Reviewerly/exHarmony huggingface dataset:
Use of Special Cases of Ontologies for Big Data Analysis in Decision Making Systems

We analyze the use of knowledge organization systems for decision making support. Various types of ontologies are considered as knowledge sources for semantic structuring information from Big Data. Proposed approach provides semantic matching of Big Data metadata with natural language definition of current user task for selection of Big Data fragments that are relevant to this task. The main advantage of this approach is reducing of set of analyzed concepts and their relations by terms of domain ontologies. But this mapping needs in task-specific ontologies for every user task on base of task semantic analysis. We propose methods of semi-automated generation of task ontology that use external information resources with semantic markup (such as Semantic Wiki), and then this ontology can be used as a source of concepts and relations for decision making task. Fragments of Big Data are selected by matching of their metadata with concepts of task thesaurus that is generated on base of task ontology. Practical use of proposed approach is considered on example of refinement of advisory system AdvisOnt.
Australia’s DIGI Code: what can we learn from the EU experience?

Disinformation and misinformation on social media platforms are growing regulatory concerns for governments. In this paper, we examine Australia's DIGI Code of Practice (CoP). The CoP is a voluntary set of commitments to address false content on platforms that is overseen by Australia's digital media industry. It was established in response to a federal government directive. We evaluate the DIGI CoP by comparing it to the code it was partially based on: the EU's Code of Practice on Disinformation (EU CPD). The EU CPD also attempted voluntary regulation, and yet the EU is now moving towards a strengthened CPD. While the DIGI CoP improves upon the original EU CPD, the original version had limited accountability measures and notable content exclusions. We conclude by discussing a recent review of the CoP, suggesting that Australia will also strengthen regulation in this area. Lastly, we address ongoing tensions around media freedom and accountability.
Services for Selecting Journals for Publishing an Article: a Comparative Analysis

Currently, there are a number of automated services for selecting journals for the publication of scientific articles, which are an auxiliary tool for the author when choosing a scientific journal. The article provides an overview and comparative analysis of services for selecting a journal for publishing an article (Master Journal List Manuscript Matcher, JournalFinder, Journal Suggester, JournalGuide, Wiley Journal Finder, NeuroAssistant of Scientific Publishing), which are available free of charge and provide the ability to automatically select journals by analyzing fragment of the manuscript and additional parameters. As a result of the study, it was revealed that of the considered services, Master Journal List Manuscript Matcher (for foreign journals) and NeuroAssistant of Scientific Publishing (for Russian-language journals) are more preferable, because give the most relevant list of journals.
Internet Memes in Network Communication: English Entertainment Sites

Internet memes are an integral part of the Internet entertainment discourse. They are often based on precedent phenomena and reflect current events, facilitate the self-expression of the author, create a comic effect, etc. Memes possess a great repetitive and transformation potential. Their textual element is compact and humor-oriented. In the English Internet discourse, memes exist as texts, pictures, pictures with captions, and videos. The most common memes are those that refer to mass and Internet culture. In social networks, their main communicative goals are entertainment, reflection of current events, and increasing personal rating. The policy of the social network and the peculiarities of the meme culture determine the cooperative speech strategy they represent: even when the meme is part of some conflictogenic tactics, the negative reaction is not its main goal.
Intelligent Recognition English Translation Model Based on Embedded Machine Learning and Improved GLR Algorithm

Most of the popular translation models are based on encoder-decoder architecture and belong to the autoregressive translation model. When autoregressive translation models decode, they generate the current sequence according to the sequence generated before. This process is not parallel. The generalized maximum likelihood ratio detection (GLR) algorithm model cannot effectively guarantee the overlapping and accurate results of English translation detection. To improve the recognition rate of English phrases and meanings, this paper proposes an intelligent model for English translation recognition based on embedded machine learning and an improved GLR algorithm. A corpus of 520000 English phrases is used for training. And we compare and analyze different corpora and compare GLR algorithm with other traditional algorithms. Words and phrases are based on analytic linear structure. The syntactic function of the table corrects the ambiguity between English and Chinese structures in some speech recognition results and finally retains the recognition content. The research shows that the recognition accuracy based on the improved GLR algorithm is more than 96.58%, which is 23% higher than the classical GLR in semantic recognition. Statistical algorithm and dynamic storage algorithm make it more suitable for intelligent translation and provide a new model method for intelligent machine translation.
Extracting Chinese events with a joint label space model

