Reviewerly/exHarmony huggingface dataset:
Intervention strategies for urban blight: A participatory approach

Urban blight has been the subject of numerous worldwide studies. However, because of blight's complexity, direct relationship with ​​urban planning and multifaceted associated variables, studies targeting certain aspects of blight require detailed conceptualization and structure to facilitate decision-making processes that address blight. Many urban, socio-economic and cultural externalities associated with blight need to be addressed in developing viable intervention strategies for prevention, attenuation and eradication. Identifying and simplifying the complex issues associated with blight is important as their intricacy creates difficulties for decision makers. We simplify the blight problem by using unique techniques for structuring complex decision problems (i.e., problem structuring methods (PSMs)). This methodology is based on multiple-criteria decision analysis (MCDA) principles, as well as a combination of participatory techniques (i.e., decision conferencing and cognitive mapping). In a subsequent phase, the decision-making trial and evaluation laboratory (DEMATEL) technique is used to enable the development of a more realistic, transparent analysis model for blight intervention strategies. This study assumes a constructivist logic, so both objective and subjective elements are included. The proposed methodology's advantages and limitations are also discussed.
Warp Consistency for Unsupervised Learning of Dense Correspondences

The key challenge in learning dense correspondences lies in the lack of ground-truth matches for real image pairs. While photometric consistency losses provide unsupervised alternatives, they struggle with large appearance changes, which are ubiquitous in geometric and semantic matching tasks. Moreover, methods relying on synthetic training pairs often suffer from poor generalisation to real data.We propose Warp Consistency, an unsupervised learning objective for dense correspondence regression. Our objective is effective even in settings with large appearance and view-point changes. Given a pair of real images, we first construct an image triplet by applying a randomly sampled warp to one of the original images. We derive and analyze all flow-consistency constraints arising between the triplet. From our observations and empirical results, we design a general unsupervised objective employing two of the derived constraints. We validate our warp consistency loss by training three recent dense correspondence networks for the geometric and semantic matching tasks. Our approach sets a new stateof-the-art on several challenging benchmarks, including MegaDepth, RobotCar and TSS. Code and models are at github.com/PruneTruong/DenseMatching.
Injury Risk Prediction of Aerobics Athletes Based on Big Data and Computer Vision

In recent years, competitive aerobics has been rapidly popularized and developed, and the level of sports skills has also been greatly improved. The performance of some events has gradually approached and reached the advanced level. Therefore, it is vital to invest in the quantitative analysis and cross-disciplinary comprehensive research of aerobics performance and related factors. This paper adopts big data analysis technology and computer vision technology based on convolutional neural network, according to the related theories of sports biomechanics and computer image recognition, to establish a loss risk prediction model for aerobics athletes. The approach firstly has used technology of big data analysis for analyzing the characteristics of competitive aerobics sports data. Secondly, the approach combines the convolutional neural network to visually recognize the aerobics sports images and establish a two-branch prediction model. Finally, the output can be fused to accurately diagnose and evaluate the level of physical fitness development of aerobics athletes, the focus and goal of training content are clarified, and the scientific degree of aerobics training is improved. The study can help injury risk prediction of aerobic athletes based on applications of big data and computer vision.
A Segment-Based Drift Adaptation Method for Data Streams

In concept drift adaptation, we aim to design a blind or an informed strategy to update our best predictor for future data at each time point. However, existing informed drift adaptation methods need to wait for an entire batch of data to detect drift and then update the predictor (if drift is detected), which causes adaptation delay. To overcome the adaptation delay, we propose a sequentially updated statistic, called drift-gradient to quantify the increase of distributional discrepancy when every new instance arrives. Based on drift-gradient, a segment-based drift adaptation (SEGA) method is developed to online update our best predictor. Drift-gradient is defined on a segment in the training set. It can precisely quantify the increase of distributional discrepancy between the old segment and the newest segment when only one new instance is available at each time point. A lower value of drift-gradient on the old segment represents that the distribution of the new instance is closer to the distribution of the old segment. Based on the drift-gradient, SEGA retrains our best predictors with the segments that have the minimum drift-gradient when every new instance arrives. SEGA has been validated by extensive experiments on both synthetic and real-world, classification and regression data streams. The experimental results show that SEGA outperforms competitive blind and informed drift adaptation methods.
CIE XYZ Net: Unprocessing Images for Low-Level Computer Vision Tasks

