Reviewerly/exHarmony huggingface dataset:
On the Influence of Masking Policies in Intermediate Pre-training

Current NLP models are predominantly trained through a two-stage "pre-train then fine-tune" pipeline.Prior work has shown that inserting an intermediate pre-training stage, using heuristic masking policies for masked language modeling (MLM), can significantly improve final performance.However, it is still unclear ( 1) in what cases such intermediate pre-training is helpful, (2) whether hand-crafted heuristic objectives are optimal for a given task, and (3) whether a masking policy designed for one task is generalizable beyond that task.In this paper, we perform a large-scale empirical study to investigate the effect of various masking policies in intermediate pre-training with nine selected tasks across three categories.Crucially, we introduce methods to automate the discovery of optimal masking policies via direct supervision or meta-learning.We conclude that the success of intermediate pre-training is dependent on appropriate pre-train corpus, selection of output format (i.e., masked spans or full sentence), and clear understanding of the role that MLM plays for the downstream task.In addition, we find our learned masking policies outperform the heuristic of masking named entities on TriviaQA, and policies learned from one task can positively transfer to other tasks in certain cases, inviting future research in this direction.
Blockchain and Data Protection: A Compatible Couple?

In recent years, blockchain, the underlying technology behind cryptocurrencies, such as Bitcoin, has emerged as one of the technological innovations with the greatest potential to transform the economy and society. As various European institutions and data protection authorities point out, this type of technologies may, by its very nature, be unable to comply with the General Data Protection Regulation (GDPR). We believe that, in order to understand the risks that the use of blockchain technology could pose, it is necessary to examine the architecture and the specific characteristics of the technology in question, in particular the way in which personal data are stored or processed. Thus, the impact of blockchain on the privacy and personal data of data subjects generally requires analysis on a case-by-case basis. We conclude that, while there are some tensions to be resolved, blockchain technology is not per se incompatible with the GDPR. blockchain, DLT, data protection, privacy, GDPR, cryptocurrency
Community-Driven Collaborative Recommendation System

Recommendation systems (RSs) are an application of community detection, becoming more significant in our daily lives. They play a significant role in suggesting information to users such as products, services, friends and so on. A novel community driven collaborative recommendation system (CDCRS) has been proposed by the authors, in this particular paper. Furthermore, K means approach has been utilized to detect communities and extract the relationship among the users. The singular value decomposition method (SVD) is also applied. Issues of sparsity and scalability of the collaborative method are considered. Experiments were conducted on MovieLens datasets. Movie ratings were predicted and top-k recommendations for the user produced. The comparative study that was performed between the proposed as well as the collaborative filtering method dependent on SVD (CFSVD) as well as the results of experiments shows that CFSVD is outperformed by the proposed CDCRS method.
Multilingual Lexicon based Approach for Real-Time Sentiment Analysis

The information on WWW has mounted to a greater height, overriding to fledgling analysis in the direction of sentiments using Artificial Intelligence. Sentiment Analysis deals with the calculus exploration of sentiments, opinions and subjectivity. In this paper, multilingual tweets are analyzed for identifying the polarities of various political parties like AAP, BJP, Samajwadi, BSP and Congress; so that the users will get an idea that to which party they should give their vote. The data is being analyzed using Natural Language Processing. Using different smoothening techniques, noise is removed from data, classified by using Machine learning algorithms and then the accuracy of the system is gauged using various evaluation precision measures. The central premise of this research is to benignant common people and politicians both. For common people; is for deciding their precious vote, to which party to give will be good for themselves and nation too. For politicians; they will have an idea about themselves i.e. after seeking the polarities of different parties, the politicians will have an idea which party is preferable and which is not preferable, so that the politicians can work accordingly. The system shows comparison among VADER and SVM algorithm; and SVM algorithm showed 90% accuracy.
Sentiment Analysis

Sentiment Analysis (SA) is an ongoing field of research in text mining field.SA (sentiment analysis) is the computational treatment of opinions, sentiments and text.This s paper deals in a comprehensive overview of the recent updates in this field.Many recently proposed algorithms amend and various SA applications are investigated and presented briefly in this paper.The related fields to SA (transfer learning, emotion detection, and building resources) that attracted researchers recently are discussed.The main objective of this paper is to give nearly full image of SA techniques and the related fields with brief details.The main contributions in this paper include the sophisticated categorizations of a large number of recent articles and the illustration of the recent trend of research in the sentiment analysis and its related areas.
Image Retrieval System using Residual Neural Network in a Distributed Environment

