Reviewerly/exHarmony huggingface dataset:
“New Regionalization”: Modernization of Russian Universities and Experience of Germany

The importance of the supporting universities in each region is quite high, because the main purpose of creating them is to provide an efficient cooperation between the region and the system of higher education. Through the supporting universities it is possible to solve the most pressing problems facing not only regions but also the state as a whole. The interaction between higher education and regions is associated with the strengthening of the “third mission” of universities, the implementation of which makes university a key «player» in the economic and social development of a region and introduces significant changes in the university’s relations with its partners: industry, business, government, civil society institutions. The first section of the article focuses on the identification of the main parameters of the regional activities of Russian universities as active participants in the socio-economic development of region. The second section systematizes the experience of German universities on the implementation of the «third mission» in the regional context.
Guiding Prosecutorial Decisions with an Interpretable Statistical Model

After a felony arrest, many American jurisdictions hold individuals for several days while police officers investigate the incident and prosecutors decide whether to press criminal charges. This pre-arraignment detention can both preserve public safety and reduce the need for officers to seek out and re-arrest individuals who are ultimately charged with a crime. Such detention, however, also comes at a high social and financial cost to those who are never charged but still incarcerated. In one of the first large-scale empirical analyses of pre-arraignment detention, we examine police reports and charging decisions for approximately 30,000 felony arrests in a major American city between 2012 and 2017. We find that 45% of arrested individuals are never charged for any crime but still typically spend one or more nights in jail before being released. In an effort to reduce such incarceration, we develop a statistical model to help prosecutors identify cases soon after arrest that are likely to be ultimately dismissed. By carrying out an early review of five such candidate cases per day, we estimate that prosecutors could potentially reduce pre-arraignment incarceration for ultimately dismissed cases by 35%. To facilitate implementation and transparency, our model to prioritize cases for early review is designed as a simple, weighted checklist. We show that this heuristic strategy achieves comparable performance to traditional, black-box machine learning models.
A Bayesian analysis for pseudo-compositional data with spatial structure

We proposed a Bayesian analysis of pseudo-compositional data in presence of a latent factor, assuming a spatial structure. This development was motivated by a dataset containing information on the number of newborns of primiparous mothers living in each of the microregions of the state of Sao Paulo, Brazil, in the year of 2015, stratified by the age of the mothers (15-18, 19-29 and 30 years or more). Considering that data on newborns are not stochastically distributed among the three age groups, but they are explained in relation to women's population structure, we adopted the expression "pseudo-compositional data" to refer to this data structure. The hypothesis of interest establishes that the age of the first pregnancy is associated with the economic conditions of the geographic area where the mother lives. The incidence of poverty was included as an independent variable. Additive log-ratio (alr) and isometric log-ratio (ilr) transformations were considered, as is usually done in the analysis of compositional data. The model included a random effect related to the spatial effect assumed to have a conditional autoregressive structure. A Bayesian Markov Chain Monte Carlo (MCMC) simulation procedure was used to get the posterior summaries of interest. The model based on the (ilr) transformation was well fitted to the data, showing that in the microregions with the highest incidence of poverty, there are higher proportions of women who have their first child in adolescence, while in the microregions with the lowest incidence of poverty, there are higher proportions of women who have their first child after the age of 30 years. From these results it is possible to conclude that this Bayesian approach was very useful in the estimation of the parameters of the proposed model. The proposed method should have a broad application to other problems involving pseudo-compositional data.
Estimation of Mining and Landfilling Activities with Associated Overburden through Satellite Data: Germany 2000–2010

Despite ever-increasing material extraction on the global scale, very few studies have focused on the relationship between mining activities, overburden, and landfilling. This is mainly due to the lack of statistical data. Yet, large mining activities cause environmental strain to the natural environment, and are often cause of irreversible alterations to the natural landscape. To circumvent this problem, we develop a methodology that employs the digital elevation model and land cover to detect and analyze mining and landfilling site over time. We test our methodology with the case of Germany for the years 2000–2010. We then confront our results with statistically available data, to verify whether this methodology can be applied to other countries. Results from the analysis of satellite data give 15.3 Pg of extracted materials and 7.8 Pg of landfilled materials, while statistics report 29.4 Pg and 1.8 Pg, respectively. This large difference was likely due to the different frequency of recording, where satellite data was updated after 10 years, while statistics were reported yearly. The analysis of the anthropogenic disturbance with spatial information can effectively contribute to observe, analyze, and quantify mining activities, overburden, and landfills, and can thus provide policy makers with useful and practical information regarding resource usage and waste management.
Automatic Breast Cancer Grading of Histological Images using Dilated Residual Network

