Reviewerly/exHarmony huggingface dataset:
Effectiveness of Multi-stakeholder Discussions for Decentralized Finance: A Conference Report of CoDeFi 2020

After the invention of Bitcoin [1], the decentralization of the finance system became a big trend in technology, business, and regulation points of view. On the one hand, the decentralization manner of technology development is the right solution for innovation dilemma, but on the other hand, it makes for regulators to achieve regulatory goals. This is an existing problem for communication technology, especially for the global Internet, but this is a new problem for the financial industry and regulators. Lack of well-organized collaboration among stakeholders, there were many scams, and financial crimes were occurred based on blockchain technology. This article summarizes the needs of multi-stakeholder discussion, the background of the CoDeFi 2020 workshop, and unconference style discussions by all stakeholders.
21st Century Reading Strategy Program to Read English Reading Material for Primary School Pupils

This paper provides a literature review on reading definitions, issues and challenges of reading English materials at primary level, parents' roles in instilling reading habits among pupils and measures that could be employed to equip the pupils with relevant strategies and knowledge in reading hypermedia reading materials.Reading in Malaysia is not a habitual action.The problem starts even at the early stage of education when the pupils are in primary school.Thus, they need to change their perception of reading.They must also change their reading strategies in accordance with the current trend of reading via websites and the internet and various applications via online.If they cannot cope with the English Language, they cannot read the English reading materials, thus, reading will not be their favorite habit.Parents' role is also one of the important factors to change the children's reading habits.Parents need to be equipped with knowledge so they can lead their children.Hopefully, with various approaches and workshops on reading, the pupils will be ready to change their reading habits.In this conceptual paper, a number of mobile applications will be introduced towards the primary school pupils to gather their attention and equip them with effective hypermedia reading strategies.Furthermore, in this 21st Century Learning, applications and gadgets are relevant to their interest.Parents, community, and the pupils themselves need to familiarize themselves with new applications to promote reading activities and develop a better understanding of reading materials.
Inter-class distribution alienation and inter-domain distribution alignment based on manifold embedding for domain adaptation

Domain adaptation (DA) aims to train a robust predictor by transferring rich knowledge from a well-labeled source domain to annotate a newly coming target domain; however, the two domains are usually drawn from very different distributions. Most current methods either learn the common features by matching inter-domain feature distributions and training the classifier separately or align inter-domain label distributions to directly obtain an adaptive classifier based on the original features despite feature distortion. Moreover, intra-domain information may be greatly degraded during the DA process; i.e., the source data samples from different classes might grow closer. To this end, this paper proposes a novel DA approach, referred to as inter-class distribution alienation and inter-domain distribution alignment based on manifold embedding (IDAME). Specifically, IDAME commits to adapting the classifier on the Grassmann manifold by using structural risk minimization, where inter-domain feature distributions are aligned to mitigate feature distortion, and the target pseudo labels are exploited using the distances on the Grassmann manifold. During the classifier adaptation process, we simultaneously consider the inter-class distribution alienation, the inter-domain distribution alignment, and the manifold consistency. Extensive experiments validate that IDAME can outperform several comparative state-of-the-art methods on real-world cross-domain image datasets.
English as a Foreign Language (EFL) Learners’ Perceptions on the Effectiveness of the Internet to Enhance their Learning

This study attempted to investigate the vital role of the internet in enhancing the learning of reading. In addition, the study aimed to measure the students’ attitudes towards the internet and to identify to what extent the internet was employed in their reading classes. Moreover, the study aimed to explore the challenges that they encountered while using the internet in their learning of reading. In order to achieve these goals, the researcher used the descriptive analytical approach and used the questionnaire as the main data collection tool. The study sample comprised 50 Saudi EFL male students in the preparatory year at King Saud University in Saudi Arabia. The findings of the study showed that students perceived that the internet is important in the process of reading comprehension learning and teaching with positive attitudes towards the internet. Results showed that the top challenges of using of the internet for reading learning/teaching are lack of students’ knowledge to know what to read on the internet, the short time of the class, and the students’ lack of the skills and knowledge to use the internet for academic purposes. Furthermore, the results showed that the students mostly used the internet for playing games, social networking, and learning vocabulary. It is recommended that English language teachers should be encouraged to use the internet in their language classes as it is valuable for their students to enhance their reading comprehension skills.
Empirical Negation, Co-Negation and the Contraposition Rule II: Proof-Theoretical Investigations

