Reviewerly/exHarmony huggingface dataset:
Expanded graph embedding for joint network alignment and link prediction

Abstract Link prediction in social networks has been an active field of study in recent years fueled by the rapid growth of many social networks. Many link prediction methods are harmed by users’ intention of avoiding being traced across networks. They may provide inaccurate information or overlook a great deal of information in multiple networks. This problem was overcome by developing methods for predicting links in a network based on known links in another network. Node alignment between the two networks significantly improves the efficiency of those methods. This research proposes a new embedding method to improve link prediction and node alignment results. The proposed embedding method is based on the Expanded Graph, which is our new novel network that has edges from both networks in addition to edges across the networks. Matrix factorization on the Finite Step Transition and Laplacian similarity matrices of the Expanded Graph has been used to obtain the embeddings for the nodes. Using the proposed embedding techniques, we jointly run network alignment and link prediction tasks iteratively to let them optimize each other’s results. We performed extensive experiments on many datasets to examine the proposed method. We achieved significant improvements in link prediction precision, which was 50% better than the peer’s method, and in recall, which was 500% better in some datasets. We also scale down the processing time of the solution to be more applicable to big social networks. We conclude that computed embedding in this type of problem is more suitable than learning the embedding since it shortens the processing time and gives better results.
EvidenceNet: Evidence Fusion Network for Fact Verification

Fact verification is a challenging task that requires the retrieval of multiple pieces of evidence from a reliable corpus for verifying the truthfulness of a claim. Although the current methods have achieved satisfactory performance, they still suffer from one or more of the following three problems: (1) unable to extract sufficient contextual information from the evidence sentences; (2) containing redundant evidence information and (3) incapable of capturing the interaction between claim and evidence. To tackle the problems, we propose an evidence fusion network called EvidenceNet. The proposed EvidenceNet model captures global contextual information from various levels of evidence information for deep understanding. Moreover, a gating mechanism is designed to filter out redundant information in evidence. In addition, a symmetrical interaction attention mechanism is also proposed for identifying the interaction between claim and evidence. We conduct extensive experiments based on the FEVER dataset. The experimental results have shown that the proposed EvidenceNet model outperforms the current fact verification methods and achieves the state-of-the-art performance.
Banknote and obstacle detection system for visually impaired people

This paper presents the development of two perception modules for visually impaired people. In the first module, a robust YOLO-based neural network model is proposed to recognise the denomination of American, European, Mexican, and Colombian banknotes, achieving a detection performance over 97 % over all denominations. In the second module, an efficient obstacle detection algorithm based on stereo-vision is developed to prevent collisions while walking. Several performance tests and simulations were carried out using each module, proving that each is accurate enough to help blind people to complete the proposed perception tasks.
Virtual reality utility and usefulness in the furniture, fixture and equipment sector: a validation of interactive and distributed immersion

Purpose The Furniture, Fixture and Equipment (FFE) sector is well placed to leverage virtual reality (VR) technology for competitive and operational advantages; however, the diffusion of VR applications in this sector has followed a steep curve. This study reports on the implementation of two novel VR applications in the FFE sector and also investigates the challenges and benefits associated with their use and adaptability. Design/methodology/approach A sequential exploratory mixed research methodology consisting of three phases was adopted for this study. This included identification of factors that affect/facilitate the implementation of VR (Challenges and Benefits) using experiments during in-house prototyping of VR applications, a rigorous literature review and questionnaire survey to solicit FFE Stakeholder's ( n = 117) opinion on the utility and usefulness of the proposed applications and to the understand factors that facilitate and inhibit their implementation in FFE's context, particularly as a design communication and coordination tool. Findings The findings of this study revealed that distributed and single-user VR has become essential to digitalising the FFE sector's design communication with improved design communication being regarded as the most important benefit of its use. Conversely, the most critical challenge that inhibits the implementation of these two VR applications in the FFE sector is the perceived cost. Originality/value This study provides valuable insight to FFE's stakeholders to devise action plans to mitigate myriad complex and interrelated factors that affect the adoption of virtual reality technology in the FFE sector that are otherwise very hard to understand, and the consequential implementation of any mitigation plans cannot be devised.
Learning to predict in-hospital mortality risk in the intensive care unit with attention-based temporal convolution network

