Reviewerly/exHarmony huggingface dataset:
Understanding Network Characteristics of Spam Users in Social Media

Timely identification of spam users, usually regarded as users creating and spreading false information in social media, has experienced a resurgence of interest. Besides the linguistic and behavioral cues, the network among users has become an informative yet robust data source towards spam users detection. However, the characteristics of spam users' behavior on networks have not been closely examined. Along this line, our study selects two datasets from e-commerce review system and social networking platform for systematically exploring network characteristics of spam users. We perform the network analysis from three aspects: homophily of edges, centrality of nodes and cohesive groups. Our analytical results yield several conclusions: (i) the homophily property is always satisfied on the e-commerce co-reviewing network, but it does not hold on multiple relations of social networking platform; (ii) spam users are likely to be on the periphery of the network on e-commerce platform, but on the innermost network core on social networking platform; (iii) spam users on e-commerce platform tend to form cohesive groups that are quite scarce on the social networking platform.
Interoperability and Synchronization Management of Blockchain-Based Decentralized e-Health System

A huge amount of data, generated by different applications in computer network, is growing up exponentially based on nonstop operational states. Such applications are generating an avalanche of information that is disruptive for predictable data processing and analytics functionality, which is perfectly handled by the cloud before explosion growth of Big Data. Blockchain technology alleviates the reliance on a centralized authority to certify information integrity and ownership, as well as mediate transactions and exchange of digital assets, while enabling secure and pseudo-anonymous transactions along with agreements directly between interacting parties. It possesses key properties, such as immutability, decentralization, and transparency that potentially address pressing issues in healthcare, such as incomplete records at point of care and difficult access to patients’ own health information. An efficient and effective healthcare system requires interoperability, which allows software apps and technology platforms to communicate securely and seamlessly, exchange data, and use the exchanged data across health organizations and app vendors. Unfortunately, healthcare today suffers from fragmented data, delayed communications, and disparate workflow tools caused by the lack of interoperability. Blockchain offers the opportunity to enable access to longitudinal, complete, and tamper-aware medical records that are stored in fragmented systems in a secure and pseudo-anonymous fashion. The proposed work carried out blockchain implementation in distributed computing environment and it also provide the automatic recovery of invalid chain. This also determines the impact of those security issues and possible solutions, providing future security-relevant directions to those responsible for designing, developing, and maintaining distributed systems.
One year of breast cancer in Ibadan, Southwestern Nigeria: a histopathological review

Background: Breast cancer is a major public health problem in most low- to medium-income countries of the world because of its high morbidity and mortality rate. Histopathological features are vital in risk assessment, selection of treatment and prognostication in breast cancer patients. This study aimed to assess the histopathological features of all breast cancer cases seen in a tertiary hospital in the year 2018. Methods: This is a retrospective review of all breast cancer cases histologically diagnosed and confirmed by three Pathologists at the University College Hospital (UCH), Ibadan over a one-year period from 1st January 2018 to 31st December, 2018. Results: A total of 236 breast cancer cases were seen during this period, 234 (99.2%) were females while only 2 (0.8%) were male. 163 (69.7%) patients were between the fourth and sixth decades of life, 65 (27.8%) patients were above sixth decade while 6 (2.5%) patients were below fourth decade. Invasive ductal carcinoma of No Special Type (NST) was the commonest histological subtype 212 (89.8%). Grade 1 cases were 25(10.6%), Grade 2 129 (54.7%) and Grade 3 cases were 40 (16.9%) while 42 (17.8%) were not graded. Lymphovascular invasion observed in 98 (41.5%) of the cases. 54 (22.9%) had immunohistochemistry out of which 23 (43.4%) were triple negative while 8 (3.4%) were triple positive. Conclusion: Histopathological features suggesting aggressive disease was predominantly observed. Efforts should be made towards early diagnosis, adequate evaluation and prompt treatment. Cancer care should be fully incorporated in the National Health Insurance Scheme (NHIS).
First- and Second-Order Sorted Local Binary Pattern Features for Grayscale-Inversion and Rotation Invariant Texture Classification

