Reviewerly/exHarmony huggingface dataset:
Flexible entity marks and a fine-grained style control for knowledge based natural answer generation

Knowledge-based natural answer generation systems are generally difficult to train because numerous entities rarely appear. One way is to replace the entities with their respective types. However, the entity type requires additional recognition with limited accuracy and ignores semantic meanings. Consequently, we propose a question answering system with flexible marks, copying, and retrieving mechanisms (MarkCRQA) to generate natural and accurate answers. By requiring random marks to be shared among all entity types, MarkCRQA attaches the marks to the entities in questions, answers, and knowledge base, which avoids the additional recognition process for entity types and reduces the training difficulty. In addition, we propose to finely control the naturalness and knowledge level of each answer for different real-world scenarios and user needs. Experiments show that MarkCRQA achieves state-of-the-art performance on two open-domain question answering datasets.
Privacy-Enhancing Technologies

Abstract An increasing amount of sensitive information is being communicated and stored online. Frequent reports of data breaches and sensitive data disclosures underscore the need for effective technologies that users and administrators can deploy to protect sensitive data. Privacy-enhancing technologies can control access to sensitive information to prevent or limit privacy violations. This chapter focuses on some of the technologies that prevent unauthorized access to sensitive information. These technologies include secure messaging, secure email, HTTPS, two-factor authentication, and anonymous communication. Usability is an essential component of a security evaluation because human error or unwarranted inconvenience can render the strongest security guarantees meaningless. Quantitative and qualitative studies from the usable security research community evaluate privacy-enhancing technologies from a socio-technical viewpoint and provide insights for future efforts to design and develop practical techniques to safeguard privacy. This chapter discusses the primary privacy-enhancing technologies that the usable security research community has analyzed and identifies issues, recommendations, and future research directions.
Facial Age Estimation Using Geometric, Local Phase Quantization and Pyramid Histogram of Oriented Gradient Features

Variations in shape and texture of a face are caused by aging across years. Factors such as genetics, lifestyle, health etc. greatly influence the aging process of a person which makes human age estimation from a facial image a very difficult problem. In this research, geometric and texture based facial feature extraction methods are presented. These methods are based on fusion between geometric features and local phase quantization (LPQ) texture features or geometric and pyramid histogram of oriented gradients (PHOG) texture features. Classification based on these methods was then carried out using the K-Nearest Neighbor (KNN) and support vector machine (SVM) to determine the accuracy of the system. Result obtained showed that fusing geometric and PHOG features provided best classification accuracy of 69.4% using the FGNET Database 1002 images of various age ranges.
Diagenetic and epigenetic origins for <scp>Cu‐Ag</scp> mineralization in the Khoemacau Zone 5 deposit, Kalahari Copperbelt, <scp>northwestern</scp> Botswana

Abstract In the Kalahari Copperbelt, the mechanism of regional scale Cu‐Ag mineralization has remained intensely debated between early diagenesis and a single pass during a regional deformation event. At the Zone 5 Cu‐Ag deposit in northwestern Botswana, the orebody is hosted preferentially by chemically reduced metasedimentary rocks that overly oxidized, hematite‐bearing arkosic sandstone. An early diagenetic mineralizing event has been identified which is characterized by fine‐grained stratiform pyrite, including recrystallized framboidal pyrite, intergrown with diagenetic mineral assemblages in the host‐rock. Diagenetic pyrite is in textural equilibrium with chalcopyrite, sphalerite, galena, and (Fe‐Co‐Ni) sulfarsenide. These minerals were subsequently overprinted by a more intense, multi‐stage, structurally‐controlled hydrothermal Cu‐Ag mineralization event related to the Damaran orogeny (~600–480 Ma). The hydrothermal Cu‐Ag mineralization was deposited from hot (~236–265°C), high salinity (19–24.6 wt% NaCl equiv.) hydrothermal ore fluids. Petrographic results reveal an apparent overlap in trace metal associations (Cu, Fe, As, Zn, Pb, Ni, Co) between the two mineralizing events, which can be explained by remobilization of precursor sulfides. The major Ag‐carriers in the ore are chalcocite, covellite, and bornite. The δ 34 S values of diagenetic pyrite range from −35.8 to +11.4‰, whereas those of hydrothermal epigenetic sulfides, including pyrite, range from −28.0 to +3.0‰. We propose that the hydrothermal sulfides had acquired some bacterially‐reduced sulfur from earlier‐formed minerals. The δ 18 O and δ 13 C values of quartz and calcite associated with the hydrothermal mineralization are typical of Neoproterozoic sediment‐hosted Cu‐Ag deposits. However, the δ 18 O isotopic values of the calcite gangue are anomalously depleted, which is likely due to recrystallization under metamorphic conditions. Our studies at Zone 5 indicate that the Zone 5 Cu‐Ag deposit is the result of a multi‐stage mineralization history that includes both diagenetic and epigenetic events (punctuated by &gt;400 m.y.) facilitated by a strong litho‐structural control.
Smart Education: Education Reform in the Age of Intelligence

