Reviewerly/exHarmony huggingface dataset:
Impact of an Artificial Intelligence Research Frame on the Perceived Credibility of Educational Research Evidence

Abstract Artificial Intelligence (AI) is attracting a great deal of attention and it is important to investigate the public perceptions of AI and their impact on the perceived credibility of research evidence. In the literature, there is evidence that people overweight research evidence when framed in neuroscience findings. In this paper, we present the findings of the first investigation of the impact of an AI frame on the perceived credibility of educational research evidence. In an experimental study, we allocated 605 participants including educators to one of three conditions in which the same educational research evidence was framed within one of: AI, neuroscience, or educational psychology. The results demonstrate that when educational research evidence is framed within AI research, it is considered as less credible in comparison to when it is framed instead within neuroscience or educational psychology. The effect is still evident when the subjects’ familiarity with the framing discipline is controlled for. Furthermore, our results indicate that the general public perceives AI to be: less helpful in assisting us to understand how children learn, lacking in adherence to scientific methods, and to be less prestigious compared to neuroscience and educational psychology. Considering the increased use of AI technologies in Educational settings, we argue that there should be significant attempts to recover the public image of AI being less scientifically robust and less prestigious than educational psychology and neuroscience. We conclude the article suggesting that AI in Education community should attempt to be more actively engaged with key stakeholders of AI and Education to help mitigate such effects.
Novel Assembled Sensorized Glove Platform for Comprehensive Hand Function Assessment by Using Inertial Sensors and Force Sensing Resistors

This study proposed a novel assembled sensorized glove combining nine-axis inertial measurement units (IMUs) and force sensing resistors (FSRs) to simultaneously measure hand kinematics and fingertip force. The sensorized glove was designed to have high flexibility and extensibility. The accuracy and reliability of the hand kinematics measurements were verified using four-finger flexion-extension tasks. The results reveal that the mean absolute errors (MAEs) of the joint angles of fingers are all <; 5°, thus indicating that the glove can precisely measure hand kinematics. The MAE of the fingertip force prediction was 1.47 N, thus revealing that the FSRs can accurately measure the fingertip force. Moreover, comprehensive evaluation was conducted to prove that the sensorized glove can not only simultaneously measure the hand kinematics and fingertip force but also distinguish between the subjects with distinct hand functions. Therefore, the sensorized glove proposed in this study is reliable and has a strong potential for application in practical rehabilitation settings.
Certificateless searchable public key encryption scheme secure against keyword guessing attacks for smart healthcare

The smart healthcare system (SHS) provides a new information service mode. It greatly improves the diagnostic efficiency by monitoring patients’ signs information via various wearable devices. To ensure the confidentiality of sensitive information, the security and privacy issues have drawn wide attention. The searchable encryption technology is suitable for addressing these issues, because it supports search over encrypted data and provides data privacy protection. Recently, many searchable public-key encryption (SPE) schemes have been designed to balance security and efficiency. However, these SPE schemes face the challenge of certificate management or key escrow. This is because they are constructed based on public key infrastructure (PKI) or identity (ID) cryptosystem. Meanwhile, most of SPE schemes are subject to great security threats, such as keyword guessing attacks (KGA). To resolve the above problems, this paper designs a secure certificateless SPE scheme for SHS, and this scheme does not require the use of secure channels, i.e., a SCF-CLSPE scheme. We prove that this SCF-CLSPE scheme can resist KGA and chosen keyword attacks (CKA) under standard model. In addition, the results of performance analysis indicate that this SCF-CLSPE scheme can achieve better efficiency.
Predictive Model for Classification of Power System Faults using Machine Learning

Power System is a combination of electric power generation, transmission, distribution and utilization systems. In brief, power system is the heart of any electrical system. In an electric power system, a fault or fault current is any abnormal electric current. As a consequence of such fault, the entire system may damage and eventually collapse. The aim of this work is to automatically classify the faults into one of the eleven faulty classes, which includes both balanced and unbalanced faults. The dataset of generated fault in overhead transmission lines is synthetic, which consists of 11 different faults for 100 kilometers. The simulation is done using MATLAB/Simulink software model. The task of classification of faults is implemented using supervised machine learning algorithms in Python and scikit-learn. Comparison is made using three commonly used classification algorithms - Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM). SVM performed excellent giving a performance with 91.6% test accuracy for the generated dataset. The predictive model will thus make the system more intelligent in bringing up reliable power supply.
Data-dependent Gaussian Prior Objective for Language Generation

