Reviewerly/exHarmony huggingface dataset:
On the role of question encoder sequence model in robust visual question answering

Generalizing beyond the experiences has a significant role in developing robust and practical machine learning systems. It has been shown that current Visual Question Answering (VQA) models are over-dependent on the language-priors (spurious correlations between question-types and their most frequent answers) from the train set and pose poor performance on Out-of-Distribution (OOD) test sets. This conduct negatively affects the robustness of VQA models and restricts them from being utilized in real-world situations. This paper shows that the sequence model architecture used in the question-encoder has a significant role in the OOD performance of VQA models. To demonstrate this, we performed a detailed analysis of various existing RNN-based and Transformer-based question-encoders, and along, we proposed a novel Graph attention network (GAT)-based question-encoder. Our study found that a better choice of sequence model in the question-encoder reduces the over-fit to language biases and improves OOD performance in VQA even without using any additional relatively complex bias-mitigation approaches.
ZenoPS: A Distributed Learning System Integrating Communication Efficiency and Security

Distributed machine learning is primarily motivated by the promise of increased computation power for accelerating training and mitigating privacy concerns. Unlike machine learning on a single device, distributed machine learning requires collaboration and communication among the devices. This creates several new challenges: (1) the heavy communication overhead can be a bottleneck that slows down the training, and (2) the unreliable communication and weaker control over the remote entities make the distributed system vulnerable to systematic failures and malicious attacks. This paper presents a variant of stochastic gradient descent (SGD) with improved communication efficiency and security in distributed environments. Our contributions include (1) a new technique called error reset to adapt both infrequent synchronization and message compression for communication reduction in both synchronous and asynchronous training, (2) new score-based approaches for validating the updates, and (3) integration with both error reset and score-based validation. The proposed system provides communication reduction, both synchronous and asynchronous training, Byzantine tolerance, and local privacy preservation. We evaluate our techniques both theoretically and empirically.
The economic potential of some metacarbonate rocks in Akoko-Edo, Edo state Nigeria

ABSTRACTThe application of metacarbonate rocks depend majorly on their physical and/or chemical properties. The mineralogical and chemical properties of the metacarbonate rocks from Enwan, Bekuma, and Ekpedo metacarbonate deposits in Akoko Edo, Nigeria were determined using combined modal petrographic analysis and XRS-FP quantitative analysis. The modal analyses show that the metacarbonate rocks from Enwan, Ekpedo and Bekuma contain averages of 96.4%, 58.5%, and 77.35% CaCO3 respectively and 3.6%, 41.5% and 13.1% MgO respectively. The mineralogical constituent include calcite, dolomite, quartz, plagioclase and muscovite. The principal constituents of the carbonate rocks are calcite and dolomite with calcite being the dominant mineral. The marbles can be used in a variety of calcium carbonate industries due to the low quartz content and the mineralogical composition. These characteristics make the metacarbonate rocks suitable for the production of paint, factory lime, pesticide and animal feed, as construction material and for use in the chemical industry.KEYWORDS: Metacarbonate rockAkoko Edogeochemistrypetrographic characteristicsindustrial materialeconomic potential Disclosure statementNo potential conflict of interest was reported by the author(s).
Construction Grammar Conceptual Network: Coordination-based graph method for semantic association analysis

Abstract In this article, we present the Construction Grammar Conceptual Network method, developed for identifying lexical similarity and word sense discrimination in a syntactically tagged corpus, based on the cognitive linguistic assumption that coordination construction instantiates conceptual relatedness. This graph analysis method projects a semantic value onto a given coordinated syntactic dependency and constructs a second-order lexical network of lexical collocates with a high co-occurrence measure. The subsequent process of clustering and pruning the graph reveals lexical communities with high conceptual similarity, which are interpreted as associated senses of the source lexeme. We demonstrate the theory and its application to the task of identifying the conceptual structure and different meanings of nouns, adjectives and verbs using examples from different corpora, and explain the modulating effects of linguistic and graph parameters. This graph approach is based on syntactic dependency processing and can be used as a complementary method to other contemporary natural language processing resources to enrich semantic tasks such as word disambiguation, domain relatedness, sense structure, identification of synonymy, metonymy, and metaphoricity, as well as to automate comprehensive meta-reasoning about languages and identify cross/intra-cultural discourse variations of prototypical conceptualization patterns and knowledge representations. As a contribution, we provide a web-based app at http://emocnet.uniri.hr/ .
Blockchain based optimized data storage with secure communication for Internet of Vehicles considering active, passive, and double spending attacks

