Reviewerly/exHarmony huggingface dataset:
Breast Ultrasound Image Processing

Breast cancer is a serious disease among women, and its early detection is very crucial for the treatment of cancer. To assist radiologists who manually delineate the tumour from the ultrasound image an automatic computerized method of detection called CAD (computer-aided diagnosis) is developed to provide valuable inputs for radiologists. The CAD systems is divided into many branches like pre-processing, segmentation, feature extraction, and classification. This chapter solely focuses on the first two branches of the CAD system the pre-processing and segmentation. Ultrasound images acquired depends on the operator expertise and is found to be of low contrast and fuzzy in nature. For the pre-processing branch, a contrast enhancement algorithm based on fuzzy logic is implemented which could help in the efficient delineation of the tumour from ultrasound image.
Entanglement generation between two solid-state qubits mediated by microwave photons

We consider a hybrid system of two interacting double quantum dots coupled to a single-mode microwave resonator, and it is demonstrated that the preparation of qubit–qubit entangled states at the steady-state mediated either by real or virtual photons is feasible when the resonator operates in an over-coupling regime. For that purpose, we perform a comprehensive analysis of the degree of entanglement in the system based on entanglement quantifiers such as the concurrence criterion and the reduced density matrix of the quantum state. In particular, our simulations take into account the current experimental scenario, and special attention is given to the reflection coefficient since it is experimentally accessible. Our findings reveal that when the two qubits energies are close to the resonance, the reflection coefficient becomes unequivocally in a witness of entanglement in the system. Our results pave the way for the experimental that desire to achieve entanglement control in the short- and long-range interactions among qubits.
A Framework for Classifying Temporal Relations with Question Encoder

Temporal-relation classification plays an important role in the field of natural language processing. Various deep learning-based classifiers, which can generate better models using sentence embedding, have been proposed to address this challenging task. These approaches, however, do not work well because of the lack of task-related information. To overcome this problem, we propose a novel framework that incorporates prior information by employing awareness of events and time expressions (time–event entities) as a filter. We name this module “question encoder.” In our approach, this kind of prior information can extract task-related information from sentence embedding. Our experimental results on a publicly available Timebank-Dense corpus demonstrate that our approach outperforms some state-of-the-art techniques.
An Open Problem

In this notes paper, we present an open problem to the Buildsys community: energy data super-resolution, referring to the task of estimating the power consumption of a home at a higher resolution given the low-resolution power consumption. Super-resolution is especially useful when the smart meters collect data at a very low-sampling rate owing to a plethora of issues such as bandwidth, pricing, old hardware, among others. The problem is motivated by the success of image super resolution in the computer vision community. In this paper, we formally introduce the problem and present baseline methods and the algorithms we used to "solve" this problem. We evaluate the performance of the algorithms on a real-world dataset and discuss the results. We also discuss what makes this problem hard and why a trivial baseline is hard to beat.
WITHDRAWN: The security in web application of cloud and IoT service

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
FUN-Agent: A 2020 HUMAINE Competition Entrant

Of late, there has been a significant surge of interest in industry and the general populace about future potential of human-AI collaboration [20]. Academic researchers have been pushing the frontier of new modalities of peer-level and ad-hoc human agent collaboration [10;22] for a longer period. We have been particularly interested in research on agents representing human users in negotiating deals with other human and autonomous agents [12;16;18]. Here we present the design for the conversational aspect of our agent entry into the HUMAINE League of the 2020 Automated Negotiation Agent Competition (ANAC). We discuss how our agent utilizes conversational and negotiation strategies, that mimic those used in human negotiations, to maximize its utility as a simulated street vendor. We leverage verbal influence tactics, offer pricing, and increasing human convenience to entice the buyer, build trust and discourage exploitation. Additionally, we discuss the results of some in-house testing we conducted.
RIFD Fibonacci Zeckendorf Hybrid Encoding and Decoding Algorithm for Medical Image Compression and Reconstruction

