Reviewerly/exHarmony huggingface dataset:
DBMS, NoSQL and Securing Data: the relationship and the recommendation

In modern days, database systems usage keep increasing where they have become an essential part of our life in storing records. As technology advances, databases receive updates or new improved methods that, alongside it, risks emerge and increase if left unresolved or unaware of such ongoing risks. Such risks would lead to data availability, integrity, and confidentiality to be broken and leaked out into the hands of cybercriminals. This paper aims to show two types of databases, analyze their security, and propose a solution to securing and maintaining the availability, integrity, and confidentiality of the data stored in the database.
Discriminative Features Generation for Mortality Prediction in ICU

Effective methods for mortality prediction for Intensive Care Unit (ICU) patients assist health professionals by producing alerts ahead of time regarding the critical changing degeneration of a patient’s health. This can guide health professionals to take immediate interventions to rescue the lives of patients. However, existing methods only use raw clinical features and ignore the compound information exhibited by Electronic Health Records (EHRs) data, i.e., the co-occurrence of different clinical events at the same point of time (or within a short time interval). In this paper we use a recently proposed method, called Temporal Tree, to capture the compound information and use them as discriminative features. In addition, to test the impact of preserving temporal information, we capture compound information in terms of patient situations (i.e., the patient’s medical condition at particular point of time), and represent a patient as a sequence of patient situations. This is contrasted with the baseline approach of representing a patient by the associated sequence of clinical events (bag-of-words like). These representations are further processed to obtain low dimensional embeddings used to represent patients and their situations. The effectiveness of the proposed methods is evaluated using a real ICU dataset with nine different baselines and state-of-the-art classifiers. The empirical results show the Temporal Tree method is able to generate discriminative patient representations. Classifiers that exploited the compounded information significantly improved the quality of ICU mortality predictions, in all cases and across both bag-of-words (up to $$7.814\%$$ improvements) and patient situations representations (up to $$2.720\%$$ improvements).
Research on Named Entity Recognition Technology for Chinese Titles

Abstract Aiming at the problem that the feature extraction of Chinese title data is insufficient in the process of named entity recognition and the marking data is difficult to obtain, this paper presents a CNN-BiLSTM-CRF model. The model trains large-scale corpus to generate word vectors with semantic information and trains the character vectors in words through CNN to generate character vectors containing character features, the word vector and the character vectors are merged as the input of BiLSTM network. Through the BiLSTM network, the front and back texts features are fully extracted, and CRF is used to restrict the relevance of tags. In addition, this paper proposes an algorithm based on active learning. This algorithm uses the proposed model construction committee to select the samples with large amount of information to mark, which effectively reduces the amount of data annotation.
Equivalence between Non-Malleability against Replayable CCA and Other RCCA-Security Notions

Replayable chosen ciphertext (RCCA) security was introduced by Canetti, Krawczyk, and Nielsen (CRYPTO'03) in order to handle an encryption scheme that is “non-malleable except tampering which preserves the plaintext.” RCCA security is a relaxation of CCA security and a useful security notion for many practical applications such as authentication and key exchange. Canetti et al. defined non-malleability against RCCA (NM-RCCA), indistinguishability against RCCA (IND-RCCA), and universal composability against RCCA (UC-RCCA). Moreover, they proved that these three security notions are equivalent when considering a PKE scheme whose plaintext space is super-polynomially large. Among these three security notions, NM-RCCA seems to play the central role since RCCA security was introduced in order to capture “non-malleability except tampering which preserves the plaintext.” However, their definition of NM-RCCA is not a natural extension of that of original non-malleability, and it is not clear whether their NM-RCCA captures the requirement of original non-malleability. In this paper, we propose definitions of indistinguishability-based and simulation-based non-malleability against RCCA by extending definitions of original non-malleability. We then prove that these two notions of non-malleability and IND-RCCA are equivalent regardless of the size of plaintext space of PKE schemes.
A Technique for Video Inpainting using Deep Learning