Cameras currently allow access to two image states: (i) a minimally processed linear raw-RGB image state (i.e., raw sensor data); or (ii) a highly-processed nonlinear image state (e.g., sRGB). There are many computer vision tasks that work best with a linear image state, such as image deblurring and image dehazing. Unfortunately, the vast majority of images are saved in the nonlinear image state. Because of this, a number of methods have been proposed to "unprocess" nonlinear images back to a raw-RGB state. However, existing unprocessing methods have a drawback because raw-RGB images are sensor-specific. As a result, it is necessary to know which camera produced the sRGB output and use a method or network tailored for that sensor to properly unprocess it. This paper addresses this limitation by exploiting another camera image state that is not available as an output, but it is available inside the camera pipeline. In particular, cameras apply a colorimetric conversion step to convert the raw-RGB image to a device-independent space based on the CIE XYZ color space before they apply the nonlinear photo-finishing. Leveraging this canonical image state, we propose a deep learning framework, CIE XYZ Net, that can unprocess a nonlinear image back to the canonical CIE XYZ image. This image can then be processed by any low-level computer vision operator and re-rendered back to the nonlinear image. We demonstrate the usefulness of the CIE XYZ Net on several low-level vision tasks and show significant gains that can be obtained by this processing framework. Code and dataset are publicly available at https://github.com/mahmoudnafifi/CIE_XYZ_NET.
Progressive Ensemble Kernel-Based Broad Learning System for Noisy Data Classification

The broad learning system (BLS) is an algorithm that facilitates feature representation learning and data classification. Although weights of BLS are obtained by analytical computation, which brings better generalization and higher efficiency, BLS suffers from two drawbacks: 1) the performance depends on the number of hidden nodes, which requires manual tuning, and 2) double random mappings bring about the uncertainty, which leads to poor resistance to noise data, as well as unpredictable effects on performance. To address these issues, a kernel-based BLS (KBLS) method is proposed by projecting feature nodes obtained from the first random mapping into kernel space. This manipulation reduces the uncertainty, which contributes to performance improvements with the fixed number of hidden nodes, and indicates that manually tuning is no longer needed. Moreover, to further improve the stability and noise resistance of KBLS, a progressive ensemble framework is proposed, in which the residual of the previous base classifiers is used to train the following base classifier. We conduct comparative experiments against the existing state-of-the-art hierarchical learning methods on multiple noisy real-world datasets. The experimental results indicate our approaches achieve the best or at least comparable performance in terms of accuracy.
Indian Sign Language Recognition using SVM Classifier

Sign language is the medium of communication for the hearing impaired people. It uses gestures instead of sound to convey meaning. It combines hand-shapes, orientation and movement of the hands, arms or body, facial expressions and lip-patterns for conveying messages. Different types of project are done against deaf, mute, hard hearing people. A system with computer human interface is proposed for sign language recognition. But there is country wide variation available in that project. The main idea of this project is design a system which is useful for communication of that people with outside world in any public places, so that no need to interpreter in public places. In that project we need the isolated images in the form of database with Indian sign language of numeric sign. A regular camera is useful for acquiring this numeric sign. Principal Component Analysis (PCA) is used for preprocessing, in which the removal of redundant and unwanted data is done.
Unsupervised Robust Domain Adaptation without Source Data

We study the problem of robust domain adaptation in the context of unavailable target labels and source data. The considered robustness is against adversarial perturbations. This paper aims at answering the question of finding the right strategy to make the target model robust and accurate in the setting of unsupervised domain adaptation without source data. The major findings of this paper are: (i) robust source models can be transferred robustly to the target; (ii) robust domain adaptation can greatly benefit from nonrobust pseudo-labels and the pair-wise contrastive loss. The proposed method of using non-robust pseudo-labels performs surprisingly well on both clean and adversarial samples, for the task of image classification. We show a consistent performance improvement of over 10% in accuracy against the tested baselines on four benchmark datasets. Our source code will be made publicly available.
Measuring the Impact of Immersive Virtual Reality on Construction Design Review Applications: Head-Mounted Display versus Desktop Monitor

Head-mounted display (HMD)–based virtual reality (VR) applications can potentially facilitate various visualization-intensive tasks in the construction industry, such as design and constructability review. However, the lack of quantitative validation on the theoretical benefits of VR has impeded its industry implementation. This paper designed four construction design review-related tasks and performed user tests with 24 construction engineering novices and 24 industry experts. Participants were randomly assigned to treatment or control groups, performing four tasks via HMD-based VR or desktop-based VR, respectively. Experimental results and statistical analysis show that users' performance was significantly enhanced by the HMD in detecting design errors and planning for construction sequence. The scale of performance improvement varied from 18.22% to 59.01%. The implementation of HMD-based VR applications should be intentional because its benefits are only significant in specific use cases. The intellectual contribution of this paper includes quantifying the impact of HMD to the tested VR applications and developing the technology assessment framework. Meanwhile, the findings from this paper can facilitate the implementation of effective VR applications in the construction industry.
An Attack on Zawadzki’s Quantum Authentication Scheme

