Reviewerly/exHarmony huggingface dataset:
Is it Nemo or Dory? Fast and accurate object detection for IoT and edge devices

Current state-of-the-art object detection neural networks, such as YOLO and SSD, are trained and developed on server-class GPUs. These neural networks do not scale down well to resource-constrained devices, with both accuracy and precision taking a significant hit at the expense of speed. This is particularly concerning as object detection algorithms are often used for low-powered devices, such as surveillance and smart-home cameras, where accuracy is critical. Therefore, these devices generally tend to forward data to servers for processing, which adds network latency. In other cases, algorithms developed for these devices shrink neural networks to reduce computation at the expense of accuracy, which is often not acceptable. We create an alternative object detection scheme for static-camera systems such as those used in surveillance and smart-home settings. Our model does not require positions of objects in training data, enabling us to retain only the relevant parts of a video frame for training data and reduce data storage cost while preserving privacy of other subjects in the video. When evaluated on static-camera video feeds using an NVIDIA Jetson Nano (a hybrid Arm and GPU IoT embedded device platform), our method increases throughput compared to Tiny YOLOv3. Further, it takes half of the time that Tiny YOLOv3 takes when run on OpenCV’s CUDA-optimized DNN framework per frame while having high accuracy of bounding box prediction and classification. The lessons learned from this work also suggest other strategies for tailoring the deployment of deep learning algorithms at the edge.
A Novel Deception Defense-Based Honeypot System for Power Grid Network

In recent years, as cyber-attacks have become more and more rampant, power grid networks are also facing more and more security threats, which have gradually become the focus attention of attackers. Traditional defense methods are represented by intrusion detection systems and firewalls, whose main purpose is to keep attackers out. However, with the diversification, concealment and complexity of attack methods, traditional defense methods are usually difficult to cope with the endless attack methods. To this end, this paper proposes a new type of honeypot system based on deception defense technology. While retaining the nature of the honeypot, it adopts dynamic deception approach to actively collect unused IP addresses in the power grid networks. Then, these unused IP addresses are used to construct dynamic virtual hosts. When an attacker initiates network access to these dynamic virtual hosts, they will proactively respond to the attacker or redirect the attack traffic to the honeypot in the background, thereby deceiving and trapping the attacker. The experimental results show that the proposed honeypot system can effectively expands the monitoring range of traditional honeypots and has a good defense effect against unknown attacks, thus effectively making up for the shortcomings of traditional defense methods.
An Ontology Based Resource Description Model for Blockchain-IoT

Blockchain-based Internet of Things (Blockchain-IoT) is becoming widespread to protect users' privacy. However, there are high barriers among heterogeneous Blockchain-IoT systems, making it difficult for massive cross-domain devices to share data among themselves. In this article, we developed an ontology-based Blockchain-IoT resource description model to exchange information on multiple Blockchain-IoT systems. In our model, we specially designed a unified representation method taking granular characteristics and blockchain historical information of Blockchain-IoT resource into account. Firstly, we extracted resource feature information from 4 device dimensions of attributes, control, status, and parameters, where the blockchain historical information is embedded into the parameters feature. Then, we designed an intelligent parking scenario to test our Blockchain-IoT resource description model. Experiment shows that the proposed model can effectively support the access and business functions for the heterogeneous Blockchain-IoT devices in the intelligent parking system with good usability and adaptability.
Effects of the Run-up on Throwing Motion and Throwing Distance in the Lower Grades of Elementary School Children

The purpose of this study was to compare the throwing motion with and without run-up of children in the lower grades of elementary school, who were in the introductory stage of learning the throwing motion, and to clarify the effect of the presence or absence of run-up the throwing motion and throwing distance.
Investigating the precision of 3D scanner systems based on digital fringe projection method for biomedical engineering applications

3D scanners are among the equipment widely used in 3D measurement and are mainly used in various areas such as robotic systems, manufacturing, medicine, etc. There are many types of scanners, including scanners that use the structured light technique, especially the digital fringe projection method for 3D imaging. This research aims to investigate the accuracy and precision of these scanners for biomedical engineering applications, specifically in prosthesis' socket design. The 3D scanner used in this paper has been developed based on digital fringe projection. The three-dimensional measurement process in this system includes two main steps: calibration and imaging of patterns projected on the object. Also, by applying appropriate adjustments, effective parameters in 3D reconstruction and calibration error were optimized. To evaluate the accuracy, speed, and quality of the output images of this system in measuring objects and their interaction with the human body, two validation, and clinical tests were designed and performed, respectively. By examining the available results and obtaining the average system error, it was found that the scanner system can calculate the dimensions of the object with 97% accuracy measurement. This accuracy would be suitable for use in clinical applications.
A Multi-Stage Approach to Breast Cancer Classification Using Histopathology Images

