Reviewerly/exHarmony huggingface dataset:
Self-supervised Anomaly Detection with Random-shape Pseudo-outliers

Anomaly detection in a medical image is a challenging yet essential task. It relies on learning patterns/distributions from health data only, and no abnormal samples are available during training. This study proposes a novel self-supervised learning method to precisely detect and localize anomalies in MRI medical images. We synthesize abnormal images by overlaying random pseudo-outliers onto normal samples and propose a discriminative model for anomaly detection. Unlike prior arts that generate abnormalities with pre-determined regular geometric shapes, we introduce a new outlier synthesis strategy capable of generating random-shape anomalies. By learning the disentanglement of pseudo-outliers and normal regions in the synthesized images, our model can capture natural anomalies in images at both the pixel level and sample level. We present our empirical experimentation on two publicly accessible datasets and demonstrate the proposed method's superiority over SOTA solutions on MRIs.
Unity Human Eye Model for Gaze Tracking with a Query-Driven Dynamic Vision Sensor

Objective measurement of gaze pattern and eye movement during untethered activity has important applications for neuroscience research and neurological disease detection. Current commercial eye-tracking tools rely on desk-top devices with infrared emitters and conventional frame-based cameras. Although wearable options do exist, the large power-consumption from their conventional cameras limit true long-term mobile usage. The query-driven Dynamic Vision Sensor (qDVS) is a neuromorphic camera which dramatically reduces power consumption by outputting only intensity-change threshold events, as opposed to full frames of intensity data. However, such hardware has not yet been implemented for on-body eye-tracking, but the feasibility can be demonstrated using a mathematical simulator to evaluate the eye-tracking ca-pabilities of the qDVS under controlled conditions. Specifically, a framework utilizing a realistic human eye model in the 3D graphics engine, Unity, is presented to enable the controlled and direct comparison of image-based gaze tracking methods. Eye-tracking based on qDVS frames was compared against two different conventional frame eye-tracking methods - the traditional ellipse pupil-fitting algorithm and a deep learning neural network inference model. Gaze accuracy from qDVS frames achieved an average of 93.2% for movement along the primary horizontal axis (pitch angle) and 93.1 % for movement along the primary vertical axis (yaw angle) under 4 different illumination conditions, demonstrating the feasibility for using qDVS hardware cameras for such applications. The quantitative framework for the direct comparison of eye tracking algorithms presented here is made open-source and can be extended to include other eye parameters, such as pupil dilation, reflection, motion artifact, and more.
Classification of Pap-smear cell images using deep convolutional neural network accelerated by hand-crafted features

The classification of cells extracted from Pap-smears is in most cases done using neural network architectures. Nevertheless, the importance of features extracted with digital image processing is also discussed in many related articles. Decision support systems and automated analysis tools of Pap-smears often use these kinds of manually extracted, global features based on clinical expert opinion. In this paper, a solution is introduced where 29 different contextual features are combined with local features learned by a neural network so that it increases classification performance. The weight distribution between the features is also investigated leading to a conclusion that the numerical features are indeed forming an important part of the learning process. Furthermore, extensive testing of the presented methods is done using a dataset annotated by clinical experts. An increase of 3.2% in F1-Score value can be observed when using the combination of contextual and local features. Clinical Relevance - Analysis of images extracted from digital Pap-test using modern machine learning tools is discussed in many scientific papers. The manual classification of the cells can be time-consuming and expensive which requires a high amount of manual labor. Furthermore the result of the manual classification can also be uncertain due to interobserver variability. Considering these, any result that can lead to a more reliable highly accurate classification method is considered valuable in the field of cervical cancer screening.
Synthesizing Contrast-enhanced Computed Tomography Images with an Improved Conditional Generative Adversarial Network

Contrast-enhanced computed tomography (CE-CT) images are used extensively for the diagnosis of liver cancer in clinical practice. Compared with the non-contrast CT (NC-CT) images (CT scans without injection), the CE-CT images are obtained after injecting the contrast, which will increase physical burden of patients. To handle the limitation, we proposed an improved conditional generative adversarial network (improved cGAN) to generate CE-CT images from non-contrast CT images. In the improved cGAN, we incorporate a pyramid pooling module and an elaborate feature fusion module to the generator to improve the capability of encoder in capturing multi-scale semantic features and prevent the dilution of information in the process of decoding. We evaluate the performance of our proposed method on a contrast-enhanced CT dataset including three phases of CT images, (i.e., non-contrast image, CE-CT images in arterial and portal venous phases). Experimental results suggest that the proposed method is superior to existing GAN-based models in quantitative and qualitative results.
Rate-Adaptive Streaming of 360-Degree Videos with Head-Motion-Aware Viewport Margins

