Reviewerly/exHarmony huggingface dataset:
Breast Tumor Segmentation Using K-Means Clustering and Cuckoo Search Optimization

Today, there are various methods for detecting tumors in breasts. But researchers are still trying to find an exact automatic way to segment the tumors from breast images. In this paper we propose a clustering-based algorithm for automatic tumor segmentation in the MRI samples. In the proposed method, we use k-means clustering algorithm for segmentation and also we use cuckoo search optimization (CSO) algorithm to initialize centroids in the k-means algorithm. We have used RIDER breast dataset to evaluate the proposed method and results clearly show that our algorithm outperforms similar methods such as simple k-means clustering algorithm and Fuzzy C-Means (FCM).
A Real-Time 3D Laparoscopic Imaging System: Design, Method, and Validation

Objective: This paper aims to propose a 3D laparoscopic imaging system that can realize dense 3D reconstruction in real time. Methods: Based on the active stereo technique which yields high-density, accurate and robust 3D reconstruction by combining structured light and stereo vision, we design a laparoscopic system consisting of two image feedback channels and one pattern projection channel. Remote high-speed image acquisition and pattern generation lay the foundation for the real-time dense 3D surface reconstruction and enable the miniaturization of the laparoscopic probe. To enhance the reconstruction efficiency and accuracy, we propose a novel active stereo method by which the dense 3D point cloud is obtained using only five patterns, while most existing multiple-shot structured light techniques require 10-40 patterns. In our method, dual-frequency phase-shifting fringes are utilized to uniquely encode the pixels of the measured targets, and a dual-codeword matching scheme is developed to simplify the matching procedure and achieve high-precision reconstruction. Results: Compared with the existing structured light techniques, the proposed method shows better real-time efficiency and accuracy in both quantitative and qualitative ways. Ex-vivo experiments demonstrate the robustness of the proposed method to different biological organs and the effectiveness to lesions and deformations of the organs. Feasibility of the proposed system for real-time dense 3D reconstruction is verified in dynamic experiments. According to the experimental results, the system acquires 3D point clouds with a speed of 12 frames per second. Each frame contains more than 40,000 points, and the average errors tested on standard objects are less than 0.2 mm. Significance: This paper provides a new real-time dense 3D reconstruction method for 3D laparoscopic imaging. The established prototype system has shown good performance in reconstructing surface of biological tissues.
Learning Bayesian Network Parameters With Small Data Set: A Parameter Extension under Constraints Method

Recent advances have illustrated substantial benefits from learning Bayesian networks (BNs).However, when the available data size is small, the BN parameter learning becomes a key challenge in many intelligent applications.By integrating both sample data and expert constraints, we propose a BN parameter learning algorithm with extension method-parameter extension under constraints (PEUC) by introducing related domain expert knowledge.Knowledge is transformed into inequality constraints which candidate parameter sets arise from the relative constraints space.The maximum entropy principle helps to estimate the parameter in statistical averaging model while candidate sets of BN parameters satisfy the constrained knowledge by bootstrapping techniques.Then BN parameters are estimated based on the real available sampled data set and extension streams of candidate parameters samples from the constraints space.The sample size is also taken into account according to the contribution to the final parameters.Experimental results of benchmark BN modeling problems demonstrate that PEUC algorithm tends to the classical MLE algorithm when the modeling data size is sufficient.Furthermore, when the available data size is small, the parameters of BN can be estimated by PEUC as well, and the learned accuracy is superior to MLE, MAP or QMAP algorithm.Finally, PEUC is also applied to a real bearing fault diagnosis case.The presented approach provides a new promising BN parameter learning way for more intelligent system modeling problems, particularly when the data sets are small.
Machine Learning Takes Laboratory Automation to the Next Level

Clinical microbiology laboratories face challenges with workload and understaffing that other clinical laboratory sections have addressed with automation. In this issue of the Journal of Clinical Microbiology, M. L. Faron, B. W. Buchan, R. F. Relich, J. Clark, and N. A. Ledeboer (J Clin Microbiol 58:e01683-19, 2020, https://doi.org/10.1128/JCM.01683-19) evaluate the performance of automated image analysis software to screen urine cultures for further workup according to their total number of CFU. Urine cultures are the highest volume specimen type for most laboratories, so this software has the potential for tremendous gains in laboratory efficiency and quality due to the consistency of colony quantification.
Low literacy in Germany: Results from the second German literacy survey

When Germany performed the first national assessment on reading and writing skills among adults in 2010 (LEO), it was late compared to other European countries such as_x000D_ England or France. Now the results of the second round of that survey reveal a higher average literacy level in Germany compared to the preceding survey. In this paper, we briefly discuss the state of literacy research in large-scale surveys and offer some critical viewpoints. Next, we present the results of the two LEO surveys from 2010 and 2018. Besides providing information about the composition of the low-literate adult population in Germany (aged 18–64 years), we selected results that might help to critically revise current stereotypes about adults who have difficulties reading and writing.
Perceptual Image Anomaly Detection

