Reviewerly/exHarmony huggingface dataset:
Ordinal regression with explainable distance metric learning based on ordered sequences

The purpose of this paper is to introduce a new distance metric learning algorithm for ordinal regression. Ordinal regression addresses the problem of predicting classes for which there is a natural ordering, but the real distances between classes are unknown. Since ordinal regression walks a fine line between standard regression and classification, it is a common pitfall to either apply a regression-like numerical treatment of variables or underrate the ordinal information applying nominal classification techniques. On a different note, distance metric learning is a discipline that has proven to be very useful when improving distance-based algorithms such as the nearest neighbors classifier. In addition, an appropriate distance can enhance the explainability of this model. In our study we propose an ordinal approach to learning a distance, called chain maximizing ordinal metric learning. It is based on the maximization of ordered sequences in local neighborhoods of the data. This approach takes into account all the ordinal information in the data without making use of any of the two extremes of classification or regression, and it is able to adapt to data for which the class separations are not clear. We also show how to extend the algorithm to learn in a non-linear setup using kernel functions. We have tested our algorithm on several ordinal regression problems, showing a high performance under the usual evaluation metrics in this domain. Results are verified through Bayesian non-parametric testing. Finally, we explore the capabilities of our algorithm in terms of explainability using the case-based reasoning approach. We show these capabilities empirically on two different datasets, experiencing significant improvements over the case-based reasoning with the traditional Euclidean nearest neighbors.
Hybrid Harris Hawks Optimization with Differential Evolution for Data Clustering

Harris Hawks optimization (HHO) is a recent population-based optimization algorithm that has been recently proposed to address several different problems. Sometimes, poor exploitation (intensification) ability influences the performance of Harris Hawks optimization. This chapter proposes a new hybridization strategy, namely, hybrid Harris Hawks optimization with differential evolution (DE) (H-HHO), to tackle the data clustering problem. The proposed method attempts to improve the local (exploitation) search skill of the Harris Hawks' optimization to achieve the optimal solution. The proposed H-HHO is handled by adding local and global search operators from the differential evolution. This idea is employed to improve the search capabilities in Harris Hawks optimization to explore the optimal solution. Thus, its solutions' positions move near the global optimal. Experiments are conducting utilizing four conventional benchmark datasets from the Machine Learning Repository (UCI), which is generally utilized in the field of machine learning. The results revealed that the proposed hybrid method (H-HHO) provided very distinct clusters, particularly in massive datasets. Moreover, the proposed H-HHO got a better convergence rate. It can overwhelm the other similar algorithms by getting better results according to the clustering processes.
Cali-sketch: Stroke calibration and completion for high-quality face image generation from human-like sketches

Image generation has received increasing attention because of its wide application in security and entertainment. Sketch-based face generation brings more fun and better quality of image generation due to supervised interaction. However, when a sketch poorly aligned with the true face is given as input, existing supervised image-to-image translation methods often cannot generate acceptable photo-realistic face images. To address this problem, in this paper we propose Cali-Sketch, a human-like-sketch to photo-realistic-image generation method. Cali-Sketch explicitly models stroke calibration and image generation using two constituent networks: a Stroke Calibration Network (SCN), which calibrates strokes of facial features and enriches facial details while preserving the original intent features; and an Image Synthesis Network (ISN), which translates the calibrated and enriched sketches to photo-realistic face images. In this way, we manage to decouple a difficult cross-domain translation problem into two easier steps. Extensive experiments verify that the face photos generated by Cali-Sketch are both photo-realistic and faithful to the input sketches, compared with state-of-the-art methods.
NFCNN: toward a noise fusion convolutional neural network for image denoising

Deep learning-based methods have achieved the state-of-the-art performance in image denoising. In this paper, a deep learning-based denoising method is proposed and a module called fusion block is introduced in the convolutional neural network. For this so-called noise fusion convolutional neural network (NFCNN), there are two branches in its multistage architecture. One branch aims to predict the latent clean image, while the other one predicts the residual image. A fusion block is contained between every two stages by taking the predicted clean image and the predicted residual image as a part of inputs, and it outputs a fused result to the next stage. NFCNN has an attractive texture-preserving ability because of the fusion block. To train NFCNN, a stage-wise supervised training strategy is adopted to avoid the vanishing gradient and exploding gradient problems. Experimental results show that NFCNN is able to perform competitive denoising results when compared with some state-of-the-art algorithms.
Immune System Algorithms to Environmental Exploration of Robot Navigation and Mapping