Digital medical images are an important source of information that help doctors in diagnosis and treatment. The raw form of a digital image requires a tremendous amount of storage memory and a longer time for transmission from one node to another through a limited bandwidth network. Thus, many algorithms are developed and implemented for image compression that eliminates the redundant information while keeping the essential ones. The decoding algorithms are able to extract this essential information and reconstruct the original images without losing the original image quality. Rounding a pixel's Intensity Followed by a Division process is called RIFD algorithm. It minimizes the information redundancy of the images and maintains the image visual quality with insignificant distortion. The integration of Fibonacci sequence and Zeckendorf's theorem produces a simple and fast variable length code for representing integer data. This article proposes a hybrid encoding and decoding algorithm for medical image compression by merging the RIFD and the suffix variable length second order Fibonacci-Zeckendorf codes. Performance metrics such as number of bits per pixel, compression ratio, memory saving percentage, run time, mean square error, peak signal to noise ratio and similarity structure index are used in evaluating the efficiency of the proposed algorithm on nine medical images. The simulation results shows the efficiency of the proposed algorithm especially in compressing images with non uniform distributed histograms.
Juxtopia® CAMMRAD Prepare: A Wearable AI-AR Platform for Clinical Training Emergency First Response Teams

This paper discusses a Juxtopia <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> Open-Wear research collaboration with the Maryland Fire & Rescue Institute (MFRI) to evaluate how an artificial intelligent (AI) wearable augmented reality (AR) intervention quantifiably improves hands-free clinical training proficiency of firefighter United States (U.S.) Emergency Medical Technicians (EMT) and prepares them for administering effective clinical skills (e.g., Tourniquet) in response to hazardous materials (hazmat) incidences. The AI-AR system, Juxtopia <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> Context-Aware Mobile Mixed Reality Assistive Device (CAMMRAD) Prepare E-Training system was evaluated in a two day controlled study including firefighter EMTs subjects who participated in simulated training exercise that mimicked their real-world operations. Results of the study indicates a need for AI-AR training to continually improve EMT clinical skill proficiency.
Anonymous IBE from Quadratic Residuosity with Fast Encryption

We develop two variants of Cocks’ identity-based encryption. One variant has faster encryption, where the most time-consuming part only requires several modular multiplications. The other variant makes the first variant anonymous under suitable complexity assumptions, while its decryption efficiency is about twice lower than the first one. Both the variants have ciphertext expansion twice more extensive than the original Cocks’ identity-based encryption. To alleviate the issue of the second variant’s large ciphertext expansion, we consider using it to construct a public-key encryption with keyword search scheme with a fast encryption algorithm by means of the transform in [1].
Multiple Target Tracking Based on Position Matching and Feature Correlation

Aiming at real-time application of multiple target tracking in automatic driving, an adaptive online multiple target tracking method based on maximum matching of IoU is proposed, which solves the mismatch problem of fixed IoU threshold matching. In order to solve the problem of trajectory fracture caused by occlusion, an offline method to extract fHOG feature and deep feature is raised to determine whether a connection is required after online multiple target tracking is completed, and the validity of this method is verified by experiments.
Winter city and the potential/space of Siberian identity

Abstract The article dwells upon making winter cities livable not by means of “survival” programs but by unlocking the positive living experience, identifying the unique winter potential of a location, and its suitability for living a fulfilling life. The relevance of this research stems from the industrial development of winter/northern cities being prioritized over the establishment of living culture, which has degraded the urban and cultural environment. Viewing a winter city as a self-sustaining territory, capitalizing on the unique features of the region in winter helps unleash the productive potential of “winter life.” The article dwells upon the relevant features of winter cities, tourism industry, and festive culture as the enablers of city development and prosperity. An important aspect of the paper lies in addressing the uniqueness of a location and the winter potential of the Siberian identity. The author’s research and project designs seek to give Irkutsk a positive winter city image; they cover winter festivals and the Baikal identity. This research shows the value of having a unique location and creating a positive winter city image, etc. in the context of making Irkutsk a comfortable and investment-attractive winter city. In general, the ideology of developing a winter city as a place that will attract investment and generate technological initiative in a blend with traditional values is what makes the winter environment livable not just as a survival system, but as a self-sustainable and unique city that has its positive vibes.
Asymptotic analysis of singular likelihood ratio of normal mixture by Bayesian learning theory for testing homogeneity

When we use the normal mixture model, the optimal number of the components describing the data should be determined. Testing homogeneity is good for this purpose; however, to construct its theory is challenging, since the test statistic does not converge to the χ2 distribution even asymptotically. The reason for such asymptotic behavior is that the parameter set describing the null hypothesis (N.H.) contains singularities in the space of the alternative hypothesis (A.H.). Recently, a Bayesian theory for singular models was developed, and it has elucidated various problems of statistical inference. However, its application to hypothesis tests has been limited. In this article, we tackled the problem of testing homogeneity using Bayesian singular learning theory, and derive the asymptotic distributions of the marginal likelihood ratios in three cases: (1) only the mixture ratio is a variable in the A.H.; (2) the mixture ratio and the mean of the mixed distribution are variables; and (3) the mixture ratio, the mean, and the variance of the mixed distribution are variables. These are obtained by applying the scaling technique to the Kullback–Leibler divergence between N.H. and A.H. The validity of the hypothesis tests based on the results is confirmed through numerical experiments.
Stable Deep Reinforcement Learning Method by Predicting Uncertainty in Rewards as a Subtask

