Reviewerly/exHarmony huggingface dataset:
Blur-robust image registration and stitching

Abstract This paper addresses the functioning of a new key point detection method for images. Such methods are useful in applications as image reconstructions, object signature recognition or video stabilization. The method, Blurred Image Matching (BIM), is based on the comparison of large areas of interest in images. BIM uses a process of blurring and shape comparison that makes the approach unique and in the vast majority of tested cases results in higher performances than other approaches. Among BIM’s key features are the amount of points used for comparison, 19 to 129 times fewer than other methods and of higher quality, allowing faster processing. Another of its key features is a high efficiency in the comparison of images, with a success rate higher than the average of existing methods by 13% to 36%, depending on the images’ characteristics. The method is still in a development stage and will significantly improve, however the results obtained so far are extremely promising, its task solving approach is new and makes a new range of applications necessiting key point detection possible. All this allows us to foresee BIM as a future standard among such methods.
A Low Cost Multi-Camera Array for Panoramic Light Field Video Capture

We present a portable multi-camera system for recording panoramic light field video content. The proposed system captures wide baseline (0.8 meters), high resolution (>15 pixels per degree), large field of view (>220°) light fields at 30 frames per second. The array contains 47 time-synchronized cameras distributed on the surface of a hemispherical, 0.92 meter diameter plastic dome. We use commercially available action sports cameras (Yi 4k) mounted inside the dome using 3D printed brackets. The dome, mounts, triggering hardware and cameras are inexpensive and the array itself is easy to fabricate. Using modern view interpolation algorithms we can render objects as close as 33-cm to the surface of the array.
An Efficient Fault Detection Method for Elliptic Curve Scalar Multiplication Montgomery Algorithm

Elliptical curve cryptography (ECC) is being used more and more in public key cryptosystems. Its main advantage is that, at a given security level, key sizes are much smaller compared to classical asymmetric cryptosystems like RSA. Smaller keys imply less power consumption, less cryptographic computation and require less memory. Besides performance, security is another major problem in embedded devices. Cryptosystems, like ECC, that are considered mathematically secure, are not necessarily considered safe when implemented in practice. An attacker can monitor these interactions in order to mount attacks called fault attacks. A number of countermeasures have been developed to protect Montgomery Scalar Multiplication algorithm against fault attacks. In this work, we proposed an efficient countermeasure premised on duplication scheme and the scrambling technique for Montgomery Scalar Multiplication algorithm against fault attacks. Our approach is simple and easy to hardware implementation. In addition, we perform injection-based error simulations and demonstrate that the error coverage is about 99.996%.
Semantic connection set-based massive RDF data query processing in Spark environment

Abstract Resource Description Framework (RDF) is a data representation of the Semantic Web, and its data volume is growing rapidly. Cloud-based systems provide a rich platform for managing RDF data. However, there is a performance challenge in the distributed environment when RDF queries, which contain multiple join operations, such as network reshuffle and memory overhead, are processed. To get over this challenge, this paper proposes a Spark-based RDF query architecture, which is based on Semantic Connection Set (SCS). First of all, the proposed Spark-based query architecture adopts the mechanism of re-partitioning class data based on vertical partitioning, which can reduce memory overhead and spend up index data. Secondly, a method for generating query plans based on semantic connection set is proposed in this paper. In addition, some statistics and broadcast variable optimization strategies are introduced to reduce shuffling and data communication costs. The experiments of this paper are based on the latest SPARQLGX on the Spark platform RDF system. Two synthetic benchmarks are used to evaluate the query. The experiment results illustrate that the proposed approach in this paper is more efficient in data search than contrast systems.
Detection of Malicious Emails through Regular Expressions and Databases

In modern days, electronic mail or in other terminology, Email is a popular way when it comes to communicating with others. The communication is mostly intended to be formal as in with companies, firms, organizations, etc. Emailing is done from anywhere and at any time. However, due to the increased usage of such mailing service, cybercriminals or unethical hackers decided to target such service to harm by any means. The paper aims to find an optimal logical method of reducing the probability of falling victim to such malicious emails by filtering them out.
Modeling and Analyzing Availability, Cost and Sustainability of IT Data Center Systems

Data center has evolved dramatically in the past few years, and this change has to do with the advent of cloud computing and the massive increase of data due to the Internet of Everything. This new paradigm demands high availability with low costs, which represents conflicting requirements. Approaches to conduct an integrated evaluation of availability, cost and sustainability of IT (Information Technology) data center systems are not trivial. This paper proposes a set of models for estimating these metrics. A case study is conducted to illustrate the applicability of the proposed models for analyzing IT data center systems. The results revealed that we were able to upraise the availability over 70% with a small increase on the cost (around 15%) and on the sustainability (5%).
Simple Refreshing in the Noisy Leakage Model

