Reviewerly/exHarmony huggingface dataset:
A compositional approach to the reconstruction of geochemical processes involved in the evolution of Holocene marine flooded coastal karst basins (Mljet Island, Croatia)

Geochemical patterns in the environment are always the result of certain processes. Therefore, it is essential to decipher a process to properly evaluate the environmental role and potential of chemical elements/compounds. This allows the distinction between natural and anthropogenic influence on elemental concentrations. However, if the compositional nature of geochemical data is neglected, erroneous or misleading conclusions regarding the processes involved are probable. In this study the reconstruction of depositional environments and processes through the Holocene in two sediment cores obtained from submerged sinkholes located on the island of Mljet, Croatia, was performed by taking into account the compositional nature of geochemical, mineralogical and grainsize data. Problems involving compositional data are always multivariate; for example, the concentration of a single element does not carry any interpretative information, as only the ratios between elements do. This has led to the discovery of a large number of geochemical proxies based on elemental ratios, which describe certain environmental conditions and processes involved. Nevertheless, some proxies have been found to be restricted to only some specific environments, thus preventing them from being used in general; therefore, some kind of relation between different proxies is necessary to obtain final conclusions. However, when using simple elemental ratios, those correlations cannot be obtained due to the nature of compositional data. With a sequential binary partition of a compositional vector, orthonormal log ratio (olr) coordinates (proxies) can be constructed. When based on expert knowledge, those proxies fully acknowledge the geochemical properties of the chosen elements with one major difference - that the correlation between newly obtained variables is mathematically well grounded. As a result, the final conclusion is more accurate. In this research, geochemical proxies obtained as a representation in olr coordinates of the elements that are enriched compared to the local soil were used to perform principal component analyses. It helped to unravel the evolution of sedimentary environments. Mineralogical (XRD and heavy mineral data) and grain size analyses supported the conclusions obtained based solely on geochemical data. Furthermore, data analysis suggests that the proxies for redox conditions described in the literature should be used with caution, as their use is somewhat limited.
Traffic sign detection and recognition based on convolutional neural networks

Traffic sign detection and recognition topic are one of the most popular topics of computer vision and image processing in recent years, as they play an important role in autonomous driving and traffic safety. This paper proposes a system that will detect and classify different types of traffic signs from images. This paper differs from other papers as it uses signs that are globally recognized and isn't limited to very few signs like many other papers. The number of signs used in this paper for classification is 28, which are used all around the globe. Two separate neural networks have been used for detection and recognition purpose; one classifies the sign and other the shape. Image augmentation has been used to create the training and validation dataset. 40,000 images have been used to train the first classifier with 28000 positive images (images that contain the traffic signs) and 12000 negative images(images that do not contain any traffic signs) and 3600 images were used to train the second classifier with 2400 positive images and 1200 negative images. The images are processed to find the region of interest, which is then fed to two CNN classifiers for classification.
Internet of Things (IoT) Instructional Devices in STEM Classrooms: Past, Present and Future Directions

This research to practice category full paper provides a survey of how Internet of Things (IoT) devices have made inroads in diverse commercial industries and how these devices have been implemented in pre- and post-secondary classrooms in the past, their current state in educational settings, and suggestions for future implementation. IoT implementation in the realm of education has primarily been limited to student transportation and food service for tracking buses, students, and school property. In this paper, the authors provide suggestions for how IoT devices can be implemented in classrooms as subject (instructional aid) and object (endproduct or deliverable) devices by instructors with the emphasis on facilitating effective teaching and hands-on learning. The authors provide two examples of such devices in this paper to illustrate the classification of instructional IoT devices into subject and object devices. The authors suggest that the current limited role of IoT in pre- and post-secondary classrooms is due to the lack of knowledge about this new technology; therefore, the focus of this paper is on the function of IoTs and the process of providing educators with the technology, instructional tools, and materials necessary to implement IoT devices within the classroom.
PASSENGER DETECTION AND COUNTING FOR PUBLIC TRANSPORT SYSTEM

Implementing accurate and reliable passenger detection and counting system is an important task for the correct distribution of available transport system. The aim of this paper is to develop an accurate computer vision-based system to track and count passengers. The proposed passenger detection system incorporates the ideas of well-established detection techniques and is optimally customised for both indoor and outdoor scenarios. The candidate foreground regions (inside an image) are extracted in the proposed method and are described using the histograms of oriented gradient descriptor. These features are trained and tested using support vector machine classifier and the detected passengers are tracked using a filter. The proposed counting system is used to count passengers automatically when they pass through a virtual line of interest. Accuracies ranging 91.2 percent to 86.24 percent were found for passenger detection using the proposed passenger detection and counting system whereas relative counting errors varied ten percent to thirteen percent.
Health Safety Training for Industry in Virtual Reality

