Reviewerly/exHarmony huggingface dataset:
Determining the Likelihood Ratio From Perceptual Attributes of Voice

To provide voice experts with a method for determining the likelihood ratio (LR) from the perceptual evaluation of distinctive voice attribute scores. The proposed method aims to obtain the similarity and typicality judgments made by forensic voice experts (FVEs) during the comparison of attributes in voice pairs.It is based on the scoring method for LR calculation. In the first stage, 17 perceptual attributes grouped into six vocalic categories are specified. A novel graphical interface is used to obtain discriminative responses both globally and for each attribute from ten pairs of test sentences produced by the same and different speakers. The FVEs should discriminate whether the attributes are similar or different in each pair and should indicate the degree to which the attributes are present. In addition, for six specific attributes, the FVEs must decide whether the attribute is typical or atypical in the reference population. In the second stage, the mean score obtained in the first stage is converted to LR using probability density functions of listeners' responses to 1680 same/different speaker pairs discriminated for female and male speakers.The responses of the FVEs to the test pairs show the discriminatory power of the attributes, the incidence of the typicality factor on the final score and the performance of each FVE. With the application of the probability density functions obtained for the responses to pairs of the same or different origin taken from the reference population, the final scores are converted into LRs that are compared with the true conditions of each pair.The application of the developed system allows the global and discriminated evaluation of the perceptual attributes with high agreement in the comparison of pairs of voices. Obtaining the LRs allows associating the perceptual evaluation method with the automatic methods that are used nowadays. The responses of the FVEs taken as a reference, will allow training and evaluating the performance of young FVEs.
LaTLS: A Lattice‐Based TLS Proxy Protocol

The function of the Internet proxy is to check and convert the data exchanged between client and server. In fact, the two-party secure communication protocol with good security is turned into an unsafe multiparty protocol. At present, there are relatively few proxy protocols that can be applied in practice. This paper analyzes the classic agent protocol mcTLS and pointed out the security issues. We focus on the security of TLS 1.3 and proposed a lattice-based multi-party proxy protocol: LaTLS. LaTLS can be proved secure in the eCK model, it can resist key-sharing attacks, counterfeiting attacks, replay attacks, and achieve forward security. Compared with traditional DH and ECDH schemes, LaTLS is more effcient. Its security is based on the shortest vector problem, therefor it has anti-quantum attack properties.
A Conceptual Architecture for Deep-learning-based Video-Objects Retrieving System

Finding content in videos or video-objects retrieving systems has been a matter of concern for many years until now. Regularly, to retrieve and explore content in videos, the traditional solution is that we oblige to use the human eyes and examine those videos manually until we obtain the content that appears in the video. To speed up the search, we can fast-forward the video or predict when the content appeared. Nevertheless, this method often consumes a lot of time, especially when we need to retrieve content in a large number of Videos. In this paper, we introduce a deep-learning-based architecture that allows retrieving objects in videos instantly to search objects in CCTV cameras. This solution promises to bring high efficiency in finding video content, thereby saving costs and human resources to operate related systems.
Re‐EnGAN: Unsupervised image‐to‐image translation based on reused feature encoder in CycleGAN

As an advanced task for image synthesis without labelled data, unsupervised image-to-image translation refers to the overall conversion of a certain characteristic image domain X to another domain Y. The key point is to learn a mapping relationship that can be transformed between different image domains. Existing methods mainly adopt GANs to generate authentic images. While the discriminators will be abandoned after the training process is completed. In order to avoid this waste of training resources, a feature encoder reusing method is proposed, which could reduce the number of parameters and accelerate the training speed. In addition, we add the adaptive perceptual loss for the purpose of paying more attention to the quality of generated images. This loss directly uses the encoder during training to perform feature-level constraints, and applies the L1-norm on the intermediate feature layer of the generated samples. The experiments illustrate that our framework can generate more natural images and provide an effective solution for unsupervised translation.
A Chaincode-based Framework for Securing Patient Health Information in Hospital Environment

Goal of any healthcare system is to provide good service to the public in a most effective manner. The hospital healthcare system is a common platform for people to access the health related services, anytime and anywhere. This service is completely driven by the regulations and policies of government and hospital administration. At present most hospital systems in India lack a secure mechanism for maintaining and managing patient health records. This is a crucial capability which a hospital healthcare system must have. Failure to have a robust system will cause multiple issues like inaccessibility when required, wrongfully sharing patient health record or allowing unethical players to access the health records. This will ultimately lead to lack of trust in the system Most of the current hospital healthcare systems store information centrally and provide access to the connected nodes as required. This architecture leads to single point of failure when central server is down or crashes or gets hacked. To overcome this problem, we have proposed a Chaincode-based framework to secure patient health information in hospital environment. Chaincode is a smart contract program which gets executed on Hyperledger Fabric Blockchain network automatically. A Chaincode stores the information in decentralized digital ledger and creates a secured and private blockchain network with trusted nodes and provide data visibility, data security, and data transparency with respect to patient health record at any time. This technology could solve the above said problem in more efficient way and provide better visibility and transparency to patient health data with more security.
Continuous Riemannian Geometric Learning for Sleep Staging Classification

