Reviewerly/exHarmony huggingface dataset:
Investigation of Variables Related to University Drop-out in Problem-Based Learning

This Research to Practice Full Paper will introduce a didactic concept with the aim of activating a large cohort in a classical lecture hall. The main objective is to reduce factors which may lead to a drop-out. The didactic concept is based on a problem-based learning approach. The students solve problems in several steps which refer to an end product according to the action-oriented learning. The problem-solving processing steps are divided according to the flipped-classroom approach. At home, students familiarize themselves with the problem and the end product and collect information on both. They prepare a plan to independently solve the problem. At the university, they develop and test the solution. The test is carried out the end product. Due to the large cohort, not every solution can be checked in the lecture hall. Because of this the end product is part of a remote lab. Thus, every student can check their solution from home. The concept was tested for the first time in 2019. The students participating in the course industrial control systems and real time systems form the experimental group. The 2018 course participants form the control group. The survey is based on a pre-post-test-design which has been answered by both groups. The results show no decrease of the factor performance problems. The factor motivation to study, by contrast, could be increased significantly.
User experience in a kinect-based conducting system for visualization of musical structure

There is a potential for immersive tools that can teach some basic aspects of conducting in a constructive way. This work presents a Kinect-based conducting system that uses a gestural interface for visualization of musical structures, aimed towards educational purpose. The system provides a solution that enables students to practice conducting without the actual need of the physical presence of an orchestra or choir. Visualization of melodic and harmonic structures through an audiovisual mapping is provided to enable the understanding of complex musical pieces, such as operas and concertos. Real-time visual and gestural feedback is generated for the conductor and audience through the display of a set of particle emitters and the trajectory traversed by the captured gestures. Focusing on delivering a better user experience (UX), the system was also evaluated by professional conductors, musicians and users without any formal musical knowledge. UX played an important part in identifying that this type of experience is viable as an additional support for conducting and musical theory learning.
Synchronization of chaotic systems and long short-term memory networks by sharing a single variable

We adopt long short-term memory (LSTM) networks to model and characterize chaotic systems rather than conventional dynamical equations. We find that a well-trained LSTM system can synchronize with its learned chaotic system via transmitting a common signal. In the same fashion, we show that when learning an identical chaotic system, the trained LSTM systems can also be synchronized. Remarkably, we find that a cascading synchronization will be achieved among chaotic systems and their trained LSTM systems in the same manner. We further validate that this synchronization behavior is robust even the transmitting signal is contaminated with relatively a high level of white noise. Our work reveals that synchronization is a common behavior linking chaotic systems and their learned LSTM networks.
Development of Real-time Assembly Work Monitoring System Based on 3D Skeletal Model of Arms and Fingers

Decreasing birth rates and an aging population in society often cause labor shortages in the manufacturing industry, making the development of methods to improve productivity based on limited human resources imperative. One way to achieve this is by enhancing product quality via the reduction of product losses and the consequent need for reassembly due to human error. Human error during assembly can arise from specific actions via the arms and fingers. We assumed that these errors can be captured based on the information on human skeletal models. The purpose of this study is to propose and develop a system that acquires information about assembly procedures by using human skeletal models, including the fingers of workers, and notifies them of skipped procedures and errors in part types. Further, we confirm the basic capability of the proposed system via experiments based on simulated assembly work. The proposed system monitors workers' motion based on color and depth images captured by a single RGB-D camera. In addition, we developed a function to detect the process and to point out errors with audible and visual feedbacks when errors were made by workers in assembly processes. In the experiment using simulated assembly work, the proposed system exhibited an accuracy rate of 98.3% with respect to acquisition of assembly processes. In the case of an error in an assembly process, the system was able to point out the error correctly and provide feedback to the worker before he had finished picking up the wrong part. In conclusion, we confirmed that the developed system exhibited the basic capability to acquire work procedures and efficiently point out errors in real time.
Triple Up-Sampling Segmentation Network With Distribution Consistency Loss for Pathological Diagnosis of Cervical Precancerous Lesions

