Reviewerly/exHarmony huggingface dataset:
Principle-Inspired Multi-Scale Aggregation Network for Extremely Low-Light Image Enhancement

The under-exposure and low-light environments are common to degrade the image-quality with invisible information. To ameliorate this case, a copious of low-light image enhancement methods are developed. However, these existing works are hard to handle extremely low-light conditions with noises, even well-known network-based methods. To address this issue, we develop a Principle-inspired Multi-scale Aggregation Network (PMA-Net) to simultaneously achieve the exposure enhancement and noises removal. Specifically, we establish a pioneering principle-inspired connection to present the physical principle in the inside of the network, to strengthen the structural depict. Subsequently, we propose a multi-scale aggregation strategy to eliminate the noises in the enhanced results. Sufficient ablation studies manifest the effectiveness of our PMA-Net. Extensive qualitative and quantitative comparisons with other state-of-the-art methods are conducted to fully indicates our outstanding performance.
My Voiceprint Is My Authenticator: A Two-Layer Authentication Approach Using Voiceprint for Voice Assistants

Voice assistants are a ubiquitous service of contemporary daily life. Their intuitive use and 24-hour-a-day convenience make them popular with an increasing user base. However, the security of voice assistants not not increased commensurate with the rising number of users and their increasing technical abilities. The lack of an authentication mechanism gives attackers an opportunity to exploit voice assistants to control and get personal information from linked services. The goal of this research is to provide an authentication method that protects voice assistants from attacks without degrading their usability. We utilize Microsoft Azure Speaker Recognition API and Google Speech API to implement an Android application to examine the approach. The results indicate that the voice authentication method can resist replay attacks and be easily learned and used.
On Effectiveness of Source Code and SSL Based Features for Phishing Website Detection

Phishing is a social engineering method to steal user credentials through data entry forms from malicious websites. Currently available anti-malware softwares can only detect black listed phishing websites. Similarity based detection methods such as visual similarity can be easily evaded by making some changes in the textual and visual contents of a phishing site. The phishing behavior of a web page can be identified from its URL, domain and source code based features. However, URL and domain based features can be easily defeated by using black hat SEO techniques. In this paper, we extract the relevant rules based on webpage source code and Secure Socket Layering (SSL) based features from a training dataset using Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm. Further, we check for the presence of these rules in a test dataset. Our implementation results show that the webpage source code based rules can identify phishing websites with an accuracy of 0.92.
Wind: Wasserstein Inception Distance For Evaluating Generative Adversarial Network Performance

In this paper, we present Wasserstein Inception Distance (WInD), a novel metric for evaluating performance of Generative Adversarial Networks (GANs). The proposed metric extends on the rationale of the previously proposed Frechet Inception Distance (FID), in the sense that GAN performance is quantified in terms of data and model distribution divergence. We extend FID by relaxing the Gaussian hypothesis of the related inception features and extend it for non-Gaussian, multimodal distributions. Gaussian Mixture Models (GMMs) are used to model data and model inception features, and the Wasserstein distance is employed as a pdf matching metric. We show that the proposed WInD metric inherits the desirable features of FID and correlates well with actual GAN performance. Furthermore, WInD can correctly evaluate cases were data and model distribution erroneously would appear as well peforming using FID. Numerical experiments on synthetic and real datasets validate our claim.
AAKA-D2D: Anonymous Authentication and Key Agreement Protocol in D2D Communications

Device-to-Device (D2D) communication is proposed as a promising technology in 5G system for communications between mobile devices geographical proximity. Despite significant benefits, new application scenarios and system architecture, for instance, open direct channel, expose D2D communications to unique security threats. Providing a secure and privacy-preserving D2D communication system is essential for the success of D2D services. In this paper, we propose AAKA-D2D, an anonymous authentication and key agreement secure protocol for D2D communications, by which two User Equipments (UE) in close proximity could mutually authenticate without leaking their real identities and negotiate a session key for secure communications in D2D session without disclosing communication contents to Core Network (CN). Formal security analysis indicates that AAKA-D2D satisfies the security requirements. The comprehensive performance evaluation show that AAKA-D2D can increase the computational performance by about 20% and decrease on communication overhead by half compared to related works.
Large Margin Gaussian Mixture Classifier With a Gabriel Graph Geometric Representation of Data Set Structure

