Reviewerly/exHarmony huggingface dataset:
Deep Shared and Specific Fusion Induced Multi-view Subspace Clustering

Exploring shared and specific structural information effectively improves the performance of multi-view clustering, which has been proved by existing methods based on original data or shallow embedding. However, these methods fail to capture the latent shared and specific structure information in the deep embedding of multi-view data. This paper proposes a novel Deep Shared and Specific Fusion induced multi-view subspace clustering method (DSSF) to address this problem. A new fusion layer is designed in DSSF, which consists of two co-learning parts: a view-shared subspace for learning the latent shared structure information, and a view-specific subspace that captures the latent specific structure information of each view. Meanwhile, our method utilizes consistency and diversity regularization terms jointly to enhance multi-view clustering performance. The former allows the structure information of deep embedding to be maintained in the view-shared subspace, while the latter maximizes the difference between the view-specific subspace of each view to enhance complementarily Experimental results on several real-world datasets demonstrate the effectiveness of our method, where the ablation study shows that the joint regularization term significantly improves the clustering performance.
Exploiting Topic Information for Joint Intent Detection and Slot Filling

Intent detection and slot filling are two important basic tasks in natural language understanding. Actually, there are multiple intents in an utterance. How to map different intents to corresponding slot becomes a new challenge for recent research. Existing models solve this problem by using neural layers to adaptively capture related intent information for each slot, while the correlation between the extracted intent information and slot is unable to verified. It is observed that there is strong consistency between intents and topics of a sentence, thus we exploit topic information for joint intent detection and slot filling via a topic fusion mechanism, where token-level topic information enriches the representation of each token for slot prediction. In addition, sentence-level topic information is also utilized to enhance the intent detection. Experiment results show explicit improvements on two public datasets, where provide 4.8% improvement in sentence accuracy on MixATIS and 0.7% improvement in intent detection on MixSNIPS.
Efficient Feature Selection based on Soft Cluster Analysis for Biological Datasets

The goal of this paper is to propose a new feature selection model which enhances the discrimination power of the selected feature subsets and minimizes their size of them. The proposed algorithm is capable of exploring the essential factors of classification problems for molecular datasets comprising a tremendous amount of input variables. Our model devotes to two accomplishments of multi-class classification tasks. Feature discretization using fuzzy clustering analysis for the improvement of feature discrimination is the first. Multivariate analysis for the investigation of information relevance and redundancy is the second achievement in this study. Experimental results convince our model acquires significant discrimination improvement for microarray classification problems.
Single Image Reflection Removal From Glass Surfaces via Multi-Scale Reflection Detection

While people take photos through glass surfaces with consumer imaging equipments, such as smartphones or digital cameras, reflections on the glass surfaces of captured images cannot be avoided. Reflections not only affect the aesthetic perception of captured images but also degrade the performance of subsequent tasks. Since the prominence of reflections depends on the contrast between the foreground reflection layer and the background transmission layer, reflections on different regions of the same glass surface may have extremely different sizes and brightness contrasts. In this paper, we propose a reflection removal network, named MRDNet, with a novel multi-scale reflection detector for foreground reflection detection and a novel Multi-scale Reflection-aware Transmission Recovery (MRTR) module for background transmission layer restoration. Our MRDNet has three main modules. First, two parallel feature extraction modules, called Reflection Extractor (R-Extractor) and Transmission Extractor (T-Extractor), are introduced to extract features of the foreground reflection layer and features of the background transmission layer contaminated by reflections, from the input image, respectively. Then, a Multi-scale Reflection-aware Transmission Recovery (MRTR) module is proposed to recover textural details of the background transmission layer. For learning our network, we also propose a large scale 3D virtual-world Physical Rendering-based Reflection Removal (PRGR) dataset.
Evaluate Solutions for Achieving High Availability or Near Zero Downtime for Cloud Native Enterprise Applications

In today’s digital world, businesses heavily rely on systems for every aspect of their operations and product life cycle. Hence, it is important to have a strong ecosystem of applications to achieve operational efficiency and positive customer experience. High availability of mission and business critical applications is a necessity as any downtime or poor performance can have a negative impact on the revenue and operations of the organization. Applications transitioning to cloud are adopting modular design and distributed system architecture. As a result, the system complexity and the number of failure points have increased. One of the promises of cloud platforms is high availability by building redundancy in the application architecture. However, enterprises opting for cloud often struggle to define the right framework for high availability. In addition, even after redundancy is built at the application layer, building a similar redundant and resilient architecture at the database layer is challenging. For near zero downtime experience, applications should be able to perform automated application and database failover with minimum manual intervention. This could be a valuable feature for applications that are expected to be available 24/7. In this paper we will define a cloud native template architecture that enterprise applications can incorporate to be highly available and evaluate techniques to perform automatic database failover for a near zero downtime experience. We will then incorporate the database failover technique as part of the recommended application architecture to review the impact during planned maintenance activities and outages.
Neural Maximum a Posteriori Estimation on Unpaired Data for Motion Deblurring

