Reviewerly/exHarmony huggingface dataset:
Cast Shadow Angle Detection in Morphological Aerial Images Using Faster R-CNN

With the tremendous advancements in digital image processing technology over the last few years, it is now possible to resolve many challenging issues. In light of this, this study proposes that digital image processing can be used to detect shadows in photographs. Since unmanned aerial vehicles and satellite devices have become more common image generating devices. The significant issue in the generated images is of its shadow. Shadows are inevitable in remote sensing photographs, particularly in metropolitan environments, due to the block of high-rise objects and the influence of the sun's altitude. This results in missing information in the shadow zone. The state-of-the-art shadow detection algorithms require manual alignment and predefined specific parameters. Most of those existing algorithms fail to deliver precise results in a variety of lighting and ecological conditions. To overcome these limitations, we propose a framework Multi Layered Linked approach with Tagged Feature Model for Shadow Angle Detection (MLTFM-SAD). The aim of the proposed model is to detect the shadows from aerial photographs and angle of those shadows. The proposed framework is a three-step approach. Initially, the image segmentation is applied on the input images. Second, hybrid Gaussian mixing mode and Otsu's approach is applied on the segmented shadow mask map and corresponding pixel set is generated. As a result, an initial shadow mask map is refined using object spectral attributes and spatial correlations between objects. Finally, the angle at which the shadow appears in the given image is recognised and analysed. The proposed method's performance is compared to that of all current approaches and the results revealed that the proposed model performance levels are superior.
An unsupervised cross project model for crashing fault residence identification

It is a critical quality assurance activity to effectively detect the root cause of faults causing the software crashes (i.e. crashing faults). Previous studies extracted features to characterise crash instances and built models to identify whether the residences of crashing faults locate inside the stack traces. These models all belong to supervised learning methods which require labelled crash data to be involved. In this study, the introduction of an unsupervised model, called Transfer Spectral Clustering (TSC), for the task of crashing fault residence identification under the unlabelled data scenario is proposed. Unlike traditional unsupervised methods which are applied to individual project data, TSC transfers the knowledge of auxiliary unlabelled data from the source project to assist the clustering task on the unlabelled data from the target project. TSC is an unsupervised transfer learning method, and simultaneously considers the data manifold information of the individual project and feature manifold information across projects to facilitate the clustering effect. Extensive experiments are conducted on a benchmark dataset containing seven software projects. Five indicators were chosen for performance evaluation. The results show that TSC achieves better performance than four clustering based unsupervised methods, and competitive performance compared with eight supervised cross-project methods.
An extendable key space integer image-cipher using 4-bit piece-wise linear cat map

Abstract This paper presents a multiplierless image-cipher, with extendable 2048-bit key-space, based on a 4-dimensional (4D) quantized piece-wise linear cat map (PWLCM). The quantized PWLCM exhibits limit-cycles of 4-bit encoded integers with periods greater than 10 7 . The synthesis of the PWLCM in a finite state space allows to eliminate the undesirable finite precision effect due to the hardware realization. The proposed image-cipher combines chaos, modular arithmetic, and lattice-based cryptography to encrypt a color image by performing pixel permutation and diffusion in a single operation. Further, an image-dependent confusion operation based on an 8-bit 2D-PWLCM is performed on the whole image to enhance security. In order to increase the key-space without key duplication, 16 × 16 sub-images are modified using sub-keys of different lattice length vectors generated from the external key. Both simulations and security analyses confirm that the proposed algorithm can resist common cipher attacks, in addition to its advantages such as simplicity, ease of implementation on low-end processors and extensibility of key-space that allows it to easily adapt even for future post-quantum computing attacks.
CDBSCAN: Density clustering based on silhouette coefficient constraints

Aiming at the problem that the edge points are difficult to be accurately divided in the DBSCAN algorithm, a density clustering algorithm based on silhouette coefficient constraints (CDBSCAN) is proposed. The CDBSCAN improves the clustering accuracy with the silhouette coefficient as the criterion. Firstly, the data is preliminary classified by the DBSCAN algorithm and the number of data points in the formed clusters is calculated. Then both noise data and clusters with fewer data are listed as potential noise data. Subsequently, each data point in the noise set is classified again according to the silhouette coefficient. Finally, experiments are conducted on both synthetic and public datasets, and the result shows that CDBSCAN has better clustering results, especially in the discrimination of data points on the clustering edge.
I-MALL An Effective Framework for Personalized Visits. Improving the Customer Experience in Stores

