Reviewerly/exHarmony huggingface dataset:
Random Style Transfer Based Domain Generalization Networks Integrating Shape and Spatial Information

Deep learning (DL)-based models have demonstrated good performance in medical image segmentation. However, the models trained on a known dataset often fail when performed on an unseen dataset collected from different centers, vendors and disease populations. In this work, we present a random style transfer network to tackle the domain generalization problem for multi-vendor and center cardiac image segmentation. Style transfer is used to generate training data with a wider distribution/heterogeneity, namely domain augmentation. As the target domain could be unknown, we randomly generate a modality vector for the target modality in the style transfer stage, to simulate the domain shift for unknown domains. The model can be trained in a semi-supervised manner by simultaneously optimizing a supervised segmentation and a unsupervised style translation objective. Besides, the framework incorporates the spatial information and shape prior of the target by introducing two regularization terms. We evaluated the proposed framework on 40 subjects from the M&Ms challenge2020, and obtained promising performance in the segmentation for data from unknown vendors and centers.
Suspicious Activity Detection from Videos using YOLOv3

Human activity detection for video system is an automated way of processing video sequences and making an intelligent decision about the actions in the video. It is one of the growing areas in Computer Vision and Artificial Intelligence. Suspicious activity detection is the process of detecting unwanted human activities in places and situations. This is done by converting video into frames and analyzing the activities of persons from the processed frames. Human detection has always been a challenging problem as human bodies are non-rigid and changes shape arbitrarily. Human recognition and detection in indoor as well as outdoor environment is a quite complex task due to the various problems that arises such as- poor lightening conditions, variation of poses etc. In this paper, YOLOv3 is used to detect different suspicious activities like bag-snatching, lock-breaking etc. Our system has a very good processing speed as well as good accuracy of detection.
Twice Mixing: A rank learning based quality assessment approach for underwater image enhancement

Objectively and accurately evaluating underwater images generated by different enhancement algorithms is an essential issue, which however is still largely under-explored. In this paper, we present a novel rank learning guided no-reference quality assessment method to evaluate different underwater image enhancement (UIE) algorithms. It is also the first work that utilizes deep learning approaches to address this problem. Our approach, termed Twice Mixing, is motivated by the observation that a mid-quality image can be generated by mixing a high-quality image and its low-quality version. Twice Mixing is trained based on an elaborately formulated self-supervision mechanism. Specifically, before each iteration, we randomly generate two mixing ratios which will be utilized for both generating virtual images and guiding the network training. In the test phase, a single branch of the network is extracted to predict the quality rankings of different UIE outputs. Additionally, to train our network, we construct a new dataset that contains over 2200 raw underwater images and their high/low-quality versions. Twice Mixing is evaluated on both synthetic and real-world datasets. Experimental results show that the proposed approach outperforms the previous methods significantly.
Secure content based image retrieval for mobile users with deep neural networks in the cloud

With the tremendous growth of smart mobile devices, the Content-Based Image Retrieval (CBIR) becomes popular and has great market potentials. Secure image retrieval has attracted considerable interests recently due to the outsourcing of CBIR onto the cloud. In this paper, we propose and implement a secure CBIR framework that performs image retrieval on the cloud without the user’s interaction. A pre-trained generic DNN model (e.g., VGG-16) is used to extract the feature vectors of an image on the user side. The cloud servers perform secure image inference with a private pre-trained DNN model and execute Approximate Nearest Neighbor (ANN) image retrieval protocols without the user’s anymore interaction. We design and implement a set of protocols for the secure evaluation of the non-linear functions in DNNs. The information about the image contents, the private DNN model parameters, the intermediate and the retrieval results is strictly concealed by the conjunctive use of the lattice-based homomorphic scheme and two-party computation (2-PC) techniques. We further propose a secure image similarity scoring and a result sorting protocols, which enable the cloud servers to compare and sort images without knowing any information about their features or contents. The comprehensive experimental results show that our framework is efficient, accurate and secure.
Introducing students to research codes: A short course on solving partial differential equations in Python

