Reviewerly/exHarmony huggingface dataset:
Abnormal Data Detection of Unmanned Aerial Vehicles Based on Double Shortcuts ZB-ResNet

Unmanned aerial vehicles (UAVs) are unmanned aircrafts operated by radio remote control and programmed control equipment. Due to their small size, low cost, and high flexibility, UAV s are widely used in military and civilian fields. Along with the broad applications of UAVs and the significant advancement of information technologies, they also face cyber threats. Among them, false spoofing is a typical cyber-attack. If this attack hits UAV s, it could result in damage to property and the release of private data or classified documents. In this paper, we propose the Double Shortcuts Zero-Bias Residual Network (Double Shortcuts ZB-ResNet) with small storage capacity and low time complexity for abnormal data detection of UAVs. It is designed by combining double shortcuts residual blocks and Zero-Bias (ZB) fully connected layer to anomaly detections. By comparing with the experimental results of the improved Convolutional Neural Networks with ZB layer, the detection accuracy of the Double Shortcuts ZB-ResNet model is improved by nearly two percentage points.
Application of 2-gram to Obtain Factor Scores of Statements Posted at Q&amp;A Sites

In order to solve the issues of mismatches between the intentions of questioners and respondents of Question and Answer (Q&A) sites, nine factors of impressions for Q&A statements have been obtained through impressive experiments. Factor scores have been then estimated through multiple regression analysis by using the feature values of statements, e.g. syntactic information, etc. The factor scores obtained have been subsequently used for inspecting the possibility of detecting respondents who are expected to post appropriate answer to a newly posted question. However, the method so far has been largely dependent on the syntactic information extracted through morphological analysis. In addition, the number of explanatory variables used for estimating factor scores has been considerably numerous and complicated. Therefore, in this paper, factor scores are estimated using the feature values of syntactic information are extracted through 2-gram, instead of morphological analysis. The feature values based on 2-gram and those other than the syntactic information are set as explanatory variable. As a result of another multiple regression analysis, estimation accuracy with 2-gram shows almost equal to or better than that with morphological analysis for all the nine factors. Moreover, monadic term is sufficient in estimating factor scores, leading to much smaller samples of explanatory variables.
GroupNet: Learning to group corner for object detection in remote sensing imagery

Due to the attractive potential in avoiding the elaborate definition of anchor attributes, anchor-free-based deep learning approaches are promising for object detection in remote sensing imagery. CornerNet is one of the most representative methods in anchor-free-based deep learning approaches. However, it can be observed distinctly from the visual inspection that the CornerNet is limited in grouping keypoints, which significantly impacts the detection performance. To address the above problem, a novel and effective approach, called GroupNet, is presented in this paper, which adaptively groups corner specific to the objects based on corner embedding vector and corner grouping network. Compared with the CornerNet, the proposed approach is more effective in learning the semantic relationship between corners and improving remarkably the detection performance. On NWPU dataset, experiments demonstrate that our GroupNet not only outperforms the CornerNet with an AP of 12.8%, but also achieves comparable performance to considerable approaches with 83.4% AP.
Measurement-based time evolution for quantum simulation of fermionic systems

Quantum simulation using time evolution in phase estimation-based quantum algorithms can yield unbiased solutions of classically intractable models. However, long runtimes open such algorithms to decoherence. We show how measurement-based quantum simulation uses effective time evolution via measurement to allow runtime advantages over conventional circuit-based algorithms that use real-time evolution with quantum gates. We construct a hybrid algorithm to find energy eigenvalues in fermionic models using only measurements on graph states. We apply the algorithm to the Kitaev and Hubbard chains. Resource estimates show a runtime advantage if measurements can be performed faster than gates, and graph states compactification is fully used. In this letter, we set the stage to allow advances in measurement precision to improve quantum simulation.
In-Browser Cryptomining for Good: An Untold Story