For typical sequence prediction problems like language generation, maximum likelihood estimation (MLE) has been commonly adopted as it encourages the predicted sequence most consistent with the ground-truth sequence to have the highest probability of occurring. However, MLE focuses on a once-for-all matching between the predicted sequence and gold-standard consequently, treating all incorrect predictions as being equally incorrect. We call such a drawback {\it negative diversity ignorance} in this paper. Treating all incorrect predictions as equal unfairly downplays the nuance of these sequences' detailed token-wise structure. To counteract this, we augment the MLE loss by introducing an extra KL divergence term which is derived from comparing a data-dependent Gaussian prior and the detailed training prediction. The proposed data-dependent Gaussian prior objective (D2GPo) is defined over a prior topological order of tokens, poles apart from the data-independent Gaussian prior (L2 regularization) commonly adopted for smoothing the training of MLE. Experimental results show that the proposed method can effectively make use of more detailed prior in the data and significantly improve the performance of typical language generation tasks, including supervised and unsupervised machine translation, text summarization, storytelling, and image caption.
Guide to Match: Multi-Layer Feature Matching With a Hybrid Gaussian Mixture Model

As a fundamental yet challenging task in computer vision, finding correspondences between two sets of feature points has received extensive attention. Among all the proposed methods, the Gaussian Mixture Model (GMM) based algorithms show their great power in formulating such problems. However, they are vulnerable to large portion of outliers in the extracted feature points. In this paper, a new Hybrid Gaussian Mixture Model (HGMM) combined with a multi-layer matching framework is proposed. Different from existing GMM based methods, HGMM uses a set of seed correspondences to guide the matching procedure. To automatically find seed correspondences, the feature points are divided into multiple layers according to their matching potential. With the help of Locality Sensitive Hashing, this can be done economically and efficiently. Correspondences found in lower layers which contain few outliers will be used as hard constraint when matching features in higher layers where a large portion of outliers exist. Extensive experiments show that the proposed method is efficient and more robust to outliers when images have large viewpoint difference or small scene overlap.
Breast tumor classification through learning from noisy labeled ultrasound images

Purpose To train deep learning models to differentiate benign and malignant breast tumors in ultrasound images, we need to collect many training samples with clear labels. In general, biopsy results can be used as benign/malignant labels. However, most clinical samples generally do not have biopsy results. Previous works have proposed generating benign/malignant labels according to Breast Imaging, Reporting and Data System (BI‐RADS) ratings. However, this approach will cause noisy labels, which means that the benign/malignant labels produced from BI‐RADS diagnoses may be inconsistent with the ground truths. Consequently, deep models will overfit the noisy labels and hence obtain poor generalization performance. In this work, we mainly focus on how to reduce the negative effect of noisy labels when they are used to train breast tumor classification models. Methods We propose an effective approach called noise filter network (NF‐Net) to address the problem of noisy labels when training breast tumor classification models. Specifically, to prevent deep models from overfitting the noisy labels, we propose incorporating two softmax layers for classification. Additionally, to strengthen the effect of clean labels, we design a teacher–student module for distilling the knowledge of clean labels. Results We conduct extensive comparisons with the existing works on addressing noisy labels. Our method achieves a classification accuracy of 73%, with a precision of 69%, recall of 80%, and F1‐score of 0.74. This result is significantly better than those of the existing state‐of‐the‐art works on addressing noisy labels. Conclusions This work provides a means to overcome the label shortage problem in training breast tumor classification models. Specifically, we can generate benign/malignant labels according to the BI‐RADS ratings. Although this approach will cause noisy labels, the design of NF‐Net can effectively reduce the negative effect of such labels.
Coupled generative adversarial network for heterogeneous face recognition

The large modality gap between faces captured in different spectra makes heterogeneous face recognition (HFR) a challenging problem. In this paper, we present a coupled generative adversarial network (CpGAN) to address the problem of matching non-visible facial imagery against a gallery of visible faces. Our CpGAN architecture consists of two sub-networks one dedicated to the visible spectrum and the other sub-network dedicated to the non-visible spectrum. Each sub-network consists of a generative adversarial network (GAN) architecture. Inspired by a dense network which is capable of maximizing the information flow among features at different levels, we utilize a densely connected encoder-decoder structure as the generator in each GAN sub-network. The proposed CpGAN framework uses multiple loss functions to force the features from each sub-network to be as close as possible for the same identities in a common latent subspace. To achieve a realistic photo reconstruction while preserving the discriminative information, we also added a perceptual loss function to the coupling loss function. An ablation study is performed to show the effectiveness of different loss functions in optimizing the proposed method. Moreover, the superiority of the model compared to the state-of-the-art models in HFR is demonstrated using multiple datasets.
A Dual-Path and Lightweight Convolutional Neural Network for High-Resolution Aerial Image Segmentation