Recent releases of open-source research codes and solvers for numerically solving partial differential equations in Python present a great opportunity for educators to integrate these codes into the classroom in a variety of ways. The ease with which a problem can be implemented and solved using these codes reduce the barrier to entry for users. We demonstrate how one of these codes, FiPy, can be introduced to students through a short course using progression as the guiding philosophy. Four exercises of increasing complexity were developed. Basic concepts from more advanced numerical methods courses are also introduced at appropriate points. To further engage students, we demonstrate how an open research problem can be readily implemented and also incorporate the use of ParaView to post-process their results. Student engagement and learning outcomes were evaluated through a pre and post-course survey and a focus group discussion. Students broadly found the course to be engaging and useful with the ability to easily visualise the solution to PDEs being greatly valued. Due to the introductory nature of the course, due care in terms of set-up and the design of learning activities during the course is essential. This course, if integrated with appropriate level of support, can encourage students to use the provided codes and improve their understanding of concepts used in numerical analysis and PDEs.
Shift Equivariance in Object Detection

Robustness to small image translations is a highly desirable property for object detectors. However, recent works have shown that CNN-based classifiers are not shift invariant. It is unclear to what extent this could impact object detection, mainly because of the architectural differences between the two and the dimensionality of the prediction space of modern detectors. To assess shift equivariance of object detection models end-to-end, in this paper we propose an evaluation metric, built upon a greedy search of the lower and upper bounds of the mean average precision on a shifted image set. Our new metric shows that modern object detection architectures, no matter if one-stage or two-stage, anchor-based or anchor-free, are sensitive to even one pixel shift to the input images. Furthermore, we investigate several possible solutions to this problem, both taken from the literature and newly proposed, quantifying the effectiveness of each one with the suggested metric. Our results indicate that none of these methods can provide full shift equivariance. Measuring and analyzing the extent of shift variance of different models and the contributions of possible factors, is a first step towards being able to devise methods that mitigate or even leverage such variabilities.
Experimental Evaluation of NISQ Quantum Computers: Error Measurement, Characterization, and Implications

Noisy Intermediate-Scale Quantum (NISQ) computers are being increasingly used for executing early-stage quantum programs to establish the practical realizability of existing quantum algorithms. These quantum programs have uses cases in the realm of high-performance computing ranging from molecular chemistry and physics simulations to addressing NP-complete optimization problems. However, NISQ devices are prone to multiple types of errors, which affect the fidelity and reproducibility of the program execution. As the technology is still primitive, our understanding of these quantum machines and their error characteristics is limited. To bridge that understanding gap, this is the first work to provide a systematic and rich experimental evaluation of IBM Quantum Experience (QX) quantum computers of different scales and topologies. Our experimental evaluation uncovers multiple important and interesting aspects of benchmarking and evaluating quantum program on NISQ machines. We have open-sourced our experimental framework and dataset to help accelerate the evaluation of quantum computing systems.
A Novel Camera Calibration Pattern Robust to Incomplete Pattern Projection

When calibrating multi-camera systems with existing camera calibration toolboxes, it is oftentimes required that calibration boards are fully captured by all cameras to establish correspondence of X-junctions automatically. It becomes impractical when different cameras share limited common field of views, resulting in the incomplete samples of the calibration board. This article proposes a practical solution containing a modified checkerboard pattern and image processing algorithms. Corresponding world and image points for calibration algorithms are provided by the proposed solution when calibration boards are partially captured. X-junctions are categorized in two types and recognized in a group as an identification unit. The proposed pattern is arranged to ensure uniqueness of each identification unit; thus X-junctions can be positioned by supporting algorithms and correspondences are founded. No burden is introduced in sampling compared to traditional calibration methods. Experimental results of calibrating a multi-camera system verify that the use of the partially captured images brings benefits to calibration accuracy. Furthermore, the proposed method can substantially facilitate the calibration process.
Path Planning for Autonomous Underwater Vehicle Based on Artificial Potential Field and Modified RRT

Autonomous underwater vehicle(AUV) plays an important role in modern aquaculture. For example, it can be applied to monitor water quality as well as the fish biomass. In these activities, path planning is significant for AUV. This paper proposed a path planning algorithm for underwater vehicles based on improved rapidly expanding random tree(RRT). The artificial potential field(APF) algorithm is introduced into the heuristic function of bidirectional RRT to improve the expansion efficiency as a global path. To solve the problem of dynamic obstacles, global path planning and local path planning are combined. The dynamic window method is used in local path planning to deal with dynamic obstacles. This approach takes the robot's speed as the reference point. It sets the window and then selects a better path by the evaluation function. In this way, the path planning of underwater vehicle in a dynamic environment is realized. Finally, the algorithm was simulated in both static and dynamic environments. The results show that the proposed algorithm can provide effective paths for underwater vehicles quickly.
Aspects of Estimation and Reporting of Mineral Resources of Seabed Polymetallic Nodules: A Contemporaneous Case Study

