Reviewerly/exHarmony huggingface dataset:
Artificial intelligence in breast ultrasound

Artificial intelligence (AI) is gaining extensive attention for its excellent performance in image-recognition tasks and increasingly applied in breast ultrasound. AI can conduct a quantitative assessment by recognizing imaging information automatically and make more accurate and reproductive imaging diagnosis. Breast cancer is the most commonly diagnosed cancer in women, severely threatening women's health, the early screening of which is closely related to the prognosis of patients. Therefore, utilization of AI in breast cancer screening and detection is of great significance, which can not only save time for radiologists, but also make up for experience and skill deficiency on some beginners. This article illustrates the basic technical knowledge regarding AI in breast ultrasound, including early machine learning algorithms and deep learning algorithms, and their application in the differential diagnosis of benign and malignant masses. At last, we talk about the future perspectives of AI in breast ultrasound.
Deep Sparse Representation Classifier for facial recognition and detection system

This paper proposes a two-layer Convolutional Neural Network (CNN) to learn the high-level features which utilizes to the face identification via sparse representation. Feature extraction plays a vital role in real-world pattern recognition and classification tasks. The details description of the given input face image, significantly improve the performance of the facial recognition system. Sparse Representation Classifier (SRC) is a popular face classifier that sparsely represents the face image by a subset of training data, which is known as insensitive to the choice of feature space. The proposed method shows the performance improvement of SRC via a precisely selected feature exactor. The experimental results show that the proposed method outperforms other methods on given datasets.
Deep Evolutionary Networks with Expedited Genetic Algorithms for Medical Image Denoising

Deep convolutional neural networks offer state-of-the-art performance for medical image analysis. However, their architectures are manually designed for particular problems. On the one hand, a manual designing process requires many trials to tune a large number of hyperparameters and is thus quite a time-consuming task. On the other hand, the fittest hyperparameters that can adapt to source data properties (e.g., sparsity, noisy features) are not able to be quickly identified for target data properties. For instance, the realistic noise in medical images is usually mixed and complicated, and sometimes unknown, leading to challenges in applying existing methods directly and creating effective denoising neural networks easily. In this paper, we present a Genetic Algorithm (GA)-based network evolution approach to search for the fittest genes to optimize network structures automatically. We expedite the evolutionary process through an experience-based greedy exploration strategy and transfer learning. Our evolutionary algorithm procedure has flexibility, which allows taking advantage of current state-of-the-art modules (e.g., residual blocks) to search for promising neural networks. We evaluate our framework on a classic medical image analysis task: denoising. The experimental results on computed tomography perfusion (CTP) image denoising demonstrate the capability of the method to select the fittest genes for building high-performance networks, named EvoNets. Our results outperform state-of-the-art methods consistently at various noise levels.
Empirical models for estimating monthly global solar radiation: A most comprehensive review and comparative case study in China

Global solar radiation is a core component of scientific research and engineering application across a broad spectrum. However, its measurement is limited by a small number of stations due to the technical and financial restricts. Estimating solar radiation with the meteorological variables using empirical models is of benefit to obtain solar radiation data at global scale. Yet, there are various options of available empirical models to select the most suitable one. This study conducted a most comprehensive collection and review of empirical models employing the commonly measured meteorological variables and geographic factors. A total of 294 different types of empirical models were collected and classified into 37 groups according to input attributes. Such collection built an empirical model library providing an overall overview of the developed empirical models in literatures. Furthermore, the collected models were calibrated and evaluated at three meteorological stations in the Three Gorges Reservoir Area in China. This study suggests that these model-comparing processes can assist the governments, scientists and engineers in tailoring the most fitted model for specific applications and in particular areas.
GraphBolt

Efficient streaming graph processing systems leverage incremental processing by updating computed results to reflect the change in graph structure for the latest graph snapshot. Although certain monotonic path-based algorithms produce correct results by refining intermediate values via numerical comparisons, directly reusing values that were computed before mutation does not work correctly for algorithms that require BSP semantics. Since structural mutations in streaming graphs render the intermediate results unusable, exploiting incremental computation while simultaneously providing synchronous processing guarantees is challenging.
CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth

Single-view depth estimation suffers from the problem that a network trained on images from one camera does not generalize to images taken with a different camera model. Thus, changing the camera model requires collecting an entirely new training dataset. In this work, we propose a new type of convolution that can take the camera parameters into account, thus allowing neural networks to learn calibration-aware patterns. Experiments confirm that this improves the generalization capabilities of depth prediction networks considerably, and clearly outperforms the state of the art when the train and test images are acquired with different cameras.
LP-3DCNN: Unveiling Local Phase in 3D Convolutional Neural Networks