The task of event extraction consists of three subtasks namely entity recognition, trigger identification and argument role classification. Recent work tackles these subtasks jointly with the method of multi-task learning for better extraction performance. Despite being effective, existing attempts typically treat labels of event subtasks as uninformative and independent one-hot vectors, ignoring the potential loss of useful label information, thereby making it difficult for these models to incorporate interactive features on the label level. In this paper, we propose a joint label space framework to improve Chinese event extraction. Specifically, the model converts labels of all subtasks into a dense matrix, giving each Chinese character a shared label distribution via an incrementally refined attention mechanism. Then the learned label embeddings are also used as the weight of the output layer for each subtask, hence adjusted along with model training. In addition, we incorporate the word lexicon into the character representation in a soft probabilistic manner, hence alleviating the impact of word segmentation errors. Extensive experiments on Chinese and English benchmarks demonstrate that our model outperforms state-of-the-art methods.
A Blockchain-Based Trust Model for Uploading Illegal Data Identification

Malicious users can upload illegal data to the blockchain to spread it, resulting in serious threats due to the tamper-proof characteristics of the blockchain. However, the existing methods for uploading illegal data identification cannot select trust nodes and ensure the credibility of the identification results, leading to a decrease in the credibility of the methods. To solve the problem, this paper proposes a blockchain-based trust model for uploading illegal data identification. The trust model mainly has the following two core modules: Reputation-based random selection algorithm (RBRSA) and incentive mechanism. By assigning reputation attributes to nodes, the proposed RBRSA will select nodes according to reputation values. RBRSA favors the nodes with high reputation value to ensure the randomness and credibility of the identification nodes. The incentive mechanism is designed to ensure the credibility of the identification results through the credibility analysis of the model based on game theory and Nash equilibrium. Identification nodes that identify illegal data correctly will obtain incentives. In order to obtain a higher income, the identification nodes must identify illegal data correctly. Credibility analysis and comparative experiments show that the probability of selecting credible nodes by RBRSA is up to 23% higher than the random selection algorithm. The probability of selecting the nodes with a reputation value of 20 by RBRSA is 27% lower than the random selection algorithm; that is, the probability that RBRSA selects untrusted nodes is lower. Therefore, the nodes selected by RBRSA have superior credibility compared with other methods. In terms of the effect of the incentive mechanism, the incentive mechanism can encourage nodes to identify data credibly and improve the credibility of identification results. All in all, the trusted model has higher credibility than other methods.
Towards an Intrinsic Interpretability Approach for Multimodal Hate Speech Detection

With the development of social media, multimodal hate speech that relies on images and text has become an emerging way of spreading hate. The detection of multimodal hate speech is gradually becoming an increasingly challenging task. While many works based on neural networks and multimodal machine learning were proposed to detect multimodal hate speech, only few attempts have been made in terms of the interpretability of the task. This leads to difficulties in analyzing prediction results and model improvement. Therefore, this paper investigates the interpretable multimodal hate speech detection task and develops an intrinsically interpretable deep learning method by leveraging the multimodal architecture. Specifically, we leverage a multimodal pretrained model as the backbone of the final detection results and parallel an interpretability module via a joint training approach, which calculates the input tokens and fine-grained tags through a filter-gate attention mechanism. The interpretability module provides an interpretable basis for the final result judgment. We conduct experiments on the hate speech detection dataset and demonstrate that our proposed method not only significantly outperforms other methods but also provides interpretable insights into the decisions of our model.
Friendship links-based privacy-preserving algorithm against inference attacks

Directly publishing the original data of social networks may compromise personal privacy because social relationship data contain sensitive information about users. To protect the social relationships against inference attacks and achieve the trade-off between privacy and utility, we propose a privacy protection algorithm that combines the friendship links of central nodes (PPCN) in a dynamic social network. In the preparation work, we design two indices for user influence based on the characteristics of social networks that can identify central nodes (Definition 1) in a network. Operating central nodes can effectively protect user privacy and improve algorithm efficiency. Then we propose the PPCN algorithm to classify the friendship links of central nodes into three levels, which achieves the trade-off between privacy and utility. Considering that the added links may increase the risk of privacy disclosure, a substitution coefficient θ (Definition 4) is designed to measure the probability of two strangers becoming friends. Experimental results show that the privacy-utility trade-off (PUTO) value of the PPCN algorithm is an average 29.43% lower than that of other methods, achieving a better trade-off between privacy and structural utility. In addition, the PPCN algorithm only runs for 3.59 s, which performs better than most algorithms.
MPSproto: An extension of EuroForMix to evaluate MPS-STR mixtures

