Reviewerly/exHarmony huggingface dataset:
Cross-Device Cross-Anatomy Adaptation Network for Ultrasound Video Analysis

Domain adaptation is an active area of current medical image analysis research. In this paper, we present a cross-device and cross-anatomy adaptation network (CCAN) for automatically annotating fetal anomaly ultrasound video. In our approach, deep learning models trained on more widely available expert-acquired and manually-labeled free-hand ultrasound video from a high-end ultrasound machine are adapted to a particular scenario where limited and unlabeled ultrasound videos are collected using a simplified sweep protocol suitable for less-experienced users with a low-cost probe. This unsupervised domain adaptation problem is interesting as there are two domain variations between the datasets: (1) cross-device image appearance variation due to using different transducers; and (2) cross-anatomy variation because the simplified scanning protocol does not necessarily contain standard views seen in typical free-hand scanning video. By introducing a novel structure-aware adversarial training module to learn the cross-device variation, together with a novel selective adaptation module to accommodate cross-anatomy variation domain transfer is achieved. Learning from a dataset of high-end machine clinical video and expert labels, we demonstrate the efficacy of the proposed method in anatomy classification on the unlabeled sweep data acquired using the non-expert and low-cost ultrasound probe protocol. Experimental results show that, when cross-device variations are learned and reduced only, CCAN significantly improves the mean recognition accuracy by 20.8% and 10.0%, compared to a method without domain adaptation and a state-of-the-art adaptation method, respectively. When both the cross-device and cross-anatomy variations are reduced, CCAN improves the mean recognition accuracy by a statistically significant 20% compared with these other state-of-the-art adaptation methods .
Pseudo time reversal operators for the state of polarization

The existence of three generalized mirrors (Martinelli and Martelli, 2017) that compensate for the effects of birefringence in retracing optical circuits allows to develop a parallel with the time reversal properties. The Wigner time reversal operator for the photon spin is considered and three pseudo time reversal operators borrowed from this original operator are introduced and analysed. Their application to different birefringence situations demonstrates the validity and the fertility of the approach.
Inferring optimal electoral roll creation, verification and management strategies: System development reviews

Fair and transparent elections play a vital role in the development of any country. In order to do this task, the identification of voters must be done with most optimum accuracy. For doing this identification, governments have incorporated electoral roll management systems that perform citizen identification, citizen verification, duplicity checking, duplicity removal, vote zone decision, and finally vote zone checking. All these operations require a lot of semi-distributed-semi-central processing tasks, which must be performed with utmost accuracy so that the entire process can be made transparent and fair for both the parties and the citizens voting for these parties. To develop such a humongous system, system designers often find it difficult to select the most optimum algorithms, architectures and protocols to be developed. In this paper, we have reviewed a set of these algorithms, architectures and protocols in order to assist the designers to select the best possible combinations for development of an optimum electoral roll management system (ERMS). Moreover, our review also suggests some further optimizations which can be performed in this area, based on the research done in the field of work.
Single Image Super-Resolution Via A Progressive Mixture Model

In this paper, a progressive mixture model (PMM) for single image super-resolution is proposed. Our model consists of an offline training stage and an online reconstruction stage, and both stages are conducted progressively by exploiting a uniform iterative scheme. In the training stage, the training dataset is clustered into finer and finer groups, and a set of mixture models are sequentially learned. In the reconstruction stage, residuals of the low-resolution (LR) input image are estimated by using the trained mixture models at increasing levels, and then they are progressively added to the LR image for producing the high-resolution (HR) image. Extensive experimental simulation results have clearly shown that the proposed model consistently delivers highly accurate and visually pleasant HR images, compared to that of the state-of the-art image super-resolution methods.
A Dynamic-Attention on Crowd Region with Physical Optical Flow Features for Crowd Counting

Crowd counting is widely used in various video surveillance applications. However, most of the existing approaches treat videos as a single frame, which increase redundant information and have low efficiency, due to ignoring the context history information of neighboring frames. In this paper, we propose a novel two-stream dynamic-attention network (DANet) to associate the temporal and spatial information. Specifically, the DANet includes two stages, one of which is to generate the region-attention map and the second is to refine the high-quality density map. In each stage, we develop a hierarchical fusion strategy to guide spatial attention, which can iteratively refine the region of crowds. Besides, the dynamic-attention module guided by the physical optical flow can be dynamically integrated into any network module to optimize the generation of features for improving the effect. Therefore, it can be plugged into many computer vision architectures. Finally, experimental results on three challenging benchmark datasets show that DANet outperforms most of the previous methods. Incorporating such dynamic-attention into a framework could boost the performance of end-to-end CNN-based methods.
Music and Language

