Reviewerly/exHarmony huggingface dataset:
An Efficient Video Steganography for Pixel Location Optimization Using Fr-WEWO Algorithm based Deep CNN Model

Video steganography is used to conserve the confidential information in various security applications.To give advance protection to the secrete message, pixels locations are optimized using nature inspired algorithm.The input video is separated into a sequence of still image frames then key frames are extracted.The proposed Required Pixel Density (RPD) value calculation and feature extraction are carried out on the extracted frames to perform the frame classification.The frame classification is done using proposed Fractional Water-Earth Worm optimization algorithm based Deep Convolutional Neural Network (FrWEWO-Deep CNN) in order to classify the frames as high, low and medium quality.Thus pixel location prediction is carried out using trained Deep CNN then secret image is hiding within high quality frame with Wavelet Transform (WT) and Inverse WT (IWT).Peak Signal to Noise Ratio (PSNR) and Correlation Coefficient (CC) are performance evaluation parameters.For efficient video steganography better imperceptibility and robustness are required.Imperceptibility is a scale of PSNR value showing similarity between original and stego video frames.The robustness of video steganography is measured by CC between embedded and extracted secret images.The proposed algorithm gives enhanced performance is compared with previous state of art such as WEWO-Deep RNN.The PSNR value is progressed from 41.8492 to 46.5728 dB and CC value improved from 0.9660 to 0.9847.
SQLDAF: SQL injection attack active defense system based on randomized method pool

Data is the most valuable resource of the Internet, attackers often use SQL injection attacks to destroy the database in order to obtain important data information in the database, and today's attack scene is complex, dynamic, multi-channel, non-linear, the existing defense detection technology cannot cope with unknown attacks, the existing instruction set randomization method may be broken by force. Aiming at the above problems, an active defense system of SQL injection attack based on randomization method pool is proposed. The randomization method pool and parallel executor are introduced to build the system framework. The result is decided whether to forward to the database after the decision maker votes, which no longer depends on prior knowledge. The attacker cannot use the system information obtained before to carry out the next effective attack. The formal representation and experimental results show that this method can effectively defend against SQL injection attacks.
A DYNAMIC CONTENT MANAGEMENT SYSTEM FOR THE VISUALIZATION OF CULTURAL INFORMATION; THE CASE OF THE STATE CONSERVATORY OF THESSALONIKI, GR

Abstract. Although there are several attempts of embedding static content in events’ brochures and posters, the increasing need for flexibility and versatility of the content, leads to the development of a mobile application with an in-app dynamic content management system. In this context, DigiOrch is an ongoing research program where, a Content Management System is developed to organize all the digital material and maintain the appropriate connection to the analogical markers. Furthermore, a mobile application is developed that lev-erages this system using in app dynamic modules, which, by utilizing the augmented reality technology, presents multimedia data such as texts, photos, videos, and 3D Models to the end-user by “superimposing” them on mobile devices screen, providing extra additional information on any valid smart-leaflet.The overall workflow of the in-app Dynamic Content Management System (DCMS) can be described as a group of modules that managing and copying content files from a remote infrastructure such as an ftp server or a local resource if network is missing, to the mobile device’s file system.The in-app DCMS consists of 4 modules: The first module is the parser, which is responsible for reading a downloaded *.json file and creating content-linked objects. The second module is the download module which is responsible for downloading the overall content by iterating the content-linked objects, created by the previous module. The third module le is the update module that, by iterating the initial content file and the local file system, suggests whether a content update is necessary. The fourth module is the loading module, which is responsible for fetching the content on runtime to fill the content-holding components, such as 3D Models and UI photo galleries, of the AR scenes on runtime.
Artificial Intelligent for Human Emotion Detection with the Mel-Frequency Cepstral Coefficient (MFCC)

Emotions are an important aspect of human communication. Expression of human emotions can be identified through sound. The development of voice detection or speech recognition is a technology that has developed rapidly to help improve human-machine interaction. This study aims to classify emotions through the detection of human voices. One of the most frequently used methods for sound detection is the Mel-Frequency Cepstrum Coefficient (MFCC) where sound waves are converted into several types of representation. Mel-frequency cepstral coefficients (MFCCs) are the coefficients that collectively represent the short-term power spectrum of a sound, based on a linear cosine transform of a log power spectrum on a nonlinear mel scale of frequency. The primary data used in this research is the data recorded by the author. The secondary data used is data from the "Berlin Database of Emotional Speech" in the amount of 500 voice recording data. The use of MFCC can extract implied information from the human voice, especially to recognize the feelings experienced by humans when pronouncing the sound. In this study, the highest accuracy was obtained when training with epochs of 10000 times, which was 85% accuracy.
Image Restoration under Cauchy Noise: A Group Sparse Representation and Multidirectional Total Generalized Variation Approach

