Reviewerly/exHarmony huggingface dataset:
The Potential of Absorbing Foreign Agricultural Investment to Improve Food Security in Developing Countries

Global food security has been in a severe condition in recent years, especially in developing countries. These countries need sufficient investment to promote their agricultural development. At the same time, the increasing foreign investment in agriculture has aroused widespread concern in the international community. It is controversial whether it is conducive to the host country’s agriculture and social development. In order to study whether developing countries can promote food security by absorbing adequate and effective foreign agricultural investment, we use principal component analysis and select 135 countries (regions) as samples to construct an indicator system from three aspects: investment demand, investment environment and investment motivation. We use cross-section data consisting of the values of the most recent year updated for each indicator. The results show that the demand for foreign investment in developing countries is strong, but overall, it does not have much investment appeal and the investment environment is bad. There is a gap between the expectations and realities in societies in each developing country in terms of the introduction of foreign agricultural investment to promote food security. Governance and capacity building should be strengthened to reconcile their investment needs with investors’ motives. Meanwhile, more inclusive rules for foreign agricultural investment will help it to play its due role in developing countries.
Medical Image Infosecurity Using Hash Transformation and Optimization-Based Controller in a Health Information System: Case Study in Breast Elastography and X-Ray Image

In recent years, several countries have increasingly promoted digital health to improve medical care quality and precision medicine. Concerns such as how to employ and manage medical data, including physiological signals and medical images, have emerged as one of the core issues in eHealth. The National Health Insurance of Taiwan has gradually generated cross-hospital medical image databases since the year 2018, including lung cancer, brain tumor, breast tumor, liver tumor, and coronary arterial diseases. Digitized medical image data can be stored in cloud databases or be transmitted via computer networks or wireless transmissions. However, patient confidentiality and transmission infosecurity are serious concerns in public channels or spaces, which raises the question of how to prevent data from being stolen, tampered, or peeked after receipt by unauthorized people. Hence, infosecurity has become an important issue in the digital era. This study proposes hash transformation with multi-secret keys and an optimization-based controller to engage a novel cryptographic method to encrypt and decrypt digital medical images in a health information system. Both the gradient descent (GD) and the particle swarm optimization (PSO)-based controllers are implemented to search the decryption key parameters. For a case study in breast elastography and X-ray images consisting of 150 benign tumors and 150 malignant tumors, the peak signal-to-noise ratio (PSNR) is used to evaluate the similarity of two images between the original images and the decrypted images. Conclusively, the PSO-based controller performed better than the GD-based controller and traditional cryptographic methods in terms of recovery reliability.
Smart speaker design and implementation with biometric authentication and advanced voice interaction capability

This publication has emanated from research supported by a research grant fromScience Foundation Ireland (SFI) under Grant Number SFI/16/RC/3918 (Confirm), and SFI/12/RC/2289 P2 (Insight) co-funded by the European RegionalDevelopment Fund.
End-to-End Abstractive Summarization for Meetings.

With the abundance of automatic meeting transcripts, meeting summarization is of great interest to both participants and other parties. Traditional methods of summarizing meetings depend on complex multi-step pipelines that make joint optimization intractable. Meanwhile, there are a handful of deep neural models for text summarization and dialogue systems. However, the semantic structure and styles of meeting transcripts are quite different from articles and conversations. In this paper, we propose a novel end-to-end abstractive summary network that adapts to the meeting scenario. We design a role vector to depict the difference among speakers and a hierarchical structure to accommodate long meeting transcripts. Empirical results show that our model considerably outperforms previous approaches in both automatic metrics and human evaluation. For example, in the ICSI dataset, the ROUGE-1 score increases from 32.00% to 39.51%.
A framework for explainable deep neural models using external knowledge graphs