In the era of intelligence, the new generation of information technology with 5G mobile communication, big data and artificial intelligence as its core has triggered a new round of education reform and promoted the transformation and upgrading of human education to smart education. As a new educational idea and practice, how should the public understand smart education? What are the characteristics of smart education? And what changes have been made in education? This paper aims to construct the ecological system of smart education by studying these three problems, and provide some useful references for the practice promotion and related research of smart education in the future.
FDI and inequality in Sub-Saharan Africa: does democracy matter?

Purpose This paper investigates whether democracy plays a mediating role in the relationship between foreign direct investment (FDI) and inequality in Sub-Saharan Africa (SSA). Design/methodology/approach The empirical analysis is conducted using fixed effects and system GMM (Generalised Method of Moments) on a panel of 38 Sub-Saharan African countries covering the period of 1990–2018. Findings The results find that FDI has no direct effect on inequality whereas democracy reduces inequality directly in both the short run and the long run. The sensitivity analyses find that democracy improves equality regardless of the magnitude of FDI, resource endowment or democratic deepening whereas FDI only reduces inequality once a moderate level of democracy has been achieved. Social implications The results discussed above thus have four policy implications. First, these results show that although democracy has inequality reducing benefits, SSA is unlikely to significantly reduce inequality unless the region purposefully diversifies its trade and FDI away from natural resources. Second, the region should continue to expand credit access to reduce inequality and attract FDI. Third, policymakers should undertake reforms that will reduce youth inequality. Lastly, the region should focus on long-run democratic reforms rather than on short-run democratization to improve governance and investor confidence. Originality/value Although there are existing studies that examine the association between FDI and inequality, FDI and democracy and democracy and inequality, this is the first study to explicitly examine the effect of democracy on the association between FDI and inequality in SSA, and the first study to separately consider the possible varied effects of contemporaneous democratization versus the long-run accumulation of democratic capital. In addition, rather than measure inequality by income alone, this study uses the more appropriate Human Development Index to account for SSA's sociological, education and income disparities.
Graph matching beyond perfectly-overlapping Erdős–Rényi random graphs

Abstract Graph matching is a fruitful area in terms of both algorithms and theories. Given two graphs $$G_1 = (V_1, E_1)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>G</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>=</mml:mo> <mml:mrow> <mml:mo>(</mml:mo> <mml:msub> <mml:mi>V</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>E</mml:mi> <mml:mn>1</mml:mn> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> and $$G_2 = (V_2, E_2)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mi>G</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mo>=</mml:mo> <mml:mrow> <mml:mo>(</mml:mo> <mml:msub> <mml:mi>V</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>E</mml:mi> <mml:mn>2</mml:mn> </mml:msub> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> , where $$V_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>V</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> and $$V_2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>V</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:math> are the same or largely overlapped upon an unknown permutation $$\pi ^*$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>π</mml:mi> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> , graph matching is to seek the correct mapping $$\pi ^*$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>π</mml:mi> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> . In this paper, we exploit the degree information, which was previously used only in noiseless graphs and perfectly-overlapping Erdős–Rényi random graphs matching. We are concerned with graph matching of partially-overlapping graphs and stochastic block models, which are more useful in tackling real-life problems. We propose the edge exploited degree profile graph matching method and two refined variations. We conduct a thorough analysis of our proposed methods’ performances in a range of challenging scenarios, including coauthorship data set and a zebrafish neuron activity data set. Our methods are proved to be numerically superior than the state-of-the-art methods. The algorithms are implemented in the R (A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, 2020) package GMPro (GMPro: graph matching with degree profiles, 2020).
Toward Gaze-Based Map Interactions: Determining the Dwell Time and Buffer Size for the Gaze-Based Selection of Map Features

