Reviewerly/exHarmony huggingface dataset:
Implementation of Machine Learning in Determining Nutritional Status using the Complete Linkage Agglomerative Hierarchical Clustering Method

Problems that often occur in the nutritional status of children can be done prevention in the form of input to the people of north Aceh on the importance of fulfilling nutrition in toddlers in order to avoid stunting. Lack of nutrition is one of the causes of problems experienced by toddlers in north Aceh. The role of local governments, hospitals and health services is needed in looking at the amount of nutritional status of children, especially areas in northern Aceh. This research aims to be able to determine the nutritional status of toddlers and can provide convenience for hospital officials and doctors in handling gradually and how to treat on a scale in diagnosing diseases with child nutritional status.  The first method of this study is to group toddlers identified nutritional status of children who are classified as stunting or not and then grouped areas that are malnourished children using hierarchical agglomerative models. The results of this study can diagnose nutritional status in children with Machine Learning using complete linkage agglomerative hierarchical clustering whose final results can see areas prone to stunting. The data to be modeled consists of 12 sub-districts with samples taken in the form of the number of cases of baktiya 12, dewantara 21, kuta makmur 83, meurah mulia 84, jambo aye 87, nibong 83, sacred store 68. the process of complete linkage agglomerative hierarchical clustering Baktiya method from Scaling Data (standardization)-1.344354111, Kuta Makmur1.376783706, Meurah Mulia 1.415109591, Cot Girek -0.462858762, Simpang Kramat0.801895435, Nisam Antara0.648591896. Based on the results of distance calculations, Prosedure was carried out up to 11 times resulting in cluster groups of 3,21,7,14.15 with a result of 0, clusters 17,23,8,13,18,20,11 with results of 1.6628305 and 1.4,10,19,26,2,9,5,12 with a value of 2.720995. The final calculation of 19,26,1,4,10 is 2.11633.
Reprint of: Endomorphism rings of reductions of Drinfeld modules

Let A=Fq[T] be the polynomial ring over Fq, and F be the field of fractions of A. Let ϕ be a Drinfeld A-module of rank r≥2 over F. For all but finitely many primes p◁A, one can reduce ϕ modulo p to obtain a Drinfeld A-module ϕ⊗Fp of rank r over Fp=A/p. The endomorphism ring Ep=EndFp(ϕ⊗Fp) is an order in an imaginary field extension K of F of degree r. Let Op be the integral closure of A in K, and let πp∈Ep be the Frobenius endomorphism of ϕ⊗Fp. Then we have the inclusion of orders A[πp]⊂Ep⊂Op in K. We prove that if EndFalg(ϕ)=A, then for arbitrary non-zero ideals n,m of A there are infinitely many p such that n divides the index χ(Ep/A[πp]) and m divides the index χ(Op/Ep). We show that the index χ(Ep/A[πp]) is related to a reciprocity law for the extensions of F arising from the division points of ϕ. In the rank r=2 case we describe an algorithm for computing the orders A[πp]⊂Ep⊂Op, and give some computational data.
CUBIST: High-Quality 360-Degree Video Streaming Services via Tile-based Edge Caching and FoV-Adaptive Prefetching

360-degree video streaming, which is becoming more and more popular as the fast development of VR/AR applications nowadays due to the immersive viewing experience it can offer, poses enormous challenges to the current network infrastructure in terms of high bandwidth and low latency requirements. To address this problem and to ensure the QoE (quality of experience) of end-users, this paper presents CUBIST, a method and system for high-quality 360-degree video streaming in networks with cache nodes at the edge. To the best of our knowledge, it is the first tile-based edge caching solution that incorporates proactive tile prefetching and hierarchical cache organization into reactive caching to maximize the caching benefit while reducing the cost of 360-degree video streaming. Experimental results show that CUBIST can achieve a cache hit ratio of 87 % and improve the effective video bitrate by 12.9 % with most rate transitions being small when compared with the latest FoV-aware edge caching scheme.
Multi-Prime RSA Verilog Implementation Using 4-Primes

In this paper, we present a new method to perform RivestShamirAdleman (RSA) key generation and decryption using FPGA. Multi-prime RSA uses more than two primes for computations. Here, we have used four primes for our hardware implementation. However, in this paper, we are not focusing on the random prime number generation. It can be externally computed and sent to the FPGA through peripheral component interconnect (PCI) port in the real scenario. We hard-coded the prime numbers and focused on the RSA key generation and decryption process. The importance of this implementation is that we use constant public e value 65537, which applies in OpenSSL with different n and d private key values for each RSA channel.
An Implementation of Secure Vehicle-to-Vehicle Communication Using Shared Key Generation with Kano Method