In real-world applications such as rescue robots, service robots, mobile mining robots, and mine searching robots, an autonomous mobile robot needs to reach multiple targets with the shortest path. This paper proposes an Immune System algorithm (ISA) for real-time map building and path planning for multi-target applications. Once a global route is planned by the ISA, a foraging-enabled trail is created to guide the robot to the multiple targets. A histogram-based local navigation algorithm is used to navigate the robot along a collision-free global route. The proposed ISA models aim to generate a path while a mobile robot explores through terrain with map building in unknown environments. In this paper, we explore the ISA algorithm with simulation studies to demonstrate the capability of the proposed ISA in achieving a global route with minimized overall distance. Simulation studies demonstrate that the real-time concurrent mapping and multi-target navigation of an autonomous robot is successfully performed under unknown environments.
fCoSE: A Fast Compound Graph Layout Algorithm with Constraint Support

Visual analysis of relational information is vital in most real-life analytics applications. Automatic layout is a key requirement for effective visual display of such information. This article introduces a new layout algorithm named fCoSE for compound graphs showing varying levels of groupings or abstractions with support for user-specified placement constraints. fCoSE builds on a previous compound spring embedder layout algorithm and makes use of the spectral graph drawing technique for producing a quick draft layout, followed by phases where constraints are enforced and compound structures are properly shown while polishing the layout with respect to commonly accepted graph layout criteria. Experimental evaluation verifies that fCoSE produces quality layouts and is fast enough for interactive applications with small to medium-sized graphs by combining the speed of spectral graph drawing technique with the quality of force-directed layout algorithms while satisfying specified constraints and properly displaying compound structures. An implementation of fCoSE along with documentation and a demo page is freely available on GitHub at https://github.com/iVis-at-Bilkent/cytoscape.js-fcose.
Visual Analysis of Emotions Using AI Image-Processing Software: Possible Male/Female Differences between the Emotion Pairs “Neutral”–“Fear” and “Pleasure”–“Pain”

Inferring the emotional state of an individual by viewing his/her facial expression seems to be present in all human cultures. Numerous studies have shown that various changes in facial muscles determine the resulting facial expression. The analysis of images of faces expressing emotional states promises to contribute to quantification of the claimed observations. Here, we use a suite of AI (artificial intelligence) algorithms, along with ML (maximum likelihood) estimated distributions to quantify the shift in facial expression from “neutral”→“fear” and “pain”→“pleasure”. The images are single frames of five emotional states (neutral, fear, pain, pleasure, laugh) expressed by actors and actresses in BDSM videos. We extract a feature vector for each image, dimension-reduce these feature vectors by mapping them onto a two-dimensional manifold and calculate the norms of the normalized displacement vectors for each emotional pair. We then find that the ML distributions of the norms are Gamma-distributed and that the modes for each pair are different for both males and females. We use Wilks lambda to determine significance. We find that the distributions for the females are significantly different, but not for the males. The methodology we present here has widespread applications: monitoring the emotional states of humans in various settings; among these: determining whether participants in BDSM and similar videos are indeed volunteering their participation or are victims of criminal activity.
Non-Contact Measurement of Motion Sickness Using Pupillary Rhythms from an Infrared Camera

Both physiological and neurological mechanisms are reflected in pupillary rhythms via neural pathways between the brain and pupil nerves. This study aims to interpret the phenomenon of motion sickness such as fatigue, anxiety, nausea and disorientation using these mechanisms and to develop an advanced non-contact measurement method from an infrared webcam. Twenty-four volunteers (12 females) experienced virtual reality content through both two-dimensional and head-mounted device interpretations. An irregular pattern of the pupillary rhythms, demonstrated by an increasing mean and standard deviation of pupil diameter and decreasing pupillary rhythm coherence ratio, was revealed after the participants experienced motion sickness. The motion sickness was induced while watching the head-mounted device as compared to the two-dimensional virtual reality, with the motion sickness strongly related to the visual information processing load. In addition, the proposed method was verified using a new experimental dataset for 23 participants (11 females), with a classification performance of 89.6% (n = 48) and 80.4% (n = 46) for training and test sets using a support vector machine with a radial basis function kernel, respectively. The proposed method was proven to be capable of quantitatively measuring and monitoring motion sickness in real-time in a simple, economical and contactless manner using an infrared camera.
SOMA-CLP for competition on bound constrained single objective numerical optimization benchmark

The paper represents a competition entry for the competition on bound constrained single objective numerical optimization at The Genetic and Evolutionary Computation Conference (GECCO) 2021 by a novel algorithm titled Self-organizing Migrating Algorithm with CLustering-aided migration and adaptive perturbation vector control (SOMA-CLP).
An integrated neural network model for pupil detection and tracking