Masking schemes are a prominent countermeasure against power analysis and work by concealing the values that are produced during the computation through randomness. The randomness is typically injected into the masked algorithm using a so-called refreshing scheme, which is placed after each masked operation, and hence is one of the main bottlenecks for designing efficient masking schemes. The main contribution of our work is to investigate the security of a very simple and efficient refreshing scheme and prove its security in the noisy leakage model (EUROCRYPT'13). Compared to earlier constructions our refreshing is significantly more efficient and uses only n random values and $${<}2n$$ operations, where n is the security parameter. In addition we show how our refreshing can be used in more complex masked computation in the presence of noisy leakage. Our results are established using a new methodology for analyzing masking schemes in the noisy leakage model, which may be of independent interest.
Fuzzy Approach for Locating Sensors in Industrial Internet of Things

Nowadays, in this era of a data driven thinking and reflection, data mining and data analysis are keys to any business survival in a competitive conjectural market. The internet of things is an emerging technology that manages to create a path for the new generation of industrial production system. This advanced technology is requirement to the proliferation of Smart factories, it represents the best tool to help this new concept of plants to organize themselves and optimize the available resources and their consumption. The purpose of this paper is two-pronged; a proposal for an architectural framework of the industrial internet of things, and a mathematical formulation based on fuzzy logic to determine the ideal location of sensors at the shop floor taking into consideration several restrictions.
Safeguarding Data Integrity by Cluster-Based Data Validation Network

Ensuring data quality is central to the digital transformation in industry. Business processes such as predictive maintenance or condition monitoring can be implemented or improved based on the available data. In order to guarantee high data quality, a single data validation system are usually used to validate the production data for further use. However, using a single system allows an attacker only to perform one successful attack to corrupt the whole system. We present a new approach in which a data validation system using multiple different validators minimizes the probability of success for the attacker. The validators are arranged in clusters based on their properties. For a validation process, a challenge is given that specifies which validators should perform the current validation. Validation results from other validators are dropped. This ensures that even for more than half of the validators being corrupted anomalies can be detected during the validation process.
Improved Performance and Execution Time of Face Recognition Using MRSRC

Madarkar, Jitendra Sharma, Poonam Singh, RimjhimFace recognition accuracy is vulnerable to environmental noise, low-resolution images, and other variations such as illumination, pose, and expression. The accuracy of the face recognition mostly relying on the features of training samples and testing samples. Recently, sparse representation based classification (SRC) has shown state-of-the-art results in face recognition and developed several extended versions of SRC methods to improve the performance. The time complexity of the SRC is depended on the size of the dictionary. In this paper, a new fusion approach MRSRC (Multi-resolution sparse representation based classification) is developed by incorporating the wavelet compressed features into the dictionary. MRSRC has shown better performance than an existing algorithm and also reduces the time complexity. The experimentation is carried out on benchmarking databases such as LFW and ORL.
Optimization Techniques for Machine Learning

This chapter outlines the fundamental of machine learning literature and provides the review of various literatures on understanding the variety of optimization techniques used for machine learning and prediction models. These techniques concern optimization either for the singular tree generation or the selection in homogeneous/heterogeneous ensembles. For the ensemble selection, various evaluation functions are studied and used with different methods of path. Comparisons with the state-and-art methods are performed on datasets or medical applications designed to validate the different techniques. The critical review of currently available optimization techniques is followed by descriptions of machine learning applications. This study will help the researcher to avoid overlapping efforts and make new basis for novice researchers.
APDRChain: ANN Based Predictive Analysis of Diseases and Report Sharing Through Blockchain

A huge amount of healthcare data (structured and unstructured) is currently available to medical specialists, indicating details of clinical symptoms. Each type of data provides information that must be properly analyzed for healthcare diagnosis. To simplify the diagnostic process, avoid misdiagnosis as well as early detection, artificial intelligence (AI) that aims to mimic human cognitive functions may be employed. Current AI techniques that are used for structured data include machine learning methods, such as the classical support vector machine, artificial neural network, and the modern deep learning. Natural language processing is mainly used for unstructured data. In this paper, we have adopted artificial neural network by using adaptive learning algorithms to handle diverse types of cardiovascular clinical data and integrate them into categorized major cardiovascular disease outputs such as heart failure, aortic aneurysm, cardiomyopathy, cerebrovascular disease, etc. These outputs are then shared as reports to patients as well as doctors by an efficient report sharing scheme called APDRChain, which combines blockchain and structured peer-to-peer network techniques with clever cryptography to create a consensus mechanism. The evaluation results show that APDRChain can achieve higher efficiency and satisfy the security requirements in report sharing.
Shouted and Normal Speech Classification Using 1D CNN

