the proposed method is efficient in terms of training time with minimal error rate and memory requirement, compared to the existing stand-alone SVM.
Vision-based hand gesture recognition from RGB video data using SVM

With the growing number of population in the world nowadays, novel human-computer interaction systems and techniques can be used to help improve their quality of life. A gesture based technology can help to maintain the safety and needs of the disable as well as the general people. Gesture recognition from video streams is a challenging task due to the high changeability in the features of each gesture with respect to different person. In this work, we propose a vision-based hand gesture recognition from RGB video data using SVM. Gesture-based interfaces are more natural, spontaneous, and straightforward. Previous works attempted to recognize hand gesture for different scenarios. Throughout our studies, gesture recognition system can be based on wearable sensor or it can be vision based. Our proposed method is applied on a vision based gesture recognition system. In our proposed system image acquisition starts from RGB videos capture using Kinect sensor. We convert the image frames from videos to blur for background noise removal. Then, we convert the images into hsv color mode. After that, we do the dilation, erosion, filtering, and thresholding the image for converting to black and white format. Finally, using the prominent classification algorithm SVM, hand gestures has been recognized. In conclusion, the framework aims to create a better vision-based hand gesture recognition system with novel techniques.
Privacy Preserving Data Publishing Anonymization Methods for Limiting Malicious Attacks in Healthcare Records

In recent times, especially since the beginning of the new millennium, governments, industry players, IT firms and business enterprises have given more consideration to the use of data for their decision and operational processes. This data, that usually contain users, clients and customers’ information, is collected using varying infrastructure, instruments and techniques. The technological breakthroughs in the health industry and the digitalization of medical records i.e., transformation into Electronic Health Records (EHRs) brings about the possibilities of accessing health records in real-time anywhere through the use of big data, aimed at reducing cost and increasing profits within the healthcare industry. However with this advancement, threats to the privacy and security of healthcare records have inevitably creeped in because of malicious attacks. This paper is directed at addressing privacy and security related issues associated with big data i.e., Privacy Preserving Data Publishing (PPDP) methods useful for the medical world. It seeks to explore various possible methods and techniques that can render data anonymously by using anonymization processes i.e., untraceable to the original data owners. This restricts the possibilities of patient privacy infraction by malicious elements, while making the data available for analytical purposes. The anonymization process here is achieved through data publishers who stand as a middleman between data owners and the data recipient and ensures that the privacy of data owners is preserved at all times.
AR Brain-Shift Display for Computer-Assisted Neurosurgery

A neuronavigation system indicates a lesion area of a patient on preoperative medical images of the patient, and hence such medical navigations have been widely used in neurosurgeries. However, the brain deformation called brain shift causes inaccurate navigation. This paper presents a method of brain shift estimation and a way to show the deformed brain to surgeons using Augmented Reality (AR) technology. By acquiring posture information of the brain and coordinating transformation, the direction of gravitational force is determined. Infrared reflective markers are attached to a camera and human head, and the markers are tracked by a motion capture system. A computer graphics (CG) model of the deformed brain is superimposed onto the camera image.
A Data Management Platform for Data Harvesting and Analysis from Autonomous Marine Measurement Platforms

As part of the Glider project, a data management platform for harvesting data from autonomous marine measurement platforms has been developed. The solution receives data continuously streamed from the autonomous measurement platforms, or Gliders, and builds a database of time-series and bulk data which can be analyzed with interactive visual analysis tools. The data management platform provides a convenient access to survey data in real-time as well as providing scientists with advanced tools for analysis. The user accessible front end is written using W3C web standards and will run on many platforms including a mobile platform solution.
A Study of Cyber Hate on Twitter with Implications for Social Media Governance Strategies

This paper explores ways in which the harmful effects of cyber hate may be mitigated through mechanisms for enhancing the selfgovernance of new digital spaces.We report findings from a mixed methods study of responses to cyber hate posts, which aimed to: (i) understand how people interact in this context by undertaking qualitative interaction analysis and developing a statistical model to explain the volume of responses to cyber hate posted to Twitter, and (ii) explore use of machine learning techniques to assist in identifying cyber hate counter-speech.
Mixed-Norm Based Broad Learning System for EEG Classification

How to design a powerful classifier with strong generalization capability is still an active topic in the brain computer interface (BCI) researches. In this paper, we propose a new classifier, which has the same structure of the recently proposed broad learning system (BLS), but the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> norm based optimization model in BLS is replaced by a mixed-nrom based one. To optimize the proposed model efficiently, the augmented Lagrange multiplier (ALM) method is utilized. The most attractive feature of the proposed classifier is that it has the potential to maintain good performance in various noise environments, by flexibly setting the value of mixed parameter. Thus, compared with the standard BLS, as well as many existing classifiers, the proposed one is expected to be a more reasonable choice for classifying electroencephalography (EEG) signals, which are usually polluted by various artifacts. The experiments on two publicly available data sets are presented to confirm the desirable performance of the new method.
Electromagnetic studies of Zvizdal-Zaliska and Brusyliv fault zones of the Ukrainian shield

