Reviewerly/exHarmony huggingface dataset:
Automated Gland Segmentation Leading to Cancer Detection for Colorectal Biopsy Images

Glandular formation and morphology along with the architectural appearance of glands exhibit significant importance in the detection and prognosis of inflammatory bowel disease and colorectal cancer. The extracted glandular information from segmentation of histopathology images facilitate the pathologists to grade the aggressiveness of tumor. Manual segmentation and classification of glands is often time consuming due to large datasets from a single patient. We are presenting an algorithm that can automate the segmentation as well as classification of H and E (hematoxylin and eosin) stained colorectal cancer histopathology images. In comparison to research being conducted on cancers like prostate and breast, the literature for colorectal cancer segmentation is scarce. Inter as well as intra-gland variability and cellular heterogeneity has made this a strenuous problem. The proposed approach includes intensity-based information, morphological operations along with the Deep Convolutional Neural network (CNN) to evaluate the malignancy of tumor. This method is presented to outpace the traditional algorithms. We used transfer learning technique to train AlexNet for classification. The dataset is taken from MCCAI GlaS challenge which contains total 165 images in which 80 are benign and 85 are malignant. Our algorithm is successful in classification of malignancy with an accuracy of 90.40, Sensitivity 89% and Specificity of 91%.
Implementation of Universal Neural Network Approximator on a ULP Microcontroller for Wavelet Synthesis in Electroencephalography

The paper describes the implementation of universal neural network approximator on microcontroller MSP430G2553 with ultralow power consumption. A multilayer perceptron is used as an approximating artificial neural network. Approximation is one of the stages of building wavelet neural network models. The approximation of electroencephalogram fragments allows obtaining wavelets adapted for its continuous wavelet transform. Implementation of neural network approximator on this microcontroller is one of the stages to create a portable system for automated electroencephalogram analysis. Two ways of implementation a neural network approximator are presented in the paper. The first way is based on the experiment with the neural network approximator without its training. The second way involves the experiment based on the interaction with the neural network approximator with training algorithm. The studies of the approximation rate depending on the number of iterations and the core clock frequency of the microcontroller have been conducted.
The Initial Value of Extraction for Visual Perception in VR(Virtual Reality) Eye-tracking Experiments

The new technology development in the VR (Virtual Reality) space is on the initial stages of developing the program that identifies designed space in architecture area, and moves or designs space components. VR should be expected to have high applicability in the architectural design area, but it was limited to research on the progress of changing user
3DCNN Performance in Hand Gesture Recognition Applied to Robot Arm Interaction

This work was funded by the Ministry of Economy, Industry and Competitiveness from the Spanish Government through the DPI2015-68087-R and the pre-doctoral grant BES-2016-078290, by the European Commission and FEDER funds through the project COMMANDIA (SOE2/P1/F0638), action supported by Interreg-V Sudoe.
Designing Normative Theories of Ethical Reasoning: Formal Framework, Methodology, and Tool Support.

A framework and methodology---termed LogiKEy---for the design and engineeringof ethical reasoners, normative theories and deontic logics is presented. Theoverall motivation is the development of suitable means for the control andgovernance of intelligent autonomous systems. LogiKEy's unifying formalframework is based on semantical embeddings of deontic logics, logiccombinations and ethico-legal domain theories in expressive classichigher-order logic. This meta-logical approach enables the provision ofpowerful tool support in LogiKEy with small effort: off-the-shelf theoremprovers and model finders for higher-order logic are assisting the LogiKEydesigner of ethical intelligent agents to flexibly experiment with underlyinglogics and their combinations, with ethico-legal domain theories, and withconcrete examples---all at the same time. Continuous improvements of theseoff-the-shelf provers, without further ado, leverage the reasoning performancein LogiKEy. Case studies, in which the LogiKEy framework and methodology hasbeen applied and tested, give early evidence that HOL's undecidability oftendoes not hinder efficient experimentation.
First-Order Methods with Increasing Iterate Averaging for Solving Saddle-Point Problems

First-order methods are known to be among the fastest algorithms for solving large-scale convex-concave saddle-point problems. Algorithms that achieve a theoretical convergence rate on the order of $1/T$ are known, but these often rely on uniformly averaging iterates in order to get the guaranteed rate. In contrast, using the last iterate has repeatedly been found to perform better in practice, but with no guarantee on convergence rate. In this paper we propose using averaging schemes with increasing weight on recent iterates, which leads to a guaranteed $1/T$ convergence rate, while capturing the practical performance of using the last iterate. We show this for Chambolle and Pock's primal-dual algorithm, and mirror prox. We present numerical results on computing Nash equilibria in matrix games, competitive equilibria in Fisher markets, and image denoising via total-variation minimization under the $\ell_1$ norm. In all cases we find that our averaging schemes lead to much better performance than uniform averaging, and sometimes even better performance than using the last iterate.
Detecting Fraudulent Bank Account Based on Convolutional Neural Network with Heterogeneous Data