In-browser cryptomining uses the computational power of a website's visitors to mine cryptocurrency, i.e., to create new coins. With the rise of ready-to-use mining scripts distributed by service providers (e.g., Coinhive), it has become trivial to turn a website into a cryptominer by copying and pasting the mining script. Both legitimate webpage owners who want to raise an extra revenue under users' explicit consent and malicious actors who wish to exploit the computational power of the users' computers without their consent have started to utilize this emerging paradigm of cryptocurrency operations. In-browser cryptomining, though mostly abused by malicious actors in practice, is indeed a promising funding model that can be utilized by website owners, publishers, or non-profit organizations for legitimate business purposes, such as to collect revenue or donations for humanitarian projects, inter alia. However, our analysis in this paper shows that in practice, regardless of their being legitimate or not, all in-browser mining scripts are treated the same as malicious cryptomining samples (aka cryptojacking) and blacklisted by browser extensions or antivirus programs. Indeed, there is a need for a better understanding of the in-browser cryptomining ecosystem. Hence, in this paper, we present an in-depth empirical analysis of in-browser cryptomining processes, focusing on the samples explicitly asking for user consent, which we call permissioned cryptomining. To the best of our knowledge, this is the first study focusing on the permissioned cryptomining samples. For this, we created a dataset of 6269 unique web sites containing cryptomining scripts in their source codes to characterize the in-browser cryptomining ecosystem by differentiating permissioned and permissionless cryptomining samples. We believe that (1) this paper is the first attempt showing that permissioned in-browser cryptomining could be a legitimate and viable monetization tool if implemented responsibly and without interrupting the user, and (2) this paper will catalyze the widespread adoption of legitimate crvptominina with user consent and awareness.
A LogTVSCAD Nonconvex Regularization Model for Image Deblurring in the Presence of Impulse Noise

This paper proposes a nonconvex model (called LogTVSCAD) for deblurring images with impulsive noises, using the log-function penalty as the regularizer and adopting the smoothly clipped absolute deviation (SCAD) function as the data-fitting term. The proposed nonconvex model can effectively overcome the poor performance of the classical TVL1 model for high-level impulsive noise. A difference of convex functions algorithm (DCA) is proposed to solve the nonconvex model. For the model subproblem, we consider the alternating direction method of multipliers (ADMM) algorithm to solve it. The global convergence is discussed based on Kurdyka–Lojasiewicz. Experimental results show the advantages of the proposed nonconvex model over existing models.
Self-supervised Fine-tuning for Efficient Passage Re-ranking

Passage retrievers based on neural language models have recently achieved significant performance improvements in ranking tasks. Such ranking models have the advantage of finding the contextual features of queries and documents better than traditional keyword based methods. However, these deep learning-based models are limited by the large amounts of training data required. We propose a new fine-tuning method based on a masked language model (MLM) that is typically used in pre-trained language models. Our model improves the ranking performance using the MLM while efficiently utilizing less training data via data augmentation. The proposed approach applies self-supervised learning to information retrieval without needing additional expensive labeled data. In addition, because masking important terms during the fine-tuning stage can undermine ranking performance, the importance values of each term and sentence in a passage are calculated using the BM25 scheme and applied to the fine-tuning task such that the more important terms are masked less often. Our model is trained with dataset from MS MARCO re-ranking leaderboard and achieves the state-of-the-art [email protected] performance in the leaderboard except for the ensemble-based method.
Solution Approaches for Breast Cancer Classification Through Medical Imaging Modalities Using Artificial Intelligence

Breast cancer (BrC) is amongst the biggest causes of death for women in today’s world. Manual identification of BrC cells is a tedious assignment that requires human blunder and, as a result, computer-aided detection (CAD) assisted components are used to achieve higher outcomes as contrasted and manual obsessive discovery frameworks. The breast-image classification mission advanced engineering of natural image classification techniques and methods of artificial intelligence have been used to a large extent. Advanced histopathology image examination is a promising way to deal with accurate and cost-effective cancer disease determination. The consideration of computerized picture arrangement gives a subsequent assessment and saving time for the specialist and the doctors. There are not many survey papers accessible that incorporate a complete outline of BrC image classification methods, selection strategies, feature extraction, classification estimating rules and results of image classification, despite the numerous publications on breast-image classification. The goal of this review is to evaluate, identify and resolve current developments in the detection of human malignancy using AI, machine learning (ML) and deep learning (DL) methods for BrC. The study illustrates how disease analysis as well as remedial action is supported by the use of ML and DL techniques. To improve analytical accuracy and reduce emotional error, the CAD system assists pathologists in classifying medical images into normal, benign and malignant cells.
MOOPer: A Large-Scale Dataset of Practice-Oriented Online Learning

