Reviewerly/exHarmony huggingface dataset:
Career path of researchers in relation to publication ethics

This paper describes the how the scholarly output of researchers impacts upon their career development. Bibliometric approaches were engaged to study the frequency of publications of prominent scientists from the Novosibirsk Scientific Center of the Siberian Branch of the Russian Academy of Sciences during their career. This included their thesis defense, assignment to leading positions in research organizations, and election as members of the Russian Academy of Sciences. The highest correlation was between the growth in the number of papers and assignment to a position of leadership. A rapid growth in scholarly output, in this case, was achieved through co-authorship. Furthermore, the thematic diversity of papers was significantly enhanced during this period. Our investigation enabled us to detect cases of violation of publication ethics through the use of "guest" and "honorary" authorship.
A Survey on SQL Injection Prevention Methods

Database plays a very important role in everyone’s life including the organizations since everything today is connected via Internet and to manage so many data. There is a need of database which helps organizations to organize, sort and manage the data and to ensure that the data which a user is receiving and sending through the mean of database is secure since the database stores almost everything such as Banking details which includes user id, Password and so. Thus, it means that the data are really valuable and confidential to us and therefore security really matters for database. SQL Injection Attacks on the database are becoming common in this era where the hackers are trying to steal the valuable data of an individual through the mean of SQL Injection Attack by using malicious query on the application. This application reveals the individual data by an efficient and the best SQL Injection Prevention technique is required in order to protect the individual data from being stolen by the hackers. Therefore, this paper will be focusing on reviewing different types of SQL Injection prevention methods and SQL injection types. The initial finding of this paper can make comparison of different types of SQL Injection Prevention methods which will enable the Database Administrator to choose the best and the efficient SQL Injection Prevention Method for their organization. Consequently, Preventing of SQL Injection Attack from happening which would ultimately result in no data loss of an user.
Local Ternary Cross Structure Pattern: A Color LBP Feature Extraction with Applications in CBIR

With the advent of medical endoscopes, earth observation satellites and personal phones, content-based image retrieval (CBIR) has attracted considerable attention, triggered by its wide applications, e.g., medical image analytics, remote sensing, and person re-identification. However, constructing effective feature extraction is still recognized as a challenging problem. To tackle this problem, we first propose the five-level color quantizer (FLCQ) to acquire a color quantization map (CQM). Secondly, according to the anatomical structure of the human visual system, the color quantization map (CQM) is amalgamated with a local binary pattern (LBP) map to construct a local ternary cross structure pattern (LTCSP). Third, the LTCSP is further converted into the uniform local ternary cross structure pattern (LTCSPuni) and the rotation-invariant local ternary cross structure pattern (LTCSPri) in order to cut down the computational cost and improve the robustness, respectively. Finally, through quantitative and qualitative evaluations on face, objects, landmark, textural and natural scene datasets, the experimental results illustrate that the proposed descriptors are effective, robust and practical in terms of CBIR application. In addition, the computational complexity is further evaluated to produce an in-depth analysis.
Synthesizing Opposites: Technical Rationality and Pragmatism in Design

(2019). Synthesizing Opposites: Technical Rationality and Pragmatism in Design. The Design Journal: Vol. 22, Running with Scissors: 13th International Conference of the European Academy of Design, Dundee, 10-12th April, 2019, edited by Louise Valentine, Joanna Bletcher and Leon Cruickshank, pp. 2031-2044.
No-reference image quality assessment based on AdaBoost BP neural network in wavelet domain

Abstract:
EyeMRTK

For head mounted displays, like they are used in mixed reality applications, eye gaze seems to be a natural interaction modality. EyeMRTK provides building blocks for eye gaze interaction in virtual and augmented reality. Based on a hardware abstraction layer, it allows interaction researchers and developers to focus on their interaction concepts, while enabling them to evaluate their ideas on all supported systems. In addition to that, the toolkit provides a simulation layer for debugging purposes, which speeds up prototyping during development on the desktop.
Strategies to Prevent and Detect Occupational Fraud in Small Retail Businesses

The objective of this multiple case study was to explore internal control strategies owners of small retail businesses in southeastern Pennsylvania used to prevent and detect occupational fraud. The results of this study indicated that there are areas for action that owners of small retail businesses could take to prevent and detect occupational fraud. Strategies business owners could implement to protect their businesses from occupational fraud include monitoring, employee identity documents to track employee activity, separation of duties, and communication with employees. Small business owners serve their communities by focusing on wealth distribution, including donations to local charities that foster economic stability with positive effects on society.
Scrum+: A scaled Scrum for the agile global software development project management with multiple models