My work (in all genres) is principally informed and guided by the question of what we can learn from our tradition and which elements from the tradition can still be identified and experienced as alive and relevant. These questions are not trivial: as I understand it, they address the crux of composition. Even though it is not important to seek conclusions, it is necessary to insist on posing these questions again and again. In addition, I have become increasingly aware of the relationship between music and language (both spoken and written) in my work and research. I have been preoccupied with this theme since I began to compose. More recently my principal concerns are to take a word—which is already a unit that comprises of sound and meaning—and combine it with its associated speech sound, and take an understanding of speech sound at its word, and finally, amidst this interplay, to expose the music and allow it to be set free.
True Online TD(λ)-Replay An Efficient Model-free Planning with Full Replay

In this paper, we present a new reinforcement learning prediction method that extends the capabilities of the true online TD(λ) to allow an agent to efficiently replay all of its past experience, online in the sequence that they appear with. We demonstrate that, for problems that benefit from experience replay, our new method outperforms true online TD(λ), albeit quadratic in complexity due to its replay capabilities. In addition, we demonstrate that our method outperforms other methods with similar quadratic complexity such as Dyna Planning and TD(0)-Replay algorithms. We showcase the capabilities of our method on two benchmarking domains, a random walk problem tested with simple binary features and on a myoelectric domain that is tested with features that are deeply extracted from sEMG signals. Experimental results confirm the particular suitability of this method for a deep architecture over other methods.
Experiment of Sentiment Analysis on Public Comments of Hot Topics

With the popularity of the Internet, the public can get news from the recent hottest news, and express their opinion in time in the network of social media, such as microblog, twitter. With the help of sentiment analysis of the comments, the government or the media can inform of the public opinion, and make corresponding decisions, so that they are able to get the positive feedback. Also, the sentiment analysis can help people block the detrimental comments. Since the sentiment analysis is useful in the daily life, the author made an experiment about the sentiment analysis of three models, namely, Naive Byes, Maximum Entropy, and SVM, to compare the results' accuracy of them. And the dataset used in this experiment is Stanford Twitter Sentiment (STS). Besides, the reference is made to Stanford Sentiment Treebank and IMDB. In addition, the determination of the emotional tone is based on emotional dictionaries like GI (General Inquirer) and How Net. By comparing the accuracy and training time of different models, SVM is selected to be the optimal model with the F-scores of 82%.
CHEAPS2AGA: Bounding Space Usage in Variance-Reduced Stochastic Gradient Descent over Streaming Data and Its Asynchronous Parallel Variants

Stochastic Gradient Descent (SGD) is widely used to train a machine learning model over large datasets, yet its slow convergence rate can be a bottleneck. As a remarkable family of variance reduction techniques, memory algorithms such as SAG and SAGA have been proposed to accelerate the convergence rate of SGD. However, these algorithms need to store per training data point corrections in memory. The unlimited space usage feature is impractical for modern large-scale applications, especially over data points that arrive over time (referred to as streaming data in this paper). To overcome this weakness, this paper investigates the methods that bound the space usage in the state-of-the-art family of variance-reduced stochastic gradient descent over streaming data, and presents CHEAPS2AGA. At each step of updating the model, the key idea of CHEAPS2AGA is always reserving N random data points as samples, while re-using information about past stochastic gradients across all the observed data points with limited space usage. In addition, training an accurate model over streaming data requires the algorithm to be time-efficient. To accelerate the model training phase, CHEAPS2AGA embraces a lock-free data structure to insert new data points and remove unused data points in parallel, and updates the model parameters without using any locking. We conduct comprehensive experiments to compare CHEAPS2AGA to prior related algorithms suited for streaming data. The experimental results demonstrate the practical competitiveness of CHEAPS2AGA in terms of scalability and accuracy.
Joint Barcode and Text Orientation Detection Model for Unmanned Retail System

The 1D barcode and specification text on the package of retail products contain rich information, such as date of manufacture and source of product. Acquiring this information quickly and accurately can improve the efficiency of unmanned retail system. However, traditional OCR methods are sensitive to text orientations. Based on the fact that 1D barcodes are usually aligned with text, this paper proposes a joint barcode and text orientation detection method. Our approach first determines the four vertices of arbitrarily aligned 1D barcode by a CNN-based barcode localization network. Then, a post processing module calculates the angle of alignment of text with the help of it. Experiments on combined public and self-collected dataset verify that our approach can localize barcode regions accurately and enhance the robustness for OCR in unmanned retail systems.
Dynamical decoupling-assisted quantum parameter estimation with initial system-environment correlations

