Reviewerly/exHarmony huggingface dataset:
Fine-Tuning for Cross-Domain Aspect-Based Sentiment Classification

Aspect-Based Sentiment Classification (ABSC) is a subfield of sentiment analysis concerned with classifying sentiment attributed to pre-identified aspects. A problem in ABSC nowadays is the limited availability of labeled data for certain domains. This study aims to improve sentiment classification accuracy for these domains where labeled data is scarce. Our proposed approach is to apply cross-domain fine-tuning to a state-of-the-art deep learning method designed for ABSC: LCR-Rot-hop++. For this purpose, we initially train the model on a domain that has a lot of labeled data available and consecutively fine-tune the upper layers with training data of the target domain. The performance of the fine-tuning method is evaluated relative to a model that is trained from scratch for each target domain. For the initial training, restaurant review data is used. For the fine-tuning and from-scratch training we use review data for laptops, books, hotels, and electronics. Our results show that when comparing the fine-tuning with the from-scratch method (for the same training set), the fine-tuning method on average outperforms the from-scratch method when the training set is small for all considered domains and is considerably faster.
UTILIZATION OF E-RESOURCES OF GUJARAT UNIVERSITY LIBRARY THROUGH MYLOFT APPLICATION DURING COVID-19 LOCKDOWN: AN ANALYTICAL STUDY

Gujarat University Library is the largest and oldest library in Gujarat state. The library was established at the university's inception and migrated to the new building in 1967. The library has a vast collection in the form of print and digital resources. In the current paper, the researcher finds the usage of the Gujarat University Library. The paper here and now showcases a comparative study of the usage of electronic contents by their patrons during the lockdown period to the new normal situation period using the MyLOFT application. This paper collects information from both the registered users in MyLOFT application whoever has logged in and whoever has not, also included and the data usage of various platforms. The paper analyses the user usage of various publishers subscribed by Gujarat University through MyLOFT application and content-based comparison of electronic databases have been evaluated in the paper for better understanding of tools. This paper also does a comparative study of various department-wise user usage of the E-resources.
cuPSO: GPU Parallelization for Particle Swarm Optimization Algorithms

Particle Swarm Optimization (PSO) is a stochastic technique for solving the optimization problem. Attempts have been made to shorten the computation times of PSO based algorithms with massive threads on GPUs (graphic processing units), where thread groups are formed to calculate the information of particles and the computed outputs for the particles are aggregated and analyzed to find the best solution. In particular, the reduction-based method is considered as a common approach to handle the data aggregation and analysis for the calculated particle information. Nevertheless, based on our analysis, the reduction-based method would suffer from excessive memory accesses and thread synchronization overheads. In this paper, we propose a novel algorithm to alleviate the above overheads with the atomic functions. The threads within a thread group update the calculated results atomically to the intra-group data queue conditionally, which prevents the frequent accesses to the memory as done by the parallel reduction operations. Furthermore, we develop an enhanced version of the algorithm to alleviate the synchronization barrier among the thread groups, which is achieved by allowing the thread groups to run asynchronously and updating to the global, lock-protected variables occasionally if necessary. Our experimental results show that our proposed algorithm running on the Nvidia GPU is about 200 times faster than the serial version executed by the Intel Xeon CPU. Moreover, the novel algorithm outperforms the state-of-the-art method (the parallel reduction approach) by a factor of 2.2.
A Non-parametric Bayesian Learning Model Using Accelerated Variational Inference on Multivariate Beta Mixture Models for Medical Applications

Clustering as an exploratory technique has been a promising approach for performing data analysis. In this paper, we propose a non-parametric Bayesian inference to address clustering problem. This approach is based on infinite multivariate Beta mixture models constructed through the framework of Dirichlet process. We apply an accelerated variational method to learn the model. The motivation behind proposing this technique is that Dirichlet process mixture models are capable to fit the data where the number of components is unknown. For large-scale data, this approach is computationally expensive. We overcome this problem with the help of accelerated Dirichlet process mixture models. Moreover, the truncation is managed using kd-trees. The performance of the model is validated on real medical applications and compared to three other similar alternatives. The results show the outperformance of our proposed framework.
An Informational Analysis of Echoic Responses in Dialogue

