Reviewerly/exHarmony huggingface dataset:
Robust Watermarking Scheme for NIfTI Medical Images

Computed Tomography (CT) scan and Magnetic Resonance Imaging (MRI) technologies are widely used in medical field. Within the last few months, due to the increased use of CT scans, millions of patients have had their CT scans done. So, as a result, images showing the Corona Virus for diagnostic purposes were digitally transmitted over the internet. The major problem for the world health care system is a multitude of attacks that affect copyright protection and other ethical issues as images are transmitted over the internet. As a result, it is important to apply a robust and secure watermarking technique to these images. Notably, watermarking schemes have been developed for various image formats, including .jpg, .bmp, and .png, but their impact on NIfTI (Neuroimaging Informatics Technology Initiative) images is not noteworthy. A watermarking scheme based on the Lifting Wavelet Transform (LWT) and QR factorization is presented in this paper. When LWT and QR are combined, the NIfTI image maintains its inherent sensitivity and mitigates the watermarking scheme's robustness. Multiple watermarks are added to the host image in this approach. Measuring the performance of the graphics card is done by using PSNR, SSIM, Q (a formula which measures image quality), SNR, and Normalized correlation. The watermarking scheme withstands a variety of noise attacks and conversions, including image compression and decompression.
Few-photon routing via chiral light-matter couplings

Abstract A quantum router is one of the essential elements in the quantum network. Conventional routers only direct a single photon from one quantum channel into another. Here, we propose a few-photon router. The active element of the router is a single qubit chirally coupled to two independent waveguides simultaneously, where each waveguide mode provides a quantum channel. By introducing the operators of the scatter-free space and the controllable space, the output state of the one-photon and two-photon scattering are derived analytically. It is found that the qubit can direct one and two photons from one port of the incident waveguide to an arbitrarily selected port of the other waveguide with unity, respectively. However, two photons cannot be simultaneously routed to the same port due to the anti-bunch effect.
Handwriting identification using random forests and score‐based likelihood ratios

Abstract Handwriting analysis is conducted by forensic document examiners who are able to visually recognize characteristics of writing to evaluate the evidence of writership. Recently, there have been incentives to investigate how to quantify the similarity between two written documents to support the conclusions drawn by experts. We use an automatic algorithm within the “handwriter” package in R, to decompose a handwritten sample into small graphical units of writing. These graphs are sorted into 40 exemplar groups or clusters. We hypothesize that the frequency with which a person contributes graphs to each cluster is characteristic of their handwriting. Given two questioned handwritten documents, we can then use the vectors of cluster frequencies to quantify the similarity between the two documents. We extract features from the difference between the vectors and combine them using a random forest. The output from the random forest is used as the similarity score to compare documents. We estimate the distributions of the similarity scores computed from multiple pairs of documents known to have been written by the same and by different persons, and use these estimated densities to obtain score‐based likelihood ratios (SLRs) that rely on different assumptions. We find that the SLRs are able to indicate whether the similarity observed between two documents is more or less likely depending on writership.
Cross-domain object detection using unsupervised image translation

Unsupervised domain adaptation for object detection addresses the adaption of detectors trained in a source domain to work accurately in an unseen target domain. Recently, methods approaching the alignment of the intermediate features proven to be promising, achieving state-of-the-art results. However, these methods are laborious to implement and hard to interpret. Although promising, there is still room for improvements to close the performance gap toward the upper-bound (when training with the target data). In this work, we propose a method to generate an artificial dataset in the target domain to train an object detector. We employed two unsupervised image translators (CycleGAN and an AdaIN-based model) using only annotated data from the source domain and non-annotated data from the target domain. Our key contributions are the proposal of a less complex yet more effective method that also has an improved interpretability. Results on real-world scenarios for autonomous driving show significant improvements, outperforming state-of-the-art methods in most cases, further closing the gap toward the upper-bound.
Weakly Supervised Ternary Stream Data Augmentation Fine-Grained Classification Network for Identifying Acute Lymphoblastic Leukemia

