Reviewerly/exHarmony huggingface dataset:
Regional Solar Irradiance Forecast for Kanto Region by Support Vector Regression Using Forecast of Meso-Ensemble Prediction System

From the perspective of stable operation of the power transmission system, the transmission system operators (TSO) needs to procure reserve adjustment power at the stage of the previous day based on solar power forecast information from global horizontal irradiance (GHI). Because the reserve adjustment power is determined based on information on major outliers in past forecasts, reducing the maximum forecast error in addition to improving the average forecast accuracy is extremely important from the perspective of grid operation. In the past, researchers have proposed various methods combining the numerical weather prediction (NWP) and machine learning techniques for the one day-ahead solar power forecasting, but the accuracy of NWP has been a bottleneck issue. In recent years, the development of the ensemble prediction system (EPS) forecasts based on probabilistic approaches has been promoted to improve the accuracy of NWP, and in Japan, EPS forecasts in the mesoscale domain, called mesoscale ensemble prediction system (MEPS), have been distributed by the Japan Meteorological Agency (JMA). The use of EPS as a machine learning model is expected to improve the maximum forecast error, as well as the accuracy, since the predictor can utilize various weather scenarios as information. The purpose of this study is to examine the effect of EPS on the GHI prediction and the structure of the machine learning model that can effectively use EPS. In this study, we constructed the support vector regression (SVR)-based predictors with multiple network configurations using MEPS as input and evaluated the forecast error of the Kanto region GHI by each model. Through the comparison of the prediction results, it was shown that the machine learning model can achieve average accuracy improvement while reducing the maximum prediction error by MEPS, and knowledge was obtained on how to effectively provide EPS information to the predictor. In addition, machine learning was found to be useful in improving the systematic error of MEPS.
Digital Pathology-based Study of Cell- and Tissue-level Morphologic Features in Serous Borderline Ovarian Tumor and High-grade Serous Ovarian Cancer

Serous borderline ovarian tumor (SBOT) and high-grade serous ovarian cancer (HGSOC) are two distinct subtypes of epithelial ovarian tumors, with markedly different biologic background, behavior, prognosis, and treatment. However, the histologic diagnosis of serous ovarian tumors can be subjectively variable and labor-intensive as multiple tumor slides/blocks need to be thoroughly examined to search for these features.We developed a novel informatics system to facilitate objective and scalable diagnosis screening for SBOT and HGSOC. The system was built upon Groovy scripts and QuPath to enable interactive annotation and data exchange.The system was used to successfully detect cellular boundaries and extract an expanded set of cellular features representing cell- and tissue-level characteristics. The performance of cell-level classification for both tumor and stroma cells achieved >90% accuracy. The performance of differentiating HGSOC versus SBOT achieved 91%-95% accuracy for 6485 imaging patches which have sufficient tumor and stroma cells (minimum of ten each) and 97% accuracy for classifying patients when aggregating the results to whole-slide image based on consensus.Cellular features digitally extracted from pathological images can be used for cell classification and SBOT v. HGSOC differentiation. Introducing digital pathology into ovarian cancer research could be beneficial to discover potential clinical implications. A larger cohort is required to further evaluate the system.
Transductive Relation-Propagation With Decoupling Training for Few-Shot Learning

Few-shot learning, aiming to learn novel concepts from one or a few labeled examples, is an interesting and very challenging problem with many practical advantages. Existing few-shot methods usually utilize data of the same classes to train the feature embedding module and in a row, which is unable to learn adapting to new tasks. Besides, traditional few-shot models fail to take advantage of the valuable relations of the support-query pairs, leading to performance degradation. In this article, we propose a transductive relation-propagation graph neural network (GNN) with a decoupling training strategy (TRPN-D) to explicitly model and propagate such relations across support-query pairs, and empower the few-shot module the ability of transferring past knowledge to new tasks via the decoupling training. Our few-shot module, namely TRPN, treats the relation of each support-query pair as a graph node, named relational node, and resorts to the known relations between support samples, including both intraclass commonality and interclass uniqueness. Through relation propagation, the model could generate the discriminative relation embeddings for support-query pairs. To the best of our knowledge, this is the first work that decouples the training of the embedding network and the few-shot graph module with different tasks, which might offer a new way to solve the few-shot learning problem. Extensive experiments conducted on several benchmark datasets demonstrate that our method can significantly outperform a variety of state-of-the-art few-shot learning methods.
Improved Fake Reviews Detection Model Based on Vertical Ensemble Tri-Training and Active Learning

