Reviewerly/exHarmony huggingface dataset:
Automated Segmentation of DCIS in Whole Slide Images

Segmentation of ducts in whole slide images is an important step needed to analyze ductal carcinoma in-situ (DCIS), an early form of breast cancer. Here, we train several U-Net architectures – deep convolutional neural networks designed to output probability maps – to segment DCIS in whole slide images and validate the optimal patch field of view necessary to achieve superior accuracy at the slide-level. We showed a U-Net trained at 5x achieved the best test results (DSC = 0.771, F1 = 0.601), implying the U-Net benefits from having wider contextual information. Our custom U-Net based architecture, trained to incorporate patches from all available resolutions, achieved test results of DSC = 0.759 (F1 = 0.682) showing improvement in the duct detecting capabilities of the model. Both architectures show comparable performance to a second expert annotator on an independent test set. This is preliminary work for a pipeline targeted at predicting recurrence risk in DCIS patients.
Generative Adversarial Network-Based Credit Card Fraud Detection

With the development of the financial industry, the number of credit cards has greatly increased. However, credit card fraud is still a major concern for financial security. Credit card fraud detection is a typical imbalanced classification problem, in which fraudulent cardholders are far less than non-fraudulent cardholders. The training on imbalanced samples will cause that the classifier ignores the minor fraudulent samples. To solve this problem, a generative adversarial network (GAN) based classification method is proposed for credit card fraud detection. GAN consists of a generative model and a discriminative model, and the two models are trained in a competitive way to get the Nash equilibrium. Specifically, the generative model tries to fit the real distribution of the non-fraudulent samples. The discriminative model determines the probability of a sample belongs to the distribution of non-fraudulent samples. To improve the discrimination performance, the fraudulent samples are also used in the training of discriminative model, which is different from the traditional GAN training scheme. The experimental results show that the recall reaches 82.7%.
Motion Segmentation Based on Model Selection in Permutation Space for RGB Sensors

Motion segmentation is aimed at segmenting the feature point trajectories belonging to independently moving objects. Using the affine camera model, the motion segmentation problem can be viewed as a subspace clustering problem—clustering the data points drawn from a union of low-dimensional subspaces. In this paper, we propose a solution for motion segmentation that uses a multi-model fitting technique. We propose a data grouping method and a model selection strategy for obtaining more distinguishable data point permutation preferences, which significantly improves the clustering. We perform extensive testing on the Hopkins 155 dataset, and two real-world datasets. The experimental results illustrate that the proposed method can deal with incomplete trajectories and the perspective effect, comparing favorably with the current state of the art.
Problem of equivalence in inverse electrometry problems of oil and gas wells

SUMMARY The method of researching and construction areas for correct solutions of inverse problems is described. The method uses the possibility of constructing a connection between the space of the solutions of the inverse problem and the space of the parameters of the model of the reservoir. Proposed method makes it possible to construct areas of equivalent solutions and to investigate the “blinds zones”. The method has been tested on a large volume of model logs and well logs. Examples are given for real wells.
FaceSurv: A Benchmark Video Dataset for Face Detection and Recognition Across Spectra and Resolutions

Existing face recognition algorithms achieve high recognition performance for frontal face images with good illumination and close proximity to the imaging device. However, most of the existing algorithms fail to perform equally well in surveillance scenarios, where videos are captured across varying resolutions and spectra. In surveillance settings, cameras are usually placed far away from the subjects, thereby resulting in variations across pose, illumination, occlusion, and resolution. Current video datasets used for face recognition are often captured in constrained environments, and thus fail to simulate the real world scenarios. In this paper, we present the FaceSurv database featuring 252 subjects in 460 videos. The proposed dataset contains over 142K face images, spread across videos captured in both visible and near-infrared spectra. Each video contains a group of individuals walking from 36ft towards the imaging device, offering a plethora of challenges common to surveillance settings. Benchmark experimental protocol and baseline results have been reported with state-of-the-art algorithms for face detection and recognition. It is our assertion that the availability of such a challenging database will facilitate the development of robust face recognition systems relevant to real world surveillance scenarios.
Development of a knowledge‐based medical expert system to infer supportive treatment suggestions for pediatric patients

