Reviewerly/exHarmony huggingface dataset:
EFFICACY OF CIELAB AND CMYK COLOR SPACES IN LEUKEMIA IMAGE ANALYSIS: A COMPARISON BY STATISTICAL TECHNIQUES

There are several color models available and which color model to be used is a question to many researchers in the field of Medical Image Processing and Analysis. In this paper, the suitability of using the CIELAB and CMYK color spaces in processing and analyzing blood smear images of leukemia is assessed for comparison. Leukemia is commonly known as blood cancer and it usually leads to an abnormal proliferation of leukocytes (White Blood Cells) in the bone marrow and blood. The diagnosis of leukemia is primarily done by Pathologists by microscopically examining the blood and bone marrow smears of the suspected patient. Image processing-based methods can be used for automated detection and classification of leukemia to assist the Pathologists for a speedy diagnosis. The proposed study used [Formula: see text]-means clustering to segment the leukocytes and leukemic blast cells. The microscopic smear images in RGB color format were transformed and processed in CIELAB as well as CMYK color spaces for comparison. Statistical analysis was performed using Student’s [Formula: see text] test. The visual observation of the segmented images revealed that, processing the images in the CIELAB color space is comparatively better than the CMYK color space and it is reflected in the results of statistical comparison by Student’s [Formula: see text] test. The study assessed the suitability of using the CIELAB and CMYK color spaces in segmentation and analysis of microscopic smear images of Leukemic and Normal cases and found that processing the images in CIELAB color space is comparatively better than CMYK and such comparative analysis is not available in the literature.
Harmony Loss for Unbalanced Prediction

In medical image analysis, in order to reduce the impact of unbalanced data sets on data-driven deep learning models, according to the characteristic that the area under the Precision-Recall curve (AUCPR) is sensitive to each category of samples, a novel Harmony loss function with fast convergence speed and high stability was constructed. Since AUCPR needs to be calculated in discrete domain, in order to ensure the continuous differentiability and gradient existence of the Harmony loss, first, the Logistic function was used to approximate the Logical function in AUCPR. Then, to improve the optimization speed of the Harmony loss during model training, a method of manually setting a certain number of classification thresholds was proposed to further approximate the calculation of AUCPR. After the above two approximate calculation processes, the Harmony loss with stable gradient and high computational efficiency was designed. In the optimization process of the model, since Harmony loss can reconcile recall and precision of each category under different classification thresholds, thereby, it can not only improve the model's ability to recognize categories with less samples, but also maintain the stability of the training curve. To comprehensively evaluate the effects of Harmony loss function, we performed experiments on image 3D reconstruction, 2D segmentation, and unbalanced classification tasks. Experimental results showed that the Harmony loss achieved the state-of-the-art results on four unbalanced data sets. Moreover, the Harmony loss can be easily combined with existing loss functions, and is suitable for most common deep learning models.
Nudging Students Toward Better Software Engineering Behaviors

Student experiences in large undergraduate Computer Science courses are increasingly impacted by automated systems. Bots, or agents of software automation, are useful for efficiently grading and generating feedback. Current efforts at automation in CS education focus on supporting instructional tasks, but do not address student struggles due to poor behaviors, such as procrastination. In this paper, we explore using bots to improve the software engineering behaviors of students using developer recommendation choice architectures, a framework incorporating behavioral science concepts in recommendations to improve the actions of programmers. We implemented this framework in class-bot, a novel system designed to nudge students to make better choices while working on programming assignments. This work presents a preliminary evaluation integrating this tool in an introductory programming course. Our results show that class-bot is beneficial for improving student development behaviors increasing code quality and productivity.
A Layer Image Auditing System Secured by Blockchain

In Additive Manufacturing (AM), auditing layer-by-layer images can detect infill defective attacks effectively. However, the auditing process itself can become a target of inside or outside attackers in Cyber-Physical Manufacturing System (CPMS) environments because pervasive connection through various types of computer networks in CPMS opens new doors for adversaries to compromise various components in an attack detection system. To maintain an effective attack detection system and prevent data from malicious data injection, this paper presents a Layer Image Auditing System (LIAS) secured by the Blockchain technology in CPMS. LIAS consists of a pre-processing system and a Multilayer Perceptron Neural Network (MLP). To evaluate the prediction accuracy of LIAS, a set of simulated infill images and physical images were used for training and testing. The effectiveness of the Blockchain implementation is demonstrated by presenting the comparative performance analysis of the proposed attack detection system with and without the Blockchain.
SDSBT: A Secure Multi-party Data Sharing Platform Based on Blockchain and TEE

