Reviewerly/exHarmony huggingface dataset:
Colour to Grey by Maximum Signed Gradient

While the magnitude of colour gradients is well defined, its direction is not. The ambiguity in the direction is due to our inability to decide if red colour is greater than green in the same way we can say that white is brighter than grey. In this paper, we present a method to calculate the maximum signed gradient and integrate it to a grey image. Experiments on natural photos show that seventy per cent of edges are due to luminance change, i.e., all the channels change in the same direction. The other gradients are due to material change such as yellow to blue or green to orange. The algorithm can be used to decompose images into luminance and chrominance. It produces noticeably better local contrast than normal luminance conversion.
Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection

The common practice in coreference resolution is to identify and evaluate the maximum span of mentions.The use of maximum spans tangles coreference evaluation with the challenges of mention boundary detection like prepositional phrase attachment.To address this problem, minimum spans are manually annotated in smaller corpora.However, this additional annotation is costly and therefore, this solution does not scale to large corpora.In this paper, we propose the MINA algorithm for automatically extracting minimum spans to benefit from minimum span evaluation in all corpora.We show that the extracted minimum spans by MINA are consistent with those that are manually annotated by experts.Our experiments show that using minimum spans is in particular important in cross-dataset coreference evaluation, in which detected mention boundaries are noisier due to domain shift.We have integrated MINA into https://github.com/ns-moosavi/coval for reporting standard coreference scores based on both maximum and automatically detected minimum spans.
A FAIR Principle Data Model for Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) and the Frederick National Laboratory Data Coordinating Center

Journal Article A FAIR Principle Data Model for Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) and the Frederick National Laboratory Data Coordinating Center Get access Paul Aiyetan, Paul Aiyetan Frederick National Laboratory for Cancer Research, Frederick, MD, USAGeorge Mason University School of Systems Biology, Manassas, VA, USA Corresponding author: paul.aiyetan@nih.gov Search for other works by this author on: Oxford Academic Google Scholar Kedar Narayan, Kedar Narayan Frederick National Laboratory for Cancer Research, Frederick, MD, USACenter for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, USA Search for other works by this author on: Oxford Academic Google Scholar David Mott, David Mott Frederick National Laboratory for Cancer Research, Frederick, MD, USA Search for other works by this author on: Oxford Academic Google Scholar Rajani Kuchipudi, Rajani Kuchipudi Frederick National Laboratory for Cancer Research, Frederick, MD, USA Search for other works by this author on: Oxford Academic Google Scholar Corinne Zeitler, Corinne Zeitler Frederick National Laboratory for Cancer Research, Frederick, MD, USA Search for other works by this author on: Oxford Academic Google Scholar Debra Hope, Debra Hope Frederick National Laboratory for Cancer Research, Frederick, MD, USA Search for other works by this author on: Oxford Academic Google Scholar Uma Mudunuri, Uma Mudunuri Frederick National Laboratory for Cancer Research, Frederick, MD, USA Search for other works by this author on: Oxford Academic Google Scholar Andrew Quong Andrew Quong Frederick National Laboratory for Cancer Research, Frederick, MD, USA Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 25, Issue S2, 1 August 2019, Pages 1368–1369, https://doi.org/10.1017/S1431927619007578 Published: 01 August 2019
Classical Machine Learning Algorithms and Shallower Convolutional Neural Networks Towards Computationally Efficient and Accurate Classification of Malaria Parasites

Malaria and its diagnosis methods need significant attention in Ethiopia. Studies show that for 46 laboratory professionals who were given 6 different positive and negative malaria slides, the detection error rates for plasmodium falciparum and vivax were 43.5% and 37%, respectively. Another similar study reports that the overall malaria diagnosis error rate is 40.4%. To circumvent these challenges, there needs to be a system that automatically and instantly analyzes and manipulates data with less bias so as to reduce misdiagnosis error, cost, workload of physicians and experts, and thereby improving the livelihood of the society. In this study, classical machine learning algorithms and shallow Convolutional Neural Networks (CNN) based classification for parasitized and uninfected malaria images are applied. While classical machine learning algorithms are requiring a feature design step for classification, CNNs learn to recognize objects end to end, implicitly without the need for feature engineering. Many machine learning algorithms such as K-Nearest Neighbors, Random Forest, Gradient Boosting, CNNs etc. are compared in this contribution. From our experimental observation, some of the classical models such as Gradient Boost, Random Forest and Stacked ensemble using color, texture and shape features achieved a classification accuracy of 96%, precision of 97%, recall of 97% and f1-score of 97% for the parasitized samples. A model ensemble of two custom CNNs and mini-VGGNet achieved a precision of 99%, recall of 96% and f1-score of 97% for the parasitized. These results of the CNN models are reproducible for each of them. All the approaches have comparable results, however, the classical machine learning models are resource efficient.
A Concept for Physiological User Description in the Context of Dual User Integration