This brief presents a geometrical approach for obtaining large margin classifiers. The method aims at exploring the geometrical properties of the data set from the structure of a Gabriel graph, which represents pattern relations according to a given distance metric, such as the Euclidean distance. Once the graph is generated, geometrical support vectors (SVs) (analogous to support vector machines (SVMs) SVs) are obtained in order to yield the final large margin solution from a Gaussian mixture model. Experiments with 20 data sets have shown that the solutions obtained with the proposed method are statistically equivalent to those obtained with SVMs. However, the present method does not require optimization and can also be extended to large data sets using the cascade SVM concept.
Mobile Cloud Forensic Readiness Process Model for Cloud-Based Mobile Applications

Nowadays, mobile cloud applications have attracted millions of smartphone users due to the proliferation of cyber technologies and a wide range of applications. Mobile cloud forensic investigation methodologies need tremendous growth due to the increasing crime incidents. The forensic readiness model plays a significant role in the forensic investigation framework by ensuring the ease of investigation to the forensic investigator. The existing forensic readiness supports either the mobile device or cloud environment, which lacks to prepare the information for the investigation. This article presents a mobile cloud forensic readiness process model to identify the factors and prepare the information that effectively supports forensic investigations. The proposed model involves requirements for the mobile cloud forensics from multiple perspectives with the aim of developing the forensic-ready system. As a result, the proposed forensic readiness model enables the mobile cloud forensic to improve the accuracy of investigation as well as reduces the investigation time significantly.
I-Vector Transformation Using K-Nearest Neighbors for Speaker Verification

Probabilistic Linear Discriminant Analysis (PLDA) is the most efficient backend for i-vectors. However, it requires labeled background data which can be difficult to access in practice. Unlike PLDA, cosine scoring avoids speaker-labels at the cost of degrading the performance. In this work, we propose a post processing of i-vectors using a Deep Neural Network (DNN) to transform i-vectors into a new speaker vector representation. The DNN will be trained using i-vectors that are similar to the training i-vectors. These similar i-vectors will be selected in an unsupervised manner. Using the new vector representation, we will score the experimental trials using cosine scoring. The evaluation was performed on the speaker verification trials of VoxCeleb-1 database. The experiments have shown that with the help of the similar i-vectors the new vectors become more discriminative than the original i-vectors. The new vectors have gained a relative improvement of 53% in terms of EER, compared to the conventional i-vector/PLDA system, but without using speaker labels.
Comparative Evaluations of Human Behavior Recognition Using Deep Learning

With the cost decrease of security monitoring facilities such as cameras, video surveillance has been widely applied to public security and safety such as banks, transportation, shopping malls, etc. which allows police to monitor abnormal events. Through deep learning, authors can achieve high performance of human behavior detection and recognition by using model training and tests. This chapter uses public datasets Weizmann dataset and KTH dataset to train deep learning models. Four deep learning models were investigated for human behavior recognition. Results show that YOLOv3 model is the best one and achieved 96.29% of mAP based on Weizmann dataset and 84.58% of mAP on KTH dataset. The chapter conducts human behavior recognition using deep learning and evaluates the outcomes of different approaches with the support of the datasets.
Local Feature-Aware Siamese Matching Model for Vehicle Re-Identification

Vehicle re-identification is attracting an increasing amount of attention in intelligent transportation and is widely used in public security. In comparison to person re-identification, vehicle re-identification is more challenging because vehicles with different IDs are generated by a unified pipeline and cannot only be distinguished based on the subtle differences in their features such as lights, ornaments, and decorations. In this paper, we propose a local feature-aware Siamese matching model for vehicle re-identification. A local feature-aware Siamese matching model focuses on the informative parts in an image and these are the parts most likely to differ among vehicles with different IDs. In addition, we utilize Siamese feature matching to better supervise our attention. Furthermore, a perspective transformer network, which can eliminate image deformation, has been designed for feature extraction. We have conducted extensive experiments on three large-scale vehicle re-ID datasets, i.e., VeRi-776, VehicleID, and PKU-VD, and the results show that our method is superior to the state-of-the-art methods.
An Attention-Based Joint Acoustic and Text on-Device End-To-End Model

Recently, we introduced a two-pass on-device end-to-end (E2E) speech recognition model, which runs RNN-T in the first-pass and then rescores/redecodes the result using a noncausal Listen, Attend and Spell (LAS) decoder. This on-device model obtained similar performance to a state-of-the-art conventional model. However, like many E2E models, it suffers from being trained only on supervised audio-text pairs and thus performs poorly on rare words compared to a conventional model which incorporates a language model trained on a much larger text corpus. In this work, we introduce a joint acoustic and text decoder (JATD) into the LAS decoder, which makes it possible to incorporate a much larger text corpus into training. We find that the JATD model obtains in a 3-10% relative improvement in WER compared to a LAS decoder trained only on supervised audio-text pairs across a variety of proper noun test sets.
Fast Training of Deep Neural Networks for Speech Recognition

