Reviewerly/exHarmony huggingface dataset:
Active learning for event detection in support of disaster analysis applications

Disaster analysis in social media content is one of the interesting research domains having an abundance of data. However, there is a lack of labeled data that can be used to train machine learning models for disaster analysis applications. Active learning is one of the possible solutions to such a problem. To this aim, in this paper, we propose and assess the efficacy of an active learning-based framework for disaster analysis using images shared on social media outlets. Specifically, we analyze the performance of different active learning techniques under several sampling and disagreement strategies. Moreover, we collect a large-scale dataset covering images from eight common types of natural disasters. The experimental results show that the use of active learning techniques for disaster analysis in social media images results in a performance comparable to that obtained using human-annotated images with fewer data samples, and could be used in frameworks for disaster analysis in images without the tedious job of manual annotation.
Curved retina-like camera array imaging system with adjustable super-resolution fovea

To achieve non-uniform imaging with a large field of view and high efficiency as well as to obtain an adjustable fovea with super-resolution, we proposed a curved retina-like camera array imaging system (CRCS), which is built by an eight-camera array distributed non-uniformly on a curved surface and a camera coaxial with Risley prisms located in the center. By the non-uniform imaging, the field of view of the developed prototype is <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:msup> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>150</mml:mn> </mml:mrow> </mml:mrow> <mml:mo>∘<!-- ∘ --></mml:mo> </mml:msup> <mml:mo>×<!-- × --></mml:mo> <mml:msup> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>40</mml:mn> </mml:mrow> </mml:mrow> <mml:mo>∘<!-- ∘ --></mml:mo> </mml:msup> </mml:math> with a reduction of data redundancy by 87.62%. Besides, the experimental results show that CRCS can obtain clear and sharp images of farther targets of interest around the fovea field of view with a constant focal length.
A comparison of an RGB-D cameras performance and a stereo camera in relation to object recognition and spatial position determination

Results of using an RGB-D camera (Kinect sensor) and a stereo camera, separately, in order to determine the 3D real position of characteristic points of a predetermined object in a scene are presented. KAZE algorithm was used to make the recognition, that algorithm exploits the nonlinear scale space through nonlinear diffusion filtering; 3D coordinates of the centroid of a predetermined object were calculated employing the camera calibration information and the depth parameter provided by a Kinect sensor and a stereo camera. Experimental results show it is possible to get the required coordinates with both cameras in order to locate a robot, although a balance in the distance where the sensor is placed must be guaranteed: no fewer than 0.8 m from the object to guarantee the real depth information, it is due to Kinect operating range; 0.5 m to stereo camera, but it must not be 1 m away to have a suitable rate of object recognition, besides, Kinect sensor has more precision with distance measures regarding a stereo camera.
An improved whale optimization algorithm with cultural mechanism for high-dimensional global optimization problems

Whale optimization algorithm (WOA) is a swarm intelligence optimization algorithm which imitates the behavior of humpback whales. It has been widely accepted to solve problems in various engineering fields because its less required parameters and excellent optimal performance. Similarly to other meta-heuristic algorithm, WOA still has the disadvantage of trap in local optima. In this paper a cultural whale optimization algorithm(C-WOA) is proposed to prevent the algorithm from falling into local optimum, which combines cultural algorithm and whale optimization algorithm. The double evolution mechanism is considered in C-WOA for integrating the advantages of different mechanisms of population space and knowledge space, which effectively improves the performance of whale optimization algorithm. The proposed C-WOA is benchmarked on six well-known test functions, and the results show that the algorithm is effective in solving complex high-dimensional problems.
A Method of Relation Extraction Using Pre-training Models

Relation Extraction (RE), as an essential task of Natural Language Processing (NLP), aims to extract potential relations between two entities in a sentence. It is a crucial step in information extraction from unstructured data and building a Knowledge Graph (KG). The performance of deep learning methods for RE, like Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), heavily depends on the quality and scale of the training set. Recently, pre-training models like BERT and ERNIE, have achieved State-Of-The-Art (SOTA) results in many NLP tasks, because they can obtain the prior semantic knowledge during the procedure of pre-training. Therefore, it is interesting to know whether the performance of RE can be improved utilizing the pre-training models. In this paper, we propose a method of RE using two kinds of pre-training models: BERT and ERNIE. First, in the input sequence, unique symbols are appended around the entities. RE is then regarded as a text classification task, and the prior semantic knowledge obtained by pre-training models is used to improve the performance. Experiments are carried on the SemEval 2010 Task 8 dataset. Results demonstrate that the method we proposed improves the performance of RE compared with previous approaches.
Unsupervised User Identity Linkage via Graph Neural Networks