Real-world dynamic scene deblurring has long been a challenging task since paired blurry-sharp training data is unavailable. Conventional Maximum A Posteriori estimation and deep learning-based deblurring methods are restricted by handcrafted priors and synthetic blurry-sharp training pairs respectively, thereby failing to generalize to real dynamic blurriness. To this end, we propose a Neural Maximum A Posteriori (NeurMAP) estimation framework for training neural networks to recover blind motion information and sharp content from unpaired data. The proposed NeruMAP consists of a motion estimation network and a deblurring network which are trained jointly to model the (re)blurring process (i.e. likelihood function). Meanwhile, the motion estimation network is trained to explore the motion information in images by applying implicit dynamic motion prior, and in return enforces the deblurring network training (i.e. providing sharp image prior). The proposed NeurMAP is an orthogonal approach to existing deblurring neural networks, and is the first framework that enables training image deblurring networks on unpaired datasets. Experiments demonstrate our superiority on both quantitative metrics and visual quality over State-of-the-art methods.
Quaternary Quantized Gaussian Modulation With Optimal Polarity Map Selection for JPEG Steganography

Recent studies have shown that side-information estimation (SIE) via JPEG image deblocking/restoration is effective in enhancing steganographic security when the side-information of JPEG rounding errors is unavailable. The polarity map of deblocking errors can work well in modulating handcrafted embedding costs. However, it may not be easy to design an optimal deblocking method that is universal to enhance security for all images, and it is unclear how to better utilize the polarity map in modulating statistical model-based embedding costs. To circumvent the difficulty of deblocking method design, we propose an optimal polarity map selection (OPMS) method leveraging existing well-performed deblocking methods. OPMS is designed based on polarity-oriented synthetic stego and minimum feature distance, so that the selected optimal polarity map (OPM) ensures a high security performance to each image. Besides, we propose a statistical model-based modulation method to better exploiting OPM in a quaternary quantized Gaussian embedding (QQGE) model. Through shifting the mean of the distribution, QQGE can derive effective modulated embedding probabilities and reduce the number of modified coefficients without increasing coding complexity. Experimental results demonstrate that the proposed overall steganographic method, called OPMS-QQGE, greatly surpasses existing state-of-the-art SIE-based methods in resisting both CNN-based and feature-based steganalyzers.
Oracle Indistinguishability and Its Applications

There are many ‘cascade-type’ constructions in symmetric key cryptography. For these, the output of a PRF (or a block cipher) is cascaded to the key of the next PRF, and this continues until all blocks of the given message are exhausted. In order to prove the security of these constructions, traditionally multi-oracle families, together with query-based hybrid arguments are used, but these produce longer proofs which cannot be extended to quantum security. In this paper, we point that the oracle indistinguishability, first formalized by Zhandry and later extended by Song and Yun, is a useful tool for proving security of cascade-type constructions, both classically and quantumly. To illustrate this, using oracle indistinguishability, we give a classical security proof of NMAC. Also, we prove that the RC (Randomized Cascade) construction, which gives a PRF out of a weak PRF, is secure, both classically and quantumly.
A Continual Learning Algorithm Based on Orthogonal Gradient Descent Beyond Neural Tangent Kernel Regime

Continual learning aims to enable neural networks to learn new tasks without catastrophic forgetting of previously learned knowledge.Orthogonal Gradient Descent algorithms have been proposed as an effective solution to mitigate catastrophic forgetting.However, these algorithms often rely on the Neural Tangent Kernel regime, which imposes limitations on network architecture.In this study, we propose a novel method to construct an orthonormal basis set for orthogonal projection by leveraging Catastrophic Forgetting Loss.In contrast to the conventional gradient-based basis that reflects an update of model within an infinitesimal range, our loss-based basis can account for the variance within two distinct points in the model parameter space, thus overcoming the limitations of the Neural Tangent Kernel regime.We provide both quantitative and qualitative analysis of the proposed method, discussing its advantages over conventional gradientbased baselines.Our approach is extensively evaluated on various model architectures and datasets, demonstrating a significant performance advantage, especially for deep or narrow networks where the Neural Tangent Kernel regime is violated.Furthermore, we offer a mathematical analysis based on higher-order Taylor series to provide theoretical justification.This study introduces a novel theoretical framework and a practical algorithm, potentially inspiring further research in areas such as continual learning, network debugging, and one-pass learning.
An Integral Projection-Based Semantic Autoencoder for Zero-Shot Learning