In this paper we describe the possibilities of using virtual reality methods for training and testing of employees of industrial companies in the field of health and safety at work. We focus on creating scenarios that aim to show correct practices and potential user failures to prevent injuries and economic damage. We show the possibilities of virtual reality in this area in specific scenarios, combining first person and third person view cameras for some scenarios. At the same time, we describe the key parts of the system and evaluate the impact of training in virtual reality compared to training without it. We focus on the key benefits of immersion and emotion induced by virtual reality on the rememberability of security practices.
HSAJAYA: An Improved Optimization Scheme for Consumer Surveillance Video Synopsis Generation

Video Surveillance is an active area of research and provides promising security measures for consumer applications. To ease consumer surveillance investigations, Video Synopsis (VS) serves as a powerful tool to assess hours of video in a shorter retro of time by projecting multiple objects concurrently. The optimization module in VS framework is considered to be a key module, yet to date, only traditional optimization techniques have been addressed for energy minimization. Amongst these, simulated annealing (SA) has been broadly employed to produce global optimal solution without getting trapped in local minima. However, the convergence time of SA is quite high as the next state is chosen randomly to achieve real-time performance. This article presents an improved energy minimization scheme using hybridization of SA and JAYA algorithm to achieve global optimal solution with faster convergence rate. The weights associated with the energy function are computed using analytic hierarchy process (AHP) instead of heuristic selection. From experimental evaluations and analysis, it is seen that the proposed scheme exhibits superior performance to minimize the overall energy cost with lesser computational time. The proposed scheme has a potential to quickly review consumer surveillance video data in a smart and efficient way.
Layer-constrained variational autoencoding kernel density estimation model for anomaly detection

Unsupervised techniques typically rely on the probability density distribution of the data to detect anomalies, where objects with low probability density are considered to be abnormal. However, modeling the density distribution of high dimensional data is known to be hard, making the problem of detecting anomalies from high-dimensional data challenging. The state-of-the-art methods solve this problem by first applying dimension reduction techniques to the data and then detecting anomalies in the low dimensional space. Unfortunately, the low dimensional space does not necessarily preserve the density distribution of the original high dimensional data. This jeopardizes the effectiveness of anomaly detection. In this work, we propose a novel high dimensional anomaly detection method called LAKE. The key idea of LAKE is to unify the representation learning capacity of layer-constrained variational autoencoder with the density estimation power of kernel density estimation (KDE). Then a probability density distribution of the high dimensional data can be learned, which is able to effectively separate the anomalies out. LAKE successfully consolidates the merits of the two worlds, namely layer-constrained variational autoencoder and KDE by using a probability density-aware strategy in the training process of the autoencoder. Extensive experiments on six public benchmark datasets demonstrate that our method significantly outperforms the state-of-the-art methods in detecting anomalies and achieves up to 37% improvement in F1 score.
Automated fibroglandular tissue segmentation in breast MRI using generative adversarial networks

Fibroglandular tissue (FGT) segmentation is a crucial step for quantitative analysis of background parenchymal enhancement (BPE) in magnetic resonance imaging (MRI), which is useful for breast cancer risk assessment. In this study, we develop an automated deep learning method based on a generative adversarial network (GAN) to identify the FGT region in MRI volumes and evaluate its impact on a specific clinical application. The GAN consists of an improved U-Net as a generator to generate FGT candidate areas and a patch deep convolutional neural network (DCNN) as a discriminator to evaluate the authenticity of the synthetic FGT region. The proposed method has two improvements compared to the classical U-Net: (1) the improved U-Net is designed to extract more features of the FGT region for a more accurate description of the FGT region; (2) a patch DCNN is designed for discriminating the authenticity of the FGT region generated by the improved U-Net, which makes the segmentation result more stable and accurate. A dataset of 100 three-dimensional (3D) bilateral breast MRI scans from 100 patients (aged 22-78 years) was used in this study with Institutional Review Board (IRB) approval. 3D hand-segmented FGT areas for all breasts were provided as a reference standard. Five-fold cross-validation was used in training and testing of the models. The Dice similarity coefficient (DSC) and Jaccard index (JI) values were evaluated to measure the segmentation accuracy. The previous method using classical U-Net was used as a baseline in this study. In the five partitions of the cross-validation set, the GAN achieved DSC and JI values of 87.0 ± 7.0% and 77.6 ± 10.1%, respectively, while the corresponding values obtained through by the baseline method were 81.1 ± 8.7% and 69.0 ± 11.3%, respectively. The proposed method is significantly superior to the previous method using U-Net. The FGT segmentation impacted the BPE quantification application in the following manner: the correlation coefficients between the quantified BPE value and BI-RADS BPE categories provided by the radiologist were 0.46 ± 0.15 (best: 0.63) based on GAN segmented FGT areas, while the corresponding correlation coefficients were 0.41 ± 0.16 (best: 0.60) based on baseline U-Net segmented FGT areas. BPE can be quantified better using the FGT areas segmented by the proposed GAN model than using the FGT areas segmented by the baseline U-Net.
AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and Results