Decoherence is the main obstacle to quantum precision measurement. One must adopt appropriate control strategies to suppress decoherence. In this paper, we study the dephasing model of the system-environment with initial correlation, and use the quantum Fisher information (QFI) as the tool to explore the effect of dynamical decoupling pulses on inhibiting decoherence and improving the estimation precision of quantum parameters. We firstly study the influence of the initial correlations and initial quantum state on the dynamical evolution of a single qubit in this mode. Based on numerical calculations, in the Ohmic and the super-Ohmic thermal reservoir, we find that the periodic dynamical decoupling (PDD) sequence can significantly reduce the amplitude of the decoherence function, except for the "anti-Zeno" effect when the number of pulses is relatively small. By a judicious choice of the PDD pulse frequency, the dynamical decoupling pulses can effectively enhance the precision of quantum parameter estimation. And due to the peak structure existing in the decoherence function, the effect of applying Uhrig dynamical decoupling (UDD) sequence on the model is not ideal.
Ahff-Net: Adaptive Hierarchical Feature Fusion Network For Image Inpainting

Generation-based image inpainting methods can capture semantic features but fail to generate consistent details and high image quality results due to highly abstract feature learning and the instability of GAN training. Current methods try to overcome these disadvantages but they either need additional marginal maps or are not suitable for different shapes of occlusion. In this paper, we introduce an adaptive hierarchical feature fusion network (AHFF-Net). Without additional maps, our method can obtain consistent edges and high-quality results with different occlusions. Specifically, to guarantee the consistency of low-level features, our hierarchical fusion generator captures and aggregates multi-scale and multi-level context features. To get the high-quality results, the conditional self-supervised discriminator pay more attention to the unknown area by conditional GAN loss and stabilize the training process by conditional rotation loss. The proposed network achieves the state-of-the-art consistently on the Paris StreetView and Places365-Standard datasets with three shapes of masks.
Cross-Scale Correlation Stereo Network

Recent work has shown that convolutional neural network models, especially end-to-end models, perform significant better over traditional methods on stereo matching. However, these models neglect that the information at coarse and fine scales is processed interactively when dealing with matching problems in human visual mechanisms, which can help improve the performance of the model. To solve this problem, we propose CSCNet based on mixed spatial pyramid module and cross-scale correlation volume. In the mixed spatial pyramid module, we propose a way to extract multi-scale context information by mixing pooling and dilated convolution. The cross-scale correlation volume perform cross-computation to obtain full correlation of different scales and the best scale of matching, which reduce the matching ambiguity by imitating the human visual mechanism, and it also provide more similarity information for the subsequent regularization process. Experiments on the KITTI and Scene Flow datasets show that our model outperforms the previous methods.
Granger Causality Analysis based on Neural Networks Architectures for bivariate cases

This work deals with an analysis of Granger Causality computation based on artificial neural networks, including a nonlinear relation between the involved variables. Information about the training parameters are exhibited in order to visualize how the conditions of the chosen model to obtain the connectivity information depend on the architecture of network. Three chaotic maps with a bivariate case built from two time series were employed to see the effect of training parameters of the models. Nonlinear autoregressive and nonlinear autoregressive with exogenous inputs were used to forecast the time series, and then, obtain the causality information based on differences of errors between both approximations. Results show that the causality computation is sensible to neural network parameters previously untreated in a detailed mode.
Prediction of chromatic visual masking with deep learning

Visual masking is a well-studied phenomenon that has been exploited for signal compression, computer graphics and data hiding. Among the different types of visual masking, chromatic masking has received very little attention despite its importance and proven potential for the aforementioned applications. In this paper, we ask whether a deep neural network can learn to predict the detection thresholds in a chromatic masking paradigm. For that, a CNN model was trained and evaluated using a dataset made of 480 image patches for which chromatic thresholds were registered in terms of log-Gabor targets, as well as Root Mean Square (RMS) error. Experimental results show the superiority of the proposed approach.
Concept-Monad Model of Technological Environment of Programming

