Reviewerly/exHarmony huggingface dataset:
Deep Feature Aggregation and Image Re-Ranking With Heat Diffusion for Image Retrieval

Image retrieval based on deep convolutional features has demonstrated state-of-the-art performance in popular benchmarks. In this paper, we present a unified solution to address deep convolutional feature aggregation and image re-ranking by simulating the dynamics of heat diffusion. A distinctive problem in image retrieval is that repetitive or bursty features tend to dominate final image representations, resulting in representations less distinguishable. We show that by considering each deep feature as a heat source, our unsupervised aggregation method is able to avoid over-representation of bursty features. We additionally provide a practical solution for the proposed aggregation method and further show the efficiency of our method in experimental evaluation. Inspired by the aforementioned deep feature aggregation method, we also propose a method to re-rank a number of top ranked images for a given query image by considering the query as the heat source. Finally, we extensively evaluate the proposed approach with pre-trained and fine-tuned deep networks on common public benchmarks and show superior performance compared to previous work.
A comparison of automated segmentation and manual tracing in estimating hippocampal volume in ischemic stroke and healthy control participants

Manual quantification of the hippocampal atrophy state and rate is time consuming and prone to poor reproducibility, even when performed by neuroanatomical experts. The automation of hippocampal segmentation has been investigated in normal aging, epilepsy, and in Alzheimer's disease. Our first goal was to compare manual and automated hippocampal segmentation in ischemic stroke and to, secondly, study the impact of stroke lesion presence on hippocampal volume estimation. We used eight automated methods to segment T1-weighted MR images from 105 ischemic stroke patients and 39 age-matched controls sampled from the Cognition And Neocortical Volume After Stroke (CANVAS) study. The methods were: AdaBoost, Atlas-based Hippocampal Segmentation (ABHS) from the IDeALab, Computational Anatomy Toolbox (CAT) using 3 atlas variants (Hammers, LPBA40 and Neuromorphometics), FIRST, FreeSurfer v5.3, and FreeSurfer v6.0-Subfields. A number of these methods were employed to re-segment the T1 images for the stroke group after the stroke lesions were masked (i.e., removed). The automated methods were assessed on eight measures: process yield (i.e. segmentation success rate), correlation (Pearson's R and Shrout's ICC), concordance (Lin's RC and Kandall's W), slope ‘a’ of best-fit line from correlation plots, percentage of outliers from Bland-Altman plots, and significance of control−stroke difference. We eliminated the redundant measures after analysing between-measure correlations using Spearman's rank correlation. We ranked the automated methods based on the sum of the remaining non-redundant measures where each measure ranged between 0 and 1. Subfields attained an overall score of 96.3%, followed by AdaBoost (95.0%) and FIRST (94.7%). CAT using the LPBA40 atlas inflated hippocampal volumes the most, while the Hammers atlas returned the smallest volumes overall. FIRST (p = 0.014), FreeSurfer v5.3 (p = 0.007), manual tracing (p = 0.049), and CAT using the Neuromorphometics atlas (p = 0.017) all showed a significantly reduced hippocampal volume mean for the stroke group compared to control at three months. Moreover, masking of the stroke lesions prior to segmentation resulted in hippocampal volumes which agreed less with manual tracing. These findings recommend an automated segmentation without lesion masking as a more reliable procedure for the estimation of hippocampal volume in ischemic stroke.
Semi-supervised feature selection analysis with structured multi-view sparse regularization

Facing abundant and various multi-view data, how to effectively combine the multi-view data information has become an important research topic in feature selection analysis. However, existing feature selection methods usually consider each view features as a whole without fully considering the individual feature in each view. In this paper, we construct a structured multi-view sparse regularization and then propose a novel semi-supervise feature selection framework, namely Structured Multi-view Hessian sparse Feature Selection (SMHFS)1. With the structured multi-view sparse regularization, SMHFS can simultaneously learn the importance of each view features and the importance of individual feature in each view. In addition, SMHFS utilizes multi-view Hessian regularization to enhance the semi-supervised learning performance. An iterative algorithm is introduced and its convergence is proven. Finally, SMHFS is applied into image annotation task and extensive experiments are conducted. The experimental results show SMHFS can effectively combine the multi-view data information to achieve better feature selection performance compared to other methods.
A novel image encryption scheme based on 3D bit matrix and chaotic map with Markov properties

