Reviewerly/exHarmony huggingface dataset:
Blockchain Application in Emergency Materials Distribution Under Disaster: An Architectural Design and Investigation

In recent years, natural disasters have been raging around the world, among which COVID-19, a public health pandemic, is still spreading all over the world. Emergency material distribution (EMD) in rescue work is facing serious challenges in the term of coordination, efficiency, traceability, identification, credibility, and security, privacy, and transparency. To address the issues, this paper develops a framework based on blockchain technology so that the operation information of EMD can be safely and promptly transmitted to the blockchain system. The scheme could achieve to query, track and record the operation status of emergency material collection, vehicle scheduling, inventory management, relief material allocation and others in real time. By using this framework, a case study on medical rescue materials distribution for COVID-19 is provided to illustrate how blockchain technology and Internet of Things (IoT) technology can be effective. The research provides insights on using the disruptive blockchain technology to effectively coordinate the deployment of emergency supplies, vehicles and personnel, and improve the transparency and credibility of rescue work to minimize the losses caused by disasters.
E-government, political system and COVID-19 in Africa: lessons for future disruptive shocks

Purpose This study aims to explore the role of mobile tracing applications as part of e-government services in combating the COVID-19 pandemic effects in Africa by analyzing the moderating role of sustainable development. This study also investigated the role of the political and economic systems in mitigating the negative consequences of COVID-19 and how e-government interacts in this relationship. Design/methodology/approach This study included the COVID-19 performance index for 94 countries belonging to different regions, including 20 African countries. Multiple linear regression was used for data analysis via Stata software. The study was conducted from the start of the pandemic to March 13, 2021. Findings The results show that less economically and technologically developed countries with generally authoritarian political systems, including African countries, could limit the spread of the pandemic better than some democratic, economically and technologically developed countries in the first wave of the pandemic. The promotion of sustainable development goals moderates the relationship between mobile tracing applications as part of the e-government service and the fight against COVID-19. Originality/value This study provides insight into the role of mobile application technology as an e-governance service in mitigating the negative consequences of the COVID-19 pandemic in a context characterized by economic limitations, fragile public health infrastructure and relatively high political instability, especially in Africa. The findings shed light on some of the difficulties African countries may face in incorporating technology into their development projects.
A Study on the Relationship between Slow Journalism and Virtual Reality: The New York Times and The Guardian Samples

The present study has highlighted the relationship between virtual reality and slow journalism in news presentation and has sought an answer to the question whether this relationship would create a journalism model that prioritizes sensation and empathy required by the society. To this end, a content analysis was conducted in the context of slow journalism on a total of 316 news stories on the YouTube channels of The New York Times and The Guardian newspapers that were prepared with virtual reality applications. The findings of the analysis have revealed that the news stories (VR news) prepared using the virtual reality technology hold the potential to enable the users to sense the events more deeply. It has also been found that VR news stories are mainly selected from timeless topics that are among the most important elements of slow journalism and prepared and presented with a narrative language. It is believed that when virtual reality experiences and slow journalism contents are brought together, it is possible to produce a sensitive journalism practice that has a potential to prioritize sensation, feeling and empathy and destroy prejudices formed over stereotypes. Through such news, the audience will be able to feel, sense and finally empathize with the event enjoying the technical features of virtual reality applications that enable storytelling. Drawing on the fact that media is an important means of cultural transmission and adopting the principles of slow journalism, it is thought that news content prepared with the virtual reality technology has a transformative effect on society.
Hadith Text Classification on Sanad Part Using Edge List

Hadith has two components consisting of sanad and the content parts. Most of the studies in hadith text classification focus on the content part named topical classification area. The sanad part, on the contrary, gains less attention in the text classification area. In this study, we design classification rules to classify hadith text based on the sanad part using the edge list concept. The rule-based classification is designed by using 700 hadith texts as a training data set and 300 hadith texts for the testing data set. We evaluate the results by using precision and recall measurement. The precision value is 1.00 by developing the rule-based classification on knowledge from experts. In our study, the recall value was 0.11 because the chronological lists for hadith text in the training set did not represent all chronological lists in Shahih Bukhari hadith, which is more than 7000. In the future, this study can be extended to analyse the overall text classification from Shahih Bukhari.
ITC Contribution in Surveys