Deep neural networks (DNNs) have become the gold standard for solving challenging classification problems, especially given complex sensor inputs (e.g., images and video). While DNNs are powerful, they are also brittle, and their inner workings are not fully understood by humans, leading to their use as "black-box" models. DNNs often generalize poorly when provided new data sampled from slightly shifted distributions; DNNs are easily manipulated by adversarial examples; and the decision-making process of DNNs can be difficult for humans to interpret. To address these challenges, we propose integrating DNNs with external sources of semantic knowledge. Large quantities of meaningful, formalized knowledge are available in knowledge graphs and other databases, many of which are publicly obtainable. But at present, these sources are inaccessible to deep neural methods, which can only exploit patterns in the signals they are given to classify. In this work, we conduct experiments on the ADE20K dataset, using scene classification as an example task where combining DNNs with external knowledge graphs can result in more robust and explainable models. We align the atomic concepts present in ADE20K (i.e., objects) to WordNet, a hierarchically-organized lexical database. Using this knowledge graph, we expand the concept categories which can be identified in ADE20K and relate these concepts in a hierarchical manner. The neural architecture we present performs scene classification using these concepts, illuminating a path toward DNNs which can efficiently exploit high-level knowledge in place of excessive quantities of direct sensory input. We hypothesize and experimentally validate that incorporating background knowledge via an external knowledge graph into a deep learning-based model should improve the explainability and robustness of the model.
Phishing Attack Detection: A Solution Based on the Typical Machine Learning Modeling Cycle

The aim of the current study has been the design and development of a model for detecting Phishing attacks using supervised Machine Learning techniques. Thus, we conducted a literature review to identify the features of infected emails by phishing. As a result, a combination of the Naive Bayes and Decision tree algorithms has been constructed using the typical cycle of Machine Learning (ML) modeling. The main tools used have been Jupiter framework and Python. The proof of concept has been performed in a controlled environment. The infected emails has been obtained using PhishTank. Finally, to yield the higher level of accuracy of phishing detection, the validation of results was accomplished using the most accepted algorithms in the scientific field such as ML Random Forest, Logistic Regression and Fictitious Classifier, according to our literature review.
Identification of markers and artificial intelligence-based classification of radical twitter data

Abstract Social networks have recently evolved from a means of connecting people to becoming a tool for social engineering, radicalization, dissemination of propaganda, and recruitment of terrorists. It is no secret that the majority of the Islamic State in Iraq and Syria (ISIS) members are Arabic speakers, and even the non-Arabs adopt Arabic nicknames. However, the majority of the literature researching the subject deals with non-Arabic languages. Moreover, the features involved in identifying radical Islamic content are shallow and the search or classification terms are common in daily chatter among people of the region. We aim at distinguishing normal conversation, influenced by the role religion plays in daily life, from terror-related content. This article presents our experience and the results of collecting, analyzing, and classifying Twitter data from affiliated members of ISIS, as well as sympathizers. We used artificial intelligence and machine learning classification algorithms to categorize the tweets, as terror-related, generic religious, and unrelated. We report the classification accuracy of the K-Nearest Neighbor, Bernoulli Naive Bayes, and Support Vector Machine (One-Against-All and All-Against-All) algorithms. We achieved a high classification F1 score of 83%. The work in this paper will hopefully aid more accurate classification of radical content.
Research on the risk of block chain technology in Internet finance supported by wireless network

Abstract In this paper, a cascaded depth learning framework is constructed. A cascaded depth model is successfully implemented by studying and analyzing the specific feature transformation, feature selection, and classifier algorithm used in the framework. A feature combination method based on enhanced feature selection and classification is proposed according to the different features learned by each layer of the model. Combining block chain cryptography technology, distributed technology, consensus accounting mechanism of technology innovation, transaction data encapsulation into specific format data unit, encapsulated into a linear list in chronological order, using encryption algorithm trading transparency, traceability of data, security, credibility, and uniqueness in financial data analysis. The experimental results show that with the increase of the number of model layers, our method can significantly improve the classification accuracy. This result also verifies that the proposed model can learn more effective data representation features and also verifies the effectiveness of the proposed feature combination method.
Online Streaming Feature Selection via Multi-Conditional Independence and Mutual Information Entropy†

The goals of feature selection are to remove redundant and irrelevant features from high-dimensional data, extract the "optimal feature subset" of the original feature space to improve the classification accuracy, and reduce the time complexity.Traditional feature selection algorithms are based on static feature spaces that are difficult to apply in dynamic streaming data environments.Existing works, such as Alpha-investing and Online Streaming Feature Selection (OSFS), and Scalable and Accurate OnLine Approach (SAOLA), have been proposed to serve the feature selection with streaming feature, but they have drawbacks, including low prediction accuracy and a large number of selected features if the streaming features exhibit characteristics such as low redundancy and high relevance.To address the limitations of the abovementioned works, we propose the algorithm of Online Streaming Feature Selection via Conditional dependence and Mutual information (OSFSCM) for streaming feature, which is found to be superior to Alpha-investing and OSFS for datasets with low redundancy and high relevance.The efficiency of the proposed OSFSCM algorithm is validated through a performance test on widely used datasets, e.g., NIPS 2003 and Causality Workbench.Through extensive experimental results, we demonstrate that OSFSCM significantly improves the prediction accuracy and requires fewer selected features compared with Alpha-investing and OSFS.
The impact of partial source dependence on belief and reliability revision.