Objective: Cervical cancer, as one of the most frequently diagnosed cancers in women, is curable when detected early. However, automated algorithms for cervical pathology precancerous diagnosis are limited. Methods: In this paper, instead of popular patch-wise classification, an end-to-end patch-wise segmentation algorithm is proposed to focus on the spatial structure changes of pathological tissues. Specifically, a triple up-sampling segmentation network (TriUpSegNet) is constructed to aggregate spatial information. Second, a distribution consistency loss (DC-loss) is designed to constrain the model to fit the inter-class relationship of the cervix. Third, the Gauss-like weighted post-processing is employed to reduce patch stitching deviation and noise. Results: The algorithm is evaluated on three challenging and public datasets: 1) MTCHI for cervical precancerous diagnosis, 2) DigestPath for colon cancer, and 3) PAIP for liver cancer. The Dice coefficient is 0.7413 on the MTCHI dataset, which is significantly higher than the published state-of-the-art results. Conclusion: Experiments on the public dataset MTCHI indicate the superiority of the proposed algorithm on cervical pathology precancerous diagnosis. In addition, the experiments on two other pathological datasets, i.e., DigestPath and PAIP, demonstrate the effectiveness and generalization ability of the TriUpSegNet and weighted post-processing on colon and liver cancers. Significance: The end-to-end TriUpSegNet with DC-loss and weighted post-processing leads to improved segmentation in pathology of various cancers.
Fast hard negative mining for deep metric learning

Deep metric learning methods aim to measure similarity of data points (e.g. images) by calculating their distance in a high dimensional embedding space. These methods are usually trained by optimizing a ranking loss function, which is designed to bring together samples from the same class while separating them from samples from all other classes. The most challenging part of these methods is the selection of samples that contribute to effective network training. In this paper we present Bag of Negatives (BoN), a fast hard negative mining method, that provides a set, triplet or pair of potentially relevant training samples. BoN is an efficient method that selects a bag of hard negatives based on a novel online hashing strategy. We show the superiority of BoN against state-of-the-art hard negative mining methods in terms of accuracy and training time over three large datasets.
Upgrade your network in-place with deformable convolution

Improving the performance of the network on is a topic that all deep learning researchers are working together. More new algorithms are proposed for different tasks. But most of these can't avoid spending a lot of time retraining the network model. Deformable convolution is a convolution structure that can extract better features of objects. This paper proposes a new method that can upgrade the standard convolution part of the network to the deformable convolution in-place, inherit the original model parameters, and reduce the time and computational resource cost for retraining. We analyzed the effects of introducing deformable convolution at different depths of the network on speed and performance. And on the detection and semantic segmentation tasks of the PASCAL VOC and COCO, a lot of experiments were carried out on our methods, and have an effective improvement.
Hybrid collaborative filtering methods for recommending search terms to clinicians

With increasing and extensive use of electronic health records (EHR), clinicians are often challenged in retrieving relevant patient information efficiently and effectively to arrive at a diagnosis. While using the search function built into an EHR can be more useful than browsing in a voluminous patient record, it is cumbersome and repetitive to search for the same or similar information on similar patients. To address this challenge, there is a critical need to build effective recommender systems that can recommend search terms to clinicians accurately. In this study, we developed a hybrid collaborative filtering model to recommend search terms for a specific patient to a clinician. The model draws on information from patients’ clinical encounters and the searches that were performed during them. To generate recommendations, the model uses search terms which are (1) frequently co-occurring with the ICD codes recorded for the patient and (2) highly relevant to the most recent search terms. In one variation of the model (Hybrid Collaborative Filtering Method for Healthcare, or HCFMH), we use only the most recent ICD codes assigned to the patient, and in the other (Co-occurrence Pattern based HCFMH, or cpHCFMH), all ICD codes. We have conducted comprehensive experiments to evaluate the proposed model. These experiments demonstrate that our model outperforms state-of-the-art baseline methods for top-N search term recommendation on different data sets.
Compact Implementation of CHAM Block Cipher on Low-End Microcontrollers

In this paper, we presented an optimized implementation of CHAM block cipher on low-end microcontrollers. In order to accelerate the performance of the CHAM block cipher, the architecture of CHAM block cipher and the full specification of 8-bit AVR microcontrollers are efficiently utilized. First, the counter mode of operation for CHAM block cipher is optimized. A number of computations for round function are replaced to look-up table accesses. Second, multiple blocks of CHAM block cipher are computed in a parallel way for high throughput. With the parallel computation, we also presented the adopted encryption. This approach is efficient for long-length data handling. Third, the state-of-art engineering technique is fully utilized in terms of instruction level and register level. The partially unrolled 8-round based implementation is adopted, which avoids a number of word-wise rotation operations. With above optimization techniques, proposed CHAM implementations for counter mode of operation outperform the state-of-art implementations by 30.1%, 9.3%, and 10.0% for CHAM-64/128, CHAM-128/128, and CHAM-128/256, respectively.
Zero-shot Learning Based on Semantic Embedding for Ship Detection

