Reviewerly/exHarmony huggingface dataset:
FisheyeDet: A Self-Study and Contour-Based Object Detector in Fisheye Images

Fisheye Images have attracted increasing attention from the research community due to their large field of view (LFOV). However, the geometric transformations inherent in fisheye cameras result in unknown spatial distortion and large variations in the appearance of objects. And this fact leads to poor performance of the state-of-the-art methods in conventional two-dimensional (2D) images. To address this problem, we propose a self-study and contour-based object detector in fisheye images, named FisheyeDet. The No-prior Fisheye Representation Method is proposed to guarantee that the network adaptively extracts distortion features without prior information such as prespecified lens parameters, special calibration patterns, etc. Furthermore, in order to tightly and robustly localize objects in fisheye images, the Distortion Shape Matching strategy is proposed, which invokes the irregular quadrilateral bounding boxes based on the contour of distorted objects as the core. By combining with the “No-prior Fisheye Representation Method” and “Distortion Shape Matching”, our proposed detector builds an end-to-end network. Finally, due to the lack of public fisheye datasets, we are on the first attempt to create a multi-class fisheye dataset VOC-Fisheye for object detection. Our proposed detector shows favorable generalization ability and achieves 74.87% mAP (mean average precision) on the VOC-Fisheye, outperforming the existing state-of-the-art methods.
Smart IOT Surveillance Multi-Camera Monitoring System

The surveillance digital monitoring process market has changed aggressively over the last 15 years. Where the process was needed for people to secure their business by a simple surveillance camera system to give them that much needed peace of mind. However, the existing monitoring systems are very expensive due to internal specific SDK configuration which was embedded in the camera itself. Some of them provide a solution such as a high-priced customized command center that has several screens view which communicates with several cameras to monitor the cameras with a specific detection analysis module. In this paper, a cheaper solution to the monitoring system for existing surveillance cameras is introduced to overcome the solution. This system only implements open source image processing methods to produce a monitoring system with customizable modules for video analytics (video content analysis) with the input of live video sources. This is to allow a wider range of camera models that can be used for this system. A real-time analysis module will assist in the use of the system to ease the user. The combined video stream and the module will help the user immensely for surveillance propose. Based on the result, the proposed system achieved a higher affordability level up to 95% with 90% usability compared to existing products.
Beyond the Prototype: Understanding the Challenge of Scaling Hardware Device Production

The hardware research and development communities have invested heavily in tools and materials that facilitate the design and prototyping of electronic devices. Numerous easy-to-access and easy-to-use tools have streamlined the prototyping of interactive and embedded devices for experts and led to a remarkable growth in non-expert builders. However, there has been little exploration of challenges associated with moving beyond a prototype and creating hundreds or thousands of exact replicas - a process that is still challenging for many. We interviewed 25 individuals with experience taking prototype hardware devices into low volume production. We systematically investigated the common issues faced and mitigation strategies adopted. We present our findings in four main categories: (1) gaps in technical knowledge; (2) gaps in non-technical knowledge; (3) minimum viable rigor in manufacturing preparation; and (4) building relationships and a professional network. Our study unearthed several opportunities for new tools and processes to support the transition beyond a working prototype to cost effective low-volume manufacturing. These would complement the aforementioned tools and materials that support design and prototyping.
Oppositional spotted hyena optimizer with mutation operator for global optimization and application in training wavelet neural network

Success behind nature inspired evolutionary metaheuristic algorithms lies in its seemly combination of operator's castoff for smooth balance between exploration and exploitation. The deficit in such combination leads to untimely convergence of an algorithm, simultaneously failed to attain global op timum by stocking in local optimum. This work represents atypical algorithm termed as OBL-MO-SHO to improve the performance of existing SHO. To deal with more intricate realistic problems and to enhance the explorative and exploitative strength of SHO, we have integrated the oppositional learning concept with mutation operator. The proposed algorithm OBL-MO-SHO (oppositional spotted hyena optimizer with mutation operator) reveals promising performance in terms of achieving global optimum and superior convergence rate which confirms its improved exploration and exploitation capability within searching region. To establish competency of proposed OBL-MO-SHO algorithm the same is appraised by means of standard functions set belongs to IEEE CEC 2017. The efficacy of said method has been proven by means of various performance metrics and the outcomes also compared with state-of-the-art algorithms. To scrutinize its uniqueness statistically, Friedman and Holms test has been performed as one non-parametric test. Additionally as an application to unravel real world intricate difficulties the said OBL-MO-SHO algorithm has been castoff to train wavelet neural network by considering datasets selected from UCI depository. The reported results unveils that the evolved OBL-MO-SHO might be one potential algorithm for enlightening different optimization difficulties effectively.
Improved well log classification using semisupervised Gaussian mixture models and a new hyper-parameter selection strategy