Development of Content-Based Image Retrieval systems supports retrieval of similar images based on selected features. Selection of appropriate features for this process is a difficult task. In this regard, deep learning concept helps in choosing appropriate features for retrieval. In this work, Content-Based Image Retrieval system is proposed using Convolution Neural Network known as Residual Neural Network model. The dataset used to build retrieval system is collection of web images 50,000 of 250 categories. The model is trained on 40% of image data and tested on 60% of data. When user submits a query image from the client-side, similar features are extracted by the model on server-side. Later, the features of query image are compared with trained images data and similarity is measured using the metric of Euclidean distance. The retrieved resultant images are displayed on Graphical User Interface. The results are comparatively higher with the existing systems. The proposed work is also compared with Google’s Image retrieval system for random query images and our proposed work has shown a better performance by 14.27%.
Reconstruction of Volumes from Soup of Faces with a Formal Topological Approach

The process of motorcycle seat styling is a grey system with partially known and partially unknown information and is influenced by various factors.In this study, Grey Modelling (GM)(1,1) is used to predict the style of a motorcycle seat, and the shape features of the seat are extracted via morphological analysis and are parameterized.The process of shape evolution is established, and the modelling characteristics are predicted by GM(1,1).The kansei study is performed using five adjectives describing the seat styles to establish the equation of kansei regression analysis.The regression analysis is employed to modify predictive modelling.A certain brand of motorcycle seats is modelled to analyse and verify the feasibility and scientific applicability of adopting GM(1,1) in predicting motorcycle seat styling, which provided a feasible and effective reference for the motorcycle seat design.
Faster R-CNN Network Based on Multi Feature Fusion for Efficient Face Detection

The putting into effect of deep learning gave all attention on deep convolutionary neural networks has gained great condition of having general approval in face discovery in near in time years. One of the still in the same way open tasks however is to make out small-scale points. The distance down of the convolutionary network can cause a quick becoming-smaller of the sent out point map for small faces, and most scale unchanging views can hardly grip less than 15×15 bit of picture faces. There are few types of Haar-like rectangle points, which send in to the hard question that the training time for the classifier is too long because of, in relation to the greatly sized number of point amounts needed to put in order the small faces. In order to get answer to this hard question, we offer the MB-LBP (Multi-scale Local based on good example) features and joined rotation-invariant LBP (nearby based on good example) features based on the quicker R-CNN classifier called convolutional neural network (put) in middle in the gave greater value to quicker field, range (EFR-CNN). Despite the shortcomings of MB-LBP point and rotation-invariant LBP point on face edge knowledge, the canny operator-based edge azimuth field purpose, use is grouped together with the above features to make statement of the sense of words of the face news given. In place, based on the grouped together point group, a given to overmuch pleasure R-CNN network of parallel form is suggested. The proposals are put on one side to three being like (in some way) given to overmuch pleasure R-CNN networks according to the different rates on a hundred that they cover on the pictures. The three networks are separated by the measures of the map and (make, become, be) different in the weight of the concatenation 8 of purpose, use maps from one another. The offered EFR-CNN system gets done giving undertaking operation on common points of comparison, including FDDB, AFW, PASCAL faces, And wider face, made a comparison of with state-of – the-art face discovery methods such as Unit Box, hyperface Fastcnn.
Binary Tree

In the previous chapter, you have learned about the basic tree structure where each node can have unlimited children. Binary trees are tree data structures in which each node has at most two children, often referred to as the left and right children. In this chapter, you will learn the main properties of binary data structure and how to implement it.
Use of Periocular Biometric in Face Recognition

Biometrics is a technology to identify people based on their physical and behavioral traits. Enhancement in the field of biometrics have led to development of a trait that is non intrusive, widely acceptable and can be captured remotely with effective features. Face biometric is widely used trait all over world. Due to less efficiency of face biometric in non cooperative environment, researchers suggest to use a sub region of face that is more effective. Periocular biometric is region surrounding the eyes having modalities including eyebrows, eyelashes, eyelids, tear duct and skin texture. This paper explores use of periocular region and its modalities in face recognition including periocular databases and its use in future for various applications.
Subatomic Many-Body Physics Simulations on a Quantum Frequency Processor