We present a novel method for image anomaly detection, where algorithms that use samples drawn from some distribution of “normal” data, aim to detect out-of-distribution (abnormal) samples. Our approach includes a combination of encoder and generator for mapping an image distribution to a predefined latent distribution and vice versa. It leverages Generative Adversarial Networks to learn these data distributions and uses perceptual loss for the detection of image abnormality. To accomplish this goal, we introduce a new similarity metric, which expresses the perceived similarity between images and is robust to changes in image contrast. Secondly, we introduce a novel approach for the selection of weights of a multi-objective loss function (image reconstruction and distribution mapping) in the absence of a validation dataset for hyperparameter tuning. After training, our model measures the abnormality of the input image as the perceptual dissimilarity between it and the closest generated image of the modeled data distribution. The proposed approach is extensively evaluated on several publicly available image benchmarks and achieves state-of-the-art performance.
Learning Dynamic Knowledge Graphs to Generalize on Text-Based Games.

Playing text-based games requires skills in processing natural language and sequential decision making. Achieving human-level performance on text-based games remains an open challenge, and prior research has largely relied on hand-crafted structured representations and heuristics. In this work, we investigate how an agent can plan and generalize in text-based games using graph-structured representations learned end-to-end from raw text. We propose a novel graph-aided transformer agent (GATA) that infers and updates latent belief graphs during planning to enable effective action selection by capturing the underlying game dynamics. GATA is trained using a combination of reinforcement and self-supervised learning. Our work demonstrates that the learned graph-based representations help agents converge to better policies than their text-only counterparts and facilitate effective generalization across game configurations. Experiments on 500+ unique games from the TextWorld suite show that our best agent outperforms text-based baselines by an average of 24.2%.
Zero-Knowledge Proof Systems for QMA

Prior work has established that all problems in NP admit classical zero-knowledge proof systems, and under reasonable hardness assumptions for quantum computations, these proof systems can be made secure against quantum attacks. We prove a result representing a further quantum generalization of this fact, which is that every problem in the complexity class QMA has a quantum zero-knowledge proof system. More specifically, assuming the existence of an unconditionally binding and quantum computationally concealing commitment scheme, we prove that every problem in the complexity class QMA has a quantum interactive proof system that is zero-knowledge with respect to efficient quantum computations. Our QMA proof system is sound against arbitrary quantum provers, but only requires an honest prover to perform polynomial-time quantum computations, provided that it holds a quantum witness for a given instance of the QMA problem under consideration. The proof system relies on a new variant of the QMA-complete local Hamiltonian problem in which the local terms are described by Clifford operations and standard basis measurements. We believe that the QMA-completeness of this problem may have other uses in quantum complexity.
Applications of Structural Equation Modelling with AMOS 21, IBM SPSS

AMOS is a highly popular software with a unique graphical interface (GUI) for solving structural equation modelling problems. The software is developed by IBM and SPSS Inc. Before 2003, AMOS software was a part of SmallWaters Corp. AMOS is extensively used by the researchers for multivariate analysis by integrating the use of various multivariate analysis methods such as regression, factor analysis, correlation and analysis of variance. AMOS provides an intuitive graphical or programmatic user interface for evaluating the complex relationships among the constructs. SPSS AMOS is available for the Windows operating system.
Recursive-iterative digital image correlation based on salient features

Measuring surface deformation of objects with natural patterns using digital image correlation (DIC) is difficult due to the challenges of the pattern quality and discriminative pattern matching. Existing studies in DIC predominantly focus on the artificial speckle patterns while seldom paying attention to the inevitable natural texture patterns. We propose a recursive-iterative method based on salient features to measure the deformation of objects with natural patterns. The method is proposed to select salient features according to the local intensity gradient and then to compute their displacements by incorporating the inverse compositional Gauss–Newton (IC-GN) algorithm into the classic image pyramidal computation. Compared with the existing IC-GN-based DIC technology, the use of discriminative subsets allows avoidance of displacement computation at pixels with poor spatial gradient distribution. Furthermore, the recursive computation based on the image pyramid can estimate the displacements of the features without the need for initial value estimation. This method remains effective even for large displacement measurements. The results of simulation and experiment prove the method’s feasibility, demonstrating that the method is effective in deformation measurement based on natural texture patterns.
Resource speed limits: maximal rate of resource variation