Automatic shouted speech detection systems usually model its spectral characteristics to differentiate it from normal speech. Mostly hand-crafted features have been explored for shouted speech detection. However, many works on audio processing suggest that approaches based on automatic feature learning are more robust than hand-crafted feature engineering. This work re-demonstrates this notion by proposing a 1D-CNN architecture for shouted and normal speech classification task. The CNN learns features from the magnitude spectrum of speech frames. Classification is performed by fully connected layers at later stages of the network. Performance of the proposed architecture is evaluated on three datasets and validated against three existing approaches. As an additional contribution, a discussion of features learned by the CNN kernels is provided with relevant visualizations.
Numerical Modeling of Suspension Force for Bearingless Flywheel Machine Based on Differential Evolution Extreme Learning Machine

The analytical model (AM) of suspension force in a bearingless flywheel machine has model mismatch problems due to magnetic saturation and rotor eccentricity. A numerical modeling method based on the differential evolution (DE) extreme learning machine (ELM) is proposed in this paper. The representative input and output sample set are obtained by finite-element analysis (FEA) and principal component analysis (PCA), and the numerical model of suspension force is obtained by training ELM. Additionally, the DE algorithm is employed to optimize the ELM parameters to improve the model accuracy. Finally, absolute error (AE) and root mean squared error (RMSE) are introduced as evaluation indexes to conduct comparative analyses with other commonly-used machine learning algorithms, such as k-Nearest Neighbor (KNN), the back propagation (BP) algorithm, and support vector machines (SVMs). The results show that, compared with the above algorithm, the proposed method has smaller fitting and prediction errors; the RMSE value is just 22.88% of KNN, 39.90% of BP, and 58.37% of SVM, which verifies the effectiveness and validity of the proposed numerical modeling method.
Recent Research Advances in Black and White Visual Cryptography Schemes

Visual Cryptography (VC) is a type of image secret sharing scheme which decrypts an original secret image with Human Visual System (HVS). In this, the original image can be alienated into n shadows or shares and allocated to n participants; stacking any k shares reveals the secret image which ensures the security measures. In this paper, we examined the recent research advances in black and white VCSs. We reviewed the existing techniques and a comparative study of VC for binary images is presented. The study is performed with respect to different parameters and draws the current barriers related to the visual cryptography schemes.
Minimal-Time Trajectories for Interception of Malicious Drones in Constrained Environments

This work is motivated by the need to improve existing systems of interception of drones by using other drones. Physical neutralization of malicious drones is recently reaching interest in the field of counter-drone technologies. The exposure time of these threats is a key factor in environments of high population densities such as cities, where the presence of obstacles can complicate the task of persecution and capture of the intruder drone. This paper is therefore focused on the development and optimization of a strategy of tracking and intercepting malicious drones in a scenario with obstacles. A simulation environment is designed in Matlab-Simulink to test and compare traditional interception methods, such as Pure Pursuit which is quite common in missile guidance field, with the proposed strategy. The results show an improvement in the interception strategy by means of a reduction in the time of exposure of the threat with the developed algorithm, even when considering obstacle environment.
Monitoring Application for Clean Water Access and Clustering using K-Means Algorithm

Abstract The purpose of this research is to develop a monitoring application of clean water access for the community and determining areas that need to improve access to clean water. Data collection was done by examining documents which were related to clean water management and conducting interviews with the Department of Housing and Settlement of West Java Province. The results of data collection are used to develop a clean water monitoring application system based on Android and Web. Data from the application will be used for clustering with the K-Means algorithm. For example, in the city of Bandung, 12 urban village areas were recommended for increasing access to clean water. The area has a percentage of ownership of clean water access approaching the value of 29.96% of the ownership of clean water sources for food and drink, 28.96% for ownership of other sources of clean water and 14.48% for ownership of reserves of clean water. The optimal number of clusters of clean water access in the city of Bandung is three clusters because it has the lowest Davies-Bouldin Index (DBI) value of 0.439.
Active Defense Strategy Selection Method Based on Two-Way Signaling Game

