Reviewerly/exHarmony huggingface dataset:
Analyzing Online Political Advertisements

Online political advertising is a central aspect of modern election campaigning for influencing public opinion.Computational analysis of political ads is of utmost importance in political science to understand the characteristics of digital campaigning.It is also important in computational linguistics to study features of political discourse and communication on a large scale.In this work, we present the first computational study on online political ads with the aim to (1) infer the political ideology of an ad sponsor; and (2) identify whether the sponsor is an official political party or a third-party organization.We develop two new large datasets for the two tasks consisting of ads from the U.S.. Evaluation results show that our approach that combines textual and visual information from pre-trained neural models outperforms a state-of-the-art method for generic commercial ad classification.Finally, we provide an in-depth analysis of the limitations of our best-performing models and linguistic analysis to study the characteristics of political ads discourse.
A Bytecode-based Approach for Smart Contract Classification

With the development of blockchain technologies, the number of smart contracts deployed on blockchain platforms is growing exponentially, which makes it difficult for users to find desired services by manual screening. The automatic classification of smart contracts can provide blockchain users with keyword-based contract searching and helps to manage smart contracts effectively. Current research on smart contract classification focuses on Natural Language Processing (NLP) solutions which are based on contract source code. However, more than 94&#x0025; of smart contracts are not open-source, so the application scenarios of NLP methods are very limited. Meanwhile, NLP models are vulnerable to adversarial attacks. This paper proposes a classification model based on features from contract bytecode instead of source code to solve these problems. We also use feature selection and ensemble learning to optimize the model. Our experimental studies on over 11K real-world Ethereum smart contracts show that our model can classify smart contracts without source code and has better performance than baseline models. Our model also has good resistance to adversarial attacks compared with NLP-based models. In addition, our analysis reveals that account features used in many smart contract classification models have little effect on classification and can be excluded.
An Analysis of Figurative Language on Joe Biden's Victory Speech

This research deals with the use of figurative language on Joe Biden victory speech. In this research, the problems are arisen about what types of figurative language are used on Joe Biden’s victory speech and what is the meaning of each figurative language used on Joe Biden’s victory speech. The methodology, which used in this research, is the qualitative research and the researchers used content analysis method. The document or content analysis is a research method applied to written or visual materials for the purpose of identifying specified characteristic of the material. The data in this research is Joe Biden’s victory speech. After investigating the types of figurative language on Joe Biden’s victory speech, the researchers found the total 86 sentences that contain examples of figurative language on Joe Biden’s victory speech. There are 5 types of figurative language used on Joe Biden’s victory speech from 7 types of figurative language proposed by Leech. The 5 types of figurative language used on Joe Biden’s victory speech are metaphor, personification, hyperbole, simile, and litotes. Among the 5 types of figurative language often appear used on Joe Biden’s victory speech is metaphor. Metaphor consists of 44 sentences, personification consists of 34 sentences, hyperbole consists of 5 sentences, simile consists of 2 sentences, and litotes consists of 1 sentence
A privacy-preserving statistics marketplace using local differential privacy and blockchain: An application to smart-grid measurements sharing

Service providers usually require detailed statistics in order to improve their services. On the other hand, privacy concerns are intensifying and sensitive data is protected by legislation, such as GDPR (General Data Protection Regulation). In this paper, we present the design, implementation, and evaluation of a marketplace that allows “data consumers” to buy information from “data providers”, which can then be used for generating meaningful statistics. Additionally, our system enables “system operators” that can select which data providers are allowed to provide data, based on filtering criteria specified by the data consumer. We leverage local differential privacy to protect the data provider's privacy against data consumers, as well as against system operators, and we build a blockchain-based solution for ensuring fair exchange, and immutable data logs. Our design targets use cases that involve hundreds or even thousands of data providers. We prove the feasibility of our approach through a proof-of concept implementation of a measurement sharing application for smart-grid systems.
Casual Leisure in Rich-Prospect

