Reviewerly/exHarmony huggingface dataset:
Chapter 8: The World-Class Gold Deposits in the Geita Greenstone Belt, Northwestern Tanzania

Abstract The Geita mine is operated by AngloGold Ashanti and currently comprises four gold deposits mined as open pits and underground operations in the Geita greenstone belt, Tanzania. The mine produces ~0.5 Moz of gold a year and has produced ~8.3 Moz since 2000, with current resources estimated at ~6.5 Moz, using a lower cut-off of 0.5 g/t. The geologic history of the Geita greenstone belt involved three tectonic stages: (I) early (2820–2700 Ma) extension (D1) and formation of the greenstone sequence in an oceanic plateau environment; (II) shortening of the greenstone sequence (2700–2660 Ma) involving ductile folding (D2–5) and brittle-ductile shearing (D6), coincident with long-lived igneous activity concentrated in five intrusive centers; and (III) renewed extension (2660–2620 Ma) involving strike-slip and normal faulting (D7–8), basin formation, and potassic magmatism. Major gold deposits in the Geita greenstone belt formed late in the history of the greenstone belt, during D8 normal faulting at ~2640 Ma, and the structural framework, mineral paragenesis, and timing of gold precipitation is essentially the same in all major deposits. Gold is hosted in iron-rich lithologies along contacts between folded metaironstone beds and tonalite-trondhjemite-granodiorite (TTG) intrusions, particularly where the contacts were sheared and fractured during D6–7 faulting. The faults, together with damage zones created along D3 fold hinges and D2–3 hydrothermal breccia zones near intrusions, formed microfracture networks that were reactivated during D8. The fracture networks served as conduits for gold-bearing fluids; i.e., lithologies and structures that trap gold formed early, but gold was introduced late. Fluids carried gold as Au bisulfide complexes and interacted with Fe-rich wall rocks to precipitate gold. Fluid-rock interaction and mineralization were enhanced as a result of D8 extension, and localized hydrofracturing formed high-grade breccia ores. Gold is contained in electrum and gold-bearing tellurides that occur in the matrix and as inclusions in pyrrhotite and pyrite. The gold mineralization is spatially linked to long-lived, near-stationary intrusive centers. Critical factors in forming the deposits include the (syn-D2–6) formation of damage zones in lithologies that enhance gold precipitation (Fe-rich lithologies); late tectonic reactivation of the damage zones during extensional (D8) faulting with the introduction of an S-rich, gold-bearing fluid; and efficient fluid-rock interaction in zones that were structurally well prepared.
Cell density features from histopathological images to differentiate non-small cell lung cancer subtypes

Histopathological evaluation plays a crucial role in the process of understanding lung cancer biology. Such evaluation consists in analyzing patterns related with tissue structure and cell morphology to identify the presence of cancer and the associated subtype. This investigation presents a multi-level texture approach to differentiate the two main lung cancer subtypes, adenocarcinoma (ADC) and squamous cell carcinoma (SCC), by estimating global spatial patterns in terms of cell density. Such patterns correspond to texture features computed from cell density distribution in a co-occurrence frame. Results using the proposed approach achieved an accuracy of 0.72 and F-score of 0.72.
Study and analysis the effectiveness of persuasive systems for behavioral change

The work on technological persuasion, initiated by Fogg at the end of the 90 years, has allowed identifying many principles of persuasion on which new technologies can rely to influence the behavior of their users. This work has also helped to implement design methods, persuasive interfaces and to experiment with technological persuasion in a variety of fields. In a research or investigation work, a questionnaire always used as a means of collecting information methodically. This data makes it possible to check the hypotheses of the research therefore according to our objective to define the strategies that adapt to each individual from different countries. In order to explore and analyze the giant mass of information obtained from the questionnaire responses. Analyzing this data allow us to have a synthetic overview concerns the domination of certain persuasion strategies in social circles and according to each country covered in our investigation. The prepared lead us to conclude that a virtualization is a strong tool able to change people's attitudes and behaviors by experiencing and touching their risks.
Research on UAV image denoising effect based on improved Wavelet Threshold of BEMD