Sleep staging classification has recently received a lot of attention because of its importance and has shown remarkable achievements through deep neural models, but lacks consideration of geometrical structure or continuous time. In this paper, we propose to exploit a diffeomorphism mapping between Riemannian manifolds and a Cholesky space. Further, in order for continuous modeling, we devise a continuous manifold learning method by integrating a manifold ordinary differential equation and a gated recurrent neural network. We demonstrate the validity of our proposed method through experiments using a publicly available SleepEDF-20 dataset.
Transformer-Based Neural Texture Synthesis and Style Transfer

Texture modeling has been a research hotspot for long, containing topics of neural texture synthesis and neural style transfer, have gained significant attention from both industry and academia. Prior arts prevalently utilized Convolutional Neural Networks as basis for performing neural texture synthesis and neural style transfer tasks, however, they hardly explore other deep neural architectures. Is convolutional network a must for texture modeling tasks? In this work, we explore this problem by introducing a novel framework along with novel optimization objectives for Transformer-based texture synthesis and style transfer. We proposed a novel texture description metric which works well in the feature space of Transformers, and more lightweight than Gram-based texture descriptors. We also proposed pixel-level and patch-level smoothing regularizations to help the generative process. Our approach shows significant improvement upon the baseline and generates favorable results, showing that we can make use of Transformers’ long-range dependencies to perform texture modeling and style transfer tasks without the help of convolutional layers.
Progressive GANomaly: anomaly detection with progressively growing GANs

In medical imaging, obtaining large amounts of labeled data is often a hurdle, because annotations and pathologies are scarce. Anomaly detection is a method that is capable of detecting unseen abnormal data while only being trained on normal (unannotated) data. Several algorithms based on generative adversarial networks (GANs) exist to perform this task, yet certain limitations are in place because of the instability of GANs. This paper proposes a new method by combining an existing method, GANomaly, with progressively growing GANs. The latter is known to be more stable, considering its ability to generate high resolution images. The method is tested using Fashion MNIST, Medical Out-of-Distribution Analysis Challenge (MOOD), and in-house brain MRI; using patches of size 16x16 and 32x32. Progressive GANomaly outperforms a one-class SVM or regular GANomaly on Fashion MNIST. Artificial anomalies are created in MOOD images with varying intensities and diameters. Progressive GANomaly detected the most anomalies with varying intensity and size. Additionally, the intermittent reconstructions are proven to be better from progressive GANomaly. On the in-house brain MRI dataset, regular GANomaly outperformed the other methods.
eSAM: Atendance System Using QR Codes in Romblon State University-Cajidiocan Campus

Checking students&#x0027; attendance has transformed from being a manual to an automatic process due to the support of technology. When it comes to putting in place attendance procedures that help the school generate attendance reports automatically, the Attendance System has played a key role. An eSAM system based on a server and mobile device is proposed in this study, along with a QR code that is provided to students at the beginning of lectures. The system generates QR codes that contain course, section, subject, date, and IN/OUT information. For attendance confirmation, students will scan a QR code on their mobile phones and upload the data to a server. Facial photos must be taken and sent to a server by students so that they can be stored in the system. On a daily or monthly basis, the system creates summaries of student attendance for teachers and the school, helping to keep an electronic record of students&#x0027; attendance while reducing paper usage to a minimum. On a particular day, students are notified by email that they successfully attended the lecture. It was found that instructors or school administrators can generate summary reports of students&#x0027; attendance status, including the date, time in/out, and number of present, absent, and late students in each subject or section handled by the instructors, as well as provide a copy of the summary reports in excel or pdf format by using the eSAM system.
Multi-Agent Reinforcement Learning with Optimal Equivalent Action of Neighborhood