Well log classification, the process of mapping well log measurements to lithofacies identified from core samples, is a common procedure in the oil and gas industry. Manually assigning lithofacies to the wire-line log measurements without core can be time consuming, and can also introduce a bias. Supervised machine learning algorithms are commonly used to automate this process, but they are prone to overfitting when the training data are scarce, which is common for well log classification problems. Semisupervised machine learning algorithms are designed for classification problems with minimal training data, and we adopt a semisupervised Gaussian mixture model (ssGMM) method to solve this problem. The dataset we consider for our study is from a machine learning competition held in 2016 and we simulate a semisupervised scenario by assuming only one out of the ten wells is the labeled data. We apply ssGMM to this well log dataset and compare its performance to the supervised method that was the winner of this competition, XGBoost. To try and improve the performance of both ssGMM and XGBoost, we also introduce a new hyper-parameter selection strategy that simultaneously uses the mean and standard deviation cross-validation scores, compared to the default procedure that only utilizes the mean cross-validation scores. Our results indicate that ssGMM is able to slightly outperform XGBoost in our semisupervised context, which supports the suggestion that semisupervised algorithms are more appropriate in low training data situations. We also show that our new hyper-parameter selection technique selects hyper-parameters for ssGMM that perform better on the testing data, but the performance is mixed for XGBoost.
Identification of Abnormal Red Blood Cells and Diagnosing Specific Types of Anemia Using Image Processing and Support Vector Machine

Blood is an important liquid that supports performs important functions for human. The general components of human blood are RBCs, WBCs, plasma, and platelets. Whereas, RBCs occupies the largest volume in blood. RBCs can be healthy or unhealthy. An unhealthy or abnormal red blood cells indicates blood related disorders, such as Anemia. RBCs can be determined by variations in size, shape, and color. The method in evaluating RBC is through conventional microscopy which leads to misreport results and experts are given hefty workload. A proposed system is aimed to support medical technicians, hematologist, and pathologist in identifying RBC by creating a device using Raspberry Pi that can measure the different parameter of the RBC such as area, perimeter, diameter, shape geometric factor (SGF), and detecting the central pallor and target flag. Related studies were already conducted using different approach one study uses Artificial Neural Network (ANN) in classifying RBC with an accuracy 90.54%. Another study uses radial basis function network it obtained an accuracy of 83.3%. With the proposed system using Support Vector Machine (SVM) classifier it acquired an accuracy of 93.33% in identifying 7 different red blood cells such as normal, echinocytes, elliptocytes, dacrocytes, spherocytes, target cell, stomatocytes, and identifying unknown cells. The classification of RBC helps in diagnosing different types of anemia such as iron-deficiency anemia, thalassemia, hereditary spherocytosis, and myelophthisic anemia. This system serves only as an aid for doctors for early diagnosis of identifying abnormal red blood cells and succeeding laboratory exams must be done to finally conclude a disease associated to abnormal RBCs.
FPGA based Deep Learning Models for Object Detection and Recognition Comparison of Object Detection Comparison of object detection models using FPGA

Real-time object detection and recognition finds extensive applications in diverse fields such as medical applications, security surveillance, and autonomous vehicles. There are many machines and deep learning techniques that are employed for object detection and recognition. The emergence of a convolutional neural network (CNN) has provided a major breakthrough for object detection and recognition. This work also includes the hardware implementation of the same with the help of Xilinx PYNQ Z2 and Intel Movidius Neural Compute Stick (NCS) which are proved to increase the performance of the system proposed. The results are compared based on three deep learning methods: Single Shot Detector (SSD), Faster Region CNN (FRCNN), You Only Look Once (YOLO). The parameters that are considered are frames per second, probability of detection, and time for computation. The results obtained are performing well compared to existing models.
Low-Power HWAccelerator for AI Edge-Computing in Human Activity Recognition Systems

In this paper, an energy efficient HW accelerator for AI edge-computing in Human Activity Recognition is proposed. The system processes samples from a tri-axial accelerometer and classifies the human activities by using a novel Hybrid Neural Network (HNN) topology, which has been designed to reduce the computational complexity of the system while preserving its accuracy. The HW design improves the characteristics of the HNN by means of an architecture that is aimed to reduce the allocated physical resources and the memory accesses. While accuracy measured on ad-hoc dataset is 97.5 %, measurements from synthesis with CMOS 65 nm standard cells report power consumption of 6.3 μW when the sensor output data rate is 25 Hz, normally used for HAR.
Low Light Image Enhancement Network With Attention Mechanism and Retinex Model

