Reviewerly/exHarmony huggingface dataset:
Oral cavity anatomical site image classification and analysis

Oral cavity cancer is a common cancer that can result in breathing, swallowing, drinking, eating problems as well as speech impairment, and there is high mortality for the advanced stage. Its diagnosis is confirmed through histopathology. It is of critical importance to determine the need for biopsy and identify the correct location. Deep learning has demonstrated great promise/success in several image-based medical screening/diagnostic applications. However, automated visual evaluation of oral cavity lesions has received limited attention in the literature. Since the disease can occur in different parts of the oral cavity, a first step is to identify the images of different anatomical sites. We automatically generate labels for six sites which will help in lesion detection in a subsequent analytical module. We apply a recently proposed network called ResNeSt that incorporates channel-wise attention with multi-path representation and demonstrate high performance on the test set. The average F1-score for all classes and accuracy are both 0.96. Moreover, we provide a detailed discussion on class activation maps obtained from both correct and incorrect predictions to analyze algorithm behavior. The highlighted regions in the class activation maps generally correlate considerably well with the region of interest perceived and expected by expert human observers. The insights and knowledge gained from the analysis are helpful in not only algorithm improvement, but also aiding the development of the other key components in the process of computer assisted oral cancer screening.
FLAG: Towards Graph Query Autocompletion for Large Graphs

Abstract Graph query autocompletion ( GQAC ) takes a user’s graph query as input and generates top- k query suggestions as output, to help alleviate the verbose and error-prone graph query formulation process in a visual interface. To compose a target query with GQAC , the user may iteratively adopt suggestions or manually add edges to augment the existing query. The current state-of-the-art of GQAC , however, focuses on a large collection of small- or medium-sized graphs only. The subgraph features exploited by existing GQAC are either too small or too scarce in large graphs. In this paper, we present Flexible graph query autocompletion for LArge Graphs, called FLAG . We are the first to propose wildcard labels in the context of GQAC , which summarizes query structures that have different labels. FLAG allows augmenting users’ queries with subgraph increments with wildcard labels to form suggestions. To support wildcard-enabled suggestions, a new suggestion ranking function is proposed. We propose an efficient ranking algorithm and extend an index to further optimize the online suggestion ranking. We have conducted a user study and a set of large-scale simulations to verify both the effectiveness and efficiency of FLAG . The results show that the query suggestions saved roughly 50% of mouse clicks and FLAG returns suggestions in few seconds.
Discriminative-region attention and orthogonal-view generation model for vehicle re-identification

Vehicle re-identification (Re-ID) is urgently demanded to alleviate the pressure caused by the increasingly onerous task of urban traffic management. Multiple challenges hamper the applications of vision-based vehicle Re-ID methods: (1) The appearances of different vehicles of the same brand/model are often similar; However, (2) the appearances of the same vehicle differ significantly from different viewpoints. Previous methods mainly use manually annotated multi-attribute datasets to assist the network in getting detailed cues and in inferencing multi-view to improve the vehicle Re-ID performance. However, finely labeled vehicle datasets are usually unattainable in real application scenarios. Hence, we propose a Discriminative-Region Attention and Orthogonal-View Generation (DRA-OVG) model, which only requires identity (ID) labels to conquer the multiple challenges of vehicle Re-ID. The proposed DRA model can automatically extract the discriminative region features, which can distinguish similar vehicles. And the OVG model can generate multi-view features based on the input view features to reduce the impact of viewpoint mismatches. Finally, the distance between vehicle appearances is presented by the discriminative region features and multi-view features together. Therefore, the significance of pairwise distance measure between vehicles is enhanced in a complete feature space. Extensive experiments substantiate the effectiveness of each proposed ingredient, and experimental results indicate that our approach achieves remarkable improvements over the state-of-the-art vehicle Re-ID methods on VehicleID and VeRi-776 datasets.
Optimized Machine Learning Algorithm to classify Phishing Websites

The common method of misleading people is Phishing, which giving up their personal information by using bogus websites. The major goal of this work is to use machine learning methods to categorize phishing websites. The internet has become a vital factor of our day-to-day life. Attackers can use smart internet-based gadgets for financial transactions as a platform to conduct a variety of assaults. The purpose of this study is to discuss phishing assaults. Phishing is a kind of friendly commercial attack that is as often as possible used to acquire individual data from users. As innovation progresses, phishing techniques have improved at a quick speed, which ought to be kept away from. As a result of the COVID-19 problems, there has been an increase in phishing assaults in which attackers have obtained users' personal information. Using phishing websites is one of the ways to begin a phishing assault. A phishing website imitates a reputable website while stealing the user's personal information. Phishing is a pernicious type of online crime that should be stayed away from assuming the public's assurance in the web is to be reestablished. Machine Learning is a viable strategy for aggressive phishing attacks. In this research, various machine learning techniques for detecting phishing websites are studied. Our suggested approach has a high degree of accuracy in detecting phishing websites.
Interpolation-based nonrigid deformation estimation under manifold regularization constraint