Vehicle-to-vehicle (V2V) communication assigned for security and safety in the vehicles communication currently becomes much more interesting, in such it is able to reduce traffic accidents such as collision among vehicles. In this system, the possibility of error shared key when obtaining received signal strength (RSS) between a pair communicating vehicles often occurs. Moreover, the possibility of stealing personal data of drivers and passengers and even controlling take over by adversaries can be happened as well. Hence, in this case, it should have reciprocity without error such that shared key between two participating vehicles is matched. Because attacking activities may happen frequently, thus a security system using shared secret key to protect these personal data in the wireless network should be mandatory. In this paper, a scenario of secure shared secret key generation in V2V communication using Raspberry Pi through 2.4 GHz WiFi connection is implemented. To increase reciprocity, Kalman Filter and BCH error code are adopted. Performance evaluation is evaluated by varying vehicle speed from 20 km/h to 60 km/h with ping time interval from 7 ms to 20 ms. The evaluation result showed that in 20 km/h with 10 ms time interval has the highest correlation 0.99 and the lowest one 0.91 in 40 km/h with 20 ms time interval. Meanwhile, Aono quantization works properly for the scenario 60 km/h with 7 ms time interval for 2881-bit of 3000 data input with key disagreement rate (KDR) 0 in 20 km/h with 10 ms time interval.
Quantum Communication: Concept, Applications, and Future Outlook

With the emergence of Quantum mechanics arrived the prospect of further development in technology. New areas of research and development came into the picture, one of them being Quantum communication. In 1991, the first protocol of Quantum cryptography and quantum non-duality attracted physicists, mathematicians, and computer scientists to explore the mysterious ways of the quantum world. Since then, quantum communication has evolved to an astonishing extent. Ultra-high-speed internet, computational power, optimization, and security have paved the way for innumerable possibilities ahead. This survey intends to investigate the concepts and applications of quantum communication. In this review article, we try to give an idea of quantum communication background by shedding light on polarization and entanglement, following which, we give a brief survey of its applications such as quantum teleportation, quantum key distribution, secure direct communication, and quantum memories. The study concludes with a discussion on the future outlook of this emerging paradigm.
Demo: Large Scale Analysis on Vulnerability Remediation in Open-source JavaScript Projects

Given the widespread prevalence of vulnerabilities, remediation is a critical phase that every software project has to go through. When comparing the studies on understanding the security vulnerabilities in software, such as vulnerability discovery and patterns, there is a lack of studies on the vulnerability remediation phase. To address this, we have done a timeline analysis for 130 of the most dependent upon open source projects written in JavaScript language, hosted on GitHub to understand the nature and the lifetime of the vulnerabilities in those projects. We used a static code analyzer on 501K commits from the repositories to identify commits that introduced new vulnerabilities to the code and fixed existing vulnerabilities in the code. In 90% of the projects, we identified that a commit that fixed an existing vulnerability had introduced one or more new vulnerabilities into the code. On average, 16% of the commits intended to fix vulnerabilities have introduced one or more new vulnerabilities from the analyzed projects. We also found that 18% of the total vulnerabilities found in those projects have originated from a commit meant to fix an existing vulnerability, and 78% of those vulnerabilities could have been avoided of introduction if the developers were to use proper internal testing. Here, we demonstrate Sequza, a visualization tool to help organizations detect such instances at the earliest possible.
Image Speckle Denoising for Securing Internet of Smart Sensors

How to improve utility performance when securing sensitive data is an important research problem in Internet of smart sensors. In this paper, we study secured image speckle denoising for networked synthetic aperture radar (SAR). Speckle noise of SAR affects image quality and has a great influence on target detection and recognition. MSTAR dataset is often used in image target recognition. In this paper, a subregion-based method is proposed in order to improve the accuracy of target recognition and better retain target information while filtering and denoising the image. The new method applies advanced encryption techniques to protect sensitive data against malicious attack. Firstly, the image is divided into marked areas and unmarked areas through edge extraction and hole filling. Secondly, we use different size windows and filtering methods to filter the image in different areas. The experimental results show that the proposed algorithm has obvious advantages over MR-NLM, SSIM-NLM, Frost, and BM3D filtering in terms of equivalent view number and preserving edge and structure.
Rapid 3D Trajectory Planning Under State Constraints Using Receding Horizon PSO Algorithm

