Reviewerly/exHarmony huggingface dataset:
Automatic cell segmentation using mini-u-net on fluorescence in situ hybridization images

Fluorescent in situ hybridization (FISH) is a molecular cytogenetic technique that provides reliable imaging biomarkers to diagnose cancer and genetic disorders in the cellular level. One prerequisite step to identify carcinoma cells in FISH images is to accurately segment cells, so as to quantify DNA/RNA signals within each cell. Manual cell segmentation is a tedious and time-consuming task, which demands automatic methods. However, automatic cell segmentation is hindered by low image contrast, weak cell boundaries, and cell touching in FISH images. In this paper, we develop a fast mini-U-Net method to address these challenges. Some special characteristics are tailored in the mini-U-Net, including connections between input images and their feature maps to accurately localize cells, mlpcon (multilayer perceptron + convolution) to segment cell regions, and morphology operators and the watershed algorithm to separate each individual cell. In comparison with the U-Net, the miniU-Net has fewer training parameters and less computational cost. The validation on 510 cells indicated that the Dice coefficients of the mini-U-Net and U-Net were 80.20% and 77.27%, and area overlap ratios were 69.17% and 68.04%, respectively. These promising results suggest that the mini-U-Net could generate accurate cell segmentation for fully automatic FISH image analysis.
Weight-Based Page Cache Management Scheme for Enhancing I/O Proportionality of Cgroups

Recently, many cloud systems, including home cloud servers, adopt Docker as their virtualization framework due to its lightweight architecture. Docker utilizes Cgroups to control the system resources. However, I/O weight of Cgroups does not consider the page cache layer, and thus, performance proportionality is degraded. In this paper, we propose a weight- based page cache management scheme that reflects I/O weight of Cgroups on the page reclamation process to improve I/O proportionality. Experimental results show that our scheme provides better proportionality than the conventional one with negligible overhead.
Sentiment Analysis on Automobile Brands Using Twitter Data

User generated contents in a very big number is freely available on different social media sites now a day. Companies to increase their competitive advantages keep an eye on their competing companies and closely analyze the data that are generated by their customers on their social media sites. Analysis of sentiments is the quickest growing field that utilizes text mining, computational linguistics and natural language processing, linguistic mining of text and calculation to extricate valuable data to assist in decision making. The automobiles business is extremely competing and needs that supplier, automobile corporations, carefully analyze and address the views of consumers with a specific end goal to accomplish an upper hand in the market. It is a great way to analyze the views of consumers through the data of social media sites; what's more, it is also helpful for automobiles companies to improve their goals and objectives of marketing. In this research, presents an analysis of sentiment on a case study of automobiles industry. Sentiment analysis and text mining are utilized to analyze and break down unstructured Twitter's tweets to take out automobile classes' polarity for example, Honda, Toyota, BMW, Audi, and Mercedes. According to the classification of the polarity, you notice that Audi has 87% of the positive tweets compared to 74% for BMW, 84% for Honda, 70% for Toyota and 81% for Mercedes. What's more, the results demonstrate that Audi has negative polarity 18% against 10% for BMW, 20% for Mercedes, 15% for Honda and 25% for Toyota.
Finding maximal 2-dimensional palindromes

This paper extends the problem of palindrome searching into a higher dimension, addressing two definitions of 2D palindromes. The first definition implies a square, while the second definition (also known as a centrosymmetric factor ) can be any rectangular shape. We present a linear time algorithm for locating all maximal square 2D palindromes in a given 2D text. For the second definition of palindromes ( rect2DP ), we present two different algorithms. Given a text of size n × n , the first algorithm has time O ( n 3 ) , which is linear in the worst case output size. The second algorithm has time O ( n 2 log ⁡ n + o c c log ⁡ n ) , where occ is the number of maximal rect2DP in the output. This provides a tradeoff in terms of output size; if the output size is small, the second algorithm is preferable, while the first would be more efficient if the output size is Θ ( n 3 ) .
Performance of Cancer Recurrence Algorithms After Coding Scheme Switch From International Classification of Diseases 9th Revision to International Classification of Diseases 10th Revision