Efficient use of available bandwidth is vital when streaming 360-degree videos as users rarely have enough bandwidth for a pleasant experience. A promising solution is the combination of viewport-dependent streaming using tiled video and rate adaptation, where the goal is to spend most of the available bandwidth for the viewport tiles. However, head motions resulting in a change in the viewport tiles briefly cause low-quality rendering until the new tiles can be replaced with high-quality versions. Previously, viewport margins-fixed regions around the viewport rendered at a medium quality-were proposed to make the viewport changes less abrupt. Later on, Head-motion-aware Viewport Margins (HMAVM) were implemented to further smooth the transitions at the expense of increased bandwidth consumption. In this paper, we manage the overall bandwidth cost of HMAVMs better by first developing a set of algorithms that trade off the quality of some viewport tiles and then making the margin selection part of the rate-adaptation algorithm.
Motion Sickness related Route Profiling for Evaluation of the Sensory Conflict in Real-Driving Studies

The risk for passengers of an automated vehicle to suffer from motion sickness symptoms increases while performing non-driving tasks. Motion sickness, whether at sea, in the air, in a car or in virtual reality, has been studied for years, but the specific motion patterns of different vehicles and the individual physiology of passengers complicate the definition of general applicable models. Technical progress in vehicles, e.g. the development of the chassis or general digitalization, is constantly changing the influences and marginal effects of motion sickness. In recent years, increasing number of investigations concentrated on the influencing factors on motion sickness. However, the relation between emesis and vehicle dynamics itself is predominantly inadequately presented. Therefore, the results can poorly be incorporate in mathematical models of the sensory conflict theory established as leading theory in the research community. In our research, we suggest a method to prepare and present route and driving information to increase the transparency of real-world driving experiments. We used determined position-based spectrograms to simplify the understanding of the provoked acceleration as well as frequency, known as important motion sickness trigger. Standardized use of this method would support review articles about driving experiments and thus support research regarding motion sickness prediction and occurrence in vehicles.
Biopsy Needle Segmentation using Deep Networks on inhomogeneous Ultrasound Images

In minimally invasive interventional surgery, ultrasound imaging is usually used to provide real-time feedback in order to obtain the best diagnostic results or realize treatment plans, so how to accurately obtain the position of the medical biopsy needle is a problem worthy of study. 2D ultrasound simulation images containing the medical biopsy needle are generated, and our images background is from the real breast ultrasound image. Based on the deep learning network, the images containing the medical biopsy needle are used to analyze the effectiveness of different networks for needle localization for the purpose of returning needle positions in non-uniform ultrasound images. The results show that attention U-Net performed best and can accurately reflect the real position of the medical biopsy needle. The IoU and Precision can reach 90.19% and 96.25%, and the Angular Error is 0.40°. Clinical Relevance- Based on the deep network, for 2D ultrasound images containing medical biopsy needle, the localization precision can reach 96.25% and the Angular Error is 0.40°.
Gauging Facial Abnormality Using Haar-Cascade Object Detector

The overriding clinical and academic challenge that inspires this work is the lack of a universally accepted, objective, and feasible method of measuring facial deformity; and, by extension, the lack of a reliable means of assessing the benefits and shortcomings of craniofacial surgical interventions. We propose a machine learning-based method to create a scale of facial deformity by producing numerical scores that reflect the level of deformity. An object detector that is constructed using a cascade function of Haar features has been trained with a rich dataset of normal faces in addition to a collection of images that does not contain faces. After that, the confidence score of the face detector was used as a gauge of facial abnormality. The scores were compared with a benchmark that is based on human appraisals obtained using a survey of a range of facial deformities. Interestingly, the overall Pearson's correlation coefficient of the machine scores with respect to the average human score exceeded 0.96.
Deep Unsupervised Blind Learning for Single Image Super Resolution

This study proposes a deep unsupervised blind learning SR framework by leveraging the powerful representation capability of deep-learning but without the requirement of prior training using external data. There are two types of unknown factor, the latent high-resolution (HR) images to be estimated and the imaging conditions (also called the degradation operations, i.e., the blur kernel and downsampling), which greatly affect performance in the SR problem. Accordingly, we present two generative networks to model the deep priors of the latent HR image and blur kernel and a specially designed degradation block to implement the imaging procedure, which constructs an end-to-end blind learning network to jointly estimate a plausible SR image and the degradation information using only the LR observation. In particular, we exploit an encoder-decoder architecture to generate the latent HR image, a fully-connected network to learn the blur kernel, and a specific depth-wise convolutional layer to realize the degradation model. Experiments on several benchmark datasets with different imaging conditions demonstrate the superiority and generalization of our method over the state-of-the-art convolutional neural network-based SR methods, as well as previously unsupervised SR methods.
Artificial Intelligence in Software Requirements Engineering: State-of-the-Art