Nowadays, carrying out a project management is difficult in any situation, even more in global projects of software development, whose environment faces different aspects that make this type of projects even more difficult to manage, for example: lack of coordination, lack of face to face communication, temporary differences, cultural diversity and application of different standards, models and approaches. With the aim of supporting agile global software development (AGSD) project management when multiple models are present, we have defined Scrum+, which is a guide based on Scrum and defines a set of activities, tasks, roles and criteria to support AGSD projects in multimodel environments. We carried out the harmonization of agile approach Scrum along with project management practices defined in multiple models such as: ISO/IEC 15504, ISO 9001 and CMMI-DEV. Likewise, the evaluation of the proposal has been conducted through: (i) its application in a focus group with experts in different related areas and (ii) its assessment of the degree of agility by means 4-DAT method. Based on the analysis of the results and the comments obtained in the focus group, Scrum+ seems to be clear, adequate, and agile. The guide proposed here can serve as reference for studying further aspects related to agile software scaled projects.
Multi Color Image Segmentation using L*A*B* Color Space

Image segmentation is always a fundamental but challenging problem in computer vision.The simplest approach to image segmentation may be clustering of pixels.my works in this paper address the problem of image segmentation under the paradigm of clustering.A robust clustering algorithm is proposed and utilized to do clustering on the L*a*b* color feature space of pixels.Image segmentation is straight forwardly obtained by setting each pixel with its corresponding cluster.We test our segmentation method on fruits images, medical and Mat lab standard images.The experimental results clearly show region of interest object segmentation.
A Synthetic Video Dataset for Video Compression Evaluation

In this paper, a new Synthetic video Texture dataset (SynTex) is introduced. It was generated using a Computer Graphics Imagery (CGI) environment and offers the capability of being able to generate many versions of the same scenes with different video parameters. This will support research in video compression enabling researchers to understand and model the relationship between video content and its coding parameters. To validate that SynTex is suitable for this purpose, firstly, typical spatio-temporal descriptors were calculated and compared against existing real video datasets with similar parameters. Then, the encoding statistics of SynTex were extracted using the HEVC reference software and compared to natural video datasets. The comparisons show that SynTex exhibits a comparable coverage over the spatial and temporal domain and it has similar encoding statistics to real video datasets.
Learning to Plan Hierarchically From Curriculum

We present a framework for learning to plan hierarchically in domains with unknown dynamics. We enhance planning performance by exploiting problem structure in several ways: First, we simplify the search over plans by leveraging knowledge of skill objectives; second, shorter plans are generated by enforcing aggressively hierarchical planning; and third, we learn transition dynamics with sparse local models for better generalization. Our framework decomposes transition dynamics into skill effects and success conditions, which allows fast planning by reasoning on effects, while learning conditions from interactions with the world. We propose a simple method for learning new abstract skills, using successful trajectories stemming from completing the goals of a curriculum. Learned skills are then refined to leverage other abstract skills and enhance subsequent planning. We show that both conditions and abstract skills can be learned simultaneously while planning, even in stochastic domains. Our method is validated in experiments of increasing complexity, with up to 2100 states, showing superior planning to classic non-hierarchical planners or reinforcement learning methods. Applicability to real-world problems is demonstrated in a simulation-to-real transfer experiment on a robotic manipulator.
Dispatching and Management Center for Optimizing Home Care Integrated Services

The paper summarizes the design specifications for a complex ICT platform for optimizing home care integrated services. It is based on the work involved in implementing the project with the same title – “Dispatching and Management Center for Optimizing Home Care Integrated Services”. The project aims to create a network of centers that offer services to home care service providers (HCPs). The centers in the network should optimize the activity of the home care service providers and support them in offering quality standardized services through the usage of a complex platform that optimizes most of the activities involved in offering home care services. The proposed system does not focus on the home care system, but concentrates on the software and hardware components that will allow a home care services provider to perform in a very efficient way. It may be compared to an ERP (Enterprise Resource Planning) system customized and dedicated to HCPs. As far as we know, there is no such system implementation currently on the market. The main beneficiaries are the home care service providers along with their end-users (elders, chronic patients or people who need ambulatory treatment), who will ultimately receive better services.
Prediction of distant melanoma recurrence from primary tumor digital H&amp;E images using deep learning.