Dynamic prediction of patient mortality risk in the ICU with time series data is limited due to high dimensionality, uncertainty in sampling intervals, and other issues. A new deep learning method, temporal convolution network (TCN), makes it possible to deal with complex clinical time series data in ICU. We aimed to develop and validate it to predict mortality risk using time series data from MIMIC III dataset.A total of 21,139 records of ICU stays were analysed and 17 physiological variables from the MIMIC III dataset were used to predict mortality risk. Then we compared the model performance of the attention-based TCN with that of traditional artificial intelligence (AI) methods.The area under receiver operating characteristic (AUCROC) and area under precision-recall curve (AUC-PR) of attention-based TCN for predicting the mortality risk 48 h after ICU admission were 0.837 (0.824 -0.850) and 0.454, respectively. The sensitivity and specificity of attention-based TCN were 67.1% and 82.6%, respectively, compared to the traditional AI method, which had a low sensitivity (< 50%).The attention-based TCN model achieved better performance in the prediction of mortality risk with time series data than traditional AI methods and conventional score-based models. The attention-based TCN mortality risk model has the potential for helping decision-making for critical patients.Data used for the prediction of mortality risk were extracted from the freely accessible MIMIC III dataset. The project was approved by the Institutional Review Boards of Beth Israel Deaconess Medical Center (Boston, MA) and the Massachusetts Institute of Technology (Cambridge, MA). Requirement for individual patient consent was waived because the project did not impact clinical care and all protected health information was deidentified. The data were accessed via a data use agreement between PhysioNet, a National Institutes of Health-supported data repository (https://www.physionet.org/), and one of us (Yu-wen Chen, Certification Number: 28341490). All methods were carried out in accordance with the institutional guidelines and regulations.
Rethinking Offensive Text Detection as a Multi-Hop Reasoning Problem

We introduce the task of implicit offensive text detection in dialogues, where a statement may have either an offensive or non-offensive interpretation, depending on the listener and context.We argue that reasoning is crucial for understanding this broader class of offensive utterances and release SLIGHT, a dataset to support research on this task.Experiments using the data show that state-of-the-art methods of offense detection perform poorly when asked to detect implicitly offensive statements, achieving only ∼11% accuracy.In contrast to existing offensive text detection datasets, SLIGHT features human-annotated chains of reasoning which describe the mental process by which an offensive interpretation can be reached from each ambiguous statement.We explore the potential for a multi-hop reasoning approach by utilizing existing entailment models to score the probability of these chains and show that even naive reasoning models can yield improved performance in most situations.Furthermore, analysis of the chains provides insight into the human interpretation process and emphasizes the importance of incorporating additional commonsense knowledge.
Linguistic-Acoustic Similarity Based Accent Shift for Accent Recognition

General accent recognition (AR) models tend to directly extract low-level information from spectrums, which always significantly overfit on speakers or channels. Considering accent can be regarded as a series of shifts relative to native pronunciation, distinguishing accents will be an easier task with accent shift as input. But due to the lack of native utterance as an anchor, estimating the accent shift is difficult. In this paper, we propose linguistic-acoustic similarity based accent shift (LASAS) for AR tasks. For an accent speech utterance, after mapping the corresponding text vector to multiple accent-associated spaces as anchors, its accent shift could be estimated by the similarities between the acoustic embedding and those anchors. Then, we concatenate the accent shift with a dimension-reduced text vector to obtain a linguistic-acoustic bimodal representation. Compared with pure acoustic embedding, the bimodal representation is richer and more clear by taking full advantage of both linguistic and acoustic information, which can effectively improve AR performance. Experiments on Accented English Speech Recognition Challenge (AESRC) dataset show that our method achieves 77.42% accuracy on Test set, obtaining a 6.94% relative improvement over a competitive system in the challenge.
THE IMPLEMENTATION OF GRAMMAR TRANSLATION METHOD TO BETTER SPEAKING COMPETENCE SUPPORTED BY DEVELOPING READING AND LISTENING SKILLS

Learning to talk is difficult. The students had difficulty communicating their views to others. A study was done to assist them in correcting their flaw. The treatment enlisted the participation of 30 students from three different schools. Each school assigned ten individuals at random. There were a total of 13 learning opportunities. A pre-test was completed by the students in order to conduct such a treatment. It is a method of determining their prior understanding of the English language. The teacher uses the grammar translation method to begin the learning process. The teacher used podcasts to help students improve their grammatical understanding. The information was presented in the form of short stories and conversations. Students learn vocabulary, hearing, speaking, and grammar by modifying them. The students took a post-test at the end of the treatment. To answer the research question, data from the pre-test, post-test, and document were analyzed using the mixmethod method. From the analysis, it can be concluded that employing the grammar translation approach and podcast material can assist students improve their speaking skills. Other teachers could use the same outcome to teach related subjects. Other researchers can build on them to get other results.
MLFFNet: multilevel feature fusion network for monocular depth estimation from aerial images

