Reviewerly/exHarmony huggingface dataset:
Aspect-Based Sentiment Analysis for Sentence Types with Implicit Aspect and Explicit Opinion in Restaurant Review Using Grammatical Rules, Hybrid Approach, and SentiCircle

Sentiment analysis can provide rough recommendations in the form of sentiment from a collection of reviews or can provide recommendations in more detail about sentiment in a particular aspect called aspect-based sentiment analysis (ABSA).Sentiment analysis based on many aspects has been carried out but its accuracy is still being developed.In previous research, most research was carried out on explicit and implicit aspects and opinions and was carried out in simple sentences.The purpose of this research is to analyze the sentiment of restaurant reviews using the rule grammar method to extract implicit aspects -explicit opinions in four sentence models, namely simple (Si-AIOE), compound (Co-AIOE), complex (Ce-AIOE), and compound-complex (CoCe-AIOE).The ABSA method is proposed using the development of a grammatical rule extraction method to extract explicit and implicit aspect words and opinion words as the basis for sentence extraction.Rules making is done to take explicit and implicit aspect words and opinion words in Si-AIOE, Co-AIOE, Ce-AIOE, and CoCe-AIOE so that the comparison of the evaluation values can be known.This research uses the Semeval 2015 dataset on Restaurant reviews from the Tripadvisor Website which has been annotated as sentence data for ABSA.The aspect categorization process is then used to categorize aspects into 4 aspect categories, namely Ambience, Food, Service, and Price using hybrid approach.The hybrid approach is combined using Elmo-Wikipedia, grammatical rule extraction, WordNet, TF-ICF, and semantic similarity methods.The results of the aspect extraction obtained value of precision, recall, and f1-measure of 0.80, 0.84, and 0.82, respectively.Meanwhile, the ABSA process uses SentiCircle to classify sentiments into two, namely positive and negative.The results of the ABSA showed that the performance of proposed method achieve for precision, recall, and f1-measure were 0.84, 0.89, and 0.87, respectively.
On the confusion coefficient of Boolean functions

Abstract The notion of the confusion coefficient is a property that attempts to characterize confusion property of cryptographic algorithms against differential power analysis. In this article, we establish a relationship between the confusion coefficient and the autocorrelation function for any Boolean function and give a tight upper bound and a tight lower bound on the confusion coefficient for any (balanced) Boolean function. We also deduce some deep relationships between the sum-of-squares of the confusion coefficient and other cryptographic indicators (the sum-of-squares indicator, hamming weight, algebraic immunity and correlation immunity), respectively. Moreover, we obtain some trade-offs among the sum-of-squares of the confusion coefficient, the signal-to-noise ratio and the redefined transparency order for a Boolean function.
Rapid Cybersecurity Assessment System for Small Business' COVID Move to Online

With employment fast becoming a key aspect of post-COVID recovery, the protection of small businesses is paramount.This will, in addition, protect the privacy and identity of the small business' customers, preventing serious personal, financial and emotional impacts [3].
A graph-based feature selection method for learning to rank using spectral clustering for redundancy minimization and biased PageRank for relevance analysis

This paper addresses the feature selection problem in learning to rank (LTR). We propose a graph-based feature selection method, named FS-SCPR, which comprises four steps: (i) use ranking information to assess the similarity between features and construct an undirected feature similarity graph; (ii) apply spectral clustering to cluster features using eigenvectors of matrices extracted from the graph; (iii) utilize biased PageRank to assign a relevance score with respect to the ranking problem to each feature by incorporating each feature?s ranking performance as preference to bias the PageRank computation; and (iv) apply optimization to select the feature from each cluster with both the highest relevance score and most information of the features in the cluster. We also develop a new LTR for information retrieval (IR) approach that first exploits FS-SCPR as a preprocessor to determine discriminative and useful features and then employs Ranking SVM to derive a ranking model with the selected features. An evaluation, conducted using the LETOR benchmark datasets, demonstrated the competitive performance of our approach compared to representative feature selection methods and state-of-the-art LTR methods.
Unbiased IoU for Spherical Image Object Detection

As one of the fundamental components of object detection, intersection-over-union (IoU) calculations between two bounding boxes play an important role in samples selection, NMS operation and evaluation of object detection algorithms. This procedure is well-defined and solved for planar images, while it is challenging for spherical ones. Some existing methods utilize planar bounding boxes to represent spherical objects. However, they are biased due to the distortions of spherical objects. Others use spherical rectangles as unbiased representations, but they adopt excessive approximate algorithms when computing the IoU. In this paper, we propose an unbiased IoU as a novel evaluation criterion for spherical image object detection, which is based on the unbiased representations and utilize unbiased analytical method for IoU calculation. This is the first time that the absolutely accurate IoU calculation is applied to the evaluation criterion, thus object detection algorithms can be correctly evaluated for spherical images. With the unbiased representation and calculation, we also present Spherical CenterNet, an anchor free object detection algorithm for spherical images. The experiments show that our unbiased IoU gives accurate results and the proposed Spherical CenterNet achieves better performance on one real-world and two synthetic spherical object detection datasets than existing methods.
A Multi-Objective Secure Optimal VM Placement in Energy-Efficient Server of Cloud Computing