Training large, deep neural network acoustic models for speech recognition on large datasets takes a long time on a single GPU, motivating research on parallel training algorithms. We present an approach for training a bidirectional LSTM acoustic model on the 2000-hour Switchboard corpus. The model we train achieves state-of-the-art word error rate, 7.5% on the Hub5-2000 Switchboard test set and 13.1% on the Callhome test set, and scales to an unprecedented 96 learners while employing only 12 global reductions per epoch of training. As our implementation incurs far fewer reductions than prior work, it does not require aggressively optimized communication primitives to reach state-of-the-art performance in a short amount of time. With 48 NVIDIA V100 GPUs training takes 5 hours; with 96 GPUs, training takes around 3 hours.
An Effective Vehicle Counting Approach Based on CNN

Vehicle counting is an essential part of the intelligent transportation system (ITS). Compared with traditional vehicle detection methods, convolutional neural network (CNN) could reach in real-time speed with high accuracy in vehicle detection. This paper presents an efficient and robust method of vehicle counting by combining CNN and the virtual coils. Experimental results indicate that our approach could remove duplicate and missing detection effectively and count the vehicle at high speed with around 98% relative accuracy and 93% absolute accuracy on average while it could also work well in the traffic jam situation with over 95% relative and 84% absolute accuracy. Besides, through identifying the traffic light color, the proposed method could also be applied in automatically capturing red light violations.
Collective pragmatic acting in networked spaces: The case of #activism in Arabic and English Twitter discourse

This paper explores the theoretical and analytical advantages of conceptualizing hashtag activism as a collective pragmatic act. After analyzing a corpus of hashtag activism, the paper identifies two types of micro acts, constituting the pragmatic act of hashtagged political tweeting: communing affiliation round sociopolitical values, and legitimizing the sociopolitical claims associated with the hashtags. The communing act is performed by a hashtag associating attitudes with an idea representing the sociopolitical claim of the campaign. The content of the political tweet, on the other hand, provides an opportunity for the tweeters to legitimize their views and attitudes in relation to the topic of the hashtag via a deontic or epistemic legitimizing act. Tweeting under a hashtag carrying a sociopolitical claim allows meaning to be negotiated collaboratively by a group of politically active agents sharing common values. This turns any cluster of hashtagged political tweets into a macro collective act performed by a collectivity of actors. Such a conceptualization provides a collective perspective to Pragmatic Act Theory and explains how political engagement can be reinvigorated by the users' sense of agency in networked spaces.
Exploratory Data Analysis and Machine Learning on Titanic Disaster Dataset

RMS Titanic was a British cruise ship said to be the largest cruise ever made in the history of world. It collided with an iceberg during its maiden journey across the pacific ocean from Southampton to New York City. With more than 2200 passengers on board, nearly half of them died after the unprecedented mishap. The infamous incident compels researchers to dig into the dataset. This research is aimed at achieving an exploratory data analysis and understand the effect or parameters key to the survival of a person had they been on the ship. The survival prediction has been done by applying various algorithms like Logistic Regression, K - nearest neighbours, Support vector machines, Decision Tree. Towards the end, accuracies of the algorithms based on features fed to them has been compared in a tabular form.
Learning Signed Graphs from Data

Signed graphs have recently been found to offer advantages over unsigned graphs in a variety of tasks. However, the problem of learning graph topologies has only been considered for the unsigned case. In this paper, we propose a conceptually simple and flexible approach to signed graph learning via signed smoothness metrics. Learning the graph amounts to solving a convex optimization problem, which we show can be reduced to an efficiently solvable quadratic problem. Applications to signal reconstruction and clustering corroborate the effectiveness of the proposed method.
Minimal Adversarial Perturbations in Mobile Health Applications: The Epileptic Brain Activity Case Study

Today, the security of wearable and mobile-health technologies represents one of the main challenges in the Internet of Things (IoT) era. Adversarial manipulation of sensitive health-related information, e.g., if such information is used for prescribing medicine, may have irreversible consequences involving patients' lives. In this article, we demonstrate the power of such adversarial attacks based on a real-world epileptic seizure detection problem. We identify the minimum perturbation required by the adversaries to declare a seizure (ictal) sample as non-seizure (inter-ictal) in emergency situations, i.e., minimal adversarial perturbation to fool the classification algorithm.
Securing the Internet of things using Blockchain