This paper aims to underline the Information and Communication Technologies (ICT) contributions in survey implementations. We used ICT tools in Chad Malaria Indicators Survey (CMIS). We found ICT are revolutionizing surveys. Their implementation considerably improves the survey operations by eliminating some over budgeting steps, enriching the quality of data and facilitating some data manipulations. This bringing leads us to highlight the contribution of ICT in smart surveys. And smart surveys in which ICT tools are used were exposed with a focus on ITC contributions in these surveys. A use case was giving through our implementation of smart survey.
Hemodynamic Analysis and Diagnosis Based on Multi-Deep Learning Models

This study employs nine distinct deep learning models to categorize 12,444 blood cell images and automatically extract from them relevant information with an accuracy that is beyond that achievable with traditional techniques.The work is intended to improve current methods for the assessment of human health through measurement of the distribution of four types of blood cells, namely, eosinophils, neutrophils, monocytes, and lymphocytes, known for their relationship with human body damage, inflammatory regions, and organ illnesses, in particular, and with the health of the immune system and other hazards, such as cardiovascular disease or infections, more in general.The results of the experiments show that the deep learning models can automatically extract features from the blood cell images and properly classify them with an accuracy of 98%, 97%, and 89%, respectively, with regard to the training, verification, and testing of the corresponding datasets.
Research on Measurement and Influencing Factors of Artificial Intelligence Development in China

As a crucial achievement of the emerging scientific and technological revolution, artificial intelligence has become a vital factor to change the economic model and industrial structure. This paper analyzes the importance of the development of artificial intelligence, and on this basis, measures the development trend of artificial intelligence in China by using statistical public data, in addition to further quantifying the influencing factors. Finally, this paper provides some suggestions for developing artificial intelligence in China.
Second Skins: Exploring the Challenges and Opportunities for Designing Adaptable Garments Using E-Textile

The process of making adaptive and responsive wearables on the scale of the body has often been a process where designers use off-the-shelf parts or hand-crafted electronics to fabricate garments. However, recent research has shown the importance of emergence in the process of making. Second Skins is a multistakeholder exploration into the creation of those garments where the designers and engineers work together throughout the design process so that opportunities and challenges emerge with all stakeholders present in the process. This research serves as a case study into the creation of adaptive caring garments for sustainable wardrobes from a multistakeholder design team. The team created a garment that can customize the colors, patterns, structures, and other properties dynamically. A reflection on the multi-stakeholder process unpacks the process to explore the challenges and opportunities in adaptable e-textiles.
Explorations and Analysis on Innovative Education's Hot Spots and Frontier Issues

After exploring innovative education research's current situation and frontier issues, the work utilized bibliometrics and scientific knowledge mapping methods to reveal research hotspots and development trends and study the literature published in the China Social Science Citation Index database from 2000 to 2020. The authors, research institutions, and keywords were analyzed by bibliometrics to draw the mapping knowledge domains. Results showed that there were only a few core authors in the innovative education field, with less papers published. Furthermore, a research institution group formed, including Nanjing Normal University, Zhejiang University, Huazhong University of science and technology, South China Normal University, and Beijing Normal University as the centers and other normal schools as the main bodies. In the past twenty years, innovation education research's hot spots in China mainly focused on innovation education ideas, innovation education mechanisms, innovation personnel training, teaching reforms, educates and other aspects. However, the frontier issue in the innovation education research in China refers to college and university students nowadays.
Chinese sentiment analysis model by integrating multi-granularity semantic features

Purpose In recent years, Chinese sentiment analysis has made great progress, but the characteristics of the language itself and downstream task requirements were not explored thoroughly. It is not practical to directly migrate achievements obtained in English sentiment analysis to the analysis of Chinese because of the huge difference between the two languages. Design/methodology/approach In view of the particularity of Chinese text and the requirement of sentiment analysis, a Chinese sentiment analysis model integrating multi-granularity semantic features is proposed in this paper. This model introduces the radical and part-of-speech features based on the character and word features, with the application of bidirectional long short-term memory, attention mechanism and recurrent convolutional neural network. Findings The comparative experiments showed that the F 1 values of this model reaches 88.28 and 84.80 per cent on the man-made dataset and the NLPECC dataset, respectively. Meanwhile, an ablation experiment was conducted to verify the effectiveness of attention mechanism, part of speech, radical, character and word factors in Chinese sentiment analysis. The performance of the proposed model exceeds that of existing models to some extent. Originality/value The academic contribution of this paper is as follows: first, in view of the particularity of Chinese texts and the requirement of sentiment analysis, this paper focuses on solving the deficiency problem of Chinese sentiment analysis under the big data context. Second, this paper borrows ideas from multiple interdisciplinary frontier theories and methods, such as information science, linguistics and artificial intelligence, which makes it innovative and comprehensive. Finally, this paper deeply integrates multi-granularity semantic features such as character, word, radical and part of speech, which further complements the theoretical framework and method system of Chinese sentiment analysis.
Hierarchical Design Space Exploration for Distributed CNN Inference at the Edge