In the study, we propose a double blockchain based vehicular system for optimizing data storage and securing communication in Internet of Vehicles (IoV) networks. We propose an incentive mechanism and a reputation system to reduce the selfish and distrustful behavior of IoV nodes. Moreover, we propose an encryption technique to protect the proposed system from passive and active attacks. The proposed technique is a hybrid of Rivest Shamir Adleman and Advanced Encryption Standard-256 (AES-256+RSA). We use this hybrid approach to efficiently and secretly share the users' secret keys. To address the issues of high resource consumption and communication delay, we propose a cache memory technique for miner and controller nodes. In addition, we propose an authentication mechanism to remove malicious nodes from the system. The simulation results depict that our system provides security, scalability, optimized data storage, node's trustworthiness, and transparent data sharing.
ICT Usage for Cross-Curricular Connections in Music and Visual Arts during Emergency Remote Teaching in Slovenia

Due to the COVID-19 pandemic, the entire process of teaching and learning moved online. This forced teachers and pupils to heavily rely on information and communications technology (ICT) and make adjustments to the new mode of teaching and learning in educational institutions. We conducted a qualitative case study by interviewing 24 teachers from Slovene primary schools focusing on the implementation of cross-curricular connections in music and visual arts content with the support of ICT during the period of emergency remote teaching. We found that when planning and implementing the cross-curricular learning process, teachers insufficiently took advantage of possibilities offered by modern ICT. The manner of implementing cross-curricular connections showed uncertainties in terms of understanding their specifics, resulting in the inefficient transfer of concepts taught, the results of which were seen in pupils’ work. This might additionally show the negative influence of parental supervision on the creative thinking and expression of pupils. The present study emphasizes the lack of ICT competences on the part of all participants in the educational process. Our findings show the need to educate teachers by eliminating the uncertainties related to the implementation of distant cross-curricular connections while meaningfully applying ICT adapted to pupils’ competences.
Cybersecurity Law-based Insurance Market

Purpose: A cyber insurance policy's purpose is to help in the recovering of a person or corporation following a cyber breach and to compensate for civil suit expenses stemming from first- and third-party responsibility claims.Methodology: The usage of cybersecurity spending has forecast a variety of security categories using F&S projection methodology. Each of these is suited to the end-user organisations of in-scope security mechanisms, as well as the particular market circumstances. Critical national infrastructure (CNI), immigration control, big events, first responding, executive branch, infrastructure, and transportation security are among the worldwide forecast categories. This segmentation is further subdivided into 16 subsegments, each with its own security forecasting system. F&S protection marketplaces are anticipated using a bottom-up technique for each nation, which adds up to worldwide market penetration. This covers 177 nations spread throughout seven zones.Findings: The cybersecurity insurer industry was valued at USD 7.36 billion in 2020 and is predicted to be worth USD 27.83 billion by 2026, growing at a compound annual growth rate (CAGR) of 24.30% during the forecast time frame (2021–2026). The expanding use of digitalisation innovations such as the cloud, big data, mobile computing, internet of things (IoT), and artificial intelligence (AI) across more lines of employment and society, as well as improved connectivity, have enhanced the burden of already overburdened information technology (IT) staff.Practical implications: Accepted the innovative Insurance Data Security Model Law (#668), which necessitates insurance providers and other agencies registered by government insurance agencies to advance, integrate, and establish an information security management system; start investigating any cybersecurity events; and advise the private insurance superintendent of such happenings. Too far, the approach has been embraced by governorates.
On Paraconsistent Belief Revision in LP

Belief revision aims at incorporating, in a rational way, a new piece of information into the beliefs of an agent. Most works in belief revision suppose a classical logic setting, where the beliefs of the agent are consistent. Moreover, the consistency postulate states that the result of the revision should be consistent if the new piece of information is consistent. But in real applications it may easily happen that (some parts of) the beliefs of the agent are not consistent. In this case then it seems reasonable to use paraconsistent logics to derive sensible conclusions from these inconsistent beliefs. However, in this context, the standard belief revision postulates trivialize the revision process. In this work we discuss how to adapt these postulates when the underlying logic is Priest's LP logic, in order to model a rational change, while being a conservative extension of AGM/KM belief revision. This implies, in particular, to adequately adapt the notion of expansion. We provide a representation theorem and some examples of belief revision operators in this setting.
Verification of Classification Model and Dendritic Neuron Model Based on Machine Learning

