Reviewerly/exHarmony huggingface dataset:
CLIN-IK-LINKS: A platform for the design and execution of clinical data transformation and reasoning workflows

Effective sharing and reuse of Electronic Health Records (EHR) requires technological solutions which deal with different representations and different models of data. This includes information models, domain models and, ideally, inference models, which enable clinical decision support based on a knowledge base and facts. Our goal is to develop a framework to support EHR interoperability based on transformation and reasoning services intended for clinical data and knowledge. Our framework is based on workflows whose primary components are reusable mappings. Key features are an integrated representation, storage, and exploitation of different types of mappings for clinical data transformation purposes, as well as the support for the discovery of new workflows. The current framework supports mappings which take advantage of the best features of EHR standards and ontologies. Our proposal is based on our previous results and experience working with both technological infrastructures. We have implemented CLIN-IK-LINKS, a web-based platform that enables users to create, modify and delete mappings as well as to define and execute workflows. The platform has been applied in two use cases: semantic publishing of clinical laboratory test results; and implementation of two colorectal cancer screening protocols. Real data have been used in both use cases. The CLIN-IK-LINKS platform allows the composition and execution of clinical data transformation workflows to convert EHR data into EHR and/or semantic web standards. Having proved its usefulness to implement clinical data transformation applications of interest, CLIN-IK-LINKS can be regarded as a valuable contribution to improve the semantic interoperability of EHR systems.
FLAT: Federated lightweight authentication for the Internet of Things

Federated Identity Management schemes (FIdMs) are of great help for traditional systems as they improve user authentication and privacy. In this paper, we claim that traditional FIdMs are mostly cumbersome and then ill-suited for IoT. As a solution to this problem, we came up with Federated Lightweight Authentication of Things (FLAT), namely a federated identity authentication protocol exclusively tailored to IoT. FLAT replaces weighty protocols and public-key cryptographic primitives used in traditional FIdMs by lighter ones, like symmetric cryptographic primitives and Implicit Certificates. Our results show that FLAT can reduce the data exchange overhead by around 31% when compared to a baseline solution. Also, the FLAT Client, the role played by an IoT device in the protocol, is more efficient than the baseline Client in terms of data exchange, storage, memory, and computation time. Our results indicate that FLAT runs efficiently, even on top of resource-constrained devices like Arduino.
A Sightseeing Spot Recommendation System That Takes into Account the Visiting Frequency of Users

The present study aimed to design, develop, operate and evaluate a sightseeing spot recommendation system that can efficiently and usefully support tourists while considering their visiting frequencies. This system was developed by integrating social networking services (SNSs), Web-geographic information systems (GIS) and recommendation systems. The system recommends sightseeing spots to users with different visiting frequencies, adopting two recommendation methods (knowledge-based recommendation and collaborative recommendation methods). Additionally, the system was operated for six weeks in Kamakura City, Kanagawa Prefecture, Japan, and the total number of users was 61. Based on the results of the web questionnaire survey, the usefulness of the system when sightseeing was high, and the recommendation function of sightseeing spots, which is an original function, received mainly good ratings. From the results of the access analysis of users’ log data, the total number of sessions in this system was 329, 77% used mobile devices, and smartphones were used most frequently. Therefore, it is evident that the system was used by different types of devices just as it was designed for, and that the system was used according to the purpose of the present study, which is to support the sightseeing activities of users.
Digital Potential of Economic Education: Information Technologies in a Management University

The changes happening in the world these days imply the inevitable digital transformation of business, science and education. To undergo such a serious transition Russian companies of any business scope need to provide the full-scale readiness of the whole system. Digital transformation without the elaborated high-quality preparation of all system elements and connections may turn out to be an untimely expensive measure hindering company’s development. There are analyzed the obstacles defined as part of the study and related to digital transformation of economy and education. Education is viewed as a leading factor of socio-economic development. Thus, the request for implementation of digital transformation is addressed primarily to education. The study raises a problem of lack of a well-founded psychological and pedagogical concept of digital learning which could be used by the subjects of education as the basic one. The results of the survey conducted among university students majoring in public management have shown the following: it is necessary to expand professional competences of modern managers needed for carrying out the activity in the epoch of digital revolution; there emerges the question about rational inclusion into public management university curricula the disciplines corresponding to real demands of the labor market.
Virtual Interpolation Images of Tumor Development and Growth on Breast Ultrasound Image Synthesis With Deep Convolutional Generative Adversarial Networks