Exploration of seabed polymetallic nodules identifies the Clarion Clipperton Zone and the Indian Ocean Nodule Field to be of economic interest. Mineral resource estimation is important to the owner of the resource (all of mankind; and managed by the International Seabed Authority; ISA) and to developers (commercial and government groups holding contracts with the ISA). The Committee for Mineral Reserves International Reporting Standards was developed for the land-based minerals industry and adapted in 2015 for ISA-managed nodules. Nodules can be sampled in a meaningful manner using mechanical devices, albeit with minor issues of bias. Grade and moisture content are measured using the established methodology for land-based minerals. Tonnage of resource is determined via the abundance of nodules in kilograms per square metre of seabed. This can be estimated from physical samples and, in some cases, from photographs. Contemporary resource reporting for nodules classify the level of confidence in the estimate, by considering deposit geology, sample geostatistics, etc. The reporting of estimates also addresses reasonable prospects for eventual economic extraction, including factors such as mining technology, the marine environment, metallurgical processing, and metals markets. Other requirements are qualified persons responsible for estimation and reporting, site inspection, and sample chain of custody.
The Effect of Exploration Mode and Frame of Reference in Immersive Analytics

The design space for user interfaces for Immersive Analytics applications is vast. Designers can combine navigation and manipulation to enable data exploration with ego- or exocentric views, have the user operate at different scales, or use different forms of navigation with varying levels of physical movement. This freedom results in a multitude of different viable approaches. Yet, there is no clear understanding of the advantages and disadvantages of each choice. Our goal is to investigate the affordances of several major design choices, to enable both application designers and users to make better decisions. In this article, we assess two main factors, exploration mode and frame of reference, consequently also varying visualization scale and physical movement demand. To isolate each factor, we implemented nine different conditions in a Space-Time Cube visualization use case and asked 36 participants to perform multiple tasks. We analyzed the results in terms of performance and qualitative measures and correlated them with participants' spatial abilities. While egocentric room-scale exploration significantly reduced mental workload, exocentric exploration improved performance in some tasks. Combining navigation and manipulation made tasks easier by reducing workload, temporal demand, and physical effort.
A Deep Dive into Multilingual Hate Speech Classification

Hate speech is a serious issue that is currently plaguing the society and has been responsible for severe incidents such as the genocide of the Rohingya community in Myanmar. Social media has allowed people to spread such hateful content even faster. This is especially concerning for countries which lack hate speech detection systems. In this paper, using hate speech dataset in 9 languages from 16 different sources, we perform the first extensive evaluation of multilingual hate speech detection. We analyze the performance of different deep learning models in various scenarios. We observe that in low resource scenario LASER embedding with Logistic regression perform the best, whereas in high resource scenario, BERT based models perform much better. We also observe that simple techniques such as translating to English and using BERT, achieves competitive results in several languages. For cross-lingual classification, we observe that data from other languages seem to improve the performance, especially in the low resource settings. Further, in case of zero-shot classification, evaluation on Italian and Portuguese dataset achieve good results. Our proposed framework could be used as an efficient solution for low-resource languages. These models could also act as good baselines for future multilingual hate speech detection tasks. Our code (Code: https://github.com/punyajoy/DE-LIMIT) and models (Models: https://huggingface.co/Hate-speech-CNERG) are available online.
Designing Environments Conducive to Interpretable Robot Behavior

Designing robots capable of generating interpretable behavior is essential for effective human-robot collaboration. This requires robots to be able to generate behavior that aligns with human expectations but exhibiting such behavior in arbitrary environments could be quite expensive for robots, and in some cases, the robot may not even be able to exhibit expected behavior. However, in structured environments (like warehouses, restaurants, etc.), it may be possible to design the environment so as to boost the interpretability of a robot's behavior or to shape the human's expectations of the robot's behavior. In this paper, we investigate the opportunities and limitations of environment design as a tool to promote a particular type of interpretable behavior - known in the literature as explicable behavior. We formulate a novel environment design framework that considers design over multiple tasks and over a time horizon. In addition, we explore the longitudinal effect of explicable behavior and the trade-off that arises between the cost of design and the cost of generating explicable behavior over an extended time horizon.
Clownfish: Edge and Cloud Symbiosis for Video Stream Analytics