9577 Background: Patients with resected melanoma are at high risk for systemic recurrence. Quantifying the risk of recurrence can help identify the need for adjuvant immunotherapies, and accelerate the acquisition of survival statistics in clinical trials. Methods: 75 patients with stages I, II, and III melanoma seen at Columbia University Medical Center between 2001 and 2014 and The Icahn School of Medicine at Mount Sinai between 2000 and 2010 were included based on availability of tissue and 24 months of clinical follow-up. Images were scanned into tiff files (Aperio Biosystems). Deep neural net (DNN) architecture was designed consisting of convolutional and recurrent neural networks (CNN, RNN). Using QuPath open source software for nuclear segmentation and cell classification, we generated cell location, density, and clustering features to identify tissue areas for training of network parameters. DNN analyzes image and feature information locally within an H&amp;E image, generating a prediction vote per region, and votes are averaged. Results: The model was validated on two independent external sets of stage I-III primary melanomas. Cohort 1 (Yale Medical Center), had n = 86 patients, of whom 49 were alive or had no evidence of disease at death (no DMR) and 37 died from melanoma. The second set, Cohort 2 (Geisnger Health Systems), had n = 29 patients, 15 without DMR and 14 with DMR. Prediction scores correlated with DMR status in both sets (AUC = 0.94 and 0.77 for Cohorts 1 and 2, respectively). A multivariable Cox proportional hazard model showed DNN recurrence prediction to be an independent prognostic factor for both Cohort 1 (HR = 2.54, 95% CI: 1.54-4.19, p = .0004***) and Cohort 2 (HR = 8.43, 95% CI: 2.58-27.51, p = .001**). Conclusions: We designed a DNN for quantitative prediction of melanoma recurrence from a H&amp;E stained tissue. The prediction score warrants further study in larger patient cohorts and may constitute a novel digital pathology tool for the selection of melanoma patients for adjuvant immunotherapy. [Table: see text]
A Systematic Mapping Study of Cross-Platform Mobile Apps

Cross-platform frameworks for mobile Application (app) development allow developers to deploy solutions on a range of platforms such as iOS and Android.With the increased use of such frameworks, it is of utmost importance to understand the contributions and challenges in this emergent field.Although there have been many studies in this area of research, there is a lack of a coherent view.To address this issue, we have conducted a Systematic Mapping Study (SMS) to categorize the available studies in the area of cross-platform mobile app development.More specifically, we aim to understand the accomplishments, contribution facets and the challenges presented in this field.Thirty (30) studies are mapped to a classification scheme.Different research gaps are identified: more research needs to be conducted using more complex and real-world apps; the point of how cross-platform apps are identified and distinguished from the native is still not clarified; and we argue that there should be more research contribution in the particular area of testing and maintenance.
Medoid-based shadow value validation and visualization

A silhouette index is a well-known measure of an internal criteria validation for the clustering algorithm results. While it is a medoid-based validation index, a centroid-based validation index that is called a centroid-based shadow value (CSV) has been developed. Although both are similar, the CSV has an additional unique property where an image of a 2-dimensional neighborhood graph is possible. A new internal validation index is proposed in this article in order to create a medoid-based validation that has an ability to visualize the results in a 2-dimensional plot. The proposed index behaves similarly to the silhouette index and produces a network visualization, which is comparable to the neighborhood graph of the CSV. The network visualization has a multiplicative parameter (c) to adjust its edges visibility. Due to the medoid-based, in addition, it is more an appropriate visualization technique for any type of data than a neighborhood graph of the CSV.
Confidence Measures for Deep Learning in Domain Adaptation

In recent years, Deep Neural Networks (DNNs) have led to impressive results in a wide variety of machine learning tasks, typically relying on the existence of a huge amount of supervised data. However, in many applications (e.g., bio–medical image analysis), gathering large sets of labeled data can be very difficult and costly. Unsupervised domain adaptation exploits data from a source domain, where annotations are available, to train a model able to generalize also to a target domain, where labels are unavailable. Recent research has shown that Generative Adversarial Networks (GANs) can be successfully employed for domain adaptation, although deciding when to stop learning is a major concern for GANs. In this work, we propose some confidence measures that can be used to early stop the GAN training, also showing how such measures can be employed to predict the reliability of the network output. The effectiveness of the proposed approach has been tested in two domain adaptation tasks, with very promising results.
Say Yes to the Dress: Shape and Style Transfer Using Conditional GANs

Objects are defined by their shape and visual style. Previous work into image manipulation has generally altered the stylistic appearance of a whole image, while maintaining the image content and object shapes. In this paper we transfer both the shape and style of chosen objects between images, leaving the remaining areas unaltered. To tackle this problem, we propose a two stage method, where each stage contains a generative adversarial network, that will alter the shape and style of objects in a subject image to reflect a donor image. We demonstrate the effectiveness of our method by transferring clothing between images.
Verifying big data topologies by-design: a semi-automated approach

