Reviewerly/exHarmony huggingface dataset:
Blockchain Analytics Reference Architecture for FinTech - A Positioning Paper

Financial services industry is one of the highly impacted industries with what is currently happening in the digital economy due to the “Digital Transformation” wave derived by the rapid evolution of the “Disruptive Emerging Technologies”. “FinTech” is the current dominating trend describing the role played by these technologies in the financial services industry. Although all financial sectors are affected, the insurance sector had been much disrupted due to not only the severe competition but also due to its data sensitivity and dependability. This makes insurance companies in an urgent need for the transparency and trust as well as the need to be “Data-Driven Enterprises” that competes through a “Data-Centric Strategy” that utilizes the power of “Big Data”. This made the “InsurTech” to become a standalone trend recently. The Blockchain technology can provide transparency and trust. However, having an "Enterprise-Wide Analytics" capability is the key strategic enabler to this needed data-centric strategy to make informed decisions that are inspired by data. Not to mention the competitive advantage that results from the synergistic effect of building a "Blockchain Analytics" capability specifically. Furthermore, many digital transformation initiatives are chaotic, Blockchain technology is under critical development and analytics is so time and money consuming. Traceability to strategic objectives are missing or implicit and no standardization is available. Something is needed to navigate these challenges and achieve this ambitious transformative vision. This paper will explain how “Value-Driven Enterprise Architecture”, with the “TOGAF” and the “Capability-Based Planning” technique, as a discipline and the “ArchiMate” as a modelling framework can provide a master blueprint for designing a “Blockchain Analytics" solution within an "Enterprise Wide Analytics" capability following the latest standards of the “Enterprise Data Lakes”.
English Speech Recognition System Model Based on Computer-Aided Function and Neural Network Algorithm

With the economic globalization continuous growth of China's socioeconomic level tends to be internationalized, China's attention to English has been significantly improved. However, the domestic English teaching level is limited, so it is impossible to correct students' English pronunciation and make a reasonable evaluation at all times so that oral training has certain disadvantages. However, the computer-aided language learning system at home and abroad focuses on the practice of words and grammar, and the evaluation indicators are less and not comprehensive. In view of the complexity of English pronunciation changes, traditional speech recognition is difficult to recognize speech speed and improve its accuracy. Furthermore, to strengthen the English pronunciation of domestic students, a nonlinear network structure is studied in depth to simulate the human brain to analyze a model of speech recognition is established Mel frequency cepstrum characteristic parameters of human ear model and deep belief network. In this paper, the traditional computer pronunciation evaluation method is improved in an all-round way, and a set of high-quality speech recognition system of speech recognition method is constructed. Aiming at the above problems, it takes the students as the research, which proves that the method adopted in this paper can give the learners accurate pronunciation quality analysis report and guidance and correct their intonation and improve the learning effect, and the experimental data verify that the improved speech recognition system model recognition ability is higher than the traditional model.
Text Mining Public Perception on Autonomous Driving

Background: The key factor for breakthrough of autonomous driving is the acceptance of the public. Social media platforms may offer an opportunity to assess public acceptance and opinion towards automated vehicle technology. Methods: Using Twitter Archiving Google Sheet, tweets were collected with the specific hashtags. Tableau and Gephi were used to visualize and aggregate the social media network. By using R Studio, the word frequency, association and sentiment analysis was carried out. Results: The social media network and sentiment analysis provide many new discoveries of public perceptions about autonomous driving. Conclusions: Information from social media can complement data sources and provide direct insights into public intentions. Besides, more complex machine learning technology is needed to gauge changes in public opinion on autonomous driving precisely.
More Constructions of Light MDS Transforms Based on Known MDS Circulant Matrices

Maximum distance separable (MDS) codes have the maximum branch number in cryptography, and they are generally used in diffusion layers of symmetric ciphers. The diffusion layer of the Advanced Encryption Standard (AES) uses the circulant MDS matrix with the row element of {2;3;1;1} in F28. It is the simplest MDS matrix in F2n4, recorded as A=Circ(2;3;1;1). In this paper, we study the more extensive MDS constructions of A in F2n4. By transforming the element multiplication operation in the finite field into the bit-level operation, we propose a multivariable operation definition based on simple operations, such as cyclic shift, shift, and XOR. We apply this multivariable operation to more lightweight MDS constructions of A and discuss the classification of the MDS clusters. We also give an example of the MDS cluster of A. Without changing the structure, elements, and the implementation cost of the known MDS matrix, the number of existing MDS transformations is expanded to n2/2 times that of its original. The constructions in this paper provide rich component materials for the design of lightweight cryptographic algorithms.
Weakly supervised spatial–temporal attention network driven by tracking and consistency loss for action detection