Breast cancer is one of the leading causes of female death worldwide. Histological evaluation of the breast biopsies is essential in the early detection. Recently, deep learning methods are developed to automatically grade breast cancer of histological images. For the critical local and global features of histological images, few existing deep learning methods effectively extract both of them. Most methods extract one at the loss of the other, with degraded multi-class classification accuracy. In this paper, we propose an effective breast cancer classification method of histology images based on a modified dilated residual network (DRN). The proposed method effectively captures the global feature while maintaining the local information, and thus achieves notably high multi-class classification accuracy. Experimental results show that for the four-class breast cancer classification problem, an accuracy of 89.5% can be obtained, which outperforms all the prevalent methods. In comparison to the manual diagnosis accuracy of 89% from pathologists, the proposed automatic diagnosis method is practical and promising.
A Neural Network Framework to Generate Caption from Images

With several applications in various different fields, computer vision can be found everywhere in our society. A fundamental problem of artificial intelligence (AI) is to give automatic description of the content in an image. In this paper, we focus on one of the visual recognition facets of computer vision, i.e., image captioning. To give the description for visual data has been studied for a long time but in the field of videos. In the recent few years, emphasis has been lead on still image description with natural text. Due to the recent advancements in the field of object detection, the task of scene description in an image has become easier. The aim of the paper was to train convolutional neural networks (CNN) with several hundreds of hyperparameters and apply it on a huge dataset of images (ImageNet) and combine the results of this image classifier with a recurrent neural network to create a caption for the classified picture. In this paper, we systematically analyze a deep neural networks-based image caption generation method. With an image as the input, the method can output an English sentence describing the content in the image. We analyze three components of the method: convolutional neural network (CNN), recurrent neural network (RNN) and sentence generation. By replacing the CNN part with three state-of-the-art architectures, we find the VGGNet which performs best according to the BLEU score. In this paper, we present the detailed architecture of the model used by us. We achieved a BLEU score of 56 on the Flickr8k dataset while the state-of-the-art results rest at 66 on the dataset.
Survey on Augmented Reality Technologies for Naval Training

Augmented Reality (AR) is a technology that extends the perception of human reality by allowing, for instance, to insert virtual objects in real environments. With Augmented Reality, learning can be more effective through annotations and interactivity when seeing and manipulating the content. The purpose of this article was to survey the AR technology amenable to support the development of solutions for training in the maritime context. For this purpose, different technologies of the existing devices were analysed in order to elicit the characteristics of devices in order the adapt their usage to a specific context of training solutions. The characteristics of the different markers used to trigger AR applications were also analysed.
A New Ontology Meta-Matching Technique with a Hybrid Semantic Similarity Measure

Ontology is the kernel technique of semantic web, which can be used to describe the concepts and their relationships in a particular domain. However, different domain experts would construct the ontologies according to different requirements, and there exists a heterogeneity problem among the ontologies, which hinders the interaction between ontology-based intelligent systems. Ontology matching technique can determine the links between heterogeneous concepts, which is an effective method for solving this problem. Semantic similarity measure is a function to calculate to what extent two concepts are similar to each other, which is the key component of ontology matching technique. Generally, multiple semantic similarity measures are used together to improve the accuracy of the concept recognition. How to combine these semantic similarity measures, i.e., the ontology meta-matching problem, is a challenge in the ontology matching domain. To address this challenge, this paper proposes a new ontology meta-matching technique, which applies a novel combination framework to aggregate two broad categories of similarity measures. The experiment uses the famous benchmark provided by the Ontology Alignment Evaluation Initiative (OAEI). Comparing results with the participants of OAEI shows the effectiveness of the proposal.
A Secure Cross-Domain Access Control Scheme in Social Networks

