Reviewerly/exHarmony huggingface dataset:
CNN based Counterfeit Indian Currency Recognition Using Generative Adversarial Network

In today's world scenario, paper currency is economical in the sense that its face value is greater than intrinsic value. It is also more elastic and stable, paper currency can be counted quickly, it is easy to move and safe to store. These all are the main reasons because of which counterfeit currency recognition is crucial. Fake currency cannot be identified by human vision and due to this recognition of forged currency notes has become crucial problem because counterfeiters are using new and improved methods. The methods currently existing to determine whether the notes are real cannot be accessed by the common people and are also complex hardware based methods. There are no applications or devices available through which fake currencies can be detected and identified easily by common people. The main purpose of the project is to identify Indian paper currency with a new methodical approach using Generative Adversarial Networks(GAN). In this system, the Indian currency note features would be primarily extracted using Convolutional Neural Networks (CNNs).The processed image data are then fed to a Generative Adversarial Network which helps to classify the currency as either real or fake. GAN consists of two main modules - Generator and Discriminator. The Generator generates fake currency images and the Discriminator identifies and labels the real and fake images.
A Comparative Study of Kalman-like Filters for State Estimation of Turning Aircraft in Presence of Glint Noise

This paper continues the study started by Kulikov and Kulikova on state estimation accuracies of various Kalman-like filtering techniques in target tracking scenarios with non-Gaussian noise in 2018. The cited authors examined a number of methods, which are grounded in the minimum-variance or maximum-correntropy criteria and cover extended-, cubature- and unscented-type Kalman filters, in the well-known turning aircraft scenario with impulsive (shot) noise or mixed-Gaussian one. Despite the success of the maximum-correntropy-based filtering methods reported on estimation of linear discrete-time stochastic systems in literature, those case studies expose the superiority of the cubature and unscented Kalman filters towards various extended Kalman methods designed in the minimum-variance sense or grounded in the maximum-correntropy criterion within the mentioned target tracking scenarios. Here, we extend that examination to the turning aircraft scenario with glint noise, which is simulated by a sum of two zero-mean Gaussian variables with difference covariances. In particular, our study reveals a valued potential of the maximum-correntropy-based accurate continuous-discrete extended Kalman filters devised by the above authors in this glint noise state estimation environment.
Data mining and Region Prediction Based on Crime Using Random Forest

Crime is one of the most critical problems of the today's world which affects the normal life of the society and it breaks the social, and economical flow of a country. The scenario is the same for Bangladesh. This paper focused on prediction of the areas based on crimes instead of the opposite. This will also be beneficial to predict crimes occurred in different cities in certain timeframe. And knowing the future trend the law enforcement and detectives will be able to take precautionary steps which will in turn reduce the rate of such events. Here, Random Forest algorithm is used to predict the regions (AKA district) based on different crime rates.
Development of flight simulation system based on leap motion controller

Aiming at the problems of low precision and discontinuous interaction in traditional human-computer interaction, A natural interactive system that can be used for flight simulation is proposed, which is based on Leap Motion combined with machine learning. Firstly, a gesture Motion suitable for flight simulation is introduced, and the Leap Motion is used to collect custom interactive gesture data. Then, linear regression and local weighted regression algorithm are respectively used to fit the collected original data, so as to obtain the interactive control curve that most conforms to the experimenter. The experimental results show that the control curve obtained by linear regression and local weighted regression algorithm have increased accuracy by 21.40% and 65.27% respectively compared with the data curve obtained by the original data, which is very helpful to improve the human-computer interaction experience in flight simulation.
MIRAGE: A consolidation aware migration avoidance genetic job scheduling algorithm for virtualized data centers

Modern virtualized data centers often rely on virtual machine (VM) migrations to consolidate workload on a single machine for energy saving. But VM migrations have many drawbacks, including performance degradation, service disruption etc. Hence, many approaches have been proposed to minimize the overhead when migrations occur. In contrast, this work aims to proactively avoid migrations from happening in the first place. We have proposed a novel consolidation aware scheduling algorithm to minimize the number of migrations for batch processing systems by taking advantage of the prior knowledge of consolidation strategy and job information. We show the problem can be formulated as an integer linear programming (ILP) problem, and an effective heuristic solution can be found by a genetic algorithm. Both real and synthetic workload traces were used to evaluate our methods. Experimental results showed that, after comparing with two popular job scheduling algorithms, our approach has reduced the number of migrations by more than 25%.
Multi-UAV Task Assignment Based on Quantum Genetic Algorithm