This paper proposes an extension of the principles of an adaptive programming environment based on the considered intersubject and interpersonal programming paradigms and the use of oracles, concepts, copmozies, and open-loop systems. The purpose of this work is to develop the foundations of an adaptive technological programming environment based on an intersubjective paradigm. In order to achieve the above goal, it is necessary to substantially expand the understanding of programming by introducing certain conceptual and logical structures. Based on such structures, you can lay the foundations for the technological process of programming, when the property of each individual subject of programming will be available to everyone. An oracle open-loop system describing the general scheme of programming is considered. In such a scheme, the active role of the subject is decisive, since the process of creating the program is regarded as determined by the subject. This suggests that the interest-based paradigm defines the programming process as the essence that is determined by the program itself. Conceptualization in the context of the oracle interaction scheme is considered. Oracle structures enrich the conception, form a point of view on the program as a structural activity, and on the concept as an appropriate structure. Conceptualization is defined as the sequence of actions of the subject who creates the program, the concept of which is a composition. Moreover, each stage of such a concept is also a specific concept. There are two types of composites. The first is composites that determine the understanding of a composition as the stages of composites, and the second includes basic composites that determine the active role of the subject in conception. Examples of using reduction to demonstrate the relationship of composition and decomposition in conceptualization. Schemes of concepts for various composite and composite concepts are given. These schemes are also important because they clearly reflect the fact of complementarity between the two poles of conception - synthesis and analysis, composition and decomposition. The meaningful meaning of reduction considered in the paper is that it naturally implements the "divide and conquer" paradigm in understanding the subject's active role in conceptualization. This allows you to depart from the general concept of programming as a subject-specific activity and to lay the foundations for its technologicalization. To this end, among the vast variety of compositions, composites stand out as the basic gene structures. Compared to them, any other composition is formed as a derivative of the consistent use of composites. An effective object programming environment is developed based on the concept of reduction. Adaptation of this environment is made for the standard system of software algebras. This demonstrates the solution of a number of representative numerical analysis problems.
Enhanced Image Compression Technique to Improve Image Quality for Mobile Applications

The concepts of image resizing and compression of images in mobile applications are discussed. The techniques which are currently existing can be enhanced to achieve improved image quality. Various types of image compressions are examined here. Other features such as coding process and image formats such as JPEG, PNG, and RAW are also discussed. The performance of the proposed resizing and compression algorithm was analyzed by means of Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR), Structured Similarity Index (SSIM) to determine the quality of the image. This technique improves the quality of the image transferred over the internet through mobile applications.
Optimal Scheduling Algorithm for Distributed Streaming of Data Flow across Edge Devices and Cloud

Edge computing is an emerging paradigm to assist intelligent decisions for cloud centric analytics. The major limitation of edge computing is the non-availability of a open-source platforms-as-a- service for various applications across cloud and edge. ECHO(an adaptive orchestration platform for streaming hybrid data flows across cloud and edge) attempts to fill this gap . It enables streaming data flows across distributed resources where user tasks are represented as vertices in a directed acyclic graph (DAG) and edges represent the routing channels between data and tasks. These DAGs are executed upon data arrival. ECHO's current scheduler schedules jobs using round robin algorithm. This paper proposes improvement in ECHO's scheduler. We consider current device health before scheduling dataflow in ECHO. The proposed scheduling algorithm makes use of CPU utilization and memory utilization as parameters. The experimental results show that the proposed algorithm improves the scheduler performance.
Infer user preferences from aggregate measurements: A novel message passing algorithm for privacy attack

Social media platforms, such as Facebook and TikTok, have triggered debates on privacy. The recent transformation of social media into an increasingly centralized service, exemplified by TikTok, only exacerbates the matter. While aggregation has been deemed an effective way to combat privacy infringement, a high degree of centralization can make aggregation ineffective. We present a randomized article-push algorithm and a message-passing reconstruction algorithm that enable social media platforms to infer user preferences from only the publicly available aggregate data of article-reads, without storing any individual users’ actions. Its O(n) complexity allows the reconstruction algorithm to scale to a large population, as is typical of social media platforms. Moreover, the feasibility of the privacy attack depends on the algorithm using as few articles as possible. We determine the minimum number of articles needed for high probability inference. Given the proportion of users, 0<ϵ<1, who prefer a given topic, we design a push algorithm and a reconstruction algorithm that achieve an article-to-user ratio β=ϵ(1−ϵ), at which phase transition occurs. The analysis of the algorithm departs from the classic density evolution due to the lack of monotonicity in the per-iteration error probability, which makes it surprising that phase transition takes place. By formulating the inference problem as a compressed sensing problem, we show that our phase transition threshold ϵ(1−ϵ) is extremely close to that of compressed sensing, even when the latter algorithm is of a worst-case O(n3) complexity and uses a dense Gaussian measurement matrix.