Zero-shot detection (ZSD) is a hot topic, which can detect unseen object without training on it. This new approach rises several challenges, e.g., the imbalance between positive and negative data, ambiguity between background and unseen classes, and how to align visual feature and semantic concept. In this paper, we apply an end-to-end zero-shot network to fulfil ZSD, where polarity loss is utilized to train detection model. polarity loss puts more importance on difficult data to reduce class imbalance and can maximize the difference between positive and negative predictions. Then, we use a semantic embedding method that use word vectors in both classification and regression subnets to better align visual features and semantic features. Finally, we use MS-COCO dataset as seen classes to train detection model, then we transfer the model for our own to detect warship object. Our model performs good on unseen classes and test accuracy of using polarity loss is apparently higher than that of using focal loss.
Feature Selection with Dynamic Classifier Ensembles

With the advance in technology, the volume of available data grows massively. Therefore, feature selection has become an essential preprocessing step to extract valuable information. Feature selection is the task of reducing the number of features by removing redundant features from data while preserving the classification accuracy. It is a multiobjective problem as there are two objectives. In general, multiobjective selection algorithms with machine learning techniques are utilized to find the most promising feature subsets; however, classification performances of these machine learning techniques are analyzed separately. In this study, we propose a new multiobjective selection model that dynamically searches for the best ensemble of five classifiers to extract the best representative feature subsets. We present the experiment results on 12 well-known datasets. The results show that the proposed method performs significantly better than all the machine learning techniques when they are executed separately. Moreover, the proposed method outperforms two existing ensemble algorithms, namely AdaBoost and Gradient Boosting.
Bird’s-eye view image acquisition from simulated scenes using geometric inverse perspective mapping

The following topics are dealt with: cameras; road vehicles; object detection; motion control; mobile robots; robot vision; image classification; image segmentation; control engineering computing; autonomous aerial vehicles.
Hacker Definitions in Information Systems Research

The IS research community has called for more research on hackers and their behavioral motivations. One of the reasons that research on hackers has been so limited is that there is no clear definition of what a hacker is, or who may or may not be considered a hacker. Researchers have attempted to define the term hacker, yet overall attempts to craft a definition have been inconsistent and partially complete. The purpose of this paper is to present results from an inductive, qualitative study of how the term hacker has been defined in IS literature. We reviewed the leading IS journals to identify previously-used hacker definitions. The review and analysis conducted in this paper yields a more comprehensive definition of the term hacker to help advance hacker-related IS research efforts.
3D Guided Weakly Supervised Semantic Segmentation

Pixel-wise clean annotation is necessary for fully-supervised semantic segmentation, which is laborious and expensive to obtain. In this paper, we propose a weakly supervised 2D semantic segmentation model by incorporating sparse bounding box labels with available 3D information, which is much easier to obtain with advanced sensors. We introduce a 2D-3D inference module to generate accurate pixel-wise segment proposal masks. Guided by 3D information, we first generate a point cloud of objects and calculate a per class objectness probability score for each point using projected bounding-boxes. Then we project the point cloud with objectness probabilities back to the 2D images followed by a refinement step to obtain segment proposals, which are treated as pseudo labels to train a semantic segmentation network. Our method works in a recursive manner to gradually refine the above-mentioned segment proposals. We conducted extensive experimental results on the 2D-3D-S dataset where we manually labeled a subset of images with bounding boxes. We show that the proposed method can generate accurate segment proposals when bounding box labels are available on only a small subset of training images. Performance comparison with recent state-of-the-art methods further illustrates the effectiveness of our method.
Identifying Patient Phenotype Cohorts Using Prehospital Electronic Health Record Data

Objective: Emergency medical services (EMS) provide critical interventions for patients with acute illness and injury and are important in implementing prehospital emergency care research. Retrospective, manual patient record review, the current reference-standard for identifying patient cohorts, requires significant time and financial investment. We developed automated classification models to identify eligible patients for prehospital clinical trials using EMS clinical notes and compared model performance to manual review.Methods: With eligibility criteria for an ongoing prehospital study of chest pain patients, we used EMS clinical notes (n = 1208) to manually classify patients as eligible, ineligible, and indeterminate. We randomly split these same records into training and test sets to develop and evaluate machine-learning (ML) algorithms using natural language processing (NLP) for feature (variable) selection. We compared models to the manual classification to calculate sensitivity, specificity, accuracy, positive predictive value, and F1 measure. We measured clinical expert time to perform review for manual and automated methods.Results: ML models’ sensitivity, specificity, accuracy, positive predictive value, and F1 measure ranged from 0.93 to 0.98. Compared to manual classification (N = 363 records), the automated method excluded 90.9% of records as ineligible and leaving only 33 records for manual review.Conclusions: Our ML derived approach demonstrates the feasibility of developing a high-performing, automated classification system using EMS clinical notes to streamline the identification of a specific cardiac patient cohort. This efficient approach can be leveraged to facilitate prehospital patient-trial matching, patient phenotyping (i.e. influenza-like illness), and create prehospital patient registries.
The use of e-learning resources in distance learning

