Reviewerly/exHarmony huggingface dataset:
QUALITY RESEARCH MANAGEMENT IMPROVES DESIGN RESEARCH EFFECTIVENESS

Design research projects are often more complex than initially perceived due to a requirement for novelty, the creativity inherent in the design process, and the limited experience of novice researchers. Also, expectations from institutions and external funding stakeholders must be met. Therefore, a framework was needed to support the research effort in the form of quality research management. The focus of this framework is primarily to align stakeholders. To this end, engineering management methods were consulted to define a quality research management framework that provides review baselines, requirements traceability, and a general process model. The framework is based on the well-known ‘house of quality’ principle that provides requirements traceability. A series of matrices was developed, with a research validation matrix resulting from the combination of traceability matrices. This matrix allows the allocation of research challenges to a need, concept solutions to research challenges, and specific research solutions to concept solutions. Also, information sources are allocated to challenges and solutions to verify their authenticity. It is thus possible to visualise clearly a summary of the research project. Additional benefits include improved communication, visualisastion of progress, traceability of requirements, and verification and validation, which illustrates the value of quality research management.
ManyLands: A Journey Across 4D Phase Space of Trajectories

Abstract Mathematical models of ordinary differential equations are used to describe and understand biological phenomena. These models are dynamical systems that often describe the time evolution of more than three variables, i.e., their dynamics take place in a multi‐dimensional space, called the phase space. Currently, mathematical domain scientists use plots of typical trajectories in the phase space to analyze the qualitative behavior of dynamical systems. These plots are called phase portraits and they perform well for 2D and 3D dynamical systems. However, for 4D, the visual exploration of trajectories becomes challenging, as simple subspace juxtaposition is not sufficient. We propose ManyLands to support mathematical domain scientists in analyzing 4D models of biological systems. By describing the subspaces as Lands, we accompany domain scientists along a continuous journey through 4D HyperLand, 3D SpaceLand, and 2D FlatLand, using seamless transitions. The Lands are also linked to 1D TimeLines. We offer an additional dissected view of trajectories that relies on small‐multiple compass‐alike pictograms for easy navigation across subspaces and trajectory segments of interest. We show three use cases of 4D dynamical systems from cell biology and biochemistry. An informal evaluation with mathematical experts confirmed that ManyLands helps them to visualize and analyze complex 4D dynamics, while facilitating mathematical experiments and simulations.
Multisensor Estimation Fusion with Gaussian Process for Nonlinear Dynamic Systems

The Gaussian process is gaining increasing importance in different areas such as signal processing, machine learning, robotics, control and aerospace and electronic systems, since it can represent unknown system functions by posterior probability. This paper investigates multisensor fusion in the setting of Gaussian process estimation for nonlinear dynamic systems. In order to overcome the difficulty caused by the unknown nonlinear system models, we associate the transition and measurement functions with the Gaussian process regression models, then the advantages of the non-parametric feature of the Gaussian process can be fully extracted for state estimation. Next, based on the Gaussian process filters, we propose two different fusion methods, centralized estimation fusion and distributed estimation fusion, to utilize the multisensor measurement information. Furthermore, the equivalence of the two proposed fusion methods is established by rigorous analysis. Finally, numerical examples for nonlinear target tracking systems demonstrate the equivalence and show that the multisensor estimation fusion performs better than the single sensor. Meanwhile, the proposed fusion methods outperform the convex combination method and the relaxed Chebyshev center covariance intersection fusion algorithm.
P74 Endometrial cancer: can the iKnife diagnose endometrial cancer?

<h3>Introduction/Background</h3> Endometrial cancer is the most common gynaecological cancer in the western world with 88’000 new cases diagnosed per year in the European Union. Diagnosis of endometrial cancer is confirmed by histological examination of endometrial curetting’s or outpatient endometrial biopsies e.g. pipelle; but can take up to two weeks. To-date there is no Point-Of-Care (POC) diagnosis available. The intelligent surgical knife (’iKnife’) analyses tissue real-time but has never been used to diagnose endometrial cancer. This study aims to establish whether the iKnife could distinguish between normal and malignant endometrial pipelle biopsy samples based on differences in their lipidomic profiles. <h3>Methodology</h3> Research pipelle biopsy samples were obtained for women needing biopsies for clinical reasons (samples concurrently sent to conventional histopathology). A Waters G2-XS Xevo Q-Tof mass spectrometer (MS) was used in this study in conjunction with a modified handheld diathermy (collectively coined the ‘iKnife’). The resultant surgical aerosol containing ionic species produced during diathermy was then analysed with this technology; producing spectra that are background subtracted, lock mass corrected and in the phospholipid range. Principal component analysis (PCA) and linear discriminant analysis (LDA) were then performed to find the variance in spectral signatures. A leave one patient out cross validation was used to obtain diagnostic accuracy. <h3>Results</h3> 134 pipelle biopsy samples (73 normal and 61 malignant) were obtained. Each tissue sample was processed as described; producing an individual spectrum per burn. The iKnife differentiated between normal and malignant endometrial tissues on the basis of differences in their unique phospholipid spectral signatures. Cross validation revealed a diagnostic accuracy of 80%. <h3>Conclusion</h3> This pilot study is the first to use the iKnife as a tool to differentiate between normal and malignant endometrial pipelle samples. These results are encouraging and suggest that the iKnife could be used as an adjunct in clinic to provide a POC diagnosis. <h3>Disclosure</h3> Nothing to disclose.
Maximum Likelihood Evidential Reasoning-Based Hierarchical Inference with Incomplete Data