Cloud Computing has been economically famous for sharing the resources of third-party applications. There may be an increase in the exploitation of the prevailing Cloud resources and their vulnerabilities as a result of the aggressive growth of Cloud Computing. In the Cache Side Channel Attack (CSCA), the attackers can leak sensitive information of a Virtual Machine (VM) which is co-located in a physical machine due to inadequate logical isolation. The Cloud Service Provider (CSP) has to modify either at the hardware level to isolate their VM or at the software, level to isolate their applications. The hardware isolation requires changes in hardware design and it also leads to performance degradation. The existing works challenge to eliminate CSCA. But our solution prevents CSCA by securing the VM placement in the energy-efficient server. This paper proposes a heuristic secure VM placement policy by using different VM placement techniques (Least VM, Multi-Objective Ant Colony System (MOACO), Previously Assigned Physical Host First (PAPHF)) based on user behaviours to prevent co-resident. The Co-Resident Rate for the three VM placement policies (Least VM, MOACO, PAPHF) has been compared with the existing First Fit Decreasing (FFD) algorithm and Discrete Firefly Algorithm-VM Placement (DFA-VMP) policy in the CloudSim environment. The Power Consumption, Resource Wastage, and Co-Resident probability are compared with the existing FFD and DFA-VMP Systems. Our Multi-Objective Secure Optimal VM Placement (MOSOVMP) proposed system proved that the Co-Resident Rate is reduced with improved server utilization.
User Survey about Exposure of Hate Speech among Instagram Users in India

This research paper presents the findings on exposure of Hate Speech among users of Instagram in India.The research objectives of this user survey include understanding the impact caused to Instagram users due to online hate speech and what actions are taken to respond to hate speech attacks.Social media like Instagram, is an excellent communication medium that lets users interact with friends and a lot of other users.However, this same potential of Instagram brings a major challenge, as it provides space for discourses that can be harmful and offensive to many individuals and communities.This challenge manifests in many ways like cyber bullying, offensive content and hate speech.This paper has provided a systematic measurement and analysis of hate speech faced by users on Instagram, which has recently implemented strict policies to combat hate speech, that include suspending and disabling accounts that tend to spread hate across the platform.This user survey is aimed at different relevant age groups of 13-17 years, 18-24 years and 25-34 years.The user survey involves Female, Male and Non-Binary users, with a total of 65 participants providing valid responses.It is found that online hate speech is highly targeted based on gender and religion of an individual.Direct messaging feature on Instagram is reported to be the most used way of attacking people with Hate Speech.Reportedly, Hate Speech has affected a majority of participants with psychological and social effects.This research will provide a starting point to designers, to create inclusive and safe social media applications, in future.
npGLC-Vis Library for Multidimensional Data Visualization

While information is growing exponentially, datasets are getting bigger and bigger containing valuable information that can expand human knowledge. To extract meaningful information from these dense datasets, the need for effective graphical representations that take advantage of the human's visual perception capabilities is revealed. The visualization of this kind of data is a complex task. These big datasets are in general inherently multidimensional (n-D), facing the challenge of finding suitable mappings from the n-D space to a 2D or 3D space. Even though multiple visualization methods have been developed for n-D data, many of them do not allow the complete restoration of the data from its reduced representation and/or do not represent the complete n-D dataset. The General Lines Coordinates (GLC) are reversible visual representations that preserve n-D information for knowledge discovery. In this paper, we present the npGLC-Vis Library, a data visualization library supporting Non-Paired General Line Coordinates (npGLC) with associated traditional interactions like brushing, zooming, and panning. npGLC-Vis is a collection of visualization methods, designed for experimenting with npGLC techniques in the development of visualization applications. We present the library design and implementation, exemplifying it through the representation of different datasets.
Engaging with Artificial Intelligence (AI) with a Bottom-Up Approach for the Purpose of Sustainability: Victorian Farmers Market Association, Melbourne Australia

