Reviewerly/exHarmony huggingface dataset:
Enhancing User Experience for Sustainable Fashion through QR code and Geo-Fencing

Fashion industry is second largest pollution generator, after oil industry. There are various reasons for this, user awareness and affordability for the sustainable fashion is also one of the reasons for the same. There are very less fashion store, which provide the full information about fashion related product to user. Or sometime information is available in such a lengthy manner, that user don't want to access. In this paper we are discussing about a model based on mobile application, which will work on the concept of Geo-fencing and QR code in a coordinated manner, and this will help to enhance user experience. There is a continuous growth on mobile phone users which also provides an easy and affordable platform to user and vendor both to know more about each other, which helps vendor to offer, personalized services as per user interest and user can also get easily all information in his/her mobile. Geo-fencing sends notification to all users who are in range of geo-fencing. Once user is in store, user can get all details about product with the help of QR (Quick response) code installed at the location product. In store mobile application also helps to find location and navigates user to product location. At any point of time, mobile application is not working or in process any stage is not working, immediately a notification will be sent to sales team, so that they can help user. As QR code can store approximately 3-4 MB of data that is good enough for storing information about product. This will help user to know more about product sustainability for environment, which will be equally good for his/her health too.
Breast microcalcifications: the UK RCR 5-point breast imaging system or BI-RADS; which is the better predictor of malignancy?

In the UK RCR 5-point breast imaging system (UKS), radiologists grade mammograms from 1 to 5 according to suspicion for malignancy, however unlike BI-RADS, no lexicon of descriptors is published. The aim of this study was to determine whether strict categorisation of microcalcifications (MCC) according to BI-RADS was a better predictor of malignancy than the UKS and whether these descriptors could be used within the UKS.A retrospective review of 241 cases, with MCC on mammography, who underwent biopsy was performed. Morphology, distribution, extent, UKS score, BI-RADS category and pathology were recorded. The positive predictive value (PPV) of each classification system for malignancy was calculated.28.6% were diagnosed with DCIS/IDC. The PPV for malignancy using the UKS was 18.9%, 69.4%, 100% for M3-5 respectively (p < 0.001) and using ΒI-RADS morphology was amorphous: 7.1%, coarse heterogeneous: 33.3%, fine pleomorphic: 48.1% and fine linear/fine linear branching: 85.2% (p < 0.001). The PPV based on distribution was grouped: 14.2%, regional: 32.3%, diffuse: 33.3% and linear/segmental: 77.8% (p < 0.001). Combining all cases of benign-appearing, amorphous and grouped coarse heterogenous and grouped fine pleomorphic MCC gave a PPV of 12.8%. Combining regional, linear or segmental coarse heterogenous and fine pleomorphic and all fine linear/branching MCC resulted in a PPV of 83.3% for malignancy.Combining morphology and distribution of MCC is accurate in malignancy prediction. Use of BI-RADS descriptors could help standardise reporting within the UKS and an algorithm using these within the UKS is proposed. Better prediction would enable more appropriate counselling and help to identify discrepancies.No guidance exists on scoring of suspicious MCC in the UK breast imaging system. Use of BI-RADS morphologic/distribution descriptors can aid malignancy prediction. Findings other than morphology of MCC are important in malignancy prediction. An algorithm for use by the UK radiologist when evaluating MCC is provided.
Blockchain Secured Electronic Health Records: Patient Rights, Privacy and Cybersecurity

There have been significant efforts in the UK to embrace health technology to improve provisions of care. Yet, healthcare offers unique challenges to innovation, particularly with regard to data siloing. Blockchain is a shared distributed ledger technology that decentralises information storage with the potential to improve health outcomes by concurrently optimising data sharing practices and data privacy. In this paper, we explore how blockchain technology may facilitate the handling of health data in the context of regulatory frameworks, patient rights, cybersecurity and provider-centric perspectives. This is essential if this developing technology is to be considered for implementation at scale.
Fine-Grained Ranked Multi-Keyword Search Over Hierarchical Data for IoT-Oriented Health System