Semantic segmentation on high-resolution aerial images plays a significant role in many remote sensing applications. Although the Deep Convolutional Neural Network (DCNN) has shown great performance in this task, it still faces the following two challenges: intra-class heterogeneity and inter-class homogeneity. To overcome these two problems, a novel dual-path DCNN, which contains a spatial path and an edge path, is proposed for high-resolution aerial image segmentation. The spatial path, which combines the multi-level and global context features to encode the local and global information, is used to address the intra-class heterogeneity challenge. For inter-class homogeneity problem, a Holistically-nested Edge Detection (HED)-like edge path is employed to detect the semantic boundaries for the guidance of feature learning. Furthermore, we improve the computational efficiency of the network by employing the backbone of MobileNetV2. We enhance the performance of MobileNetV2 with two modifications: (1) replacing the standard convolution in the last four Bottleneck Residual Blocks (BRBs) with atrous convolution; and (2) removing the convolution stride of 2 in the first layer of BRBs 4 and 6. Experimental results on the ISPRS Vaihingen and Potsdam 2D labeling dataset show that the proposed DCNN achieved real-time inference speed on a single GPU card with better performance, compared with the state-of-the-art baselines.
Pedestrian Planar LiDAR Pose (PPLP) Network for Oriented Pedestrian Detection Based on Planar LiDAR and Monocular Images

Pedestrian detection is an important task for human-robot interaction and autonomous driving applications. Most previous pedestrian detection methods rely on data collected from three-dimensional (3D) Light Detection and Ranging (LiDAR) sensors in addition to camera imagery, which can be expensive to deploy. In this letter, we propose a novel Pedestrian Planar LiDAR Pose Network (PPLP Net) based on two-dimensional (2D) LiDAR data and monocular camera imagery, which offers a far more affordable solution to the oriented pedestrian detection problem. The proposed PPLP Net consists of three sub-networks: an orientation detection network (OrientNet), a Region Proposal Network (RPN), and a PredictorNet. The OrientNet leverages state-of-the-art neural-network-based 2D pedestrian detection algorithms, including Mask R-CNN and ResNet, to detect the Bird's Eye View (BEV) orientation of each pedestrian. The RPN transfers 2D LiDAR point clouds into occupancy grid map and uses a frustum-based matching strategy for estimating non-oriented 3D pedestrian bounding boxes. Outputs from both OrientNet and RPN are passed through the PredictorNet for a final regression. The overall outputs of our proposed network are 3D bounding box locations and orientation values for all pedestrians in the scene. We present oriented pedestrian detection results on two datasets, the CMU Panoptic Dataset and a newly collected FCAV M-Air Pedestrian (FMP) Dataset, and show that our proposed PPLP network based on 2D LiDAR and monocular camera achieves similar or better performance to previous state-of-the-art 3D-LiDAR-based pedestrian detection methods in both indoor and outdoor environments.
Augmented Reality Applications in Tourism

This study aims to examine the use of the application in tourism field on the question of what augmented reality applications mean, which is one of the endpoints of technology for tourism. With the study of in-depth literature, firstly augmented reality has been determined, and the changes and developments it has undergone throughout the history has been examined. Then, its areas of use and the types have been examined. Today, it is thought that the augmented reality, being the newest dimension of technology and seems it is almost impossible not to use in the area of tourism will provide a huge amount of marketing convenience and competitive advantages. In this article, twelve case studies on the application used in many fields of tourism such as transportation, accommodation, food & beverage and museums, have been analyzed. As a result of these analyses, it is seen that augmented reality applications, which are used in this field increasingly, provide a great marketing convenience to businesses and destinations. Augmented reality applications, playing a major role in the travels of tourists, make consumers feel safer while making the travels easier. it is thought that all businesses that want to capture market progress in the coming years and aim to provide competitive advantage by making a difference in influencing tourists will intensively use augmented reality applications.
GazMon: Eye Gazing Enabled Driving Behavior Monitoring and Prediction

Automobiles have become one of the necessities of modern life, but also introduced numerous traffic accidents that threaten drivers and other road users. Most state-of-the-art safety systems are passively triggered, reacting to dangerous road conditions or driving maneuvers only after they happen and are observed, which greatly limits the last chances for collision avoidances. Timely tracking and predicting the driving maneuvers calls for a more direct interface beyond the traditional steering wheel/brake/gas pedal. In this paper, we argue that a driver's eyes are the interface, as it is the first and the essential window that gathers external information during driving. Our experiments suggest that a driver's gaze patterns appear prior to and correlate with the driving maneuvers for driving maneuver prediction. We accordingly present GazMon, an active driving maneuver monitoring and prediction framework for driving assistance applications. GazMon extracts the gaze information through a front-camera and analyzes the facial features, including facial landmarks, head pose, and iris centers, through a carefully constructed deep learning architecture. Both our on-road experiments and driving simulator based evaluations demonstrate the superiority of our GazMon on predicting driving maneuvers as well as other distracted behaviors. It is readily deployable using RGB cameras and allows reuse of existing smartphones towards more safely driving.
Efficient discrete supervised hashing for large-scale cross-modal retrieval