Artificial intelligence (AI) is impacting all aspects of food systems, including production, food processing, distribution, and consumption. AI, if implemented ethically for sustainability, can enhance biodiversity, conserve water and energy resources, provide land-related services, power smart cities, and help mitigate climate change. However, there are significant issues in using AI to transition to sustainable food systems. AI’s own carbon footprint could cancel out any sustainability benefits that it creates. Additionally, the technology could further entrench inequalities between and within countries, and bias against minorities or less powerful groups. This paper draws on findings from a study of the Victorian Farmers’ Markets Association (VFMA) that investigated the complexity of designing AI tools to enhance sustainability and resilience for the benefit of the organisation and its members. Codesign workshops, both synchronous and asynchronous, semi-structured interviews, and design innovation methods led the VFMA to experiment with an AI tool to link sustainable soil practices, nutrient rich produce, and human health. The analysis shows that the codesign process and an agile approach created a co-learning environment where sustainability and ethical questions could be considered iteratively within transdisciplinary engagement. The bottom-up approach developed through this study supports organisations who want to engage with AI while reinforcing fairness, transparency, and sustainability.
Ternary Mathematics and 3D Placement of Logical Elements Justification

Aims/ Objectives: The research presented in the following application aims to prove use of Ternary Maths for calculating machines and to simplify the process of calculating In it we will try to justify the use of triplets and describe how it works.&#x0D; An earlier research presented in “Logical Principles in Ternary Mathematics” [1,2,3] shows that we can transit from one expression of a number such as a "component form" to another, e.g a decimal, or still another, that is it’s vector form [4]. The aim of our further research is to explain why we associate Triplets of numbers in such choice {-1,0,1} and not the numbers 1,2,3 for example, or a set {1,2,3} The explanation seems obvious as a set of decimal numbers consists of 10 entries not 3 At the same time we have to prove that the mentioned set of triplets is a unique and the only one to be used as a Ternary Set or a base, as we might call it, for our calculating machines.
The Rich Still Get Richer: Empirical Comparison of Preferential Attachment via Linking Statistics in Bitcoin and Ethereum

Bitcoin and Ethereum transactions present some of the largest real-world complex networks that are publicly available for study, including a detailed picture of their time evolution. As such, they have received a considerable amount of attention from the network science community along with analyses from economic and cryptographic perspectives. Among these studies, in an analysis on the early instance of the Bitcoin network, we have shown the clear presence of the preferential attachment, or the “rich-get-richer” phenomenon. Now, we revisit this question, using a recent version of the Bitcoin network that has grown almost 100-fold since our original analysis. Furthermore, we additionally carry out a comparison with Ethereum, the second most important cryptocurrency. Our results show that preferential attachment continues to be a key factor in the evolution of both the Bitcoin and Ethereum transactoin networks. To facilitate further analysis, we publish a recent version of both transaction networks, and an efficient software implementation that is able to evaluate linking statistics necessary for learn about preferential attachment on networks with several hundred million edges.
Research on Cross-Company Defect Prediction Method to Improve Software Security

As the scale and complexity of software increase, software security issues have become the focus of society. Software defect prediction (SDP) is an important means to assist developers in discovering and repairing potential defects that may endanger software security in advance and improving software security and reliability. Currently, cross-project defect prediction (CPDP) and cross-company defect prediction (CCDP) are widely studied to improve the defect prediction performance, but there are still problems such as inconsistent metrics and large differences in data distribution between source and target projects. Therefore, a new CCDP method based on metric matching and sample weight setting is proposed in this study. First, a clustering-based metric matching method is proposed. The multigranularity metric feature vector is extracted to unify the metric dimension while maximally retaining the information contained in the metrics. Then use metric clustering to eliminate metric redundancy and extract representative metrics through principal component analysis (PCA) to support one-to-one metric matching. This strategy not only solves the metric inconsistent and redundancy problem but also transforms the cross-company heterogeneous defect prediction problem into a homogeneous problem. Second, a sample weight setting method is proposed to transform the source data distribution. Wherein the statistical source sample frequency information is set as an impact factor to increase the weight of source samples that are more similar to the target samples, which improves the data distribution similarity between the source and target projects, thereby building a more accurate prediction model. Finally, after the above two-step processing, some classical machine learning methods are applied to build the prediction model, and 12 project datasets in NASA and PROMISE are used for performance comparison. Experimental results prove that the proposed method has superior prediction performance over other mainstream CCDP methods.
Innovative solution for SARS-COV2 epidemic management

The SARS-CoV-2 pandemic challenged the authorities into taking measures that limit the spread of the virus in the community. TAMEC project (Advanced Community Approaches to Epidemic Management) developed a system that detects the contacts of patients infected with the SARS-CoV-2 virus and alerts them using mobile applications. It allows real-time analysis of confirmed patients and identified contacts, early detection of new outbreaks, and provides decisionmaking information to prevent viral transmission. The system uses its patent for homomorphic encryption, a method that allows to process and manipulate data in an encrypted format, without having access to the original data. Thus, the solution proposed in the TAMEC project fully respects the privacy rules of citizens imposed by the EU, using the concept of privacy preservation. Our solution offers the possibility to identify and validate a risk score that could become an extremely helpful tool in the stratification of COVID-19 patients. The TAMEC system is innovative in its simplicity and ability to facilitate the prevention of the spread of the SARS-CoV-2 virus in the community.
Clustering Based Approach for Novelty Detection in Text Documents