We previously developed and validated informatic algorithms that used International Classification of Diseases 9th revision (ICD9)-based diagnostic and procedure codes to detect the presence and timing of cancer recurrence (the RECUR Algorithms). In 2015, ICD10 replaced ICD9 as the worldwide coding standard. To understand the impact of this transition, we evaluated the performance of the RECUR Algorithms after incorporating ICD10 codes.Using publicly available translation tables along with clinician and other expertise, we updated the algorithms to include ICD10 codes as additional input variables. We evaluated the performance of the algorithms using gold standard recurrence measures associated with a contemporary cohort of patients with stage I to III breast, colorectal, and lung (excluding IIIB) cancer and derived performance measures, including the area under the receiver operating curve, average absolute prediction error, and correct classification rate. These values were compared with the performance measures derived from the validation of the original algorithms.A total of 659 colorectal, 280 lung, and 2,053 breast cancer cases were identified. Area under the receiver operating curve derived from the updated algorithms was 89.0% (95% CI, 82.3% to 95.7%), 88.9% (95% CI, 79.3% to 98.2%), and 80.5% (95% CI, 72.8% to 88.2%) for the colorectal, lung, and breast cancer algorithms, respectively. Average absolute prediction errors for recurrence timing were 2.7 (SE, 11.3%), 2.4 (SE, 10.4%), and 5.6 months (SE, 21.8%), respectively, and timing estimates were within 6 months of actual recurrence for more than 80% of colorectal, more than 90% of lung, and more than 50% of breast cancer cases using the updated algorithm.Performance measures derived from the updated and original algorithms had overlapping confidence intervals, suggesting that the ICD9 to ICD10 transition did not affect the RECUR Algorithm performance.
Re-Keying Scheme Revisited: Security Model and Instantiations

The re-keying scheme is a variant of the symmetric encryption scheme where a sender (respectively, receiver) encrypts (respectively, decrypts) plaintext with a temporal session key derived from a master secret key and publicly-shared randomness. It is one of the system-level countermeasures against the side channel attacks (SCAs), which make attackers unable to collect enough power consumption traces for their analyses by updating the randomness (i.e., session key) frequently. In 2015, Dobraunig et al. proposed two kinds of re-keying schemes. The first one is a scheme without the beyond birthday security, which fixes the security vulnerability of the previous re-keying scheme of Medwed et al. Their second scheme is an abstract scheme with the beyond birthday security, which, as a black-box, consists of two functions; a re-keying function to generate a session key and a tweakable block cipher to encrypt plaintext. They assumed that the tweakable block cipher was ideal (namely, secure against the related key, chosen plaintext, and chosen ciphertext attacks) and proved the security of their scheme as a secure tweakable block cipher. In this paper, we revisit the re-keying scheme. The previous works did not discuss security in considering the SCA well. They just considered that the re-keying scheme was SCA resistant when the temporal session key was always refreshed with randomness. In this paper, we point out that such a discussion is insufficient by showing a concrete attack. We then introduce the definition of an SCA-resistant re-keying scheme, which captures the security against such an attack. We also give concrete schemes and discuss their security and applications.
Metric-based evaluation of fiducial markers for medical procedures

The accurate tracking of patients during a surgery is an essential requirement of computer assisted surgery. Many tracking systems are based on permanently installed infrared camera systems to detect reflective spheres. These tracking concepts need a certain amount of installation effort and are associated with high investments. An alternative are planar fiducial markers, which can be tracked only through RGB data and can therefore be used with different camera systems. The objective of this work is to introduce a set of similarity metrics to compare fiducial markers for pose estimation. We propose eight different similarity metrics to unify the process of evaluation and comparison of marker systems. These are the size and outer margin of the marker, the distance to the camera, the pose estimation accuracy, the runtime of the algorithm, the robustness against external influences, the affection to the sensor system and the number of used markers. We also describe the methodology for evaluating these metrics. We then apply these metrics to compare the ArUco and AprilTag open source marker systems. Our tests conclude that the optical tracking of open source fiducial markers is possible at submillimeter range at distances up to one meter. In addition, the tracking result can be greatly improved by using multiple markers. Accuracy is increased and fluctuations are minimized. The similarity metrics presented by us are suitable for evaluating and comparing marker systems in detail. This can serve as a basis for selecting a suitable system for a specific medical procedure.
Emotional Speech Synthesis for Multi-Speaker Emotional Dataset Using WaveNet Vocoder

This paper studies the methods for emotional speech synthesis using a neural vocoder. For a neural vocoder, WaveNet is used, which generates waveforms from mel spectrograms. We propose two networks, i.e., deep convolutional neural network (CNN)-based text-to-speech (TTS) system and emotional converter, and deep CNN architecture is designed as to utilize long-term context information. The first network estimates neutral mel spectrograms using linguistic features, and the second network converts neutral mel spectrograms to emotional mel spectrograms. Experimental results on a TTS system and emotional TTS system, showed that the proposed systems are indeed a promising approach.
Multi-Scale Aggregation Network for Direct Face Alignment