Traditional 3D Convolutional Neural Networks (CNNs) are computationally expensive, memory intensive, prone to overfit, and most importantly, there is a need to improve their feature learning capabilities. To address these issues, we propose Rectified Local Phase Volume (ReLPV) block, an efficient alternative to the standard 3D convolutional layer. The ReLPV block extracts the phase in a 3D local neighborhood (e.g., 3 × 3 × 3) of each position of the input map to obtain the feature maps. The phase is extracted by computing 3D Short Term Fourier Transform (STFT) at multiple fixed low frequency points in the 3D local neighborhood of each position. These feature maps at different frequency points are then linearly combined after passing them through an activation function. The ReLPV block provides significant parameter savings of at least, 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> to 13 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> times compared to the standard 3D convolutional layer with the filter sizes 3 × 3 × 3 to 13 × 13 × 13, respectively. We show that the feature learning capabilities of the ReLPV block are significantly better than the standard 3D convolutional layer. Furthermore, it produces consistently better results across different 3D data representations. We achieve state-of-the-art accuracy on the volumetric ModelNet10 and ModelNet40 datasets while utilizing only 11% parameters of the current state-of-the-art. We also improve the state-of-the-art on the UCF-101 split-1 action recognition dataset by 5.68% (when trained from scratch) while using only 15% of the parameters of the state-of-the-art.
Review of harmony search with respect to algorithm structure

Harmony Search (HS) is a metaheuristic optimisation algorithm inspired by musical improvisation. So far it has been applied to various optimisation problems, and there are several application-oriented review papers. However, this review paper tries to focus on the historical development of algorithm structure instead of applications. This paper explains the original HS algorithm along with a selection of modified and hybrid HS methods: adaption of original operators of the basic harmony search, parameter adaption, hybrid methods, handling multi-objective optimisation problems and constraint handling.
<i>'Think secure from the beginning'</i>

Vulnerabilities persist despite existing software security initiatives and best practices. This paper focuses on the human factors of software security, including human behaviour and motivation. We conducted an online survey to explore the interplay between developers and software security processes, e.g., we looked into how developers influence and are influenced by these processes. Our data included responses from 123 software developers currently employed in North America who work on various types of software applications. Whereas developers are often held responsible for security vulnerabilities, our analysis shows that the real issues frequently stem from a lack of organizational or process support to handle security throughout development tasks. Our participants are self-motivated towards software security, and the majority did not dismiss it but identified obstacles to achieving secure code. Our work highlights the need to look beyond the individual, and take a holistic approach to investigate organizational issues influencing software security.
NVGaze

Quality, diversity, and size of training data are critical factors for learning-based gaze estimators. We create two datasets satisfying these criteria for near-eye gaze estimation under infrared illumination: a synthetic dataset using anatomically-informed eye and face models with variations in face shape, gaze direction, pupil and iris, skin tone, and external conditions (2M images at 1280x960), and a real-world dataset collected with 35 subjects (2.5M images at 640x480). Using these datasets we train neural networks performing with sub-millisecond latency. Our gaze estimation network achieves 2.06(±0.44)° of accuracy across a wide 30°×40° field of view on real subjects excluded from training and 0.5° best-case accuracy (across the same FOV) when explicitly trained for one real subject. We also train a pupil localization network which achieves higher robustness than previous methods.
Three-dimensional prospectivity modeling of the Jiaojia-type gold deposit, Jiaodong Peninsula, Eastern China: A case study of the Dayingezhuang deposit

The Jiaojia-type gold deposits, hosting >80% of gold resource in the Jiaodong Peninsula, Eastern China, are characterized by veinlet- and disseminated-style mineralization associated with the Mesozoic detachment faults. In this study, we performed multi-constraint geological modeling and spatial analysis involving 3D buffer analysis, shape analysis, and field analysis for the Dayingezhuang gold deposit to quantitatively assess the gold distribution and its association with geological features. The obtained spatial data were further integrated into three dimensional (3D) prospectivity modeling by fuzzy weights-of-evidence (WofE) and continuous WofE methods to evaluate mineral potential. Our results determine a quantitative correlation between phyllic alteration thickness and tectono-geochemical anomaly to construct the geometric models of alteration zone. The hydrothermal intensity extracted from the models shows a bimodal distribution and it is significantly high in the center of No. 2 orebodies, indicating an overprinting gold mineralization. The spatial analysis on the Zhaoping fault reveals that the most probable locations for gold deposition were determined to be in segments of the Zhaoping fault with a slope of 20° to 40°, dip angle changes of −5°, and undulation of near 0 m. All of the features likely result from structural controls on fluid flow and infiltration, as well as variations in the stability of Au-bearing complexes related to fault morphology. 3D prospectivity models generated by continuous transformed spatial evidence values with lower bias and uncertainty yielded a higher predictive efficiency than classified evidential layers. Our study not only highlights that gold enrichment of the Jiaojia-type deposit is essentially controlled by shape features of detachment faults, but also emphasizes the applicability of 3D prospectivity modeling in identifying potential gold mineralization at depth in the Jiaodong Peninsula.
A Novel Block Encryption Algorithm Based on Chaotic S-Box for Wireless Sensor Network