Echoic responses abound in dialogues, where a speaker reuses a portion of the text uttered by another in a preceding turn, though semantically they contribute little if any new information. The phenomenon has attracted the attention of researchers from diverse academic fields, ranging from sociolinguistics and developmental psychology, to computational linguistics and human-computer interfaces. This study reports an empirical investigation on echoic responses from an informational perspective. Drawing on statistical analyses of instances extracted from corpora of spoken dialogues in Japanese, we show that echoic responses with different timings, lengths, intonations, pitches, and speeds signal different degrees in which the speakers have integrated the repeated information into their prior knowledge. We further consider dialogue-coordination functions enacted by this informational potential of echoic responses, and identify the function of display as distinguished from the functions of acknowledgment and repair-initiation.
Data breaches reach record levels worldwide

There were 12,449 data breaches that compromised user identities in 2018 – a 424% increase compared to the previous year, according to figures just released by identity intelligence firm 4iQ. The US and China between them accounted for nearly half (47%) of these breaches.
The River’s Lizard Tail: Braiding Indigenous Knowledges with Geomorphology

Indigenous Knowledges can be accurate, rigorous, and precise, say researchers in New Zealand, and they can help geomorphologists see landscapes in a new, richer way.
Efficient String Matching Algorithm for Searching Large DNA and Binary Texts

The exact string matching is essential in application areas such as Bioinformatics and Intrusion Detection Systems. Speeding-up the string matching algorithm will therefore result in accelerating the searching process in DNA and binary data. Previously, there are two types of fast algorithms exist, bit-parallel based algorithms and hashing algorithms. The bit-parallel based are efficient when dealing with patterns of short lengths, less than 64, but slow on long patterns. On the other hand, hashing algorithms have optimal sublinear average case on large alphabets and long patterns, but the efficiency not so good on small alphabet such as DNA and binary texts. In this paper, the authors present hybrid algorithm to overcome the shortcomings of those previous algorithms. The proposed algorithm is based on q-gram hashing with guaranteeing the maximal shift in advance. Experimental results on random and complete human genome confirm that the proposed algorithm is efficient on various pattern lengths and small alphabet.
A study of resource allocation techniques in cloud computing

The process of allocation and reallocation of resources is the key to accommodating unpredictable demands and improving the return on investment from the infrastructure supporting the cloud. A cloud environment consists of multiple customers requesting for resources in a dynamic environment with possible constraints. In cloud computing allocation of resources for every client with high gain and the profit is the major task for the cloud administrator. Cloud administrator has the problem for allocating the same resources with many users. So while allocating the resources prioritisation of tasks is very important. The literature concerns seven scheduling algorithms and six allocation techniques as well as the parameter measures for effectiveness of scheduling. Methodical analysis of this research helps the researchers to find the importance of a scheduling algorithm for particular allocation technique for increasing profit and return on investment in cloud.
Performance Optimization by Task Scheduling in Cloud Computing

Cloud computing is associate on demand access to a shared pool of resources. Vendor’s of cloud computing offer application and change technology, infrastructure, hardware, software, and integration for consumer. The most aspect of cloud computing is accessibility and performance of their network association. It refers to the method of allocating users’ tasks to virtual machines (VMs) with a goal of minimizing the work time and rising the resource utilization. Tasks programming is taken into account NP onerous drawback with O (m, n) run time complexness to schedule n tasks on m resources. A computer hardware adapts its programming strategy consistent with the ever-changing setting and therefore the variety of task. We provide comparison with Max–min scheduling formula and Genetic formula. Our hybrid algorithm provides higher performance compared to different programming algorithm.
Literature Review

This chapter first categorizes the existing cloud security auditing, then elaborates each category mainly based on its coverage and adopted verification techniques, and finally presents a taxonomy based on these works. There exist mainly three categories of cloud security auditing approaches. In the following, we discuss each of these approaches with corresponding example works.
Commentary on the Emerging Energy Blockchain Technology

This article promotes the emerging technology application of the energy blockchain (EBC) innovation and the published energy source (PES). The prosumer, for the most part, expends energy of its own and utilizes an incorporated energy framework that occasionally produces energy for other customers. A complimentary multi-energy sources can improve various quality and increase the merit of the application of renewable energies. For example, an underlining case for the ocean thermal energy conversion (OTEC) is investigated. Firstly, the restriction on a traditional OTEC is ordinarily at about 3% that can be overcome with an improved efficiency which is at 6% to 12% as shown in this study. Second, the contextual investigation should shed light on the demonstration and system produced for EBC. Third, a precise investigation uses the EBC input parameters as follows: OTEC multi-energy complementarity, energy storage/ power parameter, and the smart grid power. A complimentary multi-energy sources can improve various quality, increase the merit of the application of renewable energies. The author constructs a mathematical model to study the maximal available benefit for a prosumer. This model connects the electric load to the external grid power through a gate-control that has a smart meter inside the prosumer’s EBC framework. All the above parameters usually depend on time variables. Finally, this study is to present EBC properties in the view of a PES value dependency law. Output and its optimization is to resolve the dependency law upon input parameters referenced at above. The optimum solution increases the benefit for the prosumer.
Improved CURE Clustering Algorithm using Shared Nearest Neighbour Technique