This paper discusses the design, implementation, and potential use of an ontology‐based mobile pediatric consultation and monitoring system, which is a smart healthcare expert system for pediatric patients. The proposed system provides remote consultation and monitoring of pediatric patients during their illness at places distant from medical service areas. The system not only shares instant medical data with a pediatrician but also examines the data as a smart medical assistant to detect any emergency situation. In addition, it uses an inference engine to infer instant suggestions for performing certain initial medical treatment steps when necessary. The applied methodologies and main technical contributions have three aspects: (a) pediatric consultation and monitoring ontology, (b) semantic Web rule knowledge base, and (c) inference engine. Two case studies with real pediatric patients are provided and discussed. The reported results of the applied case studies are promising, and they demonstrate the applicability, effectiveness, and efficiency of the proposed approach.
Chaotic time series prediction based on long short-term memory neural networks

Recently, the development of deep learning technology has promoted the wide application of machine learning. In particular, the technique known as reservoir computing has attracted more and more attention due to its excellent behavior on chaotic time series prediction, and has formed a new research hotspot. In this paper, we use traditional long short-term memory (LSTM) neural networks and fully connected layers as the fundamental elements to build a LSTM learning machine based on the recurrent neural network architecture. The motivation for using LSTM is that it can effectively prevent the vanishing and exploding of gradients. In the simulation experiment, we quantify the duration of accurate prediction with the average valid time and use the model to predict the state of Lorenz system. Aiming at the special dynamic properties of chaotic system, we propose four strategies to assist prediction, which are normalization and restoration, scaling down the gradients, reservoir-based initialization and preserving the optimal model. The results show that the prediction ability of the LSTM learning machine with suitable strategies is comparable to that of reservoir computing, furthermore, the complexity of the LSTM is lower. Therefore, our results indicate that there is no obvious evidence that reservoir computing can surpass the traditional method, which inspires us to further study the mechanism and methods of learning machine for predicting time series, and find more effective learning machines.
The Model of Information Security Culture Level Estimation of Organization

Deep penetration of computer networks into all spheres of organization’s life requires new approaches to ensuring security for survival in a context of continuous renewal of cyber threats. Since the object of information operation is a person, there is a need for the developing of information systems that are aimed not only at the technical protection of computer systems and technology, but also to prevent the impact of social engineering methods.In order to reduce such influences, it is necessary to work continuously to increase the level of information security culture of the organization. The model of the system of complex assessment of the information security culture (ISC) level of personnel as an important component of the overall information security organization presented in the article contains six levels and is divided into two subsystems. The first subsystem includes ranking of factors, and the second - the definition of the integral indicator of the personnel ISC level of the organization. The peculiarity of the proposed model lies in the use of fuzzy decision-making methods that allow to simulate the change in the properties of the object, expressed in the form of qualitative relationships. Also, the model takes into account the assessment of the security specialists competence, depending on the role they play at different levels of its provision.The model can be used in the development implementation and audit, of integrated information security systems.
Requirements for Gesture-Controlled Remote Operation to Facilitate Human-Technology Interaction in the Living Environment of Elderly People

The “SmartPointer” (SP) technology comprises a universal buttonless gesture-controlled handheld remote device with a simple quasi-intuitive operating structure. With this handset, elderly people will be able to control various household devices in their living environment. In order to develop an age-appropriate SP system, the aim of the study was to determine the requirements of elderly people and people with tremor. For this purpose, a mixed-method design, involving several assessments, a guideline-based interview, a task-based investigation and a questionnaire using a gesture catalog, was applied. The whole sample included 20 seniors being 60 years and older. In the process, qualitative requirements were collected on the topics of device use, operating problems, desired devices for gesture control, receiver unit, gestures, feedback and safety. The interview results emphasized the elderly participants’ needs to an easy and intuitive system use. Furthermore, concerns should be prioritized in order to the development of the system. In the quantitative evaluation, the use of various technical devices was analyzed and the frequency of used gestures was determined based the gesture catalog and the task-based investigation. The most frequently used gestures were horizontal, vertical, circular and targeting gestures. In summary, the elderly people were very interested in, and open-minded towards, the SP-system. In a comparison between healthy persons and persons with tremor, the results demonstrated only minimal differences regarding the requirements.
An inquiry into the legal status of the ECOWAS cybercrime directive and the implications of its obligations for member states