Due to the uncertainty of the environment, the captured image may not only degrade but also have uneven brightness distribution.The quality of these images can not meet the input requirements of existing computer vision tasks, so they may lead to performance degradation in the completion of computer vision tasks.Past methods are often only applicable to low-light images with uniform brightness distribution, and the performance of these methods is not ideal for low-light images with uneven brightness distribution.In order to solve the problems caused by these low-light images, a new low light image enhancement model based on attention mechanism and Retinex model is proposed.The proposed method first estimates the illumination mask of the input image, which guides the network to predict the illumination distribution.Then we use a module with attention mechanism to predict the illumination map, and the initial enhanced image is estimated based on Retinex model.We modify the color distortion and suppress noise with convolution layers to obtain final enhanced results.In experiments, the performance of our methods is demonstrated by compared with the state-of-the-art existing methods.Our approach has more positive performance in some scenarios, especially uneven lighting.
Neural Network Based End-to-End Query by Example Spoken Term Detection

This article focuses on the problem of query by example spoken term detection (QbE-STD) in zero-resource scenario. State-of-the-art approaches primarily rely on dynamic time warping (DTW) based template matching techniques using phone posterior or bottleneck features extracted from a deep neural network (DNN). We use both monolingual and multilingual bottleneck features, and show that multilingual features perform increasingly better with more training languages. Previously, it has been shown that the DTW based matching can be replaced with a CNN based matching while using posterior features. Here, we show that the CNN based matching outperforms DTW based matching using bottleneck features as well. In this case, the feature extraction and pattern matching stages of our QbE-STD system are optimized independently of each other. We propose to integrate these two stages in a fully neural network based end-to-end learning framework to enable joint optimization of those two stages simultaneously. The proposed approaches are evaluated on two challenging multilingual datasets: Spoken Web Search 2013 and Query by Example Search on Speech Task 2014, demonstrating in each case significant improvements.
A deceptive review detection framework: Combination of coarse and fine-grained features

Electronic commerce has become a popular shopping mode. To enhance their reputations, attract more customers, and finally obtain more benefits, dishonest sellers often recruit buyers or robots to post a large number of deceptive reviews to mislead users. According to the interpretability of learning results, existing methods for detecting deceptive reviews can be mainly divided into explicit feature-based mining ones and neural network-based implicit feature mining ones. The nature of these works is accurate text classification based on coarse-grained features (e.g., topic, sentence, and document) or fine-grained features (e.g., word). To take full merits of existing approaches, this paper proposes a new framework that explores a method to combine the coarse-grained features and the fine-grained features. In this framework, the coarse-grained implicit semantic features of the topic distribution are learned by the concatenation of a Latent Dirichlet Allocation (LDA) topic model and a 2-layered neural network. The fine-grained implicit semantic features from the word vectors representation of the reviews are parallelly learned by a deep learning framework. Finally, these two granular features are combined and adopted to train a Support Vector Machine (SVM) classifier for detecting whether a review is deceptive or not. To verify the effectiveness and performance of this framework, we derive three models by specifying three popular deep learning models, such as TextCNN, long short-term memory (LSTM), and Bi-directional LSTM (BiLSTM) to learn the fine-grained features. Experimental results on a mixed-domain dataset and balanced/unbalanced in-domain datasets show that all the combination models are superior to the corresponding baseline models considering single features.
Panoramic Video Quality Assessment Based on Non-Local Spherical CNN

Panoramic video and stereoscopic panoramic video are essential carriers of virtual reality content, so it is very crucial to establish their quality assessment models for the standardization of virtual reality industry. However, it is very challenging to evaluate the quality of the panoramic video at present. One reason is that the spatial information of the panoramic video is warped due to the projection process, and the conventional video quality assessment (VQA) method is difficult to deal with this problem. Another reason is that the traditional VQA method is problematic to capture the complex global time information in the panoramic video. In response to the above questions, this paper presents an end-to-end neural network model to evaluate the quality of panoramic video and stereoscopic panoramic video. Compared to other panoramic video quality assessment methods, our proposed method combines spherical convolutional neural networks (CNN) and non-local neural networks, which can effectively extract complex spatiotemporal information of the panoramic video. We evaluate the method in two databases, VRQ-TJU and VR-VQA48. Experiments show the effectiveness of different modules in our method, and our method outperforms state-of-the-art other related methods.
Empirical Detection Techniques of Insider Threat Incidents