As digital cultural collections become increasingly sophisticated in their scope and functionality, there is a need to build an in-depth understanding concerning the information behaviors of users in this new domain. Research has demonstrated that many digital museum visitors are engaged in casual leisure during exploration of a collection, suggesting that they do not have an inherent information goal but rather seek new experiences or learning opportunities based on personal curiosity and moments of discovery. Consequently, understanding how to translate casual leisure contexts into meaningful interaction design may play a critical role in designing engaging digital collections. Our study reports on the user experience of a largely unexplored user interface design framework called rich-prospect , which was originally developed to enhance browsing and discovery for complex visual collections. We performed a mixed-method, within-subjects study (N=30) that simulated a casual leisure approach to information browsing and retrieval across three different rich-prospect interfaces for digital cultural heritage. Our results show that rich-prospect scores well in the hedonic facets of its user experience, whereas pragmatic aspects have room for improvement. Additionally, through our qualitative analysis of participant feedback, we derived salient themes relating to the exploratory browsing experience. We conclude with a series of design implications to better connect interactive elements with casual leisure contexts for digital cultural collections.
A secure image cryptosystem via multiple chaotic maps

This work focused on introducing a new two-dimensional (2D) chaotic system. It is combined of some existing maps, the logistic, iterative chaotic map with infinite collapse, and Henon maps; we called it 2D-LCHM. The assessment of the actual performance of 2D-LCHM presents its sensitivity to a tiny change in the initial condition. Besides, its dynamics behavior is very complicated. It also has hyperchaotic properties and good ergodicity. The proposed system is occupied with designing a new image encryption system. Changing the image pixels’ locations is the primary step in the encryption process. The original image is splitting into four blocks to create four different keys based on 2D-LCHM; this reduces the computation time and increases the complexity. To obtain the encryption image, we have to repeat the partitioning process several times for each block. The encryption image’s efficiency is shown through some performance analysis such as; image histogram, the number of pixels changes rate (NPCR), the unified average changing intensity (UACI), pixels correlation, and entropy. The proposed system is compared with some efficient encryption algorithms in terms of chaocity attributes and image performance; the analysis result showed consistent improvement.
Measurement-device-independent quantum dialogue

Recently, measurement-device-independent quantum secure direct communication schemes were proposed by Niu et al. (Sci.Bull. 63:1345, 2018) and Zhou et al. (Sci. China-Phys. Mech. Astron. 63:230362, 2020). Inspired by their ideas, in this paper, a measurement-device-independent quantum dialogue protocol based on entanglement is designed and proved to be secure. The advantage of this scheme is that it can not only allow two communicators transmit secret message each other, making the application scenarios more extensive, but also eliminate all the security loopholes related to the measurement device and information leakage. In terms of experimental implementation, the scheme mainly involves the preparation of entangled states, the preparation of single photon, quantum storage, Bell measurement and other technologies, all of which are mature at present, therefore, the scheme is feasible by using the current technologies.
Towards Intelligent Control of Electric Wheelchairs for Physically Challenged People

The chapter deals with the use of soft computing techniques in solving the mobility problems of physically handicapped people using available signals such as face directional gesture, voice, brain and electromyogram (EMG) signals. These signals, depending on the type and degree of handicap, are used to classify commands required to drive a wheelchair. The user’s intention is transferred to the wheelchair controller through the human-computer interface (HCI), and then the wheelchair is guided to the intended direction. Additionally, the wheelchair can perform safe and reliable motions by detecting and avoiding obstacles autonomously. Several detection methods and commands classification algorithms will be discussed. For smooth and reliable operation, an intelligent controller will be proposed to drive wheelchair motors. An adaptive Neuro-fuzzy inference system (ANFIS) technique will be used in the controller. The chapter introduces a modified method to design multiple-input, multiple-output (MIMO) ANFIS using only MATLAB. This controller relies on real data received from obstacle avoidance sensors and the HCI unit. The implemented wheelchair will be equipped with path detection sensors, GPS tracking and battery level monitoring to guaranty more safety for the user. It has been tested on 3D simulation software, and the obtained results from the wheelchair prototype and 3D simulation model demonstrated the performance of the proposed real-time controller in dealing with user requirements and working environment constraints. The cost of the proposed smart wheelchair is suitable with user case. By combining the concepts of soft computing and mechatronics, the implemented wheelchair will be more sophisticated and gives people more mobility. The obtained results show that the proposed intelligent wheelchair is feasible for the disabled and the elderly with severe mobility disabilities.
A Robust Reputation-Based Group Ranking System and Its Resistance to Bribery

