Reviewerly/exHarmony huggingface dataset:
Generating Watermarked Speech Adversarial Examples

Increasing methods use deep learning for auto-generating speech adversarial examples that can easily lead speech recognition systems to make incorrect predictions. It is necessary to prevent the speech adversarial example generation model from being abused by any unauthorized users. It implies that, both the model and the adversarial examples generated by the model should be protected and monitored. One may build a complex access protocol to avoid the model leakage, which, however, has limited control when the model was obtained by authorized users. To deal with this problem, in this paper, we propose a method to mark the speech adversarial example generation model by optimizing a combined loss function allowing a watermark to be embedded into the generated adversarial examples. Accordingly, the resultant model not only generates speech adversarial examples that can fool the target model, but also allows us to identify the ownership by detecting watermarks from outputs. Moreover, by retrieving the watermark from an unknown speech signal, we can judge whether the speech signal is an adversarial example generated by a specific model. We have proved through experiments that the speech adversarial example generation model optimized with the proposed method can effectively deceive the state-of-the-art speech classification network and trace the source of the generated adversarial examples.
Security Attacks in Cloud Computing: A Systematic Review

These days cloud computing plays very major role in the world of computers. On demand services via Internet is provided by cloud computing using large amount of virtual storage. Its most important feature is that user has no need to establish costly computing infrastructure and pay very less for its services. Virtualization is the backbone of resource sharing provided by cloud computing. Security is huge challenge of cloud computing. Cloud computing allows the user to access resources anywhere anytime via internet which is actually the main reason behind the multiple varieties of attacks. Generally at different cloud layers various threats functioning like data breach, data leakage and unauthorized data access. Even there is constant implementation and improvement occurring regarding security issues and its countermeasures over cloud computing with the growth of time, unfortunately security is still a big challenge. This paper includes a examination based on a theoretical survey on cloud computing that communicated various possible threats and also taxonomy model where at each layer a number of various security attacks enter from the usage of different cloud services, moreover for these attacks proposed mechanisms and the solutions available earlier.
Self-calibrating Neural-Probabilistic Model for Authorship Verification Under Covariate Shift

We are addressing two fundamental problems in authorship verification (AV): Topic variability and miscalibration. Variations in the topic of two disputed texts are a major cause of error for most AV systems. In addition, it is observed that the underlying probability estimates produced by deep learning AV mechanisms oftentimes do not match the actual case counts in the respective training data. As such, probability estimates are poorly calibrated. We are expanding our framework from PAN 2020 to include Bayes factor scoring (BFS) and an uncertainty adaptation layer (UAL) to address both problems. Experiments with the 2020/21 PAN AV shared task data show that the proposed method significantly reduces sensitivities to topical variations and significantly improves the system’s calibration.
Post-Quantum Signal Key Agreement from SIDH

In the effort to transition cryptographic primitives and protocols to quantum-resistant alternatives, an interesting and useful challenge is found in the Signal protocol. The initial key agreement component of this protocol, called X3DH, has so far proved more subtle to replace—in part due to the unclear security model and properties the original protocol is designed for. This paper defines a formal security model for the original Signal protocol, in the context of the standard eCK and CK+ type models, which we call the Signal-adapted-CK model. We then propose a replacement for the Signal X3DH key exchange protocol based on SIDH, and provide a proof of security in the Signal-adapted-CK model, showing our protocol satisfies all security properties of the original Signal X3DH. We call this new protocol SI-X3DH. Our protocol shows that SIDH can be used to construct a secure X3DH replacement despite the existence of adaptive attacks against it. Unlike the generic constructions proposed in the literature, our protocol achieves deniability without expensive machinery such as post-quantum ring signatures. It also benefits from the small key sizes of SIDH, and its efficiency as a key-exchange protocol compared to other isogeny-based protocols such as CSIDH.
Quantum state discrimination circuits inspired by Deutschian closed timelike curves

It is known that a party with access to a Deutschian closed timelike curve (D-CTC) can perfectly distinguish multiple non-orthogonal quantum states. In this paper, we propose a practical method for discriminating multiple non-orthogonal states, by using a previously known quantum circuit designed to simulate D-CTCs. This method relies on multiple copies of an input state, multiple iterations of the circuit, and a fixed set of unitary operations. We first characterize the performance of this circuit and study its asymptotic behavior. We also show how it can be equivalently recast as a local, adaptive circuit that may be implemented simply in an experiment. Finally, we prove that our state discrimination strategy achieves the multiple Chernoff bound when discriminating an arbitrary set of pure qubit states.
Development of Sign Language Translator for Speech Impairment Person