Clustering is the unsupervised learning based grouping of data points based on the similarity between them. Traditional clustering algorithms work well with datasets which have globular/spherical shape. When it comes to non-spherical shaped clusters, they may divide large clusters into small clusters or merge two clusters.CURE(Clustering using REpresentatives) clustering algorithm overcomes the limitations of traditional clustering algorithms for clustering non-globular shaped clusters. CURE algorithm chooses random points as representative points from each cluster and shrinks them towards the centroid of the clusters. The problem arises when data sets do not have a centroid tendency. To resolve this issue, we propose an improved CURE algorithm, where instead of shrinking of representative points, the shared neighbours between the points have been used to form clusters. The representative points which share the same neighbourhood are put together in the same clusters. This allows generating clusters of non-globular shaped clusters. Our neighbourhood based clustering does not get affected by the shape of the clusters. Experimental results of our work demonstrate that CURE clustering using shared nearest neighbours has better performance than CURE
Influential factors of working adults' perceptions of mobile-assisted vocabulary learning with multimedia annotations

With the development of technology-enhanced language learning, there have been increasingly more studies on mobile-assisted vocabulary learning with multimedia annotations. However, little research has been conducted to explore working adults' use of multimedia annotations in authentic mobile learning environments, investigate their perceptions of diverse types of multimedia annotations, or discuss the factors that may influence their perceptions of multimedia annotations. To fill in these research gaps, the researchers of this project developed a multimedia-annotation-enhanced vocabulary learning app, iWORDS, and invited 24 working adults in Hong Kong to learn vocabulary using this app. After experiencing mobile-assisted vocabulary learning with four types of multimedia annotations (i.e., the textual, the pictorial, the text-plus-GIF and the text-plus-video annotations), the participants were interviewed about their perceptions and preferences. The results indicated that working adults used textual annotations most frequently but showed strongest preference for text-plus-GIF annotations. Four factors, content quality, attractiveness, efficiency and cognitive stimuli, played important roles in influencing working adults' preferences and perceptions of multimedia annotations. Based on the research results, we proposed some suggestions for the development, selection and usage of multimedia annotations in mobile-assisted vocabulary learning.
Using Classification Techniques to SMS Spam Filter

SMS is service that uses mobile phone that allows the users to exchange textual content. Spamming can be defined as sending unwanted content to a group of people for various purposes such as fraud. SMS spam is one form of spamming in which unwanted messages are delivered to many clients by spammers. Therefore, it has become necessary to develop SMS spam detection system to keep up with the current development of message services. Where the aim of this work is developing spam filter for Arabic and English languages by using two filter to be able to detect spam sms efficiently. Content based method was used to build spam filter for English and Arabic languages. based on this method, there are a number of steps should be taken which are Read English and Arabic dataset, Preprocessing phase, Feature Extraction and Classification. The first step after reading the dataset for Arabic and English languages is preprocessing phase which is important step to get more accurate results. The next step is extracting the features from the body of each message. Eight features have been extracted from English messages and six features from Arabic messages. Then features of messages for English and Arabic languages are splitted into two set: training set and testing set. Training set are used to train the algorithms while the test set are used evaluate the performance of proposed Spam filter for the English and Arabic language. In proposed system two classifiers are used. Naive Bayes is used as first classifier and neural network as second classifier. The incoming messages are passed through naive Bayes classifier. If it is classified as ham then passes to second classifier to make sure if it is spam, otherwise it doesn’t passes to second classifier. The results of the proposed system were acceptable with 97% accuracy is obtained for English language when using eight features and 80% from dataset for training .And 95% accuracy is obtained for Arabic language with six features and 70% from dataset for training.
Students' expectations from mobile devices for mobile learning