Zero-shot Learning (ZSL) classification categorizes or predicts classes (labels) that are not included in the training set (unseen classes). Recent works proposed different semantic autoencoder (SAE) models where the encoder embeds a visual feature vector space into the semantic space and the decoder reconstructs the original visual feature space. The objective is to learn the embedding by leveraging a source data distribution, which can be applied effectively to a different but related target data distribution. Such embedding-based methods are prone to domain shift problems and are vulnerable to biases. We propose an integral projection-based semantic autoencoder (IP-SAE) where an encoder projects a visual feature space concatenated with the semantic space into a latent representation space. We force the decoder to reconstruct the visual-semantic data space. Due to this constraint, the visual-semantic projection function preserves the discriminatory data included inside the original visual feature space. The enriched projection forces a more precise reconstitution of the visual feature space invariant to the domain manifold. Consequently, the learned projection function is less domain-specific and alleviates the domain shift problem. Our proposed IP-SAE model consolidates a symmetric transformation function for embedding and projection, and thus, it provides transparency for interpreting generative applications in ZSL. Therefore, in addition to outperforming state-of-the-art methods considering four benchmark datasets, our analytical approach allows us to investigate distinct characteristics of generative-based methods in the unique context of zero-shot inference.
VirFace<sup>∞</sup>: A Semi-Supervised Method for Enhancing Face Recognition via Unlabeled Shallow Data

The semi-supervised face recognition problem has become a popular research topic in recent years. However, one common and important situation, in which the unlabeled data is shallow, has rarely been considered in most existing works. In this paper, shallow data means there are only few images per identity. In the unlabeled shallow situation, the existing semi-supervised face recognition methods generally do not work well. Thus, how to effectively utilize the unlabeled shallow face data for improving face recognition performance is an important issue. In this paper, we propose a novel semi-supervised face recognition method, namely VirFace <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sup> , to enhance the face recognition performance effectively with the unlabeled shallow data. VirFace <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sup> consists of VirClass and VirDistribution components. In VirClass, we inject the unlabeled data as virtual classes into the feature space to enlarge the inter-class distance. In VirDistribution, we predict the distribution of each virtual class, namely virtual distribution, and then enhance the inter-class discriminativeness by enlarging the distances between the labeled features and the virtual distributions. To the best of our knowledge, we are among the first to tackle the face recognition problem on unlabeled shallow face data. Extensive experiments demonstrate the superiority of our proposed method.
European Commission's Public Consultation on Civil Liability - Adapting Liability Rules to the Digital Age and Artificial Intelligence

The authors welcome the opportunity to respond to the public consultation of the European Commission on ‘Civil Liability – Adapting Liability Rules to the Digital Age and Artificial Intelligence’. In this survey, the Commission first asks for feedback on whether and how to improve the Product Liability Directive (PLD), and in a second part seeks to ‘collect information on the need and possible ways to address issues related specifically to damage caused by Artificial Intelligence systems’. As the introduction to the call rightly states, the latter concerns both the PLD as well as other civil liability rules of the Member States.
Low Resource Language Analysis Using Deep Learning Algorithm for Gender Classification

Voice signals are the essential input source for applications based on human and computer interaction technology. Gender identification through voice signals is one of the most challenging tasks. For voice signal based analysis, deep learning algorithms provide an alternative to traditional and conventional algorithms for classification. To identify the gender through voice signals of female, male and ‘first-time’ transgender, the deep learning algorithm is used to improve the robustness of the identification model with the Mel Frequency Cepstrum Coefficients (MFCC) as a feature of the voice signals. This article presents the identification accuracy of gender with the help of recorded live voice signals. The voice samples of the third gender are recorded in the Hindi language. These Hindi language voice samples of transgender are very low resources and are unavailable at any recognized sources. The simulation results do not depend on the duration of the signals and are text independent. The recurrent neural network – Bidirectional Long Short-term Memory (RNN – BiLSTM) algorithm has been simulated on the recorded voice signals. The simulation outcome is compared with the earlier reported results in the literature. The gender-wise average accuracy of the proposed model is achieved as 91.44%, 94.94%, and 96.11% for males, females, and transgender, respectively, using voice signals. The identification accuracy of transgender is high in comparison to other genders. On the other hand, the average accuracy of the proposed model is obtained as 94.16%.
Personality Recognition Based on Handwriting Types Using Fuzzy Inference