Abstract Recent advances in quantum resource theories have been driven by the fact that many quantum information protocols make use of different facets of the same physical features, e.g. entanglement, coherence, etc. Resource theories formalise the role of these important physical features in a given protocol. One question that remains open until now is: how quickly can a resource be generated or degraded? Using the toolkit of quantum speed limits we construct bounds on the minimum time required for a given resource to change by a fixed increment, which might be thought of as the power of said resource, i.e., the rate of resource variation. We show that the derived bounds are tight by considering several examples. Finally, we discuss some applications of our results, which include generalisations of thermodynamic work, heat, and power to any given resource, as well as fundamental bounds on the computation rate in leading quantum processing architectures.
Operator Sampling for Shot-frugal Optimization in Variational Algorithms

Quantum chemistry is a near-term application for quantum computers. This application may be facilitated by variational quantum-classical algorithms (VQCAs), although a concern for VQCAs is the large number of measurements needed for convergence, especially for chemical accuracy. Here we introduce a strategy for reducing the number of measurements (i.e., shots) by randomly sampling operators $h_i$ from the overall Hamiltonian $H = \sum_i c_i h_i$. In particular, we employ weighted sampling, which is important when the $c_i$'s are highly non-uniform, as is typical in chemistry. We integrate this strategy with an adaptive optimizer developed recently by our group to construct an improved optimizer called Rosalin (Random Operator Sampling for Adaptive Learning with Individual Number of shots). Rosalin implements stochastic gradient descent while adapting the shot noise for each partial derivative and randomly assigning the shots amongst the $h_i$ according to a weighted distribution. We implement this and other optimizers to find the ground states of molecules H$_2$, LiH, and BeH$_2$, without and with quantum hardware noise, and Rosalin outperforms other optimizers in most cases.
SpeedNet: Learning the Speediness in Videos

We wish to automatically predict the of moving objects in videos---whether they move faster, at, or slower than their speed. The core component in our approach is SpeedNet---a novel deep network trained to detect if a video is playing at normal rate, or if it is sped up. SpeedNet is trained on a large corpus of natural videos in a self-supervised manner, without requiring any manual annotations. We show how this single, binary classification network can be used to detect arbitrary rates of speediness of objects. We demonstrate prediction results by SpeedNet on a wide range of videos containing complex natural motions, and examine the visual cues it utilizes for making those predictions. Importantly, we show that through predicting the speed of videos, the model learns a powerful and meaningful space-time representation that goes beyond simple motion cues. We demonstrate how those learned features can boost the performance of self-supervised action recognition, and can be used for video retrieval. Furthermore, we also apply SpeedNet for generating time-varying, adaptive video speedups, which can allow viewers to watch videos faster, but with less of the jittery, unnatural motions typical to videos that are sped up uniformly.
Machine Learning From Distributed, Streaming Data [From the Guest Editors]

The articles in this special section focus on machine learning from distributed, streaming media. The field of machine learning has undergone radical transformations during the last decade. These transformations, which have been fueled by our ability to collect and generate tremendous volumes of training data and leverage massive amounts of lowcost computing power, have led to an explosion in research activity in the field by academic and industrial researchers. Discusses the fields that are adopting machine learning and reports on applications for their use.
Robust image classification against adversarial attacks using elastic similarity measures between edge count sequences

Due to their unprecedented capacity to learn patterns from raw data, deep neural networks have become the de facto modeling choice to address complex machine learning tasks. However, recent works have emphasized the vulnerability of deep neural networks when being fed with intelligently manipulated adversarial data instances tailored to confuse the model. In order to overcome this issue, a major effort has been made to find methods capable of making deep learning models robust against adversarial inputs. This work presents a new perspective for improving the robustness of deep neural networks in image classification. In computer vision scenarios, adversarial images are crafted by manipulating legitimate inputs so that the target classifier is eventually fooled, but the manipulation is not visually distinguishable by an external observer. The reason for the imperceptibility of the attack is that the human visual system fails to detect minor variations in color space, but excels at detecting anomalies in geometric shapes. We capitalize on this fact by extracting color gradient features from input images at multiple sensitivity levels to detect possible manipulations. We resort to a deep neural classifier to predict the category of unseen images, whereas a discrimination model analyzes the extracted color gradient features with time series techniques to determine the legitimacy of input images. The performance of our method is assessed over experiments comprising state-of-the-art techniques for crafting adversarial attacks. Results corroborate the increased robustness of the classifier when using our discrimination module, yielding drastically reduced success rates of adversarial attacks that operate on the whole image rather than on localized regions or around the existing shapes of the image. Future research is outlined towards improving the detection accuracy of the proposed method for more general attack strategies.
Social VR : A New Form of Social Communication in the Future or a Beautiful Illusion?

