Reviewerly/exHarmony huggingface dataset:
A study on using deep autoencoders for imbalanced binary classification

Imbalanced classification represents a challenge for supervised learning, as an unequal distribution of classes in the training data set is mainly connected to poor predictive performance for the minority class. However, usually the minority class is the most relevant one, from a practical perspective. But due to the imbalancement of the training data, the classification errors for the minority class are higher, as the classifiers are usually biased to predict the majority class. In this paper we investigate the use of autoencoders for improving the predictive performance for imbalanced binary classification problems. As an application domain we consider breast cancer detection, that is an imbalanced classification problem of great interest in the medical domain. According to the World Health Organisation, breast cancer represents the primary cause of cancer mortality in women. Nowadays there is an increasing interest in applying conventional machine learning and more recently deep learning techniques in the breast cancer detection field by helping medical experts in the early detection of the disease. One of the paper’s goal is to investigate the ability of deep autoencoders to learn patterns within the classes of benign and malignant instances. Secondly, we propose and compare two autoencoders-based classification models for breast cancer detection. The performances of the proposed models were empirically assessed on data sets previously used in the breast cancer detection literature. The results show that our best model compares favourably with the results of most of the classifiers used for comparison and that it is able to handle well the data imbalancement.
SC-Political ResNet: Hashtag Recommendation from Tweets Using Hybrid Optimization-Based Deep Residual Network

Hashtags are considered important in various real-world applications, including tweet mining, query expansion, and sentiment analysis. Hence, recommending hashtags from tagged tweets has been considered significant by the research community. However, while many hashtag recommendation methods have been developed, finding the features from dictionary and thematic words has not yet been effectively achieved. Therefore, we developed an effective method to perform hashtag recommendations, using the proposed Sine Cosine Political Optimization-based Deep Residual Network (SC-Political ResNet) classifier. The developed SCPO is designed by integrating the Sine Cosine Algorithm (SCA) with the Political Optimizer (PO) algorithm. Employing the parametric features from both, optimization can enable the acquisition of the global best solution, by training the weights of classifier. The hybrid features acquired from the keyword set can effectively find the information of words associated with dictionary, thematic, and more relevant keywords. Extensive experiments are conducted on the Apple Twitter Sentiment and Twitter datasets. Our empirical results demonstrate that the proposed model can significantly outperform state-of-the-art methods in hashtag recommendation tasks.
Optimizing Probabilities in Probabilistic Logic Programs

Probabilistic Logic Programming is an effective formalism for encoding problems characterized by uncertainty. Some of these problems may require the optimization of probability values subject to constraints among probability distributions of random variables. Here, we introduce a new class of probabilistic logic programs, namely Probabilistic Optimizable Logic Programs, and we provide an effective algorithm to find the best assignment to probabilities of random variables, such that a set of constraints is satisfied and an objective function is optimized. This paper is under consideration for acceptance in Theory and Practice of Logic Programming.
r-Indexing the eBWT

The extended Burrows Wheeler Transform (\(\mathrm {eBWT}\)) was introduced by Mantaci et al. [TCS 2007] to extend the definition of the \(\mathrm {BWT}\) to a collection of strings. In our prior work [SPIRE 2021], we give a linear-time algorithm for the \(\mathrm {eBWT}\) that preserves the fundamental property of the original definition (i.e., the independence from the input order). The algorithm combines a modification of the Suffix Array Induced Sorting (SAIS) algorithm [IEEE Trans Comput 2011] with Prefix Free Parsing [AMB 2019; JCB 2020]. In this paper, we show how this construction algorithm leads to r-indexing the \(\mathrm {eBWT}\), i.e., run-length encoded \(\mathrm {eBWT}\) and \(\mathrm {SA}\) samples of Gagie et al. [SODA 2018] can be constructed efficiently from the components of the PFP. Moreover, we show that finding maximal exact matches (MEMs) between a query string and the r-index of the \(\mathrm {eBWT}\) can be efficiently supported.
Automatic Image Annotation by Sequentially Learning From Multi-Level Semantic Neighborhoods