In this paper we present I-MALL, an ICT hardware and software infrastructure that enables the management of services related to places such as shopping malls, showrooms, and conferences held in dedicated facilities. I-MALL offers a network of services that perform customer behavior analysis through computer vision and provide personalized recommendations made available on digital signage terminals. The user can also interact with a social robot. Recommendations are inferred on the basis of the profile of interests computed by the system analysing the history of the customer visit and his/her behavior including information from his/her appearance, the route taken inside the facility, as well as his/her mood and gaze.
Heralding Single Photons using Photon-number-resolving Superconducting Nanowires

We improve a single photon source based on spontaneous parametric down-conversion by heralding one of the output modes using a photon number resolving superconducting nanowire detector. We measure a reduced magnitude of the second order cross correlation of one of the output modes conditioned on detection of a single photon in the other mode.
Redefiniendo las modalidades docentes a raíz de la crisis por la COVID-19

In this paper, the authors will talk about the development of technological platforms and tools that will support the creation and implementation of techno-pedagogical paradigms to tech the XXI Century generations. By proving how the innovative model of the open and distance learning universities (UNAD, for its Spanish acronym) is not run out and can be renewed, taking as an example the COVID-19 pandemic and analyzing the carriers that provide change to a new education, where the need of innovation in higher education in open and distance modalities is an urgent matter. Undoubtedly, learning is one of the human capacities that has most influenced their development and evolution. Thus, the e-education concept is presented as an effort to develop talents and human capacities using the Internet and new information and communication technologies (ICT). This will allow introducing a generalized remote mode, which will create a different culture adjusting the school curriculum to an online strategy, and not a rigid one, that involves technological improvements, so general and specialized knowledge are combined to the needs of scientific, technological, and social development. Teachers work with students differently and provide help through more knowledge and experience. Recent studies are showing that there are more open and online options for teacher professional development and teachers access the Internet to expand their development opportunities through social media platforms. In this sense, the non-attendance process of education has improved its relevance globally. Although, the COVID-19 pandemic caused an accelerator effect on the digital transformation of the educational institutions.
Research on x-vector speaker recognition algorithm based on Kaldi

This paper presents a convolutional neural network with an attention mechanism for analysing the spectrogram in an x-vector based speaker recognition system. First, the convolutional neural network (CNN) is used to extract the features of the spectrogram. Then, an attention mechanism is designed to calculate the frame weight in the statistical pooling layer. Finally, probability linear discriminant analysis (PLDA) is used as a back end classifier. The system is implemented using Kaldi speech recognition tools and tests on the Voxceleb1 database. The experimental results show that the combination of spectrogram and CNN gains a relative improvement of 6.7% in equal error rate (EER) compared with the x-vector baseline system. The attention mechanism for the statistical layer further leads to a relative improvement of 26.1%. Overall the proposed method outperforms state-of-the-art methods on the Voxceleb1 database.
Approaching Helstrom Bound Using Single-shot Quantum Measurement Confidences

We demonstrate the first quantum measurement providing s ingle-shot self-confidence for each received symbol. Further, we use measured confidence data to implement error correction codes reducing error rates by more than five orders of magnitude for reliable communication.
Serving Hybrid-Cloud SQL Interactive Queries at Twitter

The demand for data analytics has been consistently increasing in the past years at Twitter. In order to fulfill the requirements and provide a highly scalable and available query experience, a large-scale in-house SQL system is heavily relied on. Recently, we evolved the SQL system into a hybrid-cloud SQL federation system, compliant with Twitter’s Partly Cloudy strategy. The hybrid-cloud SQL federation system is capable of processing queries across Twitter’s data centers and the public cloud, interacting with around 10PB of data per day. In this paper, the design of the hybrid-cloud SQL federation system is presented, which consists of query, cluster, and storage federations. We identify challenges in a modern SQL system and demonstrate how our system addresses them with some important design decisions. We also conduct qualitative examinations and summarize instructive lessons learned from the development and operation of such a SQL system.
Evaluation of a Distributed Kalman Filter for Autonomous Satellite Navigation Using DASEE

Distributed Application Simulation and Evaluation Environment (DASEE) is an edge computing testbed built on a cloud-native software platform. Distributed applications at the edge are resource constrained and failure-prone, have disadvantaged communications, often require local autonomy, and exhibit non-deterministic global behavior. DASEE was created to support mission concept of operations (CONOPS) development, architecture trades, and demonstration of distributed compute algorithms in a realistic test environment. DASEE was recently used to simulate the performance of a distributed Kalman filter (DKF) for autonomous satellite navigation in a 112-node Low Earth Orbit (LEO) spacecraft constellation. Preliminary results show that the DKF algorithm converges in a centralized simulation but clearly diverges when executed in a decentralized simulation. Divergence is caused by the covariance matrix some-times losing positive-definiteness. Root cause of the divergence is traced back to asymmetric partner updates between nodes, in which one node successfully completes the update while its partner fails. This result demonstrates the need for an edge computing simulation environment for distributed computing applications.
A personalized path generation for an autonomous vehicle overtaking maneuver

