Reviewerly/exHarmony huggingface dataset:
CLAHE Parameters Effects on the Quantitative and Visual Assessment of Dense Breast Mammograms

In Brazil, breast cancer is the disease with the highest mortality rate in women, mainly due to late diagnosis. The digital image processing has been used in order to provide both an improvement in the quality of the mammographic images as well as the evaluation of these images by radiologist. The goal of this work is to apply and evaluate (by an experienced radiologist) an adaptive histogram equalization algorithm and the variation of its parameters in mammographic digital images of dense breasts. We applied the Contrast-limited Adaptive Histogram Equalization (CLAHE) with different parameters (window size) in 98 images for the purpose of visual analysis and comparison with the respective original image. The peak signal-to-noise ratio (PSNR) ratio, the variance and Structural Similarity Index (SSIM) were calculated, as well as a visual evaluation of an experienced and specialized radiologist. The quantitative results showed the proximity between the averages of PSNR values for the set of windows tested. According to the radiologist assessment, the window size 15x15 provided a better contrast between fibroglandular tissue and adjacent structures. This study contributed to the contrast enhancement in dense mammograms, that is, belonging to those patients who present a higher risk of developing breast cancer. With the application of a simple mathematically and fast computational processing technique, it was possible to obtain better resultant images compared to the original ones, able to aid radiologists in a better diagnostic accuracy and in the early diagnosis of breast cancer.
Unusual customer response identification and visualization based on text mining and anomaly detection

The Vehicle Dependability Study (VDS) is a survey study on customer satisfaction for vehicles that have been sold for three years. VDS data analytics plays an important role in the vehicle development process because it can contribute to enhancing the brand image and sales of an automobile company by properly reflecting customer requirements retrieved from the analysis results when developing the vehicle’s next model. Conventional approaches to analyzing the voice of customers (VOC) data, such as VDS, have focused on finding the mainstream of customer responses, many of which are already known to the enterprise. However, detecting and visualizing notable opinions from a large amount of VOC data are important in responding to customer complaints. In this study, we propose a framework for identifying unusual but significant customer responses and frequently used words therein based on distributed document representation, local outlier factor, and TF–IDF methods. We also propose a procedure that can provide useful information to vehicle engineers by visualizing the main results of the framework. This unusual customer response detection and visualization framework can accelerate the efficiency and effectiveness of many VOC data analytics.
Blockchain-Enabled Privacy-Preserving Internet of Vehicles: Decentralized and Reputation-Based Network Architecture

With the rapid growth of the transportation systems, the Internet of Vehicles (IoV) has evolved as a new theme in both industry and academia from traditional vehicular ad hoc networks (VANETs). However, the multi-sources and multi-domain information disseminated over the network has brought huge security issues for the communications in the IoV system. In this paper, we present a lightweight blockchain-based framework for IoV to meet the requirements of security, privacy and high availability. We propose a novel hierarchical data sharing framework where two types of sub-blockchain are formed allowing for flexible access control. In addition, we propose a reconfigured blockchain structure to acclimatize itself to the vehicular network which is composed of a number of lightweight and low-energy IoT devices. Moreover, we design a lightweight reputation-based consensus algorithm with a multi-weight reputation evaluation mechanism to prevent internal collusion of network nodes. Based on the proposed architecture, security analysis is illustrated to show the security and privacy of the proposed framework.
Simultaneous segmentation and classification of breast lesions from ultrasound images using Mask R-CNN

Accurate segmentation of breast lesions from ultrasound image is challenging and crucial for automated diagnosis of breast tumor. Deep learning performs well on semantic segmentation and classification with huge labeled natural images. However, for medical images, it is costly to obtain precisely labeled dataset because experts in the field are demanded. Traditional image augmentation methods are applied to expand the dataset but it still can’t meet the need to train a deep convolutional neural network considering of the accuracy and universality. In this study, we propose an image augmentation method named super-pixel elastic deformation and employ a semantic segmentation convolutional neural network called mask region-based convolutional neural network (Mask R-CNN) to automatically segment breast lesions from ultrasound images and make the classification simultaneously.
Mathematical Reasoning in Latent Space

We design and conduct a simple experiment to study whether neural networks can perform several steps of approximate reasoning in a fixed dimensional latent space. The set of rewrites (i.e. transformations) that can be successfully performed on a statement represents essential semantic features of the statement. We can compress this information by embedding the formula in a vector space, such that the vector associated with a statement can be used to predict whether a statement can be rewritten by other theorems. Predicting the embedding of a formula generated by some rewrite rule is naturally viewed as approximate reasoning in the latent space. In order to measure the effectiveness of this reasoning, we perform approximate deduction sequences in the latent space and use the resulting embedding to inform the semantic features of the corresponding formal statement (which is obtained by performing the corresponding rewrite sequence using real formulas). Our experiments show that graph neural networks can make non-trivial predictions about the rewrite-success of statements, even when they propagate predicted latent representations for several steps. Since our corpus of mathematical formulas includes a wide variety of mathematical disciplines, this experiment is a strong indicator for the feasibility of deduction in latent space in general.
Isotope geochemistry of Zn, Pb and S in the Ediacaran strata hosted Zn-Pb deposits in Southwest China