The accurate detection and tracking of pupil is important to many applications such as human–computer interaction, driver’s fatigue detection and diagnosis of brain diseases. Existing approaches however face challenges in handing low quality of pupil images. In this paper, we propose an integrated pupil tracking framework, namely LVCF, based on deep learning. LVCF consists of the pupil detection model VCF which is an end-to-end network, and the LSTM pupil motion prediction model which applies LSTM to track pupil’s position. The proposed network was trained and evaluated on 10600 images and 75 videos taken from 3 realistic datasets. Within an error threshold of 5 pixels, VCF achieves an accuracy of more than 81%, and LVCF outperforms the state of arts by 9% in terms of percentage of pupils tracked. The project of LCVF is available at https://github.com/UnderTheMangoTree/LVCF .
Promoting Cybersecurity Culture Change in Healthcare

Cybersecurity problems have traditionally been addressed through technological solutions and staff training. Whilst technology can reduce or remove some weaknesses some attacks specifically target human users. Whilst training can educate staff on how to behave more securely, this is often not sufficient to promote actual secure behaviours . Knowing what to do is necessary but not sufficient. It is also necessary to remove barriers to the required behaviour and to intervene in a way that affords behaviour change. This is particularly true in healthcare, where environmental factors including time pressure, and staff fatigue can create barriers for cybersecurity behaviour change. Technology and training are only a partial solution. Only by taking a more holistic approach which encompasses technology, people and processes and addressing the culture change needed to facilitate more secure behaviours will any progress be made in the workplace. We conducted a series of in-depth interviews and workshops with staff across 3 healthcare organisations in Italy, Crete and Ireland. This paper reflects on our main findings, including key requirements for future cybersecurity interventions. We used this reflection to develop a secure behaviour toolkit to help healthcare organisations identify problematic behaviours, co-create interventions to increase secure staff behaviour being mindful that sometimes culture change is necessary to enable the required security behaviours. The toolkit also provides a means to evaluate the interventions identified and the final implementation of the intervention.
An Overview of Ontologies in Virtual Reality-Based Training for Healthcare Domain

Virtual reality (VR) is one of the state-of-the-art technological applications in the healthcare domain. One major aspect of VR applications in this domain includes virtual reality-based training (VRT), which simplifies the complicated visualization process of diagnosis, treatment, disease analysis, and prevention. However, not much is known on how well the domain knowledge is shared and considered in the development of VRT applications. A pertinent mechanism, known as ontology, has acted as an enabler toward making the domain knowledge more explicit. Hence, this paper presents an overview to reveal the basic concepts and explores the extent to which ontologies are used in VRT development for medical education and training in the healthcare domain. From this overview, a base of knowledge for VRT development is proposed to initiate a comprehensive strategy in creating an effective ontology design for VRT applications in the healthcare domain.
A theoretical investigation on the distinguishers of Salsa and ChaCha

Salsa and ChaCha are two of the most well-known stream ciphers in last two decades. These two ciphers came into the picture when a massively used cipher RC4 was going through severe cryptanalysis and a significant number of observed weaknesses of it showed the requirement of new stream ciphers in the market. Later, ChaCha was adopted by Google as their encryption algorithm, which further increased the importance of research work on these two ciphers. Salsa and ChaCha have gone through differential key recovery attack up to the 8-th and 7-th round respectively. Initially, this attack used an experimentally observed distinguisher by observing a single bit position up to the 4th round for Salsa and 3rd round for ChaCha. Later, Maitra (2016) improved the attack complexity by minimizing the propagation of the difference after the first round using properly chosen IV values. Also, using this distinguisher, Choudhuri et al. (FSE 2016) provided a technique to construct a distinguisher for the next round of both the ciphers by observing multiple bits. Among all these attacks which were mostly based on experimental observations, theoretical works did not get much importance for these two ciphers. In this paper, we aim to theoretically investigate the reason behind these experimentally observed distinguishers for these chosen IV distinguishers, where the difference propagation is minimized up to the first round. We provide a mathematical proof of the observed probabilities for the distinguishers of both the ciphers in the single and multiple bits.
A distributed, decoupled system for losslessly streaming dynamic light probes to thin clients

We present a networked, high-performance graphics system that combines dynamic, high-quality, ray traced global illumination computed on a server with direct illumination and primary visibility computed on a client. This approach provides many of the image quality benefits of real-time ray tracing on low-power and legacy hardware, while maintaining a low latency response and mobile form factor.
Development of Breast Cancer Detection Model Using Transfer Learning of Residual Neural Network (ResNet-50)

