Reviewerly/exHarmony huggingface dataset:
A proxy-protected proxy signature based on SM9

Proxy-protected proxy signature scheme not only has the function of signature conversion, but also realizes the protection of the proxy signer. In this paper, we present a proxy-protected proxy signature scheme based on SM9. Identity-based cryptosystem is used in our scheme, which solves the certificate management problem of Guo&#x0027;s proxy-protected proxy signature based on SM2. Furthermore, the proposed scheme satisfies verifiability, identifiability, distinguishability, non-repudiation and strong unforgability.
Important-unit Coverage for Recurrent Neural Network

Nowadays, many latest systems are typical cyber physical systems (CPS), such as self-driving systems, medical monitoring, industrial control systems and robotics systems. Some of these fields involve speech emotion recognition based on deep learning technology. Therefore, the safety issues brought by deep neural networks cannot be ignored. Recurrent neural network (RNN) is one of several mainstream directions in speech emotion recognition. However, limited research has been done on RNN testing. In this paper, we define important-unit coverage metric for a classic RNN architecture, long short-term memory network (LSTM), to guide the generation of test cases and measure the test adequacy. We implement our experiments on a speech emotion dataset named Emo-DB. We also compare our method with some existing test coverage metrics for RNN. Experimental results show that we have consistent performance comparing with these metrics and can generate more test cases than neuron coverage.
FedDeepFM: Ad CTR prediction based on Federated Factorization Machine

Personal Advertising recommendation has become one of the main sources of income for Internet companies such as Google and Facebook. The prediction of ad click-through rate achieved through machine learning, deep learning and other technologies can effectively improve the accuracy of ad recommendation. However, the large amount of user data collected and used in this process is also constantly causing data security issues and user privacy leaks. In order to protect user privacy and fulfill the requirements of relevant laws and regulations, privacy-friendly machine learning methods such as federal learning have gradually attracted the attention of relevant scholars and practitioners. In this paper, we propose a framework of federated factorization machine, which is used to implement the function of advertising click-through rate prediction under the premise of protecting user privacy. First, we designed a user-level distributed factorization machine that only uses the gradient information of each client (instead of the original preference data) to update the model. Secondly, in order to solve the linear aggregation model loss caused by the heterogeneity of user data, we introduced the idea of cluster federation learning in the factorization machine, and designed the framework, FedDeepFM. Finally, we implemented the prototype of FedDeepFM and evaluated it with a real ad click data set. Experimental results show that the accuracy of our proposed framework is 8&#x0025; higher than that of the traditional federated matrix factorization algorithm, and 2.5&#x0025; higher than the federated learning accuracy of a single global model.
Dysarthric Speech Recognition using Multi-Taper Mel Frequency Cepstrum Coefficients

Vast industrial growth has increased the demand of automatic speech recognition for various automation and human machine interaction application. Performance of various artificial intelligence based approaches is limited because of the speech disability caused due to communication disorders, neurogenic speech disorder or psychological speech disorders. The dysarthric disorder is neurogenic speech disorder that limits the human voice articulation capability. This paper presents, dysarthric speech detection using Multi-Taper Mel Frequency Cepstral coefficients (MTMFCC) that is capable to smallest variation over the dysarthric speech. The efficiency of the proposed algorithm is estimated using the K-Nearest Neighbor (KNN) classifier and support vector machine (SVM) based on accuracy, sensitivity and specificity. The system has shown 99.04 &#x0025; and 96.00 &#x0025; accuracy for the MTMFCC+KNN and MTMFCC+SVM which is superior to traditional MFCC.
Analysis of Methodologies to Model the Content for Conveying the Correct Information

Information is found in various forms like Misinformation, Dis-information, Impartial Information, legit and complete information. Content is a derived form of the information created by the content writer for conveying the information. Considering the growing volume of content, it is a tough task to decide on useful and irrelevant content. To deal with such a large volume of data processing, storage is necessary. The irrelevant content causes a waste of time and money for the content creator, consumer, and platform provider as well. Search engine Marketing and spammy techniques rank the content and thereby a website. This type of practice is encouraging inorganic methodologies to boost the rank of content. The use of organic methodologies can provide the solution up to a considerable extent. To design the organic models the research carried out earlier in this field is discussed in this paper. Methodologies like Foraging, Collaborative Filtering, Social Commerce, Micro-Video Prediction, Social Commerce.
Handwritten Devanagari Word Recognition using Customized Convolution Neural Network

