Reviewerly/exHarmony huggingface dataset:
Calibration and rectification of bi-telecentric lenses in Scheimpflug condition

In the lens-based imaging model, the Scheimpflug principle is expressed as the object plane, the image plane, and the lens plane intersect in a line. With this principle, the focused object plane in the lens's object side can be tilted by placing a tilted sensor at the image side of the lens; thereby, multi-cameras can be focused on the same object space with an overlapping field of view and depth of field. For Scheimpflug cameras, additional tilt angles between the camera sensor and the optical axis are introduced, which has been well studied in pinhole cameras' calibration methods. Telecentric lens, as a commonly used lens type, has constant magnification in the axial direction and has a wide range of applications in close-range photogrammetry. To calibrate and rectify the telecentric lenses in Scheimpflug conditions, we derived a concise imaging model by expressing the sensor tilt angles and the lens magnification into a simplified intrinsic matrix. Based on the derived imaging model, an integrated calibration algorithm without solving the tilt angles and a stereo-rectification method for stereo matching are developed. The effectiveness and accuracy of the proposed methods are verified by experiments, including the comparison with the traditional telecentric model and pinhole model. Combined with the experimental results, we analyzed the potential impact of the extrinsic rotation matrix's ambiguity, verified whether the lens distortion affects the re-projection error, and discussed how the calibration posture influences the calibration accuracy.
Comparison of document similarity algorithms in extracting document keywords from an academic paper

The idea of this study is to validate a list of keywords derived from a scientific article by a domain expert from years of knowledge with prominent document similarity algorithms. For this study, a list of handcrafted keywords generated by Electric Double Layer Capacitor (EDLC) experts are chosen, and relevant documents to EDLC are considered for the comparison. Then, different similarity calculation algorithms were employed in different settings on the documents such as using the whole texts of the documents, selecting the positive sentences of the documents, and generating similarity score with automatically extracted keywords from the documents. The experiment’s outcome provides us with findings that the machine-generated keywords are mostly similar to the curated list by the domain experts. This study also suggests the preferable algorithms for similarity calculation and automated key-phrase extraction for the EDLC domain.
FLASH: <u>F</u> ast Neura <u>l</u> <u>A</u> rchitecture <u>S</u> earch with <u>H</u> ardware Optimization

Neural architecture search (NAS) is a promising technique to design efficient and high-performance deep neural networks (DNNs). As the performance requirements of ML applications grow continuously, the hardware accelerators start playing a central role in DNN design. This trend makes NAS even more complicated and time-consuming for most real applications. This paper proposes FLASH, a very fast NAS methodology that co-optimizes the DNN accuracy and performance on a real hardware platform. As the main theoretical contribution, we first propose the NN-Degree, an analytical metric to quantify the topological characteristics of DNNs with skip connections (e.g., DenseNets, ResNets, Wide-ResNets, and MobileNets). The newly proposed NN-Degree allows us to do training-free NAS within one second and build an accuracy predictor by training as few as 25 samples out of a vast search space with more than 63 billion configurations. Second, by performing inference on the target hardware, we fine-tune and validate our analytical models to estimate the latency, area, and energy consumption of various DNN architectures while executing standard ML datasets. Third, we construct a hierarchical algorithm based on simplicial homology global optimization (SHGO) to optimize the model-architecture co-design process, while considering the area, latency, and energy consumption of the target hardware. We demonstrate that, compared to the state-of-the-art NAS approaches, our proposed hierarchical SHGO-based algorithm enables more than four orders of magnitude speedup (specifically, the execution time of the proposed algorithm is about 0.1 seconds). Finally, our experimental evaluations show that FLASH is easily transferable to different hardware architectures, thus enabling us to do NAS on a Raspberry Pi-3B processor in less than 3 seconds.
Accent Recognition with Hybrid Phonetic Features

The performance of voice-controlled systems is usually influenced by accented speech. To make these systems more robust, frontend accent recognition (AR) technologies have received increased attention in recent years. As accent is a high-level abstract feature that has a profound relationship with language knowledge, AR is more challenging than other language-agnostic audio classification tasks. In this paper, we use an auxiliary automatic speech recognition (ASR) task to extract language-related phonetic features. Furthermore, we propose a hybrid structure that incorporates the embeddings of both a fixed acoustic model and a trainable acoustic model, making the language-related acoustic feature more robust. We conduct several experiments on the AESRC dataset. The results demonstrate that our approach can obtain an 8.02% relative improvement compared with the Transformer baseline, showing the merits of the proposed method.
Pruned-YOLO: Learning Efficient Object Detector Using Model Pruning