Owing to the ill-posed problem of image restoration, how to find an effective method to obtain image prior information is still challenging.The total generalized variational model has been successfully applied to image denoising and/or deblurring.However, the highorder gradient of the image is described by using L1 norm in the traditional total generalized variational denoising and deblurring model, which can not effectively describe the local group sparse priors of the image gradient.As a result, the traditional total generalized variational model has some limitations in the ability to suppress the staircase artifacts.In order to solve this problem, one new model is proposed to restore images corrupted by Cauchy noise and/or blur in our paper, where the non-convex data fidelity term is combined with two regularization terms: group sparse representation prior and multi-directional total generalized variation.We use group sparse representation prior information to obtain the nonlocal self-similarity information of similar image block for preserving the details and texture features of the discontinuous or uneven region of the image.At the same time, the noise is fully removed in the uniform region, which improves the image visual quality.Moreover, the gradient information in multiple directions is calculated by the multidirectional total generalized variation regularization term, which can better preserve the edge information of the image.The model is divided into several sub-problems by split Bregman iteration, and each sub-problem is solved efficiently.The experimental results show that this model is superior to other existing models both in terms of visual quality and some image quality evaluation.
Competition or Cooperation? Exploring Unlabeled Data via Challenging Minimax Game for Semi-supervised Relation Extraction

Semi-Supervised Relation Extraction aims at learning well-performed RE models with limited labeled and large-scale unlabeled data. Existing methods mainly suffer from semantic drift and insufficient supervision, which severely limit the performance. To address these problems, recent work tends to design dual modules to work cooperatively for mutual enhancement. However, the consensus of two modules greatly restricts the model from exploring diverse relation expressions in unlabeled set, which hinders the performance as well as model generalization. To tackle this problem, in this paper, we propose a novel competition-based method AdvSRE. We set up a challenging minimax game on unlabeled data between two modules, Generator and Discriminator, and assign them with conflicting objectives. During the competition game, one module may find any possible chance to beat the other, which develops two modules' abilities until relation expressions cannot be further explored. To exploit label information, Discriminator is further asked to predict specific relation for each sentence. Experiment results on two benchmarks show new state-of-the-art performance over baselines, demonstrating the effectiveness of proposed AdvSRE.
TransPath: Learning Heuristics for Grid-Based Pathfinding via Transformers

Heuristic search algorithms, e.g. A*, are the commonly used tools for pathfinding on grids, i.e. graphs of regular structure that are widely employed to represent environments in robotics, video games, etc. Instance-independent heuristics for grid graphs, e.g. Manhattan distance, do not take the obstacles into account, and thus the search led by such heuristics performs poorly in obstacle-rich environments. To this end, we suggest learning the instance-dependent heuristic proxies that are supposed to notably increase the efficiency of the search. The first heuristic proxy we suggest to learn is the correction factor, i.e. the ratio between the instance-independent cost-to-go estimate and the perfect one (computed offline at the training phase). Unlike learning the absolute values of the cost-to-go heuristic function, which was known before, learning the correction factor utilizes the knowledge of the instance-independent heuristic. The second heuristic proxy is the path probability, which indicates how likely the grid cell is lying on the shortest path. This heuristic can be employed in the Focal Search framework as the secondary heuristic, allowing us to preserve the guarantees on the bounded sub-optimality of the solution. We learn both suggested heuristics in a supervised fashion with the state-of-the-art neural networks containing attention blocks (transformers). We conduct a thorough empirical evaluation on a comprehensive dataset of planning tasks, showing that the suggested techniques i) reduce the computational effort of the A* up to a factor of 4x while producing the solutions, whose costs exceed those of the optimal solutions by less than 0.3% on average; ii) outperform the competitors, which include the conventional techniques from the heuristic search, i.e. weighted A*, as well as the state-of-the-art learnable planners. The project web-page is: https://airi-institute.github.io/TransPath/.
Distributionally Robust Optimization with Probabilistic Group

Modern machine learning models may be susceptible to learning spurious correlations that hold on average but not for the atypical group of samples. To address the problem, previous approaches minimize the empirical worst-group risk. Despite the promise, they often assume that each sample belongs to one and only one group, which does not allow expressing the uncertainty in group labeling. In this paper, we propose a novel framework PG-DRO, which explores the idea of probabilistic group membership for distributionally robust optimization. Key to our framework, we consider soft group membership instead of hard group annotations. The group probabilities can be flexibly generated using either supervised learning or zero-shot approaches. Our framework accommodates samples with group membership ambiguity, offering stronger flexibility and generality than the prior art. We comprehensively evaluate PG-DRO on both image classification and natural language processing benchmarks, establishing superior performance.
A Generalized Unbiased Risk Estimator for Learning with Augmented Classes

