Reviewerly/exHarmony huggingface dataset:
Embedded Insertion Functions for Opacity Enforcement

We investigate the enforcement of opacity, an information-flow privacy property, using insertion sequences that modify the output of the system by event insertions. Previous work considered the problem of enforcing the opacity under the assumption that the insertion functions were based on the observed system strings. Now, we investigate the more powerful method of insertion sequences based on the exact system states and events. In this case, the insertion function would be embedded into the system itself, rather than being an output interface. In this article, we develop methods that verify if a valid insertion function exists in this setting; if one exists, synthesize one using a computationally effective algorithm; and investigate a special case where it is possible to verify and synthesize a valid embedded insertion function with polynomial complexity in the size of the system.
WITHDRAWN: A study on cloud computing challenges and its mitigations

Cloud computing Provides resource security service over the internet using the data center, under the risk of multiple attacks. Interoperability in cloud computing will share the information from one to another cloud environment over the internet. Sharing information between user and server, there will be a risk attacking the information. Interoperability deals with security and privacy. It is a major development; it is convenient for the individual user as well as for the enterprise-level. Frequently the private and public sectors are using. Organizations and enterprises are transferring the data into the cloud. There is a possibility of one user can operate another user’s information present on the cloud. Due to the security issues they provide a virtual machine for the user, the machine has a separate user id and password to access the cloud. CC is popular in the business, and public sectors. It has hedge method, and also provide the suspension for the existing attack. In this report, we led an analysis of the attacks in cloud computing. We discussed a whole range of different approaches to these attacks.
Attitude towards mobile learning among resident doctors involved in undergraduate medical education at a government medical college in Delhi, India

BACKGROUND: There is growing recognition of the role of mobile learning (M-learning) for undergraduate (UG) academic education and teaching purposes, but teacher attitudes toward it can be variable.OBJECTIVE: To determine the attitudes toward the incorporation of M-learning methods for UG medical education among resident doctors at a government medical college in Delhi, India.METHODS: A cross-sectional study was conducted for 3-months duration (2019) among 60 final year junior and senior resident doctors. The participants were selected from all the preclinical and paraclinical department, and one clinical department selected randomly based on the probability proportion to size method. The data was collected using self-administered instruments including the modified 20-item M-learning Perception Scale (MLPS).RESULTS: Social media (36.7%) and instant messaging platforms (85%) were routinely used by the participants for exchanging academic (medical) and health-related information. The mean score for all the MLPS item responses was >3, indicating positive attitudes toward M-Learning. The participants expressed maximum agreement with the views stating M-Learning can “supplement traditional teaching,” was “reliable for personal use,” and “improves the quality of lessons.” The responses of those participants having preexisting familiarity with health information and education portals, and those aware of massive open online courses correlated significantly with higher MLPS scores.CONCLUSION: M-learning is visualized as an increasingly relevant teaching and learning medium by early-career resident doctors involved in UG medical education in India.
IBaggedFCNet: An Ensemble Framework for Anomaly Detection in Surveillance Videos

The prevalent use of surveillance cameras in public places and advancements in computer vision warrant most sought-after research in the domain of anomalous activity detection.Anomaly detection has shown promising applications for suspicious activity detection.In this paper, we propose a bagging framework IBaggedFCNet that leverages the power of ensembles for robust classification to detect anomalies in videos.Our approach, which investigates state-of-the-art Inception-v3 image classification network, requires no video segmentation prior to feature extraction that can produce unstable segmentation results and cause a high memory footprint.We show improvement empirically on multiple benchmark datasets, most prominently on the UCF-Crime dataset.Moreover, we experiment with different ensemble fusion methods, including static and dynamic techniques, and also prove our single model's predictive accuracy in localizing anomaly in surveillance videos.
The concept of a unified geoinformational space of the region: ecological aspect

The principles of describing a single information space as a key component of the substance-energy-information triad as applied to a geographically limited area are considered. An open structure of the knowledge base is proposed, which provides storage and development of a multidimensional model of this geoinformation space based on the study of the connectivity and semantic interoperability of measurable components. An approach to modeling the intellectual space of the region as one of the layers of a single geoinformation space is proposed.
Arbitrated quantum signature scheme based on quantum walks