On 19 August 2011, the ECOWAS Council of Ministers adopted Directive C/DIR.1/08/11 on Fighting Cybercrime at its Sixty Sixth Ordinary Session in Abuja, Nigeria. The adoption of the Directive at that time arose from the need to tackle the growing trend in cybercrime within the ECOWAS region, as some Member States were already gaining global notoriety as major sources of email scams and Internet fraud. Accordingly, the Directive established a legal framework for the control of cybercrime within the ECOWAS region, and also imposed obligations on Member States to establish the necessary legislative, regulatory and administrative measures to tackle cybercrime. In particular, the Directive required Member States to implement those obligations “not later than 1st January, 2014″. This article undertakes an inquiry into the legal status of the Directive as an ECOWAS regional instrument in the domestic legal systems of Member States. In this regard, the article examines whether the requirement regarding the superiority or direct applicability of ECOWAS Community laws such as ECOWAS Acts and Regulations in the domestic legal systems of Member States also apply to ECOWAS Directives such as the Cybercrime Directive. The article also examines the legal implications of the Directive's obligations for Member States. The article argues that while some Member States have not implemented the obligations under the Directive, that those obligations however provide a legal basis for holding Member States accountable, where the failure to implement has encouraged the perpetration of cybercrime that infringed fundamental rights guaranteed under human right instruments such as the African Charter on Human and Peoples’ Rights or under their national laws.
SAFE: Scale Aware Feature Encoder for Scene Text Recognition

In this paper, we address the problem of having characters with different scales in scene text recognition. We propose a novel scale aware feature encoder (SAFE) that is designed specifically for encoding characters with different scales. SAFE is composed of a multi-scale convolutional encoder and a scale attention network. The multi-scale convolutional encoder targets at extracting character features under multiple scales, and the scale attention network is responsible for selecting features from the most relevant scale(s). SAFE has two main advantages over the traditional single-CNN encoder used in current state-of-the-art text recognizers. First, it explicitly tackles the scale problem by extracting scale-invariant features from the characters. This allows the recognizer to put more effort in handling other challenges in scene text recognition, like those caused by view distortion and poor image quality. Second, it can transfer the learning of feature encoding across different character scales. This is particularly important when the training set has a very unbalanced distribution of character scales, as training with such a dataset will make the encoder biased towards extracting features from the predominant scale. To evaluate the effectiveness of SAFE, we design a simple text recognizer named scale-spatial attention network (S-SAN) that employs SAFE as its feature encoder, and carry out experiments on six public benchmarks. Experimental results demonstrate that S-SAN can achieve state-of-the-art (or, in some cases, extremely competitive) performance without any post-processing.
PIRC Net: Using Proposal Indexing, Relationships and Context for Phrase Grounding

Phrase Grounding aims to detect and localize objects in images that are referred to and are queried by natural language phrases. Phrase grounding finds applications in tasks such as Visual Dialog, Visual Search and Image-text co-reference resolution. In this paper, we present a framework that leverages information such as phrase category, relationships among neighboring phrases in a sentence and context to improve the performance of phrase grounding systems. We propose three modules: Proposal Indexing Network (PIN); Inter-phrase Regression Network (IRN) and Proposal Ranking Network (PRN) each of which analyze the region proposals of an image at increasing levels of detail by incorporating the above information. Also, in the absence of ground-truth spatial locations of the phrases (weakly-supervised), we propose knowledge transfer mechanisms that leverages the framework of PIN module. We demonstrate the effectiveness of our approach on the Flickr 30k Entities and ReferItGame datasets, for which we achieve improvements over state-of-the-art approaches in both supervised and weakly-supervised variants.
Role differentiation of leadership knowledge and its assessment tools

Introduction. The development of liberal forms of management is accompanied by a rapid increase in the number of leaders. Therefore, the selection among the number of ordinary individuals is exactly those that meet the criteria of the leader, is an important problem. The most relevant in the situation of permanent elections is the search for such approaches to the differentiation of the role of leader, which would contribute to the development of society and the formation of those values that have deep civilizational influences. This led to the need for research and analysis of the role differentiation of leadership abilities that from ordinary people turn them into prominent personalities that change the present.&#x0D; Aim and tasks. The aim of the study is to study the existing experience of highlighting the qualities of leaders among a number of personality abilities, to study the approaches to their evaluation, use of acquired knowledge to assess the role differentiation of leadership abilities of senior and middle managers of IT market companies, testing the proposed approach to the example and unification of the proposed approach for assessing the role differentiation of leadership abilities of the first hundred richest people in the world.&#x0D; Results. The article analyzes foreign and domestic experience of highlighting the qualities of leaders among a number of personality abilities. Among the investigated approaches to assessing leadership qualities, an expert method for determining weight ratios was chosen that is best suited for companies in the gaming industry and digital distribution, to which Valve Corporation belongs. The results of the role differentiation of leadership qualities showed that in the figure of the most important are professional abilities, a little less have features related to interaction with subordinates, and the least - personal attributes. The petal diagram indicates a shift to professional abilities that plays a key role in the IT business. The proposed approach to the role-based differentiation of leadership abilities in the context of organizational changes in IT market companies can also be used in assessing the leadership abilities of the first hundred richest people in the world.&#x0D; Conclusions. The article examines foreign and domestic experience of highlighting the qualities of leaders among a number of personality abilities. The study of approaches to assessing leadership qualities and the possibilities of their use for companies in the field of gaming industry and digital distribution, which belongs to Valve Corporation. The offered approach of role differentiation of leadership abilities can be used both in the assessment of leadership abilities of the first hundred richest people in the world, and for the role differentiation of leadership abilities in the context of organizational changes in IT market companies.
A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication

In recent years, randomized methods for numerical linear algebra have received growing interest as a general approach to large-scale problems. Typically, the essential ingredient of these methods is some form of randomized dimension reduction, which accelerates computations, but also creates random approximation error. In this way, the dimension reduction step encodes a tradeoff between cost and accuracy. However, the exact numerical relationship between cost and accuracy is typically unknown, and consequently, it may be difficult for the user to precisely know (1) how accurate a given solution is, or (2) how much computation is needed to achieve a given level of accuracy. In the current paper, we study randomized matrix multiplication (sketching) as a prototype setting for addressing these general problems. As a solution, we develop a bootstrap method for \emph{directly estimating} the accuracy as a function of the reduced dimension (as opposed to deriving worst-case bounds on the accuracy in terms of the reduced dimension). From a computational standpoint, the proposed method does not substantially increase the cost of standard sketching methods, and this is made possible by an extrapolation technique. In addition, we provide both theoretical and empirical results to demonstrate the effectiveness of the proposed method.
Collaborative Framework for a Whole-Slide Image Viewer

Digital pathology is a new branch of medical imaging referring to the aggregation of equipment and software to acquire, store and display microscopic images in a distributed network environment. This article proposes an architecture and describes the implementation of a collaborative pathology web platform. The solution brings the modern collaborative concept, common in social and business networks, into Digital Pathology workflows supported by a customised PACS-DICOM infrastructure. The system assures services like the creation of working sessions, users groups, access control to sessions, synchronisation of operations in a rich web interface, replaying of the actions performed in a session, among others. The solution data management is ensured by a PACS compliant with the DICOM standard, more concretely the recent Whole Slide Imaging format and the DICOM Web communication services.
Association between organizational norms and employee productivity in higher education

Purpose The purpose of this paper is to investigate the impact of organizational norms on employee productivity within the higher education sector in UAE. Design/methodology/approach A quantitative research approach was used to investigate 89 respondents from higher education institutions. An online survey approach was used to investigate the opinions of respondents with regard to the impact of organizational norms on employee productivity in UAE. The data were then statistically analyzed using SPSS version 22. Findings The results showed a positive association between the investigated organizational norms and employee productivity. Furthermore, there was a significant relationship between age and organizational norms. Increase in employee age corresponds to an increase in employee productivity. Originality/value This study has made a novel contribution, since there is a significant lack of research surrounding the influence of organizational norms on employee productivity in the higher education institutions in UAE.
Sample complexity of partition identification using multi-armed bandits

Given a vector of probability distributions, or arms, each of which can be sampled independently, we consider the problem of identifying the partition to which this vector belongs from a finitely partitioned universe of such vector of distributions. We study this as a pure exploration problem in multi armed bandit settings and develop sample complexity bounds on the total mean number of samples required for identifying the correct partition with high probability. This framework subsumes well studied problems such as finding the best arm or the best few arms. We consider distributions belonging to the single parameter exponential family and primarily consider partitions where the vector of means of arms lie either in a given set or its complement. The sets considered correspond to distributions where there exists a mean above a specified threshold, where the set is a half space and where either the set or its complement is a polytope, or more generally, a convex set. In these settings, we characterize the lower bounds on mean number of samples for each arm highlighting their dependence on the problem geometry. Further, inspired by the lower bounds, we propose algorithms that can match these bounds asymptotically with decreasing probability of error. Applications of this framework may be diverse. We briefly discuss one associated with finance.