In order to ensure the basic security of wireless sensor networks (WSNs), a block encryption algorithm based on chaotic substitution box (S-box) is proposed.In this paper, we generated a new S-box based on the compound chaotic map, sinusoidal chaotic map, Baker map, and linear congruence generator.In addition, the limited computing power and communication capability of WSN are also considered in this paper.The method of generating round subkeys and F function is constructed based on the S-box.The extensive security and performance tests show that the proposed encryption algorithm has high security and low resource consumption which is suitable for WSN.
Automatic Image-Based Waste Classification

The management of solid waste in large urban environments has become a complex problem due to increasing amount of waste generated every day by citizens and companies. Current Computer Vision and Deep Learning techniques can help in the automatic detection and classification of waste types for further recycling tasks. In this work, we use the TrashNet dataset to train and compare different deep learning architectures for automatic classification of garbage types. In particular, several Convolutional Neural Networks (CNN) architectures were compared: VGG, Inception and ResNet. The best classification results were obtained using a combined Inception-ResNet model that achieved 88.6% of accuracy. These are the best results obtained with the considered dataset.
X-DeepSCA

This article, for the first time, demonstrates Cross-device Deep Learning Side-Channel Attack (X-DeepSCA), achieving an accuracy of > 99.9%, even in presence of significantly higher inter-device variations compared to the inter-key variations. Augmenting traces captured from multiple devices for training and with proper choice of hyper-parameters, the proposed 256-class Deep Neural Network (DNN) learns accurately from the power side-channel leakage of an AES-128 target encryption engine, and an N-trace (N ≤ 10) X-DeepSCA attack breaks different target devices within seconds compared to a few minutes for a correlational power analysis (CPA) attack, thereby increasing the threat surface for embedded devices significantly. Even for low SNR scenarios, the proposed X-DeepSCA attack achieves ~ 10× lower minimum traces to disclosure (MTD) compared to a traditional CPA.
Impacts of Mobility on Performance of Blockchain in VANET

This paper investigates how mobility affects the performance of a blockchain system operating in a vehicular ad hoc network (VANET). The mobility of nodes incurs a unique challenge to a blockchain system due to continuous change and dynamicity in the connectivity of the nodes. Specifically, the mobility makes a proof-of-work (PoW) process difficult since while moving the nodes can only have a limited length of time for a “rendezvous” to exchange a new block for verification. For this reason, accurate modeling for the block exchange behavior in a VANET is also challenging, which nevertheless has not been discussed in previous studies. Therefore, this paper provides an analysis framework that formulates the impact of mobility on a blockchain system's performance in a VANET based on three key metrics: (i) the probability of a successful addition of block to the chain; (ii) the stability of a rendezvous, and; (iii) the number of blocks exchanged during a rendezvous. The closed-form expressions and numerical results display the performance of a blockchain system in various scenarios in a VANET.
Trapdoor Hash Functions and Their Applications

We introduce a new primitive, called trapdoor hash functions (TDH), which are hash functions $$\mathsf {H}: \{0,1\}^n \rightarrow \{0,1\}^\lambda $$ with additional trapdoor function-like properties. Specifically, given an index $$i\in [n]$$ , TDHs allow for sampling an encoding key $$\mathsf {ek}$$ (that hides i) along with a corresponding trapdoor. Furthermore, given $$\mathsf {H}(x)$$ , a hint value $$\mathsf {E}(\mathsf {ek},x)$$ , and the trapdoor corresponding to $$\mathsf {ek}$$ , the $$i^{th}$$ bit of x can be efficiently recovered. In this setting, one of our main questions is: How small can the hint value $$\mathsf {E}(\mathsf {ek},x)$$ be? We obtain constructions where the hint is only one bit long based on DDH, QR, DCR, or LWE. This primitive opens a floodgate of applications for low-communication secure computation. We mainly focus on two-message protocols between a receiver and a sender, with private inputs x and y, resp., where the receiver should learn f(x, y). We wish to optimize the (download) rate of such protocols, namely the asymptotic ratio between the size of the output and the sender's message. Using TDHs, we obtain: We further consider the setting where f evaluates a RAM program y with running time $$T\ll |x|$$ on x. We obtain the first protocols with communication sublinear in the size of x, namely $$T\cdot \sqrt{|x|}$$ or $$T\cdot \root 3 \of {|x|}$$ , based on DDH or, resp., pairings (and correlated-input secure hash functions).
Multi-Pathway Generative Adversarial Hashing for Unsupervised Cross-Modal Retrieval