Data mining requires a pre-processing task where data are prepared, cleaned, integrated, transformed, reduced and discretized to ensure data quality. Incomplete data are commonly encountered during data cleaning, which can have major impact on the conclusions that will be drawn from the data. In order to effectively carry out inferential modelling or decision making from incomplete independent variables or explanatory variables and consider different types of uncertainties, this paper adopts a data-driven inferential modelling approach, Maximum Likelihood Evidential Reasoning (MAKER) framework, which takes advantage of incomplete datasets without any imputation that may be required by other conventional machine learning methods. The MAKER framework reflects the plausibility of different values of missing data and expresses data-driven support for different values of missing data.
DIGITAL LITERATION THROUGH ONLINE MAGAZINE IN LEARNING READING COMPREHENSION IN ELEMENTARY SCHOOL

The research aims to develop students' reading comprehension skills through the use of online magazines. The choice of online magazines as a source of student reading is because now students prefer to use electronic devices such as cellphones or laptops. The use of online magazines in primary school students can be an effort to instill digital literacy. This study uses a pre-experimental method with Post-test only with Nonequivalent Groups. The study population was grade IV students in the city of Cimahi while the selected sample of 28 students used the convenience technique. Meanwhile, the data collection technique uses a reading comprehension skills test and a digital literacy level questionnaire. Researchers conducted data analysis with inference statistics through the SPSS 20.0 program. The results indicated that the reading skills of group A students' understanding were better than those of group B. The researchers concluded that the online magazine had an effect on reading comprehension skills of grade IV students.
Inferring Structure and Meaning of Semi-Structured Documents by using a Gibbs Sampling Based Approach

In this paper, we present a novel and elegant method to extract the structure and derive meaning from semi-structured text documents such as resumes. Semi-structured text documents have an information hierarchy captured by use of textual styling, formatting and visual layout organization. So there is a need to build an algorithm that can both extract the structural hierarchy as well as classify the text blocks into known categories. Our algorithm proposes a generative statistical model, that generates the corpus of semi-structured documents, in a way that allows us to apply the Gibbs sampling algorithm to estimate the parameters of the underlying model that results in extraction of both structure and meaning from the documents. We have seen that applying our Gibbs sampling based algorithm on a random set of 1000 resumes has an extraction accuracy of close to 75%.
Towards Effective Technical Debt Decision Making in Software Startups

Context: Technical Debt (TD) is a metaphor used to describe outstanding software maintenance tasks or shortcuts made in the software development to achieve short-term benefits (i.e. time to market), but negatively impact the software quality in the long term. TD is quite common in a software startup, which is characterized as a young company with low resources and a small client base, aiming to accelerate time to market. Decisions related to TD can be critical for startup success. Objective: I aim to understand the relationship between TD decisions and the success or failure of software startups, and explore the best practices related to TD decisions that would better contribute to the startup success. Method: I plan to apply multiple retrospective case studies in different software startups that succeed or failed to pass the startup period and become a mature organization. Semi structured interviews will be used to collect data from the team who was involved in the software development in the startup era. Contribution: The outcome of this study will help software founders/entrepreneurs to make effective TD decisions during the startup timeframe; that can better contribute to the startup success and decrease the risk of the startup failure.
Investigation of Stem cells Technology in The Light of Jurisprudential Documents.