Accurate real-time object detection plays a key role in various practical scenarios such as automatic driving and UAV surveillance. The memory limitation and poor computing power of edge devices hinder the deployment of high performance Convolutional Neural Networks (CNNs). Iterative channel pruning is an effective method to obtain lightweight networks. However, the channel importance measurement and iterative pruning in the existing methods are suboptimal. In this paper, to measure the channel importance, we simultaneously consider the scale factor of batch normalization (BN) and the kernel weight of convolutional layers. Besides, sparsity training and fine tuning are combined to simplify the pruning pipeline. Notably, the cosine decay of sparsity coefficient and soft mask strategy are used to optimize our compact model, i.e., Pruned-YOLOv3/v5, which is constructed via pruning YOLOv3/v5. The experimental results on the MS-COCO and VisDrone datasets show that the proposed model achieves a satisfactory balance between computational efficiency and detection accuracy.
Text-Independent Speaker Verification Employing CNN-LSTM-TDNN Hybrid Networks

Time Delay Neural Network (TDNN)-based speaker embeddings extraction have become the dominant approach for text-independent speaker verification. Several single and hybrid deep learning architectures have been proposed for improving the performance. In this paper, we propose yet another hybrid configuration that employs Convolution Neural Network (CNN), TDNN and Long Short-Term Memory (LSTM) for training and extraction of speaker discriminant utterance level representations. In the proposed framework, we also use SpecAugment for on the fly data augmentation and multi-level statistics pooling from across CNN, LSTM and TDNN layers for aggregating the frame level information into utterance level speaker embeddings via fully connected layers. For performance evaluation of the proposed framework, speaker verification experiments are carried out across NIST SRE 2016, Voxceleb and Short duration Speaker Verification (SdSV) challenge 2021 corpora. Evaluation metrics chosen for performance evaluation are equal error rate (EER), minimum decision cost function (minDCF), and actual decision cost function (actDCF). Experimental results depict the proposed hybrid approach yielding improvements over the original TDNN, TDNN-LSTM hybrid architecture, as well as some other previously-proposed approaches.
Modelling and Optimal Design of Hybrid Power System Photovoltaic/Solid Oxide Fuel Cell for a Mediterranean City

This work presents a hybrid power system consisting of photovoltaic and solid oxide fuel cell (PV-SOFC) for electricity production and hydrogen production. The simulation of this hybrid system is adjusted for Bou-Zedjar city in north Algeria. Homer software was used for this simulation to calculate the power output and the total net present cost. The method used depends on the annual average monthly values of clearness index and radiation for which the energy contributions are determined for each component of PV/SOFC hybrid system. The economic study is more important criterion in the proposed hybrid system, and the results show that the cost is very suitable for the use of this hybrid system, which ensures that the area is fed continuously with the sufficient energy for the load which assumed to be 500 kW in the peak season. The optimized results of the present study show that the photovoltaic is capable of generating 8733 kW electricity while the SOFC produces 500 kW electricity. The electrolyzer is capable of producing 238750 kg of hydrogen which is used as fuel in the SOFC to compensate the energy lack in nights and during peak season.
To Split or not to Split: The Impact of Disparate Treatment in Classification

Disparate treatment occurs when a machine learning model produces different decisions for individuals based on a legally protected or sensitive attribute (e.g., age, sex). In domains where prediction accuracy is paramount, it could potentially be acceptable to fit a model which exhibits disparate treatment. To evaluate the effect of disparate treatment, we compare the performance of split classifiers (i.e., classifiers trained and deployed separately on each group) with group-blind classifiers (i.e., classifiers which do not use a sensitive attribute). We introduce the benefit-of-splitting for quantifying the performance improvement by splitting classifiers. Computing the benefit-of-splitting directly from its definition could be intractable since it involves solving optimization problems over an infinite-dimensional functional space. Under different performance measures, we (i) prove an equivalent expression for the benefit-of-splitting which can be efficiently computed by solving small-scale convex programs; (ii) provide sharp upper and lower bounds for the benefit-of-splitting which reveal precise conditions where a group-blind classifier will always suffer from a non-trivial performance gap from the split classifiers. In the finite sample regime, splitting is not necessarily beneficial and we provide data-dependent bounds to understand this effect. Finally, we validate our theoretical results through numerical experiments on both synthetic and real-world datasets.
Bundled Gradients Through Contact Via Randomized Smoothing

