Reviewerly/exHarmony huggingface dataset:
Detection of potential microcalcification clusters using multivendor for‐presentation digital mammograms for short‐term breast cancer risk estimation

We explore using the number of potential microcalcification clusters detected in for-presentation mammographic images (the images which are typically accessible to large epidemiological studies) a marker of short-term breast cancer risk.We designed a three-step algorithm for detecting potential microcalcification clusters in for-presentation digital mammograms. We studied association with short-term breast cancer risk using a nested case control design, with a mammography screening cohort as a source population. In total, 373 incident breast cancer cases (diagnosed at least 3 months after a negative screen at study entry) and 1466 matched controls were included in our study. Conditional logistic regression Wald tests were used to test for association with the presence of microcalcifications at study entry. We compared results of these analyses to those obtained using a Computer-aided Diagnosis (CAD) software (VuComp) on corresponding for-processing images (images which are used clinically, but typically not saved).We found a moderate agreement between our measure of potential microcalcification clusters on for-presentation images and a CAD measure on for-processing images. Similar evidence of association with short-term breast cancer risk was found (P = 1×10-10 and P = 9×10-09 , for our approach on for-presentation images and for the CAD measure on for-processing images, respectively) and interestingly both measures contributed independently to association with a short-term risk (P = 9×10-03 for the CAD measure, adjusted for our proposed method and P = 1×10-04 for our proposed method, adjusted for the CAD measure).Meaningful measurement of potential microcalcifications, in the context of short-term breast cancer risk assessment, is feasible for for-presentation images across a range of vendors. Our algorithm for for-presentation images performs similarly to a CAD algorithm on for-processing images, hence our algorithm can be a useful tool for research on microcalcifications and their role on breast cancer risk, based on large-scale epidemiological studies with access to for-presentation images.
Zero transfer in continuous-time quantum walks

In this paper, we show how using complex-valued edge weights in a graph can completely suppress the flow of probability amplitude in a continuous-time quantum walk to specific vertices of the graph when the edge weights, graph topology, and initial state of the quantum walk satisfy certain conditions. The conditions presented in this paper are derived from the so-called chiral quantum walk, a variant of the continuous-time quantum walk which incorporates directional bias with respect to site transfer probabilities between vertices of a graph by using complex edge weights. We examine the necessity to break the time-reversal symmetry in order to achieve zero transfer in continuous-time quantum walks. We also consider the effect of decoherence on zero transfer and suggest that this phenomenon may be used to detect and quantify decoherence in the system.
Automatic Detection and Update Suggestion for Outdated API Names in Documentation

Application programming interfaces (APIs) continually evolve to meet ever-changing user needs, and documentation provides an authoritative reference for their usage. However, API documentation is commonly outdated because nearly all of the associated updates are performed manually. Such outdated documentation, especially with regard to API names, causes major software development issues. In this paper, we propose a method for automatically updating outdated API names in API documentation. Our insight is that API updates in documentation can be derived from API implementation changes between code revisions. To evaluate the proposed method, we applied it to four open source projects. Our evaluation results show that our method, FreshDoc, detects outdated API names in API documentation with 48 percent higher accuracy than the existing state-of-the-art methods do. Moreover, when we checked the updates suggested by FreshDoc against the developers' manual updates in the revised documentation, FreshDoc detected 82 percent of the outdated names. When we reported 40 outdated API names found by FreshDoc via issue tracking systems, developers accepted 75 percent of the suggestions. These evaluation results indicate that FreshDoc can be used as a practical method for the detection and updating of API names in the associated documentation.
Two-Round PAKE Protocol over Lattices Without NIZK

Reducing the number of communication rounds of Password-based Authenticated Key Exchange ( $$\textsf {PAKE} $$ ) protocols is of great practical significance. At PKC’15, Abdalla et al. relaxed the requirements of Gennaro-Lindell’s framework for three-round PAKE protocols, and obtained a two-round PAKE protocol under the traditional DDH-based smooth projective hash function ( $$\mathsf {SPHF} $$ ). At ASIACRYPT’17, Zhang and Yu proposed a lattice-based two-round PAKE protocol via the approximate $$\mathsf {SPHF} $$ . However, the language of Zhang-Yu’s SPHF depends on simulation-sound non-interactive zero-knowledge (NIZK) proofs, for which there is no concrete construction without random oracle under lattice-based assumptions. To our knowledge, how to design a lattice-based two-round $$\textsf {PAKE} $$ protocol via an efficient $$\mathsf {SPHF} $$ scheme without NIZK remains a challenge. In this paper, we propose the first two-round $$\textsf {PAKE} $$ protocol over lattices without NIZK. Our protocol is in accordance with the framework of Abdalla et al. (PKC’15) while attaining post-quantum security. We overcome the limitations of existing schemes by relaxing previous security assumptions (i.e., both the client and the sever need IND-CCA-secure encryption), and build two new lattice-based $$\mathsf {SPHF} $$ s, one for IND-CCA-secure Micciancio-Peikert ciphertext (at the client side) and the other for IND-CPA-secure Regev ciphertext (at the server side). Particularly, our protocol attains provable security.
An optimal curvature smoothing method and the associated real-time interpolation for the trajectory generation of flying robots