For long-range Unmanned Aerial Vehicle (UAV) such as boost-glide hypersonic vehicle and unmanned combat aircraft, there is a rather large planning space for its flight trajectory due to the wide range of movement and long flight time. In this paper, a novel trajectory planning method is proposed based on Receding Horizon Particle Swarm Optimization (RHPSO) algorithm to realize rapid 3D trajectory planning despite a large planning space. First, the motion equations of mass center considering the rotation and oblateness of the Earth are established, and the change rates of attack angle, velocity roll angle and throttle setting are selected as decision variables which leads to the optimized results including the state constraints of trajectory. Then, by introducing receding horizon optimization strategy, the global optimization problem for the whole flight period is decomposed into local optimization problems within multiple receding horizon optimization windows, which reduces the computational complexity and realizes online rapid trajectory planning. In addition, in order to reduce blind search and speed up the optimization, a guidance strategy is proposed for the initialization of particle swarm, and in order to reduce the possibility of falling into local optimum caused by receding horizon optimization, a global heuristic strategy for specifying global heuristic feature points is hence proposed. The simulation results show that this method can effectively realize rapid 3D trajectory planning for long-range UAVs.
Implementation of Augmented Reality in E-Commerce Applications

The internet as a medium of shopping and buying has become a widely researched media theme. Augmented reality is a breakthrough technology that helps deliver an e-commerce online shopping experience using the internet with the quality of offline shopping. That is possible thanks to the ability of augmented reality technology which allows consumers to associate and try the product through cyberspace, as well as online stores. This study aims to implement AR in e-commerce applications. The e-commerce application here is a Web-based application that is segmental into the online shopping site with expected features to give the appearance of a new product, unique and exciting in online shopping activity. With the existence of augmented reality, it will make it easier to use web-based applications so that it helps customers find the desired product.
Applying data mining techniques in the context of social media to improve situational awareness at large-scale events

Having relevant information plays an important role in every aspect of human life. Data mining supports the extraction of additional information by discovering patterns and correlations between data within the dataset under investigation. Information obtained in this way is used in various applications ranging from education, business to crisis management etc. Enhanced situational awareness is a foundation for improved decision making. A prerequisite for optimized decision making is to quickly obtain valid information on a given topic, but also to have it presented in an understandable and easily digestible form. In this paper, we propose a method for applying data mining techniques in the context of social media to improve situational awareness in large-scale events by describing a solution for an intelligent situational information portal that leverages open data information sources and channels for improving decision support.
Letter on Convergence of In-Parameter-Linear Nonlinear Neural Architectures With Gradient Learnings

This letter summarizes and proves the concept of bounded-input bounded-state (BIBS) stability for weight convergence of a broad family of in-parameter-linear nonlinear neural architectures (IPLNAs) as it generally applies to a broad family of incremental gradient learning algorithms. A practical BIBS convergence condition results from the derived proofs for every individual learning point or batches for real-time applications.
Deep Learning for Joint Noncoherent Differential Detection and Channel Decoding

We propose a deep-learning approach for the joint noncoherent differential detection and channel decoding problem. Conventional receivers adopt a model-based approach for noncoherent differential detection and channel decoding in linear manners. For the noncoherent differential detection, the conventional way is to use symbol-by-symbol differential detection (DD) and multiple symbol differential detection (MSDD). DD is simple to implement, but it suffers from performance degradation compared with coherent detection. The performance degradation can be narrowed by MSDD that detects a block of symbols jointly. However, its complexity increases exponentially with the block size. Furthermore, MSDD is not robust in that when the channel phase changes substantively within the block, its performance could be worse than that of DD. Channel decoding needs to be determined according to the channel coding. This work applies the advantages in deep learning for the design of receivers. In particular, we employ a Deep neural network (DNN) constructed by Long Short-Term Memory (LSTM) units to solve the joint noncoherent differential detection and channel decoding problem. Our simulations show that a DNN can outperform conventional model-based linear receivers. Furthermore, it does so with faster signal processing. This performance improvement points to a new direction for future receiver design.
Information technology and local conflict: evidence from Indonesia

The positive effects of communication technology on the developmental process of a nation have been demonstrated in the literature. In this paper, we argue that communication technology may also have an adverse effect, by examining its impact on the presence of conflict. Using Indonesian subdistrict surveys from 2011 and 2014, we find that regions with stronger communication signals are more likely to experience conflict. The econometric estimates show that the presence of a strong base transceiver station (BTS) signal is associated with a 0.8 per cent higher likelihood of local conflict, while a weak signal is associated with a 0.7 per cent higher likelihood of local conflict. A better communication signal is also associated with the number of conflict events. Our findings suggest that the signal reduces conflict casualties. Our estimates also justify the need for government interventions in the form of tax or control policies, to minimise negative, technology-led externalities.
Collaborative Training and Response Communities - An Alternative to Traditional Cyber Defense Escalation