User identity linkage (UIL) aims to link identical users engaging in multiple social networks. It has received considerable attention in both academia and industry due to its profound implications for multiple applications. Although existing approaches have achieved promising progress in UIL using various graph learning methods, they usually require a large number of labeled anchor nodes which, however, are difficult to obtain in real-world social platforms due to privacy issues. We introduce a novel UIL model NWUIL (Network Wasserstein learning for UIL) to identify anchor users across social networks in a fully unsupervised manner. Instead of point vector embedding of nodes as in previous methods, NWUIL captures node distribution in Wasserstein space with graph neural networks. We also propose to reformulate the UIL task as an optimal network transport problem, and then introduce an unsupervised mapping process based on the network Wasserstein distance for UIL. In this way, our method not only improves the anchor node aligning accuracy but also alleviates the issues caused by insufficient labeled anchor nodes. We conduct extensive experiments using real-world datasets, and demonstrate that NWUIL significantly outperforms existing unsupervised baselines while showing competitive performance as some state-of-the-art supervised approaches.
Foreign Direct Investment, Information Technology and Economic Growth Dynamics in Sub-Saharan Africa

The research assesses how information and communication technology (ICT) modulates the effect of foreign direct investment (FDI) on economic growth dynamics in 25 countries in Sub-Saharan Africa for the period 1980-2014. The employed economic growth dynamics are Gross Domestic Product (GDP) growth, real GDP and GDP per capita while ICT is measured by mobile phone penetration and internet penetration. The empirical evidence is based on the Generalised Method of Moments. The study finds that both internet penetration and mobile phone penetration overwhelmingly modulate FDI to induce overall positive net effects on all three economic growth dynamics. Moreover, the positive net effects are consistently more apparent in internet-centric regressions compared to “mobile phone”-oriented specifications. In the light of negative interactive effects, net effects are decomposed to provide thresholds at which ICT policy variables should be complemented with other policy initiatives in order to engender favorable outcomes on economic growth dynamics. Practical and theoretical implications are discussed.
Enhancement of the Quantum Coherence of the Rydberg Atoms System by Weak Measurement

Abstract The influence of weak measurement along with quantum measurement reversal on the Rydberg atoms system is investigated, and is found to have an important practical significance as both weak measurement along with quantum measurement reversal and Rydberg atoms system can be experimentally realized. The quantum coherence and the entropic uncertainty relation of the system are explored, and it is found that the quantum coherence of the system is enhanced and the values of uncertainty and the uncertainty bound are reduced under some particular weak measurements along with quantum measurement reversals. This indicates that the decoherence effect of the system can be weakened by weak measurement along with quantum measurement reversal. Furthermore, the trace distance which is a reflection of the entanglement is also examined, and it is shown that weak measurement along with quantum measurement reversal can improve the entanglement of the system.
Digital Inclusion in Norwegian and Danish Schools—Analysing Variation in Teachers’ Collaboration, Attitudes, ICT Use and Students’ ICT Literacy

Abstract The capability to use digital technologies in an appropriate way has become a fundamental requirement of everyday life and wide adoption of digital technologies has gained a firm footing into the educational systems. Equity is a central goal in the Nordic model and ICT integration policies are warranted at the national level along with massive improvements in ICT infrastructures. The schools in their efforts towards realizing this objective have to integrate digital technology in teaching and learning in such a way that all children are given opportunities to participate in work, life and society. It is thus of interest to study the extent of digital inclusion, by examining the variation in computer and information literacy of students both within and between schools by addressing access and use of ICT in instruction among teachers. Data for the present study comes from 138 schools from Norway (2436 students, 1653 teachers) and 110 schools from Denmark (1767 students, 728 teachers) who took part in the International Computer and Information Literacy Study in 2013. Using a multilevel approach, variations at both levels in student computer and information literacy score and teacher collaboration in ICT use were examined. The results indicate that availability of digital technologies is a significant contributor towards student ICT achievement and teacher collaboration in both countries. There are small differences in computer and literacy score between the schools, while significant variations are noted between the students. Additionally, teachers’ attitudes are found to contribute significantly towards collaboration between teachers.
Hand Image-Based Human Age Estimation using a Time Distributed CNN-GRU