Abstract Multi UAV cooperation is an important application of multi UAV cooperation to complete complex tasks. In the aspect of multi UAV system coordination consists of some problems such as difficult to describe complex tasks, difficult to allocate load balancing, and difficult to model tasks. Therefore, making full use of all UAV resources and reasonable task modeling and task allocation to minimize the resource consumption of UAV system is the core problem of multi UAV cooperation. Task allocation is one of the important links of UAV cooperation, which has an important impact on the overall combat effectiveness of the system. This paper establishes the optimization model of multi UAV cooperative task allocation, and then designed a hybrid task allocation method. Quantum genetic algorithm is used for global task allocation in the initial state, and the grouping optimization strategy of hybrid frog leaping algorithm is used to greatly reduce the overall iteration times of the algorithm; the simulated annealing criterion is used to accept new solutions, which can better maintain the diversity of the population and help to jump out of the local extremum.
Multivariate Time Series Prediction of Pediatric ICU data using Deep Learning

The pediatric cardiac intensive care unit (ICU) is a specialized section for children with heart diseases. The patients admitted to the ICU are in a very critical condition. The data for each day were collected hourly basis. So, the time-series prediction might be beneficial for the physicians for the medication process of the patients whose lives are in danger. This paper proposes a multivariate time series prediction where multiple features with respect to timestamps are to be predicted using the deep learning methods in order to assist doctors in decision making in the tensed moment. Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) these methods are applied for the time series prediction. The comparative analysis among the RNN and LSTM prediction model is also highlighted in this paper. Doctors' advice is also taken to justify the result.
Bengali Image Captioning with Visual Attention

Attention based approaches has been manifested to be an effective method in image captioning. However, attention can be used on text called semantic attention or on image which in known as spatial attention. We chose to implement the later as the main problem of image captioning is not being able to detect objects in image properly. In this work, we develop an approach which extracts features from images using two different convolutional neural network and combines the features with an attention model in order to generate caption with an RNN. We adapted Xception and InceptionV3 as our CNN and GRU as our RNN. Moreover, we Evaluated our proposed model on Flickr8k dataset translated into Bengali. So that captions can be generated in Bengali using visual attention.
A Pragmatic Analysis of Pre- and Post-Quantum Cyber Security Scenarios

The advancements in quantum computing and quantum cryptology have recently started to gain momentum and transformation of usable quantum technologies from dream to reality has begun to look viable. This has created an immediate requirement to comprehend quantum attacks and their cryptographic implications, which is a prerequisite obligation to design cryptographic systems resistant to current and futuristic projected quantum and conventional attacks. In this context, this paper reviews the prevalent quantum concepts and analyses their envisaged impact on various aspects of modern-day communication and information security technologies. Moreover, the paper also presents six open-problems and two conjectures, which are formulated to define prerequisite technological obligations for fully comprehending the futuristic quantum threats to contemporary communication security technologies and information assets processed through these systems. Furthermore, the paper also presents some important concepts in the form of questions and discusses some recent trends adapted in cryptographic designs to thwart quantum attacks.
Static postural transition-based technique and efficient feature extraction for sensor-based activity recognition

Smartphone sensor-based activity recognition seeks broad, high-level knowledge about human behaviors from multitudes of low-level sensor readings, and makes considerable headway in healthcare domain. Our primary contribution is to study the effective pre-processing technique and the extraction of robust features for the classification of sensor data for human activity recognition (HAR). In the pre-processing stages, we investigated multiple filtering parameters for reducing waveform delay, smartphone orientation constraint by introducing magnitude and jerk-based features, and optimum window length for analyzing the trade-off between model performance and latency. Besides, we proposed a feature named "Average Height" that summarizes the average peak to trough distance of the activity and encodes any change of motion for classification. We also proposed two feature selection techniques for offline and real-time faster activity recognition, and analyzed the impact of different feature sets on classifying different activities. Moreover, after performing the classification with optimized hyperparameters, we proposed a Static Postural Transition-based Post-Processing (SPTPP) technique. This post-processing approach analyzes the existence of postural transition from previous window activity to current window activity, and helps to improve the model output by analyzing the posture change. The impact of our proposed techniques are demonstrated on three benchmark datasets named HASC, HAR, and HAPT, where we obtained the state-of-the-art results. We used HASC dataset for optimizing model parameters in different stages, and explored HAR and HAPT datasets as test-beds to verify our optimizations and postprocessing technique.
Effective Collaborative Representation Learning for Multilabel Text Categorization