With the advent of internet and social media, the number of videos being shot on a daily basis has increased manifold. However, some of these videos don't turn out the way you want them to. Video Inpainting is a process which can be used to eliminate obvious flaws in such videos and make them better. Further, we see a ton of highly enthralling videos in action movies, especially superhero movies. Most of the action sequences are not real, none of the cameramen will be able to shoot it to perfection in such unreal scenarios and surroundings. All these videos are doctored using a variety of video inpainting methods. In our paper, we present one such Video Inpainting method built using several deep learning techniques. Our Video Inpainting network uses a set of reference frames to regenerate the holes in a given target frame. It establishes suitable relationships between the reference frames and the target frame, stores them in an asymmetric key value store and goes about the process of inpainting. We validate that our model produces visually pleasing videos in all realistic scenarios.
Government ecological governance management based on heterogeneous multi-processors and dynamic image sampling

With the advent of the governance economy era, traditional government governance models face enormous challenges. Governance management is the source of power to enter the era of the knowledge economy. This is a profound change that governments have needed to implement innovative government governance models, methods of social organization, and management. Governments in many countries are now adopting the construction of e-government as a strategy and response to demonstrate their priority development competitive advantage over the international competition and the challenges of the knowledge economy that spreads around the world. Therefore, the establishment of a new model of government governance has great theoretical and practical significance for impulse and government management innovation. The Dynamic Image Classification Algorithm (DICA) system is introduced in the implementation of the image processing technology and global disaster detection proposed here, and of this dynamic image classification algorithm (DICA) of the government, sector to extract more effectively. DICA is the most important task, or it is a strong shadow, proper access to the characteristics of the disaster area, and a false impact on the impact of the disaster, overcoming false alarms. It creates patches of post-disaster aerial image data from pre-tailoring and pre-adjustment training and satellite imagery. DICA training, all patches to government-managed data modified without delay or image data extraction. Two types of proposed systems are high-speed operation and accuracy (96.6%) and detection of earth disasters.
Video Object Detection and Tracking based on Angle Consistency between Motion and Flow

Detect and Track (D&T) extracts a foreground region by using a feature map and region proposal network (RPN) and estimates an object class by using fully connected layers. A correlation layer, which is a hidden layer that obtains displacement between adjacent frames, estimates the movement and size of an object between the adjacent frames. Then, object class and regression are estimated by the feature maps obtained from the correlation layer and RPN. Finally, D&T estimates the moving direction and movement of a bounding box from the detection results obtained from the correlation layer and adjacent frames. Although D&T can achieve accurate object detection and tracking, the object detection and movement estimation of the correlation layer relies on the detection results of the RPN. Therefore, the correlation layer does not acquire local and global pixel changes in video frames and has to estimate the moving direction only from the similarity of detected regions. As a result, the estimation of the moving direction tends to fail. In this work, we propose a method to improve the moving direction estimation by performing the estimation in such a way as to maintain the consistency between the estimated direction and optical flow. Experimental results show that the proposed method can successfully estimate the moving direction and thereby improves both the detection and the tracking accuracy.
Combining Physical Modeling with Artificial Intelligence for Solar Power Forecasting

Modern renewable energy forecasting systems blend physical models with artificial intelligence (AI). This paper describes such a system being developed for the Shagaya Renewable Energy Park in Kuwait. This Kuwait Renewable Energy Prediction System uses both physical modeling and machine learning to produce forecasts for short-range forecasting (i.e. the next six hours) as well as for forecasts several days out. These systems work together to implement the best of our physical knowledge of atmospheric flow with actual observations at the site of interest. The system include numerical weather prediction, blending models, statistical learning models for short range prediction, and an analog ensemble to complete the forecast and quantify the uncertainty. This description includes a preliminary assessment of how each of these models performs.
A Survey of Blur Detection and Sharpness Assessment Methods

Blurring is almost an omnipresent effect on natural images. The main causes of blurring in images include: (a) the existence of objects at different depths within the scene which is known as defocus blur; (b) blurring due to motion either of objects in the scene or the imaging device; and (c) blurring due to atmospheric turbulence.
Developing an Expert System Application to Detect Childs' Lung Disease

The development of information technology has supported many activities, especially in terms of health. Artificial Intelligence (AI) is the application of information technology that is currently developing well. Several previous studies have evaluated models from expert systems to diagnose lung disease in children using Naïve Bayes (NB) and Support Vector Machine (SVM). However, in conducting these evaluations they do not try to make an integrated application to facilitate evaluation. In this study we propose to build a system that integrates NB and SVM classifiers. Furthermore, in this study we used a sample of data from a clinic in Indonesia. The results of this study, we conclude that the existence of this system will make it easier to evaluate the lung disease experienced by children.
WEB EFFORT ESTIMATION TECHNIQUES: A SYSTEMATIC LITERATURE REVIEW