The aim of this study is investigation of Stem cells Technology in The Light of Jurisprudential Documents.In this analytical-descriptive research, we collected the relevant data through a literature search. We have used PubMed, ScienceDirect, Google Scholar, Iranian databases like SID, Iran doc, Iranian law and also Islamic resources for this study.There are so many controversies about safety of these cells and possible dangers for human body. As in Iran, laws of stem cells are not clear. Elimination of barriers requires drafting laws compatible with regional and cultural beliefs of Iranian people. Unfortunately, available laws could not keep up with the advances.Iran juridical system should conduct and restrict actions in the area of stem cells technology by gathering experts of different political, science, medicine, social and mindful who are familiar with law, according to notions of intellectual jurists and legislators, Islam and Shia religious.
An Evaluation of Head-Mounted Virtual Reality for Special Education from the Teachers’ Perspective

In this research, we explore the use of head-mounted virtual reality for special education from the teachers’ perspective. We asked a group of special educators to assess the use of VR headset while students with mental disabilities played a VR game. The teachers concluded that head-mounted VR can be used for teaching students to follow instruction and training for work.
Extract, Transform and Filling: A Pipeline Model for Question Paraphrasing based on Template

Question paraphrasing aims to restate a given question with different expressions but keep the original meaning. Recent approaches are mostly based on neural networks following a sequence-to-sequence fashion, however, these models tend to generate unpredictable results. To overcome this drawback, we propose a pipeline model based on templates. It follows three steps, a) identifies template from the input question, b) retrieves candidate templates, c) fills candidate templates with original topic words. Experiment results on two self-constructed datasets show that our model outperforms the sequence-to-sequence model in a large margin and the advantage is more promising when the size of training sample is small.
Extending knowledge based redundancy in association rules with imprecise knowledge

Association Rules Mining is one of the most studied and widely applied fields in Data Mining. However, the discovery models usually result in a very large set of rules; so the analysis capability, from the user point of view, is diminishing. Hence, it is difficult to use the found model in order to assist decision-making process. The previous handicap is heightened in presence of redundant rules in the final set. In this work we study a way to eliminate redundancy in association rules with uncertainty, with imprecise user prior knowledge. A post-processing method is developed to eliminate this kind of redundancy, using association rules known by the user. Our proposal allows to find more compact models of association rules to ease its use in the decision-making process. The developed experiments have shown that the reduction using certainty factor has a slightly better behavior. The most important contribution of this paper is the definition of a mechanism to remove knowledge based redundancy using Dempster-Schaffer Theory and the Certainty Factor Model
A Graph Based Unsupervised Feature Aggregation for Face Recognition

In most of the testing dataset, the images are collected from video clips or different environment conditions, which implies that the mutual information between pairs are significantly important. To address this problem and utilize this information, in this paper, we propose a graph-based unsupervised feature aggregation method for face recognition. Our method uses the inter-connection between pairs with a directed graph approach thus refine the pair-wise scores. First, based on the assumption that all features follow Gaussian distribution, we derive a iterative updating formula of features. Second, in discrete conditions, we build a directed graph where the affinity matrix is obtained from pair-wise similarities, and filtered by a pre-defined threshold along with K-nearest neighbor. Third, the affinity matrix is used to obtain a pseudo center matrix for the iterative update process. Besides evaluation on face recognition testing dataset, our proposed method can further be applied to semi-supervised learning to handle the unlabelled data for improving the performance of the deep models. We verified the effectiveness on 5 different datasets: IJB-C, CFP, YTF, TrillionPair and IQiYi Video dataset.
Tone Mapping of High Dynamic Range Images Combining Co-Occurrence Histogram and Visual Salience Detection

One of the significant qualities of the human vision, which differentiates it from computer vision, is so called attentional control, which is the innate ability of our human eyes to select what visual stimuli to pay attention to at any moment in time. In this sense, the visual salience detection model, which is designed to simulate how the human visual system (HVS) perceives objects and scenes, is widely used for performing multiple vision tasks. This model is also in high demand in the tone mapping technology of high dynamic range images (HDRIs). Another distinct quality of the HVS is that our eyes blink and adjust brightness when objects are in their sight. Likewise, HDR imaging is a technology applied to a camera that takes pictures of an object several times by repeatedly opening and closing a camera iris, which is referred to as multiple exposures. In this way, the computer vision is able to control brightness and depict a range of light intensities. HDRIs are the product of HDR imaging. This article proposes a novel tone mapping method using CCH-based saliency-aware weighting and edge-aware weighting methods to efficiently detect image salience information in the given HDRIs. The two weighting methods combine with a guided filter to generate a modified guided image filter (MGIF). The function of the MGIF is to split an image into the base layer and the detail layer which are the two elements of an image: illumination and reflection, respectively. The base layer is used to obtain global tone mapping and compress the dynamic range of HDRI while preserving the sharp edges of an object in the HDRI. This has a remarkable effect of reducing halos in the resulting HDRIs. The proposed approach in this article also has several distinct advantages of discriminative operation, tolerance to image size variation, and minimized parameter tuning. According to the experimental results, the proposed method has made progress compared to its existing counterparts when it comes to subjective and quantitative qualities, and color reproduction.
Promoting Professional Development for Teachers Through a Scale of Competence Assessment