With the rise of big data analytics and artificial intelligence, an increasing number of enterprises and individuals are concerned about the security and privacy of the shared data. However, it is still challenging to achieve a data sharing scheme, which meets the security, privacy, security, and credibility requirements. This paper proposes SDSBT, a multi-party data sharing platform based on blockchain and the trusted execution environment (TEE), which effectively and securely realizes the data sharing among multiple parties. SDSBT achieves the properties including privacy-preserving, identity authentication, application security, and accountability. The security analysis and experimental results show that the scheme is secure and practical.
Design and Implementation of Chaotic Frequency Hopping Sequences based on One Dimensional Chaotic Maps utilizing FPGA

In this paper, a frequency hopping sequence (FHS) generator using one-dimensional chaotic map (1DCM) is implemented using FPGA supported by Xilinx system generator (XSG). The 1DCM methods in this paper are Logistic, Improved Logistic, Tent, Borujeni, Bernoulli-Shift, and Zigzag map. Each 1DCM is discretized using fixed-point operation and the least significant bits from some offset indices are selected and considered as frequency hopping sequence. FPGA SP605 XC6SLX45T-3FGG484 evaluation board is used to test the FHS using the 1DCM system. The chi-squared, Hamming correlation, and complexity analysis are used to test the randomness characteristic of the FHSs generated by 1DCM. Furthermore, the resource utilization of the codes with maximum frequency and throughput are summarized. The results show that FHS-Bernoulli-Shift has the lowest DSP48A1s and peak memory comparing to other maps. In addition, the results show that the FHS using 1DCM is superior compared to the conventional codes with good randomness analysis.
Separation of interreflections based on parallel single-pixel imaging

Interreflections introduced by points in a scene are not only illuminated by the light source used but also by other points in the scene. Interreflections cause inaccuracy and the failure of 3D recovery and optical measurements. In this research, a novel method for separating interreflections through parallel single-pixel imaging (PSI) is proposed, which can decompose interreflections into 1st bounce light, 2nd bounce light, and a higher order light component. PSI is used in obtaining the light transport coefficients of each camera pixel, and light transport coefficients are used in decomposing the intensity distribution of a projector and the component of interreflections. Results show that the proposed method can separate the interreflections of a real static scene in a concave surface.
ATLAS: Automated Amortised Complexity Analysis of Self-adjusting Data Structures

Abstract Being able to argue about the performance of self-adjusting data structures such as splay trees has been a main objective, when Sleator and Tarjan introduced the notion of amortised complexity. Analysing these data structures requires sophisticated potential functions, which typically contain logarithmic expressions. Possibly for these reasons, and despite the recent progress in automated resource analysis, they have so far eluded automation. In this paper, we report on the first fully-automated amortised complexity analysis of self-adjusting data structures. Following earlier work, our analysis is based on potential function templates with unknown coefficients. We make the following contributions: 1) We encode the search for concrete potential function coefficients as an optimisation problem over a suitable constraint system. Our target function steers the search towards coefficients that minimise the inferred amortised complexity. 2) Automation is achieved by using a linear constraint system in conjunction with suitable lemmata schemes that encapsulate the required non-linear facts about the logarithm. We discuss our choices that achieve a scalable analysis. 3) We present our tool $$\mathsf {ATLAS}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ATLAS</mml:mi></mml:math> and report on experimental results for splay trees , splay heaps and pairing heaps . We completely automatically infer complexity estimates that match previous results (obtained by sophisticated pen-and-paper proofs), and in some cases even infer better complexity estimates than previously published.
Performance Analysis of Evolutionary Computation Based on Tianchi Service Scheduling Problem

We choose the well-known evolution strategy (ES) in the evolutionary computation (EC) community to solve the large-scale scheduling problem provided by Alibaba cloud services. Since the problem is accompanied by multiple strong constraints, we design two additional strategies for improving the search efficiency with a given limited computational cost. Compared with widely used numerical benchmark test suits, this problem arises from the requirements of real-world applications and has strict constraints that cannot be violated, such as processing time, response timeout, load balance, and so on. The main contribution of this paper is to establish a bridge between EC algorithms and the characteristics of real-world problems so that EC algorithms can solve real-world problems more effectively and smoothly. Based on the difficulties encountered in the experiment, we summarize some of our experiences and insights, and hope that they may bring new enlightenment to the latecomers.
Understanding the information security culture of organizations: Results of a Survey