With the booming of online education, abundant data are collected to record the learning process, which facilitates the development of related areas. However, the publicly available datasets in this setting are mainly designed for a single specific task, hindering the joint research from different perspectives. Moreover, most of them collect the video-watching or course-enrollment log data, lacking of explicit user feedbacks of knowledge mastery. Therefore, we present MOOPer, a practice-centered dataset, focusing on the problem-solving process in online learning scenarios, with abundant side information organized as knowledge graph. Flexible data parts make it versatile in supporting various tasks, e.g., learning materials recommendation, dropout prediction and so on. Lastly, we take knowledge tracing task as an example to demonstrate the possible use of MOOPer. Since MOOPer supports multiple tasks, we further explore the advantage of combining tasks from different areas, namely, Deep Knowledge Tracing and Knowledge Graph Embedding. Results show that the fusion model improves the performance by over 9.5%, which proves the potential of MOOPer's versatility. The dataset is now available at https://www.educoder.net/ch/rest.
Post-training deep neural network pruning via layer-wise calibration

We present a post-training weight pruning method for deep neural networks that achieves accuracy levels tolerable for the production setting and that is sufficiently fast to be run on commodity hardware such as desktop CPUs or edge devices. We propose a data-free extension of the approach for computer vision models based on automatically-generated synthetic fractal images. We obtain state-of-the-art results for data-free neural network pruning, with ~1.5% top@1 accuracy drop for a ResNet50 on ImageNet at 50% sparsity rate. When using real data, we are able to get a ResNet50 model on ImageNet with 65% sparsity rate in 8-bit precision in a post-training setting with a ~1% top@1 accuracy drop. We release the code as a part of the OpenVINO <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> Post-Training Optimization tool <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
A Quantum Algorithm for Evaluating the Hamming Distance

We present a novel quantum algorithm to evaluate the hamming distance between two unknown oracles via measuring the degree of entanglement between two ancillary qubits. In particular, we use the power of the entanglement degree based quantum computing model that preserves at most the locality of interactions within the quantum model structure. This model uses one of two techniques to retrieve the solution of a quantum computing problem at hand. In the first technique, the solution of the problem is obtained based on whether there is an entanglement between the two ancillary qubits or not. In the second, the solution of the quantum computing problem is obtained as a function in the concurrence value, and the number of states that can be generated from the Boolean variables. The proposed algorithm receives two oracles, each oracle represents an unknown Boolean function, then it measures the hamming distance between these two oracles. The hamming distance is evaluated based on the second technique. It is shown that the proposed algorithm provides exponential speedup compared with the classical counterpart for Boolean functions that have large numbers of Boolean variables. The proposed algorithm is explained via a case study. Finally, employing recently developed experimental techniques, the proposed algorithm has been verified using IBM's quantum computer simulator.
Investigating the spatial distribution of antimony geochemical anomalies located in the Yunnan-Guizhou-Guangxi region, China

The Yunnan-Guizhou-Guangxi region is well known for its abundant mineral resources, and low-temperature hydrothermal mineralization represented by the elemental association of gold, arsenic, antimony and mercury is widely developed there. Many studies on the geological-geochemical characteristics of gold have been conducted, but a comprehensive understanding of the antimony geochemical pattern is still lacking. This paper studied the Sb distribution characteristics and the cause of geochemical anomalies based on the geochemical data of stream sediments and rocks in the study area. In addition, the geochemical data of Au, As, Sb and Hg were centered and log-ratio transformed to eliminate the closure effect, and then random forest regression (RFR) with Au, As and Hg as the characteristic variables was used to investigate the ore-related geochemical anomalies of Sb. Seven geochemical provinces were delineated from the original geochemical data, and they are not entirely consistent with the known deposits. Sb moves from the rocks to the stream sediments during weathering. The variation trend in the Sb background values in stream sediments in each tectonic unit is consistent with that in the rocks themselves, implying that Sb in the stream sediments is inherited from the background rocks. The distributions of Sb predicted by RFR are similar to the distribution pattern of Sb in stream sediments. Of the three elements considered, the influence of As on the variations in the Sb geochemical background is the greatest, followed by Au and then Hg. The geochemical anomalies extracted by the residuals produced in this algorithm are consistent with where the known Sb metallogenic district is located, indicating that this method of recognizing geochemical anomalies is feasible and effective and has theoretical and practical significance.
AutoMA: Towards Automatic Model Augmentation for Transferable Adversarial Attacks

Recent adversarial attack works attempt to improve the transferability by applying various differentiable transformations on input images. Considering the differentiable transformations and the original model together as a new model, these methods can be regarded as model augmentation that effectively derives an ensemble of models from the single original model. Despite their impressive performance, the model augmentation policies used in these methods are manually designed by experimental attempts, leaving the design of model augmentation policy an open question. In this paper, we propose an Automatic Model Augmentation (AutoMA) approach to find a strong model augmentation policy for transferable adversarial attacks. Specifically, we design a discrete search space that contains various diffierentiable transformations with different parameters and adopt reinforcement learning to search for the strong augmentation policy. The sampled augmentation policies together with the rewards they obtain during the searching process reveal several valuable observations for designing more powerful attacks using model augmentation policy: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1) Augmentation transformations on color space are less effective; 2) The transformation type diversity matters; and 3) Using small distortion for geometric transformations while larger distortion for intensity transformations.</i> Extensive experiments show that the augmentation policy found by AutoMA achieves superior performance than existing manually designed policies in a wide range of cases.
Secure storage and matching of latent fingerprints using phase shifting digital holography