Abstract In order to ensure the user's acceptance towards a product, the user has to be captured with all his facets and requirements. In this context, many user-centred design methods only focus on single aspects such as subjective expectation or ergonomic product design. Correlations and connections or a common consideration of several user parameters are often neglected, even if this can provide useful information for improving the design of products. Dual user integration tries to close this gap to a certain extent and considers the user's subjective expectation in combination with their physiological capacities. An integral part of this approach is a target-oriented evaluation of the user. Currently available methods of physiological and subjective evaluation of the user are only partially applicable for dual user integration. Especially physiological measurement techniques are time-consuming and expensive. For this reason, this contribution presents a new concept for capturing and describing the physiological capacity of the user via semantic differentials. Thereby, motor functions, cognition and perception are considered.
Reduced Complexity Architecture for Normalization of Histogram of Oriented Gradients

Histogram of oriented gradients (HOG) is an important tool in the process of object detection in many image processing applications. Normalization of the histogram data within the process, requires implementation of division and square root operations which have high hardware complexity. In this work a new approach for normalization process of the histogram is presented. The proposed method reduces the hardware complexity, the areas and the number of required modules.
Character Image Synthesis Based on Selected Content and Referenced Style Embedding

Arbitrary characters synthesis based on a few referenced examples poses a great challenge due to the diversity of characters category and style. We regard this problem as image translation problem and propose a character style transfer network consisting of content selector, style encoder, content encoder, feature embedding and embedded feature decoder to solve it. The content selector is used to select and match the most similar content (i.e., font) from our collected glyph dataset as content references. Then, we apply the style encoder and content encoder to extract the style and content representation separately and mix them for feature embedding. Finally, the embedded features are decoded to generate the target characters. We train them in an end-to-end manner and evaluate the proposed method on MC-GAN dataset and our collected dataset. The experimental results have demonstrated the effectiveness of the proposed model for character synthesis.
Mobile Robot Autonomous Navigation and Dynamic Environmental Adaptation in Large-Scale Outdoor Scenes

In this paper, the problem of dynamic obstacle recognition and dynamic obstacle avoidance path planning for mobile robots in outdoor environment is studied. Based on the odometer data and the online matching algorithm of 3D laser scanning point clouds, the topological map and the global path planning are realized in this paper firstly. Based on the analysis of the geometric characteristics of obstacles, a novel approach of dynamic obstacle recognition method is presented. At the same time, a dynamic obstacle avoidance method based on the obstacle motion prediction is adopted to solve the reliable obstacle avoidance path planning problem of outdoor mobile robot. A series of experiments are conducted with a self-designed mobile robot platform in large-scale outdoor environments, and the experimental results show the validity and effectiveness of the proposed approach.
Software Testing and Lines of Codes-A Study on Software Engineering Design Patterns

There has been n number of software development designs and test patterns in software engineering. Frameworks are available on methods to calculate the time, effort and human power required to develop a software. Our work is a novel study on the basics of how proportional is the time required to test a software based on the LOCs (Lines of Codes) and the design used. The work aims to interlink the understanding between two kinds of structural testing. This basic study experiment was conducted using 20 different programs. We argue and prove that there is an interconnection between the design, Lines of Code (LOC) and testing.
BFPA: Butterfly Strategy Flower Pollination Algorithm

Aiming at the shortcomings of slow convergence and low precision of flower pollination algorithm, a flower pollination algorithm based on butterfly pollination strategy (BFPA) was proposed. The algorithm first uses the butterfly pollination strategy to accelerate the convergence speed of the global search phase. Second, in the local search phase, the beetle antenna search help algorithm is used to jump out of the local optimum. The experiment uses five Benchmark test functions to test, and the results show that the BFPA algorithm has better performance than other versions of the pollination algorithm.
Estimation of Computational Complexity of Combinatorial-Genetic Algorithm COMBI-GA

The article presents a description of the combinatorial-genetic algorithm COMBI-GA as well as results of theoretical estimation of its computational complexity and numerical comparison of its effectiveness with known algorithms COMBI, MULTI and LASSO in test tasks.
On the asymmetry between names and count nouns: syntactic arguments against predicativism