In this article, we explore how people revise their belief in a hypothesis and the reliability of sources in circumstances where those sources are either independent or are partially dependent because of their shared, common background. Specifically, we examine people's revision of perceived source reliability by comparison with a formal model of reliability revision proposed by Bovens and Hartmann (2003). This model predicts a U-shaped trajectory for revision in certain circumstances: If a source provides a positive report for an unlikely hypothesis, perceived source reliability should decrease; as additional positive reports emerge, however, estimates of reliability should increase. Participants' updates in our experiment show this U-shaped pattern. Furthermore, participants' responses also respect a second feature of the model, namely that perceived reliability should once again decrease when it becomes known that the sources are partially dependent. Participants revise appropriately both when a specific shared reliability is observed (e.g., sources went to the same, low quality school) and when integrating the possibility of shared reliability. These findings shed light on how people gauge source reliability and integrate reports when multiple sources weigh in on an issue as seen in public debates. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Academic preference based on students’ personality analysis through k-means clustering

It is believed that the education quality increases in line with the knowledge and understanding of students’ personality. The theory and techniques associated with measurement of skills, abilities, attitudes and psychological traits are studied under the field of psychometrics. The students’ psychometric scores and their academic programme of choice from a local university in Malaysia are analysed using k-means. It is found that there are distinctive clusters to differentiate the students’ personality traits and the differences can influence them in choosing certain programmes. Hence, the results are useful to determine the suitable methods to increase the students’ academic performance.
Spatial Attentive Image Aesthetic Assessment

Image aesthetic assessment is challenging as it concerns various relative vague perceptual evaluations. A key factor among them is how to evaluate image layout, finding spatial importance in aesthetics. In this paper, we propose a spatial attentive image aesthetic assessment model to address that factor. Our method exploits the attention mechanism to learn the spatial attention map and aggregate the learned features according to it. This method preserves the image aspect ratio intrinsically, which is vital for image aesthetics as the ratio distortion usually degrades aesthetic evaluation. Numerical experimental results show that the proposed method gets the highest correlation performance with human annotation on a public benchmark, outperforming the existing state-of-arts.
CoCoGUM: Contextual Code Summarization with Multi-Relational GNN on UMLs

Code summaries are short natural language (NL) descriptions of code snippets that help developers better understand and maintain source code. Due to the pivotal role of code summaries in software development and maintenance, there is a surge of works on automatic code summarization to reduce the heavy burdens of developers. However, contemporary approaches only leverage the information within the boundary of the method being summarized (i.e., local context), and ignore that using broader context could assist with code summarization. In this paper, we explore two global context information, namely intra-class and inter-class context information, and propose the model CoCoGUM: Contextual Code Summarization with Multi-Relational Graph Neural Networks on UMLs. CoCoGUM first incorporates class names as the intra-class context, which is further fed to a Transformer-based sentence embedding model to extract the class lexical embeddings. Then, relevant Unified Modeling Language (UML) class diagrams are extracted as inter-class context and we use a Multi-Relational Graph Neural Network (MR-GNN) to encode the class relational embeddings. Class lexical embeddings and class relational embeddings, together with the outputs from code token encoder and AST encoder, are passed to the decoder armed with a two-level attention mechanism to generate high-quality context-aware code summaries. We conduct extensive experiments to evaluate our approach and compare it with other automatic code summarization models. The experimental results show that CoCoGUM outperforms state-of-the-art methods.
Social Network Analysis using RLVECN: Representation Learning via Knowledge-Graph Embeddings and Convolutional Neural-Network