We have developed MPSproto as an extension of EuroForMix to improve handling of stutter artefacts and other typing errors that commonly occur in MPS-STR data. MPSproto implements two models for read depth: gamma and negative binomial. It differs from EuroForMix in that calibration is required before mixtures are interpreted. In this study a mixture dataset (2-4 persons) was revisited, where EuroForMix interpretation of MPS-STR mixtures using the LUS+ format was first described; the performance of this model was compared to the MPSproto models. Results indicated that, overall, the MPSproto models performed better than the conventional EuroForMix model, and the gamma model implemented in MPSproto performed best. Differences were highlighted and further investigated to establish causality. Goodness of fit tests showed that the MPSproto models were adequate for the sequence reads when a low analytical threshold was applied.
Tunable Encrypted Deduplication with Attack-resilient Key Management

Conventional encrypted deduplication approaches retain the deduplication capability on duplicate chunks after encryption by always deriving the key for encryption/decryption from the chunk content, but such a deterministic nature causes information leakage due to frequency analysis. We present TED , a tunable encrypted deduplication primitive that provides a tunable mechanism for balancing the tradeoff between storage efficiency and data confidentiality. The core idea of TED is that its key derivation is based on not only the chunk content but also the number of duplicate chunk copies, such that duplicate chunks are encrypted by distinct keys in a controlled manner. In particular, TED allows users to configure a storage blowup factor, under which the information leakage quantified by an information-theoretic measure is minimized for any input workload. In addition, we extend TED with a distributed key management architecture and propose two attack-resilient key generation schemes that trade between performance and fault tolerance. We implement an encrypted deduplication prototype TEDStore to realize TED in networked environments. Evaluation on real-world file system snapshots shows that TED effectively balances the tradeoff between storage efficiency and data confidentiality, with small performance overhead.
Optimizing Higher-Order Correlation Analysis Against Inner Product Masking Scheme

In recent years, inner product masking (IPM) has been proposed as a promising code based masking scheme against side-channel attacks. However, most studies mainly focus on leakages in terms of information-theoretic metrics, and simulated experiments utilizing profiled attacks (with known templates). In this paper, we investigate the security of IPM scheme against one typical non-profiled side-channel attack, namely higher-order correlation analysis. We demonstrate that three factors mainly influence the efficiency of higher-order correlation analysis against IPM, and they are the attack order vector, output size of the target function and attack model. In particular, we propose a new method to measure the efficiency of higher-order correlation analyses. Generally speaking, this method is based on the correlation coefficient between leakage expectations with certain shares and sensitive values under the attack model. We validate our method by both simulation-based experiments and practical one with different attack factors. All experiments are running on the IPM in the different noise scenarios. The results demonstrate that our method works well as an indicator for higher-order correlation analyses against IPM.
Critique of “A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery” by SCC Team From Tsinghua University

Srivastava et al. propose a parallel framework to optimize Bayesian network learning in the SC20 article entitled “A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery”. They parallelize all the phases in network constructing algorithms to achieve high performance and scalability. In this article, we reproduce the strong scaling and weak scaling experiments in that SC article. We conduct experiments on a 4-node cluster with Intel CPUs provided by the SCC committee. We further analyze the results of communication overhead. Our results show that the proposed method in that SC article scales well on the provided cluster, in accordance with the SC article. <!--?query id="Q1"--> Author: Please confirm or add details for any funding or financial support for the research of this article. ?>
Automatic breast carcinoma detection in histopathological micrographs based on Single Shot Multibox Detector