The empirical success of derivative-free methods in reinforcement learning for planning through contact seems at odds with the perceived fragility of classical gradient-based optimization methods in these domains. What is causing this gap, and how might we use the answer to improve gradient-based methods? We believe a stochastic formulation of dynamics is one crucial ingredient. We use tools from randomized smoothing to analyze sampling-based approximations of the gradient, and formalize such approximations through the bundled gradient. We show that using the bundled gradient in lieu of the gradient mitigates fast-changing gradients of non-smooth contact dynamics modeled by the implicit time-stepping, or the penalty method. Finally, we apply the bundled gradient to optimal control using iterative MPC, introducing a novel algorithm which improves convergence over using exact gradients. Combining our algorithm with a convex implicit time-stepping formulation of contact, we show that we can tractably tackle planning-through-contact problems in manipulation.
MACE: Model Agnostic Concept Extractor for Explaining Image Classification Networks

Deep convolutional networks have been quite successful at various image classification tasks. The current methods to explain the predictions of a pretrained model rely on gradient information, often resulting in saliency maps that focus on the foreground object as a whole. However, humans typically reason by dissecting an image and pointing out the presence of smaller concepts. The final output is often an aggregation of the presence or absence of these smaller concepts. In this work, we propose MACE: a model agnostic concept extractor, which can explain the working of a convolutional network through smaller concepts. The MACE framework dissects the feature maps generated by a convolution network for an image to extract concept-based prototypical explanations. Furthermore, it estimates the relevance of the extracted concepts to the pretrained model’s predictions, a critical aspect for explaining the individual class predictions, missing in existing approaches. We validate our framework using VGG16 and ResNet50 CNN architectures and datasets like Animals With Attributes 2 (AWA2) and Places365. Our experiments demonstrate that the concepts extracted by the MACE framework increase the human interpretability of the explanations and are faithful to the underlying pretrained black-box model.
Key Aspects Augmentation of Vulnerability Description based on Multiple Security Databases

Common Vulnerabilities and Exposures (CVE) is one of the most influential security databases. With the continuous disclosure of security vulnerabilities, the characteristics of them are documented as vulnerability reports, and it is discovered that their impact on computer systems is increasing. However, as our research continues to deepen, we find that the current lack of key aspects of CVE description is more serious than before. In response to this situation, our research focuses on how to correctly and completely extract key aspect descriptions from various security vulnerability databases to supplement the CVE reports. First, we fetch almost all of semi-structured vulnerability reports from the CVE, Security Focus, and IBM X-Force Exchange databases before November 2020. We then propose a customized NER (Named entity recognition) method based on deep neural networks to extract six key aspects from unstructured descriptions. Finally, we use the corresponding security vulnerability reports in other vulnerability databases to complete the missing key aspects in the correlated CVE description. We conduct sampling surveys on various aspects of this information, and verify the accuracy of extracting key aspects, and find that our method can extract key information from vulnerability descriptions. To demonstrate the usefulness of key aspects augmentation, after completing the missing affected product, root cause, attacker type, attack vector, impact, and vulnerability type in the CVE description, we verify the effectiveness of completing the key aspects of the vulnerability in predicting the severity of the security vulnerability.
Evaluating the impact of feature selection consistency in software prediction

Many empirical software engineering studies have employed feature selection algorithms to exclude the irrelevant and redundant features from the datasets with the aim to improve prediction accuracy achieved with machine learning-based estimation models as well as their generalizability. However, little has been done to investigate how consistently these feature selection algorithms produce features/metrics across different training samples, which is an important point for the interpretation of the trained models. The interpretation of the models largely depends on the features of the analyzed datasets, so it is recommended to evaluate the potential of various feature selection algorithms in terms of how consistently they extract features from the employed datasets. In this study, we consider eight different feature selection algorithms to evaluate how consistently they select features across different folds of k-fold cross-validation as well as when small changes are made in the training data. To provide a stable and generalized conclusion, we investigate data from two different domains, i.e., six datasets from the domain of Software Development Effort Estimation (SDEE) and six datasets from the Software Fault Prediction (SFP) domain. Our results reveal that a feature selection algorithm could produce 20-100% inconsistent features with an SDEE dataset and 18.8-95.3% inconsistent features in the case of an SFP dataset. The analysis also reveals that it is not necessarily true that the most consistent feature selection algorithm results to be the most accurate one (i.e., leads to better prediction accuracy) in the case of SDEE datasets, while with SFP datasets, the analysis highlights that the most consistent feature selection algorithm also results to be the most accurate in predicting faults.
Deep robust image deblurring via blur distilling and information comparison in latent space