Local binary pattern (LBP) is sensitive to inverse grayscale changes. Several methods address this problem by mapping each LBP code and its complement to the minimum one. However, without distinguishing LBP codes and their complements, these methods show limited discriminative power. In this paper, we introduce a histogram sorting method to preserve the distribution information of LBP codes and their complements. Based on this method, we propose first- and second-order sorted LBP (SLBP) features which are robust to inverse grayscale changes and image rotation. The proposed method focuses on encoding difference-sign information and it can be generalized to embed other difference-magnitude features to obtain complementary representations. Experiments demonstrate the effectiveness of our method for texture classification under (linear or nonlinear) grayscale-inversion and rotation changes.
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RESIDUAL FACEBOOK DATA ANALYSIS IN BROWSERS

The Internet is an essential tool for everyday tasks. Aside from common use, the option to browse the Internet privately is a desirable attribute. However, this can create a problem when private Internet sessions become hidden from computer forensic investigators in need of evidence. Our primary focus in this research is to discover residual artifacts from private and portable web browsing sessions. In addition, the artifacts must contain more than just file fragments and enough to establish an affirmative link between user and session. As social media applications such as Facebook become an integral part of our society, they are also becoming an important source of information in a digital (forensics) investigation. In this paper, we examine the potential to recover artifacts of forensic interest after three popular browsers, namely: Mozilla Firefox, Google Chrome and Internet Explorer, have been used to access Facebook. Findings from this research will hopefully contribute to a better understanding to mobile device and app forensics. Certain aspects of this topic have triggered many questions, but there have never been enough authoritative answers to follow. As a result, we propose a new methodology for analyzing private and portable web browsing artifacts. Our research will serve to be a significant resource for law enforcement, computer forensic investigators, and the digital forensics research community
Solution of Partial Differential Equations on Radial Basis Functions Networks

The solution of boundary value problems described by partial differential equations on networks of radial basis functions is considered. An analysis of gradient learning algorithms for radial basis functions networks showed that the widely used first-order method, the gradient descent method, does not provide a high learning speed and solution accuracy. The fastest method of the second order, the trust region method, is very complex. A learning algorithm based on the Levenberg–Marquardt method is proposed. The proposed algorithm, with a simpler implementation, showed comparable results in comparison with the trust region method.
Performance Degradation of Multi-class Classification Model Due to Continuous Evolving Data Streams

Online machine learning plays a pivotal role in the 4th Industrial Revolution (IR 4.0). IR 4.0 requires real-time data analysis (classification or prediction) using the streaming data. However, mostly the data streams contain nonstationary (variability features) characteristics, such as concept drift and class imbalance. The issues of concept drift and class imbalance adversely affects the accuracy of the classification models. The classification accuracy is even more affected when these issues arrived at the same time (joint problem). Some efforts have been made in the literature to cope with the joint problem of class imbalance and concept drift in online learning, but the existing solutions are limited to binary class classification and these solutions do not work for multi-class classification. Besides, the literature doesn’t mention the exact correlation between the critical factors of concept drift and class imbalance. Also, the tuning parameters of the multi-class classification models, which can help in improving the classification accuracy, are unknown. However, to resolve the joint problem of concept drift and class imbalance in online multi-class classification models, it is essential to determine the exact correlation and find the tuning parameters, which could be helpful to provide a more dynamic approach for avoiding the performance degradation of the multi-class classification models. Therefore, to resolve this issue, this study aims to determine the correlation between the concept drift and class imbalance, identify the tuning parameters of multi-class classification models and propose a dynamic solution based on these findings. Our proposed dynamic approach could be effectively utilized in various online machine learning based (real-time) streams analysis, which is desirable for IR 4.0.
Phase-Sensitive Model for Temporal Action Proposal Generation

Temporal action proposal generation is an important and challenging task, aiming to localize the position where an action or event may occur in an untrimmed video. In this paper, we propose an efficient and end-to-end framework to generate temporal action proposals, named Phase-Sensitive Model (PSM), which fully understands all phases of temporal information. In particular, the PSM consists two modules: Boundary Phase Classification (BPC) and Action Phase Classification (APC). The BPC aims to provide two temporal boundary phase confidence maps by rich local information, while the APC is designed to generate an action phase confidence map by global features. Moreover, we introduce a new method boundary probability calculation to get the final score. Our experiments on ActivityNet-1.3 show a significant improvement with remarkable efficiency and generalizability.
IndiMaker - Open Data Linking Framework

Open data portals make a very important set of information available to the community. Those interested in a particular topic, retrieve data on the topic from different portals, but then, processing them together is difficult due to the different publication criteria used by each portal. To assist in this process, a tool called IndiMaker was developed, together with a framework that helps linking these files to users with little technical experience in data analysis. Within the framework, the tool allows applying different operations on the files, generating graphics in a dashboard, which makes information analysis easier. The framework was applied to the “environment” topic, in particular, to water and air quality and energy generation.
Social Presence Despite Isolation - Insights into the Relation Between Psychological Distance and Sensory Synchronization in Computer-Mediated Communication