Convolutional Neural Network (CNN) models for modern applications are becoming increasingly deep and complex. Thus, the number of different CNN mapping possibilities when deploying a CNN model on multiple edge devices is vast. Design Space Exploration (DSE) methods are therefore essential to find a set of optimal CNN mappings subject to one or more design requirements. In this paper, we present an efficient DSE methodology to find (near-)optimal CNN mappings for distributed inference at the edge. To deal with the vast design space of different CNN mappings, we accelerate the searching process by proposing and utilizing a multi-stage hierarchical DSE approach together with a tailored Genetic Algorithm as the underlying search engine.
Exploring the Relationship Between Free Media Sentiment and Financial Market Performance

This paper explores the relationship between the role of ‘freedom of media’ and ‘financial markets performance’ by examining sentiment in social media. A global ‘freedom of media’ sentiment index is developed in this study and is analysed against a selection of international financial market indices. A significant relationship is observed between free media sentiment and financial market performance. This paper uses the Valence Aware Dictionary for Sentiment Reasoning to determine free media sentiment. The method used in this study because of its sensitivity to both polarity and intensity of emotion. It was found that there is a significant positive relationship between sentiment regarding the freedom of media and financial market performance, and maps the global distribution of this effect. The impact of the ‘freedom of media’ on financial markets is sometimes greatly underestimated. In order to preserve the sustainability of economic and financial freedom, the role of freedom of media is paramount. If freedom of media protects the rights of individuals, it is reflected through the integrity of the financial markets. The attitude of policy makers towards free media therefore has an influence on portfolio investment dynamics. In this study, a descriptive model is constructed, highlighting the connectedness between the financial markets and the free media. This is achieved by using an OLS methodology. Through the promotion of world peace and free media, a stronger socio-economic environment emerges which fosters robust market performance.
Exposing Racial Dialect Bias in Abusive Language Detection: Can Explainability Play a Role?

Biases can arise and be introduced during each phase of a supervised learning pipeline, eventually leading to harm. Within the task of automatic abusive language detection, this matter becomes particularly severe since unintended bias towards sensitive topics such as gender, sexual orientation, or ethnicity can harm underrepresented groups. The role of the datasets used to train these models is crucial to address these challenges. In this contribution, we investigate whether explainability methods can expose racial dialect bias attested within a popular dataset for abusive language detection. Through preliminary experiments, we found that pure explainability techniques cannot effectively uncover biases within the dataset under analysis: the rooted stereotypes are often more implicit and complex to retrieve.
Improving the Quality of Rule-Based GNN Explanations

Recent works have proposed to explain GNNs using activation rules. Activation rules allow to capture specific configurations in the embedding space of a given layer that is discriminant for the GNN decision. These rules also catch hidden features of input graphs. This requires to associate these rules to representative graphs. In this paper, we propose on the one hand an analysis of heuristic-based algorithms to extract the activation rules, and on the other hand the use of transport-based optimal graph distances to associate each rule with the most specific graph that triggers them.
From Disentangled Representation to Concept Ranking: Interpreting Deep Representations in Image Classification Tasks