A diagnosis with histological classification by pathologists is very important for appropriate treatments to improve the prognosis of patients with breast cancer. However, the number of pathologists is limited, and assisting the pathological diagnosis by artificial intelligence becomes very important. Here, we presented an automatic breast lesions detection model using microscopic histopathological images based on a Single Shot Multibox Detector (SSD) for the first time and evaluated its significance in assisting the diagnosis.We built the data set and trained the SSD model with 1361 microscopic images and evaluated using 315 images. Pathologists and medical students diagnosed the images with or without the assistance of the model to investigate the significance of our model in assisting the diagnosis.The model achieved 88.3% and 90.5% diagnostic accuracies in 3-class (benign, non-invasive carcinoma, or invasive carcinoma) or 2-class (benign or malignant) classification tasks, respectively, and the mean intersection over union was 0.59. Medical students achieved a remarkably higher diagnostic accuracy score (average 84.7%) with the assistance of the model compared to those without assistance (average 67.4%). Some people diagnosed images in a short time using the assistance of the model (shorten by average 6.4 min) while others required a longer time (extended by 7.2 min).We presented the automatic breast lesions detection method at high speed using histopathological micrographs. The present system may conveniently support the histological diagnosis by pathologists in laboratories.
Exposure of Sensitive Data Through Blockchain Wallets: A Comparative Analysis

Blockchain adoption has been at all-time high in last one decade. With widespread acceptance, blockchain is also subject to criticism from deployment to sensitive data exploitation in blockchain applications. Blockchain wallet is an essential part of blockchain framework and contains the most sensitive data of blockchain application. In 2020, out of 122 blockchain attacks, 27 were found to be solely focused on the wallet. Even though the comparison and analysis of various types of wallets are available on the market, there is no collective mapping of the best wallets in use. This study delves into wallet types, how they work and a comparative analysis of the state of the art on wallets that are currently in use. Presented research study is also helpful in designing a wallet and also gives assistance in choosing a wallet according to the requirement. The findings seem useful for developers and end-users at large, as it brings about the features of the current wallets in use and also proposes a novel design for blockchain wallets.
IoT and Blockchain-Based Method for Device Identity Verification

The Internet of Things (IoT) is nowadays having outstanding developments in the IT industry and for research purposes. However, it actually experiences security and protection liabilities. Traditional security and protection approaches will, in general, be irrelevant for IoT, principally because of its decentralized geography and the asset imperatives of most of its gadgets. Blockchain that supports the cryptographic money Bitcoin has been as of late used to give security and protection in distributed networks with comparative geographies to IoT. In the IoT, ensuring the validity of the identity of a device accessing the network is the basis of security. A computer network consists of nodes. These nodes are linked through the communication links. The nodes are distributed geographically, and the purpose of the network is to transmit between different types of nodes. These nodes can be personal computers, workstations, sensors, etc. Users carrying wearable devices move from one place to another, and there is a possibility of linking to more than one network. That might raise the anomalies of the information in the network. We aim to remove the anomalies by validating the node with the help of blockchain technology. This paper proposes a system and method for verifying identity using IoT and blockchain technologies. The proposed model will detect anomalies by comparing node traffic profile and behavioral information of the node in the blockchain to the observed node activity.
Automatic Text Summarization of Konkani Texts Using Latent Semantic Analysis

Automatic text summarization involves extracting relevant details from the contents of input text documents for generating summaries. This area of Natural Language Processing is widely researched, especially with popular languages like English. There is a need to extend this work to less commonly spoken languages of the world. This paper presents a language-independent text summarization approach using Latent Semantic Analysis in Konkani language. Konkani is a low-resource language with limited language processing tools, stop-word list, etc. Latent Semantic Analysis (LSA) is an unsupervised algebraic method that finds latent semantic structures to be used for performing extractive text summarization. We examined well-known Latent Semantic Analysis-based sentence selection approaches on our dataset, constructed using books on Konkani folk tales written in Devanagari script. The results of the experiments indicated that LSA-based approaches can produce promising summaries, with the Cross method performing the best in most metrics.
Design of a Chatbot for Four- to Ten-Year-Old Children Based on Emotional Intelligence

The development of emotional intelligence in children begins during the early years of a child. Although it is the responsibility of parents to help a child in developing emotional awareness, studies have shown the utility of software systems in aiding this process. In this paper, the author presents the design of an emotionally intelligent chatbot for children. The outcomes of an online survey conducted among the parents reported that 70% of the respondents felt that an emotionally intelligent interactive chatbot can be useful for children to cope with intense subject matters related to low grades, no friends, bullies, and others. The study highlights various features of a chatbot like a user interface, personalization, responsiveness, security, and human intervention. From the findings, the author has suggested five design principles along with the detailed architecture of a chatbot framework. The paper will be useful for future studies that seek to design and develop a highly efficient emotionally intelligent chatbot for children which is trusted by their parents.