In contrast to the standard learning paradigm where all classes can be observed in training data, learning with augmented classes (LAC) tackles the problem where augmented classes unobserved in the training data may emerge in the test phase. Previous research showed that given unlabeled data, an unbiased risk estimator (URE) can be derived, which can be minimized for LAC with theoretical guarantees. However, this URE is only restricted to the specific type of one-versus-rest loss functions for multi-class classification, making it not flexible enough when the loss needs to be changed with the dataset in practice. In this paper, we propose a generalized URE that can be equipped with arbitrary loss functions while maintaining the theoretical guarantees, given unlabeled data for LAC. To alleviate the issue of negative empirical risk commonly encountered by previous studies, we further propose a novel risk-penalty regularization term. Experiments demonstrate the effectiveness of our proposed method.
Collaborative Knowledge Graph Fusion by Exploiting the Open Corpus

To ease the process of building Knowledge Graphs (KGs) from scratch, a cost-effective method is required to enrich a KG using the triples extracted from a corpus. However, it is challenging to enrich a KG with newly extracted triples since they contain noisy information. This paper proposes to refine a KG by leveraging information extracted from a corpus. In particular, we first formulate the task of building KGs as two coupled sub-tasks, namely join event extraction and knowledge graph fusion. We then propose a collaborative knowledge graph fusion framework, which is composed of an explorer and a supervisor, to allow the involved two sub-tasks to mutually assist each other in an alternative manner. More concretely, an explorer extracts triples from a corpus supervised by both the ground-truth annotation and the KG provided by the supervisor. Furthermore, a supervisor then evaluates the extracted triples and enriches the KG with those that are highly ranked. To implement this evaluation, we further propose a translated relation alignment scoring mechanism to align and translate the extracted triples to the KG. Experimental results verify that this collaboration can improve both the performance of our sub-tasks, and contribute to high-quality enriched knowledge graphs.
Balanced Column-Wise Block Pruning for Maximizing GPU Parallelism

Pruning has been an effective solution to reduce the number of computations and the memory requirement in deep learning. The pruning unit plays an important role in exploiting the GPU resources efficiently. The filter is proposed as a simple pruning unit of structured pruning. However, since the filter is quite large as pruning unit, the accuracy drop is considerable with a high pruning ratio. GPU rearranges the weight and input tensors into tiles (blocks) for efficient computation. To fully utilize GPU resources, this tile structure should be considered, which is the goal of block pruning. However, previous block pruning prunes both row vectors and column vectors. Pruning of row vectors in a tile corresponds to filter pruning, and it also interferes with column-wise block pruning of the following layer. In contrast, column vectors are much smaller than row vectors and can achieve lower accuracy drop. Additionally, if the pruning ratio for each tile is different, GPU utilization can be limited by imbalanced workloads by irregular-sized blocks. The same pruning ratio for the weight tiles processed in parallel enables the actual inference process to fully utilize the resources without idle time. This paper proposes balanced column-wise block pruning, named BCBP, to satisfy two conditions: the column-wise minimal size of the pruning unit and balanced workloads. We demonstrate that BCBP is superior to previous pruning methods through comprehensive experiments.
Robust Representation Learning by Clustering with Bisimulation Metrics for Visual Reinforcement Learning with Distractions

Recent work has shown that representation learning plays a critical role in sample-efficient reinforcement learning (RL) from pixels. Unfortunately, in real-world scenarios, representation learning is usually fragile to task-irrelevant distractions such as variations in background or viewpoint. To tackle this problem, we propose a novel clustering-based approach, namely Clustering with Bisimulation Metrics (CBM), which learns robust representations by grouping visual observations in the latent space. Specifically, CBM alternates between two steps: (1) grouping observations by measuring their bisimulation distances to the learned prototypes; (2) learning a set of prototypes according to the current cluster assignments. Computing cluster assignments with bisimulation metrics enables CBM to capture task-relevant information, as bisimulation metrics quantify the behavioral similarity between observations. Moreover, CBM encourages the consistency of representations within each group, which facilitates filtering out task-irrelevant information and thus induces robust representations against distractions. An appealing feature is that CBM can achieve sample-efficient representation learning even if multiple distractions exist simultaneously. Experiments demonstrate that CBM significantly improves the sample efficiency of popular visual RL algorithms and achieves state-of-the-art performance on both multiple and single distraction settings. The code is available at https://github.com/MIRALab-USTC/RL-CBM.
Alternating Layered Variational Quantum Circuits Can Be Classically Optimized Efficiently Using Classical Shadows

