Reviewerly/exHarmony huggingface dataset:
Exploring Malicious Webpages Using Machine Learning Concept

Malicious web page identification emerges to be an unavoidable process in the digital era. With increase in usage of various digital devices with applications from lot of third party service providers, classification of a web page into a legitimate or malicious is a most demanding need of the day. The parameter considered for this classification is the URL of the webpage. The proposed solution uses TF-IDF score to do this task. This score would be incorporated with existing machine learning model such as linear regression and different classifiers and a comparative analysis will be made to choose the better one. In addition to these lists called white list could be maintained. The white list has the list of URLs, which has been classified as legitimate earlier. When a new URL is seen by the system, it would check for its availability in the white list. If the URL is not found it would be fed into our machine learning algorithm for classification. This work considers logistic regression along with comparison using random forest and support vector machine. The comparative analysis shows that, logistic regression seems to produce optimal results when compared with SVM and random forest algorithm in terms of identifying false rates. Also, from the analysis results it is observed that, Term Frequency Inverse Document Frequency (TF-IDF) along with binary classifiers on white list works efficiently.
Application and Prospect of MEMS Technology to Geophysics

Earthquake early warning is one of the most effective means to reduce earthquake disasters. At present, many countries and regions in the world have established their own earthquake early warning systems. Compared with traditional geophone, the MEMS seismic sensor system has some advantages, including high sensitivity and compact structure, so it has been widely used in earthquake early warning systems. This paper mainly describes some projects of MEMS technology in earthquake early warning, and uses artificial excitation compares the MEMS seismic sensor system with traditional geophone. It is found that the sensitivity of the MEMS seismic sensor system is about 1.7 times that of the traditional geophone. Finally, some prospects are put forward for the application of MEMS technology in the field of earthquake early warning.
Pairwise constraints cross entropy fuzzy clustering algorithm based on manifold learning and feature selection

Abstract In weakly supervised learning, it is difficult for us to utilize pairwise constraints information in feature selection. In order to solve the problem, we propose Pairwise constraints cross entropy fuzzy clustering algorithm based on manifold learning and feature selection (FCPC-LEFS). There are four phases in our approach: 1) Generate pseudo label; 2) Dimension reduction by Laplacian Eigenmaps; 3) Feature increment and selection; 4) Cross-Entropy semi-Supervised Clustering Based on Pairwise Constraints. We apply our approach to three UCI datasets and a COVID19-CT image dataset. Experiments show that our manifold learning and feature selection method are able to increase improve the clustering performance.
Perception-Aware Bias Detection for Query Suggestions

Bias in web search has been in the spotlight of bias detection research for quite a while. At the same time, little attention has been paid to query suggestions in this regard. Awareness of the problem of biased query suggestions has been raised. Likewise, there is a rising need for automatic bias detection approaches. This paper adds on the bias detection pipeline for bias detection in query suggestions of person-related search developed by Bonart et al. [2]. The sparseness and lack of contextual metadata of query suggestions make them a difficult subject for bias detection. Furthermore, query suggestions are perceived very briefly and subliminally. To overcome these issues, perception-aware metrics are introduced. Consequently, the enhanced pipeline is able to better detect systematic topical bias in search engine query suggestions for person-related searches. The results of an analysis performed with the developed pipeline confirm this assumption. Due to the perception-aware bias detection metrics, findings produced by the pipeline can be assumed to reflect bias that users would discern.
Feature Extraction Method of Machine Translation Equivalent Pairs in Chinese-English Comparable Corpus based OCR Recognition

With the development of natural language processing and text mining technology, it has become a trend to mine and extract corresponding knowledge from unstructured text. Contrast is two or more corpora composed of texts of different languages or texts of different variants of the same language. Analogical corpora can also be subdivided into monolingual and bilingual/multilingual corpora. The former collects texts with similar content in a similar language environment, while the latter collects texts in different languages with similar content, register and communicative environment, which are mostly used in contrastive linguistics. Optical character recognition (OCR) is now mainly used in document recognition and certificate recognition. Deep learning can improve the application scope of OCR recognition. Text region extraction applied to OCR can enhance. the accuracy of OCR text extraction and improve the accuracy of OCR. This paper studies the feature extraction method of machine translation equivalent pair for OCR recognition based on Chinese English comparable corpus.
The Research of Automatic Classification of Ultrasound Thyroid Nodules