This paper presents a trajectory smoothing approach and corresponding real-time interpolation for the flying robot. To smoothly transit between straight line segments, the Bézier curve is introduced to guarantee continuous curvature. Subsequently, considering the constraints on approximation errors and lengths of the original straight line segments, an optimization problem pursuing maximal curvature radius of the Bézier curves is proposed to reduce the potential fluctuation in the real-time flights. With the established geometric profile of continuous curvature, a fast real-time interpolation approach that ensures smooth acceleration profile in real-time flights is proposed. To verify the effectiveness of this development, extensive simulations and experiments are conducted at last. The results show that the proposed trajectory generation approach can effectively generate reference trajectories in real-time both at two-dimensional and three-dimensional spaces with continuous curvature and smooth acceleration. With well-generated trajectory, the flying robot can closely track the reference with maximum cross-tracking error of 0.05 m.
Statistical analysis on secular records of igneous geochemistry: Implication for the early <scp>Archean</scp> plate tectonics

In recent years, application of statistical analysis to a sufficiently large geochemical database has become more and more popular to reveal the onset and evolution of plate tectonics as a consequence of the development of computer science and storage technology. Here, we introduce a robust statistical method to process a filtered geochemical dataset including predominantly continental basaltic rocks through Earth's history. The results show that the average Sr concentrations gradually increased from 3.8 to 0 Ga, indicating a progressively growing depth of magma source as a result of secular cooling of the mantle. However, the average La/Yb and Sm/Yb ratios only started to increase at ca . 3.0 Ga. Considering the La–Yb and Sm–Yb fractionations are commonly attributed to the residual garnet crystallization, we interpreted that the geotherm of the magma source of continental basaltic rocks before 3.0 Ga might be higher than the stability limit of garnet. As such, there were little or no garnets in the residue during partial melting of the upwelling asthenospheric mantle or mantle plumes before 3.0 Ga, which also implied a higher continental geotherm during that time. Such a high geotherm could not support the formation of contemporaneous high‐pressure or medium‐pressure TTGs (tonalite–trondhjemite–granodiorite) by vertical tectonic models associated with upwelling mantle heating or delaminated lower crust melting in the early Archean. We therefore conclude that the plate tectonics had occurred by ca . 3.8 Ga in a hot subduction style and contributed to the formations of high‐ and medium‐pressure TTGs subsequently.
Supervisory Control of Blockchain Networks

Blockchain is an open distributed ledger technology that enables ledger-maintainers on a network to collaboratively synchronize and update their own distributed copies of a single global ledger, with the goal of keeping the ledger copies consistent. This paper presents a theoretical control-model formulation of the founding Satoshi Nakamoto blockchain, aimed at enhancing our operational understanding and development of blockchain systems. The control model is generic of every honest ledger-maintainer's local operations on a blockchain network. The presented research is a logical systematization of operational knowledge that is understandable and explainable for blockchain system engineering and research. Using a software tool supporting a supervisory control theory applied in the formulation, the control model is synthesized and logically validated.
Graph-structured multitask sparsity model for visual tracking

The multitask framework for baseline sparse trackers has been extensively investigated and facilitates the performance improvement of tracking methods. However, the previous methods are based on the assumption that all of the particles are equally related to the templates, without learning their structural information. In this study, the particle correlations are modeled by a relational graph; subsequently, a novel graph-guided sparse learning model is proposed to incorporate the topological constraints of the relational graph into the multitask framework. By exploiting this underlying relationship among particles, the proposed method reconstructs the related particles, instead of all the particles, by common templates in accordance with the graph. An efficient smoothing proximal gradient method is then employed to solve the graph-structured sparse optimization problem. To preserve spatial and discriminative structure, we adopt an effective observation model that conducts sparse coding on local image patches and augments multiple background information for the Bayesian sequential inference framework. In addition, the particles are assigned various weights with respect to their distances among templates, and a simple yet efficient pooling scheme is proposed to estimate the optimal state. Extensive experimental results on a large-scale tracking benchmark verify that the proposed method is effective and outperforms existing trackers.
Fully Symmetric Convolutional Network for Effective Image Denoising