We continue the investigation of the first paper where we studied logics with various negations including empirical negation and co-negation. We established how such logics can be treated uniformly with R. Sylvan's CCω as the basis. In this paper we use this result to obtain cut-free labelled sequent calculi for the logics.
Security of E-Health Systems Using Face Recognition Based on Convolutional Neural Network

In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising.
Experimental cryptographic verification for near-term quantum cloud computing

An important task for quantum cloud computing is to make sure that there is a real quantum computer running, instead of classical simulation. Here we explore the applicability of a cryptographic verification scheme for verifying quantum cloud computing. We provided a theoretical extension and implemented the scheme on a 5-qubit NMR quantum processor in the laboratory and a 5-qubit and 16-qubit processors of the IBM quantum cloud. We found that the experimental results of the NMR processor can be verified by the scheme with about 1.4% error, after noise compensation by standard techniques. However, the fidelity of the IBM quantum cloud is currently too low to pass the test (about 42% error). This verification scheme shall become practical when servers claim to offer quantum-computing resources that can achieve quantum supremacy.
Latent Sparse Discriminative Learning for Face Image Set Classification

Image set classification has drawn much attention due to its promising performance to overcome various variations. Recently, the point-to-point distance-based methods have achieved the state-of-the-art performance by leveraging the distance between the gallery set and the probe set. However, there are two drawbacks that need to be defeated: 1) they do not fully exploit the discrimination that exists between different gallery sets; 2) they face the great challenge of high computational complexity as well as multi-parameters, usually caused by some obvious sparse constraints. To address these problems, this paper proposes a novel method, namely latent sparse discriminative learning (LSDL), for face image set classification. Specifically, a new term is proposed to exploit the relations between different gallery sets, which can boost the set discrimination so as to improve performance. Moreover, we use a latent sparse constraint to reduce the trade-off parameters and computational cost. Furthermore, an efficient solver is proposed to solve our LSDL. Experimental results on three benchmark datasets demonstrate the advantages of our propose.
Over-Sampling Multi-classification Method Based on Centroid Space

The problem of unbalanced data generally exists in various application fields of large data and machine learning, such as medical diagnosis, anomaly detection and so on. Researchers have proposed or adopted a variety of methods to learn unbalanced data. For example, SMOTE algorithm in data sampling, or EasyEnsemble method in ensemble learning. Oversampling method in data sampling may have some problems, such as low accuracy of over-fitting classification, while under-sampling method may lead to under-fitting. In this paper, an up-sampling method based on centroid space (CSUP) is proposed, which mainly aims at multi-classification problems. The proposed method solves the problem of data set imbalance well. Firstly, the initial centroid is obtained by solving the Euclidean distance between samples, and then the total distance is obtained by summing up the Euclidean distance of each centroid. By dividing the Euclidean distance of a single centroid by the total Euclidean distance, the sample points need to be increased can be obtained. The weight is multiplied by the number of sample points needed to be balanced, which makes the unbalanced data set more balanced in distribution and effectively improves the classification effect of the classifier. The experimental results show that the classification accuracy of CSUP is significantly higher than that of other algorithms, suvch as SMOTE. Adaboost etc.
Extending and Evaluating a Collaborative Note-Taking Application: A Pilot Study

Taking notes during lectures helps students filter information, record key points, clarify ideas and be more actively engaged in learning. This is particularly important in face-to-face lecture-based scenarios supported by slides, when students need to complete instructor handouts with their personal notes in order to facilitate understanding and foster involvement. In order to offer an alternative for handwritten notes or generic note-taking apps, we developed a mobile application with pedagogically grounded functionalities, called EduNotes, which allows students to collaborate with their peers during the note-taking process. In the current paper, we present an extension of the platform with a web version, which supports the simultaneous presentation of the lecture slides alongside the notes, together with a more advanced note management functionality. Both EduNotes versions were successfully used in an experimental study involving 38 students, which is reported in the paper.
Using GitHub Open Sources and Database Methods Designed to Auto-Generate Chinese Tang Dynasty Poetry