This paper proposes a computer-aided diagnosis system which can automatically detect thyroid nodules (TNs) and discriminate them as benign or malignant. The system firstly uses variational level set active contour with gradients and phase information to complete automatic extraction of the boundaries of thyroid nodules images. Then according to thyroid ultrasound images and clinical diagnostic criteria, a new feature extraction method based on the fusion of shape, gray and texture is explored. Due to the imbalance of thyroid sample classes, this paper introduces a weight factor to improve support vector machine, offering different classes of samples with different weights. Finally, thyroid nodules are classified and discriminated by the improved support vector machine. Experiments show that the efficiency of discrimination on benign and malignant thyroid nodules is improved.
“Clik-Thru” Terms of Service: Blockchain smart contracts to improve consumer engagement?

Technology entrepreneurship has enabled the widespread commercial adoption of internet technologies. These internet technologies have reformed consumer commercial experiences towards an online environment. The pervasiveness of the online experience raises the importance of protecting the consumer in the online context. Online services are typically delivered under “Clik-Thru” terms of service developed by the service provider alone; and accepted by the consumer with a single click and little if any consideration. The successful adoption of new internet-based technologies and commercial practices has encouraged more technology entrepreneurship in a positive feedback cycle. Efforts at improved readability are insufficient to engage consumers with these “Clik-Thru” contracts. This paper argues that some efforts at increasing consumer engagement with the “Clik-Thru” terms of service may be a useful and tractable step towards improved consumer experiences. Blockchain smart contracts appear to provide promising capabilities to enable greater consumer engagement with “Clik-Thru” contracts.
Research on the Design of Lexical-chunks Centered Mode of Writing under Artificial Intelligence in College English Course

In the era of big data and artificial intelligence, English learning resources have become more abundant, and the ways for students to obtain English learning resources have become more convenient, which has prompted changes in the traditional teacher-centered English model. More and more higher education institutions and scholars focus on cultivating students' autonomous English learning abilities and advocate a student-oriented English teaching model. This paper designs a college English writing teaching model based on big data technology to highlight the teaching focus of lexical chunks. The research found that big data technology effectively monitors the learning process of students and gives timely feedback on the learning effect. At the same time, the teaching of lexical chunk promotes students' writing input and triggers output, which may innovate English teaching and motivate students' learning motivation. Through the comparison with the study of computational linguistics, it is also found that students' understanding of lexical chunk is basic, and it is reasonable to infer that students can not fully grasp the lexical chunks used in the daily communication in terms of the proper forms and the pragmatic functions, and misusing happens fairly frequently. The study highlights the significance of integrating the teaching mode which involves lexical chunk specifically in English textbooks and lectures for EFL, especially for students with a relatively low language proficiency.
Vision-Based Sensing Systems for Autonomous Driving: Centralized or Decentralized?

The perception of the surrounding circumstances is an essential task for fully autonomous driving systems, but its high computational and network loads typically impede a single host machine from taking charge of the systems. Decentralized processing is a candidate to decrease such loads; however, it has not been clear that this approach fulfills the requirements of onboard systems, including low latency and low power consumption. Embedded oriented graphics processing units (GPUs) are attracting great interest because they provide massively parallel computation capacity with lower power consumption compared to traditional GPUs. This study explored the effects of decentralized processing on autonomous driving using embedded oriented GPUs as decentralized units. We implemented a prototype system that off-loaded image-based object detection tasks onto embedded oriented GPUs to clarify the effects of decentralized processing. The results of experimental evaluation demonstrated that decentralized processing and network quantization achieved approximately 27 ms delay between the feeding of an image and the arrival of detection results to the host as well as approximately 7 W power consumption on each GPU and network load degradation in orders of magnitude. Judging from these results, we concluded that decentralized processing could be a promising approach to decrease processing latency, network load, and power consumption toward the deployment of autonomous driving systems.
Face Detection, Tracking and Recognition with Artificial Intelligence

Due to the COVID-19 pandemic, the face masks are mandatory everywhere so it might be beneficial to automatize the detection and tracking of whether people wear a mask or not with the help of the computer vision. Furthermore, it can be implemented on mobile robots as well. Additionally, face recognition of people is discussed with two different techniques which are based on neural networks and eigenface approach in this study. Hence, a complete system which can be used with mobile platforms has been achieved with the proposed procedure.
Exploring a Layer-based Pre-implemented Flow for Mapping CNN on FPGA