Variational quantum algorithms (VQAs) are the quantum analog of classical neural networks (NNs). A VQA consists of a parameterized quantum circuit (PQC) which is composed of multiple layers of ansatzes (simpler PQCs, which are an analogy of NN layers) that differ only in selections of parameters. Previous work has identified the alternating layered ansatz as potentially a new standard ansatz in near-term quantum computing. Indeed, shallow alternating layered VQAs are easy to implement and have been shown to be both trainable and expressive. In this work, we introduce a training algorithm with an exponential reduction in training cost of such VQAs. Moreover, our algorithm uses classical shadows of quantum input data, and can hence be run on a classical computer with rigorous performance guarantees. We demonstrate 2-3 orders of magnitude improvement in the training cost using our algorithm for the example problems of finding state preparation circuits and the quantum autoencoder.
Semi-transductive Learning for Generalized Zero-Shot Sketch-Based Image Retrieval

Sketch-based image retrieval (SBIR) is an attractive research area where freehand sketches are used as queries to retrieve relevant images. Existing solutions have advanced the task to the challenging zero-shot setting (ZS-SBIR), where the trained models are tested on new classes without seen data. However, they are prone to overfitting under a realistic scenario when the test data includes both seen and unseen classes. In this paper, we study generalized ZS-SBIR (GZS-SBIR) and propose a novel semi-transductive learning paradigm. Transductive learning is performed on the image modality to explore the potential data distribution within unseen classes, and zero-shot learning is performed on the sketch modality sharing the learned knowledge through a semi-heterogeneous architecture. A hybrid metric learning strategy is proposed to establish semantics-aware ranking property and calibrate the joint embedding space. Extensive experiments are conducted on two large-scale benchmarks and four evaluation metrics. The results show that our method is superior over the state-of-the-art competitors in the challenging GZS-SBIR task.
Two Views of Constrained Differential Privacy: Belief Revision and Update

In this paper, we provide two views of constrained differential private (DP) mechanisms. The first one is as belief revision. A constrained DP mechanism is obtained by standard probabilistic conditioning, and hence can be naturally implemented by Monte Carlo algorithms. The other is as belief update. A constrained DP is defined according to l2-distance minimization postprocessing or projection and hence can be naturally implemented by optimization algorithms. The main advantage of these two perspectives is that we can make full use of the machinery of belief revision and update to show basic properties for constrained differential privacy especially some important new composition properties. Within the framework established in this paper, constrained DP algorithms in the literature can be classified either as belief revision or belief update. At the end of the paper, we demonstrate their differences especially in utility on a couple of scenarios.
Meta-Sketch: A Neural Data Structure for Estimating Item Frequencies of Data Streams

To estimate item frequencies of data streams with limited space, sketches are widely used in real applications, including real-time web analytics, network monitoring, and self-driving. Sketches can be viewed as a model which maps the identifier of a stream item to the corresponding frequency domain. Starting from the premise, we envision a neural data structure, which we term the meta-sketch, to go beyond the basic structure of conventional sketches. The meta-sketch learns basic sketching abilities from meta-tasks constituted with synthetic datasets following Zipf distributions in the pre-training phase, and can be fast adapted to real (skewed) distributions in the adaption phase. Extensive experiments demonstrate the performance gains of the meta-sketch and offer insights into our proposals.
NAS-LID: Efficient Neural Architecture Search with Local Intrinsic Dimension

One-shot neural architecture search (NAS) substantially improves the search efficiency by training one supernet to estimate the performance of every possible child architecture (i.e., subnet). However, the inconsistency of characteristics among subnets incurs serious interference in the optimization, resulting in poor performance ranking correlation of subnets. Subsequent explorations decompose supernet weights via a particular criterion, e.g., gradient matching, to reduce the interference; yet they suffer from huge computational cost and low space separability. In this work, we propose a lightweight and effective local intrinsic dimension (LID)-based method NAS-LID. NAS-LID evaluates the geometrical properties of architectures by calculating the low-cost LID features layer-by-layer, and the similarity characterized by LID enjoys better separability compared with gradients, which thus effectively reduces the interference among subnets. Extensive experiments on NASBench-201 indicate that NAS-LID achieves superior performance with better efficiency. Specifically, compared to the gradient-driven method, NAS-LID can save up to 86% of GPU memory overhead when searching on NASBench-201. We also demonstrate the effectiveness of NAS-LID on ProxylessNAS and OFA spaces. Source code:https://github.com/marsggbo/NAS-LID.