Face alignment has been extensively researched in computer vision while remaining a challenging task. Direct face alignment based on convolutional neural networks (CNN) without relying on cascaded regression has recently emerged and achieved promising performance. In this paper, we propose a multi-scale aggregation network (MAN) for direct face alignment by aggregating features from intermediate layers of a CNN. Specifically, MAN adopts a new convolutional architecture to aggregate features at all scales in different semantic levels, which establishes highly informative facial representations for accurate alignment. Moreover, we introduce the attention mechanism into the network, which drives it to focus on the spatial regions closely related to facial landmarks for further improved performance. Our MAN achieves a general end-to-end learning architecture for multi-scale feature aggregation, which, coupled with spatial attention mechanism, is well-suited for direct face alignment. Extensive experiments conducted on four benchmark datasets, including AFLW, 300W, CelebA and 300VW, show that MAN consistently produces high performance and surpasses several state-of-the-art methods in most cases.
ACE library of JENDL-4.0/HE

The Intra-Nuclear Cascade model employed into general-purpose Monte-Carlo simulation codes is not always applicable in the energy region from 20 MeV to several hundreds of MeV.In order to improve accuracy of the Monte-Carlo simulations including this energy region, the fourth version of Japanese Evaluated Nuclear Data Library High Energy File (JENDL-4.0/HE)as a new special purpose file, was released in 2015.It includes evaluated cross-sections for incident neutrons and protons up to 200 MeV for 130 and 133 nuclei, respectively.A Compact version of ENDF (ACE) library of all the neutron-and proton-induced reaction data for the MCNP, MCNPX and PHITS codes has been produced with the nuclear data processing code NJOY2016.9,which was modified to keep laboratory angle-energy distribution form (LAW=67) in the ACE library of proton because the original NJOY converts laboratory angle-energy distribution form (LAW=67) to continuum energy-angle distribution form (LAW=61) automatically and PHITS can only treat angle-energy distribution form (LAW=67) for proton.Validations for the ACE library were performed through benchmark calculations for high-energy shielding experiments with PHITS.
Group activity recognition with an interaction force based on low‐level features

Group activity recognition of humans is an important task for video surveillance systems. In complex situations, however, ambiguities usually arise as a result of chaotic movements in the scene, especially when it is solved based upon motion trajectory analysis involving object segmentation and tracking. In this work, we present an interaction force model (IFM) to recognize group activity using low‐level feature based on dense optical flow. The IFM used to define interaction among people consists of three principal forces: group motion force (GMF), interaction motion force (IMF), and reciprocal motion force (RMF), to model the behaviors of the activity. Our algorithm is evaluated using the well‐known NUS‐HGA and BEHAVE datasets. Results of our approach reveal good efficiency with high accuracy, regardless of the surrounding conditions, and is competitive with other state‐of‐the‐art methods. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.
Visualization concepts to improve spatial perception for instrument navigation in image-guided surgery

Image-guided surgery near anatomical or functional risk structures poses a challenging task for surgeons. To this end, surgical navigation systems that visualize the spatial relation between patient anatomy (represented by 3D images) and surgical instruments have been described. The provided 3D visualizations of these navigation systems are often complex and thus might increase the mental effort for surgeons. Therefore, an appropriate intraoperative visualization of spatial relations between surgical instruments and risk structures poses a pressing need. We propose three visualization methods to improve spatial perception in navigated surgery. A pointer ray encodes the distance between a tracked instrument tip and risk structures along the tool’s main axis. A side-looking radar visualizes the distance between the instrument tip and nearby structures by a ray rotating around the tool. Virtual lighthouses visualize the distances between the instrument tip and predefined anatomical landmarks as color-coded lights flashing between the instrument tip and the landmarks. Our methods aim to encode distance information with low visual complexity. To evaluate our concepts’ usefulness, we conducted a user study with 16 participants. During the study, the participants were asked to insert a pointer tool into a virtual target inside a phantom without touching nearby risk structures or boundaries. Results showed that our concepts were perceived as useful and suitable to improve distance assessment and spatial awareness of risk structures and surgical instruments. Participants were able to safely maneuver the instrument while our navigation cues increased participant confidence of successful avoidance of risk structures.
Application of Template Matching for Optical Character Recognition

Optical character recognition (OCR) is one of the common research problems in the computer vision which is used for industrial processes automation. In this work the algorithm of OCR based on template matching recognition is presented. Algorithm is adopted for character recognition with consideration of low contrast conditions and texture on the background. The main feature of this method is interest area detection and adaptive binarization before pattern matching. The proposed method does not require large set of training samples, which may be critical in some applications. Also algorithm is easy to implement and has low complexity. Testing was conducted on custom dataset under the various conditions of illumination and pattern readability. The quality of the algorithm was estimated using standard binary classifier metrics and the distribution of correlation function in comparison with the template.
Efficient Parallel Binary Operations on Homomorphic Encrypted Real Numbers