&lt;sec&gt;Quantum signature is quantum counterpart of classical digital signature, which has been widely applied to modern communication, such as electronic payment, electronic voting and electronic medical, owing to its great implication in ensuring the authenticity and the integrity of the message and the non-repudiation. Arbitrated quantum signature (AQS) is an important and practical type of quantum signature. The AQS algorithm is a symmetric key cryptography-based quantum signature algorithm, and its implementation requires the trusted arbitrator to be directly involved. In this paper, employing the key-controlled chained CNOT (KCCC) operation as the appropriate encryption (decryption) algorithm, we suggest an AQS scheme based on teleportation of quantum walks with two coins on a four-vertex cycle, which is used to transfer the message copy from the sender to the receiver. In light of the model of teleportation of quantum walks, the sender encodes the message to be signed into her or his coin state, and the necessary entangled states can be created as a result of the conditional shift between the coin state and the position state. The measurements performed on the generated entangled states are the bases of signature production and message recovery. Then according to the classical measurement results from the sender, the receiver performs the appropriate local unitary operations (i.e., Pauli operations) on his own coin state to recover the original message and further verifies the validity of the completed signature by using the appropriate verification algorithms under the aid of the trustworthy arbitrator. &lt;/sec&gt;&lt;sec&gt;The suggested AQS scheme makes the following contributions: 1) the necessary entangled states for quantum teleportation of message copy do not need preparing in advance, and they can be produced automatically by the first step of quantum walks; 2) the scheme satisfies the features of non-repudiation, un-forgeability and non-disavowal due to the use of the KCCC operation; 3) the scheme may be achieved by linear optical elements such as beam splitters, wave plates, etc., because quantum walks have proven to be realizable in different physical systems and experiments.&lt;/sec&gt;&lt;sec&gt;Analysis and discussion show that the proposed AQS scheme possesses the impossibility of disavowals by the signer and the receiver and impossibility of forgeries by anyone. Comparisons reveal that the designed AQS protocol is favorable. Furthermore, it provides an idea by employing the quantum computing model into quantum communication protocols with a possible improvement with respect to its favorable properties, for example, the automatic generation of entangled states via the first step of quantum walks on different models. In the near future, we will further investigate the production of entanglement by quantum walks and its applications with some improvements in designing the quantum communication protocols.&lt;/sec&gt;
3D shape measurement in the presence of strong interreflections by using single-pixel imaging in a camera-projector system

Optical 3D shape measurements, such as fringe projection profilometry (FPP), are popular methods for recovering the surfaces of an object. However, traditional FPP cannot be applied to measure regions that contain strong interreflections, resulting in failure in 3D shape measurement. In this study, a method based on single-pixel imaging (SI) is proposed to measure 3D shapes in the presence of interreflections. SI is utilized to separate direct illumination from indirect illumination. Then, the corresponding points between the pixels of a camera and a projector can be obtained through the direct illumination. The 3D shapes of regions with strong interreflections can be reconstructed with the obtained corresponding points based on triangulation. Experimental results demonstrate that the proposed method can be used to separate direct and indirect illumination and measure 3D objects with interreflections.
Representing and Linking Dunhuang Cultural Heritage Information Resources Using Knowledge Graph

This study employs a knowledge graph approach to realize the representation and association of information resources, promote the research, teaching, and dissemination of Dunhuang cultural heritage (CH). The Dunhuang Mogao Grottoes is a UNESCO world CH site, and digitization of Dunhuang CH has produced a large amount of information resources. However, these digitized resources continue to lack the systematic granular semantic representation required to correlate Dunhuang cultural heritage information (CHI) in order to facilitate efficient research and appreciation. To respond to this need, new approaches for representing CHI are being developed. This study identifies five facets and their semantic relationship to Dunhuang CH, constructs an ontology model to regulate the entities, attributes, and relationships of Dunhuang CH knowledge, and subsequently processes the resulting data using various techniques (such as semantic annotation and entity association) to facilitate rendering the data in a knowledge graph construction. Finally, we constructed a DH-oriented knowledge graph service platform in order to provide a user friendly visual display and semantic retrieval service.
WITHDRAWN: People detection and counting using YOLOv3 and SSD models

Abstract Object detection has become a crucial task for the various applications used in the real world such as surveillance, security, and automated vehicle system. The counting of the numbers of peoples at any junction also having various applications to provide integrity to any task. To count the number of peoples at any junction, we have various methods. Among the present methods, we analyzed the two algorithms that are You Only Look Once (YOLOv3) and Single Shot multi-box Detector (SSD). Two tasks were performed independently; one is for object detection by using the image dataset, and the other one is the counting of objects by using the video dataset. In this research, these two methods are analyzed for counting as well as detection efficiency, and comparison is presented. The results have shown that the precision, recall, and F1 measure achieved for SSD is higher than YOLOV3 v3.
Review of Methods for Ensuring Anonymity and De-Anonymization in Blockchain