Neural-network-based image denoising is one of the promising approaches to deal with problems in image processing. In this work, a deep fully symmetric convolutional–deconvolutional neural network (FSCN) is proposed for image denoising. The proposed model comprises a novel architecture with a chain of successive symmetric convolutional–deconvolutional layers. This framework learns convolutional–deconvolutional mappings from corrupted images to the clean ones in an end-to-end fashion without using image priors. The convolutional layers act as feature extractor to encode primary components of the image contents while eliminating corruptions, and the deconvolutional layers then decode the image abstractions to recover the image content details. An adaptive moment optimizer is used to minimize the reconstruction loss as it is appropriate for large data and noisy images. Extensive experiments were conducted for image denoising to evaluate the FSCN model against the existing state-of-the-art denoising algorithms. The results show that the proposed model achieves superior denoising, both qualitatively and quantitatively. This work also presents the efficient implementation of the FSCN model by using GPU computing which makes it easy and attractive for practical denoising applications.
An Efficient Encoding Algorithm Using Local Path on Huffman Encoding Algorithm for Compression

Keywords: encoding algorithm; compression; Huffman encoding algorithm; arithmetic coding algorithm
Multi-scale segmentation strategies in PRNU-based image tampering localization

With the rapid development of advanced media technology, especially the popularization of digital cameras and image editing software, digital images can be easily forged without leaving visible clues. Therefore, image forensics technology for identifying the accuracy, integrity, and originality of digital images has become increasingly important. Photo-response non-uniformity (PRNU) noise, a unique fingerprint of imaging sensors, is a valuable forgery detection tool because of its consistently good detection performance. All kinds of forgeries, including copy-move and splicing, can be dealt with in a uniform manner. This paper addresses the problem of forgery localization based on PRNU estimation and aims to improve the resolution of PRNU-based algorithms. Different from traditional overlapping and sliding window-based methods, in which PRNU correlations are estimated on overlapped patches, the proposed scheme is analyzed based on nonoverlapping and irregular patches. First, the test image is segmented into nonoverlapped patches with multiple scales. Second, correlations of PRNU are estimated on nonoverlapped patches to obtain the real-valued candidate tampering probability map for each individual scale. Then, all of the candidate maps are fused into a single and more reliable probability map through an adaptive window strategy. In the final step, the final decision map is obtained by adopting a conditional random field (CRF) to model neighborhood interactions. The contributions of this work include the following: a novel PRNU-based forgery localization scheme using multi-scale nonoverlapping segmentation is proposed for the first time. Furthermore, the adaptive fusion strategy involves selecting the best candidate tampering probability individually for each location in the image. Additionally, the experimental results prove that the proposed scheme can achieve much better detection results and robustness compared with the existing state-of-the-art PRNU-based methods.
Ubiquitous Requirements Engineering: A Paradigm Shift That Affects Everyone

In recent years, we have witnessed profound changes in business and society. The use of digital technologies has brought about disruptive changes in every domain, changes that are widely known as the "digital transformation." Systems are growing increasingly interconnected and complex with cyberphysical systems even sensing and actuating in the physical world. Typical computer, tablet, and smartphone users include anyone from children to the elderly. A single software product can now easily reach audiences of millions with unprecedented opportunities to obtain feedback.
Dynamic Phase Measuring Profilometry Based on Tricolor Binary Fringe Encoding Combined Time-Division Multiplexing

A dynamic phase measuring profilometry (PMP) based on tricolor binary fringe combined time-division multiplexing principle is proposed. Only one tricolor binary fringe combined by red (R), green (G), and blue (B) binary fringes with the same fringe width but without any color overlapping one another is needed and sent into the flash memory of a high-speed digital light projector (HDLP) in advance. A specialized time-division multiplexing timing sequence is designed to control the HDLP to project the tricolor binary fringe saved in the flash memory onto the measured dynamic object separately and sequentially at 234 fps, at the same time, the projected light source mode is set as monochrome mode which means that all the RGB LEDs remain lighting. Meanwhile, it also triggers a high frame rate monochrome camera synchronized with the HDLP to capture the corresponding deformed patterns in R, G and B channels. By filtering, the nearly unbroken phase-shifting sinusoidal deformed patterns for three-step PMP can be extracted from the captured deformed patterns. It is equivalent to the three-dimensional (3D) shape reconstruction of the measured dynamic object at 78 fps. Experimental results verify the feasibility and the validity of the proposed method. It is effective for measuring the dynamic object and can avoid the color cross-talk effectively.
Quantum Deep Learning Neural Networks