People’s increasingly frequent online activity has generated a large number of reviews, whereas fake reviews can mislead users and harm their personal interests. In addition, it is not feasible to label reviews on a large scale because of the high cost of manual labeling. Therefore, to improve the detection performance by utilizing the unlabeled reviews, this article proposes a fake reviews detection model based on vertical ensemble tri-training and active learning (VETT-AL). The model combines the features of review text with the user behavior features as feature extraction. In the VETT-AL algorithm, the iterative process is divided into two parts: vertical integration within the group and horizontal integration among the groups. The intra-group integration is to integrate three original classifiers by using the previous iterative models of the classifiers. The inter-group integration is to adopt the active learning based on entropy to select the data with the highest confidence and label it, and as the result of that, the second generation classifiers are trained by the traditional process to improve the accuracy of the label. Experimental results show that the proposed model has a good classification performance.
HSLF: HTTP Header Sequence Based LSH Fingerprints for Application Traffic Classification

Distinguishing the prosperous network application is a challenging task in network management that has been extensively studied for many years. Unfortunately, previous work on HTTP traffic classification rely heavily on prior knowledge with coarse grained thus are limited in detecting the evolution of new emerging application and network behaviors. In this paper, we propose HSLF, a hierarchical system that employs application fingerprint to classify HTTP traffic. Specifically, we employ local-sensitive hashing algorithm to obtain the importance of each field in HTTP header, from which a rational weight allocation scheme and fingerprint of each HTTP session are generated. Then, similarities of fingerprints among each application are calculated to classify the unknown HTTP traffic. Performance on a real-world dataset of HSLF achieves an accuracy of 96.6%, which outperforms classic machine learning methods and state-of-the-art models.
LOSP: Overlap Synchronization Parallel With Local Compensation for Fast Distributed Training

When running in Parameter Server (PS), the Distributed Stochastic Gradient Descent (D-SGD) incurs significant communication delays and huge communication overhead due to the model synchronization. Moreover, considering the heterogeneity of computational capability among workers, traditional synchronization modes incur under-utilization of computational resources because fast workers have to wait for slow ones finishing the computation. Although our previous work OSP can effectively solve these problems by overlapping the computation and communication procedures and allowing adaptive multiple local updates in distributed training, it causes the staleness problem brought by the overlap, yielding a performance degradation. In this paper, we propose a new method named LOSP by introducing local compensation to our previous synchronization mechanism, which mitigates adverse effects caused by the overlapping synchronization. We theoretically prove that LOSP (1) preserves the same convergence rate as the sequential SGD for non-convex problems, and (2) exhibits good scalability due to the linear speedup property with respect to both the number of workers and the average number of local updates. Evaluations show that LOSP significantly improves performance over the state-of-the-art ones in terms of both convergence accuracy and communication cost.
Pyramid Orthogonal Attention Network based on Dual Self-Similarity for Accurate Mr Image Super-Resolution

For magnetic resonance (MR) images sharing visual characteristics, the internal structure repetitions of different scales are considerable image-specific priors. Following the traditional algorithms, we try to combine external dataset-driven learning with the internal self-similarity for MR image super-resolution (SR). We propose a pyramid orthogonal attention network (POAN) based on dual self-similarity. On the one hand, by combining the point-similarity and the pyramid-similarity, sufficient spatial autocorrelation is explored to alleviate less training data limitation. On the other hand, the non-reduction channel attention mechanism maximizes inter-channel dependence. It increases the probability of the high-frequency region (e.g., structural textures and edges) being activated while suppresses low-frequency regions (e.g., background) adaptively. Out proposed POAN reconstructs the MR image under the guidance of pyramid orthogonal attention. Extensive experiments demonstrate that our method obtains the best results compared with state-of-the-art MR image SR methods quantitatively and visually.
Maximum Margin Multi-Dimensional Classification

Multi-dimensional classification (MDC) assumes heterogeneous class spaces for each example, where class variables from different class spaces characterize semantics of the example along different dimensions. The heterogeneity of class spaces leads to incomparability of the modeling outputs from different class spaces, which is the major difficulty in designing MDC approaches. In this article, we make a first attempt toward adapting maximum margin techniques for MDC problem and a novel approach named M3MDC is proposed. Specifically, M3MDC maximizes the margins between each pair of class labels with respect to individual class variable while modeling relationship across class variables (as well as class labels within individual class variable) via covariance regularization. The resulting formulation admits convex objective function with nonlinear constraints, which can be solved via alternating optimization with quadratic programming (QP) or closed-form solution in either alternating step. Comparative studies on the most comprehensive real-world MDC datasets to date are conducted and it is shown that M3MDC achieves highly competitive performance against state-of-the-art MDC approaches.
Artificial Neural Network Based Sensor Ontology Matching Technique