Breast cancer disease is one of the most common and dangerous as well as being considered as the second most common world cause of cancer death in women. However, the early diagnostics and detection can provide a significant chance for correct treatment and survival. One of the most powerful tools that have shown extraordinary and superior results is the deep convolutional neural network. In this work, we propose an accurate and inclusive computational breast cancer diagnosis framework using ResNet-50 convolutional neural network to classify histopathological microscopy images. The proposed model employs transfer learning technique of the powerful ResNet-50 CNN pretrained on ImageNet to train and classify BreakHis dataset into benign or malignant. The simulation results showed that our proposed model achieves exceptional classification accuracy of 99% outperforming other compared models trained on the same dataset. Based on our novel approach, earlier detection to breast cancer as whether it is being benign or malignant can be stimulated and classified and thus save life and efforts.
Entrepreneurship and agriculture resources on national productivity in Africa: exploring for complementarities, synergies and thresholds

Purpose Existent literature suggests that Africa is heavily endowed with agriculture resources and entrepreneurship remains an important mechanism for promoting national productivity and other economic outcomes. Despite these, empirical evidence on how agriculture resources promote the effect of entrepreneurship on national productivity in Africa is nonexistent given the abundance of agriculture resources and the need for Africa to increase its productivity, which has implications for improving welfare. Hence, this study aims to examine the interplay of how agriculture resources and entrepreneurship influence national productivity by way of exploring for threshold and complementarity effects of agriculture resources in Africa. Design/methodology/approach This uses panel data of 29 Africa economies between 2006 and 2016 in a bootstrap quantile regression model. Findings First, it is reported that initial levels of agriculture resources in the form of crop and arable lands reduce national productivity while the extreme increase in agriculture resources promotes national productivity in Africa. This implies a nonlinear direct U-shape effect of agriculture resources on national productivity indicating that the enhancing effect of agriculture resources on national productivity is only achieved beyond a certain threshold of average agriculture resources. Second, agriculture resources complement entrepreneurship (which initially reduced national productivity) to promote national productivity. This implies that there is a synergetic-complementarity relationship between entrepreneurship and agriculture resources on national productivity. Practical implications These findings suggest that governments that are interested in boosting national productivity through agriculture resources may have to commit more financial resources to develop and reclaiming more agriculture resources (in the form of crop and arable lands) given that some threshold of agriculture resources are needed to promote national productivity. Similarly, developing agriculture resources by policymakers can help complement entrepreneurship to further improve the effects of entrepreneurship on national productivity. Originality/value This study attempts to present first-time evidence on the interplay between agriculture resources and entrepreneurship on national productivity by way of exploring for threshold and complementarity effects of agriculture resources in Africa.
Edge/Structure-Preserving Texture Filter via Relative Bilateral Filtering With a Conditional Constraint

Image texture
Rethinking Ensemble-Distillation for Semantic Segmentation Based Unsupervised Domain Adaption

Recent researches on unsupervised domain adaptation (UDA) have demonstrated that end-to-end ensemble learning frameworks serve as a compelling option for UDA tasks. Nevertheless, these end-to-end ensemble learning methods often lack flexibility as any modification to the ensemble requires retraining of their frameworks. To address this problem, we propose a flexible ensemble-distillation framework for performing semantic segmentation based UDA, allowing any arbitrary composition of the members in the ensemble while still maintaining its superior performance. To achieve such flexibility, our framework is designed to be robust against the output inconsistency and the performance variation of the members within the ensemble. To examine the effectiveness and the robustness of our method, we perform an extensive set of experiments on both GTA5→Cityscapes and SYNTHIA→Cityscapes benchmarks to quantitatively inspect the improvements achievable by our method. We further provide detailed analyses to validate that our design choices are practical and beneficial. The experimental evidence validates that the proposed method indeed offer superior performance, robustness and flexibility in semantic segmentation based UDA tasks against contemporary baseline methods.
Issue Auto-Assignment in Software Projects with Machine Learning Techniques

Usually, managers or technical leaders in software projects assign issues manually. This task may become more complex as more detailed is the issue description. This complexity can also make the process more prone to errors (misassignments) and time-consuming. In the literature, many studies aim to address this problem by using machine learning strategies. Although there is no specific solution that works for all companies, experience reports are useful to guide the choices in industrial auto-assignment projects. This paper presents an industrial initiative conducted in a global electronics company that aims to minimize the time spent and the errors that can arise in the issue assignment process. As main contributions, we present a literature review, an industrial report comparing different algorithms, and lessons learned during the project.