Graphology is the psycho-analysis study of personality and individual characteristics of writers based on the handwriting types. Graphology is a time-consuming and complicated task. Due to small number of expert graphologists, computerized measurement of graphology is an essential need. In this research, in addition to extraction of handwriting features, it is tried to categorize them in terms of regularity and inequality of letters and recognize person’s characteristics via graphology tables. This classification includes these types of handwriting: spontaneous, with difference in pen pressure, uncoordinated, coordinated with partial variance, uniform, sharp, regular, with inequality of letter’s inflexion and unequal. In this paper, 140 typical Farsi handwriting samples are used which are interpreted after giving MMPI (Minnesota Multiphasic Personality Inventory) questionnaire. The novel proposed features are used as the Mamdani fuzzy classifier inputs. Variance of baseline curves, variance of distances between words, variance of vertical letter height, variance of pen width, number of zero, acute and obtuse angles and their ratios to the number of text lines and slope angle are used as features for Mamdani fuzzy classifier inputs. In this study, there are nine handwriting types for personality recognition and 24 rules are defined for the nine fuzzy systems. Compared to our ground truth (MMPI results for the handwriting database), the proposed method showed an accuracy of 82.5% for personality recognition that demonstrated promising results.
Simulation and Experimental Research of Thermal Power Plant Pipeline Displacement via Deep Learning Algorithms

The displacement of pipelines in thermal power plants is critical to ensure the safe operation of the thermal power unit. A 3D online displacement monitoring network, the Deep Displacement Network (DDN), has been designed to obtain real-time displacement data for the entire pipeline. The DDN utilizes a displacement network comprising a 3D CNN-Attention- Gate Recurrent Unit (GRU)-3D transposed CNN structure and a dataset established by CAESAR II to establish a displacement prediction model for four key piping systems. Additionally, to address the limitations of traditional mechanical pointers, which can only measure two-dimensional displacement and cannot measure displacement online, a real-time stereo displacement measurement device (SDMD) has been developed based on the theory of stereoscopic vision and artificial neural networks. The reliability of the DDN and SDMD has been validated through simulations, experiments, and field applications in power plants. Field application results have demonstrated that the absolute error of the SDMD is less than 2mm compared to traditional mechanical pointers, and the relative error between the predicted value by DDN and the measured value of SDMD is less than 9%. Furthermore, the attention layer can improve efficiency in reducing the relative error, while the 3D transposed CNN structure can minimize the maximum relative error. Finally, the DDN can solve the issue of requiring a large number of measurement points for the SDMD and significantly reduce the cost of measurement equipment.
A Method for Speaker Recognition Based on the ResNeXt Network Under Challenging Acoustic Conditions

Speaker recognition is an indispensable technology for biometrics. It distinguishes individuals based on their vocal patterns. In this paper, a joint confirmation method based on the Akaike Information Criterion (AIC) of reconstruction error (REE) and time complexity (AIC-Time joint confirmation method) is proposed to select the optimal decomposition rank of NMF. Furthermore, non-negative matrix factorization (NMF) is applied to the spectrogram to generate speaker features. The network for speaker recognition is based on Convolutional Neural Networks combining Squeeze Excitation (SE) modules with ResNeXt, and the best combination is explored experimentally. The SE module conducts a channel-level adaptive adjustment of the feature maps, reducing redundancy and noise interference while improving feature extraction efficiency and accuracy. The ResNeXt convolutional neural network concurrently executes multiple convolutional kernels, acquiring richer feature information. The experimental results demonstrate that compared to speaker recognition based on Gaussian mixture models (GMM), Visual Geometry Group Network (VGGNet), ResNet, and SE-ResNeXt using spectrograms, this method increases the accuracy by an average of 5.8% and 16.24% under the overlaid of babble and factory1 noise with different signal-to-noise ratios, respectively. In the short speech test, the test set is short speech of 1s and 2s, and the noise is superimposed. Compared with other methods, the recognition rate is increased by an average of 8.67% and 11.72%, respectively.