Although many assistive technologies have been developed to help disabled people, however, in Malaysia the technology for the speech impairment person is still left behind and currently researchers are exploring the technologies. Assistive technology is defined as assistive, adaptive, and rehabilitative devices for Person with Disabilities (PWD) and for the elderly generation. This research focuses on the development of a sign language translator device specialty for a speech impairment person. By capturing a real-time sign language and gesture movement using a high-resolution camera, the system will process the signal and translate the data into visual information. The device is programmed to recognize different alphabets and digits based on the Malaysian Sign Language (MSL) standard. The accuracy of the system obtained was 65.3% for alphabets and 90.6% for digits. It is aimed that this intelligent device invention will bridge the communication barrier and help disabled people to experience a more healthy lifestyle and feel appreciated among the society. The engagement with society will be much easier with the translator device.
Is Wikipedia Easy to Understand?: A Study Beyond Conventional Readability Metrics

Wikipedia has emerged to be one of the most prominent sources of information available on the Internet today. It provides a collaborative platform for editors to edit and share their information, making Wikipedia a valuable source of information. The Wikipedia articles have been duly studied from an editor's point of view. But, the analysis of Wikipedia from the reader's perspective is yet to be studied. Since Wikipedia serves as an encyclopedia of information for its users, its role as an information securing tool must be examined. The readability of a written text plays a major role in imparting the intended comprehension to its readers. Readability is the ease with which a reader can understand the underlying piece of text. In this paper, we study the readability of various Wikipedia articles. Apart from judging the readability of Wikipedia articles against standard readability metrics, we introduce some new parameters related specifically to the comprehension of the text present in Wikipedia articles. These new parameters, combined with standard readability metrics, help classify the Wikipedia articles into comprehensible and non-comprehensible classes through the SVM classification technique.
Situated Epistemic Updates

One way to model epistemic states of agents more realistically is to represent these states by sets of situations rather than possible worlds. In this paper we discuss representations of epistemic update in terms of situations. After linking epistemic update based on deleting epistemic accessibility arrows with update of situations, we discuss two specific kinds of public epistemic update; monotonic update in intuitionistic dynamic epistemic logic, and non-monotonic update in substructural dynamic epistemic logic. Our investigation is mainly conceptual, but leads to completeness results using reduction axioms, and lays the groundwork for future investigation into the concept of situated epistemic update.
Human Action Recognition Using Spatio-Temporal Multiplier Network and Attentive Correlated Temporal Feature

Nowadays, action recognition has gained more attention from the computer vision community. Normally for recognizing human actions, spatial and temporal features are extracted. Two-stream convolutional neural network is used commonly for human action recognition in videos. In this paper, Adaptive motion Attentive Correlated Temporal Feature (ACTF) is used for temporal feature extractor. The temporal average pooling in inter-frame is used for extracting the inter-frame regional correlation feature and mean feature. This proposed method has better accuracy of 96.9% for UCF101 and 74.6% for HMDB51 datasets, respectively, which are higher than the other state-of-the-art methods.
Critical-weight based locking scheme for DNN IP protection in edge computing

It is a challenging task to resist illegal usage of Deep Neural Network (DNN) models in applications from edge computing. The existed protection method is developed based on encrypting all weights of DNN models and has achieved a promising result, which however suffers from a high computation cost. In this paper, we design a critical-weight based method to lock the DNN model to defend unauthorized usage, leading to a significant decrease in the time cost. To be specific, we analyse and figure out the critical weights of a DNN model, and then encrypt the critical weights to lock the DNN model. A set of preliminary experiments are conducted to testify the effectiveness of the proposed approach.
Feature selection using different evaluate strategy and random forests

Aiming at the dimensional disaster and over-fitting problems in data analysis, this paper proposes a feature selection method using hybrid integration of difference models and random forests (Integrate-RF), firstly, Integrate-RF use CART, CHAID, SVM, BN, NN, K-Means, Kohonen to evaluate the importance of features, and then, for the above seven sorts, Integrate-RF use the arithmetic average method to calculate the importance of the features; secondly, Integrate-RF select the most important features from the remaining features into features subset, and use random forest classification to get the corresponding out-of-bag(OOB) data classification error rate; finally, the optimal features subset can be selected based on the OOB data classification error rate. Experiments show that feature selection methods proposed in this paper effectively reduces the data dimension, selects features better and more adaptable.
Brahmi Script Recognition System using Deep Learning Techniques