Big data architectures have been gaining momentum in recent years. For instance, Twitter uses stream processing frameworks like Apache Storm to analyse billions of tweets per minute and learn the trending topics. However, architectures that process big data involve many different components interconnected via semantically different connectors. Such complex architectures make possible refactoring of the applications a difficult task for software architects, as applications might be very different with respect to the initial designs. As an aid to designers and developers, we developed OSTIA (Ordinary Static Topology Inference Analysis) that allows detecting the occurrence of common anti-patterns across big data architectures and exploiting software verification techniques on the elicited architectural models. This paper illustrates OSTIA and evaluates its uses and benefits on three industrial-scale case-studies.
A Genetic XK-Means Algorithm with Empty Cluster Reassignment

K-Means is a well known and widely used classical clustering algorithm. It is easy to fall into local optimum and it is sensitive to the initial choice of cluster centers. XK-Means (eXploratory K-Means) has been introduced in the literature by adding an exploratory disturbance onto the vector of cluster centers, so as to jump out of the local optimum and reduce the sensitivity to the initial centers. However, empty clusters may appear during the iteration of XK-Means, causing damage to the efficiency of the algorithm. The aim of this paper is to introduce an empty-cluster-reassignment technique and use it to modify XK-Means, resulting in an EXK-Means clustering algorithm. Furthermore, we combine the EXK-Means with genetic mechanism to form a genetic XK-Means algorithm with empty-cluster-reassignment, referred to as GEXK-Means clustering algorithm. The convergence of GEXK-Means to the global optimum is theoretically proved. Numerical experiments on a few real world clustering problems are carried out, showing the advantage of EXK-Means over XK-Means, and the advantage of GEXK-Means over EXK-Means, XK-Means, K-Means and GXK-Means (genetic XK-Means).
A Fast Algorithm for Multi-Class Learning from Label Proportions

Learning from label proportions (LLP) is a new kind of learning problem which has attracted wide interest in machine learning. Different from the well-known supervised learning, the training data of LLP is in the form of bags and only the proportion of each class in each bag is available. Actually, many modern applications can be successfully abstracted to this problem such as modeling voting behaviors and spam filtering. However, time-consuming training is still a challenge for LLP, which becomes a bottleneck especially when addressing large bags and bag sizes. In this paper, we propose a fast algorithm called multi-class learning from label proportions by extreme learning machine (LLP-ELM), which takes advantage of an extreme learning machine with fast learning speed to solve multi-class learning from label proportions. Firstly, we reshape the hidden layer output matrix and the training data target matrix of an extreme learning machine to adapt to the proportion information instead of the real labels. Secondly, a robust loss function with a regularization term is formulated and two efficient solutions are provided to different cases. Finally, various experiments demonstrate the significant speed-up of the proposed model with better accuracies on different datasets compared with several state-of-the-art methods.
Technology enhanced learning in teachers’ professional development

There is a lot of attention focused on the development of digital competency already in early age.One of the key person is teacher.To develop teachers' digital skills, technology enhanced learning can be used in their professional development.There is large change in Latvia' education system.One of the changes is subject "Computing" that will be compulsory in all schools from 2019.From 2015 Liepaja University participates in a pilot project for this purpose and is only one that offers teachers' professional development courses for the subject.Article's aim is to introduce "Computing" course, mainly the last one what was organized mostly in webinar mode.The article shows first data from teachers' survey about their attitude to a webinar.This is the first article in a series about research made on this course.
Diagnostic Utility of Cell Population Data (CPD) In Sepsis Using Automated Hematology Analysers

Background: Sepsis still remains an important cause of morbidity and mortality posing a diagnostic challenge to clinicians across the globe.Lack of a single specific and sensitive test paved the way to many researches.Our study evaluated clinical significance of cell population data (CPD) as biomarkers in sepsis.CPD assesses morphological and functional characteristics of leucocytes using automated hematology analyzer. Methods:The study population consisted of 100 healthy subjects and 100 clinically suspected cases of sepsis with quick sequential organ failure assessment (qSOFA) score >2.The various cell population data (CPD) were collected using automated hematology analyzer, Sysmex XN1000 during a span of 6 months from January to June 2018 in a tertiary care center and the results were statistically analyzed using Z test. Result:The WBC count and CPD parameters were assessed.Except for neutrophil , monocyte cell size (NE-FSC, MO-Z) and lymphocyte fluorescence intensity(LY-Y) all other CPD parameters show significant difference (p<0.001) in sepsis group compared to healthy controls. Conclusion:This study suggested utility of CPD parameters which provide details of size and internal structure of leucocytes to be considered as an important biomarker for diagnosis and management of sepsis.
Reduction-Based Problem Mapping for Quantum Computing

In recent years, industry and academia have made tremendous research attempts to implement quantum computing technologies. But quantum computing is still grounded by numerous critical barriers, leading to its low accessibility and practicality. To overcome this problem, we propose an end-to-end framework for mapping computationally hard problems on a quantum computer via reduction.