Nowadays a lot of blockchain solutions are presented, in which to some degree implemented methods of anonymization, that allows network participants to hide their operations. Among existing projects, cryptocurrencies stand out: Bytecoin, Monero, Dash, TON (Telegram Open Network), ZCash, Loki, Grin, Beam, Verge, ZCoin, Horizen (ZenCash), BlackCoin, NavCoin, SmartCash, ZeroVert, PIVX, AEON, Fantomcoin and Ring Coin. There are also the privacy enhancing technologies on which they are based: CoinJoin, Mimblewimble, ZK-SNARKs and others. This article studies renowned technologies and methods of de-anonymization and suggests possible directions for further research.
Explainable Deep One-Class Classification

Deep one-class classification variants for anomaly detection learn a mapping that concentrates nominal samples in feature space causing anomalies to be mapped away. Because this transformation is highly non-linear, finding interpretations poses a significant challenge. In this paper we present an explainable deep one-class classification method, Fully Convolutional Data Description (FCDD), where the mapped samples are themselves also an explanation heatmap. FCDD yields competitive detection performance and provides reasonable explanations on common anomaly detection benchmarks with CIFAR-10 and ImageNet. On MVTec-AD, a recent manufacturing dataset offering ground-truth anomaly maps, FCDD sets a new state of the art in the unsupervised setting. Our method can incorporate ground-truth anomaly maps during training and using even a few of these (~5) improves performance significantly. Finally, using FCDD's explanations we demonstrate the vulnerability of deep one-class classification models to spurious image features such as image watermarks.
Leveraging Automated Machine Learning for Text Classification: Evaluation of AutoML Tools and Comparison with Human Performance

Recently, Automated Machine Learning (AutoML) has registered increasing success with respect to tabular data. However, the question arises whether AutoML can also be applied effectively to text classification tasks. This work compares four AutoML tools on 13 different popular datasets, including Kaggle competitions, and opposes human performance. The results show that the AutoML tools perform better than the machine learning community in 4 out of 13 tasks and that two stand out.
Orthogonalizing Convolutional Layers with the Cayley Transform

Recent work has highlighted several advantages of enforcing orthogonality in the weight layers of deep networks, such as maintaining the stability of activations, preserving gradient norms, and enhancing adversarial robustness by enforcing low Lipschitz constants. Although numerous methods exist for enforcing the orthogonality of fully-connected layers, those for convolutional layers are more heuristic in nature, often focusing on penalty methods or limited classes of convolutions. In this work, we propose and evaluate an alternative approach to directly parameterize convolutional layers that are constrained to be orthogonal. Specifically, we propose to apply the Cayley transform to a skew-symmetric convolution in the Fourier domain, so that the inverse convolution needed by the Cayley transform can be computed efficiently. We compare our method to previous Lipschitz-constrained and orthogonal convolutional layers and show that it indeed preserves orthogonality to a high degree even for large convolutions. Applied to the problem of certified adversarial robustness, we show that networks incorporating the layer outperform existing deterministic methods for certified defense against l2-norm-bounded adversaries, while scaling to larger architectures than previously investigated.
Fake Job Prediction using Sequential Network

With increased number of data and privacy breaches day-by-day it becomes extremely difficult for one to stay safe online. Number of victims of fake job posting is increasing drastically day by day. The companies and fraudsters lure the job-seekers by various methods, majority coming from digital job-providing web sites. We target to minimize the number of such frauds by using Machine Learning to predict the chances of a job being fake so that the candidate can stay alert and take informed decisions, if required. The model will use NLP to analyze the sentiments and pattern in the job posting. The model will be trained as a Sequential Neural Network and using very popular GloVe algorithm. To understand the accuracy in real world, we will use trained model to predict jobs posted on Linked In. Then we worked on improving the model through various methods to make it robust and realistic.
A plexus‐convolutional neural network framework for fast remote sensing image super‐resolution in wavelet domain

Satellite image processing has been widely used in recent years in a number of applications such as land classification, Identification transfer, resource exploration, super-resolution image, etc. Due to the orbital location, revision time, quick view angle limitations, and weather impact, the satellite images are challenging to manage. There are many types of resolution, such as spatial, spectral, and temporal. Still, in our case, we concentrated on spatial image resolution to super resolve the images from low-resolution images. For remote sensing image super-resolution fast wavelet-based super-resolution (FWSR), we propose a novel, fast wavelet-based plexus framework that performs super-resolution convolutional neural network (SRCNN)-like extraction of features based on three hidden layers. First, wavelet sub-band images are combined into a pre-defined full-scale data training factor, including approximation and interchangeable stand-alone units (frequency sub-bands). Second, to speed up image recovery, mapping the sub-band image of the wavelet is then measured using its approximate image. Third, the added sub-pixel layer at the end of the network model is intended to reproduce image quality using a plexus framework. The approximation sub-band images obtained after discrete wavelet transform wavelet decomposition are used as input rather than the original image because of their high-frequency data and preserved characteristics. Five current super-resolution neural network approaches are compared with the proposed technique and tested on three pubic satellite image datasets and two benchmark datasets. The experimental findings are well compared qualitatively and quantitatively.
E-waste information security protection motivation: the role of optimism bias