Background and Objectives: Breast cancer is one of the deadliest diseases worldwide amongst women. Early diagnosis and proper treatment can save many lives. Breast image analysis is one of the prime ways of diagnosing breast cancers. Computer-aided diagnosis of breast images helps radiologists to do the task more efficiently and appropriately. In this work, we develop a deep learning-based method to classify breast cancer using histopathological images.Methods: We propose a patch classification model to classify the image patches, where we divide the images into patches and pre-process these patches with stain normalization, regularization, and augmentation methods. We use machine learning based classifiers and ensembling methods to classify the image patches into four classes as Normal, Benign, In-situ and Invasive. Next, we use the patch information from this model to classify the images into two classes (Cancerous and Non-Cancerous) and four classes (Normal, Benign, In-situ and Invasive). We also introduce a model to classify images from 2-class classification to 4-class classification.Results: The proposed method yields promising results and achieves a classification accuracy of 97.50% for 4-class image classification and 98.6% for 2-class image classification on the ICIAR BACH dataset.Conclusion: We demonstrated a DL based method that produces image patches from the high resolution histopathological images and uses the patch information to classify the images into two classes and four classes. The method shows promising results and can be used as a reliable diagnostic tool for breast cancer classification from histopathological breast cancer images.
Societal Gene Acceptance Index-Based Crossover in GA for Travelling Salesman Problem

Selection of genetic material based on sociocultural inheritance in nature is the central theme of dual inheritance theory (DIT) or gene-culture co-evolution theory. Recent studies attribute cases of lactase persistence, high-altitude adaptation in Tibetans, adaptation of heat-generating body fat in cold areas etc. to such inheritance. In DIT, societal and Darwinian selection interact in a closed loop to select attributes. Genetic algorithms (GA) are based on Darwinian theory, and we propose to embrace the concept of DIT in GA by selecting attributes from parents based upon Societal Gene Acceptance Index (SGAI). This paper introduces an SGAI crossover for travelling salesman problem (TSP). This new crossover retains the attributes of the parent genetic structure, while being guided by the society for selection of genes. Also, a framework is presented to compare the new crossover and existing crossovers in an environment, amalgamating the established ideas of social disaster techniques (SDT) and no duplicate policy to achieve novelty search (NS). Testing of crossovers in the framework is done on well-known TSP problems presented in TSPLIB. Statistical evaluation of results shows dominance of proposed crossovers in the test cases.
DeepGuard: A DeepBillboard Attack Detection Technique against Connected and Autonomous Vehicles

Artificial intelligence technology is leading the innovations in connected and automation vehicles (CAVs). This technology revolution mainly relies on embedded smart devices and high-tech sensors along with deep learning-based modules to provide the data and intellect necessary for automated decisions and responses. Generally, imagery data captured from dash cameras are fed into deep neural network models to identify street signs, traffic lights, and surrounding obj ects to augment steering decisions. Such neural networks are proven to be vulnerable to a wide range of adversarial attacks. Despite the emergence of adversarial manipulations, there has been a dramatic increase in the sophisticated methods of these attacks. One of these methods is the DeepBillBoard attack which uses machine-generated imagery applied to roadside billboards to induce errors to the steering model with the capability to dictate whether this error should cause the vehicle to veer to the left or the right. As the sheer risk of such attacks continues to grow, the safety, security, and reliability concerns grow even more. Such concerns cannot be tolerated given the safety-critical environment where CA V s operate. This paper proposes a novel approach, DeepGuard, to detect, counter, and neutralize DeepBillBoard attacks against CA V s. DeepGuard leverages advanced deep learning techniques to boost its generalization capabilities for detecting adversarial patterns used in DeepBillboard attacks. Experimental evaluation is conducted using existing driving datasets that reflect dynamic real-life scenarios. The evaluation results demonstrate that our solution achieves high detection effectiveness and computational efficiency.
Conceptual Design and Implementation of Training Scenarios for Human-Centered Artificial Intelligence in Learning Factories

The number of AI solutions within industry and especially manufacturing has been steadily increasing in recent years. Consequently, AI diffuses into many different areas of employees’ everyday work life. In order to avoid a mismatch or conflict within employee-AI-interactions, a human-centric development of such technology is required. For instance, AI systems need to be adaptable to specific work situations. At the same time, employees need to be able to operate and manage these adaptable AI systems, which requires corresponding expertise. Establishing according skills represent the decisive factor to keep up not only with current but also future technology. However, approaches dedicated to learning how to handle AI have yet to be developed. We fill this gap by establishing a training concept within a learning factory for human-centered adaptive AI-work systems using adaptable demonstrators. The concept is part of an extensive training series and is based on a didactic model. Within this scenario, we follow a socio-technical perspective by considering human, technological, and organizational changes at different levels of observation that result from the introduction of AI in the work system. The concept is implemented within the learning and research factory of the Chair of Production Systems at Ruhr-University Bochum. In this paper, we contribute not only to science but also to practice by deriving action-oriented implications and the description of numerous demonstrators. The development and validation of the learning scenario takes place within the BMBF-funded project human-centered AI network (HUMAINE).
Report on the 4th international workshop on narrative extraction from texts (Text2Story 2021) at ECIR 2021