Web Effort Estimation is an important estimation measure for predicting the effort required to develop a web application. The completion of web projects within stipulated time and budget is not possible without accurate effort estimation. The numerous effort estimation models are present these days and they have achieved a pinnacle of success, but the uncertainty features are daunting its progress due to deviations in the data set collected, types of projects, and data set characteristics. The literature studied for this research task elaborated that this field still lacks in a significant direction for consolidated documentation, which guides the researchers to choose a specific technique in order to predict the effort required for web application development. The wide and versatile nature of this domain daunting the researchers to mine the literature in a more appropriate way and deploy ensemble techniques of effort prediction models in order to achieve better results for web application viz., schedule delays, budget overruns. The systematic literature review (SLR) in this research task has been done to inspect the various aspects affecting the prediction accuracy of web applications and these identified characteristics lead to a better effort estimation model. The literature review is conducted on a collection of 143 papers retrieved from online journals and conference proceedings. Only 53 relevant papers are selected for broad investigation.Â  The study reveals that the expert judgment and algorithm-based models are very popular and used frequently for effort prediction, instead the machine learning (ML) based models are rare in use but cater comparatively better prediction accuracy. The authors suggest taking cognizance of this research domain for developing ensembles of early effort prediction models to overcome delays in schedule and budget.
Improved Facial Recognition for Attendance Systems

Attendance is an important part of educational life. The attendance methodology which exists currently is either totally manual or requires some human assistance. In traditional methods, the teacher manually marks the attendance of each student on paper. There are many flaws in this method. As the process is manual there is a chance of error in the marking of attendance. Similarly, marking of false attendance by the students is also possible. Moreover, the attendance is then required to be updated in the online database of the institute through which it is accessible to the students. Also, after every month a report of all the students is to be generated. This overall process is cumbersome and time consuming. There also exist some automated systems for attendance like fingerprint verification and RFID but they also require some human support. Here, the automated attendance system using facial recognition plays an important role which requires no human intervention and is fully automatic. Face recognition has always remained a major focus of research because of its non-invasive nature and because it is people's primary method of person identification.
Information Security in the Design of Web-Based Software Systems

This article explores trends and practices in the development, integration, maintenance and operation of web-based systems. From this point of view, issues with the information security of web applications are analyzed. Attention is drawn to the fact that information security is a phenomenon in which the greater the degree of automation of the software process, the greater number of solutions for the information security problems are concentrated in the functionality of the software. We are highlighting the advantages of modular localization in the implementation of modular web-based software systems. Even though there are various studies in this area, there are no commonly accepted solutions and guidelines for solving the problem. The article attempts to fill up this gap by proposing possible guidelines. As a result of analyzing trends in the information security applied to the web-based software systems, a forecast has been made for an increase in the regulatory requirements for software development, the need for an increased general knowledge in the society for the safe and secure use of software applications, as well as the growth in demand for highly specialized information security professionals. Modern practices are introduced for providing information security in software web-based systems through authentication and authorization modules. As a result of the studies and the analysis, guidelines are proposed for the development of a method that will provide information security.
Reservoir generation via simulating the non-local connections between brain functional columns

The Liquid state machines(LSMs) have higher bio-plausibility than the echo state networks (ESNs), but their reservoirs are also more difficult to optimize. Compared with artificial networks with complex structures, the STDP-based methods have higher bio-plausibility, and are more effective in improving the performance of the reservoirs, but they rely more on training signals and parameters. There are also some methods applying the laws in the real biological nervous system that do not require training, but may lead to very complicated optimizing algorithms. In this paper, an approach that can construct a reservoir with higher bio-plausibility and noise resistance is proposed. The reservoir is generated following the non-local connection rules between the functional columns of the brain, i.e. the gamma distribution, and does not need any optimization. Experimental results demonstrate that compared with Gaussian distribution and STDP-based methods, the reservoir generated with gamma distribution has better accuracy on sequential classification tasks and object recognition tasks.
Pedestrian Detection Using Regression-Based Feature Selection and Disparity Map

In this paper, the pedestrian detection using a regression-based feature selection and a disparity map method is proposed for improving the processing speed. Using many features helps to improve detection performance, but slows down processing. Therefore, it is important to select and use features efficiently. Our proposed method consists of three stages, such as a disparity map-based detection stage, a segmentation stage using a transformed disparity map, and a recognition stage with regression-based feature analysis. Through experiments with the ETH database, we show that the proposed method improves detection performance and especially processing speed.
GMCAST