A strong information security culture in organizations contributes to reduce incidents related to leaks of sensitive and private information. Considering that one of the main factors that cause such leaks is human action, it is necessary to evaluate the current state of organizations’ culture. This work aims to identify methods for assessing the culture of information security in organizations and to characterize the current state of this topic. We conducted a survey using an evaluation instrument proposed in the literature that includes dimensions to assess the information security culture. The survey received 75 responses, mostly from employees of private institutions. We observed that there is a need for training of employees on information security, and there is incongruity between knowing, understanding and applying the procedures described in the information security policy. This work provided an understanding of the current status of the information security culture in organizations whose results can be expanded and used in future studies to improve security practices in organizations.
A Combined Approach For Collaborative Filtering Based Recommender Systems with Matrix Factorisation and Outlier Detection

Recommender system is a data sifting tool that can recommend items that can be of interest to the user. Collaborative filtering (CF) makes recommendations based on the ratings the users give to items. But noisy or inaccurate ratings reduce the quality of the recommendations. In spite of extensive studies carried on CF-based recommenders, a robust recommender to handle outlier in dataset is a challenging problem. In this study, a Factor wise Matrix Factorisation model (FWMF) is proposed for the prediction of item rating in recommender systems. To further strengthen the proposed FWMF model, a meta learning model that combines density-based outlier detection and bagging outlier detection is proposed to detect outliers. The outliers predicted are eliminated, and a comparative analysis is carried with FWMF to find the effect of outliers in making recommendations. The experiments were analysed with various error metrics conducted on benchmark dataset show that the proposed outlier extent recommendation model outperforms the conventional CF-based systems.
Tutorial Intervention’s Affective Model Based on Learner’s Error Identification in Intelligent Tutoring Systems

Intelligent Tutoring Systems (ITS) environments have the ability to adapt to each learner’s individual needs and thus provide immediate and personalized instructions, both in content and in form. This personalization can consider several aspects, such as the interaction, the level of knowledge, the error, and the affective state of the learner aiming to improve the teaching strategies. One of the strategies is the possibility of presenting tutorial interventions when verifying an error made in solving an exercise, or when detecting that the learner is unmotivated or frustrated. These tutorial interventions can improve teaching methods in order to improve performance and the level of knowledge acquired by the learner. In this sense, this research presents a model that allows the automatic presentation of tutoring interventions based on identification of mathematical error kind committed by the learner, in addition to inferring his affective state. Experiments were carried out in a real learning environment, using the proposed model implemented in a fraction game. In general, the results presented indicate that personalized tutorial interventions favor greater engagement and motivation of learners and improvement in learning outcomes.
Corrective Filter Based on Kinematics of Human Hand for Pose Estimation

Depth-based 3D hand trackers are expected to estimate highly accurate poses of the human hand given the image. One of the critical problems in tracking the hand pose is the generation of realistic predictions. This paper proposes a novel “anatomical filter” that accepts a hand pose from a hand tracker and generates the closest possible pose within the real human hand’s anatomical bounds. The filter works by calculating the 26-DoF vector representing the joint angles and correcting those angles based on the real human hand’s biomechanical limitations. The proposed filter can be plugged into any hand tracker to enhance its performance. The filter has been tested on two state-of-the-art 3D hand trackers. The empirical observations show that our proposed filter improves the hand pose’s anatomical correctness and allows a smooth trade-off with pose error. The filter achieves the lowest prediction error when used with state-of-the-art trackers at 10% correction.
CNNA: A study of Convolutional Neural Networks with Attention

Point-of-Interest (POI) recommendation systems can give the user a list of locations that the user may be interested in, but the recent check-ins may contain some daily check-ins that users are not really interested in. To address the issues of mining users’ actual preferences from users’ recent check-ins, this paper proposes a Convolutional Neural Networks with Attention (CNNA), which consists of a latent representation method and a deep convolutional neural network. We have carried out several sets of experiments to test the validity of CNNA, and the experimental results show the effectiveness of CNNA.
Efficient and Reactive Planning for High Speed Robot Air Hockey

Highly dynamic robotic tasks require high-speed and reactive robots. These tasks are particularly challenging due to the physical constraints, hardware limitations, and the high uncertainty of dynamics and sensor measures. To face these issues, it’s crucial to design robotics agents that generate precise and fast trajectories and react immediately to environmental changes. Air hockey is an example of this kind of task. Due to the environment’s characteristics, it is possible to formalize the problem and derive clean mathematical solutions. For these reasons, this environment is perfect for pushing to the limit the performance of currently available general-purpose robotic manipulators. Using two Kuka Iiwa 14, we show how to design a policy for general-purpose robotic manipulators for the air hockey game. We demonstrate that a real robot arm can perform fast-hitting movements and that the two robots can play against each other on a medium-size air hockey table in simulation.
M-arguments