Whilst social distancing (e.g., during a pandemic), electronic communication allows users to keep in touch with social contacts, thereby fulfilling needs for human interaction. Accordingly, it is important to examine social closeness in the context of computer-mediated communication in more detail and to take a closer look at the relation between the feeling of presence to achieve the maximum potential for such communication. In this paper we introduce the concept of sensory synchronization as a predictor for perceived social presence. To this end, social activities conducted virtually during the Covid-19 pandemic were surveyed and analyzed to understand the connection between a feeling of presence and possible sensory synchronization of communication partners. Context of activities were collected in preliminary interviews and examined more closely in an online survey (N = 234). Most frequently reported activities included video calls, playing virtual games and virtual meals. The results showed that there is a significant positive correlation between sensory synchronization and social presence in all activity categories. The activity categories significantly explain different variances of the evaluated social presence and sensory synchronization. The results of this work motivate further research on the topic of sensory synchronization and social presence. New predictors in social presence research were identified and a questionnaire for the assessment of sensory synchronization and social presence was developed.
Modified Transaction Against Double-Spending Attack Using Blockchain to Secure Smart Cities

Blockchain paves the way to fill the research gaps in terms of security, database process, cryptography, data center, etc. in the research fields like networking, big data and cloud computing in recent days. Generally, blockchain contains blocks of chain where each block is referring to the previous blocks and difficulty in the recreation of a chain. It provides a set of bitcoins, which is nothing but the digital currency utilized for cryptocurrency to manage several transactions based on a fully distributed environment. As bitcoins decentralizes for the mining process, mining processes are performed for the creation of bitcoin too. While considering the security, the bitcoin transaction has several attackers shows while making transactions. The most severe attack that we have found here is the double-spending attacks to modify and manipulate the transaction performed. Based on the blockchain framework existence, all the transactions stores into the transaction part of each block. Transactions perform a hash function which hashes each transaction and repeat it for pairing again and again based on the Merkle tree. Merkle tree is the block header that stores the hash of the previous block header. These chaining process helps the transaction to ensure no modifications done without changing the earlier blocks in the chain network. The transaction in blockchain denotes the bitcoin wallet, which tells the information about bitcoin's movement. Each spend transaction of bitcoin has the previous bitcoin transaction. Double spending attack occurs when a single transaction creates multiple output transaction while sending to several destination addresses. In the blockchain, each output transaction is provided based on one input. If any attempt of the same bitcoin uses for two or more times for a transaction, the double-spending attack is possible. Based on the existing survey related to the double-spending attack ratio, there is a possibility of a double-spending attack in the blockchain. Based on the double-spending attack problem, the modified bitcoin transaction chaining technique proposes with integrated the electronic codebook based on cryptography. As to provide more security, modes of electronic code operations are considered. Based on the block cipher of block length and in the case of multiple blocks of information are processed, security attacks are possible as block chaining added up into the transaction between the sender and receiver, which ensures authentication and confidentiality. The initial constant provides integrated with the transaction to provide maximum security and protect against unauthorized changes. To provide additional security constant number is considered as the random number which gets varied for each transaction based on output feedback mode operation.
More Accurate Streaming Cardinality Estimation With Vectorized Counters

Cardinality estimation, also known as count-distinct, is the problem of finding the number of different elements in a set with repeated elements. Among the many approximate algorithms proposed for this task, HyperLogLog (HLL) has established itself as the state of the art due to its ability to accurately estimate cardinality over a large range of values using a small memory footprint. When elements arrive in a stream, as in the case of most networking applications, improved techniques are possible. We specifically propose a new algorithm that improves the accuracy of cardinality estimation by grouping counters, and by using their new organization to further track all updates within a given counter size range (compared with just the last update as in the standard HLL). Results show that when using the same number of counters, one configuration of the new scheme reduces the relative error by approximately 0.86x using the same amount of memory as the streaming HLL and another configuration achieves a similar accuracy reducing the memory needed by approximately 0.85x.
Biometric Traits Share Patterns

