Reviewerly/exHarmony huggingface dataset:
Comparative Study of Machine Learning Algorithms for Breast Cancer Prediction

Breast cancer is the most common cancer occurring in women and is estimated to be 270,000 new cases diagnosed in 2019. This is the reason detection software is needed to detect it before it gets fatal. By the use of machine learning algorithms, software can be made to detect this dangerous cancer and treat it before it can cause fatality of the patient. It is also the most frequently occurring cancer among Indian women and the chance for survival of a woman suffering from breast cancer is 50%. For Breast Cancer Detection many machine learning algorithms can be used. In this paper, 2 machine learning algorithms is proposed to compare namely Logistic Regression and Decision Tree algorithm on the Wisconsin (Diagnostic) Data Set and use the algorithm with the best accuracy for predicting Breast Cancer.
Unsupervised Denoising for Satellite Imagery Using Wavelet Directional CycleGAN

Multispectral satellite imaging sensors acquire various spectral band images and have a unique spectroscopic property in each band. Unfortunately, image artifacts from imaging sensor noise often affect the quality of scenes and have a negative impact on applications for satellite imagery. Recently, deep learning approaches have been extensively explored to remove noise in satellite imagery. Most deep learning denoising methods, however, follow a supervised learning scheme, which requires matched noisy image and clean image pairs that are difficult to collect in real situations. In this article, we propose a novel unsupervised multispectral denoising method for satellite imagery using a wavelet directional cycle-consistent adversarial network (WavCycleGAN). The proposed method is based on an unsupervised learning scheme using adversarial loss and cycle-consistency loss to overcome the lack of paired data. Moreover, in contrast to the standard image-domain cycleGAN, we introduce a wavelet directional learning scheme for effective denoising without sacrificing high-frequency components such as edges and detailed information. Experimental results for the removal of vertical stripes and wave noise in satellite imaging sensors demonstrate that the proposed method effectively removes noise and preserves important high-frequency features of satellite images.
Texting with Humanlike Conversational Agents: Designing for Anthropomorphism

Conversational agents (CAs) are natural language user interfaces that emulate human-to-human communication. Because of this emulation, research on CAs is inseparably linked to questions about anthropomorphism—the attribution of human qualities, including consciousness, intentions, and emotions, to nonhuman agents. Past research has demonstrated that anthropomorphism affects human perception and behavior in human-computer interactions by, for example, increasing trust and connectedness or stimulating social response behaviors. Based on the psychological theory of anthropomorphism and related research on computer interface design, we develop a theoretical framework for designing anthropomorphic CAs. We identify three groups of factors that stimulate anthropomorphism: technology design-related factors, task-related factors, and individual factors. Our findings from an online experiment support the derived framework but also reveal novel yet counterintuitive insights. In particular, we demonstrate that not all combinations of anthropomorphic technology design cues increase perceived anthropomorphism. For example, we find that using only nonverbal cues harms anthropomorphism; however, this effect becomes positive when nonverbal cues are complemented with verbal or human identity cues. We also find that CAs’ disposition to complete computerlike versus humanlike tasks and individuals’ disposition to anthropomorphize greatly affect perceived anthropomorphism. This work advances our understanding of anthropomorphism and contextualizes the theory of anthropomorphism within the IS discipline. We advise on the directions that research and practice should take to find the sweet spot for anthropomorphic CA design.
Heterogeneous Fusion of Semantic and Collaborative Information for Visually-Aware Food Recommendation

Visually-aware food recommendation recommends food items based on their visual features. Existing methods typically use the pre-extracted visual features from food classification models, which mainly encode the visual content with limited semantic information, such as the classes and ingredients. Therefore, such features may not cover the personalized visual preferences of users, termed collaborative information, e.g. users may attend to different colors and textures of food based on their preferred ingredients and cooking methods. To address this problem, this paper presents a heterogeneous multi-task learning framework, termed privileged-channel infused network (PiNet). It learns the visual features that contain both the semantic and collaborative information by training the image encoder to simultaneously fulfill the ingredient prediction and food recommendation tasks. However, the heterogeneity between the two tasks may lead to different visual information in need and different directions in model parameter optimization. To handle these challenges, PiNet first employs a dual-gating module (DGM) to enable the encoding and passing of different visual information from the image encoder to individual tasks. Secondly, PiNet adopts a two-phase training strategy and two prior knowledge incorporation methods to ensure an effective model training. Experimental results from two real-world datasets show that the visual features generated by PiNet better attend to the informative image regions, yielding superior performance.
Towards End-to-End Video-Based Eye-Tracking