Indian civilization is one of the ancient one and has a very unique and diverse culture. It contributes so much to the word in mathematics, science, medicine, literature, education and religion. Especially, Tamil literature has great epics, poems, plays, stories and which helps to understand the ancient lifestyle of Indian civilization which includes the architecture, treatments and medicine. Tamil Brahmi inscription have been found on cave, entrances, potsherds and coins have provided historians. The Tamil Brahmi character is the most important one to get an information from cultures and heritage. One of the barrier stands between the recent technologies and ancient history is the scripts. Ancient Tamil scripts are written in Tamil-Brahmi format which is not used in recent literature. This project will help us to convert the Ancient Tamil-Brahmi letters into readable Tamil scripts which will help us to understand and use the knowledge of our ancestors using image processing and deep learning technology.
Deep Session Interest Network Based on the Time Interval Encoding for the Click-through Rate Prediction

The click-through rate prediction is with significance in the field of recommendation systems, especially in advertising recommendation systems. At present, some sequence models based on deep learning have been directly used in the field of the click-through rate prediction to dig out the rule of user behavior and have achieved good results, but they ignored the influence of time information on the rule of user behavior. To solve the above problems, we propose a model named Time Interval Encoding Deep Session Interest Network (TIED-DSIN). In the TIED-DSIN model, a time interval encoding method is designed to integrate time interval information into the sequence model, and time decay factor is introduced in the encoding process to make the model consider the influence of time information fully when mining the rule of users' dynamic behaviors. Correspondingly, a comparative experiment is conducted on the real Alimama public data set, and the results show that the accuracy of the TIED-DSIN model is better than other models that commonly used in the click-through rate prediction.
RLCFR: Minimize counterfactual regret by deep reinforcement learning

Counterfactual regret minimization (CFR) is a popular method to deal with decision-making problems of two-player zero-sum games with imperfect information. Unlike previous studies that mostly explored solving large-scale problems or accelerating the solution efficiency, we propose a framework, RLCFR, which aims at improving the generalization ability of the CFR method. In RLCFR, the game strategy is solved by CFR-based methods in a reinforcement learning (RL) framework. The dynamic procedure of the iterative interactive strategy updating is modeled as a Markov decision process (MDP). Our method then learns a policy to select the appropriate method of regret updating in the iteration process. In addition, a stepwise reward function is formulated to learn the action policy, which is proportional to how well the iteration strategy performs at each step. Extensive experimental results on various games showed that the generalization ability of our method is significantly improved compared with existing state-of-the-art methods.
Approaches to Video Real time Multi-Object Tracking and Object Detection: A survey

The world is living a major shift from information era to artificial intelligence (AI) era. Machines are giving the ability to sense the surrounding world and to take decisions. Computer vision and especially multi-object tracking(MOT), which relies on Deep Learning, is at the heart of this shift. Indeed, with the growth of deep learning, the methods and algorithms that are tackling this problem have gained better performance from the integration of deep learning models. Deep Learning has been demonstrated as MOT, which tackles the challenges of in-and-out objects, unlabeled data, confusing appearance and occlusion. Deep learning, which relied on MOT techniques, has recently gained a fast ground from representation learning to modelling the networks thanks to the advancement of deep learning hypothesis and benchmark arrangement. This paper sums up and analyzes deep learning based MOT techniques which are at a highest level. The paper also offers a comprehensive review about the different techniques applied in MOT of deep learning based on different methods. Furthermore, this study analyzes the benefits and the constraints of current strategies, techniques and methods.
Temporal Knowledge Consistency for Unsupervised Visual Representation Learning

The instance discrimination paradigm has become dominant in unsupervised learning. It always adopts a teacher-student framework, in which the teacher provides embedded knowledge as a supervision signal for the student. The student learns meaningful representations by enforcing instance spatial consistency with the views from the teacher. However, the outputs of the teacher can vary dramatically on the same instance during different training stages, introducing unexpected noise and leading to catastrophic forgetting caused by inconsistent objectives. In this paper, we first integrate instance temporal consistency into current instance discrimination paradigms, and propose a novel and strong algorithm named Temporal Knowledge Consistency (TKC). Specifically, our TKC dynamically ensembles the knowledge of temporal teachers and adaptively selects useful information according to its importance to learning instance temporal consistency. Experimental result shows that TKC can learn better visual representations on both ResNet and AlexNet on linear evaluation protocol while transfer well to downstream tasks. All experiments suggest the good effectiveness and generalization of our method. Code will be made available.
Resampling methods in ANOVA for data from the von Mises-Fisher distribution