Argumentation has been investigated mainly as an abstract formalism. However, when applying argumentation techniques in practice, abstract arguments need to be instantiated through a logic language into structured argumentation frameworks. Although structured argumentation frameworks provide practical use of argumentation, we think that computational arguments could give light to richer structures. In this work, we introduce a new category of computational arguments named M-arguments. M-arguments are not meta-arguments as the name could suggest, they are computational arguments with rich structure. Such a rich structure allows us to represent a large number of computational models of arguments. Further, M-arguments allows us not only to represent the usual structure of arguments, but also to represent relevant information that concerns the dialectic and rhetoric aspects of argumentation.
On the exploitation of neuroevolutionary information

The final outcome of neuroevolutionary processes commonly is the best structure found during the search, and a good amount of residual information from which valuable knowledge that can be extracted is usually omitted. We propose an approach that extracts this information from neuroevolutionary runs, and use it to build a Bayesian network-based metamodel that could positively impact future neural architecture searches. The metamodel is learned from the best found solutions in previous GAN structural searches and it is used to improve subsequent neuroevolutionary searches.
A generalised Dickman distribution and the number of species in a negative binomial process model

Abstract We derive the large-sample distribution of the number of species in a version of Kingman’s Poisson–Dirichlet model constructed from an $\alpha$ -stable subordinator but with an underlying negative binomial process instead of a Poisson process. Thus it depends on parameters $\alpha\in (0,1)$ from the subordinator and $r&gt;0$ from the negative binomial process. The large-sample distribution of the number of species is derived as sample size $n\to\infty$ . An important component in the derivation is the introduction of a two-parameter version of the Dickman distribution, generalising the existing one-parameter version. Our analysis adds to the range of Poisson–Dirichlet-related distributions available for modeling purposes.
APEHR: Automated Prognosis in Electronic Health Records using multi-head self-attention

Automated prognosis has been a topic of intense research. Many works have sought to learn from Electronic Health Records using Recurrent Neural Networks that, despite promising results, have been overcome by novel techniques. We introduce APEHR, a Transformer approach that leverages medical prognosis using the latest technology Neural Network Transformer, which has demonstrated superior results in problems whose data is organized in sequential fashion. We contribute with an innovative problem modeling along with a detailed discussion of how Transformers can be used in the medical domain. Our results demonstrate a prognostic performance that surpasses previous works by at least 6% for metric Recall@k in the public dataset MIMIC-III.
Multicultural Education of Autochthons and Migrants in Russia

The study aims to analyze modern scientific approaches to multicultural education of autochthons, migrants, and foreign students to assess the real state and prospects of such education development in Russia. The 2019 RPORC (Russian Public Opinion Research Center) survey “Higher education: in Russia or abroad?” was used in the study. A total of 1,600 respondents took part in the study. Based on the Pareto Optimality principle, a model of an integrated multicultural education process has been developed. Survey data analysis has shown that for the majority of Russians (78%) it is not important whether their children/grandchildren will receive higher education abroad. Russian society has a tolerant attitude towards domestic and world educational systems. The practical application of the research results is possible with the improvement of training programs, the exchange of international experience and further research into the problems of multicultural education development.
Addressing the multiplicity of solutions in optical lens design as a niching evolutionary algorithms computational challenge

Optimal Lens Design constitutes a fundamental, long-standing real-world optimization challenge. Potentially large number of optima, rich variety of critical points, as well as solid understanding of certain optimal designs per simple problem instances, provide altogether the motivation to address it as a niching challenge. This study applies established Niching-CMA-ES heuristic to tackle this design problem (6-dimensional Cooke triplet) in a simulation-based fashion. The outcome of employing Niching-CMA-ES 'out-of-the-box' proves successful, and yet it performs best when assisted by a local searcher which accurately drives the search into optima. The obtained search-points are corroborated based upon concrete knowledge of this problem-instance, accompanied by gradient and Hessian calculations for validation. We extensively report on this computational campaign, which overall resulted in (i) the location of 19 out of 21 known minima within a single run, (ii) the discovery of 540 new optima. These are new minima similar in shape to 21 theoretical solutions, but some of them have better merit function value (unknown heretofore), (iii) the identification of numerous infeasibility pockets throughout the domain (also unknown heretofore). We conclude that niching mechanism is well-suited to address this problem domain, and hypothesize on the apparent multidimensional structures formed by the attained new solutions.