Deep Learning models such as Convolutional Neural Networks (CNNs) are particularly successful in computer vision tasks. They have proven to be tremendously effective and popular in the last decade, reaching great accuracy in tasks such as image classification and object recognition. Despite their success, it is well known that conveying what the model learnt to humans remains challenging. This is due to the fact that a CNN is still mostly a black-box model, and images are very rich input data. In this work, we build upon the idea of disentangled representation produced from a trained CNN, and explore how such disentangled representation can be used to describe what the model has learned in terms of semantic concepts. Specifically, we aim at providing a ranked list of the concepts that are related to both a specific instance or image (local explainability) and a class (global explainability). In this preliminary work we use a simple linear classifier for concept ranking. Results are promising since we reached 95% precision at both local and global level. This indicates potential in developing our idea further by leveraging external knowledge bases to associate and validate specific properties and relations among the ranked concepts at both local and global level as discussed in the final section of this paper.
Bitpaths: Compressing Datasets Without Decreasing Predictive Performance

The ever growing amount of data that becomes available necessitates more memory to store it. Machine learned models are becoming increasingly sophisticated and efficient in order to navigate this growing amount of data. However, not all data is relevant for a certain machine learning task and storing that irrelevant data is a waste of memory and power. To address this, we propose bitpaths: a novel pattern-based method to compress datasets using a random forest. During inference, a KNN classifier then uses the encoded training examples to make a prediction for the encoded test example. We empirically compare bitpaths’ predictive performance with the uncompressed setting. Our method can achieve compression ratios up to 80 for datasets with a large number of features without affecting the predictive performance.
A Social Media Tool for Domain-Specific Information Retrieval - A Case Study in Human Trafficking

In a world increasingly present online, people are leaving a digital footprint, with valuable information scattered on the Web, in an unstructured manner, beholden to the websites that keep it. While there are potential harms in being able to access this information readily, such as enabling corporate surveillance, there are also significant benefits when used, for example, in journalism or investigations into Human Trafficking. This paper presents an approach for retrieving domain-specific information present on the Web using Social Media platforms as a gateway to other content existing on any website. It begins by identifying relevant profiles, then collecting links shared in posts to webpages related to them, and lastly, extracting and indexing the information gathered. The tool developed based on this approach was tested for a case study in the domain of Human Trafficking, more specifically in sexual exploitation, showing promising results and potential to be applied in a real-world scenario.
Intelligently Detecting Information Online-Weaponisation Trends (IDIOT)

This paper discusses a detailed study on existing natural language processing open source and commonly used sentiment analysis toolboxes and looks at how various combinations of those toolboxes’ results can be used to accurately classify a sinister intent in a statement. For example, can the toolboxes’ results for different features, such as Attacks, Toxicity and Aggression be combined together predict an Attacks class with more accuracy than just the Attacks classification alone? Can that combination be used to predict any other intimidating intent within text, and can it also help identify a trajectory of an online threatening trend quicker? The main findings so far conclude that the open sourced and massively used sentiment analysis toolboxes for the English language provided by Python and Java work better for Attacking and Aggressive language, compared to general Toxic language. Also, within this experiment, Support Vector machines, although have the largest overheads and take the longest time, give a more reliable accuracy prediction. Finally, Multi-class aggregates of the toolboxes provide on average a much-improved performance result than just using a single class from a single toolbox.
The Effect of Task Fidelity on Learning Curves: A Synthetic Analysis

There have been discussions about the value of fidelity since simulation-based training systems have been created. A primary question, which has yet to be fully answered, is what is the effect of level of simulation fidelity on learning on a target task? We present a new analysis method and use it for several analyses of a training simulation for an electronic maintenance task with two levels of fidelity: a high-fidelity simulation that basically takes as much time as the real-world task and a low-fidelity simulation with minimal delays and many actions removed or reduced in fidelity and time. The analyses are based on the Keystroke-Level Model (KLM) and the power law of learning. The analyses predict that the performance on the low-fidelity simulation initially takes between one quarter and one eighth of the time of the high, and thus starts out providing between four and eight times as many practice trials in a given time period. The low-fidelity curve has a lower intercept and a steeper slope. Learners that move from low to high appear to not be adversely affected. For a small number of practice trials, this makes a significant difference. We also explore the effect of missing subtasks in the low-fidelity simulation. This effect varies with the tasks included: If the low-fidelity simulation does not train an important task, learners can be slower when they transfer. We also analyze a simulation that we have built and are studying. These analyses demonstrate that using lower fidelity training situations helps most where there is less time to practice, and that if there is extensive time to practice full fidelity has nearly the same outcome (but perhaps not the same costs or risks). We discuss how this analysis approach can help choose the level of fidelity of future training simulations.