Deep learning (DL) has shown promising results on complex computer vision tasks for video stream analytics recently. However, DL-based analytics typically requires intensive computation, which imposes challenges to the current computing infrastructure. In particular, cloud-only solutions struggle to maintain stable real-time performance due to the streaming over the best-effort Internet, while edge-only solutions require the DL model to be optimized (e.g., pruned or quantized) carefully to fit on resource-constrained devices, affecting the analytics quality. In this paper, we propose Clownfish, a framework for efficient video stream analytics that achieves symbiosis of the edge and the cloud. Clownfish deploys a lightweight optimized DL model at the edge for fast response and a complete DL model at the cloud for high accuracy. By exploiting the temporal correlation in video content, Clownfish sends only a subset of video frames intermittently to the cloud and enhances the analytics quality by fusing the results from the cloud model with these from the edge model. Our evaluation based on a system prototype shows that Clownfish always runs in real time and is able to achieve analytics quality comparable to that of cloud-only solutions, even under highly variable network conditions. Clownfish is generally applicable to all video stream analytics tasks that can leverage temporal correlations.
A too-good-to-be-true prior to reduce shortcut reliance

Despite their impressive performance in object recognition and other tasks under standard testing conditions, deep networks often fail to generalize to out-of-distribution (o.o.d.) samples. One cause for this shortcoming is that modern architectures tend to rely on ǣshortcutsǥ superficial features that correlate with categories without capturing deeper invariants that hold across contexts. Real-world concepts often possess a complex structure that can vary superficially across contexts, which can make the most intuitive and promising solutions in one context not generalize to others. One potential way to improve o.o.d. generalization is to assume simple solutions are unlikely to be valid across contexts and avoid them, which we refer to as the too-good-to-be-true prior. A low-capacity network (LCN) with a shallow architecture should only be able to learn surface relationships, including shortcuts. We find that LCNs can serve as shortcut detectors. Furthermore, an LCN's predictions can be used in a two-stage approach to encourage a high-capacity network (HCN) to rely on deeper invariant features that should generalize broadly. In particular, items that the LCN can master are downweighted when training the HCN. Using a modified version of the CIFAR-10 dataset in which we introduced shortcuts, we found that the two-stage LCN-HCN approach reduced reliance on shortcuts and facilitated o.o.d. generalization.
Blockchain‐based patient centric health care communication system

Summary The rapid increase in health care data breaches with the existing centralized systems emphasizes a decentralized health care system while ensuring reliability, privacy, security, and trust. Further, to ensure trust in the medical community, scientist, and pharmaceutical, it is essential to improve the quality of health care data management. In this direction, we proposed a blockchain‐based decentralized privacy‐preserving EMR management (DPEM), which can ensure accountability and integrity. We propose a four‐layered framework for DPEM consisting of a data preparation layer, access control and security layer, data sharing layer, and data storage layer with the objectives: (i) To provide privacy‐preserving in DPEM, we propose a new elliptic curve‐based content extraction signature (EC‐CES) through which patients can exclude EMR's sensitive information to eradicate leakage of privacy information in the data sharing process. (ii) To provide secure data sharing, blockchain smart contracts are used to define the predefined access permissions of the patients. (iii) To provide secure storage, we use a cloud facility to store actual EMRs, and consortium blockchain is used to store respective indexes of EMRs so that the data leakages of EMRs could be optimized and simultaneously, indexes in consortium blockchain will take care the integrity of EMRs. (iv) To provide access control in data sharing, we adopted ciphertext‐policy attribute‐based encryption (CP‐ABE) access control policy to empower the owners of data to secure the cloud storage and give access to authorized users through the encrypted link to the cloud storage with access control policies blinded. Finally, the security analysis demonstrates that DPEM is an optimized way of achieving EMRs secure data sharing.
Domain Adaptation for Outdoor Robot Traversability Estimation from RGB data with Safety-Preserving Loss

Being able to estimate the traversability of the area surrounding a mobile robot is a fundamental task in the design of a navigation algorithm. However, the task is often complex, since it requires evaluating distances from obstacles, type and slope of terrain, and dealing with non-obvious discontinuities in detected distances due to perspective. In this paper, we present an approach based on deep learning to estimate and anticipate the traversing score of different routes in the field of view of an on-board RGB camera. The backbone of the proposed model is based on a state-of-the-art deep segmentation model, which is fine-tuned on the task of predicting route traversability. We then enhance the model's capabilities by a) addressing domain shifts through gradient-reversal unsupervised adaptation, and b) accounting for the specific safety requirements of a mobile robot, by encouraging the model to err on the safe side, i.e., penalizing errors that would cause collisions with obstacles more than those that would cause the robot to stop in advance. Experimental results show that our approach is able to satisfactorily identify traversable areas and to generalize to unseen locations.