Convincing depth estimation from monocular aerial images is a fundamental task in environment perception. We propose an efficient multilevel feature fusion network (MLFFNet) for estimating depths from aerial images. Specifically, the proposed MLFFNet consists of a multi-level attention pyramid (MAP) module and an adaptive feature fusion (AFF) module. The MAP module extracts the low-level and high-level useful information from the perspective of nonlocal spatial attention and channel attention, while the AFF module adaptively integrates the extracted multilevel features to enhance the estimated effect. Moreover, since images taken by drones have a large depth range, we designed a loss function suitable for aerial images. The evaluation experiments are performed on the MidAir dataset. Experimental results denote that our MLFFNet outperforms other depth estimation methods in predicting the depths. We also test several images from real-life scenarios, and our method can obtain reasonable outputs.
Convolutional Neural Network-Based Radar Antenna Scanning Period Recognition

The antenna scanning period (ASP) of radar is a crucial parameter in electronic warfare (EW) which is used in many applications, such as radar work pattern recognition and emitter recognition. For antennas of radars and EW systems, which perform scanning circularly, the method based on threshold measurement is invalid. To overcome this shortcoming, this study proposes a method using the convolutional neural network (CNN) to recognize the ASP of radar under the condition that antennas of the radar and EW system both scan circularly. A system model is constructed, and factors affecting the received signal power are analyzed. A CNN model for rapid and accurate ASP radar classification is developed. A large number of received signal time–power images of three separate ASPs are used for the training and testing of the developed model under different experimental conditions. Numerical experiment results and performance comparison demonstrate high classification accuracy and effectiveness of the proposed method in the condition that antennas of radar and EW system are circular scan, where the average recognition accuracy for radar ASP is at least 90% when the signal to-noise ratio (SNR) is not less than 30 dB, which is significantly higher than the recognition accuracy of NAC and AFT methods based on adaptive threshold detection.
Situational Teaching Based Evaluation of College Students’ English Reading, Listening, and Speaking Ability

To effectively enhance college students’ English Reading, Listening, and Speaking (RLS) ability, it is important to explore the teaching contents, teaching methods, and the factors affecting the development of college students’ English RLS ability. The existing literature has not clearly defined college students’ English RLS ability, or selected a proper method to evaluate the ability scientifically. To solve the problems, this paper tries to evaluate college students’ English RLS ability based on situational teaching. Firstly, the authors analyzed the teaching idea of college students’ English RLS courses, and established an evaluation index system (EIS) for their RLS ability. After examining the evaluation method, the backpropagation neural network (BPNN) was optimized by genetic algorithm, and used to construct an evaluation model for college students’ English RLS ability. The feasibility of the model was demonstrated through experiments.
Multisource Data Hiding in Digital Images

In this paper, we propose a new data-hiding framework: multisource data hiding, in which multiple senders (multiple sources) are able to transmit different secret data to a receiver via the same cover image symmetrically. We propose two multisource data-hiding schemes, i.e., separable and anonymous, according to different applications. In the separable scheme, the receiver can extract the secret data transmitted by all senders using the symmetrical data-hiding key. A sender is unable to know the content of the secret data that is not transmitted by them (non-source sender). In the anonymous scheme, it is unnecessary to extract all secret data on the receiver side. The content extracted by the receiver is a co-determined result of the secret data transmitted by all senders. Details of the secret data are unknown to the receiver and the non-source senders. In addition, the two proposed schemes achieve multisource data hiding without decreasing the undetectability of data hiding.
Gaussian Kernel Variance for an Adaptive Learning Method on Signals Over Graphs

This paper discusses a special kind of a simple yet possibly powerful algorithm, called single-kernel Gradraker (SKG), which is an adaptive learning method predicting unknown nodal values in a network using known nodal values and the network structure. We aim to find out how to configure the special kind of the model in applying the algorithm. To be more specific, we focus on SKG with a Gaussian kernel and specify how to find a suitable variance for the kernel. To do so, we introduce two variables with which we are able to set up requirements on the variance of the Gaussian kernel to achieve (near-) optimal performance and can better understand how SKG works. Our contribution is that we introduce two variables as analysis tools, illustrate how predictions will be affected under different Gaussian kernels, and provide an algorithm finding a suitable Gaussian kernel for SKG with knowledge about the training network. Simulation results on real datasets are provided.
Revealing the Reflections of the Pandemic by Investigating COVID-19 Related News Articles Using Machine Learning and Network Analysis