Devanagari language comprises 47 primary characters that include 14 vowels and 33 consonants. Nearly 120 scripts have been developed from it like Marathi, Hindi, Bengali, etc. This paper deals with a Marathi handwritten word recognition system. Marathi is an Indian language that is spoken primarily in the state of Maharashtra, most of the government paperwork is done in this language only. This system will mainly be used in recognizing various handwritten words present in the titles of different municipal documents. The main concern regarding these documents was the task of storing them digitally. The existing works have used the method of segmentation and have proposed a hidden Markov model to recognize pseudo characters, but the accuracy can be increased. We have devised a system that will overcome this limitation. In our system, a dataset is created for 2000 samples and by applying different augmentation techniques the dataset size is further increased to 12000 images. The proposed system is implemented using deep learning based Customized Convolution Neural Network which is pre-trained on a manually collected dataset containing various Marathi handwritten words. Out of which 75&#x0025; of the dataset is used for training and 25&#x0025; for testing purposes. This system has proved better results on augmented data with 94&#x0025; accuracy.
Digital Image Forensic: Comparative Scrutiny of Foregoing Techniques

CMF (Copy-move forgery) is one of the most primitive types of image doctoring with least complex tools and effectiveness of manipulation. In its primary form, CMF is nothing but a copy-n-paste process of digital image doctoring wherein some parts of an input image is taken as a copy and then pasted over another location in an image. Hence it is harder to believe what one sees in an image and doubt always remains part of whatever viewed in this digital world. Thus aim of copy-move forgery detection (CMFD) is to find regions in an image which matches different part of an image. Thus, to find similarity is ultimate goal of CMFD. Rotating or resizing of tampered area of image after attachment, is dominant problem that decline the performance of some of the major algorithms. This survey aims to explore different image manipulation detection algorithms and techniques already implemented in digital forensic research field. Depending upon fundamental methodology used these methods can be categorized and then differentiated.
Dynamic Color Recognition for Video Game Controller

In this paper, we have introduce a real-time color recognition framework using openCv and PyObjC API. Hand Gestures with color detection are a natural form of communication between humans as well as between humans and machines. They are also useful in fields like sign language recognition, human-machine interface, and behavior understanding. The focus of creating colors recognition to create better communication between humans and computers for conveying information. The proposed project aims to create a video game controller wherein we can use our color detection and play the video Game on the computer. Several combinations of hand motions can be detected using deep learning and neural networks, rather than simple image processing and machine learning.
Trust Evaluation and Cloud Service Recommendation System Based On Machine Learning Techniques

Cloud computing is a vast platform which provides users with ample services. It offers various benefits such as expense reduction, resource elasticity, managing data and easier services for hosting. At the same time, the aspects such as security, performance and reliability are the key concerns in cloud paradigm. Therefore, trustworthiness in adopting a cloud service becomes an important issue from the end user&#x0027;s viewpoint. Trust represents the degree to which user&#x0027;s expectations about the capabilities of a service are met. This paper proposes trust evaluation and cloud service recommendation system (TECSRS) which concentrates on evaluation of trust using Quality of Service (QoS) parameters. TECSRS uses Machine Learning techniques which include Linear Regression, Artificial Neural Networks (ANN), Random Forest Regression and Support Vector Regression (SVR), in trust computation. TECSRS facilitates recommending a cloud service to the end user and forecasting the trust values of cloud services. Experimental results depict that the Random Forest Regression model outperforms the other machine learning models with regard to accuracy of trust computation.
Analysis of System Requirements by Aspects-J Methodology

Methodology of aspects is a combination of multiple concerns. Multiple types of concerns are facts, logic, area of interest, security, properties of systems. In the Development phase of the aspect model Aspect considers different systems concerns, aspects divide these system concerns into software modules, Different aspects modules use as a way for analysis of systems requirements. In some aspects, methodology use UML design modeling for understanding system requirements, in aspects Methodology UML class is for knowing the system necessities in modeling phases. a UML class structure is used in the Aspects process model for requirement analysis of the system. Class design in UML design consists of various types of attributes, classes, objects, methods, Join of points &#x0026; Point of cut, various approaches to defining various concerns of the system. Few tools for crafting graph grammar rules for analysis system concerns, crafting G-graph grammar rules start from pre-condition G-grammar rules after that crafting post condition G-graph grammar rules, after crafting G-graph pre and post condition grammar rules G-graph transformation process done on rules in tools, next step is a method of creating a matrix, a matrix is basically cross applying rules to each other and find output, Two types of the matrix created first is a matrix of dependency and matrix of conflicts, this matrix for analysis conflict and dependency in crafted G-graph grammar rules, these G-rules apply as input to aspect methodology Tool. Next step transformation G-graph grammar, G-graph rules shows pre and post transformations of G-graph grammar rules when applying matrix of dependencies with a matrix of conflict, the conflict shows clash in G-graph rules, dependencies show requirement among the G-graph rules.
A QFT Approach to Data Streaming in Natural and Artificial Neural Networks