The modes of interaction (e.g., mouse and touch) between maps and users affect the effectiveness and efficiency of transmitting cartographic information. Recent advances in eye tracking technology have made eye trackers lighter, cheaper and more accurate, broadening the potential to interact with maps via gaze. In this study, we focused exclusively on using gaze to choose map features (i.e., points, polylines and polygons) via the select operation, a fundamental action preceding other operations in map interactions. We adopted an approach based on the dwell time and buffer size to address the low spatial accuracy and Midas touch problem in gaze-based interactions and to determine the most suitable dwell time and buffer size for the gaze-based selection of map features. We conducted an experiment in which 38 participants completed a series of map feature selection tasks via gaze. We compared the participants’ performance (efficiency and accuracy) between different combinations of dwell times (200 ms, 600 ms and 1000 ms) and buffer sizes (point: 1°, 1.5°, and 2°; polyline: 0.5°, 0.7° and 1°). The results confirmed that a larger buffer size raised efficiency but reduced accuracy, whereas a longer dwell time lowered efficiency but enhanced accuracy. Specifically, we found that a 600 ms dwell time was more efficient in selecting map features than 200 ms and 1000 ms but was less accurate than 1000 ms. However, 600 ms was considered to be more appropriate than 1000 ms because a longer dwell time has a higher risk of causing visual fatigue. Therefore, 600 ms supports a better balance between accuracy and efficiency. Additionally, we found that buffer sizes of 1.5° and 0.7° were more efficient and more accurate than other sizes for selecting points and polylines, respectively. Our results provide important empirical evidence for choosing the most appropriate dwell times and buffer sizes for gaze-based map interactions.
A Survey on the Cervical Cancer Detection using Deep Learning methods

Cervical cancer is the fourth common type of cancer mainly occurs in the cervix of the women. Cervical cancer can be caused by the Human Papilloma Virus (HPV) infection. It can be eradicated by vaccinating all the women from the early age. But this difficult in the mid or low income countries because of limited medical facilities. If the cervical cancer detected at an early stage, it can be successfully treated and can improve the survivability rate. Deep learning approaches are extensively used to automate the cervical cancer detection. Deep learning algorithms and architectures are used to detect the cervical cancer and provides very high detection accuracy. The comprehensive review of the latest developments of deep learning techniques in automating the cervical cancer detection is presented.
Extractive Text Summarization for COVID-19 Medical Records

The method of reducing information from an original text document while maintaining the vital information is known as text summarizing. The amount of text data available has increased dramatically in recent years from a variety of sources. A large volume of text is an excellent source of information and knowledge of the source is essential for efficiently summarizing information that must be useful. Summarization facilitates the acquisition of vital and required information in a short period of time. Text summarization is required in a variety of domains, including news article summaries, email summaries and information summaries in the medical profession to track a patient&#x0027;s medical history for future treatment and so on. In summarization, there are two methods: extractive summarization and abstractive summarization. In this work, extractive summarization is used on the COVID-19 dataset. Different models and their results have been discussed.
Generative adversarial attacks on motion-based continuous authentication schemes

Traditional authentication mechanisms use passwords, Personal Identification Numbers (PINs) and biometrics, but these only authenticate at the point of entry. Continuous authentication schemes instead allow systems to verify identity and mitigate unauthorised access continuously. However, recent developments in generative modelling can significantly threaten continuous authentication systems, allowing attackers to craft adversarial examples to gain unauthorised access and may even limit a legitimate user from accessing protected data in the network. The research available on the use of generative models for attacking continuous authentication is relatively scarce. This paper explores the feasibility of bypassing continuous authentication using generative models, measuring the impact of the damage, and advising the usage of metrics to compare the various advertised attacks in such a system. Our empirical results demonstrate that generative models cause a higher Equal Error Rate and misclassification error in attack scenarios. At the same time, training and detection time during attack scenarios is increased compared to perturbation models. The results prove that data samples crafted by generative models can be a severe threat to continuous authentication schemes using motion sensor data.
A Scalable Real-Time Distributed Multiagent Decision Making Algorithm with Cost

We present a distributed algorithm for a multiagent system in a collaborative setting including the cost of actions. We revise the distributed Max-Plus algorithm by presenting a new Cost Distributed Max-Plus (CD Max-Plus) algorithm by including the cost of actions in the interactions of agents, named Factored Value-MCTS-CD Max-Plus algorithm. The proposed Factored Value-MCTS-CD Max-Plus algorithm is online, anytime, distributed, and scalable in number of agents and their interactions. Our contribution is an alternative solution for solving cost Dec-POMDPs competing with the state of art methods and algorithms using MCTS and Max-Plus algorithms to exploit the locality of agent interactions for planning and acting.
Robot Testing from a machine learning perspective