The Sichuan–Yunnan–Guizhou (SYG) area is a world-class Zn-Pb metallogenic zone and contains over four hundred of carbonate-hosted hydrothermal Zn-Pb deposits, six of which are super large-scale deposits with significant sources of Cd, Ge, and Ag, including the Huize, Wusihe, Xiaoshifang, Maozu, Tianbaoshan and Daliangzi deposits. Although previous studies focused fully on these deposits, the ore genesis of which are still in controversial. Interestedly, four of those super large scale deposits are hosted in the Ediacaran Dengying Formation, including the Maozu, Wusihe, Tianbaoshan and Daliangzi deposits. Based upon previous studies, we investigate the extent, causes and consequences of Zn and Pb isotope signatures at hand specimen scale and ore genesis of the Daliangzi and Tianbaoshan deposits, associated with published data for the Wusihe deposit. Lead isotope ratios, coupled with published data, demonstrate that Pb in the studied deposits was predominantly derived from two components: basements and sedimentary rocks. Meanwhile, a strong positive linear correlation between δ66Zn and δ34SCDT values was observed in hand specimen D-2084, suggesting the variations may be resulted by fluid mixing. Furthuremore, the Zn isotope and Cd/Zn dataset from the Tianbaoshan, Daliangzi and Wusihe deposits defines a logarithmic curve (R2 = 0.82), which, to our knowledege, is firstly reported and lies between two indentified components: a low Cd/Zn end-member with high δ66Zn values and a high Cd/Zn end-member with low δ66Zn values. Based upon previous studies, we suggest that Zn and Pb in the Wusihe and Tianbaoshan deposits were predominantly derived from basements and Ediacaran sediments; in contrast, Zn and Pb in the Daliangzi deposit were predominantly derived from Ediacaran sediments and upper sedimentary rocks, but we cannot rule out metal contributions of the basements. Furthermore, we suggest that fluid mixing is more propreate to better explain Zn isotope signatures in the studied deposits. In summary, Zn and Pb isotope ratios reflect similar ore genesis and metal sources of the Daliangzi, Tianbaoshan and Wusihe Zn-Pb deposits in the SYG area. The application to the studied deposits with the discovery of Zn, S and Pb isotope compositions for sulfides indicates the potential of Zn isotope, combined with Cd/Zn raito, as new geochemical tracers, which may be useful for distinguishing the metal sources in large hydrothermal systems as suggested previously.
An Exploration on Text Classification with Classical Machine Learning Algorithm

Text classification is an essential part of the NLP, which aims to predict the categories for given texts in a particular classification system. There are many ways of feature selection and classification models. However, most researchers would like to use the encapsulated methods of third-party libraries to achieve their goals. Therefore, in this paper, we propose to implement code to achieve functions, instead of using third-party libraries. We evaluate our code in different classification models, and the result of our experiment shows that our code is feasible.
Smart Contracts: A Requiem

Smart contracts” are technologies that facilitate the generation and transfer of blockchain-based crypto-assets. The unfortunate labeling of these technologies as contracts has spawned a plethora of legal theories, which are built on unsubstantiated technical claims, terminological misunderstandings as well as on a general disillusionment with traditional institutions. Concepts such as “validation” or “self-enforcement,” both of which constitute permanent fixtures of the “smart contract” narrative, seem to have hijacked common sense with promises of certainty and guaranteed performance to the point where a structured and logical argument is rendered difficult. It is necessary to clarify the meaning of the term and remind those who debate the legal enforceability and validity of “smart contracts” of the basic principles of contract law. In particular, it is necessary to recall that contract law is indifferent to the manner parties express their agreement.
Weakly Supervised One Shot Segmentation

One-shot learning is a challenging discipline of machine learning since it gnaws at the concept of learning from large amounts of data. This is akin to making machine learning algorithms generalize from a few examples, much like how humans learn. We explore another novel dimension to this problem, of using weak supervision (labels only) in the one-shot domain, and specifically analyse it in the context of semantic segmentation. This is a challenging problem since we operate in the scarcity of data and supervision. We present a simple yet effective approach, whereby exploiting information from the base training classes in the current one-shot segmentation set-up allows for weak supervision to be easily used. We show that this strategy can be leveraged to achieve nearly the same results as full supervision, but with no pixel annotations, allowing fully automated segmentation. Comparisons to several fully supervised methods show convincing results. As well as better results than a weakly supervised baseline. Also presented is a baseline for generalized segmentation under one-shot and weak supervision assumptions.
Warm-Started Optimized Trajectory Planning for ASVs