Detecting fraudulent accounts by using their transaction networks is helpful for proactively preventing illegal transactions in financial scenarios. In this paper, three convolutional neural network models, i.e., NTD-CNN, TTD-CNN, and HDF-CNN, are created to identify whether a bank account is fraudulent. The three models, same in model structure, are different in types of the input features. Firstly, we embed the bank accounts’ historical trading records into a general directed and weighted transaction network. And then, a DirectedWalk algorithm is proposed for learning an account’s network vector. DirectedWalk learns social representations of a network’s vertices, by modeling a stream of directed and time-related trading paths. The local topological feature, generating by accounts’ network vector, is taken as input of NTD-CNN, and TTD-CNN takes time series transaction feature as input. Finally, the two kinds of heterogeneous data, being integrated into a novel feature matrix, are fed into HDF-CNN for classifying bank accounts. The experimental results, conducted on a real bank transaction dataset, show the advantage of HDF-CNN over the existing methods.
A landscape of data – working with digital resources within and beyond DARIAH

The way researchers in the arts and humanities disciplines work has changed significantly. Research can no longer be done in isolation as an increasing number of digital tools and certain types of knowledge are required to deal with research material. Research questions are scaled up and we see the emergence of new infrastructures to address this change. The DigitAl Research Infrastructure for the Arts and Humanities (DARIAH) is an open international network of researchers within the arts and humanities community, which revolves around the exchange of experiences and the sharing of expertise and resources. These resources comprise not only of digitised material, but also a wide variety of born-digital data, services and software, tools, learning and teaching materials. The sustaining, sharing and reuse of resources involves many different parties and stakeholders and is influenced by a multitude of factors in which research infrastructures play a pivotal role. This article describes how DARIAH tries to meet the requirements of researchers from a broad range of disciplines within the arts and humanities that work with (born-)digital research data. It details approaches situated in specific national contexts in an otherwise large heterogeneous international scenario and gives an overview of ongoing efforts towards a convergence of social and technical aspects.
A Comparative Study on Hierarchical Navigable Small World Graphs.

Hierarchical navigable small world (HNSW) graphs get more and more popular onlarge-scale nearest neighbor search tasks since the source codes were releasedtwo years ago. The attractiveness of this approach lies in its superiorperformance over most of the nearest neighbor search approaches as well as itsgenericness to various distance measures. In this paper, several comparativestudies have been conducted on this search approach. The role of hierarchicalstructure in HNSW and the function of HNSW graph itself are investigated. Wefind that the hierarchical structure in HNSW could not achieve a much betterlogarithmic complexity scaling as it was claimed in the paper, particularly onhigh dimensional data. Moreover, we find that similar high search speedefficiency as HNSW could be achieved with the support of flat k-NN graph aftergraph diversification. Finally, we point out the difficulty, faced by most ofthe graph based search approaches, is directly linked to curse ofdimensionality. HNSW, like other graph based approaches, is unable to addresssuch difficulty.
Optimizing Software Team Performance with Cultural Differences

Software development is primarily a team task that requires a high degree of coordination among team members [1].Prior research has indicated that the composition of team member traits such as personality and culture can influence the performance of software teams [2].However, this line of research does not give practical guidance on how to build teams with personnel constraints.Some research has built teams by starting with personality [2].However, cultural traitswhich are also known to influence team performancehave not been examined in the same manner.This research, therefore, builds upon this stream by: 1) examining the effects of Hofstede's [3] latest sixdimensional model of national culture [4], 2) segmenting potential software team members into distinct cultural clusters, and 3) testing the outcomes of teams built upon homogeneous versus heterogeneous cultural compositions over time.Our results indicate that-consistent with prior research-homogeneous team compositions are initially better for performance.However, this effect reverses over time, and heterogeneous team compositions are superior.
DataDrawingDroid: A Wheel Robot Drawing Planned Path as Data-Driven Generative Art

This paper introduces DataDrawingDroid, a wheel robot to visualize data and draw data-driven generative art onto a floor. In our user study, 24 participants watched videos of three types of data drawing. T-tests for the results on five-point Likert scales indicated that it attracted them and suggested the importance of balancing functionality and aesthetics.
Granger Causality for Heterogeneous Processes