Identification schemes are interactive cryptographic protocols typically involving two parties, a prover, who wants to provide evidence of their identity and a verifier, who checks the provided evidence and decides whether or not it comes from the intended prover. Given the growing interest in quantum computation, it is indeed desirable to have explicit designs for achieving user identification through quantum resources. In this paper, we comment on a recent proposal for quantum identity authentication from Zawadzki. We discuss the applicability of the theoretical impossibility results from Lo, Colbeck and Buhrman et al. and formally prove that the protocol must necessarily be insecure. Moreover, to better illustrate our insecurity claim, we present an attack on Zawadzki’s protocol and show that by using a simple strategy an adversary may indeed obtain relevant information on the shared identification secret. Specifically, through the use of the principal of conclusive exclusion on quantum measurements, our attack geometrically reduces the key space resulting in the claimed logarithmic security being reduced effectively by a factor of two after only three verification attempts.
Transformer-Based Online Speech Recognition with Decoder-end Adaptive Computation Steps

Transformer-based end-to-end (E2E) automatic speech recognition (ASR) systems have recently gained wide popularity, and are shown to outperform E2E models based on recurrent structures on a number of ASR tasks. However, like other E2E models, Transformer ASR also requires the full input sequence for calculating the attentions on both encoder and decoder, leading to increased latency and posing a challenge for online ASR. The paper proposes Decoder-end Adaptive Computation Steps (DACS) algorithm to address the issue of latency and facilitate online ASR. The proposed algorithm streams the decoding of Transformer ASR by triggering an output after the confidence acquired from the encoder states reaches a certain threshold. Unlike other monotonic attention mechanisms that risk visiting the entire encoder states for each output step, the paper introduces a maximum look-ahead step into the DACS algorithm to prevent from reaching the end of speech too fast. A Chunkwise en-coder is adopted in our system to handle real-time speech inputs. The proposed online Transformer ASR system has been evaluated on Wall Street Journal (WSJ) and AIShell-1 datasets, yielding 5.5% word error rate (WER) and 7.1% character error rate (CER) respectively, with only a minor decay in performance when compared to the offline systems.
Assessing poorly crystalline and mineral-organic species by extracting Al, Fe, Mn, and Si using (citrate-) ascorbate and oxalate

Poorly crystalline and mineral-organic species are commonly assessed by oxalate (Ox) extraction of aluminium (Al), iron (Fe), manganese (Mn), and silicon (Si). Drawbacks regarding selectivity towards analytical targets led to test alternatively, but mostly with topsoils, citrate-ascorbate (CA), combined with a separate citrate extraction. Here, we tested the extraction by Ox, CA, and citrate with 61 carbonate-free subsoils characterized by pedogenic element accumulation, and with 17 minerals, including primary silicates, clay minerals, and (Al, Fe) oxides. Iron extraction from the minerals, apart from pyrite, by citrate was negligible (≤2% of total Fe), like from Fe oxides (≤1.5% of total Fe), while oxalate-extractable Al, Fe, and Mn contents were larger than those extracted by CA, the relative fraction of which was below 9%, except for allophane. In subsoils, extractable element contents spanned several orders of magnitude whatever the extractant. With several samples, Mn was entirely solubilized by any extractant. The AlOx, FeOx, and MnOx amounts were very similar among soils lacking allophanic minerals. However, the ratio FeCA:FeOx increased with Bl and Bg horizons, which received Fe2+ by ground- or stagnic water. Citrate-extraction data revealed, depending on the soil type, a potentially large contribution of organic forms to CA- and Ox-extractable Al and Fe. Thus, we recommend combining Ox and CA extraction with citrate extraction to approximate the contents of poorly crystalline pedogenic Al and Fe species, rather than using CA- or oxalate-extraction data alone. Our results challenge the appropriateness of oxalate-extraction data for species identification and mineral quantification.
Handwritten Character Recognition of Kannada Language Using Convolutional Neural Networks and Transfer Learning

Abstract Recognition of Kannada Handwritten Characters in recent times is one among the active research fields of study. It is one of the challenging topics in the pattern recognition field due vocabulary in large scale, complicated structural hierarchy and different people have different handwriting styles. In this study, we put forward a technique for recognition of characters in kannada which are handwritten based on convolutional neural networks and transfer learning. We have first pre-processed each character to remove noise, cropped each character image and resize the images. After the pre-processing the enhanced pixel values in the image helps in training of the neural network for an efficient classification of characters to their respective classes. We have considered vowels, consonants and numerals of Kannada language which are handwritten. Even though some of the characters in kannada language have similarities in structures our model is capable of classifying the character to their correct respective class. We have also used transfer learning to further improve the model so that any new style of handwritten character can be correctly predicted. Our method is efficient in neural network approach and it achieves competitive performance on the rest of the other traditional recognition methods in the literature.
On 2-dimensional insertion-deletion Reed-Solomon codes with optimal asymptotic error-correcting capability