Competencies are a key factor in the professional development of teachers. Additionally, Information and Communication Technologies (ICTs) in education are becoming increasingly important due to their potential in the field of education. The combination of both makes it easier to perform a comprehensive assessment of the level of competences of students in the Teacher Degree. The design of tasks and activities in order to promote their assessment enable classroom intervention. Indeed, this paper presents how ICTs have been used in university teaching. In order to respond to the evolution of training needs, we present a valid and reliable evaluation tool for professional competencies and learning results for prospective teachers (Pre-School and Primary Education). We applied for students from the Teacher Degree in their practicum period. Indeed, different learning and assessment tasks have been designed, some related to “correct” or “incorrect” teaching practices. This implies that the students have not only to do their tasks but to identify and to differentiate what is correct in their professional development. Finally, the instrument enables the assessment of the level of professional competencies acquired by prospective teachers during their training period.
Application of Reservoir Computing for the Modeling of Nano-Contact Vortex Oscillator

The Nano-Contact Vortex Oscillator (NCVO) is a highly nonlinear spintronic device that can depict chaotic and nonchaotic behaviors according to the current flowing through it. The potential use of such a device in the future-generation computing systems requires the knowledge of a realistic model capable of describing its exact dynamics. In this paper, we firstly investigate the behavior of NCVO based on the power spectral analysis. Furthermore, we propose and demonstrate two efficient approaches of reservoir computing for the modeling of such a device. The performances of the proposed models are addressed in two ways. First, the generated time-varying signals are compared with the simulated magnetizations of the NCVO at different operating currents. Then, the power spectral analysis of one of the two models is carried out to examine its overall behavior over the complete DC current operating range and its ability to diagnose chaotic and non-chaotic regimes. The proposed models show quite promising results that can be counted on for further research.
Resource Allocation in Blockchain System Based on Mobile Edge Computing Networks

Blockchain is widely adopted in many applications as a promising distributed data management framework. However, the high demanding on computing and communication resources may pose a bottleneck for blockchain to be applied in wireless connected users, which are assumed a main constituent part for the future digital society. In this paper, in order to solve the problems of insufficient computing resources faced in the “Mining” process, we consider a blockchain system based on the mobile edge computing (MEC) network. The computation-intensive tasks of blockchain users are offloaded to MEC servers, and the calculation tasks offloading problem in the system is formulated as a large-scale mixed integer nonlinear programming (MINLP) problem. The MINLP problem proposed in the blockchain system is solved by an algorithmic framework based on the Benders decomposition method. Meanwhile, we propose the branch-and-bound method and the dichotomy-the alternating direction method of multipliers (ADMM) method instead of the dinkelbach-ADMM to solve the mixed integer programming master problem and the fractional programming sub-problem, respectively. Simulation results demonstrate that the proposed algorithm can save the energy consumption in the blockchain system and reduce computing time of the “Mining” process.
Convex Hulls and the size and the Size of the Hidden Layer in a MLP Based Classifier

Designing a feedforward neural network has always posed many questions regarding the number of hidden layers and the number of neurons on each hidden layer. While there is no unique and global solution, it is known that the particularities of the problem to solve, especially for classification, offer some guidance about how to solve these questions. One heuristic approach, when only a hidden layer is involved, analyzes how the involved classes are separated from each other using a finite number of hyperplanes, thereby defining the size of the hidden layer on the network. On this article, using computational geometry concepts, an automated and time efficient method is presented and discussed for estimating the quantity of neurons in the hidden layer by computing the number of hyperplanes separating the classes, based on convex hulls and approximation to alpha shapes. Examples on different situations that may arise and the results on using it are illustrated. It can be seen from the results that the proposed method gives very good estimation for the number of hidden neurons.
Detection of Spatiotemporal Changes of Surface Mining Area in Changting County Southeast China

Quantifying the spatiotemporal changes of Open Pit Mining Area (OPMA) are of important to understand the influence of mining activities on ecological, environmental and socioeconomic impact assessment procedure. This study uses the morphological mining feature index (MMFI) to identify and map surface mining areas and its dynamic changes in Changting county southeast china during 1998 to 2010, with the specific goal of understanding surface mining activities on vegetation deterioration and recovery. Serials of Landsat datasets during 1988-2010 period were collected, and the spatial distributions and change patterns of OPMA disturbances were detected and mapped. The results indicated that the changes of OPMA in Changting county have shown two stages, where the prior was the out- of-order exploitation of rare earth elements, and the next was dominated by the mining of quarry due to urbanization process in recent years.