Writing a Tang-Dynasty poetry in Chinese is very popular and useful in the tradition Chinese culture education field, In this paper we use GitHub open sources “jieba” Algorithm for text segmentation of Chinese Tang Dynasty poetry and database methods to auto-generate Chinese old Tang-Dynasty poetry, focus on AI for education field and poetry making project, through Chinese word semantic matching, short sentence combination and verse rhyme pattern rules.
Research on Medical Image Encryption Method Based on Chaotic Scrambling and Compressed Sensing

In order to improve the security of medical images, a new image encryption method is proposed in this paper. In this method, the chaos theory is used to set the initial state and key, and then the measurement equation constructed by compressed sensing is used to complete the fusion encryption. The fusion of the two theories achieves the effect of chaotic uniform scrambling on the one hand, and on the other hand, the encrypted image can still accurately recover the real image when it is compressed to a fairly small size. Experiments are carried out on three groups of medical images. The experimental results show that the proposed encryption method achieves an ideal encryption effect for medical images. Under the condition that the encrypted image is compressed to 1/4 of the original image, the decryption can still be completed effectively.
GLOD: Gaussian Likelihood Out of Distribution Detector.

Discriminative deep neural networks (DNNs) do well at classifying input associated with the classes they have been trained on. However, out-of-distribution (OOD) input poses a great challenge to such models and consequently represents a major risk when these models are used in safety-critical systems. In the last two years, extensive research has been performed in the domain of OOD detection. This research has relied mainly on training the model with OOD data or requiring additional computation for OOD detection. Such methods may not be applicable in many real world use cases. In this paper, we propose GLOD -- Gaussian likelihood out of distribution detector -- an extended DNN classifier capable of efficiently detecting OOD samples with no additional runtime overhead and without auxiliary training data. GLOD uses a layer that models the Gaussian density function of the trained classes. The layer outputs are used to estimate a Log-Likelihood Ratio which is employed to detect OOD samples. We evaluate GLOD's detection performance on SVHN, CIFAR-10 and CIFAR-100.
Optimizing the Reservoir Connection Structure Using Binary Symbiotic Organisms Search Algorithm: A Case Study on Electric Load Forecasting

ESN (Echo state network) is a novel recurrent neural network and it is also acknowledged as a powerful temporal processing method, especially in real-valued, time-series forecasting fields. The current research results believe that the connection structure of the reservoir has significant effect for ESN’s forecasting performance. However, the randomly generated reservoir is hard to establish a optimal reservoir structure for a given task. Optimizing the connection structure of reservoir can be considered as a feature selection issue and this issue can be solved by binary optimization algorithm. SOS (Symbiotic organisms search) is a recently proposed heuristic algorithm and its superior performance is confirmed via many mathematical benchmark functions and engineering design problems. It’s worth noting that the original SOS is only suitable for continuous numerical optimization problems. In this paper, a binary SOS, called BSOS, is employed to optimize the connection structure of reservoir of the standard ESN. To verify the effectiveness of the proposed model, a real electric load series derived from New South Wales in Australia is adopted as benchmark dataset. The experimental results demonstrate that the proposed model can significantly improve the forecasting accuracy and it is a hopeful forecasting model.
Comparison of OAuth/OpenID Connect Security in America and Japan

When we use an electronic commerce (EC) web application, the EC website authenticates users for a so-called social login that uses a social media account, e.g., Facebook, Google, or Twitter. In this case, it is mostly implemented using OAuth and OpenID Connect. However, the implementation of a website can involve privacy concerns or be vulnerable to various attacks. In this paper, we investigate the implementation of social logins for 500 American EC sites. We observed eight websites that acquired more user permissions from SNS than necessary, and 76 websites that may be vulnerable to attacks due to improper implementation or use of grant type not recommended by RFC. We compared these results .to the results of similar investigations into Japanese website.
KidsDoodlePass: An Exploratory Study of an Authentication Mechanism for Young Children