The need to scale software test automation while managing the test automation process within a reasonable time frame remains a crucial challenge for software development teams (DevOps). Unlike hardware, the software cannot wear out but can fail to satisfy the functional requirements it is supposed to meet due to the defects observed during system operation. In this era of big data, DevOps teams can deliver better and efficient code by utilizing machine learning (ML) to scan their new codes and identify test coverage gaps. This study introduces robot testing and machine learning to manage the test automation process to guarantee software reliability and quality within a reasonable timeframe.
Threat Assessment for Aerial Targets Based on Three-way Multi-criteria Decision Making

Aiming at the threat assessment of aerial targets for intelligent unmanned platforms, a three-way multi-criteria decision model is proposed with ELECTRE-I method. Firstly, ELECTRE-I is used to obtain the outranked sets and the conditional probabilities of targets with respect to the threat degree. Next, the loss function matrix for misclassification is constructed through a transformation mechanism, which is introduced into the three-way decision model along with conditional probability. Furthermore, the objective threat classification and ranking are obtained according to the Bayesian minimum risk evaluation criterion. Finally, the example analysis and experimental comparison are provided to illustrate the validity and superiority of the model.
Automatic Arrangement of Sports Dance Movement Based on Deep Learning

Sports dance is a new form of sports that integrates sports, dance, music, and other elements. The core content of "dance" is an important carrier for athletes to display their body art. This article aims to study the automatic arrangement of sports dance based on deep learning. This article first introduces the development process of deep learning. As the latest research direction developed from artificial neural network technology in machine learning, deep learning has attracted widespread attention from the society. And then proposing a shallow regression model based on deep learning, a convolutional neural network based on deep learning, and an offline sorting regression model, given the general process of deep learning, then, based on the clustering algorithm, the deep learning was researched, and the sport dance movement arrangement was analyzed based on the deep learning. The experimental results of this article show that deep learning can effectively enhance the artistic ability of automatic choreography in sports dance and increase the accuracy of dance movements by 80%. At the same time, on the basis of deep learning, the practical ability is strengthened on the basis of consolidating theory, to further improve one's own business ability and educational technology level, actively absorb advanced teaching methods, and earnestly delve into reasonable teaching methods. It is also used in curriculum training practice to actively gain insight into new development trends in educational methods and skills, to enhance the artistic creativity of students' arrangements.
ACC: Blockchain Based Trusted Management of Academic Credentials

Justification of authenticity of academic credentials is a very clumsy task from a global perspective. For digital certificates to be widely accepted requires secure verifications of the issuer, credential holders, and secure data sharing. Physical verification and a traditional centralized digital system are entirely viable for the current era. This paper proposes ACC, a blockchain-based Academic Credentials Chain(ACC), for global authenticity verifications and sharing. The proposed system recognizes from a credential who is certifying whom. In the proposed Blockchain network, a decentralized application allows users to store their credentials data privately. The evaluation results in proof of the feasibility, security and exhibits the level of performance.
Face Shape Classification Based on MTCNN and FaceNet

Deep learning has made great progress in the field of face recognition. Most face shape classification methods use the distance and Angle between facial features and the landmarks of facial outline, and calculate some values through formulas. With the development of deep learning, many methods take face shape as the learning object. After comparing some networks, this paper uses MTCNN and FaceNet, and the accuracy is 92.32%, which is higher than other networks. In addition, Center Loss is used instead of Triplet Loss to achieve a better effect.
HBRO: A Registration Oracle Scheme for Digital Rights Management Based on Heterogeneous Blockchains

As the Internet enters the era of big data, massive amounts of data are flooding people's mobile phones and computers.The emerging self-media industry produces many videos every day, which also exposes many security issues in digital rights management (DRM).The works of original creators can easily be infringed on public networks, and it is urgent to protect the copyright of digital content.Traditional digital rights management (TDRM) has many problems, including unqualified copyright confirmation, difficulty obtaining evidence, long time-consuming, high price, and high centralization.The combination of blockchain technology and digital rights management is one of the most popular blockchain application scenarios, the characteristics of the blockchain match DRM market demand.This paper proposes a registration oracle scheme for digital rights management based on heterogeneous blockchains, HBRO, which uses review and voting as a means to judge whether a work can be registered for copyright.HBRO is more rigorous than TDRM and DDRM in the copyright confirmation stage, rejecting many unnecessary and unqualified contents.In addition, a secure cross-chain solution is used to ensure the integrity and correctness of data transmission on heterogeneous blockchains.