Automatic image annotation is a key technology in image understanding and pattern recognition, and is becoming increasingly important in order to annotate large-scale images. In the past decade, the nearest neighbor model-based AIA methods have been proved to be the most successful in all classical models. This model has four major challenges including semantic gap, label-imbalance, wider range labels, and weak-labeling. In this paper, we propose a novel annotation model based on three-pass KNN (k-Nearest Neighbor) to address the aforementioned challenges. The key idea is to identify appropriate neighbors at each pass KNN. In the first pass KNN, we identify the several most relevant categories based on label feature rather than visual feature as traditional models. In the second pass KNN, we determine the relevant images based on multi-modal (visual and textual label) embedding features. As the test image has not been annotated with any label, we propose a pre-annotation strategory before image annotation to improve the semantic level. In the third pass KNN, we capture relevant labels from semantically and visually similar images and propagate them to the given unlabeled image. In contrast with traditional nearest neighbor based methods, our method can inherently alleviate the problems of semantic gap, label-imbalance, and wider range labels. In addition, to alleviate the issue of weak-labeling, we propose label refinement for training images. Extensive experiments on three classical benchmark datasets demonstrate that the proposed method significantly outperforms the state-of-the-art in terms of per-label and per-image metrics.
Comparison of word embeddings in text classification based on RNN and CNN

Abstract This paper presents a comparison of word embeddings in text classification using RNN and CNN. In the field of image classification, deep learning methods like as RNN and CNN have shown to be popular. CNN is most popular model among deep learning techniques in the field of NLP because of its simplicity and parallelism, even if the dataset is huge. Word embedding techniques employed are GloVe and fastText. Use of different word embeddings showed a major difference in the accuracy of the models. When it comes to embedding of rare words, GloVe can sometime perform poorly. Inorder to tackle this issue, fastText method is used. Deep neural networks with fastText showed a remarkable improvement in the accuracy than GloVe. But fastText took some time to train when compared to GloVe. Further, the accuracy was improved by minimizing the batch size. Finally we concluded that the word embeddings have a huge impact on the performance of text classification models
Exploring More Capacity for Grayscale-Invariance Reversible Data Hiding

Grayscale-invariant reversible data hiding (RDH) in color images was developed recently in which the graysclaes of the marked color image are required to be the same as that of the host color image.The grayscale-invariant RDH technique is very useful for many applications on color images in which the image processing algorithms are designed based on the gray version of the input color image.Some state-of-the-art grayscale-invariant RDH schemes were proposed recently.However their performance on embedding capacity and distortion is not satisfactory.For these existing works, the main bottleneck is the large number of correction bits generated for losslessly recovering the host green scales.In this work, we propose a novel grayscale-invariant RDH method which is able to reduce the need for correction bits.Our main contribution is to design a self-correcting mechanism which can modify the green scales for keeping grayscales unchanged while remembering the information of the host green scales.In order to obtain this task, we first observe that all previous methods generated correction bits in a functional approach which mainly results in a lot of correction bits.Instead of the functional approach, we use a relational approach to preserve the grayscales when embedding message bits.By this relational approach, the proposed algorithm only has to generate a small number of correction bits for losslessly recovering green scales.Moreover, this relational approach allows the proposed algorithm to embed more message bits into some selected green scales.The experimental results show that the proposed method significantly improves the embedding capacity and the rate-distortion performance compared to previous works.
NudgeSeg: Zero-Shot Object Segmentation by Repeated Physical Interaction

Recent advances in object segmentation have demonstrated that deep neural networks excel at object segmentation for specific classes in color and depth images. However, their performance is dictated by the number of classes and objects used for training, thereby hindering generalization to never seen objects or zero-shot samples. To exacerbate the problem further, object segmentation using image frames rely on recognition and pattern matching cues. Instead, we utilize the ‘active’ nature of a robot and their ability to ‘interact’ with the environment to induce additional geometric constraints for segmenting zero-shot samples.In this paper, we present the first framework to segment unknown objects in a cluttered scene by repeatedly ‘nudging’ at the objects and moving them to obtain additional motion cues at every step using only a monochrome monocular camera. We call our framework NudgeSeg. These motion cues are used to refine the segmentation masks. We successfully test our approach to segment novel objects in various cluttered scenes and provide an extensive study with image and motion segmentation methods. We show an impressive average detection rate of over 86% on zero-shot objects.
Silhouette Vectorization by Affine Scale-Space

Silhouettes are building elements of logos, graphic symbols and fonts. These shapes can be designed and exchanged in vector form, but more often they are drawn, printed, scanned, or directly found in digital images. Such raster forms require vectorization to get scale-invariant exchangeable formats. There is a need for a mathematically well-defined and justified shape vectorization process, which also provides a minimal set of control points with geometric meaning. In this paper, we propose a new silhouette vectorization paradigm. It extracts the outline of a 2D shape from a raster binary image and converts it to a combination of cubic Bézier polygons and perfect circles. The proposed method uses the sub-pixel curvature extrema and affine scale-space for silhouette vectorization. By construction, our control points are geometrically stable under affine transformations. The proposed method can also be used as a reliable feature point detector for silhouettes. Compared to state-of-the-art image vectorization software, our algorithm demonstrates a superior reduction in the number of control points while maintaining high accuracy.
An Efficient AES 32-Bit Architecture Resistant to Fault Attacks