Videos contain various types and strengths of motions that may look unnaturally discontinuous in time when the recorded frame rate is low. This paper reviews the first AIM challenge on video temporal super-resolution (frame interpolation) with a focus on the proposed solutions and results. From low-frame-rate (15 fps) video sequences, the challenge participants are asked to submit higher-frame-rate (60 fps) video sequences by estimating temporally intermediate frames. We employ the REDS_VTSR dataset derived from diverse videos captured in a hand-held camera for training and evaluation purposes. The competition had 62 registered participants, and a total of 8 teams competed in the final testing phase. The challenge winning methods achieve the state-of-the-art in video temporal super-resolution.
Object Detection-Based Video Retargeting With Spatial–Temporal Consistency

This study proposes a video retargeting method using deep neural network-based object detection. First, the meaningful regions of the input video denoted by bounding boxes of the object detection are extracted. In this case, the area is defined considering the size and number of bounding boxes for objects detected. The bounding boxes of each frame image are considered as regions of interest (RoIs). Second, the Siamese object tracking network is used to address high computational complexity of the object detection network. By dividing the video into scenes, object detection is performed for the first frame image of each scene to obtain the first bounding box. Object tracking is performed for the next sequential frame image until a scene change is detected. Third, the image is resized in the horizontal direction to alter the aspect ratio of the image and obtain the 1D RoIs of the image by projecting bounding boxes in the vertical direction. Then, the proposed method computes the grid map from the 1D RoIs to calculate new coordinates of each column data of the image. Finally, the retargeted video is obtained by rearranging all retargeted frame images. Comparative experiments conducted with various benchmark methods show an average bidirectional similarity score of 1.92, which is higher than other conventional methods. The proposed method was stable and satisfied viewers without causing cognitive discomfort as conventional methods.
To Adapt or Not to Adapt: A Quantification Technique for Measuring an Expected Degree of Self-Adaptation

Self-adaptation and self-organization (SASO) have been introduced to the management of technical systems as an attempt to improve robustness and administrability. In particular, both mechanisms adapt the system’s structure and behavior in response to dynamics of the environment and internal or external disturbances. By now, adaptivity has been considered to be fully desirable. This position paper argues that too much adaptation conflicts with goals such as stability and user acceptance. Consequently, a kind of situation-dependent degree of adaptation is desired, which defines the amount and severity of tolerated adaptations in certain situations. As a first step into this direction, this position paper presents a quantification approach for measuring the current adaptation behavior based on generative, probabilistic models. The behavior of this method is analyzed in terms of three application scenarios: urban traffic control, the swidden farming model, and data communication protocols. Furthermore, we define a research roadmap in terms of six challenges for an overall measurement framework for SASO systems.
Computational model for generating interactions in conversational recommender system based on product functional requirements

Conversational recommender system is a tool to help customer in deciding products they are going to buy, by conversational mechanism. By this mechanism, the system is able to imitate natural conversation between customer and professional sales support, for eliciting customer preference. However, many customers are not familiar with the technical features of multi-function and multi-feature products. A more natural way to explore customer preferences is by asking what they want to use with the product they are looking for (product functional requirements). Therefore, this paper proposes a computational model incorporating product functional requirements for interaction. The proposed model covers ontology and its structure as well as algorithms for generating interaction that comprises asking question, recommending products and presenting explanation of why a product is recommended. Based on our user studies, both expert users (familiar with product technical features) and novice users (not familiar with product technical feature) prefer our proposed interaction model than that of the flat interaction model (interaction model based on technical features). Meanwhile, functional requirements-based explanation is able to improve user trust in recommended products by 30% for novice users and 17% for expert users.
Learning selection channels for image steganalysis in spatial domain