Textual passwords are problematic for young children, whose cognitive, memory and linguistic capabilities are still developing. A possible alternative to using text for authentication systems for young children is drawings. In this paper, we describe an authentication system called KidsDoodlePass, which use simple drawings (“doodles”) that the children themselves create. An initial evaluation of the system was undertaken with 19 children aged 6 to 9 years of age. Success of logging in with KidsDoodlePass was high, only on few occasions did a child need more than one attempt, demonstrating that the system is effective. Selection times dropped significantly on the second use of the KidsPassDoodle and were typically under 10 s per grid. Most children thought their KidsDoodlePass would be easier to remember than a text password, a significant proportion. These positive results suggest that KidsDoodlePass could be a useful mechanism for young children to use as a first experience of authentication and a useful first step toward adult authentication systems.
Attention-BLSTM-CRF Based Method for Named Entity Recognition in Judicial Domain

Abstract Although the texts in the judicial field are relatively standardized, the entity categories are rich and the structure is different, and the entity expression is special in some legal documents, electronic files and guiding cases. In order to improve the effect of named entity recognition in the field of justice, this paper entity type can be divided into four categories, and presents a model of named entity recognition based on attention mechanism, structure improvement of input vector fusion CNN embed mode, BLSTM neural network at the same time we constantly study the characteristics of the context, finally by CRF decoding output sequence. The experimental results show that the method is helpful to the application research in the judicial field, and the experiment on the self-built corpus test set has achieved a good entity identification effect.
Quantitative Information Security Vulnerability Assessment for Norwegian Critical Infrastructure

A single information security vulnerability exploitation within Norwegian critical infrastructure can have a significant impact on Norwegian society, even causing cascading effects on other countries. Therefore, it is essential to conduct a quantitative vulnerability assessment to secure the weakest link. However, quantifying vulnerabilities to the entire Norwegian critical infrastructure has not been properly conducted in the literature. Defining the sectors responsible for or involved in providing vital functions in Norwegian society as the scope, we propose a methodology of six processes to conduct a quantitative vulnerability assessment by integrating the information from three sources: (1) the regional Internet registry, (2) the banner crawlers, and (3) the vulnerability database. We present and visualize the results of the vulnerability assessment from four different aspects: (1) vulnerability, (2) window of exposure, (3) impact, and (4) exploitability. Based on the results, we can easily identify power supply and transport as the weakest link. Compared to the entire country, the vital societal functions are better secured. Such assessment should be conducted continuously and automatically by specified public authorities to identify, classify, quantify, and prioritize the time-varying vulnerabilities.
Model Extraction Oriented Data Publishing with k-anonymity

Machine learning with personal data has been successfully employed in recent years. Protecting the privacy of a given personal dataset with utilities is therefore a major subject within the privacy community. The classifier trained on an anonymized dataset typically has poorer generalization performance than the classifier trained on the original dataset. We adopt the concept of Tramer et al.’s Model Extraction attack to place models trained on anonymized datasets similar to the original model. This approach also prevents the target variable from being published as it is. Using three open datasets, we do experiments to determine how close the original model is to the model trained on a dataset constructed by our method. Particularly when using Nursery Data set, the model trained on our anonymized dataset with $$k = 20$$ -anonymity gives a model that has almost the same ( $$95\%$$ ) predictions on a given test dataset as the original model.
Towards Abnormal Behavior Detection of Elderly People Using Big Data

Nowadays, smart living technologies are increasingly used to support older adults so that they can live longer independently with minimal support of caregivers. In this regard, there is a demand for technological solutions able to avoid the caregivers’ continuous, daily check of the care recipient. In the age of big data, sensor data collected by smart-living environments are constantly increasing in the dimensions of volume, velocity and variety, enabling continuous monitoring of the elderly with the aim to notify the caregivers of gradual behavioral changes and/or detectable anomalies (e.g., illnesses, wanderings, etc.). The aim of this study is to compare the main state-of-the-art approaches for abnormal behavior detection based on change prediction, suitable to deal with big data. Some of the main challenges deal with the lack of “real” data for model training, and the lack of regularity in the everyday life of the care recipient. At this purpose, specific synthetic data are generated, including activities of daily living, home locations in which such activities take place, as well as physiological parameters. All techniques are evaluated in terms of abnormality-detection performance and lead-time of prediction, using the generated datasets with various kinds of perturbation. The achieved results show that unsupervised deep-learning techniques outperform traditional supervised/semi-supervised ones, with detection accuracy greater than 96% and prediction lead-time of about 14 days in advance.