Estimating eye-gaze from images alone is a challenging task, in large parts due to un-observable person-specific factors. Achieving high accuracy typically requires labeled data from test users which may not be attainable in real applications. We observe that there exists a strong relationship between what users are looking at and the appearance of the user's eyes. In response to this understanding, we propose a novel dataset and accompanying method which aims to explicitly learn these semantic and temporal relationships. Our video dataset consists of time-synchronized screen recordings, user-facing camera views, and eye gaze data, which allows for new benchmarks in temporal gaze tracking as well as label-free refinement of gaze. Importantly, we demonstrate that the fusion of information from visual stimuli as well as eye images can lead towards achieving performance similar to literature-reported figures acquired through supervised personalization. Our final method yields significant performance improvements on our proposed EVE dataset, with up to \(28\%\) improvement in Point-of-Gaze estimates (resulting in \(2.49^\circ \) in angular error), paving the path towards high-accuracy screen-based eye tracking purely from webcam sensors. The dataset and reference source code are available at https://ait.ethz.ch/projects/2020/EVE.
Graph Few-shot Learning with Attribute Matching

Due to the expensive cost of data annotation, few-shot learning has attracted increasing research interests in recent years. Various meta-learning approaches have been proposed to tackle this problem and have become the de facto practice. However, most of the existing approaches along this line mainly focus on image and text data in the Euclidean domain. However, in many real-world scenarios, a vast amount of data can be represented as attributed networks defined in the non-Euclidean domain, and the few-shot learning studies in such structured data have largely remained nascent. Although some recent studies have tried to combine meta-learning with graph neural networks to enable few-shot learning on attributed networks, they fail to account for the unique properties of attributed networks when creating diverse tasks in the meta-training phase---the feature distributions of different tasks could be quite different as instances (i.e., nodes) do not follow the data i.i.d. assumption on attributed networks. Hence, it may inevitably result in suboptimal performance in the meta-testing phase. To tackle the aforementioned problem, we propose a novel graph meta-learning framework--Attribute Matching Meta-learning Graph Neural Networks (AMM-GNN). Specifically, the proposed AMM-GNN leverages an attribute-level attention mechanism to capture the distinct information of each task and thus learns more effective transferable knowledge for meta-learning. We conduct extensive experiments on real-world datasets under a wide range of settings and the experimental results demonstrate the effectiveness of the proposed AMM-GNN framework.
A novel load balancing technique for cloud computing platform based on PSO

In cloud computing environment tasks are allocated among virtual machines (VMs) having different length, starting time and execution time. Therefore, balancing these loads among VM is a key factor. Load balancing has to be carried out in such a manner that all VMs should have balanced to achieve optimal utilization of its capabilities and improve the system performance. In this paper, we proposed a load balancing technique by using modified PSO task scheduling (LBMPSO) to schedule tasks over the available cloud resources that minimizes the makespan and maximizes resource utilization. This is achieved by having proper information among the tasks and resources within the datacenter. Our proposed scheduling algorithm is implemented by using CloudSim simulator. Simulation results clearly shows that proposed scheduling algorithm performs better in reducing makespan and increases the resource utilization than other existing techniques.
IPMGAN: Integrating physical model and generative adversarial network for underwater image enhancement

Autonomous underwater vehicles (AUVs) highly depend on the quality of captured underwater images to perform a variety of tasks. However, compared with everyday images taken in air, underwater images are hazy, with color shift, and in relatively low quality, posing significant challenges to available mature vision algorithms to achieve expected performance. There are, currently, two major lines of approaches to tackle these challenges: the physical image formation model-based and the neural-network-based approaches. In this paper, we propose an integrated approach, where the revised underwater image formation model, i.e., the Akkaynak-Treibitz model, is embedded into the network design for the benefit of combining the advantages of these two approaches. The embedded physical model guides for network learning, and the generative adversarial network (GAN) is adopted for coefficients estimation. We conduct extensive experiments and compare with state-of-the-art approaches quantitatively and qualitatively on nearly all the available underwater datasets, and our method achieves significant improvements.
Inquiry‐based mobile learning in secondary school science education: A systematic review

Abstract Recent years have seen a growing call for inquiry‐based learning in science education, and mobile technologies are perceived as increasingly valuable tools to support this approach. However, there is a lack of understanding of mobile technology‐supported inquiry‐based learning (mIBL) in secondary science education. More evidence‐based, nuanced insights are needed into how using mobile technologies might facilitate students' engagement with various levels of inquiry and enhance their science learning. We, therefore, conducted a robust systematic literature review (SLR) of the research articles on mIBL in secondary school science education that have been published from 2000 to 2019. We reviewed and analysed 31 empirical studies (34 articles) to explore the types of mIBL, and the benefits and constraints of mIBL in secondary school science education. The findings of this SLR suggest new research areas for further exploration and provide implications for science teachers' selection, use and design of mIBL approaches in their teaching.
E$$^2$$Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans

Developing an effective liver and liver tumor segmentation model from CT scans is very important for the success of liver cancer diagnosis, surgical planning and cancer treatment. In this work, we propose a two-stage framework for 2D liver and tumor segmentation. The first stage is a coarse liver segmentation network, while the second stage is an edge enhanced network (E\(^2\)Net) for more accurate liver and tumor segmentation. E\(^2\)Net explicitly models complementary objects (liver and tumor) and their edge information within the network to preserve the organ and lesion boundaries. We introduce an edge prediction module in E\(^2\)Net and design an edge distance map between liver and tumor boundaries, which is used as an extra supervision signal to train the edge enhanced network. We also propose a deep cross feature fusion module to refine multi-scale features from both objects and their edges. E\(^2\)Net is more easily and efficiently trained with a small labeled dataset, and it can be trained/tested on the original 2D CT slices (resolve resampling error issue in 3D models). The proposed framework has shown superior performance on both liver and liver tumor segmentation compared to several state-of-the-art 2D, 3D and 2D/3D hybrid frameworks.
DeepAbstract: Neural Network Abstraction for Accelerating Verification

While abstraction is a classic tool of verification to scale it up, it is not used very often for verifying neural networks. However, it can help with the still open task of scaling existing algorithms to state-of-the-art network architectures. We introduce an abstraction framework applicable to fully-connected feed-forward neural networks based on clustering of neurons that behave similarly on some inputs. For the particular case of ReLU, we additionally provide error bounds incurred by the abstraction. We show how the abstraction reduces the size of the network, while preserving its accuracy, and how verification results on the abstract network can be transferred back to the original network.
Adv-watermark: A Novel Watermark Perturbation for Adversarial Examples

Recent research has demonstrated that adding some imperceptible perturbations to original images can fool deep learning models. However, the current adversarial perturbations are usually shown in the form of noises, and thus have no practical meaning. Image watermark is a technique widely used for copyright protection. We can regard image watermark as a king of meaningful noises and adding it to the original image will not affect people's understanding of the image content, and will not arouse people's suspicion. Therefore, it will be interesting to generate adversarial examples using watermarks. In this paper, we propose a novel watermark perturbation for adversarial examples (Adv-watermark) which combines image watermarking techniques and adversarial example algorithms. Adding a meaningful watermark to the clean images can attack the DNN models. Specifically, we propose a novel optimization algorithm, which is called Basin Hopping Evolution (BHE), to generate adversarial watermarks in the black-box attack mode. Thanks to the BHE, Adv-watermark only requires a few queries from the threat models to finish the attacks. A series of experiments conducted on ImageNet and CASIA-WebFace datasets show that the proposed method can efficiently generate adversarial examples, and outperforms the state-of-the-art attack methods. Moreover, Adv-watermark is more robust against image transformation defense methods.
The Wearable VOSTARS System for Augmented Reality-Guided Surgery: Preclinical Phantom Evaluation for High-Precision Maxillofacial Tasks

In the context of guided surgery, augmented reality (AR) represents a groundbreaking improvement. The Video and Optical See-Through Augmented Reality Surgical System (VOSTARS) is a new AR wearable head-mounted display (HMD), recently developed as an advanced navigation tool for maxillofacial and plastic surgery and other non-endoscopic surgeries. In this study, we report results of phantom tests with VOSTARS aimed to evaluate its feasibility and accuracy in performing maxillofacial surgical tasks.An early prototype of VOSTARS was used. Le Fort 1 osteotomy was selected as the experimental task to be performed under VOSTARS guidance. A dedicated set-up was prepared, including the design of a maxillofacial phantom, an ad hoc tracker anchored to the occlusal splint, and cutting templates for accuracy assessment. Both qualitative and quantitative assessments were carried out.VOSTARS, used in combination with the designed maxilla tracker, showed excellent tracking robustness under operating room lighting. Accuracy tests showed that 100% of Le Fort 1 trajectories were traced with an accuracy of ±1.0 mm, and on average, 88% of the trajectory's length was within ±0.5 mm accuracy.Our preliminary results suggest that the VOSTARS system can be a feasible and accurate solution for guiding maxillofacial surgical tasks, paving the way to its validation in clinical trials and for a wide spectrum of maxillofacial applications.
Virtual Reality in Computer Science Education: A Systematic Review