The prediction of human age is more and more widely used in various fields such as forensic medicine or national security. Several studies based on biometrics (face, voice, fingerprint) have been conducted. This paper proposes an age estimation method based on the color image of the hand using a time distributed convolutional neural network combined with Gated Recurrent Unite (GRU) followed by a stack of fully connected layers. An adjustment is used to eliminate model bias caused by the gender and the skin color, experiments demonstrate good performance of the model.
Blockchain and IoT based Vehicle Tracking System for Industry 4.0 Applications

Abstract Vehicle automation is one of the main applications of industry 4.0 especially for tourism companies who provide their vehicles to migrants for traveling purposes. It is difficult to maintain trust among the travel agency and the customers. Although there are many existing solutions in the research that addresses the trust issues, but most of the solutions are based on centralized architecture such as cloud computing where the data is prone to various security threats. Motivated by the aforementioned discussion, the authors have proposed a vehicle tracking system based on the integration of blockchain and IoT in this paper. The proposed system will increase the trust among the entities by providing them transparency in tracking details of the vehicle. As the trip details are immutable and decentralized in nature, so, blockchain is used to store the details. Also, the system will generate the trip summary that contains the route detail, the number of kilometres travelled and the total cost of the trip. Tools such as Ethereum, Remix IDE, MetaMask, and RinkeyBy have been used to measure the performance of the system.
Dual-lens system in enlarging the viewing angle for the Michelson interferometer

Shearography has been widely accepted as a non-destructive evaluation tool in engineering. However, the field of view is usually limited due to the use of the Michelson optical arrangement if the working distance is strictly constrained. In order to improve efficiency, we propose a dual-lens system to expand the view angle of the Michelson optical arrangement. Two imaging lenses, which are entitled as the objective lens and camera lens, are placed in front of and behind the Michelson interferometer, respectively. The optical arrangement has been set up and compared with the well-known 4f optical system. It is found that the proposed optical arrangement is simple to construct, and the brightness distribution is uniform over the whole image. The new setup has been applied to the inspection of a cylindrical steel shell with a rubber plate bonding on its inside surface. The debonding defects have been evaluated using the proposed shearography system at a working distance of 200 mm. By selecting an objective lens with a focal length of 2.8 mm, the viewing angle was enlarged in inspecting a field with 500 mm by 500 mm at one shot.
Unsupervised Discovery, Control, and Disentanglement of Semantic Attributes With Applications to Anomaly Detection

Our work focuses on unsupervised and generative methods that address the following goals: (1) learning unsupervised generative representations that discover latent factors controlling image semantic attributes, (2) studying how this ability to control attributes formally relates to the issue of latent factor disentanglement, clarifying related but dissimilar concepts that had been confounded in the past, and (3) developing anomaly detection methods that leverage representations learned in the first goal. For goal 1, we propose a network architecture that exploits the combination of multiscale generative models with mutual information (MI) maximization. For goal 2, we derive an analytical result, lemma 1 , that brings clarity to two related but distinct concepts: the ability of generative networks to control semantic attributes of images they generate, resulting from MI maximization, and the ability to disentangle latent space representations, obtained via total correlation minimization. More specifically, we demonstrate that maximizing semantic attribute control encourages disentanglement of latent factors. Using lemma 1 and adopting MI in our loss function, we then show empirically that for image generation tasks, the proposed approach exhibits superior performance as measured in the quality and disentanglement of the generated images when compared to other state-of-the-art methods, with quality assessed via the Fréchet inception distance (FID) and disentanglement via mutual information gap. For goal 3, we design several systems for anomaly detection exploiting representations learned in goal 1 and demonstrate their performance benefits when compared to state-of-the-art generative and discriminative algorithms. Our contributions in representation learning have potential applications in addressing other important problems in computer vision, such as bias and privacy in AI.
Randomness-based macroscopic Franson-type nonlocal correlation

Franson-type nonlocal correlation is related to Bell inequality violation tests and has been applied for quantum key distributions based on time bin methods. Using unbalanced Mach-Zehnder interferometers, Franson correlation measurements result in an interference fringe, while local measurements do not. Here, randomness-based macroscopic Franson-type correlation is presented using polarization-based two-mode coherent photons, where the quantum correlation is tested by a Hong-Ou-Mandel scheme. Coherent photons are used to investigate the wave properties of this correlation. Without contradicting the wave-particle duality of quantum mechanics, the proposed method provides fundamental understanding of the quantum nature and opens the door to deterministic quantum information science.
CBDC - Financial System Implications and Control