Purpose Electronic waste (e-waste) such as discarded computers and smartphones may contain large amounts of confidential data. Improper handling of remaining information in e-waste can, therefore, drive information security risk. This risk, however, is not always properly assessed and managed. The authors take the protection motivation theory (PMT) lens of analysis to understand intentions to protect one's discarded electronic assets. Design/methodology/approach By applying structural equation modeling, the authors empirically tested the proposed model with survey data from 348 e-waste handling users. Findings Results highlight that (1) protection intention is influenced by the perceived threat of discarding untreated e-waste (a threat appraisal) and self-efficacy to treat the discarded e-waste (a coping appraisal) and (2) optimism bias plays a dual-role in a direct and moderating way to reduce the perceived threat of untreated e-waste and its effect on protection intentions. Originality/value Results support the assertions and portray a unique theoretical account of the processes that underline people's motivation to protect their data when discarding e-waste. As such, this study explains a relatively understudied information security risk behavior in the e-waste context, points to the role of optimism bias in such decisions and highlights potential interventions that can help to alleviate this information security risk behavior.
Emotional Semantics-Preserved and Feature-Aligned CycleGAN for Visual Emotion Adaptation

Thanks to large-scale labeled training data, deep neural networks (DNNs) have obtained remarkable success in many vision and multimedia tasks. However, because of the presence of domain shift, the learned knowledge of the well-trained DNNs cannot be well generalized to new domains or datasets that have few labels. Unsupervised domain adaptation (UDA) studies the problem of transferring models trained on one labeled source domain to another unlabeled target domain. In this article, we focus on UDA in visual emotion analysis for both emotion distribution learning and dominant emotion classification. Specifically, we design a novel end-to-end cycle-consistent adversarial model, called CycleEmotionGAN++. First, we generate an adapted domain to align the source and target domains on the pixel level by improving CycleGAN with a multiscale structured cycle-consistency loss. During the image translation, we propose a dynamic emotional semantic consistency loss to preserve the emotion labels of the source images. Second, we train a transferable task classifier on the adapted domain with feature-level alignment between the adapted and target domains. We conduct extensive UDA experiments on the Flickr-LDL and Twitter-LDL datasets for distribution learning and ArtPhoto and Flickr and Instagram datasets for emotion classification. The results demonstrate the significant improvements yielded by the proposed CycleEmotionGAN++ compared to state-of-the-art UDA approaches.
Intracranial volume segmentation for neurodegenerative populations using multicentre FLAIR MRI

Intracranial volume (ICV) segmentation, also known as brain extraction or skull-stripping, is a critical preprocessing step in analytical pipelines for studying neurodegenerative diseases in magnetic resonance imaging (MRI). While the fluid-attenuated inversion recovery (FLAIR) MRI modality has emerged as an important sequence for analyzing cerebrovascular and neurodegenerative disease, most existing automated ICV segmentation methods have been developed for T1-weighted or multi-modal inputs. Additionally, many methods have been designed using single centre data of healthy subjects and encounter difficulties using images with varying acquisition parameters and neurodegenerative pathology. In this work, we develop and evaluate 2 traditional and 8 deep learning algorithms for ICV segmentation in FLAIR MRI. Training and testing were completed on 175 ​vol (8317 images) from 2 dementia and 1 vascular disease cohort. A human phantom FLAIR MRI dataset from a repeatedly scanned, healthy individual was also utilized for reliability analysis. Images were acquired from 47 imaging centres with varying scanners and parameters. To measure and compare performance, we present a novel framework for evaluating the effectiveness of computer generated segmentations on multicentre datasets. The evaluation framework includes assessments of algorithm accuracy, generalization capabilities, robustness to pathology and spatial location, and volumetric measurement reliability – all important dimensions for establishing proof of effectiveness (a prerequisite to clinical translation). The top performing method was a multiple resolution U-Net (MultiResUNet), which achieved a mean Dice similarity coefficient greater than 98% and was robust across pathology levels and spatial locations. Our results confirm a FLAIR-based ICV analytical pipeline can alone be utilized for large-scale neurodegenerative disease research. The presented evaluation framework can be deployed by other researchers to assess the viability of tools proposed for automated analysis of diverse, clinical MRI datasets.