Objectives We sought to generate realistic synthetic breast ultrasound images and express virtual interpolation images of tumors using a deep convolutional generative adversarial network (DCGAN). Methods After retrospective selection of breast ultrasound images of 528 benign masses, 529 malignant masses, and 583 normal breasts, 20 synthesized images of each were generated by the DCGAN. Fifteen virtual interpolation images of tumors were generated by changing the value of the input vector. A total of 60 synthesized images and 20 virtual interpolation images were evaluated by 2 readers, who scored them on a 5‐point scale (1, very good; to 5, very poor) and then answered whether the synthesized image was benign, malignant, or normal. Results The mean score of overall quality for synthesized images was 3.05, and that of the reality of virtual interpolation images was 2.53. The readers classified the generated images with a correct answer rate of 92.5%. Conclusions A DCGAN can generate high‐quality synthetic breast ultrasound images of each pathologic tissue and has the potential to create realistic virtual interpolation images of tumor development.
Online Hashing With Bit Selection for Image Retrieval

Online hashing methods have been intensively investigated in semantic image retrieval due to their efficiency in learning the hash functions with one pass through the streaming data. Among the online hashing methods, those based on the target codes are usually superior to others. However, the target codes in these methods are generated heuristically in advance and cannot be learned online to capture the characteristics of the data. In this paper, we propose a new online hashing method in which the target codes are constructed according to the data characteristics and are used to learn the hash functions online. By designing a metric to select the effective bits online for constructing the target codes, the learned hash functions are resistant to the bit-flipping error. At the same time, the correlation between the hash functions is also considered in the designed metric. Hence, the hash functions have low redundancy. Extensive experiments show that our method can achieve comparable or better performance than other online hashing methods on both the static database and the dynamic database.
Automatic Duplicate Bug Report Detection using Information Retrieval-based versus Machine Learning-based Approaches

Nowadays, there are many software repositories, especially on the web, which have many challenges to be automated. Duplicate bug report detection (DBRD) is an excellent problem of software triage systems like Bugzilla since 2004 as an essential online software repository. There are two main approaches for automatic DBRD, including information retrieval (IR)-based and machine learning (ML)-based. Many related works are using both approaches, but it is not clear which one is more useful and has better performance. This study focuses on introducing a methodology for comparing the validation performance of both approaches in a particular condition. The Android dataset is used for evaluation, and about 2 million pairs of bug reports are analyzed for 59 bug reports, which were duplicate. The results show that the ML-based approach has better validation performance, incredibly about 40%. Besides, the ML-based approach has a more reliable criterion for evaluation like accuracy, precision, and recall versus an IR-based approach, which has just mean average precision (MAP) or rank metrics.
Depth image super-resolution using correlation-controlled color guidance and multi-scale symmetric network

Depth image super-resolution (DISR) is an effective solution to improve the quality of depth images captured by real world low-cost cameras. In this paper, we propose a multi-scale symmetric network with the correlation-controlled color guidance block (CCGB) for DISR. The proposed network consists of two multi-scale sub-networks to respectively provide guidance and estimate depth. A symmetric unit (SU), which is a mini-encoder-decoder structure with residual learning, is designed and used as a basic network atom. The encoder part in SU aims to extract essential features, while the decoder part works to restore edge details. The way the SU processes information matches well with the textureless and sharp-edge characteristics of depth images. The two sub-networks present a high-level symmetric structure connected by dense guidance links in between. Based on the correlation analyses between the two sub-networks, each guidance link will transfer information trough a CCGB designed to implement channel-wise re-weighting mechanism. The accurate color guidance from CCGB helps avoiding artifacts introduced by non-co-occurrence of depth discontinuities and color edges. Experimental results demonstrate the superiority of the proposed method over several state-of-the-art DISR works.
Feature Selection Is Important: State-of-the-Art Methods and Application Domains of Feature Selection on High-Dimensional Data

With the advancement of technologies in the big data field, feature selection plays a vital role in most of the prediction problems and many application domains including healthcare, government sectors, network attacks prediction, microarray data analysis, etc. Nowadays, due to the existence of enormous volume of data with high-dimensional attributes and data types, it has led to a problem to find and classify informative features from noninformative ones. To solve these issues, filter, wrapper, embedded, and hybrid methods are used. In this chapter, we provide a detailed introduction about the feature selection with recent state-of-the-art techniques with respect to filter, wrapper, embedded, and hybrid models and discuss taxonomy of the dimensionality reduction techniques and fuzzy logic-based feature selection techniques. Further, we have given importance to feature selection among various application domains such as text analytics, video analytics, audio analytics, microarray analysis, intrusion detection systems, and feature selection in stream data analysis. Finally, we conclude by explaining application domains of feature selection with elaborate discussions.
Effects of financial reform on business entry in sub‐Saharan African countries: Do resource dependence and institutional quality matter?