Current deep deblurring methods pay main attention to learning a transferring network to transfer synthetic blurred images to clean ones. Though achieving significant performance on the training datasets, they still suffer from a weaker generalization capability from training datasets to others with different synthetic blurs, thus resulting in significantly inferior performance on testing datasets. In order to alleviate this problem, we propose a latent contrastive model, Blur Distilling and Information Reconstruction Networks (BDIRNet), to learn image prior and improve the robustness of deep deblurring. The proposed BDIRNet consists of a blur removing network (DistillNet) and a reconstruction network (RecNet). Two kinds of images with almost the same information but different qualities are input into DistillNet to extract identical structure information via contrast latent information and purify the perturbations from other unimportant information like blur. While the RecNet is utilized to reconstruct sharp images based on the extracted information. In addition, inside the DistillNet and RecNet, a statistical anti-interference distilling (SAID) and anti-interference reconstruction (SAIR) modules are proposed to further enhance the robustness of our methods, respectively. Extensive experiments on different datasets show that the proposed methods achieve improved and robust results compared to recent state-of-the-art methods.
Stable analysis of large-size signals and images by Racah’s discrete orthogonal moments

In this paper, a detailed theoretical and experimental study on some computational aspects of high order discrete orthogonal Racah polynomials (RPs) and their corresponding moments is carried out. Initially, the numerical overflow problem related to RPs computation is solved by using modified recurrence relations of RPs with respect to the polynomial order n and the variable s. Moreover, the recursive nature of the resulting relations considerably accelerates the computation of RPs. Then, the problem of numerical errors propagation that occurs during the recursive computation of RPs is solved. Indeed, the proposed solution relies on the use of a new numerical method that detects unstable values and sets them to zero. This ensures the numerical stability of high-order RPs. Next, a fast method is presented to significantly reduce the required time for reconstructing large-size 1D signal. This method involves the transformation of a 1D signal into a 2D array, then using matrix reconstruction formulas in the 2D domain. The simulation and comparison results clearly show that the proposed computation methods are very useful for the fast and stable analysis of large-size signals and 2D/3D images by Racah moments (RMs).
Geological and geochemical characteristics of hydrothermal alteration in the Wangu deposit in the central Jiangnan Orogenic Belt and implications for gold mineralization

Hydrothermal alteration commonly occurs in lode gold deposits, and contributes to metal precipitation through water–rock interactions. In the Wangu gold deposit in northeastern Hunan, South China, bleaching of slates enclosing auriferous quartz veins widespreadly occur, with the colour transformed from dark-grey into pale yellow-grey. The bleaching has been used to guide gold exploration, but its nature and genesis, as well as the relationship with gold mineralization, remains unclear. Field and petrographic work, combined with Tescan Integrated Mineral Analyzer (TIMA), demonstrates that the bleaching is mainly caused by the combination of sericitization and carbonatization. Combined with fluid inclusion studies, it is inferred that bleaching is caused by the pre-ore hydrothermal event characterized with CO2-bearing fluids. Large amounts of siderite are distributed within the bleaching zones, and they are locally crosscut by sulfides, suggesting that the bleaching happened before gold mineralization. Mass balance calculation and μ-XRF analysis indicate that despite the abundance of siderite, Fe is not gained during alteration. Geochemical path modeling further suggests that siderite is efficient in precipitating Au by decreasing S contents in fluids and destroying Au-bearing complexes. In addition, the siderite contents in altered slates and Au contents in sulfides suggest that the chemical interaction between siderite and ore fluids can produce ores with economic significance. The siderite developed during the pre-mineralization alteration, provided ideal chemical traps and prepared the ground for gold mineralization. When the gold-bearing fluids overprinted the altered rocks, sulfides formed through reaction with siderite. It is concluded that the sulfidation, which resulted in the destruction of Au-S complexes, is the major mechanism for gold mineralization within the bleached slates.
Polarization dehazing method based on spatial frequency division and fusion for a far-field and dense hazy image

Polarization dehazing technology is effective in imaging through scattering media because of additional information different from the light intensity and spectrum. However, the existing methods relying on the manual choice of bias factor are non-universal in different imaging conditions. In addition, these methods are not suitable for dense scenes with long distances. Aiming at the dehazing application requirements in far-field and dense hazy weather, a polarization dehazing method based on spatial frequency division and fusion (SFDF) is proposed in this paper. In addition, we optimize the interpolation process before dehazing so that the spatial resolution can be maintained without the noise influence. The experimental results indicate that the proposed method outperforms the existing schemes in dense hazy weather more than kilometer distances. Furthermore, we discuss that the effects of bias factors only act on the low-frequency parts of the polarization images, and their influence is greatly weakened after being fused with the high-frequency parts. This robust advantage without manual intervention causes the proposed SFDF method to have a broader prospect in practical application scenarios.