With the booming of deep learning, massive attention has been paid to developing neural models for multilabel text categorization (MLTC). Most of the works concentrate on disclosing word-label relationship, while less attention is taken in exploiting global clues, particularly with the relationship of document-label. To address this limitation, we propose an effective collaborative representation learning (CRL) model in this article. CRL consists of a factorization component for generating shallow representations of documents and a neural component for deep text-encoding and classification. We have developed strategies for jointly training those two components, including an alternating-least-squares-based approach for factorizing the pointwise mutual information (PMI) matrix of label-document and multitask learning (MTL) strategy for the neural component. According to the experimental results on six data sets, CRL can explicitly take advantage of the relationship of document-label and achieve competitive classification performance in comparison with some state-of-the-art deep methods.
Merkle<sup>2</sup>: A Low-Latency Transparency Log System

Transparency logs are designed to help users audit untrusted servers. For example, Certificate Transparency (CT) enables users to detect when a compromised Certificate Authority (CA) has issued a fake certificate. Practical state-of-the-art transparency log systems, however, suffer from high monitoring costs when used for low-latency applications. To reduce monitoring costs, such systems often require users to wait an hour or more for their updates to take effect, inhibiting low-latency applications. We propose Merkle <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , a transparency log system that supports both efficient monitoring and low-latency updates. To achieve this goal, we construct a new multi-dimensional, authenticated data structure that nests two types of Merkle trees, hence the name of our system, Merkle <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Using this data structure, we then design a transparency log system with efficient monitoring and lookup protocols that enables low-latency updates. In particular, all the operations in Merkle <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> are independent of update intervals and are (poly)logarithmic to the number of entries in the log. Merkle <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> not only has excellent asymptotics when compared to prior work, but is also efficient in practice. Our evaluation shows that Merkle <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> propagates updates in as little as 1 second and can support 100× more users than state-of-the-art transparency logs.
Privacy Protection in Deep Multi-modal Retrieval

Deep learning techniques have ushered in significant progress in large-scale multi-modal retrieval. Nevertheless, the advanced techniques may be used nefariously to conduct a search that violates the privacy of individuals. In this paper, we propose a novel PrIvacy Protection method (PIP) against malicious multi-modal retrieval models, which proactively transfers original data into adversarial data with quasi-imperceptible perturbations before releasing them. Consequently, unauthorized malicious parties are not able to use deployed deep models to find out desired sensitive information with them. In addition to privacy preserving, PIP synchronously learns an effective multi-modal retrieval model to facilitate authorized uses, endowed with strong resilience to the perturbations. To the best of our knowledge, it is a very first attempt to consider privacy issues in multi-modal retrieval, and encapsulate both privacy protection against unauthorized retrieval and robust multi-modal learning for authorized uses into a unified framework. This work is conducted in the challenging no-box and unsupervised settings, where neither target malicious models nor supervised information is known. The optimization objective of our versatile PIP is achieved through a two-player game between different components with both the intra- and inter-modality graph alignments and the domain distribution alignment considered. Besides, a high-level similarity matrix is developed to obtain reliable guidance for learning. Empirically, we apply the proposed PIP to hashing based multi-modal retrieval scenarios and prove its effectiveness on a range of benchmarks and tasks.
Itinerary-aware Personalized Deep Matching at Fliggy