Abstract This paper examined the effects of the financial liberalization strategy adopted on the African continent over 25 years ago in promoting new business entry using data from 22 sub‐Saharan African (SSA) countries in 2006–2017. Results from the dynamic generalized method of moments models show that: financial development via a policy of financial liberalization does not have a uniform effect on entrepreneurship; the interest rate gap significantly undermines the entrance of new firms; the ratio of broad money/gross domestic product (GDP) was positive and statistically significant while real interest rate had mixed findings; interactive effects of interest rate spread and real interest rate with regulatory quality was negative; the interaction of interest rate spread and real interest rate with natural resources confirms its destabilizing effect, although there was evidence suggesting that natural resources do not directly undermine entrepreneurship growth. Other results show real GDP and private credit have a significantly positive effect, and the cost of getting electricity significantly undermines entrepreneurship. The study calls for the need to deepen the financial sector though targeted reforms across SSA countries to reap its growth‐inducing effects on economic outcomes, while promoting institutional quality and efficient use of natural resources to achieve a non‐declining infusion of SMEs on the continent.
Representation learning of image composition for aesthetic prediction

Photo quality assessment (PQA) aims at computationally and precisely evaluating the quality of images from the aspect of aesthetic. Image aesthetic is strongly correlated with composition. However, few existing works have taken composition into consideration. Besides, existing composition features are typically hand-crafted. In this paper, we propose a novel end-to-end framework for representation learning of image composition. Specially, we build a fully connected graph based on deep features in Convolutional Neural Networks (CNNs). In the graph, edge attributes i.e. similarities between deep features at different positions are used for representing image composition. Besides, we use global attributes of the graph to represent miscellaneous aesthetic aspects. Finally, we use a gate unit to combine both composition features and miscellaneous aesthetic features for aesthetic prediction. The whole network can be trained in an end-to-end manner. Experimental results show that the proposed techniques significantly improves the prediction precision of aesthetic and composition over various datasets. We have released our codes at: .
Learning Cross-Lingual Word Embeddings from Twitter via Distant Supervision

Cross-lingual embeddings represent the meaning of words from different languages in the same vector space. Recent work has shown that it is possible to construct such representations by aligning independently learned monolingual embedding spaces, and that accurate alignments can be obtained even without external bilingual data. In this paper we explore a research direction that has been surprisingly neglected in the literature: leveraging noisy user-generated text to learn cross-lingual embeddings particularly tailored towards social media applications. While the noisiness and informal nature of the social media genre poses additional challenges to cross-lingual embedding methods, we find that it also provides key opportunities due to the abundance of code-switching and the existence of a shared vocabulary of emoji and named entities. Our contribution consists of a very simple post-processing step that exploits these phenomena to significantly improve the performance of state-of-the-art alignment methods.
Dilated residual networks with multi-level attention for speaker verification

With the development of deep learning techniques, speaker verification (SV) systems based on deep neural network (DNN) achieve competitive performance compared with traditional i-vector-based works. Previous DNN-based SV methods usually employ time-delay neural network, limiting the extension of the network for an effective representation. Besides, existing attention mechanisms used in DNN-based SV systems are only applied to a single level of network architectures, leading to insufficiently extraction of important features. To address above issues, we propose an effective deep speaker embedding architecture for SV, which combines a residual connection of one-dimensional dilated convolutional layers, called dilated residual networks (DRNs), with a multi-level attention model. The DRNs can not only capture long time-frequency context information of features, but also exploit information from multiple layers of DNN. In addition, the multi-level attention model, which consists of two-dimensional convolutional block attention modules employed at the frame level and the vector-based attention utilized at the pooling layer, can emphasize important features at multiple levels of DNN. Experiments conducted on NIST SRE 2016 dataset show that the proposed architecture achieves a superior equal error rate (EER) of 7.094% and a better detection cost function (DCF16) of 0.552 compared with state-of-the-art methods. Furthermore, the ablation experiments demonstrate the effectiveness of dilated convolutions and the multi-level attention on SV tasks.
Path planning of mobile robot based on improved genetic algorithm