In a multi-agent system, the complex interaction among agents is one of the difficulties in making the optimal decision. This paper proposes a new action value function and a learning mechanism based on the optimal equivalent action of the neighborhood (OEAN) of a multi-agent system, in order to obtain the optimal decision from the agents. In the new Q-value function, the OEAN is used to depict the equivalent interaction between the current agent and the others. To deal with the non-stationary environment when agents act, the OEAN of the current agent is inferred simultaneously by the maximum a posteriori based on the hidden Markov random field model. The convergence property of the proposed methodology proved that the Q-value function can approach the global Nash equilibrium value using the iteration mechanism. The effectiveness of the method is verified by the case study of the top-coal caving. The experiment results show that the OEAN can reduce the complexity of the agents’ interaction description, meanwhile, the top-coal caving performance can be improved significantly.
Sentiment Analysis for Arabic Tweets on Covid-19 Using Computational Techniques

The Coronavirus pandemic has affected the regular course of life. Usage of social media like Twitter is rapidly increasing in Arab&#x2019;s world regarding this phenomenon that has taken over the world by storm. This platform allows Arabian to easily write comments and share their feelings, thoughts and suggestions that can be positive or negative comments. This paper examines the Arabic sentiment analysis of Coronavirus-related tweets, as well as how Arab sentiment has changed over time in various countries. The goal of this study is to extract Arabic tweets from different periods of the pandemic and apply various preprocessing operations to them. Furthermore, the different state-of-art deep learning and machine learning classifiers are applied on the dataset and the accuracy of the classifiers are evaluated using several visualization tools. This paper focused on predictive modelling of tweets to find how the people&#x2019;s opinion keeps on dwindling with the change of time during the course of time before, during and post pandemic. It also analyzes the different facts reveled before and after lockdown post pandemic, performing sentiment analysis to justify the claims that social media is not the reliable source to take preventive measures by the government agencies, imposing any decision. Deep learning algorithm has achieved more accuracy than machine learning in both periods, in the peck pandemic period, the RFC accuracy was around 83&#x0025; where the DNN had 84&#x0025;.
Ensemble updating of categorical state vectors

Abstract An ensemble updating method for categorical state vectors is proposed. The method is based on a Bayesian view of the ensemble Kalman filter (EnKF). In the EnKF, Gaussian approximations to the forecast and filtering distributions are introduced, and the forecast ensemble is updated with a linear shift. Given that the Gaussian approximation to the forecast distribution is correct, the EnKF linear update corresponds to conditional simulation from a Gaussian distribution with mean and covariance such that the posterior samples marginally are distributed according to the Gaussian approximation to the filtering distribution. In the proposed approach for categorical vectors, the Gaussian approximations are replaced with a (possibly higher order) Markov chain model, and the linear update is replaced with simulation based on a class of decomposable graphical models. To make the update robust against errors in the assumed forecast and filtering distributions, an optimality criterion is formulated, for which the resulting optimal updating procedure can be found by solving a linear program. We explore the properties of the proposed updating procedure in a simulation example where each state variable can take three values.
Detection of Malicious Cyber Fraud using Machine Learning Techniques

As the technology and internet have come to their dawn, the rate of cyber-crimes has also increased. This increases the risk of information insecurity and the spread of crimes such as spam, farming and phishing, financial fraud, etc. Particularly, the attackers/hackers spread malicious uniform resource locators (URLs) to exploit vulnerabilities of the system and gain the personal information of the users. Thus, a study on malicious URL detection is necessary to prevent such attacks. Several studies exist which show numerous ways to determine malicious URLs based on machine learning (ML) and deep learning (DL), but there are some problems, for example, malicious features cannot be extracted efficiently. In this research, a model is proposed to ascertain malicious URLs, which is formulated on random forest, support vector machine (SVM), deep neural network (DNN), convolutional neural network (CNN). The several datasets are considered containing malicious and benign URLs to train the model to detect URL behaviour and attributes. The empirical results show that the suggested method can detect malicious URLs efficiently, based on URL behaviour and attributes. Thus, the solution may be advised as an efficient and reliable solution for the problem of malicious URL detection.
Comparison and Analysis between Different Versions of FXAA

FXAA is a significant algorithm to study image anti - aliasing algorithm. FXAA was introduced by Nvidia&#x0027;s Timothy Lottes in 2009 and has evolved over time to its current version, FXAA3.11. The idea behind FXAA&#x0027;s algorithm is simple: to do anti-aliasing in an image, you need to distinguish which pixels are aliased and which are not. Based on the causes and everyday experience of image jagging, we know that it usually occurs at the edges of objects that present a high contrast with the background. So FXAA algorithm is the core idea of just two points: edge decision algorithm and edge pixels of mixed factor calculation, because FXAA algorithm in the process of our development spawned many variant versions. Our paper will focus on analysis of the three versions. We also conduct both visualization and objective evaluation among different versions, from which we can find different versions of the FXAA about image quality and the performance of the algorithm.
On Character Sums in Finite Fields