Based on 3D cat map, reverse 3D cat map and an improved class of chaotic maps with Markov properties, an effective bit-level image encryption scheme is proposed. Firstly, we convert the plain-image into binary matrix, and use the sum of all bits in the binary matrix as a part of secret keys to resist chosen-plaintext attack and known-plaintext attack. Secondly, we design a mapping which maps a random bit position to another random bit position rather than using traditional sequential visiting the plain-image. Thirdly, a chaotic map with Markov properties is used in the diffuse process. This kind of chaotic map has good chaotic natures than the Logistic map, which keeps the uniformity and low autocorrelation. The results of simulation and security analysis show that the proposed image encryption scheme is able to resist various attacks and has excellent encryption performance.
Stochastic Learning Under Random Reshuffling With Constant Step-Sizes

In empirical risk optimization, it has been observed that stochastic gradient implementations that rely on random reshuffling of the data achieve better performance than implementations that rely on sampling the data uniformly. Recent works have pursued justifications for this behavior by examining the convergence rate of the learning process under diminishing step-sizes. This work focuses on the constant step-size case and strongly convex loss function. In this case, convergence is guaranteed to a small neighborhood of the optimizer albeit at a linear rate. The analysis establishes analytically that random reshuffling outperforms uniform sampling by showing explicitly that iterates approach a smaller neighborhood of size $O(\mu^2)$ around the minimizer rather than $O(\mu)$. Furthermore, we derive an analytical expression for the steady-state mean-square-error performance of the algorithm, which helps clarify in greater detail the differences between sampling with and without replacement. We also explain the periodic behavior that is observed in random reshuffling implementations.
Towards Unsupervised Speech-to-text Translation

We present a framework for building speech-to-text translation (ST) systems using only monolingual speech and text corpora, in other words, speech utterances from a source language and independent text from a target language. As opposed to traditional cascaded systems and end-to-end architectures, our system does not require any labeled data (i.e., transcribed source audio or parallel source and target text corpora) during training, making it especially applicable to language pairs with very few or even zero bilingual resources. The framework initializes the ST system with a cross-modal bilingual dictionary inferred from the monolingual corpora, that maps every source speech segment corresponding to a spoken word to its target text translation. For unseen source speech utterances, the system first performs word-by-word translation on each speech segment in the utterance. The translation is improved by leveraging a language model and a sequence denoising autoencoder to provide prior knowledge about the target language. Experimental results show that our unsupervised system achieves comparable BLEU scores to supervised end-to-end models despite the lack of supervision. We also provide an ablation analysis to examine the utility of each component in our system.
Identifying suspicious groups of affiliated-transaction-based tax evasion in big data

Affiliated-transaction-based tax evasion (ATTE) is a new strategy in tax evasion that is carried out via legal-like transactions between a group of companies that have heterogeneous, complex and covert interactive relationships to evade taxes. Existing studies cannot effectively detect ATTE behaviors since (i) they perform well only for determining the abnormal financial status of individuals and ineffectively address the interactive relationships among companies, (ii) they aim at detecting ATTE from the perspective of structural characteristics, which leads to a poor false-positive rate, and (iii) few of them perform well in most sectors of companies. Effectively detecting suspicious groups according to both structural characteristics of ATTE groups and business characteristics of ATTE means (BC-ATTEM) remains an open issue. In this paper, we propose an affiliated-parties interest-related network (APIRN) for modeling affiliated parties, interest-related relationships, and their properties for identifying ATTE. Then, we identify the behavioral patterns of ATTE via topological pattern abstraction from APIRN and theoretical inference of BC-ATTEM. Based on the above, we further propose a hybrid method, namely, 3TI, for identifying ATTE suspicious groups via three steps: tax rate differential detection, topological pattern matching and tax burden abnormality identification. Experimental tests that are based on two years of real-world tax data from a province in China demonstrate that 3TI can identify ATTE suspicious groups with higher accuracy and better generality than existing works. Moreover, we identify various interesting implications and provide useful guidance for ATTE inspection based on an analysis of our experimental results.
Class-specific synthesized dictionary model for Zero-Shot Learning

Zero-Shot Learning (ZSL) aims at recognizing unseen classes that are absent during the training stage. Unlike the existing approaches that learn a visual-semantic embedding model to bridge the low-level visual space and the high-level class prototype space, we propose a novel synthesized approach for addressing ZSL within a dictionary learning framework. Specifically, it learns both a dictionary matrix and a class-specific encoding matrix for each seen class to synthesize pseudo instances for unseen classes with auxiliary of seen class prototypes. This allows us to train the classifiers for the unseen classes with these pseudo instances. In this way, ZSL can be treated as a traditional classification task, which makes it applicable for traditional and generalized ZSL settings simultaneously. Extensive experimental results on four benchmark datasets (AwA, CUB, aPY, and SUN) demonstrate that our method yields competitive performances compared to state-of-the-art methods on both settings.
The PV potential of vertical façades: A classic approach using experimental data from Burgos, Spain