In the actual panorama of machine learning (ML) algorithms, the issue of the real-time information extraction/classification/manipulation/analysis of data streams (DS) is acquiring an ever-growing relevance.They arrive generally at high speed and always require an unsupervised real-time analysis for individuating long-range and higher order correlations among data that are continuously changing over time (phase transitions).This emphasizes the infinitary character of the issue, i.e., the continuous change of the signifying number of degrees of freedom characterizing the statistical representation function, challenging the classical ML algorithms, both in their classical and quantum versions, as far as all are based on the (stochastic) search for the global minimum of some cost/energy function.The physical analogue must be studied in the realm of quantum field theory (QFT) for dissipative systems as biological and neural systems, which are able to map between different phases of quantum fields, using the formalism of the Bogoliubov transform (BT).By applying the BT in a reversed way, on the system-thermal bath energetically balanced states, it is possible to define the powerful computational tool of the "doubling of the degrees of freedom" (DDF), making the choice of the signifying finite number of the degrees of freedom dynamic and then automatic, so to suggest a different class of unsupervised ML algorithms for solving the DS issue.
Analyzing the Effectiveness of Image Augmentations for Face Recognition from Limited Data

This work presents an analysis of the effectiveness of image augmentations for the problem of face recognition from limited data. We considered basic manipulations, generative methods, and their combinations for augmentations. Our results show that augmentations, in general, can considerably improve the quality of face recognition systems and the combination of generative and basic approaches performs better than the other tested techniques.
Use Machine Learning Based Smart Sampling to Improve System Level Testing Efficiency

System level tests (SLTs) are important and expensive procedures to ensure high IC quality. In volume production stage with stable high yields, efforts such as random sampling have been used to improve testing efficiency. However random sampling doesn&#x2019;t fully utilize information gathered before SLT and is not optimal. In this paper we propose both supervised (SVM) and unsupervised (AutoEncoder) machine learning algorithms to predict or estimate SLT failures based on earlier stage Final Test (FT) test data and further use the estimated pseudo probabilities to guide the selecting of dies for system level testing. Experiments on a real product dataset, consisting of 158 wafers, each with 3118 FT testing variables reveal robustness of the models. Through the gains chart of the models, we provide a flexible smart sampling strategy and demonstrate its potential of reducing SLT testing cost by 40&#x0025; with minor impact on Defective Parts Per Million (DPPM). Our cases also show that such smart sampling approach is very well suited for engaging adaptive test flow optimization achieving balanced goals of improving test efficiency, reducing cost and ensuring high product quality at the same time
COOT OPTIMIZATION ALGORITHM FOR PARAMETER ESTIMATION OF PHOTOVOLTAIC MODEL

Because of the technical and environmental advantages of many solar energy sources, their use has recently been rapidly rising. The extraction of the unknown parameters in photovoltaic models is one of the key challenges in the modeling and simulation of solar energy sources. To satisfy the behavior of the solar photovoltaic (SPV) cells, the Single-Diode Model (SDM) is recommended as a more dependable modeling method. In this study, we applied the recently introduced meta-heuristic optimization method that inspires the behavior of the swarm of birds called Coot. This Coot Algorithm is used to estimate the unknown parameters of an SPV cell/module at 33 ºC. Simulation results of this study were compared with other nine different previous Optimization Algorithms, which are Moth Flame Optimization (MFO), Dragonfly Algorithm (DA), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Ant Lion Optimization (ALO), Harris Hawk Optimization (HHO), Hybrid of Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO), Marine Predator Algorithm (MPA), and African Vultures Optimization Algorithm (AVOA). The obtained results of this comparison showed that the Coot algorithm outperformed the previous algorithms in terms of the root mean square error (RMSE) and the degree of convergence between the power versus voltage curve and the current versus voltage curve compared with the measured data. Moreover, the results confirm that the Coot optimization algorithm is favorable in reducing time and improving accuracy.
Discriminative aging subspace learning for age estimation