Artificial neural networks have achieved a great success in simulating the information processing mechanism and process of neuron supervised learning, such as classification. However, traditional artificial neurons still have many problems such as slow and difficult training. This paper proposes a new dendrite neuron model (DNM), which combines metaheuristic algorithm and dendrite neuron model effectively. Eight learning algorithms including traditional backpropagation, classic evolutionary algorithms such as biogeography-based optimization, particle swarm optimization, genetic algorithm, population-based incremental learning, competitive swarm optimization, differential evolution, and state-of-the-art jSO algorithm are used for training of dendritic neuron model. The optimal combination of user-defined parameters of model has been systemically investigated, and four different datasets involving classification problem are investigated using proposed DNM. Compared with common machine learning methods such as decision tree, support vector machine, k-nearest neighbor, and artificial neural networks, dendritic neuron model trained by biogeography-based optimization has significant advantages. It has the characteristics of simple structure and low cost and can be used as a neuron model to solve practical problems with a high precision.
Multiple userids identification with deep learning

People are becoming increasingly active on social media. It is common knowledge that most users register more than one social media account. Some users use multiple accounts to publish fake reviews to disorder the regular social network. To identify multiple accounts of the same author, multiple userids identification problem is defined. Existing methods for multiple userids identification problem focus on human-designed shallow statistical features and do not fully utilize the deep semantic information in user-generated texts. This paper uses the deep learning model to extract deep semantic features at the document level and user level. Then the most similar user pairs can be selected by computing the similarity of their writing styles indicated by text features. The experimental results demonstrate that our deep learning method outperforms state-of-the-art methods.
Defining Decentralisation in Permissionless Blockchain Systems

The term decentralised as a description of the architecture, operation, and governance of permissionless blockchain systems has become ubiquitous. However, in these contexts, the term decentralised has no clear definition. Blockchain ecosystems are complex, and thus it is essential to address confusion among stakeholders about their nature and promote understanding of the intentions and consequences of their implementation. This article offers a theoretical definition of the term decentralised in the context of permissionless blockchain systems. It is proposed that five inextricable and interconnected aspects are required, at a minimum, to warrant a claim that a permissionless blockchain system is decentralised. These aspects are disintermediation, a peer-to-peer network, a distributed blockchain data structure, algorithmic trust, and open-source principles. The relationship between the five aspects is discussed, and it is argued that decentralisation is not binary but exists on a spectrum. Any variation in one or more aspects may impact the system’s decentralised nature as a whole. The researchers identify areas where further investigation in this field is required and propose instances where the knowledge garnered may be used.
Extracting Prominent Aspects of Online Customer Reviews: A Data-Driven Approach to Big Data Analytics

Sentiment analysis on social media and e-markets has become an emerging trend. Extracting aspect terms for structure-free text is the primary task incorporated in the aspect-based sentiment analysis. This significance relies on the dependency of other tasks on the results it provides, which directly influences the accuracy of the final results of the sentiment analysis. In this work, we propose an aspect term extraction model to identify the prominent aspects. The model is based on clustering the word vectors generated using the pre-trained word embedding model. Dimensionality reduction was employed to improve the quality of word clusters obtained using the K-Means++ clustering algorithm. The proposed model was tested on the real datasets collected from online retailers’ websites and the SemEval-14 dataset. Results show that our model outperforms the baseline models.
Research on Data Fusion Method Based on Multisource Data Awareness of Internet of Things

The diversity of big data in Internet of Things is one of the important characteristics that distinguish it from traditional big data. Big data of Internet of Things is often composed of a variety of data with different structural forms. The description of the same thing by these different modal data has certain independence and strong relevance. Accurately and efficiently extracting and processing the hidden fusion information in the big data of the Internet of Things is helpful to solve various multimodal data analysis tasks at present. In this paper, a multimodal interactive function fusion model based on attention mechanism is proposed, which provides more efficient and accurate information for emotion classification tasks. Firstly, a sparse noise reduction self-encoder is used to extract text features, Secondly, features are extracted by encoder. Finally, an interactive fusion module is constructed, which makes text features and image features learn their internal information then the combination function is applied to the emotion classification task.
Improved mayfly algorithm based on hybrid mutation