We consider warm-started optimized trajectory planning for autonomous surface vehicles (ASVs) by combining the advantages of two types of planners: an A* implementation that quickly finds the shortest piecewise linear path, and an optimal control-based trajectory planner. A nonlinear 3-degree-of-freedom underactuated model of an ASV is considered, along with an objective functional that promotes energy-efficient and readily observable maneuvers. The A* algorithm is guaranteed to find the shortest piecewise linear path to the goal position based on a uniformly decomposed map. Dynamic information is constructed and added to the A*-generated path, and provides an initial guess for warm starting the optimal control-based planner. The run time for the optimal control planner is greatly reduced by this initial guess and outputs a dynamically feasible and locally optimal trajectory.
Vehicle Recognition Via Sensor Data From Smart Devices

This study explored the activity of moving vehicle and public transportation, as a next stage of smart device use, and its evolution with data science. Smart devices have been enabled to help people carry out their daily activities. Some smart device operating systems have special apps to make it easy for users to record information about walking, running, jogging, and stepping through the Activity Recognition process, even recording physiological information. Advanced smart devices can now be used to record information about moving vehicles, as it has begun by enabling paid advertising on personal motor vehicles. However, with advancements in smart device technology, as analyzed and proposed by this paper, people will be able to utilize this technology with new data science methods to analyze far more information related to moving vehicles and public transportation. In this paper, we have proposed a method to recognize the vehicle that the subject is currently using by recorded accelerometer and gyroscope sensor data embedded in a smart device. In other words, we have implemented a machine learning model Artificial Neural Network to classify vehicles from the sensor data.
Differentially Private Release of Synthetic Graphs

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (SODA)Differentially Private Release of Synthetic GraphsMarek Eliáš, Michael Kapralov, Janardhan Kulkarni, and Yin Tat LeeMarek Eliáš, Michael Kapralov, Janardhan Kulkarni, and Yin Tat Leepp.560 - 578Chapter DOI:https://doi.org/10.1137/1.9781611975994.34PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract We propose a (ϵ, δ)-differentially private mechanism that, given an input graph G with n vertices and m edges, in polynomial time generates a synthetic graph G’ approximating all cuts of the input graph up to an additive error of . This is the first construction of differentially private cut approximator that allows additive error o(m) for all m > n logC n. The best known previous results gave additive O(n3/2) error and hence only retained information about the cut structure on very dense graphs. Thus, we are making a notable progress on a promiment problem in differential privacy. We also present lower bounds showing that our utility/privacy trade-off is essentially the best possible if one seeks to get purely additive cut approximations. Previous chapter Next chapter RelatedDetails Published:2020eISBN:978-1-61197-599-4 https://doi.org/10.1137/1.9781611975994Book Series Name:ProceedingsBook Code:PRDA20Book Pages:xxii + 3011
Path Planning of 6-DOF Venipuncture Robot Arm Based on Improved A-star and Collision Detection Algorithms

In this paper, we use a compact 6 degree-of- freedom (DOF) robot arm as the actuator of the venipuncture system. Aiming at the robot arm's requirements for speed and smoothness and considering that the robot arm may collide with obstacles in the course of movement,we simplify the model of the robot arm and obstacles according to the geometry of the robot arm and obstacles in the environment. Then we proposed an improved A-star algorithm and a three-dimensional (3D) collision detection algorithm, which can plan a collision-free optimal path for the blood taking needle fixed in the end of the robot arm, so that the blood taking needle can penetrate into the target point of the venous vessel continuously,smoothly and accurately driven by the robot arm. The simulation and actual path planning experiments indicate that the proposed path planning method is effective and feasible.
DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums

Online health communities such as the online breast cancer forum enable patients (i.e., users) to interact and help each other within various subforums, which are subsections of the main forum devoted to specific health topics. The changing nature of the users' activities in different subforums can be strong indicators of their health status changes. This additional information could allow health-care organizations to respond promptly and provide additional help for the patient. However, modeling complex transitions of an individual user's activities among different subforums over time and learning how these correspond to his/her health stage are extremely challenging. In this paper, we first formulate the transition of user activities as a dynamic graph with multi-attributed nodes, then formalize the health stage inference task as a dynamic graph-to-sequence learning problem, and hence propose a novel dynamic graph-to-sequence neural networks architecture (DynGraph2Seq) to address all the challenges. Our proposed DynGraph2Seq model consists of a novel dynamic graph encoder and an interpretable sequence decoder that learn the mapping between a sequence of time-evolving user activity graphs and a sequence of target health stages. We go on to propose dynamic graph hierarchical attention mechanisms to facilitate the necessary multi-level interpretability. A comprehensive experimental analysis of its use for a health stage prediction task demonstrates both the effectiveness and the interpretability of the proposed models.
Impact of Participants’ Experiences with Immersive Multimedia on 360° Video Quality Assessment