Abstract This study proposes a novel network model for video action tube detection. This model is based on a location-interactive weakly supervised spatial–temporal attention mechanism driven by multiple loss functions. It is especially costly and time consuming to annotate every target location in video frames. Thus, we first propose a cross-domain weakly supervised learning method with a spatial–temporal attention mechanism for action tube detection. In source domain, we trained a newly designed multi-loss spatial–temporal attention–convolution network on the source data set, which has both object location and classification annotations. In target domain, we introduced internal tracking loss and neighbor-consistency loss; we trained the network with the pre-trained model on the target data set, which only has inaccurate action temporal positions. Although this is a location-unsupervised method, its performance outperforms typical weakly supervised methods, and even shows comparable results with some recent fully supervised methods. We also visualize the activation maps, which reveal the intrinsic reason behind the higher performance of the proposed method.
Impact of Personalized Recommendation on Today’s News Communication through Algorithmic Mechanism in the New Media Era

In recent years, the continuous innovation of technology has greatly contributed to the great changes in the media industry. The rapid development of big data and artificial intelligence technologies has enabled people to transition from the era of new media to the era of intelligent media. While the automation of news production brings broad prospects for intelligent media, it also accelerates the challenge of information explosion. Facing the massive amount of news and information, how to get the information users want quickly has become a big problem. In order to solve the audience’s information anxiety, personalized news recommendation system is born. In fact, news gate-keeping is an important part of news distribution. In the era of smart media, algorithmic distribution has impacted the original distribution mode and brought challenges to news gate-keeping. Personalized news recommendation is one of the gate-keeping methods of intelligent media. At the same time, with the rapid development of the Internet and information technology, today’s society has entered a period of information explosion. In terms of news, the rapid development of the Internet has made it easier to publish and read news on the Internet. As a result, online news has become an important way for people to get information. However, the previous news websites had a large amount of news information, but only collected and consolidated the news. As a result, users were left to passively receive news information from news sites and find the content they needed. Consequently, although the Internet has a huge amount of complicated news information, it is unable to meet the diversified and personalized news needs of users. In order to solve this issue, researchers are constantly looking for solutions. The emergence of recommendation system is an effective measure to cope with the above problem. The mainstream models of recommendation systems are collaborative filtering model and content-based recommendation model. However, there are two essential problems with collaborative filtering. The first one is the cold start problem, and the second one is that the preference matrix of item users becomes sparse as the number of items and users grows. These two issues can seriously affect the recommendation accuracy of the recommendation system. As a result, a hybrid recommendation system is built by fusing common recommendation algorithms. This system can not only deliver personalized information to different users, but also compensate the shortcomings of a single algorithm to a certain extent. To be specific, the newly constructed hybrid recommendation system can push news of interest to users according to their demographic attributes, behavioral attributes, and interests, thus expanding the scope of news communication.
A Novel Cross-Network Embedding for Anchor Link Prediction with Social Adversarial Attacks

Anchor link prediction across social networks plays an important role in multiple social network analysis. Traditional methods rely heavily on user privacy information or high-quality network topology information. These methods are not suitable for multiple social networks analysis in real-life. Deep learning methods based on graph embedding are restricted by the impact of the active privacy protection policy of users on the graph structure. In this paper, we propose a novel method which neutralizes the impact of users’ evasion strategies. First, graph embedding with conditional estimation analysis is used to obtain a robust embedding vector space. Secondly, cross-network features space for supervised learning is constructed via the constraints of cross-network feature collisions. The combination of robustness enhancement and cross-network feature collisions constraints eliminate the impact of evasion strategies. Extensive experiments on large-scale real-life social networks demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of precision, adaptability, and robustness for the scenarios with evasion strategies.
Emoji as promising tools for emotional evaluation in orthodontics