Social Network Analysis (SNA) has become a very interesting research topic with regard to Artificial Intelligence (AI) because a wide range of activities, comprising animate and inanimate entities, can be examined by means of social graphs. Consequently, classification and prediction tasks in SNA remain open problems with respect to AI. Latent representations about social graphs can be effectively exploited for training AI models in a bid to detect clusters via classification of actors as well as predict ties with regard to a given social network. The inherent representations of a social graph are relevant to understanding the nature and dynamics of a given social network. Thus, our research work proposes a unique hybrid model: Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN). RLVECN is designed for studying and extracting meaningful representations from social graphs to aid in node classification, community detection, and link prediction problems. RLVECN utilizes an edge sampling approach for exploiting features of the social graph via learning the context of each actor with respect to its neighboring actors.
From data conditioning, depth imaging and reservoir characterization to machine learning

Disclaimer Text and Data Mining. Any automated analytical technique aimed at analysing text and data in digital form in order to generate information including - but not limited to - patterns, trends and correlations (so-called Text and Data Mining) may only be applied if it is done by a research organisation or cultural heritage institution solely for the purpose of (non-commercial) scientific research as mentioned in Clause 3 of Directive (EU) 2019/790 (the Digital Single Market Directive) and Clause 15n of the Dutch Copyright Act [Auteurswet]. Text and Data Mining for commercial purposes is not allowed; all rights in this respect are reserved pursuant to Clause 4 paragraph 3 of the Digital Single Market Directive and Clause 15o paragraph 1 of the Dutch Copyright Act.
Big Data and Cloud Computing

With evolving technology, a huge data is being generated from everywhere in various forms. The driving factors for the evolution of data, such as retail, media, banking, healthcare, and education, leads to a very large and complex collection of data popularly coined as big data. Handling, management, and analysis of big data seem to be a complicated process. Utilising cloud environment for analysing big data is a recent research trend. Big data analytics can provide cost-effective ways to analyse information quickly and helps in decision making, improvement in services or products. This paper aims to critically review the literature to find current issues and research gaps. This study illustrates the existing solutions and methods provided for big data and its rise in cloud computing technology. Furthermore, this paper throws light on the open research challenges in this domain, stating the scope of future work.
Context Aware Video Caption Generation with Consecutive Differentiable Neural Computer

Recent video captioning models aim at describing all events in a long video. However, their event descriptions do not fully exploit the contextual information included in a video because they lack the ability to remember information changes over time. To address this problem, we propose a novel context-aware video captioning model that generates natural language descriptions based on the improved video context understanding. We introduce an external memory, differential neural computer (DNC), to improve video context understanding. DNC naturally learns to use its internal memory for context understanding and also provides contents of its memory as an output for additional connection. By sequentially connecting DNC-based caption models (DNC augmented LSTM) through this memory information, our consecutively connected DNC architecture can understand the context in a video without explicitly searching for event-wise correlation. Our consecutive DNC is sequentially trained with its language model (LSTM) for each video clip to generate context-aware captions with superior quality. In experiments, we demonstrate that our model provides more natural and coherent captions which reflect previous contextual information. Our model also shows superior quantitative performance on video captioning in terms of BLEU (BLEU@4 4.37), METEOR (9.57), and CIDEr-D (28.08).
Optimizing execution for pipelined‐based distributed deep learning in a heterogeneously networked GPU cluster

Summary Exorbitant resources (computing and memory) are required to train a deep neural network (DNN). Often researchers deploy an approach that uses distributed parallel training to acquire larger models faster on GPUs. This approach has its detriments, though; on one hand, a GPU's expanded capacity to compute also produces bigger bottlenecks in inter‐GPU's communications during model training, and multi‐GPU systems lead to complex connectivity. Workload schedulers then end up having to consider hardware topology and requirements for workload communication, in hopes of allocating GPU resources to optimize execution time and improve usage in a heterogeneous environment. On the other hand, the high memory requirements to train a DNN model make running the training processes on GPUs onerous. To contend with this, we introduce two execution optimization methods based on pipeline‐hybrid parallelism (using both data and model parallelism) in a GPU cluster with heterogeneous networking. First, we propose a model partition algorithm that accelerates pipeline‐hybrid parallelism training between heterogeneously network‐connected GPUs. Second, we introduce a cost‐balanced recomputing algorithm to reduce memory usage in the pipeline mode. Experiments show that our solution (Pipe‐Torch) averages a speedup of 1.4 × compared with data parallelism, and reduces the memory footprint while maintaining pipelined load‐balanced training.