Requirements Engineering (RE) is a very important activity in the software development life cycle. Poorly executed RE steps can result in poor quality software and expensive maintenance cost. Although researchers have previously related and applied artificial intelligence (AI) to RE, little is known about the specific role of AI in RE process. In particular, there are insufficient understandings about how AI should be incorporated in the RE process to produce high quality, clear and detailed requirements. In this paper, we present the current state-of-the-art of AI in RE. We reviewed the literature published between January 2015 to December 2021 in order to understand how the state of the art of AI branches such as machine learning, classification, and natural language processing (NLP) has advanced the field of RE. Each recent study is summarized and the advancement to the RE field is presented. There is an apparent direction of applying NLP techniques and supervised learning techniques such as classification to requirements documents. This study provides a summary and direction of the AI applications in the field of RE.
Action Semantic Alignment for Image Captioning

Image captioning is one of the main goals in vision and language processing, which aims to generate proper descriptions of images. Recently, the attention mechanisms became crucial in captioning tasks, as they can capture global dependencies between modalities. Moreover, some works have used objects detected from the input image as anchor points, so called object tags, to ease such alignments resulting in good performance for this task. In this paper, we newly introduce action information as a prior to further improve this, by adding action tags for training. The action tags can learn alignment at action semantic level and catch the previously ignored dimension of action, that could be very important in image captioning. We found that training with action tags can be used to describe images in a dynamic style. Furthermore, we found it can actually lead to a significant improvement compared with other methods in captioning performance measured by common metrics.
On a hypergraph structuring semantic information for robots navigating and conducting their task in real-world, indoor environments

Robotic systems operating in the real world would benefit from a clear semantic model to understand their interactions with the real world. Such semantics are typically captured in an ontology. Unfortunately, existing world models in robotics focus on its navigation task. They adopt a hierarchical structure decomposing the environment from large spaces into small objects having a position, thereby limiting the robot's interactions as a “go-to-object” task. To allow a richer understanding of the real world this hierarchical structure should be replaced with an ontology, yet one that does not limit the real-time requirements of the robot when it is queried or updated with new observations extracted from sensors. Such an ontology is presented in this article. For now the ontology also focusses on the navigation aspect of robots, yet it is open to model other aspects of the real world as well. Experiments show that multiple environments are successfully modelled supporting the robot to go from one room to another to search for humans.
Droneport: From Concept To Prototype

The paper deals with the development of an experimental mechatronic system for battery management of drones called Droneport. It was developed as an automatic system with battery swapping and recharging feature set outside the drone, allowing a quick return to a mission. The first part presents its mechanical design, installed instrumentation and software environment. The next section is dedicated to the description of the individual hardware components, highlighting particular problems that had to be solved to optimize the size, weight and fulfill robustness demands. The last section summarizes our observations regarding the contribution of this tool to the drone or UAV autonomy in general.
Advances in Quantum Computation and Quantum Technologies: A Design Automation Perspective

Universal and fault-tolerant quantum computation is a promising new paradigm that may efficiently conquer difficult computation tasks beyond the reach of classical computation. It motivates the development of various quantum technologies. The rapid progress of quantum technologies accelerates the realization of quantum computers. In this paper, we survey the recent advances in quantum technologies and quantum computation from the design automation perspective.
Global Context-Based Threshold Strategy for Drone Identification Under the Low SNR Condition

Regulation of drones is already an important research topic. The drone identification method based on the radio frequency (RF) signal analysis technology is an efficient approach to regulate mainstream civilian drones. However, existing research on drone identification has been conducted under high signal-to-noise ratio (SNR) conditions. In fact, long-range drone identification in urban environments is performed under low SNR conditions. In this article, a global context (GC)-based threshold strategy is proposed to solve the above problem. The threshold is automatically determined during the deep architecture optimization. Meanwhile, the unimportant features in the network are removed by threshold denoising. First, the weight of each feature channel is obtained by modeling cross-channel dependencies, and then a weighted average is performed for each position along the feature channel on the feature map to obtain the thresholds. The method reduces the impact of unimportant features on performance without complex data preprocessing. In the experiments, the performance and complexity of the proposed method are extensively evaluated by 11 different drone RF signals. The experimental results show that in comparison with other deep learning-based threshold calculation methods, the proposed method has a more outstanding performance and lower computational complexity for drone RF signal identification under the low SNR condition.
Internal Boundary Between Entanglement and Separability Within a Quantum State