We demonstrate a photonic quantum simulation on a quantum frequency processor, computing the effective interaction potential between composite particles in the Schwinger model and determining the binding energies of three nuclei (3H, 3He, and 4He).
Evolutionary computation for digital art

tutorial Share on Evolutionary computation for digital art Authors: Aneta Neumann The University of Adelaide, Australia The University of Adelaide, AustraliaView Profile , Frank Neumann The University of Adelaide, Australia The University of Adelaide, AustraliaView Profile Authors Info & Claims GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2019 Pages 1056–1076https://doi.org/10.1145/3319619.3323375Published:13 July 2019Publication History 1citation92DownloadsMetricsTotal Citations1Total Downloads92Last 12 Months8Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Real Time Meteorological Solar Data Based Modified Solar Aware LEACH

In hostile and non-accessible remote area, energy conservation plays vital role in the performance of Wireless Sensor Network (WSN). Shorter life of battery operated sensors tends to lower lifespan of the WSN which further degrades the dense network performance. In this paper, we propose the modified solar aware LEACH for efficient routing in WSN to maximize the network lifespan. This proposed scheme uses real time solar meteorological data for the implementation of solar aware LEACH (sLEACH), advance solar aware LEACH (A-sLEACH), improved solar aware LEACH (IS-LEACH).The proposed algorithm is simulated using MATLAB and performance is evaluated on the basis of data throughput, energy consumption and network lifetime which show improved performance than existing techniques.
Live forensics method for acquisition on the Solid State Drive (SSD) NVMe TRIM function

SSD currently has a new storage media technology namely Solid State Drive Non-volatile Memory Express (SSD NVMe). In addition, SSD has a feature called TRIM. The TRIM feature allows the operating system to tell SSDs which blocks are not used. TRIM removes blocks that have been marked for removal by the operating system. However, the TRIM function has a negative effect for the digital forensics specifically related to data recovery. This study aimed to compare the TRIM disable and enable functions to determine the ability of forensics tools and recovery tools to restore digital evidence on the NVMe SSD TRIM function. The operating system used in this study was Windows 10 professional with NTFS file system. Typically, acquisition is conducted by using traditional or static techniques. Therefore, there was a need of a technique to acquire SSD by using the live forensics method without shutting down the running operating system. The live forensics method was applied to acquire SSD NVMe directly to the TRIM disable and enable functions. The tools used for live acquisition and recovery were FTK Imager Portable. The inspection and analysis phases used Sleutkit Autopsy and Belkasoft Evidence Center. This research found that in the recovery process of TRIM disabled and enabled, TRIM disabled could find evidence while maintaining the integrity of evidence. It was indicated by the same hash value of the original file and the recovery file. Conversely, when TRIM is enabled, the files were damaged and could not be recovered. The files were also not identical to the original so the integrity of evidence was not guaranteed.
The Audit Investigation and Accounting Forensicin Detecting Fraud in Digital Environment

The Audit Investigation and Accounting Forensicin Detecting Fraud in Digital Environment Prastika Suwandi Tjeng, Rina Nopianti Abstract A forensic accountant helps organizations or individuals primarily to provide management support in the form of reports to detect fraud and support litigation, primarily through expert witness testimony. The objectives to be achieved in this study are to explore how investigative and forensic accounting audits are applied in detecting fraud in the digital environment and to find out why investigative audits and forensic accounting in detecting fraud in the digital environment need to be applied. Researchers conducted a library study by collecting several economic journals and books relating to the problem under study. The data collection method in this research is done by interpreting and analyzing qualitative data. The results of this study indicate that forensic accounting in detecting fraud in the digital environment can be done by computer forensic and investigations that must be done by making copies of the entire log data, making fingerprints from numerical data, making fingerprints from copies, making master list hashes and documenting data that has been done. Full Text: PDF DOI: 10.15640/ijat.v8n1a6
Design and Development of Prosthetic Hand Controlled by Wireless Gestures for Differently Able People

This paper focuses on the design and development of Prosthetic hand to help differently-able people who lost their hands due to accidents and diseases. Our research purpose is to develop a master and slave robotic system that will be a substitute for the lost hand to do the day-to-day activities of a person. The person has to wear smart gloves in the hand to do gesture action. The gloves will able to transfer the hand gestures of differently-able people to react suitably and move the hand gripper (which contains spring coils similar to bones in human hand) based on the data from smart gloves. The methodology behind this research is that the analog signals produced in the flex sensor due to the gesture action are transferred to the servo motors to do a similar action in the 3D printed prosthetic hand through the Wi-Fi module. This research project involves two Arduino microcontrollers for communicating and controlling applications in both master and slave sections. A number of flex sensors are placed in the glove to get readings of the motion of human fingers and it is transmitted through the Wi-Fi module by using the Arduino microcontroller. The transmitted signals are received by the Wi-Fi module in the slave section through the Arduino microcontroller and further uses this signal to control various servo motors and it controls the slave robotic hand by using the ropes attached between the servo motor and 3D printed parts. Not only for differently-able people, but the enlarged model of this project can also be used in industries to handle hazardous, harmful, high temperatures and harmful things.
Adaptive Particle Swarm Optimization based Credentialed Extreme Learning Machine Classifier (APSO-CELMC) for High Dimensional Datasets