This paper describes the secure storage and matching of latent fingerprints using phase shifting digital holography. A phase shifting digital holographic system is proposed for imaging of latent fingerprints, which can be applied in the criminal investigation field. Digital holography is one of the accurate and fast technique for measuring 3D information, by acquiring both amplitude and phase information from the object. Digital holography uses a Charge-Coupled Device (CCD) to record the interference pattern. In digital holography, the reconstructed image unavoidably contains the conjugate image and the zeroth order beam, resulting in low resolution. This problem can be overcomed by using phase shifting digital holography. Phase shifting digital holography is a technique by which multiple holograms are taken corresponding to the various phase differences between the object beam and the reference beam. One of the most secure storage methods is digital holographic data storage which provides a credible way in the fingerprint acquisition in the criminal investigation field. The phase shifting digital holograms of the latent fingerprint are acquired and then numerically reconstructed. The reconstructed fingerprint is then compared with the database after further processing to find whether it is a match or no match. The investigations have been conducted through experiments and computer simulations. The parameters used for analyzing the performance of the system are False Matching Rate (FMR), False Non-Matching Rate (FNMR) etc. This system shows a remarkable performance improvement over digital holographic techniques. The method can be explicitly used in forensic applications.
Corpus-level and Concept-based Explanations for Interpretable Document Classification

Using attention weights to identify information that is important for models’ decision making is a popular approach to interpret attention-based neural networks. This is commonly realized in practice through the generation of a heat-map for every single document based on attention weights. However, this interpretation method is fragile and it is easy to find contradictory examples. In this article, we propose a corpus-level explanation approach, which aims at capturing causal relationships between keywords and model predictions via learning the importance of keywords for predicted labels across a training corpus based on attention weights. Based on this idea, we further propose a concept-based explanation method that can automatically learn higher level concepts and their importance to model prediction tasks. Our concept-based explanation method is built upon a novel Abstraction-Aggregation Network (AAN), which can automatically cluster important keywords during an end-to-end training process. We apply these methods to the document classification task and show that they are powerful in extracting semantically meaningful keywords and concepts. Our consistency analysis results based on an attention-based Naïve Bayes classifier (NBC) also demonstrate that these keywords and concepts are important for model predictions.
Deep Learning for Bias Detection: From Inception to Deployment

To create a more inclusive workplace, enterprises are actively investing in identifying and eliminating unconscious bias (e.g., gender, race, age, disability, elitism and religion) across their various functions. We propose a deep learning model with a transfer learning based language model to learn from manually tagged documents for automatically identifying bias in enterprise content. We first pretrain a deep learning-based language-model using Wikipedia, then fine tune the model with a large unlabelled data set related with various types of enterprise content. Finally, a linear layer followed by softmax layer is added at the end of the language model and the model is trained on a labelled bias dataset consisting of enterprise content. The trained model is thoroughly evaluated on independent datasets to ensure a general application. We present the proposed method and its deployment detail in a real-world application.
Lexically Aware Semi-Supervised Learning for OCR Post-Correction

Abstract Much of the existing linguistic data in many languages of the world is locked away in non- digitized books and documents. Optical character recognition (OCR) can be used to produce digitized text, and previous work has demonstrated the utility of neural post-correction methods that improve the results of general- purpose OCR systems on recognition of less- well-resourced languages. However, these methods rely on manually curated post- correction data, which are relatively scarce compared to the non-annotated raw images that need to be digitized. In this paper, we present a semi-supervised learning method that makes it possible to utilize these raw images to improve performance, specifically through the use of self-training, a technique where a model is iteratively trained on its own outputs. In addition, to enforce consistency in the recognized vocabulary, we introduce a lexically aware decoding method that augments the neural post-correction model with a count-based language model constructed from the recognized texts, implemented using weighted finite-state automata (WFSA) for efficient and effective decoding. Results on four endangered languages demonstrate the utility of the proposed method, with relative error reductions of 15%–29%, where we find the combination of self-training and lexically aware decoding essential for achieving consistent improvements.1