Most network security research studies based on signaling games assume that either the attacker or the defender is the sender of the signal and the other party is the receiver of the signal. The attack and defense process is commonly modeled and analyzed from the perspective of one-way signal transmission. Aiming at the reality of two-way signal transmission in network attack and defense confrontation, we propose a method of active defense strategy selection based on a two-way signaling game. In this paper, a two-way signaling game model is constructed to analyze the network attack and defense processes. Based on the solution of a perfect Bayesian equilibrium, a defense strategy selection algorithm is presented. The feasibility and effectiveness of the method are verified using examples from real-world applications. In addition, the mechanism of the deception signal is analyzed, and conclusions for guiding the selection of active defense strategies are provided.
Research and Implementation on Serial Communication Based on Qt

Serial communication is a very common and stable communication method. This paper introduces the Qt-based serial communication application development program in detail, analyzes the design concept of serial communication software development, and completes the visual interface design of Qt-based serial communication software. The software test results show that the real-time data communication between the two computers can be realized. The operation of the software is simple and convenient, and the design interface is elegant. The test software which has excellent anti-interference ability is stable and reliable.
Differences between smart speakers and graphical user interfaces for music search considering gender effects

The ubiquitous availability of smart speakers allows hands- and eyes-free interaction through Voice User Interfaces (VUIs). Control-ling music playback is the most commonly used feature of VUIs. Previous work investigated how users naturally interact with smart speakers and suggested that users' gender could affect the devices' usability. The usability of commercial devices compared to other interactive systems and the effects of users' gender is, however, unclear. Therefore, we conducted a study with 20 participants using an Amazon Echo Dot and a laptop device. Participants searched for artists and titles using a Graphical User Interface (GUI) and a VUI. In addition, they performed different tasks such as saving a song in a playlist or adding songs into a queue. The analysis revealed that the VUI provides significantly lower usability because it lacks features, requires higher mental effort, and provides confusing answers. In contrast to previous concerns, the analysis did not reveal significant device×gender effects.
The Optimized Path For A Mobile Robot Using Fuzzy Decision Function

The proposed paper presents the efficient obstacle avoidance mechanism in a complex environment based on the fuzzy optimized decision function. The origin point, goal point, obstacles, optimized paths, and efficient decisions mechanism are the requirements of the navigational system in two-dimensional space. During navigation, the distances measured in Euclidean Space, but the decision comprises with the proposed fuzzy function. This fuzzy function’s domain, co-domain, and range are finite. The domain lies with the set of obstacles, co-domain lies with the set of paths and the range lies with the decision. The robot follows the fuzzy rule generated by the proposed fuzzy function presented in the form of a matrix and the combination of the cell by row and column provides the resultant decision. This decision is formulated as the compact fuzzification of the two-dimensional rule over row and column of the matrix. Thus, every decision is unique and optimized. The experimental and simulational results are validated here to show the effectiveness of the proposed controller.
Proactive Cyber Situation Awareness via High Performance Computing

Cyber situation awareness technologies have largely been focused on present-state conditions, with limited abilities to forward-project nominal conditions in a contested environment. We demonstrate an approach that uses data-driven, high performance computing (HPC) simulations of attacker/defender activities in a logically connected network environment that enables this capability for interactive, operational decision making in real time. Our contributions are three-fold: (1) we link live cyber data to inform the parameters of a cybersecurity model, (2) we perform HPC simulations and optimizations with a genetic algorithm to evaluate and recommend risk remediation strategies that inhibit attacker lateral movement, and (3) we provide a prototype platform to allow cyber defenders to assess the value of their own alternative risk reduction strategies on a relevant timeline. We present an overview of the data and software architectures, and results are presented that demonstrate operational utility alongside HPC-enabled runtimes.
A New Multiple Hypothesis Tracker Integrated with Detection Processing

In extant radar signal processing systems, detection and tracking are carried out independently, and detected measurements are utilized as inputs to the tracking procedure. Therefore, the tracking performance is highly associated with detection accuracy, and this performance may severely degrade when detections include a mass of false alarms and missed-targets errors, especially in dense clutter or closely-spaced trajectories scenarios. To deal with this issue, this paper proposes a novel method for integrating the multiple hypothesis tracker with detection processing. Specifically, the detector acquires an adaptive detection threshold from the output of the multiple hypothesis tracker algorithm, and then the obtained detection threshold is employed to compute the score function and sequential probability ratio test threshold for the data association and track estimation tasks. A comparative analysis of three tracking algorithms in a clutter dense scenario, including the proposed method, the multiple hypothesis tracker, and the global nearest neighbor algorithm, is conducted. Simulation results demonstrate that the proposed multiple hypothesis tracker integrated with detection processing method outperforms both the standard multiple hypothesis tracker algorithm and the global nearest neighbor algorithm in terms of tracking accuracy.