Virtual reality (VR) technologies have become more affordable and accessible in recent years. This is opening up new methods and opportunities in the field of digital learning. VR can offer new forms of interactive learning and working, especially for subjects from the STEM (Science, technology, engineering, and mathematics) area. In this context we investigate the potential and application of VR for computer science education with a systematic review in this paper. We present a formal literature review on the use of VR technologies in computer science education. We focus on the identification of factors such as learning objectives, technologies used, interaction characteristics, and challenges and advantages of using fully immersive VR for computer science education.
Verifying Recurrent Neural Networks Using Invariant Inference

Deep neural networks are revolutionizing the way complex systems are developed. However, these automatically-generated networks are opaque to humans, making it difficult to reason about them and guarantee their correctness. Here, we propose a novel approach for verifying properties of a widespread variant of neural networks, called recurrent neural networks. Recurrent neural networks play a key role in, e.g., speech recognition, and their verification is crucial for guaranteeing the reliability of many critical systems. Our approach is based on the inference of invariants, which allow us to reduce the complex problem of verifying recurrent networks into simpler, non-recurrent problems. Experiments with a proof-of-concept implementation of our approach demonstrate that it performs orders-of-magnitude better than the state of the art.
Comparative analysis of China’s Health Code, Australia’s COVIDSafe and New Zealand’s COVID Tracer Surveillance Apps: a new corona of public health governmentality?

The onset of the COVID-19 pandemic and the subsequent lockdown of cities worldwide generated a dramatic increase in the use of public health trac(k)ing technologies. This article presents an empirical analysis of China’s Health Code on WeChat and Alipay, Australia’s COVIDSafe and New Zealand’s COVID Tracer. We ask: how does app-based public health monitoring differ from prior forms of state tracking and corporate surveillance, and interface with public and private ideals of health and citizenship? Based on a comparative analysis of the selected apps and the political economy that surrounds their code and implementation, we argue that there is a new corona of surveillance to address COVID-19 crises by intensifying the diffusion of national surveillance technologies and framing these into justifiable moral practice. In conclusion, we identify a new ‘corona’ of public health governmentality during COVID-19 pandemic through an intensification of top-down institutional data extraction from human bodies.
Adaptive Variance Based Label Distribution Learning for Facial Age Estimation

Estimating age from a single facial image is a classic and challenging topic in computer vision. One of its most intractable issues is label ambiguity, i.e., face images from adjacent age of the same person are often indistinguishable. Some existing methods adopt distribution learning to tackle this issue by exploiting the semantic correlation between age labels. Actually, most of them set a fixed value to the variance of Gaussian label distribution for all the images. However, the variance is closely related to the correlation between adjacent ages and should vary across ages and identities. To model a sample-specific variance, in this paper, we propose an adaptive variance based distribution learning (AVDL) method for facial age estimation. AVDL introduces the data-driven optimization framework, meta-learning, to achieve this. Specifically, AVDL performs a meta gradient descent step on the variable (i.e. variance) to minimize the loss on a clean unbiased validation set. By adaptively learning proper variance for each sample, our method can approximate the true age probability distribution more effectively. Extensive experiments on FG-NET and MORPH II datasets show the superiority of our proposed approach to the existing state-of-the-art methods.
Seamless High- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>Q</mml:mi></mml:math> Microwave Cavities for Multimode Circuit Quantum Electrodynamics

Multimode cavity quantum electrodynamics ---where a two-level system interacts simultaneously with many cavity modes---provides a versatile framework for quantum information processing and quantum optics. Due to the combination of long coherence times and large interaction strengths, one of the leading experimental platforms for cavity QED involves coupling a superconducting circuit to a 3D microwave cavity. In this work, we realize a 3D multimode circuit QED system with single photon lifetimes of $2$ ms and cooperativities of $0.5-1.5\times10^9$ across 9 modes of a novel seamless cavity. We demonstrate a variety of protocols for universal single-mode quantum control applicable across all cavity modes, using only a single drive line. We achieve this by developing a straightforward flute method for creating monolithic superconducting microwave cavities that reduces loss while simultaneously allowing control of the mode spectrum and mode-qubit interaction. We highlight the flexibility and ease of implementation of this technique by using it to fabricate a variety of 3D cavity geometries, providing a template for engineering multimode quantum systems with exceptionally low dissipation. This work is an important step towards realizing hardware efficient random access quantum memories and processors, and for exploring quantum many-body physics with photons.