Convolutional Neural Networks are compute-intensive learning models that have demonstrated ability and effectiveness in solving complex learning problems. However, developing a high-performance FPGA accelerator for CNN often demands high programming skills, hardware verification, precise distribution localization, and long development cycles. Besides, CNN depth increases by reuse and replication of multiple layers. This paper proposes a programming flow for CNN on FPGA to generate high-performance accelerators by assembling CNN pre-implemented components as a puzzle based on the graph topology. Using pre-implemented components allows us to use the minimum of resources necessary, predict the performance, and gain in productivity since there is no need to synthesize any HDL code. Furthermore, components can be reused for a different range of applications. Through prototyping, we demonstrated the viability and relevance of our approach. Experiments show a productivity improvement of up to 69% compared to a traditional FPGA implementation while achieving over 1.75× higher Fmax with lower resources and power consumption.
Score-Based Explanations in Data Management and Machine Learning: An Answer-Set Programming Approach to Counterfactual Analysis

We describe some recent approaches to score-based explanations for query answers in databases and outcomes from classification models in machine learning. The focus is on work done by the author and collaborators. Special emphasis is placed on declarative approaches based on answer-set programming to the use of counterfactual reasoning for score specification and computation. Several examples that illustrate the flexibility of these methods are shown.
Face Mask Detection using Machine Learning Techniques

COVID-19 pandemic has bought a lot of change in our daily routine. To protect ourselves from getting infected, we need to follow certain precautions. One such safety measures are wearing a face mask. Viruses spread from one person to other respiratory droplets. When an infected person talks, shouts, sneezes or coughs, the droplets land in the mouth or nose of other people who are nearby. So, wearing a face mask over nose and mouth acts as a barrier for preventing the transmission of respiratory droplets. But many people ignore wearing a mask in public areas. It is good to have a system to identify people without masks in public places. This system, when integrated with embedded systems ensures the public safely. Our project aims to detect the people without masks on a video by developing a machine learning algorithm which identifies the person without a face mask. Machine learning is one of the techniques of Artificial Intelligence which is used for extracting valuable knowledge from large databases[1]. We collected the training sets from Kaggle, few open-source libraries and Google consisting of faces with and without a face mask. There are around 1500 images of with and without a face mask.
Cover or Pack

This paper considers the worst-case complexity of multi-round join evaluation in the Massively Parallel Computation (MPC) model. Unlike the sequential RAM model, in which there is a unified optimal algorithm based on the AGM bound for all join queries, worst-case optimal algorithms have been achieved on a very restrictive class of joins in the MPC model. The only known lower bound is still derived from the AGM bound, in terms of the optimal fractional edge covering number of the query.
Real-Time Deep Video Analytics on Mobile Devices

Real-time mobile video analytics plays an increasingly important role in our daily life, such as smart driving, unmanned delivery, cashier free stores, and video surveillance. The existing video analytics runs complex deep models to detect and recognize objects in video frames. However, running deep models on mobile devices can not meet the real-time requirement. This paper develops a novel mobile video analytics system. Its unique features include (i) high accuracy, (ii) real-time, and (iii) running exclusively on a mobile device without the need of edge/cloud server or network connectivity. At its heart lies an effective technique to reliably extract motion from video frames and use the motion to speed up video analytics. Unlike the existing motion extraction, our technique is robust to background noise and changes in object sizes. Extensive evaluation results show that we can support real-time object tracking at 30 frames/second (fps) on Nvidia Jetson TX2. For single-object tracking, Sight improves the average Intersection-over-Union (IoU) by 88%, improves the mean Average Precision (mAP) by 207% and reduces the average hardware resource usage by 45% over state-of-the-art approach. For multi-object tracking, Sight improves IoU by 69%, improves mAP by 173% and reduces resource usage by around 32% over state-of-the-art approach.
A Mortality Risk Assessment Approach on ICU Patients Clinical Medication Events Using Deep Learning