In recent years, a variety of tasks have been accomplished by deep reinforcement learning (DRL). However, when applying DRL to tasks in a real-world environment, designing an appropriate reward is difficult. Rewards obtained via actual hardware sensors may include noise, misinterpretation, or failed observations. The learning instability caused by these unstable signals is a problem that remains to be solved in DRL. In this work, we propose an approach that extends existing DRL models by adding a subtask to directly estimate the variance contained in the reward signal. The model then takes the feature map learned by the subtask in a critic network and sends it to the actor network. This enables stable learning that is robust to the effects of potential noise. The results of experiments in the Atari game domain with unstable reward signals show that our method stabilizes training convergence. We also discuss the extensibility of the model by visualizing feature maps. This approach has the potential to make DRL more practical for use in noisy, real-world scenarios.
Dimensionality Reduction of Massive I/O Log Data Flow in Power System

Every day, the power system receives massive I/O logs. The amount of data in these logs is so large that it takes huge computational resources to analyze. Therefore, it is necessary to reduce the size of the massive I/O logs and only analyze the key log data, thereby reducing the workload of invalid analysis. This paper takes the I/O log of the substation as the research object, and studies the dimension reduction method of the massive I/O data flow log, which reduces the computational load brought by the high-dimensional I/O data flow log data and reduces the massive I/O data flow log. This paper proposes a method of secondary dimensionality reduction. Firstly, the high dimensional I/O log data stream is classified, so that the data is transformed from high-dimensional to low-dimensional. Then, the dimension is reduced again in each category, so that the most simplified massive I/O logs are achieved. Through theoretical analysis, we can come to the conclusion that the computational time complexity of the data after dimension reduction is reduced by more than 80%.
Inaudible acoustic signal based key agreement system for IoT devices

Secure Device-to-Device (D2D) communication is becoming increasingly important with the ever-growing number of Internet-of-Things (IoT) devices in our daily life. To achieve secure D2D communication, the key agreement between different IoT devices without any prior knowledge is becoming desirable. Although various approaches have been proposed in the literature, they suffer from a number of limitations, such as low key generation rate and short pairing distance. In this paper, we present an inaudible acoustic signal based key generation protocol for mobile devices. Based on acoustic channel reciprocity, our system exploits channel frequency response of two legitimate devices as a common secret to generate keys. Extensive experiments are conducted to evaluate the proposed system in different real environments. Evaluation results show that the proposed system can generate the same secret key for two mobile devices with high probability.
Research on the Strategy for Constructing Smart Kindergarten System

It has been a hot topic that needs to be paid special attention to in promoting the informatization of preschool education how to effectively integrate intelligent technology and education, so that technology can better accelerate the educational reform in line with the times. After analyzing the intelligent level of kindergartens from the perspectives of space, teaching and management, it has been found that there are a series of problems in the transformation process to a smart kindergarten, such as the lack of overall coordination, the insufficient use of intelligent teaching resources, and the inadequate exploration of data value. Therefore, it is necessary to make full use of a variety of intelligent technologies to build an integrated intelligent space, construct a game-based teaching activity system, and improve the systematic intelligent management system based on the real space of kindergartens as well as children's cognitive characteristics.
Prediction of Breast Cancer Using Electronic Health Record

With increasing in the death rate due to the breast cancer is the second most cause. Breast cancer prediction is one of the challenging research problem. It is essential that early prediction, diagnosis and the prevention of breast cancer. Existing system cannot produce an output with good accuracy. This paper adopts the Decision tree algorithm to extract the feature selection and compare it with prior dataset to predict the breast cancer. Our algorithm can predict breast cancer at the early stage and by providing the treatment at the earlier stage and the death rate will be reduced. In phase 1 the Electronic Health Record (EHR) has the patient dataset are used to process the value and provide the avoidance and regulation of breast cancer. In phase2 the Decision tree algorithm process the dataset and extract the feature from the dataset and compare it with prior data to predict the breast cancer.
Overwater Image Dehazing via Cycle-Consistent Generative Adversarial Network