Due to the high incidence of acute lymphoblastic leukemia (ALL) worldwide as well as its rapid and fatal progression, timely microscopy screening of peripheral blood smears is essential for the rapid diagnosis of ALL. However, screening manually is time-consuming and tedious and may lead to missed or misdiagnosis due to subjective bias; on the other hand, artificially intelligent diagnostic algorithms are constrained by the limited sample size of the data and are prone to overfitting, resulting in limited applications. Conventional data augmentation is commonly adopted to expand the amount of training data, avoid overfitting, and improve the performance of deep models. However, in practical applications, random data augmentation, such as random image cropping or erasing, is difficult to realistically occur in specific tasks and may instead introduce tremendous background noises that modify actual distribution of data, thereby degrading model performance. In this paper, to assist in the early and accurate diagnosis of acute lymphoblastic leukemia, we present a ternary stream-driven weakly supervised data augmentation classification network (WT-DFN) to identify lymphoblasts in a fine-grained scale using microscopic images of peripheral blood smears. Concretely, for each training image, we first generate attention maps to represent the distinguishable part of the target by weakly supervised learning. Then, guided by these attention maps, we produce the other two streams via attention cropping and attention erasing to obtain the fine-grained distinctive features. The proposed WT-DFN improves the classification accuracy of the model from two aspects: (1) in the images can be seen details since cropping attention regions provide the accurate location of the object, which ensures our model looks at the object closer and discovers certain detailed features; (2) images can be seen more since erasing attention mechanism forces the model to extract more discriminative parts' features. Validation suggests that the proposed method is capable of addressing the high intraclass variances located in lymphocyte classes, as well as the low interclass variances between lymphoblasts and other normal or reactive lymphocytes. The proposed method yields the best performance on the public dataset and the real clinical dataset among competitive methods.
VR-based training of craniotomy for intracranial aneurysm surgery

Intracranial aneurysms can be treated micro-surgically. This procedure involves an appropriate head position of the patient and a proper craniotomy. These steps enable a proper access, facilitating the subsequent steps. To train the access planning process, we propose a VR-based training system.We designed and implemented an immersive VR access simulation, where the user is surrounded by a virtual operating room, including medical equipment and virtual staff. The patient's head can be positioned via hand rotation and an arbitrary craniotomy contour can be drawn. The chosen access can be evaluated by exposing the aneurysm using a microscopic view.The evaluation of the simulation took place in three stages: testing the simulation using the think-aloud method, conducting a survey and examining the precision of drawing the contour. Although there are differences between the virtual interactions and their counterparts in reality, the participants liked the immersion and felt present in the operating room. The calculated surface dice similarity coefficient, Hausdorff distance and feedback of the participants show that the difficulty of drawing the craniotomy is appropriate.The presented training simulation for head positioning and access planning benefits from the immersive environment. Thus, it is an appropriate training for novice neurosurgeons and medical students with the goal to improve anatomical understanding and to become aware of the importance of the right craniotomy hole.
Mobileware: A High-Performance MobileNet Accelerator with Channel Stationary Dataflow

MobileNet models have been gaining popularity with lighter computational load than previous convolutional neural network models. However, mapping depthwise convolutional layers in the MobileNets to the conventional hardware accelerators experiences significantly low utilization of multipliers, which leads to large performance degradation. The utilization becomes low because the depthwise convolutional layers have 1) different input/weight reuse patterns and 2) much smaller number of multiplications for each dot product computation compared to standard convolutional operations. To overcome such limitations, we propose an architecture called Mobileware which uses a channel stationary dataflow. Experimental results show that the Mobileware can achieve up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.4-29.5\times$</tex> higher overall system performance than the previous neural network accelerators.
Reservoir computing with random and optimized time-shifts

We investigate the effects of application of random time-shifts to the readouts of a reservoir computer in terms of both accuracy (training error) and performance (testing error). For different choices of the reservoir parameters and different "tasks," we observe a substantial improvement in both accuracy and performance. We then develop a simple but effective technique to optimize the choice of the time-shifts, which we successfully test in numerical experiments.
Anomalous Sound Detection Using Attentive Neural Processes

A typical approach for unsupervised anomaly detection of machine sounds learns an autoencoder model for reconstructing the spectrograms of normal sounds. During inference, fidelity of the reconstruction can be used to identify anomalous sounds different from normal sounds encountered during training. Recent improvements to the baseline autoencoder approach mask certain regions of the spectrogram at the input to the autoencoder, and then use the reconstruction error over masked regions as the anomaly score. We propose an alternative approach based on the attentive neural process, a recently proposed meta-learning technique for estimating distributions over signals. A benefit of our approach is that masked regions of the spectrogram do not need to be pre-specified at training time, and can determined based on signal properties or prior knowledge. Furthermore, we present an iterative approach that finds difficult-to-reconstruct spectrogram regions, and uses the reconstruction error over only those regions as the anomaly score. We demonstrate the effectiveness of our approach on experiments with the six machines of the DCASE 2020 Task 2 dataset, including in the case of zero-shot domain adaptation, where our approach outperforms baseline approaches in predicting anomalies for unseen machine instances.
Spontaneous facial expression database of learners’ academic emotions in online learning with hand occlusion