Emoji are pictograms frequently used in social networks capable of expressing emotions. These tools can provide insights into people's behavior that could not be obtained with the use of textual communication. Recently, emoji have been introduced to various research fields as successful alternatives to word-based questionnaires for measure emotional responses. The objective of this study was to preliminarily evaluate the discriminating ability and relationship of these tools with different occlusal conditions/malocclusions.Online surveys were applied to adult individuals (n = 201; mean age = 27.4 ± 5.7; 37.3% males, 62.7% females). Subjects issued acceptance scores (10-point scale) and expressed their emotional status using a 30-emoji list in relation to nine occlusal conditions: C1-crowding, C2-anterior open bite, C3-interincisal diastema, C4-increased overjet + deep bite (Class II div. 1), C5-anterior crossbite (Class III), C6-ideal occlusion, C7-unilateral posterior crossbite, C8-anterior open bite plus bilateral posterior crossbite plus crowding, and C9-deep bite (Class II div. 2). Cochran's Q and McNemar tests were used to compare the frequencies of choice of emoji between conditions. Correspondence analyses were applied to assess the association between occlusal conditions and emoji. Kendall's correlation coefficient was calculated to evaluate the relationship between mean acceptance scores and frequency counts of each emoji.The frequency of choice between conditions showed a significant difference for 25 of the 30 emoji (P < 0.05), indicating an adequate discriminating ability of these tools. Emoji were grouped predominantly based on their emotional valence (positive/negative) and arousal/activation (high/low). Positive emoji were associated with the most accepted conditions (i.e., C6, C3), while negative emoji with the most rejected ones (i.e., C8, C1, C2). Although only weak, positive correlations between acceptance and positively valenced emoji, and negative correlations between acceptance and negatively valenced emoji were observed (P < 0.05).Emoji have an adequate discriminatory ability and would allow determining emotional profiles in the face of different occlusal conditions. Further research is necessary to consolidate the use of these tools in an instrument that allows measuring emotional responses.
Transactions privacy on blockchain using web of trust concept

Exchange of information through the web took place inside a trusted environment and thus user privacy was assured by default. Nowadays, ensuring user privacy is becoming one of the most desirable features of new technology, and Blockchain is not an exception. The Blockchain is a decentralized technology, open, and public platform where all transactions are stored and viewed from nodes, an approach known as "Web of Trust." Although these transactions tend to be anonymous but in the case of the banking sector, user privacy requires special attention. In centralized systems, the implementation of privacy is no longer a challenge, using a hierarchical approach such as Public Key Infrastructure. This paper presents a broad landscape and state of art of user transaction privacy in Blockchain technology using the Web of Trust approach. Furthermore, we present a novel approach using the Public Key Infrastructure for assuring user privacy adding an optionally encrypted field in blocks in transactions. We have used Bithomp, as a free and open-source tool with the Testnet platform, and Ripple as the best known in the implementation of the payment system to implement the proposed approach. The paper concludes with the strengths and limitations of the proposed approach.
A SLAM-based augmented reality app for the assessment of spatial short-term memory using visual and auditory stimuli

A SLAM-based Augmented Reality (AR) app has been designed, developed, and validated to assess spatial short-term memory. Our app can be used with visual and auditory stimuli and can run on mobile devices. It can be used in any indoor environment. The anchors and data of the app are persistently stored in the cloud. As an authoring tool, the type of stimulus, its number, and specific positions in the real environment can be customized for each session. A study involving 48 participants was carried out to analyze the performance outcomes comparing the location and remembering of stimuli in a real environment using visual versus auditory stimuli. The number of objects placed correctly was similar for the two different stimuli used. However, the group that used the auditory stimulus spent significantly more time completing the task and required significantly more attempts. The performance outcomes were independent of age and gender. For the auditory stimuli, correlations among all of the variables of the AR app and the variables of two other tasks (object-recall and map-pointing) were found. We also found that the greater the number of correctly placed auditory stimuli, the greater the perceived competence and the less mental effort required. The greater the number of errors, the less the perceived competence. Finally, the auditory stimuli are valid stimuli that may benefit the assessment of the memorization of spatial-auditory associations, but the memorization of spatial-visual associations is dominant, as our results suggest.
Cross-Countries Comparison Toward Digital Currency Acceptance: Integrating UTAUT2 Into ITM