This paper addresses the image/surface deformation problem by estimating interpolation functions pixel by pixel(or voxel by voxel) between control point pairs using labeled control points and unlabeled feature points as input. The labeled control points are usually selected by users and labeled through user operations; the unlabeled feature points are extracted from the source image. We formulate the interpolation function estimation at each pixel as a weighted semi-supervised learning problem. Specially, we employ moving least squares to estimate the nonrigid deformation function according to the weights between each pixel and the labeled control points and exploit manifold regularization to preserve the intrinsic geometric information of the unlabeled feature points contained in the object. Moreover, we define the nonrigid deformation function in a reproducing kernel Hilbert space to derive a closed-form solution. To reduce the computational complexity, we also adopt a sparse approximation to realize a fast implementation. It is worth mentioning that our proposed method is a unified framework with two different basis functions. Both basis-function-based methods are applied to 2D image deformation, 3D surface deformation, and medical image registration. Extensive experiments on the data and the resulting mean opinion score (MOS) on the 2D deformation demonstrate that our methods are superior to state-of-the-art ones.
A Hybrid Approach of Adam Optimization with CNN to Remove Noise on Images

A digital picture was tainted by noise as a result of the surroundings. There is a novel convolutional neural network for eliminating pictures. The suggested solution incorporates planning approaches with a convolutional neural network. a superior normalizing procedure, the digital picture is initially subjected to the Batch preprocessing step. The picture would then be fed into a Convolution Neural Network (CNN) with a Leaky rectified linear component, which extracts the feature. The CNN algorithm reconfigures the properties of the image that has been retrieved. Ultimately, the rectified picture was integrated Adam optimization technique and Deep CNN with MSE error function to educate and optimize the extracted features. The suggested platform’s performance was evaluated at different sound levels as well as contrasted to different pads and steps. The suggested Deep CNN with Adam optimization method surpasses earlier denoising techniques and gives a satisfactory performance in terms of peak signal-to-noise ratio (PSNR).
Cache Shaping

Cache-based website fingerprinting attacks can infer which website a user visits by measuring CPU cache activities. Studies have shown that an attacker can achieve high accuracy with a low sampling rate by monitoring cache occupancy of the entire Last Level Cache. Although a defense has been proposed, it was not effective when an attacker adapts and retrains a classifier with defended data. In this paper, we propose a new defense, referred to as cache shaping, to preserve user privacy against cache-based website fingerprinting attacks. Our proposed defense produces dummy cache activities by introducing dummy I/O operations and implementing with multiple processes, which hides fingerprints when a user visits websites. Our experimental results over large-scale datasets collected from multiple web browsers and operating systems show that our defense remains effective even if an attacker retrains a classifier with defended cache traces. We demonstrate the efficacy of our defense in the closed-world setting and the open-world setting by leveraging deep neural networks as classifiers.
Predicting the Next Word of a Sinhala Word Series Using Recurrent Neural Networks

Making accurate predictions before occurrence have been a useful phenomenon lately with the advancement of technology. Numerous technologies are used to make countless word predicting applications that make typing easier. Unlike low inflected languages like English, it is not easy to build word predictors for highly inflected languages such as Sinhala language, which comprises a rich set of vocabulary and grammar rules. The aim of this research is to find possible ways to build a word predictor for Sinhala language, by identifying patterns using Recurrent Neural Networks, considering a sequence length of two to predict the next word.
UGDAS: Unsupervised graph-network based denoiser for abstractive summarization in biomedical domain

Abstractive summarization models can generate summary auto-regressively, but the quality is often impacted by the noise in the text. Learning cross-sentence relations is a crucial step in this task and the graph-based network is more effective to capture the sentence relationship. Moreover, knowledge is very important to distinguish the noise of the text in special domain. A novel model structure called UGDAS is proposed in this paper, which combines a sentence-level denoiser based on an unsupervised graph-network and an auto-regressive generator. It utilizes domain knowledge and sentence position information to denoise the original text and further improve the quality of generated summaries. We use the recently-introduced dataset CORD-19 (COVID-19 Open Research Dataset) on text summarization task, which contains large-scale data on coronaviruses. The experimental results show that our model achieves the SOTA (state-of-the-art) result on CORD-19 dataset and outperforms the related baseline models on the PubMed Abstract dataset.
On Different Criteria for Optimizing the Two-bit Uniform Quantizer