Academic emotions refer to various emotional experiences in connection with learners’ academic activities while learning, and these emotions are vital to the development of learners’ physiology and mentality. Facial expression recognition (FER) technology has been widely used in online learning to identify learners’ academic emotions. However, learners often inadvertently cover part of their face with their hands during online learning, which affects the accuracy of the technology's recognition of academic emotions. Most existing databases lack facial expression data with hand occlusion, which makes it difficult for researchers to further improve recognition accuracy. Therefore, this research established an online learners’ facial expression database with hand occlusion (OLFED-HO) to solve the above problem. This database has a total of 92,947 facial expression images of online learners, including four different hand occlusion situations (no occlusion, left occlusion, middle occlusion, and right occlusion) and seven academic emotions (confusion, curiosity, distraction, enjoyment, fatigue, depression, and neutrality). Then, to indicate the high reliability of the database established in this study, we analyzed the confusion matrix and concluded that the expression labels marked by different external coders have a high internal consistency. The database is expected to further promote the application of expression recognition technology in the field of education and provide online learners’ facial expressions with hand occlusion for the academic emotion database. In addition, an automatic facial expression recognition method with transfer learning based on region attention networks (RAN) is proposed in this paper, which efficiently reduces the impact of hand occlusion. The proposed architecture achieves an accuracy of 89% on the test set of our database.
A new metric for morphologic variability using landform shape classification via supervised machine learning

The frequency and degree of change in landform-profile shape can provide insights into the evolution of complex landforms. Here, we present a workflow that can be used to quantify this aspect of morphologic variability along any landform, leveraging both the expertise of a geomorphologist and the efficiency of a machine-learning algorithm. As a case study, we tackle the problem of degradation of fault scarps in jointed bedrock. We made field observations of seven fault scarps in jointed bedrock from Hawai'i, California and Iceland and collected aerial imagery for Structure-from-Motion (SfM) photogrammetry. From these observations, we first manually classify fault-scarp profiles extracted from SfM-derived point clouds into six morphologic categories defined by a geomorphologist with a view towards geologic process. Then, we use principal component analysis with singular value decomposition to quantitatively distinguish morphologic classes. We follow this by employing the support vector machine (SVM) method to build a supervised classifier, using the principal-component coordinates of the classified profiles in principal component space as a training set. Classification performance was assessed using 5-fold cross validation (81% accuracy) and with independent test data (80% accuracy). Finally, we define a morphologic variability metric and calculate it by determining the number of classes represented and the standard deviation of their proportions in a moving window along a fault scarp. By analyzing the covariance between the morphologic variability metric and other geomorphic parameters, we can quantitatively determine the drivers of scarp form. We find that morphologic variability decreases with scarp maturity. Our results suggest that the morphologic variability metric is a promising tool to understand the evolution of complex landforms.
Multi-source data fusion method based on nearest neighbor plot and track data association

In response to the multi-sensor data fusion about intelligent vehicle, an improved algorithm for vehicle based on plot and track data association is proposed. This method takes Euclidean distance and cosine similarity as well as target track as indexes and sets up a reasonable maximum threshold for tracking based on the target history state. Then, the targets identified from the visual system and radar system are fused by using the probability distribution to achieve the maximum utilization of the detection range of sensors. On-vehicle experiments are executed to evaluate the performance of this proposed method, and results demonstrate that through the comparison of fusion data and single sensor data, this proposed algorithm integrates the advantages of fusion system to accurately perceive the environmental information.
AegisDNN: Dependable and Timely Execution of DNN Tasks with SGX