Social media data can provide a general idea of people’s response towards the COVID-19 outbreak and its reflections, but it cannot be as objective as the news articles as a source of information. They are valuable sources of data for natural language processing research as they can reveal various paradigms about different phenomena related to the pandemic. This study uses a news collection spanning nine months from 2019 to 2020, containing COVID-19 related articles from various organizations around the world. The investigation conducted on the collection aims at revealing the repercussions of the pandemic at multiple levels. The first investigation discloses the most mentioned problems covered during the pandemic using statistics. Meanwhile, the second investigation utilizes machine learning to determine the most prevalent topics present within the articles to provide a better picture of the pandemic-induced issues. The results show that the economy was among the most prevalent problems. The third investigation constructs lexical networks from the articles, and reveals how every problem is related through nodes and weighted connections. The findings exhibit the need for more research using machine learning and natural language processing techniques on similar data collections to unveil the full repercussions of the pandemic.
Learning Robust Representation Through Graph Adversarial Contrastive Learning

Existing studies show that node representations generated by graph neural networks (GNNs) are vulnerable to adversarial attacks, such as unnoticeable perturbations of adjacent matrix and node features. Thus, it is requisite to learn robust representations in graph neural networks. To improve the robustness of graph representation learning, we propose a novel Graph Adversarial Contrastive Learning framework (GraphACL) by introducing adversarial augmentations into graph self-supervised learning. In this framework, we maximize the mutual information between local and global representations of a perturbed graph and its adversarial augmentations, where the adversarial graphs can be generated in either supervised or unsupervised approaches. Based on the Information Bottleneck Principle, we theoretically prove that our method could obtain a much tighter bound, thus improving the robustness of graph representation learning. Empirically, we evaluate several methods on a range of node classification benchmarks and the results demonstrate GraphACL could achieve comparable accuracy over previous supervised methods.
National EPOS initiatives and participation to the EPOS integration plan

European Plate Observing System (EPOS) is designed on a three-level architecture. The national research infrastructures (NRIs) constitute the backbone of the EPOS delivery framework, where data are generated, processed, analyzed and archived. These data are then integrated by thematic core services (TCS) and distributed through the centralized integrated core services (ICS). In this architecture, data provision from the NRIs is an essential element for the sustainable operation of the EPOS research infrastructure (RI). National EPOS initiatives in various countries in Europe are developed thanks to the increased awareness of the importance of FAIR (Findable, Accessible, Interoperable and Reusable) data management in science. As such, out of the 14 countries (13 members and one observer) that constitute the EPOS European Research Infrastructure Consortium (EPOS-ERIC), 11 have dedicated EPOS consortia established and included in the national roadmaps for research infrastructures. Moreover, there are in total 24 countries involved in the EPOS delivery framework where 10 are not yet members of EPOS-ERIC. However, the diversity of regulations and procedures adopted in different countries, hampers the development of dedicated EPOS consortia contributing to sustainability. In this paper, the national EPOS initiatives are discussed in order to emphasize synergies achieved and the shared efforts to build the EPOS RI during its life-cycle (the design, preparation, implementation, and pilot operational phases), tackling the challenge of sustainable operation.
An Information System Risk Management of a Higher Education Computing Environment

Cyber risks, data loss or data leakage, loss exposure are one of the most customer and business significant threats.Those data contained information and were stored in electronic form that made them vulnerable to be hacked.The major target of hackers intruding is the higher education institutions.Therefore, many organizations perform information system risk management to identify their weaknesses and enforce the security of their system.The study aims to identify, analyze, and measure the risks associated with information systems specifically evolve in the higher education sector environment.Then it provides solutions and recommendations for the higher education sector to improve the quality of their information systems.Information system risk management was performed in the computing environment of the Faculty of Medicine, X University.It was conducted using the OCTAVE Allegro framework.The framework can streamline and optimize the information system risk management process through eight steps and various worksheets and questionnaire sheets for guidelines.After completing all the required data, the analysis was conducted to determine the critical information assets for the organization.The results showed that there were 8 (eight) critical information assets.One of them is the Student Profile.It was continued to be assessed using a chronological approach of information system risk management for improving security awareness and formulating mitigation strategies as the control actions.This paper's analysis and results are expected to contribute to the implementation of information system risk management for real case applications in different sectors.