In this paper, we address the problem of determining the most influential parameter of the two-bit uniform quantizer, i.e. the support region threshold ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x$</tex> <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</inf> ), according to different optimization criteria. We analyze the dependences of the quantized neural network (QNN) model quality indicators on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{x_{\max}}$</tex> , for the case of applying two-bit uniform quantization of weights in the post-training phase for the MNIST dataset. In addition to the theoretical quality indicator of quantized signal for the Laplacian distribution - theoretical SQNR, our quality indicators are also the accuracy of QNN and the experimentally determined SQNR for the Laplacian-like weight distribution in the three-layer fully connected neural network. The goal is to determine the results of optimizing these quality indicators per X <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</inf> for the considered research framework and to make their comparison for setting a good foundation for future research.
Prediction of Potentially Hazardous Asteroids using Deep Learning

An impact by an asteroid is one of the very few natural disasters that could be mitigated or even entirely prevented if accurate predictions are made early enough. Classifying Near-Earth Asteroid population to identify Potentially Hazardous Asteroids well before an impact is one of the most important problems that need to be solved to avoid humanity from facing the same fate as that of the dinosaurs 65 million years ago. This study presents an approach to classify the Near-Earth Asteroid Population as potentially hazardous or non-hazardous by allowing deep neural networks to learn complex representations that exist in the distribution of available asteroid orbital data. We believe that the generation of an automatic potentially hazardous asteroid detector would contribute to speed up the characterization rate of the rapidly growing asteroid data that are made available through advances of science.
Semi-supervised echo state network with temporal–spatial graph regularization for dynamic soft sensor modeling of industrial processes

Echo state network (ESN) has been successfully applied to industrial soft sensor field because of its strong nonlinear and dynamic modeling capability. Nevertheless, the traditional ESN is intrinsically a supervised learning technique, which only depends on labeled samples, but omits a large number of unlabeled samples. In order to eliminate this limitation, this work proposes a semi-supervised ESN method assisted by a temporal-spatial graph regularization (TSG-SSESN) for constructing soft sensor model with all the available samples. Firstly, the traditional supervised ESN is enhanced to construct the semi-supervised ESN (SSESN) model by integrating both unlabeled and labeled samples in the reservoir computing procedure. The SSESN computes the reservoir states under high sampling rate for better process dynamic information mining. Furthermore, the SSESN's output optimization objective is modified by applying the local adjacency graph of all training samples as a regularization term. Especially, in view of the dynamic data characteristic, a temporal-spatial graph is constructed by considering both the temporal relationship and the spatial distances. The applications to a debutanizer column process and a wastewater treatment plant demonstrate that the TSG-SSESN model can build much smoother model and has better generalization capability than the basic ESN models in terms of soft sensor prediction results.
Towards More Direct Text Editing With Handwriting Interfaces

The conventional handwriting interface on pen computers uses two parts of the screen. The user must put a text cursor where they want to insert the text and then write on a separate pop-up window that recognizes ink. The separation of the two spaces may cause user fatigue and discomfort because it requires additional gaze and hand movement, which may cause a decrease in text editing efficiency and additional cognitive burden. As an alternative to alleviate these potential problems, we revisited and explored the concept of direct-writing interfaces as used in the past. In this study, we designed and implemented a direct- and indirect-writing text editor. Subsequently, we conducted an experiment to compare the usability of both text editors. The results showed that users' gaze movement distance and frequency could be reduced, and constant time could be saved for each text editing operation using the direct-writing interface. In addition, participants preferred the direct-writing interface when simple and frequent text editing was required. However, the overall throughput of the text editing task was not affected. Lastly, we provided guidelines for further development of a direct-writing interface by combining empirical results, comments, and observations.
Air Writing in Japanese: A CNN-based character recognition system using hand tracking

Air writing, commonly known as &#x2018;k&#x016B;sho&#x2019; is a technique used commonly in Japan as a way to memorize or remember kanji. Air writing has been researched many different times for its different writing style. In this paper, a method of writing Japanese in the air was proposed and a system was created using a PS Move controller to allow for movement and button tracking through the use of OpenCV and the PSMove API. Using a demo provided by OpenVINO, we were able to compare the results of the proposed system, 92.5%, alongside the accuracy of the data tested using the Nakayosi and Kondate database, 98.16%.
Performance evaluation and experimental validation of different empirical models for predicting photovoltaic output power