With the rapid advance of the Internet of Things (IoT) and cloud computing technologies, the IoT-oriented health is expected to greatly improve the quality of healthcare service. However, data security and privacy concerns have become one of the biggest issues in smart health applications. As a potential and promising solution, attribute-based keyword search (ABKS) can provide fine-grained keyword search and access control over the encrypted data at the same time. Nevertheless, prior ABKS schemes cannot simultaneously support fine-grained, effective, and accurate data retrieval over hierarchical data. In this paper, to tackle these issues, we propose a fine-grained ranked multi-keyword search scheme over hierarchical data by leveraging ciphertext-policy hierarchical attribute-based encryption (CP-HABE) and ranked multi-keyword search (RMKS) technologies. Then, we prove that our proposed scheme is selectively secure through security analysis and we also show the practicability and feasibility of the proposed scheme by performance evaluation.
Real time surveillance for low resolution and limited data scenarios: An image set classification approach

This paper proposes a novel image set classification technique based on the concept of linear regression. Unlike most other approaches, the proposed technique does not require any training. We represent the gallery image sets as subspaces in a high dimensional space. Class specific gallery subspaces are used to estimate regression models for each image in the test image set. Images of the test set are then projected onto the gallery subspaces. The residuals, calculated using the Euclidean distance between the original and the projected test images, are used as the distance metric. Three different strategies are devised to decide on the final class of the test image set. We extensively evaluated the proposed technique using both low resolution and noisy images and with less gallery data to assess the suitability of our technique for the tasks of surveillance and video-based face recognition. The experiments show that the proposed technique achieves superior classification accuracy and has a faster execution time compared with existing techniques, especially under the challenging conditions of low resolution and a limited amount of gallery and test data.
Deep Learning for Logo Detection

We present a deep learning system for automatic logo detection in real world images. We base our detector on the popular framework of FasterR-CNN and compare its performance to other models such as Mask R-CNN or RetinaNet. We perform a detailed empirical analysis of various design and architecture choices and show how these can have much higher influence than algorithmic tweaks or popular techniques such as data augmentation. We also provide a systematic detection performance comparison of various models on multiple popular datasets including FlickrLogos-32, TopLogo-10 and recently introduced QMUL-OpenLogo benchmark, which allows for a direct comparison between recently proposed extensions. By careful optimization of the training procedure we were able to achieve significant improvements of the state of the art on all mentioned datasets. We apply our observations to build a detector to detect logos of the Red Bull brand in online media and images.
Strengthening Smart Contracts to Handle Unexpected Situations

Decentralized application users may face unexpected situations that the smart contract implementing the application should handle, but cannot, because the smart contract cannot be modified once it is deployed. Therefore, we need 'stronger' smart contracts with flexible structures that are resilient in such unexpected situations. In this paper, we propose a generic mechanism to strengthen smart contracts and handle possible unexpected situations. Given a smart contract, this mechanism automatically generates an action list which offers actions as interfaces to change parameters of smart contracts and a voting system that utilizes a limited voter group randomly chosen from the peers. Each action in the action list can change a corresponding parameter of smart contracts. The actions, when approved by the majority, are executed to change the parameters. When users face unexpected situations in a transaction, they choose some actions as the solution and pass them to the voting system. Since a smart contract has finite parameters, there are finite actions. By arranging and combining these actions, our mechanism offers solutions that can handle wide-ranging unexpected situations. Also, to execute a solution, the majority of voters need to approve it, thus not violating the protocol of the original smart contract. Voters are rewarded based on quadratic rules for peer prediction, which makes telling true preferences the only way to maximize rewards. Using machine learning, we predict users' preferences based on the voting records. The predictions are provided as default values for future votes to avoid users' need to vote manually each time.
Database NewSQL Performance Evaluation for Big Data in the Public Cloud

For very years, relational databases have been the leading model for data storage, retrieval and management. However, due to increasing needs for scalability and performance, alternative systems have emerged, namely NewSQL technology. NewSQL is a class of modern relational database management systems (RDBMS) that provide the same scalable performance of NoSQL systems for online transaction processing (OLTP) read-write workloads, while still maintaining the ACID guarantees of a traditional database system. In this research paper, the performance of a NewSQL database is evaluated, compared to a MySQL database, both running in the cloud, in order to measure the response time against different configurations of workloads.
The Impact of Feature Selection Methods for Classifying Arabic Texts

Text classification becomes the most important research issues and common technique in data mining and its main challenge is the high dimensionality problem where the feature space of the documents is very huge with redundancy and noisy data. Feature selection aims to enhance the accuracy of the classifier by reduce the dimensionality in that space. Due to its richness, Arabic text classification consider as the most language with high dimensionality. This paper aims to study the effectiveness of using feature selection techniques to enhance the Arabic text classifiers performance. Two feature selection techniques: InfoGain and Chi-square statistic (CHI) and two machines supervised machine learning models were investigated. The results showed that the feature selection enhances the performance of the modes. InfoGain feature selection technique outperforms the Chi-Square Statistic (CHI) feature selection technique when implemented the NB classifier and worked equally when implemented SMO classifier.
Secure Message Embedding in Audio