Discovery of temporal structures and finding causal interactions among time series have recently attracted attention of the data mining community. Among various causal notions graphical Granger causality is well-known due to its intuitive interpretation and computational simplicity. Most of the current graphical approaches are designed for homogeneous datasets i.e. the interacting processes are assumed to have the same data distribution. Since many applications generate heterogeneous time series, the question arises how to leverage graphical Granger models to detect temporal causal dependencies among them. Profiting from the generalized linear models, we propose an efficient Heterogeneous Graphical Granger Model (HGGM) for detecting causal relation among time series having a distribution from the exponential family which includes a wider common distributions e.g. Poisson, gamma. To guarantee the consistency of our algorithm we employ adaptive Lasso as a variable selection method. Extensive experiments on synthetic and real data confirm the effectiveness and efficiency of HGGM.
Projection-Based Augmented Reality Robot Prototype with Human-Awareness

Since projection augmented reality (AR) robot can provide a lot of information through projector, it can be useful in museums and art galleries that need to provide information to the crowd. Therefore, it is necessary to continue to interact with people, and human-aware path planning is also needed. We prototyped projection AR mobile robot implemented human-aware path planning and wrote about future research direction.
Knowledge Graph Rule Mining via Transfer Learning

Mining logical rules from knowledge graphs (KGs) is an important yet challenging task, especially when the relevant data is sparse. Transfer learning is an actively researched area to address the data sparsity issue, where a predictive model is learned for the target domain from that of a similar source domain. In this paper, we propose a novel method for rule learning by employing transfer learning to address the data sparsity issue, in which most relevant source KGs and candidate rules can be automatically selected for transfer. This is achieved by introducing a similarity in terms of embedding representations of entities, relations and rules. Experiments are conducted on some standard KGs. The results show that proposed method is able to learn quality rules even with extremely sparse data and its predictive accuracy outperformed state-of-the-art rule learners (AMIE+ and RLvLR), and link prediction systems (TransE and HOLE).
Improving Eight-State Continuous Variable Quantum Key Distribution by Applying Photon Subtraction

We propose a new method to effectively improve the performance of a quantum key distribution with eight-state continuous variables by the photon subtraction method. This operation is effective in increasing and distilling Gaussian entanglement between quantum states, and can be easily realized by existing technology. Simulation results show that the channel-loss tolerance of the eight-state continuous variable quantum key distribution (CVQKD) protocol can be extended by the appropriate photon subtraction algorithm; namely, single-photon subtraction.
The evaluation performance of letter-based technique on text steganography system

Steganography is a part of information hiding in covering the hidden message in any medium such as text, image, audio, video and others. This paper concerns about the implementation of steganography in text domain called text steganography. It intends to concentrate on letter-based technique as one of the representative techniques in text steganography. This paper displays some techniques of letter-based that is integrated in one system technique displayeed in a logical and physical design. The integrated system is evaluated using some parameter that is used in order to discover the performance in term of capacity after embedding process and the time consuming in the development process. This paper is anticipated to contribute in describing the implementation of the techniques in one system and to display the performance some parameter evaluation.
MS EXCEL-BASED APPLICATION FOR ENCRYPTION AND DECRYPTION OF ENGLISH TEXTS USING FIBONACCI LINEAR FEEDBACK SHIFT REGISTERS

MS EXCEL-BASED APPLICATION FOR ENCRYPTION AND DECRYPTION OF ENGLISH TEXTS USING FIBONACCI LINEAR FEEDBACK SHIFT REGISTERS
Efficient impulse noise removal using hybrid neuro‐fuzzy filter with optimized intelligent water drop technique

Abstract Impulse noise (IN) affects the digital image, during transmission, digital storage, and image acquisition. IN removal from an image is necessary as it retains the quality of the image. This work concentrates on the IN. A neuro‐fuzzy (NF) system based on a fuzzy technique which is trained by a learning algorithm derived from neural network theory was implemented for the removal of noise. A NF network for noise filtering in grayscale images that combines two NF filters with a postprocessor to produce the output was presented. However, Sugeno‐type is not intuitive technique and it also less accurate. To overcome these problems, a hybrid NF filter with optimized intelligent water drop (IWD) technique is introduced, where hybridized Sugeno–Mamdani‐based fuzzy interference system is implemented in both the NF filters to obtain more efficient noise removal system. To improve the accuracy of the assignment of membership values to each input pixels, the optimized IWD technique is utilized, as the choice of membership function decides the efficiency of the noise removal in the images. Here, Fuzzy rules have been used to obtain the filtered output. The Hybrid method maintains the accuracy of the Sugeno model and also the interpretable capability of the Mamdani model. This method is robust against the IN and it is flexible, efficient, and accurate than existing filtering method in both noise attenuation and detail preservation and it has a great scope for better real‐time applications.
Efficient Computation of Longest Common Subsequences with Multiple Substring Inclusive Constraints