To improve the diversity and performance of the mayfly algorithm (MA), this letter adopts the mutation strategies in the process of MA. The opposition-based learning (OBL) and Cauchy mutation strategies are used to mutate the global optimal solution, and the artificial mutation operator is used in the offspring population. The hybrid mutation strategies are used in a cascaded structure. The performance of the proposed algorithms is demonstrated in simulations comparatively.
Multimodal Image Aesthetic Prediction with Missing Modality

With the increasing growth of multimedia data on the Internet, multimodal image aesthetic assessment has attracted a great deal of attention in the image processing community. However, traditional multimodal methods often have the following two problems: (1) Existing multimodal image aesthetic methods are based on the assumption that full modalities are available in all samples, which is unapplicable in most cases since textual information is more difficult to obtain. (2) They only fuse multimodal information at a single level and ignore their interaction at different levels. To address these two challenges, we proposed a novel framework termed Missing-Modility-Multimodal-Bert networks (MMMB). To achieve the completeness, we first generate the missing textual modality conditioned on the available visual modality. We then project the image features to the token space of the text, and use the transformer’s self-attention mechanism to make the two different modalities information interact at different levels for earlier and more fine-grained fusion, rather than only at the final layer. A large number of experiments on two large benchmark datasets in the field of image aesthetic quality evaluation: AVA and Photo.net demonstrate that the proposed model significantly improves image aesthetic assessment performance under both textual missing modality condition and full-modality condition.
Walsh–Hadamard Kernel Feature-Based Image Compression Using DCT with Bi-Level Quantization

To meet the high bit rate requirements in many multimedia applications, a lossy image compression algorithm based on Walsh–Hadamard kernel-based feature extraction, discrete cosine transform (DCT), and bi-level quantization is proposed in this paper. The selection of the quantization matrix of the block is made based on a weighted combination of the block feature strength (BFS) of the block extracted by projecting the selected Walsh–Hadamard basis kernels on an image block. The BFS is compared with an automatically generated threshold for applying the specific quantization matrix for compression. In this paper, higher BFS blocks are processed via DCT and high Q matrix, and blocks with lower feature strength are processed via DCT and low Q matrix. So, blocks with higher feature strength are less compressed and vice versa. The proposed algorithm is compared to different DCT and block truncation coding (BTC)-based approaches based on the quality parameters, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) at constant bits per pixel (bpp). The proposed method shows significant improvements in performance over standard JPEG and recent approaches at lower bpp. It achieved an average PSNR of 35.61 dB and an average SSIM of 0.90 at a bpp of 0.5 and better perceptual quality with lower visual artifacts.
Enhance Cross-Domain Aspect-Based Sentiment Analysis by Incorporating Commonsense Relational Structure (Student Abstract)

Aspect Based Sentiment Analysis (ABSA) aims to extract aspect terms and identify the sentiment polarities towards each extracted aspect term. Currently, syntactic information is seen as the bridge for the domain adaptation and achieves remarkable performance. However, the transferable syntactic knowledge is complex and diverse, which causes the transfer error problem in domain adaptation. In our paper, we propose a domain-shared relational structure incorporated cross-domain ABSA model. The experimental results show the effectiveness of our model.
Metode Prototype pada Sistem Informasi Manajemen Tugas Akhir Mahasiswa Berbasis Website

Final Project as a Research is one of the main requirements for the graduation of undergraduate students from universities. Supervisory guidance is one way to find solution for student research problems. The final project guidance process involves multiple parts: campus administration, supervisors, and students. However, this process is often difficult to carry out properly because of the busyness of each individual, both lecturers and students. As a result, the process for carrying out the final project was hindered. Another thing that makes the completion of the thesis slow is the administrative service procedure to complete the final project which is too time consuming. This research aims to simplify and speed up the process of completing the student's final project. The prototype method is used to develop the final project management information system. This information system is designed using use case diagrams, rich picture diagrams, and entity relation diagrams. Tests were carried out using the black box method. The result of the research is a final project management information system which makes the student's final project completion process more efficient and effective. This information system facilitates administrative services and monitoring of the final project because it is performed systematically and can be controlled in real time.