In this paper, we study the impact that the level of experience of participants with immersive multimedia has on 360° video quality assessment. An extensive subjective experiment using the modified absolute category rating with hidden reference (M-ACR-HR) method was conducted. The opinion scores obtained from the participants in this experiment along with other sensor data support this study. The statistical analysis is performed in terms of screening participants’ distribution of opinion scores for consistency, average rating time, box plots related to mean opinion scores (MOSs) and differential MOS (DMOS). Detailed results for MOS and DMOS are also provided for reference and test sequences covering a wide range of qualities. Major findings include: (1) Screening detects many outliers for participants with no experience, (2) Average rating times show that experts learn the quality assessment task fast, (3) Participants with no experience provide lower opinion scores in average, (4) DMOS may alleviate the bias in quality rating caused by different levels of experience.
Online Continual Learning on Sequences

Online continual learning (OCL) refers to the ability of a system to learn over time from a continuous stream of data without having to revisit previously encountered training samples. Learning continually in a single data pass is crucial for agents and robots operating in changing environments and required to acquire, fine-tune, and transfer increasingly complex representations from non-i.i.d. input distributions. Machine learning models that address OCL must alleviate catastrophic forgetting in which hidden representations are disrupted or completely overwritten when learning from streams of novel input. In this chapter, we summarize and discuss recent deep learning models that address OCL on sequential input through the use (and combination) of synaptic regularization, structural plasticity, and experience replay. Different implementations of replay have been proposed that alleviate catastrophic forgetting in connectionists architectures via the re-occurrence of (latent representations of) input sequences and that functionally resemble mechanisms of hippocampal replay in the mammalian brain. Empirical evidence shows that architectures endowed with experience replay typically outperform architectures without in (online) incremental learning tasks.
When one vulnerable primitive turns viral: Novel single-trace attacks on ECDSA and RSA

Microarchitecture based side-channel attacks are common threats nowadays. Intel SGX technology provides a strong isolation from an adversarial OS, however, does not guarantee protection against side-channel attacks. In this paper, we analyze the security of the mbedTLS binary GCD algorithm, an implementation that offers interesting challenges when compared for example with OpenSSL, due to the usage of very tight loops in the former. Using practical experiments we demonstrate the mbedTLS binary GCD implementation is vulnerable to side-channel analysis using the SGX-Step framework against mbedTLS based SGX enclaves.We analyze the security of some use cases of this algorithm in this library, resulting in the discovery of a new vulnerability in the ECDSA code path that allows a single-trace attack against this implementation. This vulnerability is three-fold interesting:&#x0D; &#x0D; It resides in the implementation of a countermeasure which makes it more dangerous due to the false state of security the countermeasure currently offers.&#x0D; It reduces mbedTLS ECDSA security to an integer factorization problem.&#x0D; An unexpected GCD call inside the ECDSA code path compromises the countermeasure.&#x0D; &#x0D; We also cover an orthogonal use case, this time inside the mbedTLS RSA code path during the computation of a CRT parameter when loading a private key. The attack also exploits the binary GCD implementation threat, showing how a single vulnerable primitive leads to multiple vulnerabilities. We demonstrate both security threats with end-to-end attacks using 1000 trials each, showing in both cases single-trace attacks can be achieved with success rates very close to 100%.
A Plaintext-Related Dynamical Image Encryption Algorithm Based on Permutation-Combination-Diffusion Architecture

In this paper, a novel high security plaintext-related dynamical image encryption algorithm is proposed. Compared with the classical permutation-diffusion encryption architecture, we add the combination operation before the diffusion, which uses the 3D intertwining Logistic map (3D-ILM) to generate dynamic interference matrix with the same size of the grayscale plaintext image, and then combine the permutated image with interference matrix. It can be seen as an improvement to enhance the connection between permutation and diffusion operation, which makes adversaries are difficult to crack the permutation and diffusion algorithm separately. Besides, the permutated pixels are hidden in the dynamic interference matrix during the combination process, which reduces the correlation among local adjacent pixels. Thus, it can increase the resistance of chosen-plaintext attack. After the permutation, combination, and diffusion operation, a dynamic color cipher image is generated. Note that the generated color cipher image is dynamic changing even the same plaintext image is encrypted with identical secret keys. Ultimately, simulation results and security analysis show that the proposed algorithm has high-security performance and can resist most known common attacks.