With the rising demand for emerging DNN applications in safety-critical systems, much attention has been given to the reliability and trustworthiness of DNN inference output against malicious attacks. Although prior work has been conducted to improve the privacy of DNN inference by executing the entire DNN model inside Intel SGX enclaves, existing approaches pose severe performance challenges to achieve dependable and timely execution simultaneously. In this paper, we propose AegisDNN, a DNN inference framework to address this problem. AegisDNN leverages secure SGX enclaves for protecting only the critical part of real-time DNN tasks which are vulnerable to potential fault injection attacks. To choose the right set of layers for protection while ensuring the timeliness of task execution, AegisDNN includes a dynamic-programming based algorithm that finds a layer protection configuration for each task to meet the real-time and dependability requirements based on the layer-wise DNN time and SDC (Silent Data Corruption) profiling mechanism. AegisDNN also utilizes a machine-learning based SDC prediction method to significantly reduce the time for estimating SDC rates for all possible layer protection configurations. We implemented AegisDNN on Caffe, PyTorch, and Tensorflow with Eigen BLAS ported into SGX enclaves to comprehensively demonstrate the effectiveness of AegisDNN against state-of-the-art DNN fault-injection attacks. Experiment results indicate that AegisDNN could satisfy both dependability and real-time requirements simultaneously, when none of the other compared approaches could do so.
Drawing Elon Musk: A Robot Avatar for Remote Manipulation

The fast growth of communication technologies such as 5G provides high bandwidth and low latency wireless internet access. This enables both high definition video stream and real-time robot commands transmitted between robots and operators in the context of telepresence and teleoperation. Although there has been substantial research to establish algorithms that convert images to robot motions and telerobotic systems, little effort was made in establishing a clear scheme that enable artists to draw portraits using telerobotic systems. In this paper, we provide an easy-to-follow structure and implementation of a robot avatar for portrait drawing by artists through remote manipulation. The proposed telerobotic system uses a digital tablet and motion capture suit as input devices, which provides accurate drawing and continuous motion data stream respectively. With sensor fusion of the input data on the robot side, the drawing process presented in this work uses a unified force and impedance controller to ensure smooth and uniform pen-strokes. The proposed scheme was used to synthesise a system that was used by an artist to successfully finish the portrait drawing of Elon Musk. Finally, we show the effectiveness of the introduced control framework through an experiment. In particular, we validate the benefit of combining unified force and impedance control with sensor fusion of the digital tablet and motion capture suit data.
Telepresence with Hologram Effect: Technological Ecosystem for Distance Education

One of the most significant challenges of telepresence distance education is to bring the professor and the students closer together in a synchronistic educational experience where the professor is perceived as anatomically proportionate. Telepresence, an educational technology ecosystem using holograms, offers a way to solve this technological challenge. Our mixed exploratory research investigating this methodology had two purposes: (1) propose the key elements to teach distance courses synchronously in an educational technology ecosystem, and (2) demonstrate the technological, didactic practices that result in positive student learning outcomes in several specified courses. This methodology included applying a student questionnaire to collect their perceptions of the educational experience. The scores and written comments from the questionnaire were analyzed using Grounded Theory. On a Likert scale from 1 to 5, the students scored their educational experience, attaining a mean of 4.05. The positive perception affirmed that they valued: (a) recreating the natural dynamics of face-to-face classes, where the students perceived their professors as being physically present in the classroom; (b) professors renowned in their disciplines; (c) professor–student and campus and intercampus learning community interactions, and, finally, (d) class design and content. The main conclusions of this research were that students positively perceived the “wow” effect of the technology, feeling comfort, amazement, interest, and engagement. In addition, we found that professors and keynote speakers with excellent pedagogical skills and experts in their disciplines were well appreciated. Key elements for the success of the experience were professor-student, campus, and intercampus interactions and the quality of the technological and communication infrastructure.
Hybrid recommendations and dynamic authoring for AR knowledge capture and re-use in diagnosis applications

In Industry 4.0, integrated data management is an important challenge due to heterogeneity and the lack of structure of numerous existing data sources. A relevant research gap involves human knowledge integration, especially in maintenance operations. Augmented Reality (AR) can bridge this gap, but it requires improved augmented content to enable effective and efficient knowledge capture. This paper proposes dynamic authoring and hybrid recommender methods for accurate AR-based reporting. These methods aim to provide maintainers with augmented data input formats and recommended datasets for enhancing the efficiency and effectiveness of their reporting tasks. The proposed contributions have been validated through experiments and surveys in two failure diagnosis reporting scenarios. Experimental results indicated that the proposed reporting solution can reduce reporting errors by 50% and reporting time by 20% compared to alternative recommender and AR tools. Besides, survey results suggested that testers perceived the proposed reporting solution as more effective and satisfactory for reporting tasks than alternative tools. Thus, proving that the proposed methods can improve the effectiveness and efficiency of diagnosis reporting applications. Finally, this paper proposes future works towards a framework for automatic adaptive authoring in AR knowledge transfer and capture applications for human knowledge integration in the context of Industry 4.0.