A number of homomorphic encryption application areas could be better enabled if there existed a general solution for combining sufficiently expressive logical and numerical circuit primitives. This paper examines accelerating binary operations on real numbers suitable for somewhat homomorphic encryption. A parallel solution based on Single Instruction Multiple Data (SIMD) can be used to efficiently perform combined addition, subtraction and comparison-based operations on packed binary operands in a single step. The result maximises computational efficiency, memory space usage and minimises multiplicative circuit depth. General application and performance of these accelerated binary primitives are demonstrated in a number of case studies, including min-max and sorting operations.
Multi-Party Mixed Reality Interaction for Earth Sciences Education

Collaborative learning has been shown to be beneficial for children's learning performance, increasing the curiosity and intensity of the ability of cooperation. Mixed-Reality with collaborative learning is the trending research topic in the Human-Computer Interaction (HCI) area. Additionally, with the rise of attention to global warming which brings in more extreme weather and climate conditions, the earth science education would be one of the crucial topics for the next generation. Moreover, there are few augmented reality and mixed reality applications on earth science subject. In this paper, we propose a Mixed Reality Tornado Simulator which offers an earth science education in a collaborative setting. Students and the instructor can cooperate on learning the knowledge of the formation and its damage cause on human-built structures, farming, and vegetation by using our mixed reality application with the Microsoft HoloLens. Also, for evaluating the learning performance in this mixed reality setting, we propose to study the cognitive load while the student is learning the abstract knowledge in Earth Science. We will separate the student into a control group and experimental groups and use different teaching instruments to test the difference of cognitive load.
Sparse Associative Memory

It is still unknown how associative biological memories operate. Hopfield networks are popular models of associative memory, but they suffer from spurious memories and low efficiency. Here, we present a new model of an associative memory that overcomes these deficiencies. We call this model sparse associative memory (SAM) because it is based on sparse projections from neural patterns to pattern-specific neurons. These sparse projections have been shown to be sufficient to uniquely encode a neural pattern. Based on this principle, we investigate theoretically and in simulation our SAM model, which turns out to have high memory efficiency and a vanishingly small probability of spurious memories. This model may serve as a basic building block of brain functions involving associative memory.
Introducing a Traceability Based Recommendation Approach Using Chatbot for E-Learning Platforms

For many years, the use of recommender systems has increased and reached a new level recently. These systems are widely used in different areas, as they tend to offer and provide customers with the most suitable products or items that can satisfy their needs to the best. Especially in e-Learning environments, the purpose of such systems is to help students improve their learning by suggesting relevant resources and increase their knowledge through a selection of tips, hints, and tutorials. The process of recommendation considers the user’s profile which is a set of preferences and interests and is based on various techniques, often combined to provide improved results. However, due to the huge amount of data, the selection process becomes more and more difficult to handle and the same items get proposed repeatedly. Traceability, on the other hand, can either mean the history or the composition of something. It is a collection of actors, actions, objects and most importantly, time. In this paper, we introduce the use and the benefits of traceability and discuss how it can enhance recommendations. We also propose a traceability based approach for recommendations in e-Learning platforms, by means of a chatbot.
Natural and Intuitive Deformation Gestures for One-handed Landscape Mode Interaction

The landscape orientation of smartphone offers a better aspect ratio and extensive view for watching media and photography. However, it presents challenges of occlusion, reachability, and frequent re-gripping in one-handed interactions. To address these issues we took the opportunity of deformation gestures to interact with future flexible smartphones. A preliminary survey was conducted to understand one-handed landscape mode usage patterns. Then, the 1st study was conducted to identify 3 most preferred one-handed landscape mode grips. In the 2nd study, we gathered unique user-defined deformation gestures to identify the set of most natural and intuitive gestures corresponding to each grip. We also found 3 gestures that can be performed in more than one grip. Finally, we discuss the influence of the grips on performing gestures.
An Efficient Model of Text Categorization Based on Feature Selection and Random Forests: Case for Business Documents

Huge amount of information on the internet is saved as text documents that are not classified, therefore the remaining issue is the recognition and classification of these text documents into the corresponding categories. Such text categorization task requires the use of feature selection techniques in order to reduce dimensionality which could enhance the models selectivity, accuracy and robustness. The present paper proposes a text categorization model based on feature selection using genetic algorithm and random forest method. The proposed approach has been tested on a publicly available dataset of business documents and the results show a classification accuracy of about 98.7% which outperforms other techniques tested on the same dataset.