Human age estimation from facial images has become an active research topic in computer vision field because of various real-world applications. Temporal property of facial aging display sequential patterns that lie on the low-dimensional aging manifold. In this paper, we propose hidden factor analysis (HFA) model-based discriminative manifold learning method for age estimation. The hidden factor analysis decomposes facial features into independent age factor and identity factor. Various age invariant face recognition systems in the literature utilize identity factor for face recognition; however, the age factor remains unutilized. The age component of the hidden factor analysis model depends on the subject's age. Thus it carries significant age-related information. In this paper, we demonstrate that such aging patterns can be effectively extracted from the HFA-based discriminant subspace learning algorithm. Next, we have applied multiple regression methods on low-dimensional aging features learned from the HFA model. Effect of reduced dimensionality on the accuracy has been evaluated by extensive experiments and compared with the state-of-the-art methods. Effectiveness and robustness in terms of MAE and CS of the proposed framework are demonstrated using experimental analysis on a large-scale aging database MORPH II. The accuracy of our method is found superior to the current state-of-the-art methods.
Do emotions influence safe browsing? Toward an electroencephalography marker of affective responses to cybersecurity notifications

Cybersecurity notifications play an important role in encouraging users to use computers safely. Emotional reactions to such notifications are known to positively influence users' adherence to these notifications, though it is challenging for researchers to identify and quantify users' emotional reactions. In this study, we explored electroencephalography (EEG) signals that were elicited by the presentation of various emotionally charged image stimuli provided by the International Affective Picture System (IAPS) and compared signals to those elicited by images of cybersecurity notifications and other computer-related stimuli. Participants provided behavioral assessments of valence and arousal elicited by the images which were used to cross-reference the results. We found that EEG amplitudes corresponding to the late positive potential (LPP) were elevated in reaction to images of cybersecurity notifications as well as IAPS images known to elicit strong positive and negative valence, when compared to neutral valence or other computer-related stimuli. These findings suggest that the LPP may account for emotional deliberation about cybersecurity notifications, which could be a useful measure when conducting future studies into the role such emotional reactions play in encouraging safe computer behavior.
Pronoun <i>TA</i> as a facilitator of empathy in Chinese digital narratives

Abstract This article presents findings pertaining to digital you-narratives in which ungendered third person Chinese pronoun ta is embedded. The study asks what implications the script choice ta, as opposed to gendered 他 ta ‘he’ and 她 ta ‘she’, has for the facilitation of situational empathy when used in conjunction with specific and generic 你 ni ‘you’. The study draws on 131 digital texts from celebrity verified accounts on social media platform Sina Weibo in October 2015. From a Discourse Analytical perspective, the study utilizes pragmatic and textual approaches under a constructivist narrative analysis framework to examine the facilitative role of ta in relation to empathy invoked in readers. The study proposes that ta is a pragmatic resource used to facilitate the co-construction of emergent narratives based on situational empathy. The implications of said findings are discussed along with future avenues of research.
Cyber Security and Internet Banking: Issues and Preventive Measures

The internet brought a new revolution to the financial sector and it has changed the way of operations in the last two decades. Now, people have the option to carry out banking transactions from a place of their choice without having to go to a nearby bank office. E-banking has become an integral part of the banking system and has become a popular method of transaction for the majority of people. A user has a wide range of options for managing his money through numerous internet banking methods. While internet banking is an aid for customers, they still have to be vigilant to keep their accounts safe from cybercriminals and hackers, as everything on the internet is prone to security threats. Internet security measures followed by the majority of the bank sites to protect their information are not up-to-date as compared to the dynamic cyber threats. Such problems have made it easy for confidential financial information to fall into the hands of third parties and cybercriminals. Although there are several security measures to stop data breaches, there are still flaws in these systems. The goal of this study article is to look at the number of cyber security issues in internet banking in India and the consumer’s awareness of these issues and preventive measures used by them. Our research is particularly based on primary data.
Adaptive Text Denoising Network for Image Caption Editing

Image caption editing, which aims at editing the inaccurate descriptions of the images, is an interdisciplinary task of computer vision and natural language processing. As the task requires encoding the image and its corresponding inaccurate caption simultaneously and decoding to generate an accurate image caption, the encoder-decoder framework is widely adopted for image caption editing. However, existing methods mostly focus on the decoder, yet ignore a big challenge on the encoder: the semantic inconsistency between image and caption. To this end, we propose a novel A daptive T ext D enoising Net work (ATD-Net) to filter out noises at the word level and improve the model’s robustness at sentence level. Specifically, at the word level, we design a cross-attention mechanism called Textual Attention Mechanism (TAM), to differentiate the misdescriptive words. The TAM is designed to encode the inaccurate caption word by word based on the content of both image and caption. At the sentence level, in order to minimize the influence of misdescriptive words on the semantic of an entire caption, we introduce a Bidirectional Encoder to extract the correct semantic representation from the raw caption. The Bidirectional Encoder is able to model the global semantics of the raw caption, which enhances the robustness of the framework. We extensively evaluate our proposals on the MS-COCO image captioning dataset and prove the effectiveness of our method when compared with the state-of-the-arts.