Web of Things (WoT) is capable of promoting the knowledge discovery and address interoperability problems of various Internet of Things (IoT) applications. To integrate the semantic information on WoT, Semantic Sensor Networks (SSN) based knowledge engineering is utilized for the uniform representation of identical knowledge by using the sensor ontology. However, it is arduous to link numerous heterogeneous sensor entities, and the Sensor Ontology Matching (SOM) is a newly emerging technique for solving the ontology heterogeneity problem, which aims at finding the semantically identical sensor entities in two ontologies. In this paper, the problem of SOM is deemed as regression problem that to integrate a number of Entity Similarity Measure (EMS) to estimate the real similarity score between two sensor entities. To address it, we propose a Artificial Neural Network (ANN)-based sensor ontology matching technique (ANN-OM), which employs the representative entities for enhancing the quality of alignment and the matching efficiency. The experimental results illustrate that ANN-OM is capable of determining superior alignment which is better than the state-of-the-art ontology matchers.
Multiscale Unified Network for Simultaneous Segmentation of Nerves and Micro-vessels in Histology Images

Among the analytic factors to study tumor aggressiveness and disease recurrence, density of micro-vessels (MVD), Lymphovascular Invasion (LVI) and Perineural Invasion (PNI) are considered key prognostic factors. The manual identification of micro-vessels and nerves is time consuming, laborious and highly prone to human error. Computational pathology is an emerging field striving to improve patient care by incorporating modern algorithms to the traditional analysis procedures of microscopic slides. To overcome the challenges of multi-scale, multi-shape and slight intensity variant histopathology structures, we have proposed a deep neural network based hybrid semantic segmentation architecture. The framework is specifically designed to improve the accuracy by focusing mega to minor object details. The encoder uses Multi-scale feature extraction block formed of ResNeXt Blocks. This organization is effective to encode coarse to fine grained features from all specifications and dimensions while limiting the number of learnable parameters. The decoder is a combination of feature fusion and feature erudition while step by step mapping them back to the pixel map. The proposed architecture is trained and tested on generated data set comprising 17,300 samples, prepared from 18 histopathological WSIs of oral cell carcinoma tissues. The trained architecture outperformed the existing segmentation networks like FCN, Unet, SegNet, Deeplabv3+ and a significant rise in accuracy regarding certain scenarios is observed.
Privacy-preserving multikey computing framework for encrypted data in the cloud

Preparing large amounts of training data is the key to the success of machine learning. Due to the public's concern about individual privacy, different techniques are proposed to achieve privacy preserving machine learning. Homomorphic encryption enables calculation on encrypted data in the cloud. However, current schemes either focus on single key or a specific algorithm. Cooperation between different institutions is quite common in this era of big data. Encrypting data from different institutions under one single key is a risk to data privacy. Moreover, constructing secure scheme for a specific machine learning algorithm lacks universality. Based on an additively homomorphic encryption supporting one multiplication, we propose a general multikey computing framework to execute common arithmetic operations on encrypted data such as addition, multiplication, comparison, sorting, division and etc. Our scheme can be used to run different machine learning algorithms. Our scheme is proven to be secure against semi-honest attackers and the experimental evaluations demonstrate the practicality of our computing framework.
Probing transfer learning with a model of synthetic correlated datasets

Abstract Transfer learning can significantly improve the sample efficiency of neural networks, by exploiting the relatedness between a data-scarce target task and a data-abundant source task. Despite years of successful applications, transfer learning practice often relies on ad-hoc solutions, while theoretical understanding of these procedures is still limited. In the present work, we re-think a solvable model of synthetic data as a framework for modeling correlation between data-sets. This setup allows for an analytic characterization of the generalization performance obtained when transferring the learned feature map from the source to the target task. Focusing on the problem of training two-layer networks in a binary classification setting, we show that our model can capture a range of salient features of transfer learning with real data. Moreover, by exploiting parametric control over the correlation between the two data-sets, we systematically investigate under which conditions the transfer of features is beneficial for generalization.
An Improved Gesture Segmentation Method for Gesture Recognition Based on CNN and YCbCr