Since the rise of the Internet, information security has been one of the most important factors in networking. Cryptography and steganography are the arts and sciences of secret communication. This paper proposes two different implementations of a double-layer message security scheme. In both schemes, the first layer is a cryptographic algorithm, employing the AES-128, while the second layer is a steganographic technique, employing the least significant bit (LSB) embedding with range manipulation of an audio as a cover. Performance is measured in terms of the mean squared error (MSE), the peak signal to noise ratio (PSNR), hearing test and waveform plots of the audio. Numerical results show that the proposed techniques are superior to some of the recurring approaches in the literature.
Case Based Interpretation Model for Word Sense Disambiguation in Gurmukhi

Language is a medium through which we can communicate our thoughts with each other. To duplicate the same communication between a human and a machine, we require Natural Language Processing. Under Artificial Intelligence, natural language processing is one of the major case study. What makes this case study more complex is the fact that a word has multiple meanings and the way in which the word is being used (context) explains the meaning of the word. This phenomenon where a single word could have multiple meaning and the correct meaning is deduced from the context it is being used as is known as Word Sense Disambiguation. In this paper, we are using Case Based Reasoning interpretation model for word sense disambiguation on Indian Regional Language - Gurmukhi, popularly known as Punjabi. The basic idea behind case based reasoning approach is to apply the solution of previously solved problems in finding the solutions for new problems. The inspiration for the case based reasoning approach came from the role of reminding in human reasoning.
Implementation of Target Tracking Methods on Images Taken from Unmanned Aerial Vehicles

Traditional object detection algorithms generate proposals and implement feature extraction. Then, a classification algorithm is implemented to label object classes. This process is slow, and the accuracy may not be adequate for UAV's real-time application tasks due to their movement in the air. We specified and practically implemented an object detection and localization scheme on images taken from a UAV, and provided the UAV with an advanced vision. We used YOLOv2 model. The YOLOv2 is a suitable object detection approach based on deep learning, and it presents a network architecture with accurate results in high speed. The object detection and localization were successfully implemented for people, car, and motorcycle classes within the threshold confidence scores. We pre-trained the model on COCO dataset and tested the model with our test images. The confidence scores were higher in altitudes from 5 to 15 meters and the confidence scores varied between %45 - %79 mAP.
Exploiting potential of deep neural networks by layer-wise fine-grained parallelism

Deep neural networks (DNNs) have become more and more important for big data analysis. They usually use data parallelism or model parallelism for extreme scale computing. However, the two approaches realize the performance improvement mainly by using coarse-grained parallelization schemes. Neither can fully exploit the potentials of the parallelism of many-core systems (such as GPUs) for neural network models. Here, a new fine−grained parallelism strategy (named FiLayer) is presented based on layer-wise parallelization. It has two components: inter-layer parallelism and intra-layer parallelism. The inter-layer parallelism makes several neighboring layers be processed by using a pipeline manner in a network model. For intra-layer parallelism, the operations in one layer are separated into several parts and processed concurrently. To implement above fine-grained parallelism methods, CUDA streams are used. A mathematical analysis is presented for the influence of fragment number on performance of the inter-layer parallelism, and also an analysis for the influence of CUDA stream number on the performance of the intra-layer parallelism is given. The proposed approach is realized based on Caffe. Some representative datasets including CIFAR100 and ImageNet, are applied for experiments. The evaluation results show that it can help Caffe realize remarkable speedups, which makes much sense to big data analysis.
Geochemistry and origin of Mn deposits in the Bela ophiolite complex, Balochistan, Pakistan