IT progress and its application to the financial industry have inspired central banks and academics to reflect about the merits of central bank digital currencies (CBDC) accessible to the broad public. This paper first briefly recalls the advantages that have been associated with CBDC and reviews some relevant background from the history of the issuance of different forms of central bank money. It then discusses two key arguments against CBDC, namely (i) risk of structural disintermediation of banks and centralization of the credit allocation process within the central bank and (ii) risk of facilitation systemic runs on banks in crisis situations. The paper proposes as solution a two-tier remuneration of CBDC, as a tested and simple tool to control the quantity of CBDC both in normal and crisis times. It compares this solution with the one of Kumhof and Noone (2018). It is however also acknowledged that controlling the quantity of CBDC is not necessarily sufficient to control its impact on the financial system. Finally, the paper compares the financial account implications of CBDC with the one of crypto assets, stable coins, and narrow bank digital money, noting the similarity and differences in terms of implications on the financial system. It is concluded that well-controlled CBDC seems feasible, without this implying that CBDC would not catalyze change in the financial system.
An Intelligent Chatbot Using NLP and TF-IDF Algorithm for Text Understanding Applied to the Medical Field

This is the era of intelligent machines. Due to the progress and advancements of machine learning, deep learning, and artificial intelligence, machines are impersonating humans. Natural language processing-based conversational software agents are known as chatbots. These chatbots are the best example of such machines. To spend a healthy life, health care is much important. But it is not easy to have a consultation with the appropriate doctor while having health problems. Our proposed solution for this problem is to create an artificial intelligence-based medical chatbot, which will be able to detect the ailment and also to give the necessary details about any condition. The purpose of this chatbot is to minimize the healthcare cost and to improve the approachability to medical knowledge. This chatbot will act as a medical reference book that will help the patients to diagnose their sickness and also to recover their health. A patient can only gain the actual benefit of the chatbot only when a chatbot can diagnose the accurate disease and also provide the necessary information about the condition. A text-to-text verdict bot involves people in the discussion about their health problems and also provides a personalized diagnosis depending on the symptoms. Hence, people will get ideas related to their health conditions and will get the right safety. In this work, we performed a detailed survey on recent literature. We examined many publications from the last 5 years, which are related to chatbots. We also presented a hybrid architecture based on deep learning models like NLP and the TF-IDF algorithm.
Stable ant‐antlion optimiser for feature selection on high‐dimensional data

High-dimensional data exists widely in the real world, such as gene, magnetic resonance imaging (MRI), text, web data and so on. Feature selection is an effective and powerful method that is often adopted to reduce dimensions of high-dimensional data for promoting learning algorithm's ability to obtain useful information from them. However, feature selection stability lacks attention for a long time, which plays an important role in getting compelling results. This study proposes a stable feature selection approach called stable ant-antlion optimiser (SALO) that is a modified hybrid evolutionary method combining ant colony optimisation and antlion optimiser. Several relative stable filter methods are employed to rank features as guide information, fisher discriminant rate is used to evaluate features as heuristic information, and F1 indicator and the similarity between feature subset and stable ranked feature list are taken as optimisation objects. Comprehensive experiments show the fantasy stability and excellent classification performance of our proposed sophisticated method.
A Group Discovery Method Based on Collaborative Filtering and Knowledge Graph for IoT Scenarios

With the massive growth of Internet-of-Things (IoT) devices, how to provide users with recommendation services in the IoT environment has become a research hotspot. Group discovery, as a prerequisite step of group recommendation that can be used to assist groups of users to select services in IoT-enriched environments, has an important impact on recommendation performance. However, existing group recommendation solutions assume that a user belongs to a specific group and ignore the possible correlation between the user’s preferences and other groups’ preferences. In addition, existing solutions treat group members as equal individuals and assign them equal weights, which makes it hard to meet the user’s accurate recommendation requirements. Furthermore, these methods focus on group members’ explicit preference information while ignoring implicit preferences. To address these problems, we propose a group discovery method based on collaborative filtering and knowledge graph (GD-CFKG). This method first uses the attention mechanism to learn the embedding of service entities from knowledge graphs and interaction between users and services to achieve users’ own preferences embedding. Considering that the preferences of similar users will help to attain accurate target user’s preferences, we then train users’ final preferences embedding by collaborative filtering and word2vec method. We conduct experiments to evaluate our approach using the MovieLens and Douban data sets. Experimental results show that our proposed method has better group recommendation performance than those baseline methods.