At present, the networks are vulnerable to various security problems like intrusion, phishing, hacking, to mention a few, and various encryption techniques are employed to curb the same. IoT environments are susceptible to various attacks that compromise the basic Confidentiality-Integrity-Availability triad of network security. IoT applications range from baby monitors to high-end military systems. With a plethora of devices transmitting sensitive information, the security of IoT environments becomes pivotal. However, it is not viable to extend the traditional dense encryption methodology to resource-inhibited IoT devices. Thus, lightweight algorithms are devised to meet the needs of their constrained architecture. Blockchain is a technology that helps in achieving a peer-peer secure and tamper-proof transfer of data between two objects. Blockchains can thus empower integrity in networks. Extending the concept of Blockchains to constrained IoT ecosystems, however, meets with its challenges. This paper focuses on the applications and challenges of Blockchain technology in securing IoT environments.
Human Presence Detection via Deep Learning of Passive Radio Frequency Data

Human presence detection is a critical field in certain circumstances such as natural disasters and surveillance systems. This paper presents a new approach that utilizes software defined radio to passively collect radio frequency data and applying deep learning neural network to detect human presence. It provides a low cost and environment friendly solution. The long term goal of this study is to develop a deep learning based spectrum monitoring system.
A new intelligent pattern classifier based on structured sparse representation

A new intelligent pattern classifier based on structured sparse representation is developed, aiming to utilise the subspace structure to simplify the training process and reduce the required training sample size. It is seen that no mathematical optimization is involved in the training stage. In the developed classifier, the classification is made by selecting the optimal linear subspace that can represent data with the highest probability. A nonlinear mapping is designed to ensure data vectors in the feature space are more likely to live in their own subspaces. The sparse representation vectors of input data can then be computed in terms of a dictionary constructed by membership vectors of these subspaces. After that, the Bayesian inference based decision-making algorithm is implemented to use the statistical information imbedded in sparse representation vectors to classify the data efficiently. Lastly, a self-learning mechanism is developed to adjust the membership vectors of the dictionary to ensure the classifier's response to the subsequent application of similar input patterns will be enhanced. The experiment results show excellent classification performance and strong robustness of proposed intelligent pattern classifier.
Polarizing Front Ends for Robust Cnns

The vulnerability of deep neural networks to small, adversarially designed perturbations can be attributed to their excessive linearity. In this paper, we propose a bottom-up strategy for attenuating adversarial perturbations using a nonlinear front end which polarizes and quantizes the data. We observe that ideal polarization can be utilized to completely eliminate perturbations, develop algorithms to learn approximately polarizing bases for data, and investigate the effectiveness of the proposed strategy on the MNIST and Fashion MNIST datasets.
Learning Blind Denoising Network for Noisy Image Deblurring

Noisy image deblurring is to recover the blurry image in the presence of the random noise. One key to this problem is to know the noise level in each iteration. The existing methods manually adjust the regularization parameter for varying noise levels, which is quite inaccuracy and tedious for practical application. In this work, we discover that the noise level and the denoiser is tightly coupled. Consequently, we propose efficient blind denoising convolutional neural network (BDCNN) consisting of two stages: a down-sampling regression network for estimating noise level and a fully convolutional network for denoising, such that our model could adaptively handle the unknown noise level during iteration. Further, the BDCNN functions as a discriminative prior and is plugged into the iterative deblurring framework for noisy image deblurring. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods in terms of practicability and performance.
Body Movement Generation for Expressive Violin Performance Applying Neural Networks

Generating body movements based on given music audio recordings is an emerging research topic. This problem remains challenging particularly for string instruments, considering the fact that the relationship between the musical note sequences and the body movement sequences in string instruments does not have an one-to-one correspondence and is highly context-dependent. In this paper, we take a divide-and-rule approach to tackle the multifaceted characteristics of musical movement, and propose a framework for generating violinists' body movements. Both objective and subjective evaluations show that the proposed framework improves the stability as well as the perceptual quality of the generation outputs by using the task-specific models for bowing and expressive movement. To the best of our knowledge, this work represents the first attempt to generate violinists' body movements considering music expression.
Scene Text Recognition with Temporal Convolutional Encoder

Texts from scene images typically consist of several characters and exhibit a characteristic sequence structure. Existing methods capture the structure with the sequence-to-sequence models by an encoder to have the visual representations and then a decoder to translate the features into the label sequence. In this paper, we study text recognition framework by considering the long-term temporal dependencies in the encoder stage. We demonstrate that the proposed Temporal Convolutional Encoder with increased sequential extents improves the accuracy of text recognition. We also study the impact of different attention modules in convolutional blocks for learning accurate text representations. We conduct comparisons on seven datasets and the experiments demonstrate the effectiveness of our proposed approach.