Mobile devices are becoming ever more widely available, with growing functionality, giving students access to educational material anytime and anywhere. Thus, mobile devices have become more attractive for education. However, owing to the nature of mobile devices, such as small screen size, it poses number of challenges to create a usable learning environment. Therefore, it becomes noteworthy to explore usability of these devices and the students' expectations. The present empirical study regarding the use of personal digital assistants (PDAs) aimed to explore expectations of students from mobile devices in order to support mobile learning. Data was collected from 30 university students through pre and post interviews while students were conducting a set of e-learning actions, such as uploading files, communicating and acquiring course contents, on a mobile device. With the analyses of interviews using constant comparative approach, expectations of the students were classified into 11 categories which are related to interaction and usability.
Distributed data mining in grid computing environment

With the rapid development of computer technology, the data generated in the scientific research, industrial and commercial fields is increasing at an alarming rate. Traditional data mining techniques are limited to mining a single data source. How to mine distributed data sources and how to perform parallel mining is one of the hot topics in the field of data mining. The purpose of this article is to study distributed data mining in a grid computing environment. This paper studies the existing grid technology and data mining technology, and discusses the possibility of combining the two. Then based on this, a grid-based distributed data mining service framework is proposed, and the service framework is developed detailed design. This paper tests the framework, the experimental results show that applying the grid framework to distributed mining can improve the computing performance and data size. In this paper, the calculation speedup of the framework under 1 to 8 nodes is tested, and the speedup ratios are 1, 2, 3, 4, 5, 6, 7, and 8 respectively. It can be seen that the performance of the framework is directly proportional to the size of the calculation.
Visualization in multiobjective optimization

tutorial Visualization in multiobjective optimization Share on Authors: Bogdan Filipič Jožef Stefan Institute, Ljubljana, Slovenia Jožef Stefan Institute, Ljubljana, SloveniaView Profile , Tea Tušar Jožef Stefan Institute, Ljubljana, Slovenia Jožef Stefan Institute, Ljubljana, SloveniaView Profile Authors Info & Claims GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference CompanionJuly 2020 Pages 775–800https://doi.org/10.1145/3377929.3389867Online:08 July 2020Publication History 1citation69DownloadsMetricsTotal Citations1Total Downloads69Last 12 Months25Last 6 weeks1 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
Online Booking System for Marine Tourism in Pulau Perhentian, Terengganu, Malaysia

Online booking system is a web-based system that is designed for reservation management. The booking system is accepted by allowing potential customers to make bookings and payment for an activity or service through the website. This service is managed online, which significantly reduces staff’s burden and eliminating any occurrence of double bookings. This paper presents the design of an online booking of room and ferry tickets for the marine tourism industry in Terengganu. This system provides facilities to customers to make the room and ferry ticket reservation online and print out their tickets themselves. Nowadays, companies are still using the manual system where the booking is done on paper and recorded using a manual filing system. The new system is needed to substitute the manual booking system to increase more tourists, both local and foreign, to travel in Malaysia. The Software Development Life Cycle (SDCL) and Waterfall Model are used in the software development processes which consist of 6 phases. The development of this system includes features such as user-friendly, data management, payment process and inventory management.
Data Preprocessing

Before applying a neural network model, the data must be preprocessed in advance. Data normalization must be made to avoid the difference of data variability for inputs and outputs. It also simplifies the parameter range of a network model. Furthermore, data should be split for different purposes such as training, validation and testing. In this chapter, these data normalization and data split are explained in detail.
An Optimized Associative Classifier for Incremental Data Based On Non-Trivial Data Insertion

Associative classification (AC) is an interesting approach in the domain of data mining which makes use of the association rules for building a classification system, which are easy for interpretation by the end user. The previous work [1] showed excellent performance in a static large data base but there existed a question of same performance when applied in an incremental data. Many of the Associative Classification methods have left the problem of data insertion and optimization unattended that results in serious performance degradation. To resolve this issue, we used new technique C-NTDI for building a classifier when there is an insertion of data that take place in a non-trivial fashion in the initial data that are used for updating the classification rules and thereafter to apply the PPCE technique for the generating of rules and further Proportion of Frequency occurrence count with BAT Algorithm (PFOCBA) is applied for optimizing the rules that are generated. The experiments were conducted on 6 different incremental data sets and we found that the proposed technique outperforms other methods such as ACIM, E-ACIM, Fast Update (FUP), Galois Lattice theory (GLT) and New Fast Update (NFUP) in terms of accuracy and time complexity.