An important problem in directional statistics is to test the null hypothesis of a common mean direction for several populations. The Analysis of Variance (ANOVA) test for vectorial data may be used to test the hypothesis of the equality of the mean directions for several von Mises-Fisher populations. As this test is valid only for large concentrations, we propose in this paper to apply the resampling techniques of bootstrap and permutation to the ANOVA test. We carried out an extensive simulation study in order to evaluate the performance of the ANOVA test with the resampling techniques, for several sphere dimensions and different sample sizes and we compare with the usual ANOVA test for data from von Mises-Fisher populations. The purpose of this simulation study is also to investigate whether the proposed tests are preferable to the ANOVA test, for low concentrations and small samples. Finally, we present an example with spherical data.
Overview of SAR Image Denoising Based on Transform Domain

Synthetic aperture radar (SAR) image denoising processing is a particularly important issue. This paper mainly discusses SAR image denoising based on the transform domain. First, this paper describes the formation mechanism of coherent speckle noise and the basic methods of suppressing coherent speckle. Then, the denoising algorithms based on wavelet transform and multi-scale analysis are introduced, respectively. The representative algorithms are verified by simulation. Finally, the paper summarizes the advantages and disadvantages of existing algorithms, and proposes the development trend of SAR image denoising in transform domain.
TACN: A Topical Adversarial Capsule Network for textual network embedding

Combining topological information and attributed information of nodes in networks effectively is a valuable task in network embedding. Nevertheless, many prior network embedding methods regarded attributed information of nodes as simple attribute sets or ignored them totally. In some scenarios, the hidden information contained in vertex attributes are essential to network embedding. For instance, networks that contain vertexes with text information play an increasingly important role in our life, including citation networks, social networks, and entry networks. In these textual networks, the latent topic relevance information of different vertexes contained in textual attributes information are valuable in the network analysis process. Shared latent topics of nodes in networks may influence the interaction between them, which is critical to network embedding. However, much prior work for textual network embedding only regarded the text information as simple word sets while ignored the embedded topic information. In this paper, we develop a model named Topical Adversarial Capsule Network (TACN) for textual network embedding, which extracts a low-dimensional latent space of the original network from node structures, vertex attributes, and topic information contained in text of nodes. The proposed TACN contains three parts. The first part is an embedding model, which extracts the embedding representation from the topological structure, vertex attributes, and document-topic distributions. To ensure a consistent training process by back-propagation, we generate document-topic distributions by the neural topic model with Gaussian Softmax constructions. The second part is a prediction model, which is used to exploit labels of vertices. In the third part, an adversarial capsule model is used to help distinguish the latent representations from node structure domain, vertex attribute domain, or document-topic distribution domain. The latent representations, which may come from the three domains, are the output of the embedding model. We incorporate the adversarial idea into the adversarial capsule model to combine the information from these three domains, rather than to distinguish the representations conventionally. Experiments on seven real-world datasets validate the effectiveness of our method.
Understanding generalization error of SGD in nonconvex optimization

The success of deep learning has led to a rising interest in the generalization property of the stochastic gradient descent (SGD) method, and stability is one popular approach to study it. Existing generalization bounds based on stability do not incorporate the interplay between the optimization of SGD and the underlying data distribution, and hence cannot even capture the effect of randomized labels on the generalization performance. In this paper, we establish generalization error bounds for SGD by characterizing the corresponding stability in terms of the on-average variance of the stochastic gradients. Such characterizations lead to improved bounds on the generalization error of SGD and experimentally explain the effect of the random labels on the generalization performance. We also study the regularized risk minimization problem with strongly convex regularizers, and obtain improved generalization error bounds for the proximal SGD.
Exploring Sentence Embedding Structures for Semantic Relation Extraction

Sentence embeddings encode natural language sentences as low-dimensional, dense vectors and have improved NLP tasks, including relation extraction, which aims at identifying structured relations defined in a knowledge base from unstructured text. A promising and more efficient approach would be to embed both the text and structured knowledge in low-dimensional spaces and discover alignments between them. We develop such an alignment procedure and evaluate the extent to which sentences carrying similar senses are embedded in close proximity sub-spaces, using that structure to align them to a knowledge graph. Our experimental results show that embedding spaces generated from simple models outperform those from more complicated approaches for the alignment and relation extraction task. We also show that clusterability can serve as a proxy for alignment accuracy, leading us to conclude that better structured spaces drive better semantic applications.