Potential photovoltaic (PV) production from vertical façades facing the four cardinal points of the compass are calculated from daily average vertical global insolation readings measured in Burgos, Spain. Ten-minute data sets are collected over forty-five months, from January 2014 to September 2017, in the experimental campaign to produce estimates of daily average insolation levels, from which the PV potential of the vertical surfaces was calculated. Given the scarcity of data on Global Vertical Insolation (GVI), the main sky-related variables were processed in four classic decomposition models (Isotropic, Circumsolar, Klucher, and Hay) to predict the insolation values. Both the experimentally measured GVI values and those calculated with the models were then compared using the statistical indicators RMSE and MBE. The results highlighted the economic viability of Building Integrated PhotoVoltaic (BIPV) facilities, even on the north-facing façades, in comparison with the horizontal facility at the same location.
Determination of surface profiles of transparent plates by means of laser interferometry with wavelength tuning

An interferogram obtained from a transparent plate contains information about the profiles of both surfaces of the plate. This information can be extracted by processing fringe patterns measured at different wavelengths. The conventional Fourier analysis applied to solve such problems for a set of a restricted number of the fringe patterns is quite sensitive to the error of detuning the wavelength shifting and suffers from fringe patterns interference noise. This study proposes a method for finding surface profiles of a transparent plate using a series of fringe patterns obtained at different phase shifts caused by wavelength changes. The data treatment is based on the analytical approach for describing spatial distributions of irradiance in interference patterns. The results show that the proposed method is feasible. The experimental setup used herein was a Fizeau interferometer with tunable wavelength laser. A glass plate approximately 1 cm thick was as the test sample. According to the obtained results, the root mean square (RMS) errors for determining both surface profiles did not exceed 2.3 nm.
Towards a robust and affordable Automatic Weather Station

The frequency and severity of extreme weather events have increased over the last 30 years, making predictability of weather a challenge. Weather extreme events often cause adverse impacts to lives and property. Thus, accurate and timely provision of weather data is becoming crucial to improve the skill of weather prediction and to strengthen resilience to the impacts of the adverse weather conditions. Uganda and many developing countries have challenges in acquiring accurate and timely weather data due to their sparse weather observation networks. The sparse weather observation networks are in part attributed to the high cost of acquiring an Automatic Weather Station (AWS) and limited funding to national meteorological services of the respective countries. The inability of developing countries to manufacture their own AWSs leads to high recurring costs accruing from importation and maintenance. In this study, we propose an AWS based on Wireless Sensor Networks. We plan to design three generations of the AWS prototype, the first being the subject of this paper. The purpose of this paper is therefore to evaluate the first-generation AWS prototype and to propose improvements for the second-generation, based on needs and requirements. Results from the AWS prototype data suggest improving non-functional requirements such as reliability, data accuracy, power consumption and data transmission in order to have an operational AWS. The non-functional requirements combined with cost reduction produces a robust and affordable AWS. Therefore, developing countries like Uganda will be able to acquire the AWSs in reasonable quantities, hence improvement in weather forecasts.
Empirical research for software architecture decision making: An analysis

Despite past empirical research in software architecture decision making, we have not yet systematically studied how to perform such empirical research. Software architecture decision making involves humans, their behavioral issues and practice. As such, research on decision making needs to involve not only engineering but also social science research methods. This paper studies empirical research on software architecture decision making. We want to understand what research methods have been used to study human decision making in software architecture. Further, we want to provide guidance for future studies. We analyzed research papers on software architecture decision making. We classified the papers according to different sub-dimensions of empirical research design like research logic, research purpose, research methodology and process. We introduce the study focus matrix and the research cycle to capture the focus and the goals of a software architecture decision making study. We identify gaps in current software architecture decision making research according to the classification and discuss open research issues inspired by social science research. We show the variety of research designs and identify gaps with respect to focus and goals. Few papers study decision making behavior in software architecture design. Also these researchers study mostly the process and much less the outcome and the factors influencing decision making. Furthermore, there is a lack of improvements for software architecture decision making and in particular insights into behavior have not led to new practices. The study focus matrix and the research cycle are two new instruments for researchers to position their research clearly. This paper provides a retrospective for the community and an entry point for new researchers to design empirical studies that embrace the human role in software architecture decision making.
On Two Classes of Primitive BCH Codes and Some Related Codes