In contrast to images taken on land scenes, images taken over water are more prone to degradation due to the influence of the haze. However, existing image dehazing methods are mainly developed for land scenes and perform poorly when applied to overwater images. To address this problem, we collect the first overwater image dehazing dataset and propose an OverWater Image Dehazing GAN (OWI-DehazeGAN). Due to the difficulties of collecting paired hazy and clean images, the dataset is composed of unpaired hazy and clean images taken over water. The proposed OWI-DehazeGAN learns the underlying style mapping between hazy and clean images in an encoder-decoder framework, which is supervised by a forward-backward translation consistency loss for self-supervision and a perceptual loss for content preservation. In addition to qualitative evaluation, we design an image quality assessment network to rank the dehazed images. Experimental results on both real and synthetic test data demonstrate that the proposed method performs superiorly against several state-of-the-art land dehazing methods.
Detailed 3D Face Reconstruction from Single Images Via Self-supervised Attribute Learning

We present a novel approach to reconstruct high-fidelity geometric human face model from a single RGB image. The main idea is to add details into a coarse 3D Morphable Model (3DMM) based model in a self-supervised way. Our observation is that most of the facial details like wrinkles are driven by expression and intrinsic facial characteristics which here we refer to as the facial attribute. To this end, we propose an expression related details recovery scheme and a facial attribute representation.
An Efficient RGB-D Scene Recognition Method Based on Multi-Information Fusion

RGB-D scene recognition is a challenging problem due to lack of RGB-D datasets and inefficient RGB-D fusion. In this paper, we exploit several factors that affect the RGB-D scene recognition performance, including the representations of the RGB-D data and objects in the scene. We propose an effective multi-information fusion method composed of two modules: a revised detection-based method, and a multi-feature fusion based classifier. The revised detection-based method leverage the auxiliary RGB data. And the multi-feature fusion based classifier select the optimal feature configuration for RGB-D data description. The proposed method is validated on two publicly available datasets: the SUN RGB-D dataset, and the NYU Depth v2 dataset. The obtained results show that the proposed fusion method is effective and is comparable with the state-of-the-art method. Furthermore, the proposed framework contains much less parameters than the state-of-the-art model and thus requires much less time for training. The code and the fine-tuned model parameters are available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zhangbin28/MulInfo_RGBDScene</uri> .
Decision Boundary Extraction of Classifiers

Abstract In the field of machine learning, explicitly extracting the decision boundary of a classifier not only helps to visualize the differences between different classifiers, but also provides a more convenient way to determine the class of a sample. We proposed a boundary extraction method that relies on the output result of the classifier but not on the classifier mechanism. We find the initial decision boundary based on Voronoi diagram, and then take the smoothness and simplicity as the driving goal, adaptively adjust the shape of the boundary until convergence. In order to verify the effectiveness and usefulness of the algorithm, the decision boundaries generated by four different classifiers, ANN, SVM, Random Forest, and ELM, were visualized on the four data sets, the classification accuracy is also analyzed based on the extracted decision boundaries. The test results show that the visualization results of decision boundaries and the classification accuracy based on explicit decision boundaries are highly consistent with the classification accuracy of the classifier, which can simulate the decision mechanism of the classifier well.
3D SaccadeNet: A Single-Shot 3D Object Detector for LiDAR Point Clouds

3D object detection is an essential step towards holistic scene understanding. Currently, the existing 3D object detection methods focus on certain object's areas once and predict the object's locations. The way does not conform to the habit of human observing targets. Hence, this work proposes a fast and accurate object detector called 3D SaccadeNet, which regards one 3D object as nine keypoints. In the training process, the corner loss, center loss, and classification loss are computed. However, the center is only used to predict a 3D object. Performed experiments on the KITTI dataset show that the proposed method is highly efficient and effective, and the 3D object detection reaches (91.18%,82.80%,79.90%).
Free gap estimates from the exponential mechanism, sparse vector, noisy max and related algorithms

Private selection algorithms, such as the exponential mechanism, noisy max and sparse vector, are used to select items (such as queries with large answers) from a set of candidates, while controlling privacy leakage in the underlying data. Such algorithms serve as building blocks for more complex differentially private algorithms. In this paper we show that these algorithms can release additional information related to the gaps between the selected items and the other candidates for free (i.e., at no additional privacy cost). This free gap information can improve the accuracy of certain follow-up counting queries by up to 66%. We obtain these results from a careful privacy analysis of these algorithms. Based on this analysis, we further propose novel hybrid algorithms that can dynamically save additional privacy budget.