In this article, we consider a generalized longest common subsequence (LCS) problem with multiple substring inclusive constraints. For the two input sequences X and Y of lengths n and m, and a set of d constraints of total length r, the problem is to find a common subsequence Z of X and Y including each of constraint string in P as a substring and the length of Z is maximized. A new dynamic programming solution to this problem is presented in this article. The correctness of the new algorithm is proved. The time complexity of our algorithm is . In the case of the number of constraint strings is fixed, our new algorithm for the generalized LCS problem with multiple substring inclusive constraints requires time and space.
Iterative Mirror Decomposition for Signal Representation

In this paper it is shown how to describe any finite-energy continuous or discrete signal through an ordered set of positions to uniquely represent it. This is obtained by designing an iterative decomposition through a series of mirror operations around those positions. The purpose is to find at any step of the decomposition the location that provides for the maximum decoupling between the even and odd components of the signal with respect to it. The algorithm can then be iterated at infinity determining a sequence of positions. The per location information determines the optimal energy decoupling strategy at each stage providing remarkable sparsity in the representation. Thanks to the sparsity of the resulting representation, experimental simulations demonstrate superior approximation capabilities of this proposed non-linear mirror transform.
A Novel Adaptable Approach for Sentiment Analysis

The internet has provided many novel ways for people to express their ideas and views about different topics, ideas and trends. The contents generated by the users which are present on various mediums like internet blogs, discussion forums, and groups paves a strong base for decision making in diverse fields such as digital advertising, election polls, scientific predictions, market surveys and business zones etc. Sentiment analysis is the process of mining the sentiments from the data that are available in online platforms and categorizing the opinion towards a particular entity that falls on three different categories which are positive, neutral and negative. In this paper, the problem of sentiment classification of election dataset in twitter has been addressed. This paper summarizes the ensemble method, the best way to achieve classification. And also about the ada boosting algorithm and artificial neural networks by which the optimized prediction accuracy is achieved.
Sky-CNN: A CNN-based Learning Approach for Skyline Scene Understanding

Skyline scenes are a scientific matter of interest for some geographers and urbanists.These scenes have not been well-handled in computer vision tasks.Understanding the context of a skyline scene could refer to approaches based on hand-crafted features combined with linear classifiers; which are somewhat side-lined in favor of the Convolutional Neural Networks based approaches.In this paper, we proposed a new CNN learning approach to categorize skyline scenes.The proposed model requires a pre-processing step enhancing the deep-learned features and the training time.To evaluate our suggested system; we constructed the SKYLINEScene database.This new DB contains 2000 images of urban and rural landscape scenes with a skyline view.In order to examine the performance of our Sky-CNN system, many fair comparisons were carried out using well-known CNN architectures and the SKYLINEScene DB for tests.Our approach shows it robustness in Skyline context understanding and outperforms the hand-crafted approaches based on global and local features.
Median Filtering for Removal of Maximum Impulse Noise from Images with a Decision Based Model

Filtering the unwanted parameters will guarantee the quality of images for further operations. Sensors and other devices used to capture the images may be subjected to situations where it might not function as programmed. The medium in which the images are transformed may also face the same difficulties. In such cases, salt and pepper noise should be removed before the images are taken into considerations. Otherwise, processes like edge detection, image segmentation and object recognition will be facing hurdles and may not reveal the desired output. This proposal intends to solve such an issue by using an algorithm for median filtering for using filters with medians on highly affected images. The previous techniques used for filtering out noise are Standard Median Filtering (SMF), Adaptive Median Filtering (AMF) and they have shown relatively lesser performance than the proposed approach. Implementation includes a FPGA set up and strives to remove impulse noise to a great extent consuming lesser computation time. The quality of output's visual and quantitative metrics has outperformed the discussed previous models.
WCCC 2018: The 24th World Computer Chess Championship

WCCC 2018, the 24th World Computer Chess Championship, continued the ICGA's longitudinal computer chess experiment begun in 1974. This event was held in Stockholm, featured eight chess engines and was won by KOMODO after a play-off against GRIDGINKGO.
Analysis of Primary Care Computerised Medical Records with Deep Learning.

The analysis of primary care data plays an important role in understanding health at an individual and population level. Currently the utilization of computerized medical records is low due to the complexities, heterogeneities and veracity associated with these data. We present a deep learning methodology that clusters 11,000 records in an unsupervised manner identifying non-linear patterns in the data. This provides a useful tool for visualization as well as identify features driving the formation of clusters. Further analysis reveal the features that differentiate sub-groups that can aid clinical decision making. Our results uncover subsets that contain the highest proportion of missing data, specifically Episode type, as well as the sources that provide the most complete data.