ICU patients are vulnerable to medications, especially infusion medications, and the rate and dosage of infusion drugs may worsen the condition. The mortality prediction model can monitor the real-time response of patients to drug treatment, evaluate doctors’ treatment plans to avoid severe situations such as inverse Drug-Drug Interactions (DDI), and facilitate the timely intervention and adjustment of doctor’s treatment plan. The treatment process of patients usually has a time-sequence relation (which usually has the missing data problem) in patients’ treatment history. The state-of-the-art method to model such time-sequence is to use Recurrent Neural Network (RNN). However, sometimes, patients’ treatment can last for a long period of time, which RNN may not fit for modelling long time sequence data. Therefore, we propose to use the heterogeneous medication events driven LSTM to predict the outcome of the patient, and the Natural Language Processing and Gaussian Process (GP), which can handle noisy, incomplete, sparse, heterogeneous and unevenly sampled patients’ medication records. In our work, we emphasize the semantic meaning of each medication event and the sequence of the medication events on patients, while also handling the missing value problem using kernel-based Gaussian process. We compare the performance of LSTM and Phased-LSTM on modelling the outcome of patients’ treatment and data imputation using kernel-based Gaussian process and conduct an empirical study on different data imputation approaches.
The effect of brain GYM on the dementia and depression reduction of the elderly‎

This study aims to determine the effect of brain gym on decreasing the level of dementia and depression in the elderly in the Health Center Community of Mulyorejo in Deli Serdang Regency.The population of this study was 5765 elderly people.The sampling technique uses the Slovin formula and it is determined through inclusion criteria using a purposive consecutive method so that the total sample of 30 respondents is divided into two groups, 15 respondents with dementia and 15 respondents with depression.A quasi-experimental pre-post test design was used, where a control group wasn't used, then a pre-test is followed by intervention in each group and at the end a post-test in each group was conducted.Data analysis used the Wilcoxon test due to the unusual distribution of the data.The results of the study on the dementia variable obtained p-value (p = 0.000 <0.05) and the depression variable value (p-value = 0.006 <0.05), so it can be concluded that the brain gym has an effect on decreasing the level of dementia and depression in the elderly.The use of brain gym can be one of the considerations in nursing intervention as a complementary therapy to reduce the level of dementia and depression in the elderly, and is expected to be carried out routinely during the activities of the elderly of each Posyandu (integrated health care center).
A new heuristic model for monthly streamflow forecasting

Streamflow plays an important role in several hydraulic and hydrologic applications. Therefore, forecasting streamflow accurately is critical for better understanding of streamflow characteristics and variability over the time. This chapter proposed and discussed four heuristic extreme learning machine (ELM) models used for forecasting streamflows. Subsequently, the chapter includes a case study where application of the four heuristic ELM models is demonstrated in forecasting monthly streamflows using data from two stations in Turkey, namely, Topluca and Tozkoy stations. Furthermore, the case study involves investigating the comparative feasibility and robustness of four heuristic methods. The proposed heuristic models are (i) the outlier-robust extreme learning machine (ORELM), (ii) the regularized extreme learning machine (RELM), (iii) the weighted regularized extreme learning machine (WRELM), and (iv) the original ELM. Results obtained using the proposed models were compared with those obtained using the multiple linear regression models. Using monthly streamflow dataset measured between 1994 and 2007, the models were developed using 70% of the dataset as training data, and 30% as validation data, according to six scenarios. A total of four statistical metrics were used to evaluate performance of the models: correlation coefficient (R2), Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and mean absolute error. At Topluca Station, the ORELM models provided the R2 values ranging from 0.604 to 0.858, NSE values from 0.313 to 0.735, and RMSE values from 11.121 to 17.827 m3/s, respectively. At Tozkoy Station, the ORELM models provided the R2 values ranging from 0.615 to 0.911, NSE values from 0.291 to 0.829, and RMSE values from 3.164 to 6.439 m3/s, respectively. Overall, the results suggested that the proposed ELM models have capability to forecast monthly streamflow with reliable accuracy.
Algorithms for Right-Sizing Heterogeneous Data Centers

Power consumption is a dominant and still growing cost factor in data centers. In time periods with low load, the energy consumption can be reduced by powering down unused servers. We resort to a model introduced by Lin, Wierman, Andrew and Thereska (23,24) that considers data centers with identical machines, and generalize it to heterogeneous data centers with d different server types. The operating cost of a server depends on its load and is modeled by an increasing, convex function for each server type. In contrast to earlier work, we consider the discrete setting, where the number of active servers must be integral. Thereby, we seek truly feasible solutions. For homogeneous data centers (d=1), both the offline and the online problem were solved optimally in (3,4)
RLWE/PLWE equivalence for totally real cyclotomic subextensions via quasi-Vandermonde matrices

We propose and justify a generalized approach to prove the polynomial reduction of the RLWE to the PLWE problem attached to the ring of integers of a monogenic number field. We prove such equivalence in the case of the maximal totally real subextension of the [Formula: see text]th cyclotomic field, with [Formula: see text] arbitrary prime.