The Advanced Encryption Standard cryptographic algorithm, named AES, is implemented in cryptographic circuits to ensure high security level to any system which required confidentiality and secure information exchange.One of the most effective physical attacks against the hardware implementation of AES is fault attacks which can extract secret data.Until now, a several AES fault detection schemes against fault injection attacks have been proposed.In this paper, so as to ensure a high level of security against fault injection attacks, a new efficient fault detection scheme based on the AES architecture modification has been proposed.For this reason, the AES 32-bit round is divided into two half rounds and input and pipeline registers are implemented between them.The proposed scheme is independent of the procedure the AES is implemented.Thus, it can be implemented to secure the pipeline and iterative architectures.To evaluate the robustness of the proposed fault detection scheme against fault injection attacks, we conduct a transient and permanent fault attacks and then we determine the fault detection capability; it is about 99.88585% and 99.9069% for transient and permanent faults respectively.We have modeled the AES fault detection scheme using VHDL hardware language and through hardware FPGA implementation.The FPGA results demonstrate that our scheme can efficiently protect the AES hardware implementation against fault attacks.It can be simply implemented with low complexity.In addition, the FPGA implementation performances prove the low area overhead and the high efficiency and working frequency for the proposed AES detection scheme.
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We consider the algorithmic problem of computing a primitive idempotent of a central simple algebra over the field of rational functions over a finite field. The algebra is given by a set of structure constants. The problem is reduced to the computation of a division algebra Brauer equivalent to the central simple algebra. This division algebra is constructed as a cyclic algebra, once the Hasse invariants have been computed. We give an application to skew constacyclic convolutional codes.
Quantum deep transfer learning

Quantum machine learning (QML) has aroused great interest because it has the potential to speed up the established classical machine learning processes. However, the present QML models can merely be trained on the dataset of single domain of interest. This severely limits the application of the QML to the scenario where only small datasets are available. In this work, we have proposed a QML model that allows the transfer of the knowledge from one domain encoded by quantum states to another, which is called quantum transfer learning. Using such a model, we demonstrate that the classification accuracy can be greatly improved for the training process on small datasets, comparing with the results obtained by former QML algorithm. Last but not least, we have proved that the complexity of our algorithm is basically logarithmic, which can be considered an exponential speedup over the related classical algorithms.
Adversarially Trained Object Detector for Unsupervised Domain Adaptation

Unsupervised domain adaptation, which involves transferring knowledge from a label-rich source domain to an unlabeled target domain, can be used to substantially reduce annotation costs in the field of object detection. In this study, we demonstrate that adversarial training in the source domain can be employed as a new approach for unsupervised domain adaptation. Specifically, we establish that adversarially trained detectors achieve improved detection performance in target domains that are significantly shifted from source domains. This phenomenon is attributed to the fact that adversarially trained detectors can be used to extract robust features that are in alignment with human perception and worth transferring across domains while discarding domain-specific non-robust features. In addition, we propose a method that combines adversarial training and feature alignment to ensure the improved alignment of robust features with the target domain. We conduct experiments on four benchmark datasets and confirm the effectiveness of our proposed approach on large domain shifts from real to artistic images. Compared to the baseline models, the adversarially trained detectors improve the mean average precision by up to 7.7%, and further by up to 11.8% when feature alignments are incorporated. Although our method degrades performance for small domain shifts, quantification of the domain shift based on the Frechet distance allows us to determine whether adversarial training should be conducted.
Calibration of a camera-array-based microscopic system with spatiotemporal structured light encoding

The strong need in life science and medicine research has prompted the development of wide-field, high-resolution and real-time microscopic imaging based on camera array. For large-scale sub-image stitching on the order of billion pixels, it is highly challenging to calibrate the camera array with low-overlapping fields of view (FOVs). To address the challenge, we propose a camera array calibration method to obtain the complete camera model and global mapping coordinates. The method utilizes spatiotemporal structured light encoding provided by a spatial light modulator to establish the correspondence between the physical coordinates of the keypoints in the object plane and their pixel coordinates in the sub-image plane. This achieves fast and accurate calibration of a camera-array-based microscopic system without the requirement of sub-FOV overlap. A series of experiments, including reprojection evaluation and global image stitching on the resolution chart and biological specimen slice, verified the effectiveness of the proposed approach. The method supports calibration of global geometric position with 0.2-pixel accuracy and has a broad potential for application to general camera array-based imaging systems.
STEREO VISION INFORMATION SYSTEM USING MEDIAN THEOREM AND ATTITUDE COMPENSATION WITH NONLINEAR DIFFERENTIAL EQUATIONS