Abstract Aiming at the noise generated by external or internal factors reduces the sharpness of unmanned aerial vehicle (UAV) images, an improved bi-dimensional empirical mode decomposition (BEMD) de-noising algorithm is proposed to solve the problem. Firstly, an intrinsic mode function (IMF) is obtained after BEMD decomposition of noisy image, and then the high frequency apply particle swarm optimization (PSO) after wavelet decomposition to the high-frequency IMF to obtain the optimal threshold filtering. As for the low frequency, it is filtered by the exponential attenuation threshold algorithm of wavelet semi-soft threshold. Finally, the filtered both high-frequency and low-frequency IMF are reconstructed after inverse wavelet transformation. This algorithm is applied to the processing of UAV images, and compared with the traditional UAV image denoising method and the advanced UAV image denoising method. Simulation results demonstrate that the proposed denoising method outperforms other de-noising methods in terms of peak signal-to-noise ratio and mean square error.
AN ANALYSIS ON TEACHER’S COMMUNICATION STRATEGIES IN TEACHING SPEAKING AT MTS NW BONJERUK

The objectives of this study are to reveal what communication strategies used by an English teacher in communication with first-grade students of MTs NW Bonjeruk. The research used a qualitative approach, more specifically classroom discourse analysis in analyzing this study since the source of the data was communication strategies which are used by an English teacher in communicating with students during an English teaching-learning activity. The result of the study showed that there were four of five communication strategies proposed that appeared in the teaching-learning process. They were achievement or compensatory strategies, stalling or time gaining strategies, self-monitoring strategies, and interactional strategies. The strategy which was not found in the transcription is avoidance or reduction strategy. Based on the results of the study, the teacher often uses code-switching more than other strategies.
Security Assessment of a Partially Decentralized Blockchain System

As a new technology with subversive significance in the Internet field, blockchain has received extensive attention from research and applications sector in recent years. Its decentralized structure and unchangeable data nature have a wide application prospect in Internet transactions. However, it is difficult to implement supervision in a partially decentralized system. Therefore, a blockchain system with a single or a few central nodes has emerged during the development of blockchain. For such special partially decentralized blockchain systems, more scientific development is needed. This paper takes X insurance trading chain as an example to study this partially decentralized blockchain system, to make a more scientific security assessment of this kind of system, and put forward corresponding countermeasures to improve the security of this kind of system.
An inception deep architecture to differentiate close-related Gleason prostate cancer scores

Histopathological tissue analysis is the most effective and definitive method to prognosis cancer and stratify the aggressiveness of the disease. The Gleason Score (GS) is the most powerful grading system based on architectural tumor pattern quantification. This score characterizes cancer tumor tissue, such as the level of cell differentiation on histopathological images. The reported GS is described as the sum of two principal grades present in a particular image, and ranged from 6 (cancer grow slowly) to 10 (cancer cells spread more rapidly). A main drawback of GS is the pathological dependency on histopathological region stratification, which strongly impacts the clinical procedure to treat the disease. The agreement among experts has been quantified with a kappa index of: ~0.71. Even worse, a higher uncertainty is reported for intermediate grade stratification. This work presents a like-inception deep architecture that is able to differentiate between intermediate and close GS grades. Each image herein evaluated was split-up into regional patches that correspond to a single GS grade. A set of training patches were augmented according to appearance image variations of each grade. Then, a transfer learning scheme was implemented to adapt a bi-Gleason tumor patterns prediction among close levels. The proposed approach was evaluated on public set of 886 tissue H&amp;E stained images with different GS grades, achieving an average accuracy of 0.73% between grades three and four.
On the Geometry of Rectifier Convolutional Neural Networks

While recent studies have shed light on the expressivity, complexity and compositionality of convolutional networks, the real inductive bias of the family of functions reachable by gradient descent on natural data is still unknown. By exploiting symmetries in the preactivation space of convolutional layers, we present preliminary empirical evidence of regularities in the preimage of trained rectifier networks, in terms of arrangements of polytopes, and relate it to the nonlinear transformations applied by the network to its input.
A New One-Class Classification Method with Multiple Encoder-Decoder Pairs for Images

One of the main destinations of image classification methods is to screen out the images belonging to the target class (positive) and identify the images of other classes (negative). Although most classifiers are trained on both positive samples and negative samples, in reality, negative samples are often unavailable. One-class classifiers trained only on positive samples, are proposed to solve this problem. However, how to train effectively a classifier remains a daunting challenge. Considering the success of deep learning in the field of computer vision in recent years, we propose a one-class classification model for images based on convolutional neural network (CNN). The model consists of two parts of networks with different responsibilities. The network of the first part works as the discriminator, used to identify whether the images are positive. The networks of the second part, each of which consists of an encoder-decoder pair work as the guiders of the discriminator. They guide the discriminator to learn what images it should identify to be negative. Different encoder-decoder pairs can restore images to different degrees. Images restored to different degrees can be used to train the discriminator. The discriminator learns that images under a specific restored degree don't belong to the target class. The experiment results on CIFAR-10 show that our method can achieve good performance, with less difficulty in training than the GAN-based counterparts'.
Decentralized Labor Record System Based on Wavelet Consensus Protocol