Data mining is a key research field in the computer science research arena. Feature selection is performed once the dataset got cleansed. Optimization algorithms are considered to be helpful for the feature selection task. Also the obtained suitable features will contribute considerably for the classifier. Machine learning classifiers are comparatively performing better than that of traditional data mining classification algorithms. In this part of research work an adaptive particle swarm optimization algorithm is employed in order to perform feature selection task. Extreme learning machine classifier is added with credential weights. Twenty datasets are taken for performance analysis. From the obtained results it is evident that Adaptive Particle Swarm Optimization based Credentialed Extreme Learning Machine Classifier (APSO-CELMC) performs better in terms of predictive accuracy and time taken for classification.
KM-MBFO: A Hybrid Hadoop Map Reduce Access for Clustering Big Data by Adopting Modified Bacterial Foraging Optimization Algorithm

K-Means Clustering is a very powerful and frequently used algorithm for the clustering, it has got its own limitation. The prevalent K-Means clustering algorithm used for grouping have inadequacies, for example, slow convergence rate, local optima trap, and so on. Therefore, many swarm knowledge based procedures combined with KM for clustering were presented and demonstrated their presentation, its variations and its applications in data grouping. In this paper we intend to propose a parallel organizing strategy for KM-MBFO mechanism that actualized in Hadoop Distributed File System (HDFS) for diminishing the execution time. This Mapper approach produces the populace for given data set for grouping. The Modified Bacterial Foraging Optimization (MBFO) algorithm finds the wellness of the populace to choose the optimal K values as far as execution time and classification error. Through simulated test results, we assess the demonstration of the proposed KM-BFO conspire
The Winograd Schemas from Hell

The Winograd Challenge has been advocated as a test of computer understanding with respect to commonsense reasoning. The challenge is based on Winograd Schemas: sentences that contain correferential ambiguities. Most Winograd Schemas are relatively easy for human subjects, and today the best computer systems for the Winograd Challenge can work close to human performance. In this paper, we examine the assumptions behind the Winograd Challenge, and investigate how far we can push the difficulty level of Winograd Schemas, proposing various strategies to build really challenging schemas.
Improved quantum secret sharing scheme based on GHZ states

With the rapid progress of quantum cryptography, secret sharing has been developed in the quantum setting for achieving a high level of security, which is known as quantum secret sharing (QSS). The first QSS scheme was proposed by Hillery et al. in 1999 [Phys. Rev. A, Vol. 59, p.1829 (1999)] based on entangled Greenberger-Horne-Zeilinger (GHZ) states. However, only 50% of the entangled quantum states are effective for eavesdropping detection and secret splitting in the original scheme. In this paper, we introduce a possible method, called measurement-delay strategy, to improve the qubit efficiency of the GHZ-based QSS scheme. By using this method, the qubit efficiency of the improved QSS scheme can reach 100% for both security detection and secret distribution. The improved QSS scheme can be implemented experimentally based on current technologies.
A Circumscribed Research of Load Balancing Techniques in Cloud Computing

Cloud Computing (CC) is a very big field in the computer industry. It has systems that are connected through communication networks such as Internet. The LBA is an important cloud computing area to prevent overloaded workload and provide the same and valuable service. The various algorithms are used to solve the load balancing complexity. In this paper, we explain various standard load balancing techniques in different algorithms and challenges in assigning tasks to Cloud Computing. The load balancing algorithm can be used to better utilize and better understand user needs
Communication-Cognition ‘Butterfly’ (Geometrical Model of Verbal Communication in Case of Ambiguity)

The present paper deals with the traditional semantic triangle from the perspective of successful / unsuccessful communication in case of linguistic ambiguity and examines the limitations of the triangle in description of communication-cognition process.It is maintained that the semantic triangle merely explains the relationship between sign, concept and referent and describes how the received linguistic signs are decoded but fails to demonstrate the process of communication which is the main function of human language.An attempt is made to model and demonstrate communication-cognition process in case of linguistic ambiguity geometrically.Thus, instead of the semantic triangle, the author suggests a new geometrical shape dubbed "communication-cognition "butterfly" which includes both the source and recipient of linguistic signs and is tested by means of English linguistic humor based on ambiguity.