As the number of people using social networks increases, users are often assigned to different domains for better digital right management. However, they are no longer satisfied with accessing data just in one domain with the rapid expansion of information, so there is an urgent need for a way to access data among different domains while guarantee their privacy and security. In this paper, we propose a cross-domain access control scheme based on multi-authority attribute-based encryption for big data from social networks. In our construction, we use a hybrid encryption algorithm that based on symmetric encryption and CP-ABE not only to achieve cross-domain access control, but also to improve the efficiency of the system. Besides, the trusted proxy users for proper storage of symmetric keys ensures the security of the system. Meanwhile, the use of an inter-domain trusted proxy user, instead of an intra-domain data owner to formulate an access control policy, reduces the huge computational overhead associated with the ciphertext re-encryption technology and greatly improves the efficiency of the entire system. Finally, security analysis and performance analysis show that the proposed scheme is a secure and trusted networking.
The Hard Problem of Theory Choice: A Case Study on Causal Inference and Its Faithfulness Assumption

The problem of theory choice and model selection is hard but still important when useful truths are underdetermined, perhaps not by all kinds of data but by the kinds of data we can have access to ethically or practicably—even if we have an infinity of such data. This article addresses a crucial instance of that problem: the problem of inferring causal structures from nonexperimental, nontemporal data without assuming the so-called causal Faithfulness condition or the like. A new account of epistemic evaluation is developed to solve that problem and justify a standard practice of causal inference in data science.
Objective estimation of subjective image quality assessment using multi‐parameter prediction

Objective evaluation of a subjective image quality assessment plays a significant role in the various image processing applications, such as compression, interpolation and noise reduction. The subjective image quality assessment does not only depend on some objective measurable artefacts, but also on image content and kind of distortions. Thus, a multi-parameter prediction of the objective image quality assessment is proposed in this study. The prediction parameters are found minimising the mean square error related to the known subjective image quality measure (DMOS). This approach includes mostly used image quality metrics (peak signal-to-noise ratio, multi-scale structural similarity image measure, feature similarity image measure, video quality measure) and two-dimensional image quality metrics (2D IQM). The proposed multi-parameter prediction has been verified on the test image database (LIVE) for compression, noise and blur distortions with available subjective image quality measures (DMOS). More reliable estimations are obtained using multi-parameter prediction instead of only one measure. The best results are reached when an image content indicator is combined with the 2D IQM measure separately for different kinds of distortions.
Varieties generated by completions

We prove that persistently finite algebras are not created by completions of algebras, in any ordered discriminator variety. A persistently finite algebra is one without infinite simple extensions. We prove that finite measurable relation algebras are all persistently finite. An application of these theorems is that the variety generated by the completions of representable relation algebras does not contain all relation algebras. This answers Problem 1.1(1) from Maddux’s 2018 Algebra Universalis paper in the negative. At the same time, we confirm the suggestion in that paper that the finite maximal relation algebras constructed in M. Frias and R. Maddux’s 1997 Algebra Universalis paper are not in the variety generated by the completions of representable relation algebras. We prove that there are continuum many varieties between the variety generated by the completions of representable relation algebras and the variety of relation algebras.
Assessment of High Performance Computing Services Potential of SMEs

High Performance Computing (HPC) enables solving complex problems that would be otherwise impossible to compute with ordinary desktop computers in an acceptable time. HPC services, offered in cloud, could foster new products and/or services development, faster time to market and optimization of production processes for Small and Medium sized enterprises (SME). However, cloud HPC services adoption among SMEs is still scarce and not all SMEs are viable to use HPC. The goal was to assess the cloud HPC potential among SMEs, select the viable SMEs and support them in implementation of the technology. For this purpose we have developed a web-based multi-criteria assessment tool to measure the potential of the SMEs to use cloud HPC services. In this paper we present the analysis of the 61 assessed organizations, among which 38 were SMEs. The analysis offers a limited insight into the state of the European SMEs and other organizations, but is important for understanding the needs and opportunities, that the cloud HPC services offer.
Transformation of Supply Chain Provenance Using Blockchain—A Short Review

Blockchain is a decentralized and distributed digital ledger that maintains a continuously growing list of time-sequenced records of transactions. Records are validated by the participating nodes and confirmed before it finally gets added to the immutable ledger. Information stored in blockchain is hardened against any sort of tampering or revision by any of the participating nodes. Verifying and transferring ownership by third-party intermediaries is redundant, as it is done by the decentralized protocol using complex math and cryptography. Inherent characteristics of blockchain are to enhance trust between the parties through transparency and traceability by lowering friction in transactions between the participants. Global supply chains are growing more complex and complicated. Current supply chain system operates under disparate methods between disconnected systems where there is no clarity of a single state of truth. Everyone in the ecosystem maintains their own current state in isolation, which leads to challenges like lack of transparency, traceability, accountability, and efficiency. In this paper, an attempt has been made to explore and analyze how blockchain or distributed ledger will aid efforts to improve supply chain provenance tracking.
Generalized public-key cryptography with tight security