Reed-Solomon codes have gained a lot of interest due to its encoding simplicity, well structuredness and list-decoding capability [6] in the classical setting. This interest also translates to other metric setting, including the insertion and deletion (insdel for short) setting which is used to model synchronization errors caused by positional information loss in communication systems. Such interest is supported by the construction of a deletion correcting algorithm of insdel Reed-Solomon code in [22] which is based on the Guruswami-Sudan decoding algorithm [6]. Nevertheless, there have been few studies [3] on the insdel error-correcting capability of Reed-Solomon codes. In this paper, we discuss a criterion for a 2-dimensional insdel Reed-Solomon codes to have optimal asymptotic error-correcting capabilities, which are up to their respective lengths. Then we provide explicit constructions of 2-dimensional insdel Reed-Solomon codes that satisfy the established criteria. The family of such constructed codes can then be shown to extend the family of codes with asymptotic error-correcting capability reaching their respective lengths provided in [3, Theorem 2] which provide larger error-correcting capability compared to those defined in [25].
A safe feature elimination rule for L1-regularized logistic regression

The L1-regularized logistic regression (L1-LR) is popular for classification problems. To accelerate its training speed for high-dimensional data, techniques named safe screening rules have been proposed recently. They can safely delete the inactive features in data so as to greatly reduce the training cost of L1-LR. The screening power of these rules is determined by their corresponding safe regions, which is also the core technique of safe screening rules. In this paper, we introduce a new safe feature elimination rule (SFER) for L1-LR. Compared to existing safe rules, the safe region of SFER is improved in two aspects: (1) a smaller sphere region is constructed by using the strong convexity of dual L1-LR twice; (2) multiple half-spaces, which correspond to the potential active constraints, are added for further contraction. Both improvements can enhance the screening ability of SFER. As for the complexity of SFER, an iterative filtering framework is given by decomposing the safe region into multiple "domes". In this way, SFER admits a closed form solution and the identified features will not be scanned repeatedly. Experiments on ten benchmark data sets demonstrate that SFER gives superior performance than existing methods on training efficiency.
Improvement of Earthquake Risk Awareness and Seismic Literacy of Korean Citizens through Earthquake Vulnerability Map from the 2017 Pohang Earthquake, South Korea

Earthquake activities in and around the Korean Peninsula are relatively low in number and intensity compared with neighboring countries such as Japan and China. However, recent seismic activity caused great alarm and concern among citizens and government authorities, and uncovered the level of preparedness toward earthquake disasters. A survey has been conducted on 1256 participants to investigate the seismic literacy of Korean citizens, including seismic knowledge, awareness and management using a questionnaire of citizen earthquake literacy (CEL). The results declared that the citizens had low awareness and literacy, which means that they are not properly prepared for earthquake hazards. To develop an earthquake risk reduction plan and program efficiently and effectively, not only must it appropriately characterize the target audience, but also indicate high potential earthquake zones and potential earthquake damage. Therefore, this study mapped and analyzed the seismic vulnerability in southeast Korea using LogitBoost, logistic model tree (LMT), and logistic regression (LR) machine learning algorithms based on a building damage inventory map. The damaged buildings’ locations were generated after the 2017 Pohang earthquake using the damage proxy map (DPM) method from the Sentinel-1 synthetic aperture radar (SAR) data. DPMs detected coherence loss, which indicates damaged buildings in urban areas in the Pohang earthquake and shows a good correlation with the Korea Meteorological Administration (KMA) report with modified Mercalli intensity (MMI) scale values of more than VII (seven). The damage locations were randomly divided into two datasets: 50% for training the vulnerability models and 50% for validating the models in terms of accuracy and reliability. Fifteen seismic-related factors were used to construct a model of each algorithm. Model validation based on the area under the receiver operating curve (AUC) was used to determine model accuracy. The AUC values of seismic vulnerability maps using the LogitBoost, LMT, and LR algorithms were 0.769, 0.851, and 0.749, respectively. We suggest that earthquake preparedness efforts should focus on reconstruction, retrofitting, renovation, and seismic education in areas with high seismic vulnerability in South Korea. The results of this study are expected to be beneficial for engineers and policymakers aiming at developing disaster risk reduction plans, policies, and programs due to future seismic activity in South Korea.