Abstract The article presents the results of research on the implementation of electronic educational resources in distance learning in order to develop the ICT (information and communication technology) competence of teachers. The author of the article did a theoretical analysis on the concept of "professional competence of teachers", its structural content. Based on the current situation associated with the spread of coronavirus infection and the transition of all educational institutions to distance learning, the author suggested that not all school and university teachers were ready to teach using information and communication technologies. Having conducted a thorough analysis of studying problem and identified the level of developing the ICT competence of a modern teacher, the author selected and implemented Electronic Learning Resources (ELR) as a means of teaching, which, in his opinion, can improve or develop this competence. The basis of the research presented in the article is one of the secondary schools of the Ural region. The results of the implementation of the proposed ELR allowed the author to conclude that this ICT competence should be necessarily formed and improved by teachers themselves continuously and purposefully, since the system of advanced training courses does not keep pace with the rapid development of the information environment.
Digital Twins, Augmented Reality and Explorer Maps Rising Attractiveness of Rural Regions for Outdoor Tourism

Hidden landscape features, visualized by very high resolution digital elevation models (DEM) in combination with augmented reality (AR) apps are able to rise the tourist attractiveness of hiking trails away from the known travel destinations, especially for outdoor adventure tourists. Within the work described, explorer maps as a new map format in combination with the enrichment of landscape perception using digital twins of geographic features were developed. One big challenge was the credible placement of the virtual objects in three-dimensional space. Natural markers at selected points of interest (POI) are used for the tracking of the device in the 3D room and the enrichment of the camera view with the hidden objects of the landscape (e.g. bomb craters, railway features), preprocessed before as virtual representation. This alternative view of the landscape has not only the potential to rise the attractiveness of destinations, but also to attract new target groups in hiking tourism, such as young people and technology-savvy adults.
Mining Pool Selection Problem in the Presence of Block Withholding Attack

Mining, the process where multiple miners compete to add blocks to Proof-of-Work (PoW) blockchains, is of great importance to maintain the tamper-resistance feature of blockchains. In current blockchain networks, miners usually form groups, called mining pools, to improve their revenues. When multiple pools exist, a fundamental mining pool selection problem arises: which pool should each miner join to maximize its revenue? In addition, the existence of mining pools also leads to another critical issue, i.e., Block WithHolding (BWH) attack, where a pool sends some of its miners as spies to another pool to gain extra revenues without contributing to the mining of the infiltrated pool. This paper therefore aims to investigate the mining pool selection issue (i.e., the stable population distribution of miners in the pools) in the presence of BWH attack from the perspective of evolutionary game theory. We first derive the expected revenue density of each pool to determine the expected payoff of miners in that pool. Based on the expected payoffs, we formulate replicator dynamics to represent the growth rates of the populations in all pools. Using the replicator dynamics, we obtain the rest points of the growth rates and discuss their stability to identify the Evolutionarily Stable States (ESSs) (i.e., stable population distributions) of the game. Simulation and numerical results are also provided to corroborate our analysis and to illustrate the theoretical findings.
ET-CycleGAN: Generating Thermal Images from Images in the Visible Spectrum for Facial Emotion Recognition

Facial thermal imaging has in recent years shown to be an efficient modality for facial emotion recognition. However, the use of deep learning in this field is still not fully exploited given the small number and size of the current datasets. The goal of this work is to improve the performance of the existing deep networks in thermal facial emotion recognition by generating new synthesized thermal images from images in the visual spectrum (RGB). To address this challenging problem, we propose an emotion-guided thermal CycleGAN (ET-CycleGAN). This Generative Adversarial Network (GAN) regularizes the training with facial and emotion priors by extracting features from Convolutional Neural Networks (CNNs) trained for face recognition and facial emotion recognition, respectively. To assess this approach, we generated synthesized images from the training set of the USTC-NVIE dataset, and included the new data to the training set as a data augmentation strategy. By including images generated using the ET-CycleGAN, the accuracy for emotion recognition increased by 10.9%. Our initial findings highlight the importance of adding priors related to training set image attributes (in our case face and emotion priors), to ensure such attributes are maintained in the generated images.