The acceptability by a population for new technology is conditioned by its benefits and its adaptability. While an autonomous vehicle could be a nice solution as an intelligent transportation system to decrease road crashes, traffic jams, etc., the choice of a driving style (relaxed/sporty) could be a good factor of adaptability. This study presents a method to optimize a local overtaking trajectory defined as a sigmoid function integrating constraints of comfort, safety, and path continuity. An only one driving style parameter allows obtaining different paths from smooth to tight shapes respecting previous constraints. Simulations of use cases show good performance of the developed algorithm.
Empirical Analysis of Novel Differential Evolution for Molecular Potential Energy Problem

In bio-chemistry, molecular potential energy problem is a highly complex problem which is multi-model in nature as well. In the present study, a novel differential evolution algorithm (NDE) is applied to handle this problem and to efficiently minimize the molecular potential energy function. The NDE algorithm with its efficient nature provides enhanced search capability with reduced space and time complexities. This variant of DE made use of adaptive control parameter settings along with unit population structure which amplifies the execution of simple DE algorithm without compromising the standards of the solution. The performance of NDE is analysed on eight unconstrained benchmark functions and molecular potential energy function as well in terms of mean fitness and standard deviation. The numerical outcomes showed that NDE achieved good results for all the benchmark cases and maintained a trade-off between convergence rate and efficiency.
Identifying Cyberspace Users’ Tendency in Blog Writing Using Machine Learning Algorithms

A blog is a form of direct interactive communication technology, which allows users to interact and communicate with each other through posting comments and sharing links as well. A blog is a platform where a writer or group of writers gives their opinion on a specific topic. Many issues and topics that are in a certain country being censored and controlled by the government from being presented through the mass media. Nevertheless, blogs have the space to provide a wide platform for exchanging ideas and opinions on various issues. There is a specific proportion between blog features and bloggers’ tendency to social, political, and cultural patterns of different countries and nations that create trends among the bloggers in these countries. In this paper, we use an existing data set from previous research, which has 100 records of data, and manipulate the data by applying three machine learning algorithms for implementing classification and regression tasks. The algorithms are Decision Tree (c4.5), Linear Regression (LR), and Decision Forest (DF) with a 10-fold cross-validation method for training and testing. The results showed that C4.5 achieves the best overall results of 81% accuracy, 83% precision, and 91% recall, compared with the other two algorithms.
Nonlinear three-state quantum walks

The dynamics of a three-state quantum walk with amplitude-dependent phase shifts is investigated. We consider two representative inputs whose linear evolution is known to display either full dispersion of the wave packet or intrinsic localization on the initial position. The nonlinear counterpart presents much more involved dynamics featuring self-trapping, solitonic pulses, radiation, and chaoticlike behavior. We show that nonlinearity leads to a metastable self-trapped wave-packet component that radiates in the long-time regime with a survival probability $\ensuremath{\propto}{t}^{\ensuremath{-}1/2}$. A sudden dynamical transition from such a metastable state to the point when the radiation process is triggered is found for a set of parameters.
Amplitude Scintillation Forecasting Using Bagged Trees

Electron density irregularities present within the ionosphere induce significant fluctuations in global navigation satellite system (GNSS) signals. Fluctuations in signal power are referred to as amplitude scintillation and can be monitored through the S4 index. Forecasting the severity of amplitude scintillation based on historical S4 index data is beneficial when real-time data is unavailable. In this work, we study the possibility of using historical data from a single GPS scintil-lation monitoring receiver to train a machine learning (ML) model to forecast the severity of amplitude scintillation, either weak, moderate, or severe, with respect to temporal and spatial parameters. Six different ML models were evaluated and the bagged trees model was the most accurate among them, achieving a forecasting accuracy of 81% using a balanced dataset, and 97% using an imbalanced dataset.
Design and implementation of a framework for blockchain based security using IoT