We have studied geochemical characteristics, mineralogy and origin of the manganese deposits in Bela ophiolitic complex. Geochemical investigation was conducted in order to discuss the elemental correlations and to infer the probable origin of manganese deposits in Bela ophiolites. Ore microscopy was conducted to identify different manganese minerals and their paragenesis. Psilomelane and braunite were found to be the major ore minerals. Mineral paragenesis sequence as observed was braunite forming first. Psilomelane formed in later stages because it has been found that psilomelane is altering the braunite. Magnetite was observed as a secondary mineral as vein filling and cutting through both the braunite and psilomelane, therefore, younger than the psilomelane and braunite. The gangue minerals observed in the studied samples were quartz, cryptocrystalline silica and calcite. The correlations among different major and trace elements showed diversity of relations. MnO showed negative correlation with Fe2O3 (− 0.73), Si2O (− 0.27), positive correlation with Al2O3 (0.54), TiO2 (0.36), MgO (0.22), Pb (0.23), Ni (0.07), Cr (0.12), and no correlation was established with Zn, Cu, Co. Binary diagram of Si versus Al and ternary discrimination diagrams of Fe–(Ni + Co + Cu) × 10–Mn and Ni–Zn–Co showed hydrothermal-diagenetic-type deposits. Furthermore, the present study suggests that the enriched manganiferous fluid during its upward movement within the Tethys oceanic crust near spreading center started precipitating Mn along with Fe on the sea floor. Later on, these manganese minerals were obducted on land between Indian plate and Helmond block of Eurasian plate in existing position accompanying pillow basalt and pelagic sediments.
Utilization of HOG-SVD based Features with Connected Component Labeling for Multiple Copy-move Image Forgery Detection

Copy-move forgery is one of the most regarded image forgery technique to tamper information conveyed by the image. In this technique, segment of original image is replicated and pasted across the same image to produce forged image. This technique is capable to hide selective information or to add fictitious details in image. Detection of this form of forgery is one of the significant area of information security. In this paper, we propose block-based approach for copy-move image forgery detection to secure information conveyed through the image by identifying the forged images and to prevent spreading of tampered subject matter. Proposed model divides suspicious image in overlapping blocks. We extracted block features using Histogram of Oriented Gradients (HOG) and Singular Value Decomposition (SVD). Lexicographical sorting is performed over feature matrix followed by Euclidean distance computation to recognize similar feature vectors. To remove false match detection, Connected component labeling is utilized. Our scheme achieves highest F-measure than former techniques, when forged image sustain plain multiple copy-move, multiple copy-move with contrast adjustment, color reduction, and image blurring attacks.
An Investigation of Interpretable Deep Learning for Adverse Drug Event Prediction

A variety of deep learning architectures have been developed for the goal of predictive modelling in regards to detecting health diagnoses in medical records. Several models have placed strong emphases on temporal attention mechanisms and decay factors as a means to include highly temporally relevant information regarding the recency of medical event occurrence while facilitating medical code-level interpretability. In this study we utilise such models with a novel Electronic Patient Record (EPR) data set consisting of both diagnoses and medication data for the purpose of Adverse Drug Event (ADE) prediction. As such, a main contribution of this work is an empirical evaluation of two state-of-the-art deep learning architectures in terms of objective performance metrics for ADE prediction. We also assess the importance of attention mechanisms in regards to their usefulness for medical code-level interpretability, which may facilitate novel insights pertaining to the nature of ADE occurrence within the health care domain.
Feature Extraction Model in Systems of Face Images for Person Identification

One of the currently important and rapidly developing areas of modern information technology is related to the tasks of person identification by biometric characteristics (features). The advantages of face recognition systems include discreetness, passiveness, and relatively low cost. Despite all this, the issues of extracting the features of face images have not been adequately investigated. The paper considers the stage of face feature extraction, since it is the main stage in creating systems for person identification based on the features. To solve the problem under consideration, a model for extraction of the identification features of face images has been proposed. The basic idea of the proposed model is to form a set of preferred features that would simplify the process of building a decision rule when recognizing patterns given as images. To test the working efficiency of the proposed model, experimental investigations have been carried out in solving a model problem of face recognition.
Smartphone picture organization: A hierarchical approach

We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 40 persons. Experimental results demonstrate major user satisfaction with respect to state of the art solutions in terms of organization.
Power of Deep Learning for Amplitude-phase Signal Modulation Recognition

This article presents our initial results in deep learning for signal modulation recognition in satellite communication system. In this article, we propose a signal classification model based on the recurrent neural network. Different from existing methods that need estimating the signal parameters or design the features by the experienced experts, we use LSTM (Long Short-Term Memory) to extract deep features of signal sequence from time domain. Using this architecture, we realized the modulation recognition for target signals end-to-end finally. And the method is insensitive to frequency offset and timing deviation, which has a strong application prospect in engineering. From our simulation results, the deep learning based approach can achieve considerable performances that the signals recognition rate is close to 98% in 6dB. Furthermore, the deep learning based approach is more robust than conventional methods when there is frequency offset, timing deviation and low Signal-to-noise ratio.