Large-scale data-driven DNN models have been proven to achieve great performance in various computer vision challenges, and transfer learning is proposed recently to take advantage of pre-trained DNN on a small database. Under such a framework, an innovative classification model for both identity and gender classification with hand vein information is proposed in this paper. By adopting pre-trained VGG and AlexNet model with ImageNet database and the corresponding fine-tuned ones with PolyU fingerprint and palmprint database, state-of-the-art classification results are obtained with the fine-tuned ones, which indicates that domain-specific model performs better than a generic one, and similar experimental results with faces further indicate that biometric traits share latent patterns. On the other hand, to evaluate the distribution of shared patterns, a quantized shared-index calculated as the number of correlated dictionary atoms is realized based on a sparse representation model.
OEBSA-Sketch: a Combination of Sketch and Hash Table for the Measurement of Stream Data

Sketch is a hash-based data structure that can record traffic characteristic information in a high-speed network environment. Sketch only occupies a small amount of space resources while maintaining theoretically provable estimation accuracy. Although various sketches have been proposed, most of them achieve either high accuracy or high speed in limited memory, especially for skewed data sets. In order to achieve both goal, we combined our prior work, CBFSketch, with the odd-even cuckoo hash table which is a data structure that provides storage and accurate lookup. The new scheme is named OEBSA- Sketch, we compared our sketch with the several typical sketches in effectiveness and efficiency. The results showed the accuracy of Heavy Hitter in our scheme is 4 times higher than other sketches at most, and the accuracy of Heavy Changer is up to 16 times at most. The average absolute error is reduced by about 50% compared with CBFSketch. At the same average relative error, the space used can be reduced by about 50%.
A semantic model of morphological information retrieval: A comparative accumulative analysis

The main function of information retrieval (IR) system is to obtain efficient and exactly a minimum subset of document that is related to user concern. Synonymy and polysemy act as a barrier for natural language processing algorithms due to overestimation and misrepresentation. The proposed model uses the implicit of higher rank structure in combing terms with document to optimize the identification of relevant document based on terms used in queries with an enhanced automatic indexing approach has been suggested. The study benefited from the use of Term Frequency Inverse Document Frequency (TF-IDF) method to assign weight for each term in the document. Each document is presented as a vector of weight in the space. Also, the user query is represented as vector of weight. Finally, a Singular Value Decomposition (SVD) approach has been used in which a huge weight of term-document matrix is factorized into collection of vectors for approximation of the original matrix. The cosine similarity is also used to determine the closed vector of document to the user query. In regard to English information retrieval, It was observed that TF-IDF showed higher performance before term percentage 0.3 while Latent Semantic Indexing (LSI) was more stable than TF-IDF, especially in terms of the use of word association.
A writing assistant en route to a full computational grammar for Sepedi

A detailed user study and observations by lecturers indicate that the correct compilation of sentences in the eight verbal moods as well as for a number of similar grammatically complicated constructions in Sepedi poses a challenge in any text production situation. Feedback from target users indicated that there is a need for a computational writing assistant for the compilation and verification of correct constructions. To address this need, an extended computational sentence builder for verbal moods, adjectives and possessive constructions in Sepedi was designed and built to assist in text production and to serve as a model for other African languages. This article introduces a prototype of such an extended computational sentence builder for verbal moods, adjectives and possessive constructions in Sepedi. The emphasis throughout is on the grammatical complexity of Sepedi and how the Sepedi Helper can assist users to produce correct sentences. In contrast to typical traditional grammars of Sepedi, the tool also provides the required cognitive information through basic clickable help screens. The Sepedi Helper is a dynamic lightweight tool aimed at combining user knowledge with a text production tool, i.e. user-involved, step-by-step production of Sepedi phrases. The emphasis in the design is on simplicity.
RKC-H: A Rich Knowledge Based Model for Multi-turn Dialogue Generation

When conversational communication, people often draw upon their rich world knowledge in addition to the dialogue context. The commonsense world fact can facilitate natural language understanding. In the paper, we present a rich knowledge cognition hierarchical (RKC-H) multi-turn dialogue model in open-domain to improve language generation. Given the input, the model selects the corresponded seed-graphs and encodes the seed-graph nodes with a seed-graph attention mechanism. Then, the hierarchical encoder captures the multi-granularity of current utterance and history dialogue text features. We apply graph-to-sequence generator to the responses and provide Exponential Maximum Mutual Information loss function. Automatic and human evaluations show that the proposed model can complete rich meaning and coherent multi-turn dialogue. Our model outperforms over the baseline.