In order to solve the problems of slow convergence speed and avoid local optimum in the path planning of mobile robot, the basic genetic algorithm was improved and a method for path planning of mobile robot in static environment was proposed. In this paper, the shortest planning path and the adaptive smoothness are combined as the influencing factors of the individual fitness function value of the path, and a certain weight is assigned to these two factors. It improves the local optimal solution of the basic genetic algorithm, overcomes the shortcoming of precocity, and improves the global search ability of the algorithm. The simulation results show that the improved genetic algorithm is feasible and effective in the path planning of mobile robots.
Time-Space Tradeoffs and Short Collisions in Merkle-Damgård Hash Functions

We study collision-finding against Merkle-Damgård hashing in the random-oracle model by adversaries with an arbitrary S-bit auxiliary advice input about the random oracle and T queries. Recent work showed that such adversaries can find collisions (with respect to a random IV) with advantage $$\varOmega (ST^2/2^n)$$ , where n is the output length, beating the birthday bound by a factor of S. These attacks were shown to be optimal. We observe that the collisions produced are very long, on the order of T blocks, which would limit their practical relevance. We prove several results related to improving these attacks to find shorter collisions. We first exhibit a simple attack for finding B-block-long collisions achieving advantage $$\tilde{\varOmega }(STB/2^n)$$ . We then study if this attack is optimal. We show that the prior technique based on the bit-fixing model (used for the $$ST^2/2^n$$ bound) provably cannot reach this bound, and towards a general result we prove there are qualitative jumps in the optimal attacks for finding length 1, length 2, and unbounded-length collisions. Namely, the optimal attacks achieve (up to logarithmic factors) on the order of $$(S+T)/2^n$$ , $$ST/2^n$$ and $$ST^2/2^n$$ advantage. We also give an upper bound on the advantage of a restricted class of short-collision finding attacks via a new analysis on the growth of trees in random functional graphs that may be of independent interest.
Using BERT to Predict Bug-Fixing Time

Prediction of the resolution time of a newly-submitted bug is a relevant aspect during the bug triage process since it can help project managers to better estimate software maintenance efforts and better manage software projects. Once a bug is reported, it is typically recorded in a Bug Tracking System, and it is assigned to a developer in order to solve the issue. The contribution of this paper is to provide a deep learning approach for the resolution of the bug-fixing time prediction, proposing a new feature set, consisting of the description of the issue and comments of the developers, in order to perform transfer learning on a pre-trained language representations model, called BERT. The problem of predicting the resolution time of a bug is therefore formulated as a supervised text categorization task. BERT makes use of a self-attention mechanism that allows learning the bidirectional context representation of a word in a sentence, which constitutes one of the main advantages over the previously proposed solutions. Experimental results show the proposed approach has effective bug-fixing time prediction ability.
Digital transformation of education: From changing funds to developing activities

The problem of distance between the processes of world transition to a new technological platform, information technologies, to the digital economy and digitalization of various spheres of activity, including education, on the one hand, and not sufficiently deep studies of the phenomenon of digital transformation, including the digital transformation of education, on the other hand, is studied. Lack of elaboration of the essence of digital transformation provokes the free and often marketing use of the concepts of digitalization, digital technologies, and digital transformation of education, and in practice leads to low efficiency of actions taken to implement projects and programs of digital transformation. The lack of strict assumptions leads to a lack of meanings, a multiple increase in the managerial and organizational complexity of implementing the actual practice of digital transformation in education, and as a result, there is an imitation or formal implementation of promising projects. The purpose of the study is to substantiate the General model of digital transformation and to specify it in relation to the digital transformation of education, demonstrating all the elements of the structure of the transformed object and the nature of the cardinal transformations. The hypothesis being tested — the transformation process covers not only the means of educational activity, in particular, digital technologies, but also other elements of it — the goal, object, subject, changing the essence of the educational activity itself, contrasting it with the processes of training and preparation. The system-based research methodology is used. Research methods: main — theoretical analysis and synthesis of existing scientific positions, observation of practical artifacts of digital transformation of education; accompanying — experimental method for testing the results of research. The main results were: distinguishing digital transformation projects, highlighting their unique properties and characteristics that differ from projects of optimization, improvement, development; substantiating the model of digital transformation of education, including proof that in the process of transformation, not only the means (digital technologies), but also the content of the educational process, its goals, subjects and methods of communication between them change dramatically. This suggests a new approach to changing the education process under the influence of digital transformation, its distance from the processes of education and training, which will become a starting point in the self-organization of teams of projects of digital transformation of education. The results will be useful to heads of education authorities at all levels, managers and development teams of educational organizations of basic and additional education, who are planning or implementing digital transformation projects.