The Fourth International Workshop on Narrative Extraction from Texts (Text2Story'21) was held on the April 1 st , 2021, in conjunction with the 43 rd European Conference on Information Retrieval (ECIR 2021). Due to the Covid-19 outbreak, the workshop was held online on Zoom platform. During the course of the day, an average of more than 80 attendees had the opportunity to follow-up and discuss the recent advances in extraction and formal representation of narratives. The workshop consisted of two invited keynotes and ten paper presentations. The proceedings of the workshop are available online at http://ceur-ws.org/Vol-2860/ Date : 1 April, 2021. Website : https://text2story21.inesctec.pt/.
A Blockchain-Based Continuous Query Differential Privacy Algorithm

In the differential privacy interactive framework, data sets need to be used to answer multiple queries. With the gradual consumption of the privacy budget, the risk of privacy disclosure increases. Therefore, it is essential to save and track the consumption of the privacy budget, which should not exceed the limit given by the privacy budget. Therefore, firstly, this paper optimizes the Gaussian mechanism to reduce the query response time; Then a Continuous Query Differential Privacy Mechanism (CQDPM) is designed to save the overhead of privacy budget and improve the availability of data; Use the blockchain to record the privacy budget to facilitate the query of the usage of the privacy budget; Finally, a data integrity verification algorithm is proposed by using blockchain. Experiments show that the proposed mechanism reduces the query response time, effectively saves the privacy budget overhead under the same privacy budget limit, and has higher data availability.
Digital image forensics using EXIF data of digital evidence

Pie Information Extractor is one of the applications to data mining from digital photos. The global position system (GPS) with suitable connection with digital technologies enables to put additional layer to data. Exif (Exchangeable image file) information of photographs contains various information, such as date and time the picture was taken, type of camera used, types of settings on the camera, GPS Coordinates where the picture was taken, etc. Today not only digital cameras but also various devices with camera technology could put geotagged layers to photos. This information could be linked with location, camera model name, revised software, created date. Cyberattacks used not only the image layer but EXIF data are very useful. For example, the time and date of geotagged information are potentially useful for thieves and also criminologists on the other side. More and more photos are often shared on social media without any caution about private information. This paper presents another approach of image visual recognition in the interconnection with geotagged information.
Exploring Sub-skeleton Trajectories for Interpretable Recognition of Sign Language

Recent advances in tracking sensors and pose estimation software enable smart systems to use trajectories of skeleton joint locations for supervised learning. We study the problem of accurately recognizing sign language words, which is key to narrowing the communication gap between hard and non-hard of hearing people. Our method explores a geometric feature space that we call ‘sub-skeleton’ aspects of movement. We assess similarity of feature space trajectories using natural, speed invariant distance measures, which enables clear and insightful nearest neighbor classification. The simplicity and scalability of our basic method allows for immediate application in different data domains with little to no parameter tuning. We demonstrate the effectiveness of our basic method, and a boosted variation, with experiments on data from different application domains and tracking technologies. Surprisingly, our simple methods improve sign recognition over recent, state-of-the-art approaches.
TIME HISTORY RESPONSE ANALYSIS CONSIDERING REPEATED DEFORMATION DEPENDENCE OF THE OIL DAMPER UNDER EXTREMELY STRONG EARTHQUAKE GROUND MOTIONS

In recent years in Japan, various research and developments using oil dampers have been carried out to find a way to suppress the displacement of the seismic isolation structure in response to a large-amplitude seismic motion exceeding design specifications. As a countermeasure, the authors have proposed a velocity-dependent, multi-staged oil damper. For the performance of the seismic isolation device responds to a large-amplitude seismic motion, the repeated deformation dependence of the oil damper has been evaluated using time history response analysis by changing the damping coefficient from moment to moment and so far, there has been little research of detailed evaluation. In this paper, we report about a new function newly introduced in OpenSees, which takes into account the repeated deformation dependence of the oil damper, and verified the response suppression effect by the proposed damper, while conducting a seismic response analysis for the test seismic building. As a result, it is concluded that the velocity-dependent, multi-staged oil damper works effectively when the repeated deformation dependence of the seismic isolation device is taken into consideration.
Outdoor Mask - Wearing Surveillance System Using Facial Recognition