As the information is overloaded over the internet accessing of information from the internet according to a given query provides redundant and irrelevant information. It is necessary to retrieve relevant and novel information from a given query by the user. With the result of this the user will require minimum effort to access the information need. In this work we proposed a clustering based approach for novelty detection which will provide the relevant and novel documents for the information need. Based on the user query the incoming stream of documents will be clustered using k-means algorithm. Then the cluster heads are selected from the various clusters with the minimum distance. These cluster heads are the novel documents from a collection of documents from different clusters having the large distance. The proposed technique can be further used in the field of information retrieval.
Proof of Engagement: A Flexible Blockchain Consensus Mechanism

Consensus mechanism plays an important role in blockchain. At present, mainstream consensus mechanisms include proof of work (PoW), proof of stake (PoS), and delegated proof of stake (DPoS). PoW, as is widely used in virtual currency, results in significant energy consumption; PoS and DPoS are proposed to reduce energy waste caused by PoW, but their disadvantage is that they tend to create Matthew Effect (ME): “the rich get richer.” In order to balance the discourse power of new nodes and elder ones, this paper proposes a flexible consensus mechanism called proof of engagement (PoE), based on the activity and contribution of network nodes. We analyze the incentive compatibility of PoE from the perspective of mechanism design. In our simulation experiments, we tested the profit changes under PoW, PoS, and PoE. The results illustrate it is easier for new nodes to accumulate their profits under PoE than under PoW or PoS, so as to reduce the negative impacts of ME.
Effect of Collaborative Writing Combined with Blog Online Learning on Indonesian EFL Learners’ Writing Skill across Motivation

Abstract:&#x0D; &#x0D; This study aims to investigate collaborative writing combined with blog online learning on the writing skill of Indonesian English foreign language (EFL) learners’ across their motivation. This research included a quasi experimental with the comparative time series. A quasi experimental study was employed by involving 61 learners of Senior High School. This research applied in into two groups: experimental and control. The treatment was conducted at 7 sessions, including the pre-test and post-test. The scores of pre-test and post-test in writing were used as a base of quantitative data analysis whereas the learners’ responses to motivation questionnaire were used to categorize their motivation. The result revealed that the collaborative writing combined with blog online learning on the writing was effective to enhance the learners’ writing skill. In addition, to apply the combination of collaborative writing and online blog learning, teacher should not worry about different levels of motivation among students where the students were motivated to use language.&#x0D;
Governance in Systems Based on Distributed Ledger Technology (DLT): A Comparative Study

The (experimental) use of DLT is growing, also in the financial sector. DLT systems themselves advocate the fact that they use (consensus) algorithms and cryptography to create a leaderless system (horizontal). How is it possible to ensure that decisions in these DLT systems are in the interest of all stakeholders and the public interest, including supervisory authorities? Given the popularity of the Bitcoin and Ethereum blockchains, for example, it is not inconceivable that a part of the FMI will be based on a public DLT system in the future. It is therefore important to gain a better understanding of the governance of such DLT systems, and what the risks are for FIs when using DLT. This paper compares the governance of financial institutions (FIs) and financial market infrastructures (FMIs) on the one hand with the governance of systems based on distributed ledger technology (DLT systems), on the other, to discover how they differ, and where potential risks and benefits lie for their future use, also in the financial markets. We answer two questions: i) What are the differences in governance between traditional FIs, FMIs and DLT systems? and ii) What are the consequences of decentralized governance of DLT systems for supervisory authorities?
A weighted region-based level set method for image segmentation with intensity inhomogeneity

Image segmentation is a fundamental task in image processing and is still a challenging problem when processing images with high noise, low resolution and intensity inhomogeneity. In this paper, a weighted region-based level set method, which is based on the techniques of local statistical theory, level set theory and curve evolution, is proposed. Specifically, a new weighted pressure force function (WPF) is first presented to flexibly drive the closed contour to shrink or expand outside and inside of the object. Second, a faster and smoother regularization term is added to ensure the stability of the curve evolution and that there is no need for initialization in curve evolution. Third, the WPF is integrated into the region-based level set framework to accelerate the speed of the curve evolution and improve the accuracy of image segmentation. Experimental results on medical and natural images demonstrate that the proposed segmentation model is more efficient and robust to noise than other state-of-the-art models.