Quantum states are the key mathematical objects in quantum mechanics, and entanglement lies at the heart of the nascent fields of quantum information processing and computation. However, there has not been a general, necessary and sufficient, and operational separability condition to determine whether an arbitrary quantum state is entangled or separable. In this paper, we show that whether a quantum state is entangled or not is determined by a threshold within the quantum state. We first introduce the concept of finer and optimal separable states based on the properties of separable states in the role of higher-level witnesses. Then we show that any bipartite quantum state can be decomposed into a convex mixture of its optimal entangled state and its optimal separable state. Furthermore, we show that whether an arbitrary quantum state is entangled or separable, as well as positive partial transposition (PPT) or not, is determined by the robustness of its optimal entangled state to its optimal separable state with reference to a crucial threshold. Moreover, for an arbitrary quantum state, we provide operational algorithms to obtain its optimal entangled state, its optimal separable state, its best separable approximation (BSA) decomposition, and its best PPT approximation decomposition. And the open question of how to calculate the BSA in high-dimension systems is partially done.
Regularized Multi-Output Gaussian Convolution Process With Domain Adaptation

Multi-output Gaussian process (MGP) has been attracting increasing attention as a transfer learning method to model multiple outputs. Despite its high flexibility and generality, MGP still faces two critical challenges when applied to transfer learning. The first one is negative transfer, which occurs when there exists no shared information among the outputs. The second challenge is the input domain inconsistency, which is commonly studied in transfer learning yet not explored in MGP. In this paper, we propose a regularized MGP modeling framework with domain adaptation to overcome these challenges. More specifically, a sparse covariance matrix of MGP is constructed using convolution process, where penalization terms are added to adaptively select the most informative outputs for knowledge transfer. To deal with the domain inconsistency, a domain adaptation method is proposed by marginalizing inconsistent features and expanding missing features to align the input domains among different outputs. Statistical properties of the proposed method are provided to guarantee the performance practically and asymptotically. The proposed framework outperforms state-of-the-art benchmarks in comprehensive simulation studies and one real case study of a ceramic manufacturing process. The results demonstrate the effectiveness of our method in dealing with both the negative transfer and the domain inconsistency.
Low Latency Implementations of CNN for Resource-Constrained IoT Devices

Convolutional Neural Network (CNN) inference on a resource-constrained Internet-of-Things (IoT) device (i.e., ARM Cortex-M microcontroller) requires careful optimization to reduce the timing overhead. We propose two novel techniques to improve the computational efficiency of CNNs by targeting low-cost microcontrollers. Our techniques utilize on-chip memory and minimize redundant operations, yielding low-latency inference results on complex quantized models such as MobileNetV1. On the ImageNet dataset for per-layer quantization, we reduce inference latency and Multiply-and-Accumulate (MAC) per cycle by 22.4% and 22.9%, respectively, compared to the state-of-the-art mixed-precision CMix-NN library. On the CIFAR-10 dataset for per-channel quantization, we reduce inference latency and MAC per cycle by 31.7% and 31.3%, respectively. The achieved low-latency inference results can improve the user experience and save power budget in resource-constrained IoT devices.
Adaptive Particle Swarm Optimizer Combining Hierarchical Learning With Variable Population

Particle swarm optimizer (PSO) is an optimization technique that has been applied to solve various problems. In its variants, hierarchical learning and variable population are two commonly used learning strategies. The former is used to employ more potentially good particles to lead the swarm, which is very effective in the early search phase. However, in the later search phase, such mechanism impedes PSO’s convergence. This work proposes an adaptive particle swarm optimizer combining hierarchical learning with variable population (PSO-HV), in which a heap-based hierarchy is first proposed to organize particles to hierarchically learn from the ones with better fitness in the same and upper levels. The levels of particles are determined and updated according to their current fitness in each iteration. Meanwhile, an adaptive variable population strategy is introduced and eliminates redundant particles based on the population’s evolution state. In this way, the swarm is more explorative upon the hierarchical structure and improves its exploitation capability due to the variable population mechanism. Ten state-of-the-art PSO contenders, including two hierarchical ones and two variable population-based ones, are compared with the proposed method on 57 benchmark functions and the experimental results verify its effectiveness and efficiency.