This study surveys the current status of Quantum Deep Learning Neural Networks. Exciting breakthroughs may soon bring real quantum neural networks, specifically deep learning neural networks, to reality. Three main obstacles have been limiting quantum growth in the deep learning area, and this study has found that new discoveries have changed these obstacles. The first obstacle was the lack of a real quantum computers to experiment with, not simulators. Several companies have significantly increased their inventory of quantum computers in the last year, including IBM. The second obstacle was the impossibility of training quantum networks, but a new algorithm solves this problem. The third obstacle was that neural networks have nonlinear functions, but that has been solved with a new quantum perceptron. This study explains the historical background briefly for context and understanding, then describes these three major accomplishments that will likely lead to real quantum deep learning neural networks.
Modal posterior clustering motivated by Hopfield’s network

Motivated by the Hopfield's network, a conditional maximization routine is used in order to compute the posterior mode of a random allocation model. The proposed approach applies to a general framework covering parametric and nonparametric Bayesian mixture models, product partition models, and change point models, among others. The resulting algorithm is simple to code and very fast, thus providing a highly competitive alternative to Markov chain Monte Carlo methods. Illustrations with both simulated and real data sets are presented.
Uncle Traps

Mining pools in Proof-of-Work cryptocurrencies allow miners to pool their computational resources as a means of reducing payout variance. In Ethereum, uncle blocks are valid Proof-of-Work solutions which do not become the head of the blockchain, yet yield rewards if later referenced by main chain blocks. Mining pool operators are faced with the non-trivial task of fairly distributing rewards for both block types among pool participants.
Discovering author interest evolution in order-sensitive and Semantic-aware topic modeling

Modeling the interests of authors over time from documents has important applications in broad applications such as recommendation systems, authorship identification and opinion extraction. In this paper, we propose an Ordering-sensitive and Semantic-aware Dynamic Author Topic Model (OSDATM), which monitors the evolution of author interest in time-stamped documents. The model further uses the discovered author interest information to discover better topics. Unlike traditional topic models, OSDATM is sensitive to the ordering of words, thus it extracts more information from the semantic meaning of the context. The experimental results show that OSDATM learns better topics than state-of-the-art topic models. In addition, the dynamic interests of authors that the OSDATM model discovers are interpretable and consistent with the truth.
Designing Mid-Air Gesture Interaction with Mobile Devices for Older Adults

This chapter presents a user-centered perspective on the design of effective mid-air gesture interaction with mobile technology for seniors. Starting from the basic characteristics of mid-air gesture interaction, we introduce the main design challenges of this interaction and report on a case study in which we implemented a user-centered design process focused on the engagement of older adults in each design stage. We present a series of studies in which we aimed at involving older users as active creators of the interaction, taking into account their feedback and values in the design process. Finally, we propose a set of recommendations for the design of mid-air gesture interaction for older adults. These recommendations are elaborated combining research on HCI, ageing and ergonomic principles, and the results from the user studies we conducted in the process.
Detection based long term tracking in correlation filter trackers

Correlation filter based object tracking has recently gained popularity due to continuous improvements in the tracking accuracy and robustness. However, these trackers are limited by the model drift problem due to wrong target appearances learned from an incorrectly tracked frame. The model drift increases as more frames are processed and restricts the ability of long term tracking in correlation filter trackers. The proposed method introduces tracking resumption in correlation trackers using a detector mechanism that re-initializes the tracker upon a target loss identified using an adaptive threshold. Online training of both the tracker and detector modules incorporates temporal information into the proposed framework, making it robust to appearance changes of the object. The tracker and detector stages complement each other in correcting the false appearances learned from any frame, thereby mitigating the model-drift problem. A similarity matching technique estimates the final target location. Extensive experimental analysis on benchmark datasets indicate that the proposed tracker is well suited for robust long-term tracking and is superior to other state of the art methods both qualitatively and quantitatively.
Memory Mechanisms for Discriminative Visual Tracking Algorithms With Deep Neural Networks

Deep-neural-networks-based online visual tracking methods have achieved state-of-the-art results. One of the core components of these methods is the memory pool, in which a number of samples consisting of image patches and the corresponding labels are stored to update the online tracking network. Hence, the mechanism of updating the stored samples determines the performance of the tracking method. In this paper, a novel memory mechanism is proposed to control the writing and reading accesses of the memory pool using credit assignment network H, which learns features of the target object. This memory mechanism comprises the writing and reading mechanisms. In the writing mechanism, network H produces credits for the current tracked object and the samples in the memory pool. This ensures that the reliable samples are written into the memory pool and the unreliable samples are replaced if the memory pool is full. In the reading mechanism, network H assigns an importance score to each sample selected to update the online tracking network. The state-of-the-art tracking methods with and without the proposed memory mechanism are evaluated on the CVPR2013 and OTB100 benchmarks. The experimental results demonstrated that the proposed memory mechanism improves tracking performance significantly.