Using computer vision technology to obtain and analyze biomechanical information is an important research direction in recent years. However, the linear model in the computer vision system cannot accurately describe the geometric relationship of the camera imaging, so it is difficult to realize human posture recognition in high-precision mechanics information. Therefore, how to improve the recognition accuracy is very important. In this paper, we apply nonlinear differential equations to stereo computer vision (SCV) information systems. And based on the median theorem, a nonlinear posture recognition and error compensation algorithm based on BP neural network is proposed to reduce the recognition error. The test set uses the Leeds Motion Pose (LSP) dataset to verify the performance of the algorithm. Experimental results show that the compensated median filter of BP neural network can eliminate glitches in attitude data. Superimposing the output attitude error compensation value with the attitude estimation value can greatly reduce the root-mean-square error of the attitude angle. The result of gesture recognition is closer to reality. Compared with traditional algorithms, the cyclomatic complexity of the proposed BP neural network algorithm has a much lower growth rate in high-order calculations, which indicates that the proposed BP neural network algorithm is more concise and scalable.
Real-Time Audio-Guided Multi-Face Reenactment

Audio-guided face reenactment aims to generate authentic target faces that have matched facial expression of the input audio, and many learning-based methods have successfully achieved this. However, most methods can only reenact a particular person once trained or suffer from the low-quality generation of the target images. Also, nearly none of the current reenactment works consider the model size and running speed that are important for practical use. To solve the above challenges, we propose an efficient <i>A</i>udio-guided <i>M</i>ulti-face reenactment model named <i>AMNet</i>, which can reenact target faces among multiple persons with corresponding source faces and drive signals as inputs. Concretely, we design a <i>Geometric Controller</i> (GC) module to inject the drive signals so that the model can be optimized in an end-to-end manner and generate more authentic images. Also, we adopt a lightweight network for our face reenactor so that the model can run in real-time on both CPU and GPU devices. Abundant experiments prove our approach&#x2019;s superiority over existing methods, <i>e.g</i>., averagely decreasing FID by 0.12<inline-formula><tex-math notation="LaTeX">$\downarrow$</tex-math></inline-formula> and increasing SSIM by 0.031<inline-formula><tex-math notation="LaTeX">$\uparrow$</tex-math></inline-formula> than APB2Face, while owning fewer parameters (<inline-formula><tex-math notation="LaTeX">$\times 4 \downarrow$</tex-math></inline-formula>) and faster CPU speed (<inline-formula><tex-math notation="LaTeX">$\times 4 \uparrow$</tex-math></inline-formula>).
Helmet Detection using Single Shot Detector (SSD)

The higher death risk in motorcycle crashes and construction worker accidents can be due to riders' and employee's inability to wear helmets. To avoid such collisions, it is important to always identify people who are not wearing helmets, and also this model could be of a great help for the motor vehicle department. This paper describes an electronic method for increasingly and simultaneously separating motorbike riders without a helmet from traffic management videos and employees without helmets. The helmet detection problem in this article is solved using a single shot detector model. This model will assess the bounding box region of the motorcycle and the rider with just one unit. After the area has been picked, the proposed model will classify whether the biker is wearing or not wearing a helmet in real-time. The Convolutional Neural Network is used to recognize motorcycles without helmets and to distinguish motorcycles among moving targets.
Research on Optimization of Blockchain Network and Data Communication in the Ecological Structure of Sports Industry

Problems such as excessive centralization of sports industry operations, unclear value, and lack of IP protection hinder the benign interaction between various formats of the sports industry and the healthy operation of the industrial ecosystem. This article is aimed at studying the use of the decentralization, openness and transparency, smart contracts, copyright traceability, and other characteristics of the BT to construct a PCMR model, which can solve the pain points faced by the sports industry ecosystem in a targeted manner, and forming a way to reconstruct trust, the specific implementation path of the integration of BT and sports industry with mechanism as the core. This paper proposes blockchain network and data communication algorithms such as consensus algorithm, signature core algorithm, and practical Byzantine fault-tolerant algorithm to provide technical support for the application of blockchain technology in the sports industry ecosystem and, at the same time, provide technical support for blockchain network and data communication. The feasibility analysis of the application of the sports industry ecosystem is proposed, and the implementation path of the blockchain network and data communication to reconstruct the sports industry ecosystem is analyzed. Finally, the empirical test of the support of blockchain sports to the sports industry is carried out. By analyzing the panel data of 240 observations of 48 listed companies in 5 years as a sample of the sports industry, the experimental results show that the dominant modulus of the sample does not have other differences, and the leftovers all pass significance test at the 10% level at least, denoting that the fundamental sample is more rational.