Tightly secure public-key cryptographic schemes enjoy the advantage that the selection of the security parameter can be optimal to achieve a certain security level. Security models in the multi-user setting with corruptions (MU-C) consider more realistic threats in practice. Many efforts have been devoted to constructing tightly MU-C secure schemes. To date, we have many concrete constructions. Nevertheless, the study on how to generally achieve tight security in public-key cryptography remains lacking. In this paper, we take an insight into the key generations in public-key cryptography. We first generalize the key generation algorithms of traditional schemes and discuss the requirements of achieving tight security. We notice that for some schemes (e.g. key-unique schemes), these requirements inherently cannot be satisfied and hence these schemes cannot achieve tight security. This is in accordance with the impossibility results of tight reductions by Bader et al. (EUROCRYPT 2016). To further study possible constructions, we extend the key generations of public-key cryptographic schemes to obtain a different framework. To demonstrate its applications, we illustrate how to construct tightly secure key-unique schemes under the extended framework. This circumvents the impossibility results of tight security for key-unique schemes.
A Facial Authentication Method Robust to Postural Changes in e-Testing

Examinee posture changes during e-Testing, making it difficult to perform facial authentication throughout the duration of the exam. We propose two methods for addressing this. The first method is to combine facial detection and tracking of facial information during the test. The second method is to pre-register facial information at various postures for authentication by computing the most similar image at the time of authentication. We conducted certification experiments using 21 examinees. The results showed authentication accuracy of 82.8%, an improvement of 16.8% over previous research, thus demonstrating the effectiveness of the proposed method.
Multi-color Space Features Analysis from Rock Shin-section Image for Rock-type classification

Abstract To fully analyze the features extracting from multi-color space for rock classification, each feature will be evaluated in this paper. And, to enhance correlation between features and reduce dimensionality of feature space, a PCA approach will be used and the result of PCA will also be fully analyzed. A C-SVM model is chosen to test analysis result. Data set consist of 500 images from Ordos basin. The classification result between single color space and multi-color space will be compared, and the cooperation result shows that features from multi-color space can support classifier like C-SVM to take higher accuracy and higher reliability.
Analyzing Happiness: Investigation on Happy Moments using a Bag-of-Words Approach and Related Ethical Discussions

In this research paper, we analyzed what moments and activities make people happy, based on a collection of happy moments. We are focusing on specific happy moments from a collection of text responses that people have shared through the crowd-sourcing platform: Amazon Mechanical Turk (MTurk). Using crowd-sourcing to collect our data allows us to advance our understanding of the cause of happiness, by focusing on words and real human experiences. Workers of MTurk were asked to reflect on what makes them happy in a given period and share three specific moments in complete sentences. Through text-based analysis, we will look to see what other components have a role in making a specific event happy and further analyze how we can classify such words. Also, we dive deeper into specific subcategories of classifiers in an attempt to form insights about their happiness level based on specific factors. With the goal to extract features from the text in HappyDB, in this study we used the bag of words approach. Through doing so, our results were successful at predicting the happiness category, concerning both accuracy and context. Our models were able to accomplish the goal of understanding a happy moment and fit such a moment into one of the seven ground truth happiness categories we set at the beginning of this study. We finished the article with the ethical perspective of such research works and related social implications.
Identifying Buildings with Ramp Entrances Using Convolutional Neural Networks

The Americans with Disabilities Act (ADA) is a civil rights law that was signed into law in 1992 by President George H.W. Bush. The law requires wheelchair access be made available for buildings built after 1992. Buildings under the law include retail stores, hotels, banks and most other public buildings. However, there are a large percentage of buildings built before 1992 that are not wheelchair accessible. In addition, ADA does not require the location of ramp to be at the front of the building. This is an inconvenience for individuals who use wheelchairs to access a building, as a) the building may not have a ramp or b) they may have to roll around the building to where the ramp may be located. Hence, in this paper, we describe a prototype artificial intelligent system, which takes the input of a building image, and produces the output prediction for whether the building has a ramp. The system uses a deep learning technique, convolution neural network (CNN) to classify building images. We evaluated our method on a sample dataset of building images that we collected and building images from online sources. Training and validation accuracies were very high, 98.9 and 95.6 percentages respectively.