The labor market involves several untrusted actors with contradicting objectives. We propose a blockchain based system for labor market, which provides benefits to all participants in terms of confidence, transparency, trust and tracking. Our system would handle employment data through new Wavelet blockchain platform. It would change the job market enabling direct agreements between parties without other participants, and providing new mechanisms for negotiating the employment conditions. Furthermore, our system would reduce the need in existing paper workflow aswell as in major internet recruiting companies. The key differences of our work from other blockchain based labor record systems are usage of Wavelet blockchain platform, which features metastability, directed acyclic graph system and Turing complete smart contracts platform and introduction of human interaction inside the smart contracts logic, instead of automatic execution of contracts. The results are promising while inconclusive and we would further explore potential of blockchain solutions for labor market problems.
Adaptive Constrained Differential Evolution Algorithm by Using Generalized Opposition-Based Learning

Differential evolution is a global optimization algorithm based on greedy competition mechanism, which has the advantages of simple structure, less control parameters, higher reliability and convergence. Combining with the constraint-handling techniques, the constraint optimization problem can be efficiently solved. An adaptive differential evolution algorithm is proposed by using generalized opposition-based learning (GOBL-ACDE), in which the generalized opposition-based learning is used to generate initial population and executes the generation jumping. And the adaptive trade-off model is utilized to handle the constraints as the improved adaptive ranking mutation operator is adopted to generate new population. The experimental results show that the algorithm has better performance in accuracy and convergence speed comparing with CDE, DDE, A-DDE and. And the effect of the generalized opposition-based learning and improved adaptive ranking mutation operator of the GOBL-ACDE have been analyzed and evaluated as well.
Application of Bayesian Decision Tree Algorithm in Breast Cancer Prediction

Aiming at the low classification accuracy of common decision tree C4.5 algorithm and the condition attribute problem of decision tree splitting point, a Bayesian decision tree algorithm combining ordinary decision tree C4.5 algorithm and naive Bayesian correlation algorithm is proposed in this paper. On this basis, the consistency test coefficient(kappa coefficient) is introduced to improve the correlation algorithm in the information gain rate in C4.5. Finally, the newly generated decision tree is pruned according to the most suitable splitting point. The improved algorithm proposed in this paper has a distinct improvement compared with the algorithm proposed in other literatures considering both time and accuracy.
‘Copyright Work and its Definition with Regard to Originality and AI’ – Conference Report on the Fourth Binational Seminar of TU Dresden and Charles University in Prague, 27 June 2019

Abstract Bill Gates once wrote ‘I am in the camp that is concerned about super intelligence’ and positioned himself on the question of Artificial Intelligence.1 Mr Gates was, however, concerned about the future of AI in order to be able to supply not only intelligent but also exceptional products. Following the third binational seminar in November 2018 on the topic ‘Software and Artificial Intelligence – Old and New Challenges for Patent Law’,2 colleagues from the IGETeM, TU Dresden and Charles University in Prague met in Dresden on 27 June 2019 to focus on this question from the perspective of copyright. They also dealt with other current issues involving copyright, such as definition of work and the notion of originality.
Deep in the Wild

In this chapter we are interested in how from high-resolution images and videos passing them through a Deep convolutional neural network we get reduced dimension which finally allows a classification decision. We are interested in two operations: convolution and pooling and trace analogy with these operations in a classical Image Processing framework.
Personal Research Assistant for Online Exploration of Historical News

We present a novel environment for exploratory search in large collections of historical newspapers developed as a part of the NewsEye project. In this paper we focus on the intelligent Personal Research Assistant (PRA) component in the environment and the web interface. The PRA is an interactive exploratory engine that combines results of various text analysis tools in an unsupervised fashion to conduct autonomous investigations on the data according to users' needs. The PRA is freely available online together with some datasets of European historical newspapers. The methods used by the assistant are of potential benefit to other exploratory search applications.
High-Order Markov Random Field Based Image Registration for Pulmonary CT