Supervised cross-modal hashing has gained increasing research interest on large-scale retrieval task owning to its satisfactory performance and efficiency. However, there are still some issues to be further addressed: (1) most of them fail to capture the inherent data structure effectively due to the complex correlations among heterogeneous data points; (2) most of them obtain continuous solutions firstly and then quantize the continuous solutions to generate hash codes directly, which causes large quantization error and consequent suboptimal retrieval performance; (3) most of them suffer from relatively high memory cost and computational complexity during training procedure, which makes them unscalable. In this paper, to address above issues, we propose a supervised hashing method for cross-modal retrieval dubbed Efficient Discrete Supervised Hashing (EDSH). Specifically, the sharing space learning with collective matrix factorization and semantic embedding with class labels are seamlessly integrated to learn hash codes. Therefore, the feature based similarities and semantic correlations are both preserved in hash codes, which makes the learned hash codes more discriminative. Then an efficient discrete optimal scheme is designed to handle the scalable issue. Instead of learning hash codes bit-by-bit, hash codes matrix can be obtained directly which is more efficient. Extensive experimental results on three public datasets show that our EDSH produces a superior performance in both accuracy and scalability over several existing cross-modal hashing approaches.
An Integrated approach for Path Planning for Mobile Robot Using Bi-RRT

Abstract In this paper, an integrated effort has been exploited for path planning along with path smoothness for a mobile robot. An extension of randomly exploring tree is used to plan the path for robot along with path length smoothness. Our proposed system influences the concept of Bi-directional Rapidly Exploring Random Trees (Bi-RRTs) for path planning together with path smoothness for minimizing the processing time to search for the path length. We compared our system with standard method in trials with the autonomous robotic platform. Produced results show that the presented approach as high robustness and use less computational effort.
NAIS: Neural Architecture and Implementation Search and its Applications in Autonomous Driving

The rapidly growing demands for powerful AI algorithms in many application domains have motivated massive investment in both high-quality deep neural network (DNN) models and high-efficiency implementations. In this position paper, we argue that a simultaneous DNN/implementation co-design methodology, named Neural Architecture and Implementation Search (NAIS), deserves more research attention to boost the development productivity and efficiency of both DNN models and implementation optimization. We propose a stylized design methodology that can drastically cut down the search cost while preserving the quality of the end solution. As an illustration, we discuss this DNN/implementation methodology in the context of both FPGAs and GPUs. We take autonomous driving as a key use case as it is one of the most demanding areas for high quality AI algorithms and accelerators. We discuss how such a co-design methodology can impact the autonomous driving industry significantly. We identify several research opportunities in this exciting domain.
FakeTalkerDetect: Effective and Practical Realistic Neural Talking Head Detection with a Highly Unbalanced Dataset

Detecting realistic fake images and videos is an increasingly important and urgent problem because they can be maliciously used. In this work, we propose FakeTalkerDetect, which is based on siamese networks to detect the recently proposed realistic talking head with few-shot learning. Unlike conventional methods, we propose to use pre-trained models with only a few real images for fine-tuning in siamese networks to effectively detect the fake images in a highly unbalanced data setting. Our FakeTalkerDetect achieves the overall accuracy 98.81% accuracy in detecting fake images generated from the latest neural talking head models. In particular, our preliminary work also demonstrates the effectiveness for the highly unbalanced dataset.
Knowledge based domain adaptation for semantic segmentation

Domain adaptation for semantic segmentation is a challenging problem for two reasons. One reason is that annotating labels is an extremely high cost work. Another reason is that the domain gap between the source and target domains limits the performance of semantic segmentation. In this paper, we propose an unsupervised knowledge based domain adaptation method for semantic segmentation. The proposed method consists of three steps. First, the common knowledge is loaded from the source and target domains. Then, the loaded knowledge is filtered according to the specific input image. In the end, the filtered knowledge is fused with the high-level features to guide domain adaptation. Our main contributions are: (1) a first novel knowledge based domain adaptation approach for semantic segmentation and (2) a triangular constraint for knowledge loading, in which the semantic vectors are smoothly imported. Experimental results on three datasets indicate that our method achieves competitive results in some scenarios compared with the state-of-the-art approaches.