Abstract The further development of 5G technology provides new development possibilities for the application of VR technology in various fields. The application of VR in the social field has derived the concept of “social VR”. This paper focuses on where social VR is new and where is the future of social VR going. By means of comparing with traditional ways of socializing, this paper proposes three new features of social VR: high immersion, diverse interactive modes, and contextualized social content. We classify social VR applications and take two popular social VR games: VRChat and Facebook Horizon , as examples to illustrate the current development of social VR applications and the possible existence of social VR. Moreover, a series of problems and countermeasures are explained and further considered. This paper is of great significance in the development of social VR. It indicates that there is a good prospect for the future of social VR.
Wearables for something good: aid, dataveillance and the production of children’s digital bodies

The article contributes to the emergent critical literature on the creation of data in aid by exploring how the use of digital technologies creates corresponding ‘digital bodies’. The article argues that dataveillance is becoming a central practice of aid. To explore this proposition, the article interrogates the making of children’s digital bodies as objects of intervention through an examination of the development of ‘Khushi baby’, a wearable digital necklace for tracking infants immunization records in India.
Privacy-preserving activity and health monitoring on databox

Activity recognition using deep learning and sensor data can help monitor activities and health conditions of people who need assistance in their daily lives. Deep Neural Network (DNN) models to infer the activities require data collected by in-home sensory devices. These data are often sent to a centralised cloud to be used for training the model. Centralising the data introduces privacy risks. The collected data contain sensitive information about the subjects. The cloud-based approach increases the risk that the data be stored and reused for other purposes without the owner's control. We propose a system that uses edge devices to implement activity and health monitoring locally and applies federated learning to facilitate the training process. The devices use the Databox platform to manage sensor data collected in people's homes, conduct activity recognition locally, and collaboratively train a DNN model without transferring the collected data into the cloud. We illustrate the applicability of the processing time of activity recognition on edge devices. We use a hierarchical model in which a global model is generated in the cloud, without requiring the raw data, and local models are trained on edge devices. The activity inference accuracy of the global model converges to a sufficient level after a few rounds of communication between edge devices and the cloud.
Efficient and Round-Optimal Oblivious Transfer and Commitment with Adaptive Security

We construct the most efficient two-round adaptively secure bit-OT in the Common Random String ( $$\text {CRS} $$ ) model. The scheme is UC secure under the Decisional Diffie-Hellman (DDH) assumption. It incurs $$\mathcal {O}(1)$$ exponentiations and sends $$\mathcal {O}(1)$$ group elements, whereas the state of the art requires $$\mathcal {O}(\kappa ^2)$$ exponentiations and communicates $$\text {poly}(\kappa )$$ bits, where $$\kappa $$ is the computational security parameter. Along the way, we obtain several other efficient UC-secure OT protocols under DDH: We also obtain the first non-interactive adaptive string UC-commitment in the CRS model which incurs a sublinear communication overhead in the security parameter. Specifically, we commit to $$\text {polylog}(\kappa )$$ bits while communicating $$\mathcal {O}(\kappa )$$ bits. Moreover, it is additively homomorphic. We can also extend our results to the single $$\text {CRS} $$ model where multiple sessions share the same $$\text {CRS} $$ . As a corollary, we obtain a two-round adaptively secure MPC protocol in this model.
Adjusted Quick Shift Phase Preserving Dynamic Range Compression method for breast lesions segmentation

Ultrasound imaging systems produce images that are affected by speckle. This is mainly due to the interference of the returning wave at the transducer aperture that degrades images quality. As the filtering stage affects the quality of segmentation for breast ultrasound images, significant research recently concentrated on eliminating the speckle using different mathematical methods. In this paper, we propose a new approach for segmentation of breast ultrasound images called Adjusted Quick Shift Phase Preserving Dynamic Range Compression (AQS-APPDRC). AQS-APPDRC consists of three steps: a preprocessing step by applying APPDRC Filter and Frost Filter, followed by proposed Adjusted Quick Shift segmentation for superpixel extraction, and a post processing step of Binary Thresholding for blob selection. The results of our proposed AQS-APPDRC segmentation is compared with two other conventional segmentation methods namely: QS-FR, and QS-PPDRC. In addition, this study considers two state-of-the-art Convolutional Neural Networks (CNNs), i.e. U-Net and FCNs (FCN-AlexNet, FCN-32s and FCN-16s) for comparison. The segmentation results are evaluated on two small breast ultrasound datasets, where Dataset A has 306 images and Dataset B has 163 images. The proposed AQS-APPDRC approach achieved the best performance amongst two conventional methods and the CNNs in terms of Dice, Specificity, and MCC, when evaluated on Dataset A. For Dataset B, FCN-16s showed the best Dice, Specificity, and MCC, but the proposed AQS-APPDRC achieved comparable results. For Sensitivity, FCN-32s showed the best result for both datasets. Our results revealed that, for CNNs, the size of dataset is always the key indicator for its performance. The conventional methods produce comparable results on small datasets.