Cross-modal hashing aims to map heterogeneous cross-modal data into a common Hamming space, which can realize fast and flexible retrieval across different modalities. Unsupervised cross-modal hashing is more flexible and applicable than supervised methods, since no intensive labeling work is involved. However, existing unsupervised methods learn the hashing functions by preserving inter- and intra-correlations while ignoring the underlying manifold structure across different modalities, which is extremely helpful in capturing the meaningful nearest neighbors of different modalities for cross-modal retrieval. Furthermore, existing works mainly focus on pairwise relation modeling while ignoring the correlations within multiple modalities. To address the above-mentioned problems, in this paper, we propose a multi-pathwaygenerativeadversarialhashing approach for unsupervised cross-modal retrieval, which makes full use of a generative adversarial network's ability for unsupervised representation learning to exploit the underlying manifold structure of cross-modal data. The main contributions can be summarized as follows: First, we propose a multi-pathwaygenerativeadversarialnetwork to model cross-modal hashing in an unsupervised fashion. In the proposed network, given the data of one modality, the generative model tries to fit the distribution over the manifold structure and selects informative data of other modalities to challenge the discriminative model. The discriminative model learns to distinguish the generated data and the true positive data sampled from the correlation graph to achieve better retrieval accuracy. These two models are trained in an adversarial way to improve each other and promote hashing function learning. Second, we propose a correlation graph-based approach to capture the underlying manifold structure across different modalities so that data of different modalities but within the same manifold can have a smaller Hamming distance to promote retrieval accuracy. Extensive experiments compared with state-of-the-art methods on three widely used datasets verify the effectiveness of our proposed approach.
SoftSKU

The variety and complexity of microservices in warehouse-scale data centers has grown precipitously over the last few years to support a growing user base and an evolving product portfolio. Despite accelerating microservice diversity, there is a strong requirement to limit diversity in underlying server hardware to maintain hardware resource fungibility, preserve procurement economies of scale, and curb qualification/test overheads. As such, there is an urgent need for strategies that enable limited server CPU architectures (a.k.a "SKUs") to provide performance and energy efficiency over diverse microservices. To this end, we first undertake a comprehensive characterization of the top seven microservices that run on the compute-optimized data center fleet at Facebook.
Blockchain based permission delegation and access control in Internet of Things (BACI)

Access control with permission delegation mechanism allows fine granular access to secure resources. In the literature, existing architectures for permission delegation and access control are either event-based or query-based. These previous works assume a single trusted delegation service, which however is likely biased or fails to service. Also, they fail to allow users to verify delegation service operations, as such cannot be directly applied to IoT (Internet of Things) due to low power, low-bandwidth, ad-hoc and decentralized nature. This paper proposes a novel decentralized architecture for permission delegation and access control for IoT application, with demands on event and query base permission delegation. We further apply Blockchain (BC) technology to make delegation services secure, trusted, verifiable and decentralized. We investigate our proposed approach in Simple PROMELA INterpreter (SPIN) model checker using PROMELA (Process Meta Language). The “Platform Verification”, “Delegation”, “Mutual Exclusion” properties written in Linear Temporal Logic (LTL) are also verified against the PROMELA model.
World of Code: An Infrastructure for Mining the Universe of Open Source VCS Data

Open source software (OSS) is essential for modern society and, while substantial research has been done on individual (typically central) projects, only a limited understanding of the periphery of the entire OSS ecosystem exists. For example, how are tens of millions of projects in the periphery interconnected through technical dependencies, code sharing, or knowledge flows? To answer such questions we a) create a very large and frequently updated collection of version control data for FLOSS projects named World of Code (WoC) and b) provide basic tools for conducting research that depends on measuring interdependencies among all FLOSS projects. Our current WoC implementation is capable of being updated on a monthly basis and contains over 12B git objects. To evaluate its research potential and to create vignettes for its usage, we employ WoC in conducting several research tasks. In particular, we find that it is capable of supporting trend evaluation, ecosystem measurement, and the determination of package usage. We expect WoC to spur investigation into global properties of OSS development leading to increased resiliency of the entire OSS ecosystem. Our infrastructure facilitates the discovery of key technical dependencies, code flow, and social networks that provide the basis to determine the structure and evolution of the relationships that drive FLOSS activities and innovation.