The standard versions of predicativism are committed to the following two theses: (1) proper names are count nouns in all their (literal) occurrences (the syntactic aspect of predicativism), and (2) names do not refer to objects but express (non-relational) name-bearing properties (the semantic aspect of predicativism). The main motivation for predicativism is to provide a uniform explanation of referential names and predicative names. According to predicativism, predicative names are fundamental and referential names are explained by appealing to a null determiner functioning like “the” or “that.” This paper has two goals. The first is to reject the predicativists’ explanation of the two types of names. I present three syntactic counterexamples to the predicativists’ account of referential names: incorporation, modification, and measure phrase uses. The second goal is to present a novel strategy to explain the two types of names. I propose that referential names are fundamental but that there are null morphemes available for transforming a name into a count noun (and possibly into other syntactic categories).
Offline Handwritten Script Recognition Based on Texture Descriptors

The recognition of different script systems is a challenging task in the context of document image analysis. In this paper, we develop and evaluate an approach to the script recognition problem using writing as texture information to represent a manuscript class. Distinct descriptors are tested and analyzed. As main contributions, we proposed the use of a texture compression scheme, a partitioning of the document into blocks, as well as their further combination. According to our experiments, high accuracy rates are achieved for the script recognition problem when compared to other approaches available in the literature.
Implementation and Comparison of SPA and DPA Countermeasures for Elliptic Curve Point Multiplication

The core operation of all cryptosystems based on Elliptic Curve Cryptography is Elliptic Curve Point Multiplication. Depending on implementation it can be vulnerable to different Side Channel Analysis attacks exploiting information leakage, such as power consumption or execution time. Multiple countermeasures against these attacks have been developed over time, each having different impact on parameters of the cryptosystem. This paper summarizes popular countermeasures for simple and differential power analysis attacks on Elliptic Curve cryptosystems. Presented secure algorithms were implemented in Verilog hardware description language and synthesized to logic gates for power trace generation.
Methods and Special Processors of Entropy Signal Processing

The analysis of applied tasks and methods of entropy signal processing are carried out in this article. The theoretical comments about the specific schemes of special processors for the determination of probability and correlation activity are given. The perspective of the influence of probabilistic entropy of C. Shannon as cipher signal receivers is reviewed. Examples of entropy-manipulated signals and system characteristics of the proposed special processors are given.
An Improved Code-Based Encryption Scheme with a New Construction of Public Key

There are substantial variants of McEliece cryptosystem from coding theory. In general, these attempts always utilize two basic ways to enhance performance of the original cryptosystem: reducing the public key size and improving the efficiency and ability of decoding algorithms. Meanwhile, security level is the goal that has always been pursued. Sometimes we need to sacrifice some advantage to get other claims. Recently, Baldi et al. proposed a novel scheme (BBCRS scheme for short) in which the public code is no longer permutation equivalent to the secret code and this distinctly increases the security level. In this paper, we propose a new construction of public key following the idea of BBCRS scheme. The improvement enhances the correction capability and results in a better protection of the secret code. We also discuss some known attacks in detail as in BBCRS scheme which shows that our new scheme is secure under the current means of attack.
A Pruned-CELP Speech Codec Using Denoising Autoencoder with Spectral Compensation for Quality and Intelligibility Enhancement

A codec based on the excited linear prediction (CELP) speech compression method adopting a denoising autoencoder with spectral compensation (DAE-SC) for quality and intelligibility enhancement is proposed in this paper. The sizes of CELP parameters in the encoder are carefully pruned to achieve a higher compression rate. To recover the speech quality and intelligibility degradation due to the pruned CELP parameters, a DAE-SC network with three hidden layers is employed in the decoder. Compared with the conventional CELP codec at a 9.6k bps transmission rate, the proposed speech codec achieves extra 21.9% bit rate reduction with comparable speech quality and intelligibility that are evaluated by four commonly used speech performance metrics.
Using Gamification and Fear Appeal Instead of Password Strength Meters to Increase Password Entropy

Abstract It is very common for users to create weak passwords. Currently, the majority of websites deploy password strength meters to provide timely feedback. These meters are in wide use and their effects on the security of passwords have been relatively well studied. In this paper another type of feedback is studied: a gamified approach supported by fear appeal. In this approach, users are encouraged to make passwords stronger through the use of visual and textual stories. This approach is supported by data-driven suggestions about how to improve password security as well as by fear appeal. To prove the effectiveness of this gamified password creation process, an experiment was performed in which users changed their passwords in two ways: without any feed-back, and with gamified feedback with fear appeal. To support the initial findings a questionnaire was completed by participants at the end of research.
Feature Extraction from Smartphone Images by Using Elliptical Fourier Descriptor, Centroid and Area for Recognizing Indonesian Sign Language SIBI (Sistem Isyarat Bahasa Indonesia)