The Internet of Things (IoT) has altered the world in the last few years due to its capacity to impact almost every part of life. However, IoT raises concerns about data security and privacy because it collects data from devices via the cloud, increasing its vulnerability to hacking. IoT security is a serious issue that has delayed its widespread adoption. Several security and privacy solutions have been proposed for IoT contexts that meet prevalent security criteria such as authentication, integrity, and secrecy. However, due to resource restrictions and heterogeneous IoT devices, present solutions are unable to address the security requirements of the approaching large-scale IoT paradigm. Blockchain, well known for bitcoin and Ethereum, provides an intriguing approach for IoT security. The IoT and blockchain technologies may be combined and significant improvements in distributed systems have been made as a result of the widespread use of IoT technology. A novel framework with a unique design was proposed to improve security in bitcoin transaction by combining blockchain and SHA-256 hash algorithm. Additionally, the performance of proposed framework is compared with the state-of-the-art algorithms like MD5 and SHA1 in term of encryption time, power consumption, latency, speed and security. It is observed that the proposed framework takes 12 ms lesser latency than MD5 and consumes 2.7Wh lesser power consumption than SHA1 and provides better security than both the techniques.
A Proposed Blockchain-Based Model for Online Social Network to Detect Suspicious Accounts

Online social network (OSN) is a social media platform to remain connected with their families and friends and others. The major challenge of OSN is security, because malicious attackers can compromise the legitimate user account or create fake accounts to spread fake information which causes more damage in social activities. Detection of these fake accounts or compromised accounts is a difficult process due to the dynamic nature of OSN services and users. For that purpose, we proposed blockchain-based online social network (Block-OSN) method which includes five consecutive phases for detecting Sybil attacks. In the first phase, blockchain-based user authentication is performed for ensuring the legitimacy of the users, which helps to mitigate malicious users in an earlier stage. In the second phase, preprocessing is performed to improve the quality of the data, in which the data normalization is performed. In the third phase, we perform suspicious account detection using SiasmeNet algorithm by considering tweet and network-based data. In the fourth phase, we provide security to the normal account. In the fifth phase, differential privacy is applied to the suspicious account.
Unified Framework for NER and RE Tasks with Strong Interpretability on Chinese Medicine Instruction Parsing

Medicine instructions and related medical guide-lines could serve as valuable knowledge source to serious medicine Knowledge graph (KG) construction. To formulate these nonstructural texts into a well-defined structure, a pre-cise and efficient way to extract information from medicine instructions is needed. Two basic tasks of the extraction, i.e. the Naming Entity Recognition (NER) task and the Relation Extraction (RE) task, have been well studied in traditional way (such as templates) as well as deep neural networks (such as BERT-based models). However, sound interpretability is key to serious medical scenario, which almost rules out the direct application of deep neural networks. In this work, we propose a unified framework for NER task and RE task on Chinese medicine instructions, in which hierarchical dynamic templates serve as basic tools for both tasks. These templates are precise, flexible and strong-interpretable, and they are initially manually constructed and easily expanded in a bootstrap session with the help of distant supervision and BERT-based models. In another point of view, BERT-based models are effectively distilled by rules and manual check, resulting into a cluster of strong-interpretable dynamic templates. At the end of the day, a Chinese medical KG based on the exacted parse result of more than 30 thousand medical instructions was established, and then partly applied to a prescription pre-audit system, which led to more than 10% improvement to the prescription audit accuracy in eight primary hospitals.
Semantic and Context understanding for sentiment analysis in Hindi Handwritten Character Recognition Using a Multiresolution Technique

The rapid growth of Web 2.0, which enables people to generate, communicate, and share information, has resulted in an increase in the total number of users. In developing countries, online users’ sentiment influences decision-making, social views, individual consumption decisions, and entity quality monitoring. As a result, more accurate sentiment analysis, particularly in their native language such as Hindi, is preferred over crude binary categorization. Because of the abundance of web-based data in Indian languages such as Hindi, Marathi, Kannada, Tamil, and so on. Analyzing this data and recovering valuable and relevant information from handwritten text has become extremely important. Despite years of research and development, no optical writing recognition (OCR) system has ever been certified as completely reliable. The first step in any pattern recognition system is feature selection. In many fields, feature selection is studied as a combinatorial optimization problem. The primary goal of feature selection is to reduce the number of redundant and ineffective traits in the recognition system. This feature selection is used to maintain or improve the performance of the classifier used by the recognition system: A support vector machine (SVM) technique could be used to solve this character recognition problem. The Hindi character recognition system recognizes Hindi characters by employing morphological operations, edge detection, HOG feature extraction, and an SVM-based classifier. The proposed model outperformed the current state-of-the-art method, achieving an accuracy of 96.77 %.