Many empirical models for predicting the photovoltaic output power are available in the literature. However, selecting the most suitable for a specific climate is a difficult task as no comparative studies have been addressed. This work aims to evaluate eighteen empirical models existing in the literature until today and which only use meteorological data as input. The models selected according to their simplicity and accuracy were validated and their accuracy has been evaluated versus the experimental measurements recovered from the photovoltaic installation of the Faculty of Science and Technology of Tangier. Coefficient of determination (R2), mean absolute error, mean absolute percentage error, root mean square error, and its normalised version have been used as evaluation indexes of the prediction model. Results show a good correlation between the measured values and those predicted by the models, where recorded values of R2 were between 99.8% and 96.4% as the maximum and minimum values. Furthermore, this study gave good normalised root mean square error scores between 7.115% and 27.347% as the most favourable and unfavourable errors. This indicator generally provides better information on the prediction model quality. The selection of the most suitable model to use was also carried out taking into account climatic changes such as sunny, cloudy and rainy days. Therefore, the models are tested based on three days of different climates for high-resolution photovoltaic simulation output.Abbreviations ANN: artificial neural network; FST: Faculty of Science and Technology; FSTT: Faculty of Science and Technology of Tangier; PV: photovoltaic; STC: standard test conditions.
Source language classification of indirect translations

Abstract One of the major barriers to the systematic study of indirect translation – that is, translations of translations – is the lack of efficient methods to identify these translations. In this article, we use supervised machine learning to examine whether computers can be harnessed to identify indirect translations. Our data consist of a monolingual comparable corpus that includes (1) nontranslated Finnish texts, (2) direct translations from English, French, German, Greek, and Swedish into Finnish, and (3) indirect translations from Greek (the ultimate source language) via English, French, German, and Swedish (mediating languages) into Finnish. We use n-grams of various types and lengths as feature sets and random forests as the statistical classification technique. To maximize the transferability of the method, the feature sets were implemented in accordance with the Universal Dependencies framework. This study confirms that computers can distinguish between translated and nontranslated Finnish, as well as between Finnish translations made from different source languages. Regarding indirect translations, the ultimate source language has a greater impact on the linguistic composition of indirect Finnish translations than their respective mediating languages. Hence, the indirect translations could not be reliably identified. Therefore, our results suggest that the reliable computational identification of indirect translations and their mediating languages requires a way to control for the effect of the ultimate source language.
Contrastive Learning for Event Extraction

Event extraction is an important information extraction task, aiming at extracting event information from text. Each event consists of triggers and arguments with specific roles. Event extraction methods first identify the trigger and classify it into specific types, and then find out the argument and its role. Traditional methods suffer from scarcity of supervised data. Most existing methods use pre-trained language models to solve this problem. Although these models greatly improved event extraction after fine-tuning, they still need a lot of supervised data to achieve the most advanced downstream task performance. In this work, we propose a simple but effective event extraction model CLEE, which leverages contrastive learning to pre-train language models. Our model first uses a pre-trained model to obtain the context representation of the word, and then uses a contrastive target to reduce the distance between triggers and arguments of the same event, and push the distance between triggers and arguments that are not in an event further. Experiments on ACE 2005 dataset show that CLEE achieves significant improvement.
The Effect of Physical Training of Athletes Based on Parametric Bayesian Estimation in the Context of Big Data

Athletes must maintain their physical fitness in order to compete in any sport. The event can be organized for a single person or multiple people. Irrespective of the number of people or team participation in a sport, the people should have perfect training. The performance and physical fitness of the candidate will be measured under various categories, and the data will be stored in the database. The data to be collected about each event, player, coach, and others will result in the creation of big data with the aid of artificial intelligence and wireless networking. Wireless networking aids in the collection of data around the globe in a shorter period with the aid of intelligent servers. In this study, a recursive Bayesian estimation algorithm is implemented to perform the analysis of training and testing of the athlete’s performance with physical training. The proposed algorithm achieved an accuracy of 99%, which is a minimum increase in nine (09) percentage points over the neural network and an 18% growth over the fuzzy set model. The proposed models are able to analyze players’ success at a higher level based on their scores at each factor level. The experimental results show that the proposed model outperforms well in enhancing player performance.