Advanced steganography modifies the complex regions of digital media to embed secret messages, while steganalysis aims to detect whether the digital media contains secret messages or not. It is well recognized that the content adaptivity which adopted in steganography should also be considered for steganalysis to improve detection accuracy, and thus the embedding locations are weighted (so called selection channel) to build steganalytic detectors. However, the existing selection channels incorporated into steganalysis are all manually designed and keep constant even in the whole training stage of deep learning based steganalysis. Therefore, the handcrafted and fixed selection channels leave much room for improvement in steganalysis. In this paper, we propose to learn the selection channels in an end-to-end manner. Our steganalytic scheme has two parts: selection channel network and steganalysis network. These two networks are trained together. The selection channel network learns and outs the selection channels for the steganalysis network, and the steganalysis network integrated with the learned selection channels predicts the final steganalysis results. Our experiments under various conditions show that the learned selection channels considerably improve the detection accuracy of steganalytic schemes against content-adaptive steganography, and also exhibit high universality and robustness in real-world environments.
A study of factors related to patients’ length of stay using data mining techniques in a general hospital in southern Iran

The length of stay (LOS) in hospitals is a widely used indicator for goals such as health care management, quality control, utilizing hospital services and resources, and determining the degree of efficiency. Various methods have been used to identify the factors influencing the LOS. This study adopts a comparative approach of data mining techniques for investigating effective factors and predict the length of stay in Shahid-Mohammadi Hospital, Bandar Abbas, Iran.Using a dataset consists of 526 patient records of the Shahid-Mohammadi Hospital from March 2016 to March 2017, factors affecting the LOS were ranked using information gain and correlation indices. In addition, classification models for LOS prediction were created based on nine data mining classifiers applied with and without feature selection technique. Finally, the models were compared.The most important factors affecting LOS are the number of para-clinical services, counseling frequency, clinical ward, the specialty and the degree of the doctor, and the cause of hospitalization. In addition, regarding to the classifiers created based on the dataset, the best accuracy (83.91%) and sensitivity (80.36%) belongs to the Logistic Regression and Naïve Bayes respectively. In addition, the best AUC (0.896) belongs to the Random Forest and Generalized Linear classifiers.The results showed that most of the proposed models are suitable for classification of the length of stay, although the Logistic Regression might have a slightly better performance than others in term of accuracy, and this model can be used to determine the patients' Length of Stay. In general, continuous monitoring of the factors influencing each of the performance indicators based on proper and accurate models in hospitals is important for helping management decisions.
Non-Visual to Visual Translation for Cross-Domain Face Recognition

Reducing the cross-modality gap between two different domains is a challenging problem for heterogeneous face recognition (HFR).The current visual domain face recognition system is not easy to solve the discrepancy of cross-modality when two comparing domains are heterogeneous.Moreover, the amount of HFR dataset is significantly insufficient, making it considerable difficulty in training.This paper proposes a novel two-step framework that consists of the image translation module and the feature learning module to obtain an enhanced cross-modality matching system for heterogeneous datasets.First, the image translation module consists of a Preprocessing Chain (PC) method, CycleGAN, and the Siamese network.It enables to meet the conditions for preserving contents along with changing the styles from the source domain to the target domain.Second, in the feature learning module, the training dataset and its translated images are used together for fine-tuning the pre-trained backbone model in the visual domain.This allows for discriminative and robust feature matching of the probe and gallery test datasets in the visual domain.The experimental results are evaluated with two scenarios, using the CUHK Face Sketch FERET (CUFSF) dataset and the CASIA NIR-VIS 2.0 dataset.The proposed method achieves a better recognition performance in comparison to the state-of-the-art methods.
Practical Anti-Counterfeit Medicine Management System Based on Blockchain Technology

Leveraging the transparency, immutability, and security of Blockchain technology for the drug supply chain, a variety of researches on counterfeit medicines has recently implemented. However, the majority of Blockchain-based medicine management proposals lack practicality. Additionally, the problem of cloning of drug has not yet been resolved explicitly. In this paper, we propose a novel Blockchain-based product ownership management method for anti-counterfeit medicine system to resist the cloning of drug and improve the practical applicability. Analysis and evaluation results of our proposed system outperform the related proposals based on criteria about a practical application, anti-clone, low cost, customer-oriented, and scalability. Furthermore, experimental implementation on a small scale shows that our proposed system works appropriately in a real environment.