With the continuous improvement of people’s requirements for interactive experience, gesture recognition is widely used as a basic human-computer interaction. However, due to the environment, light source, cover, and other factors, the diversity and complexity of gestures have a great impact on gesture recognition. In order to enhance the features of gesture recognition, firstly, the hand skin color is filtered through YCbCr color space to separate the gesture region to be recognized, and the Gaussian filter is used to process the noise of gesture edge; secondly, the morphological gray open operation is used to process the gesture data, the watershed algorithm based on marker is used to segment the gesture contour, and the eight-connected filling algorithm is used to enhance the gesture features; finally, the convolution neural network is used to recognize the gesture data set with fast convergence speed. The experimental results show that the proposed method can recognize all kinds of gestures quickly and accurately with an average recognition success rate of 96.46% and does not significantly increase the recognition time.
FPN-GAN: Multi-class Small Object Detection in Remote Sensing Images

Despite the recent dramatic advances in object detection, detecting a small object in general and in remote sensing images is still a challenging problem. One main reason for this is the appearance of small objects in images. Specifically low resolution and noisy representation makes it hard to detect small objects. We tickle down this problem by proposing a novel object detector based on Generative adversarial network (GAN), which we called FPN-GAN in short. The proposed method is composed of GAN, Resnet-50 as a backbone, and Feature Pyramid Network for detection. We combine both of these methods to achieve a single end to end GAN model for multi class-small object detection and image enhancement simultaneously. Extensive experiments on a challenging benchmark DIOR remote sensing dataset demonstrate the superiority of the proposed method for small objects as well as large and the medium size objects.
Leveraging Domain Adaptation as a Defense Against Membership Inference Attacks

Deep Learning (DL) techniques allow ones to train models from a dataset to solve tasks. DL has attracted much interest given its fancy performance and potential market value, while security issues are amongst the most colossal concerns. However, the DL models may be prone to the membership inference attack, where an attacker determines whether a given sample is from the training dataset. Efforts have been made to hinder the attack but unfortunately, they may lead to a major overhead or impaired usability. In this article, we propose and implement DAMIA, leveraging Domain Adaptation (DA) as a defense aginist membership inference attacks. Our observation is that during the training process, DA obfuscates the dataset to be protected using another relate and similar dataset, and derives a model that underlyingly extracts the features from both datasets. Seeing that the model is obfuscated, membership inference fails, while the extracted features provide supports for usability. Extensive experiments have been conducted to validates our intuition. The model trained by DAMIA has a negligible footprint to the usability and introduces slight overhead compared with other defenses. Our experiment also excludes factors that may hinder the performance of DAMIA, and comparisons with other defenses, providing a potential guideline to vendors and researchers to benefit from our solution in a timely manner.
Depicting a Neural Model for Lemmatization and POS Tagging of Words from Palaeographic Stone Inscriptions

Lemmatization is essential before POS (Part-of-Speech) Tagging for analysis of morphology and the removal of inflections by returning the base of the word without the endings. POS is to indicate tagging the words into categories of grammatical terms in analysis of text and marking up linguistic words in a script. Considering the combinations and inflections in the words of Tamil language, there is difficulty in Lemmatization and POS Tagging classification and prediction of Tags of the words. As the automated tools are very rare for modern Tamil language there is a lack of such statistical methods and techniques for the Paleographic Tamil language such as the texts from inscriptions of stone where the words are combined, staked, overlapped and compounded without splitting up into morphemes or lemmas. The proposed work overcomes the complexity of splitting up and classifying ancient words. The proposed work is based on designing the Neural Model for POS Tag Classification and Prediction of Words from the Paleographic 11 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> century stone inscription script. Bi-LSTM model is implemented with the embedding layer of vectors of words for training the POS Tagging model and classifying the words into tags and prediction of Tags of words for any novel script given that involves syntactic tag assigning and predicting tag for concerning words efficiently. The proposed model provides 96.43% accuracy compared to the existing works in the stream.
Deploying Different Clustering Techniques on a Collaborative-based Movie Recommender

Recommendation systems are involved in many industries, for example (e-health, transportation, e-commerce, and agriculture), where Recommendation systems aim to benefit both market and user levels. They help consumers make the right decision based on their preferences without being exposed to data overload. Nowadays, there is a wide range of recommenders based on different filtering approaches, such as Collaborative-based, Content-based, hybrid-based, demographic-based filtering approaches. In this paper, we present clustering-based recommendation systems. We also experiment and show the results for a collaborative-based movie recommender using different clustering techniques such as Kmeans, BIRCH Balanced Iterative Reducing and Clustering using Hierarchies) and DBSCAN (Density-based Spatial Clustering of Applications with Noise). We intended to choose different clustering approaches such as partitional, hierarchical, and density-based clustering approaches. We incorporated Item-based Collaborative filtering, then applied multiple clustering techniques on the dataset based on the users' ratings. We checked the performance using accuracy measures such as MAE (Mean Absolute Error), RMSE (Root mean square error), and the computed time. These measures were calculated for analysis and comparison purposes.