BCH codes are an interesting type of cyclic codes and have wide applications in communication and storage systems. Generally, it is very hard to determine the minimum distances of BCH codes. In this paper, we determine the weight distributions of two classes of primitive BCH codes C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(q,m,δ(2))</sub> and C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(q,m,δ(3))</sub> and their extended codes, which solve two problems proposed by Ding et al. It is shown that the extended codes C̅ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(q,m,δ(2))</sub> have four nonzero weights. We also employ the Hartmann-Tzeng bound to present the minimum distance of the dual code C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">⊥</sup> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(q,m,δ(2))</sub> for q ≥ 5. Inspired by the idea, we then determine the dimensions of a class of cyclic codes and give lower bounds on their minimum distances, which is greatly improved comparing with the BCH bound. Some optimal codes are obtained.
Robust contrast enhancement forensics based on convolutional neural networks

Contrast enhancement (CE) is frequently applied to conceal traces of forgery and therefore can provide indirect forensic evidence of tampering when investigating composite images. The performance of existing CE forensic methods however, suffers fatal degradation when detecting enhanced images stored in the JPEG format. In this paper, we propose a new JPEG-robust CE forensic method based on a modified convolutional neural network (CNN). Unlike traditional CNNs, the first layer of our CNN architecture accepts a potentially enhanced image as the input and outputs its Gray-Level Co-occurrence Matrix (GLCM), which contains CE fingerprints; termed a GLCM layer. A cropping layer is used for noise reduction in GLCMs. In addition, the output of the cropping layer becomes input when extracting multiple features for further classification using a tailor-made CNN, which significantly extracts residual CE features under JPEG compression. Extensive experimental results show that the proposed method achieves significant improvements in both global and local CE detection.
Multiscale Symmetric Dense Micro-Block Difference for Texture Classification

A dense micro-block difference (DMD)-based method was proposed for performing texture representation that is a fundamental task of image and video analysis. However, it cannot capture effectively the rotation invariance and multiscale spatial information of textures. To alleviate these problems, in this paper, we propose a multiscale symmetric DMD (MSDMD) method for texture classification. In particular, we first combine K-rotation and Gaussian distribution to analyze the Symmetric DMD in order to capture the rotation invariance of textures. Furthermore, we propose a high-order vector of locally aggregated descriptor called HVLAD by incorporating the second-order and third-order statistics into the original vector of VLAD. To effectively extract the spatial information of textures, we implement the above-mentioned steps in a Gaussian pyramid structure to construct an MSDMD feature and use a support vector machine (SVM) to perform texture classification. The experimental results on five available published texture datasets (KTH-TIPS, CUReT, UIUC, UMD, and KTH-TIPS2-b) reveal that our proposed method is effective when compared with 15 representative texture classification methods.
Mini-batch algorithms with online step size

Mini-batch algorithms have been proposed as a way to speed-up stochastic optimization methods and good results for mini-batch algorithms have been reported previously. A major issue with mini-batch algorithms is how to timely and readily acquire step size while running the algorithm. Usually, mini-batch algorithms employ a diminishing step size, or a best-tuned step size by mentor, which, in practice, are time consuming. To solve this problem, we propose using a hypergradient to compute an online step size (OSS) for mini-batch algorithms. Specifically, we incorporate online step size into advanced mini-batch algorithms, mini-batch nonconvex stochastic variance reduced gradient (MSVRG), thereby generating a new method, MSVRG-OSS. When computing step size in MSVRG-OSS, mini-batch samples are used. In addition, MSVRG-OSS, which needs little additional computation, requires only one extra copy of the original gradient to be stored in memory. We prove that MSVRG-OSS converges linearly in expectation and analyze its complexity. We present numerical results on problems arising with machine learning that indicate the proposed method shows great promise. We also show that, with slightly large batch samples, MSVRG-OSS is insensitive to the initial parameters, which are the key factor for controlling the performance of the algorithm.
T2FCS filter: Type 2 fuzzy and cuckoo search-based filter design for image restoration

A novel filter design for the restoration of the corrupted digital image is proposed in this paper. The filter design incorporates type II fuzzy system and cuckoo search optimization algorithm (T2FCS) based filter design for the restoration of the noise in the images. The noisy pixels in the images are detected using the proposed circular based searching scheme and the detected corrupt pixels are removed using the cuckoo search algorithm. The enhanced pixels in place of the corrupt pixels are acquired using the proposed type II fuzzy system. The proposed filter adapts to various noisy conditions such as random noise, salt and pepper noise and scratch noise. The experimentation of the proposed filter design is carried out over two images. The performance of the proposed T2FCS filter design is compared over the existing image restoration algorithms using metrics; Peak Signal to Noise ratio (PSNR), Structural Similarity Index (SSIM), Second Derivative Like Measure of Enhancement (SDME). The result obtained favours the performance of the proposed filter in the restoration of the noisy images.