Spatial-temporal distribution pattern of geomagnetic variations and electric field in the western part of the Ukrainian shield has been obtained as a result of experimental studies carried out by the methods of deep magnetotelluric sounding and magnetovariational profiling in 2018 along two profiles (15 points). It makes possible to estimate the value of electroconductivity and geoelectric structure of the section along the vertical and horizontal directions. Processing has been conducted with the help of modern software system PRC-MTMV that guarantees joint noise immunity of evaluation of impedance and induction parameters of synchronous МТ/МВ-records. Tipper appraisals have been obtained for the periods of geomagnetic variations from 50 to 3400 s, the curves of apparent resistance and phases of impedance from 20 to 10000 s. The principal result of high quality interpretation of geoelectric studies is revealing of high electro-conductivity anomalies (4 surface ones, 1 subsurface) in the Earth crust with complex system of faults in jointing zone of the Podolia and Rossinsky megablocks. Anomalies are characterized by different electroconductivity, the depth of occurrence and shape. Appraisals obtained with different approaches to formal interpretation of magnetotelluric sounding agree with regional three-dimensional geoelectric model of the western part of the Ukrainian shield and add to the concept on debatable deep structure. Surface anomalies of electroconductivity have been discovered: 1) in the central part of Zvizdal-Zalessky fault zone, 2) between the Pogrebynsky and the Kocherov faults, 3) along the Velykoerchyk and 4) Ulashev faults. Their nature is explained by heightened content of graphite and by ore formation confined to tectono-metasomatic zones. Deep anomaly of electroconductivity has been allocated with its surface projection spatially coinsiding with the area of cross-section of the Brussilov and the Nemyrov fault zones (and mineraginetic zones of the same name). Its upper edge deepens eastward from Zvizdal-Zalessky fault zone. High electroconductivity anomalies are spatially attached to tectonic fault zones and the zones of metasomatic alterations. Their territorial arrangement corresponds to metallogenic ore zones. Therefore they can be used as geoelectric criteria for the search of ore manifestations.
Teknik Cerapan Anak Bulan: Satu Penelitian Literatur (Observation Techniques of Crescent: A Literature Review)

Along with the development of technology, various observation techniques have been introduced to assist observers for crescent visibility. Hence, this study was conducted to review the literatures which are related with the techniques that have been used during the observation of crescent starting from Babylon era until now. The qualitative method has been used in this study to collect data based on library research. Then, the inductive method is used to analyse the data. The results from these literature reviews showed that many writings, ideas and discussions that are related with the techniques used during observations have been analyzed by scholars and academicians. Nevertheless, there is no specific study and discussion about these observation techniques for determining the crescent from Shariah perspective.
QoE of Video Streaming in Multihomed Vehicular Networks

With the ever-increasing interest and availability of vehicular networks, it comes now of importance to study the Quality-of-Experience (QoE) provided by these networks. Either motivated by safety or commercial applications, video streaming in such highly mobile environments offers multiple challenges. In this paper we evaluate the QoE of a multihoming communication strategy, supported simultaneously by WAVE and Wi-Fi, for increasing the reliability and performance of video streams in these environments. Furthermore, we also investigate how distinct network functionalities, such as multihoming load balance, buffering, throughput and latency affect the overall QoE observed. The results obtained led to the proposal of a multihoming load balance rule for video applications based on access technologies, aiming to improve QoE. The overall results show a QoE improvement by using the proposed approach.
The Weighted Multiple Meta-Models Stacking Method for Regression Problem

When solving classification and regression problems using various machine learning models, we often choose the model that has the best performance. However, each model has advantages and disadvantages when it comes to the specific problem and scenario. If we combine the benefits of multiple models, then we will get better results. In the past, many methods for improving the prediction performance of various models are proposed. In this paper, we introduce a method called stacking. The stacking method introduces the concepts of base model and meta-model, which takes the predictions of base models as the input of the meta-model and the output of the meta-model as a final prediction. We have made some improvements to the stacking method. Instead of using single meta-model, we use the output of each base model as the input to multiple meta-models, then combine the outputs of multiple meta-models as a final output. The experimental results show that the weighted multiple meta-models stacking method outperform the traditional stacking method by reducing RMSE to 0.1245, while the best score of other models is 0.1292.
Data Mining Predictive Modeling for Prediction of Gold Prices Based on Dollar Exchange Rates, Bi Rates and World Crude Oil Prices

Gold is an investment instrument that is quite safe from inflationary attacks, and gold is one aspect of initiating investment. Can by buying gold in physical form and then selling when the price has risen high or by digitally investing gold. One of them is by trading gold online. To maximize the benefits of gold trading, a gold price prediction (XAUUSD) is needed for traders. This study aims to (1) Analyze various factors that influence the price of gold (2) Provide recommendations about the prediction of gold prices. Materials that will be used as objects of research to produce gold price predictions include historical XAUUSD (Gold) data itself, historical crude oil data, historical dollar data (USD IDR) and BI 7-Day Repo Rate (BI Rate). ), in producing the prediction of the gold price used Mining Predictive Modeling data using the linear regression function. The results to be achieved from this study is to provide accurate gold price predictions so that it can be used as a reference in making decisions to buy / sell positions in trading. The prediction of the XAUUSD gold price generated is expected to provide significant interest to the investment players (traders) in order to maximize the profit generated.From the results of the trading tests that have been carried out, the implementation of predictive modeling data mining using a linear regression function produces recommendations for gold price predictions (XAUUSD) with an accuracy of 85%.
BP Neural Network based on Dropout Applied to the EDXRF Quantitative Analysis of Heavy Metal Elements

BP neural network has been used in EDXRF quantitative analysis, and the improvement of calculating model is one of the vital research fields. Dropout is considered as an optimization method to solve the over-fitting problem of BP. Therefore, the new experiment of applying the optimized dropout on small sample BP neural network in the quantitative analysis of XRF is proposed. 100 groups of experimental samples are used for establishing model and comparative analysis. The results verify that the optimized BP algorithm can effectively reduce over-fitting problem, and ensure the quality of data, with high confidence and fast operation time.