The spread of online reviews and opinions and its growing influence on people’s behavior and decisions boosted the interest to extract meaningful information from this data deluge. Hence, crowdsourced ratings of products and services gained a critical role in business and governments. Current state-of-the-art solutions rank the items with an average of the ratings expressed for an item, with a consequent lack of personalization for the users, and the exposure to attacks and spamming/spurious users. Using these ratings to group users with similar preferences might be useful to present users with items that reflect their preferences and overcome those vulnerabilities. In this article, we propose a new reputation-based ranking system, utilizing multipartite rating subnetworks, which clusters users by their similarities using three measures, two of them based on Kolmogorov complexity. We also study its resistance to bribery and how to design optimal bribing strategies. Our system is novel in that it reflects the diversity of preferences by (possibly) assigning distinct rankings to the same item, for different groups of users. We prove the convergence and efficiency of the system. By testing it on synthetic and real data, we see that it copes better with spamming/spurious users, being more robust to attacks than state-of-the-art approaches. Also, by clustering users, the effect of bribery in the proposed multipartite ranking system is dimmed, comparing to the bipartite case.
Fine-grained Classification of Political Bias in German News: A Data Set and Initial Experiments

We 1 present a data set consisting of German news articles labeled for political bias on a fivepoint scale in a semi-supervised way.While earlier work on hyperpartisan news detection uses binary classification (i.e., hyperpartisan or not) and English data, we argue for a more fine-grained classification, covering the full political spectrum (i.e., far-left, left, centre, right, far-right) and for extending research to German data.Understanding political bias helps in accurately detecting hate speech and online abuse.We experiment with different classification methods for political bias detection.Their comparatively low performance (a macro-F 1 of 43 for our best setup, compared to a macro-F 1 of 79 for the binary classification task) underlines the need for more (balanced) data annotated in a fine-grained way.
IMPROVING STUDENTS’ READING COMPREHENSION THROUGH QUESTION ANSWER RELATIONSHIP STRATEGY (QARS)

This research was intended to find out the empirical evidence of the influence the (QARS) Question Answer Relationship strategy on student reading comprehension ability in reading comprehension explanation text. The research used quantitative method with quasi experimental design This research was used purposive sampling with the research sample totally 20 students with classified into two classes, namely experimental group and control group, the population of this research was eight grade of SMP YPK Pematangsiantar. The instrument used is reading comprehension test, before giving treatment the student from both classes were give pre-test to know the ability of student reading comprehension in reading explanation text, the test consists 10 multiple choice question.at the end of treatment the student given post-test the data from both tests were analyzed by using mean to measure the score of student the result of mean score of pre-test reveal a significant improvement between pre-test and post-test. In the pre-test the experimental group got 61 while the control group 66 in post-test
Unsupervised Method to Localize Masses in Mammograms

Breast cancer is one of the most common and prevalent type of cancer that mainly affects the women population. chances of effective treatment increases with early diagnosis. Mammography is considered one of the effective and proven techniques for early diagnosis of breast cancer. Tissues around masses look identical in mammogram, which makes automatic detection process a very challenging task. They are indistinguishable from the surrounding parenchyma. In this paper, we present an efficient and automated approach to segment masses in mammograms. The proposed method uses hierarchical clustering to isolate the salient area, and then features are extracted to reject false detection. We applied our method on two popular publicly available datasets (mini-MIAS and DDSM). A total of 56 images from mini-mias database, and 76 images from DDSM were randomly selected. Results are explained in-terms of ROC (Receiver Operating Characteristics) curves and compared with the other techniques. Experimental results demonstrate the efficiency and advantages of the proposed system in automatic mass identification in mammograms.
Tsallis generalized entropy for Gaussian mixture model parameter estimation on brain segmentation application

Among statistical models, Gaussian Mixture Models (GMMs) have been used in numerous applications to model the data in which a mixture of Gaussian curves fits them. Several methods have been introduced to estimate the optimum parameters to a GMM fitted to the data. The accuracy of such estimation methods is crucial to interpret the data. In this paper, we proposed a new approach to estimate the parameters of a GMM using critical points of Tsallis-entropy to adjust each parameter's accuracy. To evaluate the proposed method, seven GMMs of simulated random (noisy) samples generated by MATLAB were used. Each simulated model was repeated 1000 times to generates 1000 random values obeying the GMM. In addition, five GMM shaped samples extracted from magnetic resonance brain images were used, aiming for image segmentation application. For comparison assessment, Expectation-Maximization, K-means, and Shannon's estimator were employed on the same dataset. These four estimation methods using accuracy, Akaike information criterion (AIC), Bayesian information criterion (BIC), and Mean Squared Error (MSE) were evaluated. The mean accuracies of the Tsallis-estimator for simulated data, i.e., the mean values, variances, and proportions, were 99.9(±0.1), 99.8(±0.2), and 99.7(±0.3)%, respectively. For both datasets, the Tsallis-estimator accuracies were significantly higher than EM, K-means, and Shannon. Tsallis-estimator, increasing the estimated parameters' accuracy, can be used in statistical approaches and machine learning.
<scp>Nova</scp> : Value-based Negotiation of Norms