A mask on the face has become a new normal lifestyle due to the Covid-19 pandemic. Even after a year, spread of corona virus is increasing and people cannot go out without wearing a mask. All service providers in day- to-day life don’t allow their customers avail their services without mask. Face mask detection has really been one of the most common and trendy as well as an important topic in today’s time to help people take precautions and don’t forget to wear their masks. In this article, we have presented a face mask detector using some basic machine learning packages in-built in python like TensorFlow, Keras, OpenCV, MobileNet.
A Meshless Fragile Points Method for Rule-Based Definition of Myocardial Fiber Orientation

Rule-based methods are commonly used to determine myocardial fiber arrangement. These methods provide a continuous vector field by solving the Laplace problem with appropriate Dirichlet boundary conditions. Rotation rules are subsequently applied to determine the orientation of the fibers according to histology observations. Usually, the Finite Element Method (FEM) is employed to solve the Laplace-Dirichlet problem, as this is the state-of-the-art method for cardiac electromechanical simulations. Recently, novel meshless methods that alleviate the mesh requirement of FEM have been proposed for cardiac electrophysiology simulation. In this context, a mesh representation of the model may not be available and a meshless rule-based method is required to estimate the fiber arrangement. In this work, we propose an implementation of rule-based methods using the novel Fragile Points Method. This is a recently developed method that has promising properties, such as simple discontinuous trial functions and single-point exact integration for linear trial functions. We prove that the Fragile Points Method can generate myocardial fiber arrangements in very good agreement with the FEM for both ventricular and atrial geometry.
Incentivising honest behaviour in P2P networks using blockchain-based reputation

Blockchain-based reward systems have recently been proposed to motivate decentralized execution of various tasks. Our work introduces the notion of reputation in this setting and aims to assess the feasibility of providing incentives for honest user behavior in P2P networks. In this paper, we focus on file sharing and utilize the Proof-of-Prestige reward system as the basis for user incentivisation to mitigate content poisoning. Our evaluation results show that the use of reputation on the Proof-of-Prestige reward system resulted in honest users consistently mining several times more coins than malicious ones. We posit that such an indirect coupling of reputation with rewards can prove highly beneficial for blockchain-based P2P systems by providing counter-incentives against malicious behavior.
Influencing Factors on Application of Digital Educational Resources among Rural Teachers in Underdeveloped Areas

Basing on UTAUT2 model, this research investigated the influencing factors of rural teachers’ digital educational resources (DER) application. The researchers built a theoretical influencing factor model of teachers’ application of DER, and collected quantitative data from 363 rural teachers in underdeveloped areas across country. After structural equation model analysis through AMOS22.0, the 9 hypotheses were tested and the final model was built. It is found that the performance expectation, social influence and sense of pleasure have direct impact on rural teachers’ usage intention of DER, and the promoting factors and effort expectations have indirect impact on it. These findings have some implications on the evaluation and guidance of rural teachers’ DER application especially in underdeveloped area.
Learning in Medicine: The Importance of Statistical Thinking

In many fields, including medicine and biology, there has been in the last years an increasing diffusion and availability of complex data from different sources. Examples include biological experiments or data from health care providers. These data encompass information that can potentially enhance therapeutic advancement and constitute the core of modern system medicine. When analyzing these complex data, it is important to appropriately quantify uncertainty, avoiding using only algorithmic and automated approaches, which are not always appropriate. Improper application of algorithmic approaches, which ignore domain knowledge, could result in filling the literature with imprecise and/or misleading conclusions. In this chapter, we highlight the importance of statistical thinking when leveraging complex data and models to enhance science progress. In particular, we discuss the reproducibility and replicability issues, the importance of uncertainty quantification, and some common pitfalls in the analysis of big data.
Research on Speaker Identification Models Based on CNN and Additive Angular Margin Loss

In view of the traditional speaker identification methods which usually have poor generalization ability and high computational complexity, three text-independent closed-set speaker identification models named Res-SD, Res-SArc and Rep-SArc respectively are designed based on the Red-Song dataset constructed by us. Res-SD is trained by using the traditional cross-entropy loss, while Res-SArc and Rep-SArc are trained by using additive angular margin loss that optimizes the feature embedding to realize higher intra-class similarity and higher inter-class diversity. In terms of the number of model parameters, Rep-SArc accounts for only 1/3 of Res-SArc. Test experiments for evaluating three models are conducted on the Red-Song dataset's test set. In terms of identification accuracy, Rep-SArc and Res-SArc are more competitive among three models, and their identification accuracies reach 97.90% and 97.19%, respectively. The experimental results validate that these three models are effective for the text-independent closed-set speaker identification tasks.