Deformable image registration is an important tool in medical image analysis. In the case of lung four dimensions computed tomography (4D CT) registration, there is a major problem that the traditional image registration methods based on continuous optimization are easy to fall into the local optimal solution and lead to serious misregistration. In this study, we proposed a novel image registration method based on high-order Markov Random Fields (MRF). By analyzing the effect of the deformation field constraint of the potential functions with different order cliques in MRF model, energy functions with high-order cliques form are designed separately for 2D and 3D images to preserve the deformation field topology. For the complexity of the designed energy function with high-order cliques form, the Markov Chain Monte Carlo (MCMC) algorithm is used to solve the optimization problem of the designed energy function. To address the high computational requirements in lung 4D CT image registration, a multi-level processing strategy is adopted to reduce the space complexity of the proposed registration method and promote the computational efficiency. Compared with some other registration methods, the proposed method achieved the optimal average target registration error (TRE) of 0.93 mm on public DIR-lab dataset with 4D CT images, which indicates its great potential in lung motion modeling and image guided radiotherapy.
The Effect of High School Rank in English and Math on College Major Choice

Using unique data on preference rankings for all high school students who apply for college in Ireland, we investigate whether, conditional on absolute achievement, within school-cohort rank in English and math affects choice of college major. We find that higher rank in math increases the likelihood of choosing STEM and decreases the likelihood of choosing Arts and Social Sciences.Similarly, a higher rank in English leads to an increase in the probability of choosing Arts and Social Sciences and decreases the probability of choosing STEM. The rank effects are substantial, being about one third as large as the effects of absolute performance in math and English. We identify subject choice in school as an important mediator – students who rank high in math are more likely to choose STEM subjects in school and this can partly explain their subsequent higher likelihood of choosing STEM for college.We also find that English and math rank have significant explanatory power for the gender gap in the choice of STEM as a college major – they can explain about 36% as much as absolute performance in English and math. Overall, the tendency for girls to be higher ranked in English and lower ranked in math within school-cohorts can explain about 6% of the STEM gender gap in mixed-sex schools and about 16% of the difference in the STEM gender gap between mixed-sex schools and same-sex schools. Notably, these effects occur even though within-school rank plays no role whatsoever in college admissions decisions.
Smart Homes and Assisted Living as an Additional Service Offered to the Users

For several years, ENEA has been conducting a research project funded by the Ministry of Economic Development, within a broader context that envisages the of an integrated Smart Urban District model. Among the objectives of the project, there is the development of a replicable Smart Home model capable of stimulating energy savings and, at the same time, offering innovative services that aim to improve the quality of life of end users, in particular fragile users as well as older people. This paper presents the integration on the Smart Home developed architecture of a useful additional assisted living service, namely MySignals service, which is able to monitor the vital parameters of home users.
Secure Data Deduplication with Resistance to Side-Channel Attacks via Fog Computing

Deduplication could greatly save the storage overhead of cloud server by eliminating duplicated data and retaining one copy. In order to ensure the data privacy, many researchers try to make deduplication feasible in ciphertext. A typical scheme is message-locked encryption (MLE) which takes cryptographic hash value of message as encryption key. However, MLE is vulnerable to side-channel attacks. To our knowledge, the existing schemes try to mitigate these attacks with either security drawbacks or expensive overhead. In this paper, we propose two new techniques to solve two typical side-channel attacks named probe attack and key-cache attack via fog computing with new security and efficiency tradeoffs. Built on the new techniques, we propose a secure data deduplication system in fog computing environment. Our evaluation shows that our system has better performance compared with previous works.
Correction to: Transfer Hierarchical Attention Network for Generative Dialog System

The article Transfer Hierarchical Attention Network for Generative Dialog System written by Xiang Zhang and Qiang Yang, was originally published on vol. 16, no. 6 of International Journal of Automation and Computing without Open Access. After publication, the authors decided to opt for Open Choice and to make the article an Open Access publication. Therefore, the copyright of the article has been changed to © The Author(s) 2020 and the article is forthwith distributed under the terms of the Creative Commons Attribution 4.0 International License ( https://doi.org/creativecommons.org/licenses/by/4.0/ ), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Semantic Network Analysis of Korean Virtual Assistants’ Review Data

The interest in artificial intelligence (AI) is increasing significantly. “Virtual Assistant” is a service that allows consumers to easily access AI technology. This study aims to provide a clear and thorough overview of the current consumer experience through reviews written by consumers in South Korea. We suggest possible solutions for future developments of virtual assistants that could further benefit consumers. This study provides insights into consumer experience with virtual assistants through their review data. We categorize each virtual assistant and their related issues and suggest possible solutions for improving future virtual assistants to enhance the quality of consumers’ life.