In the United States, the State of Colorado“s Department of Transportation successfully defended and recovered from a recent severe malware attack. The 2018 attack at the Colorado Department of Transportation was mitigated by a rapid multi-agency incident response. This is the first case in the United States where a state's National Guard responded to a governor's declaration of a cyber emergency response. In anticipation of advanced threats to Colorado citizens, Regis University has hosted collaborative exercises with government, organizations, and industry as part of a larger Collaborative Training Response Community (CTRC) effort to facilitate collaborative physical exercises, governmental policy development, and relationship building. The resulting capabilities allowed for an effective response to this incident. The authors present a new incident response model, based on this case in the context of existing cybersecurity organizations extant in the U.S., that may be useful to private and public sector communities where collaborative incident response is appropriate.
A Fast Correction Algorithm for Inverse Longitude and Latitude Mapping of Fish Eye Image

Abstract In order to solve the problem of large barrel distortion in fisheye lens, an image distortion correction algorithm based on double longitude is proposed to improve the visual effect. Firstly, in the case of unknown lens angle and non-circular fisheye image, the spherical center and its radius are obtained by using fisheye image features, and the radius is optimized to reduce pole distortion; on this basis, the fisheye image is mapped to the spherical surface by orthogonal projection strategy using double longitude model method, which is transformed into the transverse longitude and longitudinal longitude coordinates of the sphere The projection is a square plane image based on the horizontal and vertical double longitude coordinates to realize the fast and accurate image transformation.
An Elasticity Framework for Smart Contracts

Smart contracts provide computation facilities to blockchains, enabling many application scenarios where automatic analysis and complex transactions can be performed. However, in situations where the flow of information is variable and there are time, quality, and/or cost constraints imposed, smart contracts do not usually offer enough performance. In this article, we describe an elasticity framework that enables dynamic adaptation of smart contracts with respect to used resources, analytics quality, and incurred costs in a transparent manner for both smart contract developers and users. We validate our framework in an Internet of Things and Smart City scenario where several analytics are computed using Elastic Smart Contracts that dynamically adapt the appropriate elasticity variables.
Solar Power based Hybrid Wheelchair

Nowadays the ability to uplift own work is a major concern for the physically disabled people. This makes them not to depend on other and let them to carry their own water. This automated solar powered wheelchair with the accessibility to ease of access to the surrounding with the help of the smart screen packed with the scanner into the hybrid mode of this wheelchair helps to go anywhere without asking anyone and get dependent on others. Also, this wheelchair has the automatic stabilization to the balance of the user sitting on the wheelchair while using it. The inbuilt operations along with the hardware of the smart screen being implanted next to the alternate handle of the user helps them to navigate whereas on the other side there will be the rotating ball for the steering of the wheelchair. The speed also be controlled with the help of the screen as well just with a click. The patent for the proposed wheelchair has been published from Patent office of India, Application number 202011035447, 17th Aug 2020.
Exploring the Network-Sensitive Scheduling on Distributed Shared Memory

Distributed shared memory abstraction can organize a cluster of machine nodes to empower the application execution with the big memory spaces and abundant parallelism. However, when deploying an application on the distributed shared memory, these loosely-coupled machine nodes may also make the execution of its tasks suffer from the frequent network traffic based on the existing scheduling strategies. In this paper, we conduct a detailed case study to reveal that the network-intensive task and poor data locality contribute to the major part of network traffic, which could impose the network burden on the execution. To address this issue, we propose the network-sensitive scheduling that fully takes the network traffic into the consideration of scheduling on DSM. The experimental results reveal that this network-sensitive scheduling can reduce the execution time by up to 32%, and decrease the network traffic by 18% under the real-world cases.
Intuitiveness in Active Teaching

While machine learning (ML) gives rise to astonishing results in automated systems, it is usually at the cost of large data requirements. This makes many successful algorithms from ML unsuitable for human-machine interaction, where the machine must learn from a small number of training samples that can be provided by a user within a reasonable time frame. Fortunately, the user can tailor the training data they create to be as useful as possible, severely limiting its necessary size—as long as they know about the machine’s requirements and limitations. Of course, acquiring this knowledge can in turn be cumbersome and costly. This raises the question of how easy ML algorithms are to interact with. In this work, we address this issue by analyzing the intuitiveness of certain algorithms when they are actively taught by users. After developing a theoretical framework of intuitiveness as a property of algorithms, we introduce an active teaching paradigm involving a prototypical two-dimensional spatial learning task as a method to judge the efficacy of human-machine interactions. Finally, we present and discuss the results of a large-scale user study into the performance and teaching strategies of 800 users interacting with two prominent ML algorithms in our system, providing first evidence for the role of intuition as an important factor impacting human-machine interaction.