For polynomial character sums in finite fields, an analog of the known conjecture on the distribution of values of the Kloosterman sums is proposed.
Efficient Light-Weight Deep Neural Network for Person Detection in Drone Images

In this paper, we propose an efficient light-weight deep neural network model for small object (person) detection in drone images. The proposed method performs light-weight as well as efficient small object detection by removing the head layers that detects large and medium-sized objects. In addition, the feature was extracted by focusing the weight on the small object while performing feature fusion through the Weighting Module. Finally, since the class imbalance problem between the object and the background is more serious in the drone image, the problem is alleviated by using the focal loss. As a result, the light-weight that can be mounted on the drone and the inference time are faster, and the Average Precision (AP) is higher than the original model.
Robust Filter-Based Visual Navigation Solution with Miscalibrated Bi-Monocular or Stereo Cameras

This work addressed the problem of miscalibration or decalibration of mobile stereo/bi-monocular camera setups. We especially focused on the context of autonomous vehicles. In real-world conditions, any optical system is subject to various mechanical stresses, caused by vibration, rough handling, collisions, or even thermal expansion. Such mechanical stresses change the stereo pair geometry, and as a consequence, the pre-calculated epipolar geometry or any geometric-based approach is no longer valid. The standard method, which consists of estimating the calibration online, fails in such harsh conditions. The proposed method was based on a robust linearly constrained state estimation technique able to mitigate the model mismatch without estimating the model parameters. Therefore, our solution was able to mitigate the errors with negligible use of additional computing resources. We propose to use a linearly constrained extended Kalman filter for a stereo-based visual odometry or simultaneous localization and mapping approach. Simulations confirmed that the method kept the system (and objects of the map) localized in real-time even with huge miscalibration errors and parameter variations. The results confirmed that the method was robust to a miscalibration of all the extrinsic calibration parameters even when the standard online calibration procedure failed.
Fuzzy-Dedup: A secure deduplication model using cosine based Fuzzy interference system in cloud application

Storage consumption is increasing significantly these days, with consumers trying to find an effective approach to safe storage space. In these situations, a deduplication in cloud storage services is a significant way to reduce bandwidth and service space by omitting unnecessary information and keeping only a single copy of the information. This raises computational, privacy and storage issues when large numbers of handlers outsource the similar data to cloud service storage. To overcome these problems, an effective Fuzzy-Dedup framework is designed in this research by integrating four steps namely is introduced, which breaks down the data into fixed size chunks and is immediately fingerprinted by a hashing algorithm for ensuring data authentication and then indexing is done with the help of traditional b-tree indexing, similarity function is calculated to compute the similarity value in the documents. After calculating the similar values, the fuzzy interference system is designed by formulating appropriate rules for the decision-making process that determines duplicate and non-duplicate files by obtaining an effective de-duplication ratio over existing methods. After detecting duplicate files, the inline based deduplication policy checks that the new data is ready to send for storage against existing data and does not store any redundant data it discovers. The proposed model is implemented in MATLAB software is carried out several performance metrics and these parameter attained better performance such as, deduplication ratio of 1.2, memory utilization of 12500 bytes in inline and 9550 bytes in offline, throughput of 32500 Mb/s in inline and 25500 Mb/s in offline and processing time of 0.4494 s in inline and 0.1139 s in offline. Thus when compared to previous methods, such as Two Thresholds Two Divisors deduplication (TTTD) approach proposed design shows high range of performance.
Distributed Feature Sets for Document Specific Key-Phrase Extraction

The need for knowledge and the satisfaction of this need is the fastest growing market in the world. Among all types of data, the textual documents are an incredible source of knowledge. In order not to lose track of this rapid development of information diversity, it is necessary to provide schemes that make it possible to filter out and present specific information from this huge amount of textual data. Since then, key-phrase extraction methods have started to gain importance. A key-phrase extraction system provides a selection of relevant data in textual documents based on languages and corresponding extraction rules. To extract the important concepts from the documents, the system should be able to use special features and self-identify properties of the words in the texts and properties of the documents. In this article, we developed and discussed different sets of features which are unrestricted to form, size and organization of the documents, i.e. a novel set of regular, advanced and external knowledge-based features are proposed. To selectively combine the best features from all three sets of features here we deploy the two different automatic feature selection techniques. Four different datasets are used here to evaluate the performance of the individual, combined and best selected features. The dynamic programming-based feature selection approaches significantly improves the performance in contrast to the different feature sets (individual and combined both) and state-of-the-art.