In the context of digital monetary market integration, the importance of cross-border digital currency research is receiving prominent attention. This study integrated Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) and initial trust factors (ITM) into an integrative framework, which synthetically complemented the objective measures and subjective insights of digital currencies. The results indicated the integrated framework, which verified its robustness predicting the acceptance and recommendation intention of digital currency. By analyzing the two different features of digital currencies, this research puts forward a set of targeted solutions to ensure that users of Chinese and Korean digital currencies make a long-term policy for the sustainability, eventually benefitting the cross-border digital monetary transactions and economic cooperation in Asia, which leads the world to the sustainable development in the digital currencies field.
Dataset authorization control: protect the intellectual property of dataset via reversible feature space adversarial examples

Training high performance Deep Neural Networks (DNNs) models require large-scale and high-quality datasets. The expensive cost of collecting and annotating large-scale datasets make the valuable datasets can be considered as the Intellectual Property (IP) of the dataset owner. To date, almost all the copyright protection schemes for deep learning focus on the copyright protection of models, while the copyright protection of the dataset is rarely studied. In this paper, we propose a novel method to actively protect the dataset from being used to train DNN models without authorization. Experimental results on on CIFAR-10 and TinyImageNet datasets demonstrate the effectiveness of the proposed method. Compared with the model trained on clean dataset, the proposed method can effectively make the test accuracy of the unauthorized model trained on protected dataset drop from 86.21% to 38.23% and from 74.00% to 16.20% on CIFAR-10 and TinyImageNet datasets, respectively.
Approximate Nearest Neighbor Search Using Enhanced Accumulative Quantization

Approximate nearest neighbor (ANN) search is fundamental for fast content-based image retrieval. While vector quantization is one key to performing an effective ANN search, in order to further improve ANN search accuracy, we propose an enhanced accumulative quantization (E-AQ). Based on our former work, we introduced the idea of the quarter point into accumulative quantization (AQ). Instead of finding the nearest centroid, the quarter vector was used to quantize the vector and was computed for each vector according to its nearest centroid and second nearest centroid. Then, the error produced through codebook training and vector quantization was reduced without increasing the number of centroids in each codebook. To evaluate the accuracy to which vectors were approximated by their quantization outputs, we realized an E-AQ-based exhaustive method for ANN search. Experimental results show that our approach gained up to 0.996 and 0.776 Recall@100 with eight size 256 codebooks on SIFT and GIST datasets, respectively, which is at least 1.6% and 4.9% higher than six other state-of-the-art methods. Moreover, based on the experimental results, E-AQ needs fewer codebooks while still providing the same ANN search accuracy.
Incorporate temporal topology in a deep‐time knowledge base to facilitate data‐driven discovery in geoscience

Abstract Data‐driven discovery in geoscience requires an enormous amount of FAIR (findable, accessible, interoperable and reusable) data derived from a multitude of sources. Many geology resources include data based on the geologic time scale, a system of dating that relates layers of rock (strata) to times in Earth history. The terminology of this geologic time scale, including the names of the strata and time intervals, is heterogeneous across data resources, hindering effective and efficient data integration. To address that issue, we created a deep‐time knowledge base that consists of knowledge graphs correlating international and regional geologic time scales, an online service of the knowledge graphs, and an R package to access the service. The knowledge base uses temporal topology to enable comparison and reasoning between various intervals and points in the geologic time scale. This work unifies and allows the querying of age‐related geologic information across the entirety of Earth history, resulting in a platform from which researchers can address complex deep‐time questions spanning numerous types of data and fields of study.
A Primal–Dual Fixed-Point Algorithm for TVL1 Wavelet Inpainting Based on Moreau Envelope

In this paper, we present a novel variational wavelet inpainting based on the total variation (TV) regularization and the l1-norm fitting term. The goal of this model is to recover incomplete wavelet coefficients in the presence of impulsive noise. By incorporating the Moreau envelope, the proposed model for wavelet inpainting can better handle the non-differentiability of the l1-norm fitting term. A modified primal dual fixed-point algorithm is developed based on the proximity operator to solve the proposed variational model. Moreover, we consider the existence of solution for the proposed model and the convergence analysis of the developed iterative scheme in this paper. Numerical experiments show the desirable performance of our method.