Matching items for a user from a travel item pool of large cardinality have been the most important technology for increasing the business at Fliggy, one of the most popular online travel platforms (OTPs) in China. There are three major challenges facing OTPs: sparsity, diversity, and implicitness. In this paper, we present a novel Fliggy ITinerary-aware deep matching NETwork (FitNET) to address these three challenges. FitNET is designed based on the popular deep matching network, which has been successfully employed in many industrial recommendation systems, due to its effectiveness. The concept itinerary is firstly proposed under the context of recommendation systems for OTPs, which is defined as the list of unconsumed orders of a user. All orders in a user itinerary are learned as a whole, based on which the implicit travel intention of each user can be more accurately inferred. To alleviate the sparsity problem, users’ profiles are incorporated into FitNET. Meanwhile, a series of itinerary-aware attention mechanisms that capture the vital interactions between user’s itinerary and other input categories are carefully designed. These mechanisms are very helpful in inferring a user’s travel intention or preference, and handling the diversity in a user’s need. Further, two training objectives, i.e., prediction accuracy of user’s travel intention and prediction accuracy of user’s click behavior, are utilized by FitNET, so that these two objectives can be optimized simultaneously. An offline experiment on Fliggy production dataset with over 0.27 million users and 1.55 million travel items, and an online A/B test both show that FitNET effectively learns users’ travel intentions, preferences, and diverse needs, based on their itineraries and gains superior performance compared with state-of-the-art methods. FitNET now has been successfully deployed at Fliggy, serving major online traffic.
Realization of single-photon transport in one-dimensional coupled-resonator waveguides via phase control

We study theoretically the influence of the phase difference of the coupling strength changes on the transport of a single-photon in coupled-resonator waveguides (CRWs). Two cases are considered: single-photon transport in a one-dimensional CRW with nonlocal coupling to a cavity, and a single-photon in a waveguide consisting of two coupled semi-infinite CRWs, which are coupled to a cavity and two cavities. The results show that the phase difference of the coupling strength can not only improve the conversion efficiency of single-photon transport in CRWs but also can adjust the irregular energy band spectrum to become regular. This will provide an important physical parameter for the experimental realization of quantum solid-state devices such as quantum switches and quantum routers.
Reversible Data Hiding in Encrypted DICOM Images Using Cyclic Binary Golay (23, 12) Code

In this paper, a novel reversible data hiding method for encrypted images (RDHEI) is proposed. An efficient coding scheme based on cyclic binary Golay (23, 12) code is designed to embed additional data into the least significant bits (LSBs) of the encrypted image. The most significant bits (MSBs) are used to ensure the reversibility of the embedding process. The proposed scheme is lossless, and based on the receiver’s privileges, allows recovery of marked data, original data and embedded data. Furthermore, the scheme can be used with any type of data, however it is best suited to 16-bit DICOM images of monochrome photometric interpretation. A modification to the standard DICOM network model was also introduced, to point out an example application of the proposed RDHEI method, i.e. an anonymized data storage outsourcing. A computer-based analysis has been carried out and simulation results are shown at the end of this paper.
Novel Technique for Script Translation using NLP: Performance Evaluation

Data can be represented in various languages, such as Hindi, Marathi and English in social media and also in various knowledge resources. The data is open to users in a different format. Information comprehension is very difficult for uneducated individuals. Often it is difficult for people to read or comprehend meaningful data available on social media. Thus, researchers used NLP to develop different machine translation techniques, such as rule-based, statistical-based and some hybrid machine translation. Natural language is incredibly rich in form and composition and highly vague. Natural language comprehension is much harder compared to natural language development. We present the MVET (Marathi video to English Text translation) technique in this research manuscript which helps to translate Marathi Video content into English text format. That will be very useful for people who really aren't Marathi. Sometimes so many people have lost valuable information because of the regional language crisis. We have developed the MVET machine translation technique to solve this problem, which will achieve more precision compared to existing methods such as statistical, rule-based and hybrid.
COVTrac: Covid-19 Tracker and Social Distancing App

Das Mohapatra, Subhashish Nayak, Suvendu Chandan Parida, Sasmita Panigrahi, Chhabi Rani Pati, BibudhenduIn order to break the chain of SARS-CoV-2 virus infection, most countries are taking the help of mobile apps. The existing apps lack the necessary features to address the major issues of detection and alert of social distancing, detection and prevention of large gatherings, easy generation of travel permit during lock down, etc. The existing apps do not provide any solution for alerting user of the users social distance violation. Though many of these apps are being used for contact tracing, they do not say which places the user has visited recently which is a crucial parameter in contact tracing and preventing community spread. Smartphones empowered with the latest technology like Google geo-location and low energy Bluetooth (BLE) with such an app can complement a country's general Covid-19 control strategies—comprising testing, contact tracing, seclusion and social distancing. In this work, we develop a Covid-19 tracker and social distancing app named as COVTrac, an Android app that uses various capabilities of the smartphone to address these issues with the existing mobile app along with a number of other concerns such as privacy, accurate contact tracing and prevention of socio-physical interactions.