In the research of data center network structure, routing algorithm is one of the important research fields, it is mainly to find a suitable path to transfer data information from the source node to the destination node. In this paper, multicast routing algorithm is designed for three layers of graphite structure with optimal performance. Graphite structure will be closer to practical application due to its own advantages. But the author did not design its routing algorithm. In recent years, group communication is becoming more popular. Multicast is becoming more important in group communication in current data center network. This paper introduces the construction of the three-layer graphite structure and designs the multicast routing algorithm of the three-layer graphite structure.
SaraBotTagger - A Light Tool to Identify Bots in Twitter

In this work we present SaraBotTagger, a tool for identifying bots in Twitter based only in the metadata of the accounts based on a Random Forest Classifier, able to efficiently identify bots in Twitter. We also propose a validation methodology that verifies the ability of the classifier in identifying bots that are actually suspended by Twitter. We apply our methodology to the context of two real-world discussion, regarding STF (the Supreme Court of Brazil) and anxiety. We analyze the description of the accounts, the tweets in the timelines and the retweet networks in order to investigate the difference between bots and humans.
BOUNDS FOR INDEXES OF NILPOTENCY IN COMMUTATIVE RING THEORY: A PROOF MINING APPROACH

Abstract It is well-known that an element of a commutative ring with identity is nilpotent if, and only if , it lies in every prime ideal of the ring. A modification of this fact is amenable to a very simple proof mining analysis. We formulate a quantitative version of this modification and obtain an explicit bound. We present an application. This proof mining analysis is the leitmotif for some comments and observations on the methodology of computational extraction. In particular, we emphasize that the formulation of quantitative versions of ordinary mathematical theorems is of independent interest from proof mining metatheorems.
Mobile Twin Recognition

This study focused on one of the most advanced problems in facial recognition - twin differentiation. In recent years, an increasing number of mobile phones have been hacked using the face of the phone owner's sibling/twin, and there are hundreds of videos about this available on the internet. Our main goal is to improve mobile security and protect user data from outside interventions, and therefore we propose a technique which helps to recognize twins to the same extent as humans are able to do so. The main idea involves combining a modern convolutional neural network (CNN) approach with classical handcrafted features, which describe particular characteristics of the human face, such as an asymmetry. Our method was optimized for low performance mobile platforms and it can be simply used by any system with limited resources.
Research and Improvement of Intrusion Detection Based on Isolated Forest and FP-Growth

The current anomaly intrusion detection system has shortcomings such as low detection rate, high false alarm rate and poor performance in processing large amounts of data. In response to the above problems, some improvement measures are put forward for the isolated forest algorithm and the FP-Growth algorithm. The improved isolated forest algorithm considers the correlation between dimensions and makes the dimension division more reasonable for abnormal analysis. The improved FP growth algorithm reduces the time of processing a large amount of data, used for correlation analysis of abnormal data. Applying the above two improved algorithms to intrusion detection can further improve the anomaly detection performance. The results show that the false alarm rate of the joint improved algorithm is relatively reduced by 25%, and the overall detection rate is 96.24%.
Feature Selection Optimization Using a Hybrid Genetic Algorithm

The curse of dimensionality plays a vital role in data mining and pattern recognition applications. There are two methods which can address curse of dimensionality namely—feature reduction and feature selection (FS). The application of FS is such that it selects the most relevant subset of features with the less redundancy. Main objective of the proposed method is to manipulate irrelevant features and redundant features in (high–medium–low) dimensional data. We will aim to provide higher classification accuracy. In this proposed method, it is implemented with genetic algorithm. In this paper, we are working on different datasets to train and test our model.
Exact and Gerchberg-Saxton solutions of the one-dimensional Pauli problem with Gaussian probability densities

The exact phase of one-dimensional quantum states with given Gaussian probability densities in the position and momentum representations is retrieved. The number of Pauli partners that are found depends on whether the Heisenberg uncertainty relation for position and momentum is saturated or not. Without saturation, two Pauli partners are found. They differ in the sign of the time derivative of their position uncertainties. The same problem is solved by an exact implementation of the Gerchberg-Saxton algorithm. Its convergence depends on uncertainty saturation as well.