Vital organizations have faced increasing challenges of how to defend against insider threats that may cause a severe damage to their assets.The nature of insider threats is more challenging than external threats, as insiders have a privileged access to sensitive assets of an organization.In fact, there are several studies that reviewed the insider threat detection approaches from taxonomical and theoretical perspectives.However, the protection against insider threat incidents requires empirical defense solutions.Hence, our study uniquely focuses on empirical detection approaches that are validated with empirical results.We propose a 10-question model that highlights different prospective of empirical detection approaches.Significant factors are also proposed to reveal the extent to which the detection approaches are effective against insider threat incidents (e.g., feature domains, protection coverage, classification techniques, simulated scenarios, performance and accuracy metrics, etc.).The objective of this paper is to enhance researchers' efforts in the domain of insider attack by systemizing the detection techniques in comparable manner.It also highlights the challenges and gaps for further research to institute more effective solutions that can predict, detect, and prevent emerging attack incidents.Some recommendations for future research directions are also presented.
Evaluation of an interactive educational system in urban knowledge acquisition and representation based on students' profiles

Abstract The aim of this article is to study how students currently understand the conception of space through different media and how that understanding helps them to intervene in the space. Firstly, the process of teaching and learning as well as innovative supporting technologies are analysed, revealing the characteristics of the contemporary student profile and better ways of teaching according to it. Secondly, we describe the evaluation of an experiment with a virtual reality (VR) system for urban project design with students of architecture from two universities. The premise is that the technology used in VR is familiar to the current profile of students. This paper aims to study the advantages and disadvantages of this trend and to find a balance. To do so, we use a quantitative method to evaluate students' profiles and their level of satisfaction with the system. The results were obtained by a questionnaire and a survey, which show the role and use of technologies in the students' environment and the degree of satisfaction when using it in the educational processes. In line with our assumptions, the value of satisfaction in the use of an advanced visualization technology in the classroom reveals a high level of motivation, in general, with differentiation between students in their first and last phases of studies.
Embracing Digital Technology in Science Classrooms—Secondary School Teachers’ Enacted Teaching and Reflections on Practice

Abstract The aim of this case study was to investigate what happens in science classrooms when teaching is almost entirely based on the use of digital technology. Two secondary school science teachers participated, together with their seventh grade classes. Data were collected through eight observation sessions (altogether about 9.5 h) and 9 h of interviews with the teachers. For analysis, a modified version of the Technological Pedagogical and Content Knowledge framework was used. The results showed the science teachers’ general approach in the classroom and revealed that they were self-confident in using digital technology, and utilised predetermined digital study material and, when it was felt necessary, supplemental materials. The teachers were positive about using digital technology since they thought it motivated their students and made assessment easier. The teachers claimed that digital technology had improved their teaching, providing more breadth because of access to varied digital tools; teaching had also become more individualised. Few differences were identified between different lessons, whether in physics, chemistry or biology, and unfortunately the identified relationship between the use of digital technology and content knowledge was limited. The teachers also reflected on the challenges they faced, especially in supporting low-achieving students and effectively using inquiry-based teaching through digital technology. Despite some acknowledged limitations, the study enhances our knowledge about how the Technological Pedagogical Content Knowledge framework can be used as an analytical tool in authentic teaching, with specific contexts and, above all, when education is largely based on the comprehensive use of digital technology rather than its occasional integration.
A survey on blockchain-based platforms for IoT use-cases

Abstract The Internet of Things (IoT) has recently emerged as an innovative technology capable of empowering various areas such as healthcare, agriculture, smart cities, smart homes and supply chain with real-time and state-of-the-art sensing capabilities. Due to the underlying potential of this technology, it already saw exponential growth in a wide variety of use-cases in multiple application domains. As researchers around the globe continue to investigate its aptitudes, a collective agreement is that to get the best out of this technology and to harness its full potential, IoT needs to sit upon a flexible network architecture with strong support for security, privacy and trust. On the other hand, blockchain (BC) technology has recently come into prominence as a breakthrough technology with the potential to deliver some valuable properties such as resiliency, support for integrity, anonymity, decentralization and autonomous control. Several BC platforms are proposed that may be suitable for different use-cases, including IoT applications. In such, the possibility to integrate the IoT and BC technology is seen as a potential solution to address some crucial issues. However, to achieve this, there must be a clear understanding of the requirements of different IoT applications and the suitability of a BC platform for a particular application satisfying its underlying requirements. This paper aims to achieve this goal by describing an evaluation framework which can be utilized to select a suitable BC platform for a given IoT application.