Sistem Isyarat Bahasa Indonesia (SIBI) is the official sign language in Indonesia. This research aims to create a translator for SIBI to be installed on a smartphone. The translator will analyze gestures for inflectional words, which are root words combined with prefixes, and/or suffixes. The feature extraction method that was used in this research is Elliptical Fourier Descriptor (EFD), additionally centroid and area data were added to retain information on hand orientation, position and shape. The extracted features will be fed into, a Long Short-Term Memory (LSTM) model which will then recognize gestures into text. The method used in this research produced 99% accuracy for root word gestures, 71% accuracy for prefix gestures, 86% accuracy for suffix gestures.
Specular Surface Measurement with Laser Plane Constraint to Reduce Erroneous Points

For the purpose of reducing erroneous points generated by specular reflections, multiple line structured-light stripes extraction algorithm and laser plane constraint method are proposed for line structu-red-light scanners. At first, all laser stripes in images are extracted with multiple line structured-light stripes extraction algorithm. Then, the three dimensional (3D) points within which include the erroneous data caused by specular reflections are computed by optical-triangulation. Finally, false measurements generated by specular reflections are eliminated with the laser plane constraint method. The effectiveness of the method is verified by eliminating erroneous points in the experiment. The percentage of erroneous points before and after processing is counted to evaluate the performance of the laser plane constraint. The experimental results show that the percentage of erroneous points is 12.61% before processing and is 3.05% after processing, which indicate that the method can effectively eliminate erroneous points with good feasibility.
A Comparative Study of Similarity and Centrality Measures for Friends Recommendation

Nowadays Online Social Networks have gained the most popularity among different communication platforms, and an extensive applications variety is measured for them. Since with the increase in the popularity of Online Social Network, friendship is one of the essential connections amongst members of a social network. Matching of friends is a practical technique to recommend people to each other. In this paper centrality measures are used in Friends of Friends network and similarity measures in group and events network to recommend friend. The Facebook 2010 dataset was used which include the data of friends, groups and events of different users. Friends are recommended based on two different techniques. The first technique is based on friend of friends where friends are recommended based on mutual friends and centrality measure. The second technique used similarity measures to recommend the friend. Lastly, the comparative analysis of both the techniques was done by evaluating accuracies of each technique. The accuracy was calculating by comparing 2010 dataset with the 2018 dataset.
Adaptive multi-group fruit fly optimization algorithm

Aiming at the defects that the basic fruit fly optimization algorithm has low control precision and is easy to fall into local optimum, an adaptive multi-group fruit fly optimization algorithm is proposed. Due to the constant step size, the basic fruit fly algorithm has a lack of convergence efficiency and optimization precision. For this problem, the radius adjustment coefficient is introduced in the search process, so that the search radius decreases with the increase of iterations. In order to avoid the premature phenomenon caused by the lack of population diversity in the search process, the degree of utilization of the whole information during the evolution of the population is improved by simultaneously learning the local optimal individual and the global optimal individual of the subpopulation. At the same time, adding individual variation mechanism to further increase the diversity of the population makes the algorithm jump out of the local optimal solution. The simulation results show that the proposed algorithm has better performance in terms of convergence efficiency and optimization accuracy.
A Lightweight Neural Network Based Human Depth Recovery Method

Human depth recovery is an essential task for tele-immersive video interaction systems with limited bandwidth consumption. This paper is motivated by the first human depth recovery method, i.e., the weighted large margin nearest center (WLMNC) distance based method, which compresses each human depth map into several skeletal block structures by learning a WLMNC distance at the remote stage and then recovers it by a rough-to-fine approach at the local stage. Since the WLMNC distance is equivalent to employing a linear transformation on the human pixels, for human postures with complex self-occlusion, the depth recovery performance of WLMNC is limited. To address the problem, this paper proposes to learn a nonlinear WLMNC distance via a lightweight neural network. Different from traditional classification or clustering problems, the neural network is introduced for storing skeletal block structure information instead of predicting new information. The quantitative experimental results on the VGA-sized benchmark dataset demonstrates that such a slight modification over WLMNC can achieve an inspiring performance improvement: the average depth recovery MAD error is reduced from 3.56cm to 2.77cm and the total running cost is reduced from about 150 seconds to 43 seconds on a laptop with just a little loss on the sampling rate, from 64x to 51x.