Specifying a normative multiagent system (nMAS) is challenging, because different agents often have conflicting requirements. Whereas existing approaches can resolve clear-cut conflicts, tradeoffs might occur in practice among alternative nMAS specifications with no apparent resolution. To produce an nMAS specification that is acceptable to each agent, we model the specification process as a negotiation over a set of norms. We propose an agent-based negotiation framework, where agents’ requirements are represented as values (e.g., patient safety, privacy, and national security), and an agent revises the nMAS specification to promote its values by executing a set of norm revision rules that incorporate ontology-based reasoning. To demonstrate that our framework supports creating a transparent and accountable nMAS specification, we conduct an experiment with human participants who negotiate against our agent. Our findings show that our negotiation agent reaches better agreements (with small p -value and large effect size) faster than a baseline strategy. Moreover, participants perceive that our agent enables more collaborative and transparent negotiations than the baseline (with small p -value and large effect size in particular settings) toward reaching an agreement.
Comparative Analysis of Numerous Approaches in Machine Learning to Predict Financial Fraud in Big Data Framework

Nowadays, financial frauds that are taking place across the globe generate much more threats and thus have a thoughtful impact on the financial subdivision. Due to this fact, financial institution is forced to improve their fraud detection mechanism so that these various kinds of financial frauds can be detected in early stages. The various studies done in past few years show that the usage of ML and big data analytics has improved the efficiency of these methodologies. This paper basically proposed a state of art on different kinds of financial frauds and specify various financial frauds, detection and prevention techniques used in financial frauds. The key purpose of the work done is to discuss in detail various fraud detection methodologies and technologies, their comparison and performance efficiency. It provides a complete comparison of all techniques based on machine learning that are used in detecting and preventing the different kinds of financial frauds. It also delivers a complete study of all the methods which were used in recent past with their efficiency and capability for the detecting various kinds of frauds in financial sector.
Entropy-based network traffic anomaly classification method resilient to deception

Entropy-based network traffic anomaly detection techniques are attractive due to their simplicity and applicability in a real-time network environment. Even though flow data provide only a basic set of information about network communications, they are suitable for efficient entropy-based anomaly detection techniques. However, a recent work reported a serious weakness of the general entropy-based anomaly detection related to its susceptibility to deception by adding spoofed data that camouflage the anomaly. Moreover, techniques for further classification of the anomalies mostly rely on machine learning, which involves additional complexity. We address these issues by providing two novel approaches. Firstly, we propose an efficient protection mechanism against entropy deception, which is based on the analysis of changes in different entropy types, namely Shannon, R?nyi, and Tsallis entropies, and monitoring the number of distinct elements in a feature distribution as a new detection metric. The proposed approach makes the entropy techniques more reliable. Secondly, we have extended the existing entropy-based anomaly detection approach with the anomaly classification method. Based on a multivariate analysis of the entropy changes of multiple features as well as aggregation by complex feature combinations, entropy-based anomaly classification rules were proposed and successfully verified through experiments. Experimental results are provided to validate the feasibility of the proposed approach for practical implementation of efficient anomaly detection and classification method in the general real-life network environment.
On Strengthening SMEs and MEs Threat Intelligence and Awareness by Identifying Data Breaches, Stolen Credentials and Illegal Activities on the Dark Web

During the last decades, Dark Web content has risen in necessity in an increasingly connected world, where international anonymous networks provide access to data marketplaces and illicit multimedia material through the TOR or I2P networks. The motivation behind this paper is to gauge the current state and growth of the Dark Web in relation to the role it plays with special focus on Small and Medium-sized Enterprises (SMEs and MEs). More specifically, we devise Machine Learning and specialised Information Retrieval techniques to extract insights and investigate how the Dark Web enables cybercrime, maintains marketplaces with breached enterprise data collections and pawned email accounts. The research questions that we address concern: a) the role that the Dark Web plays for SMEs, MEs, and society in general; b) the criticality of cybercriminal activities and operations in the Dark Web exploiting threat taxonomies and scoring schemes; and c) the maturity and efficiency of technical tools and methods to curb illegal activities on the Dark Web through raising awareness via efficient text analytics, visual reporting and alerting mechanisms.