Reviewerly/exHarmony huggingface dataset:
Intelligent Fuzzy PD+I Controller with Stabilizer for Nano Satellite Attitude Control System

Abstract This paper discusses the variation of proportional and derivative gains in a system under the control of PD+I controller that uses fuzzy inference method an effort is made to design the fuzzy proportional-derivative (PD) + I controller for a nonlinear the proposed fuzzy PD + I structure shows superior performance in the Nano-satellite attitude control system. Given that 2-input PD type controllers are commonly used to control Nano satellite attitude systems (Innovative Satellite InnoSAT), This paper analysis the performance of Fuzzy PD+I on satellite system. Fuzzy PD+I controller design can control Nano satellite attitude control system nicely and utilized the Fuzzy PD+I controller which optimize the stability of the Nano Satellite control system. In addition, the scheme is robust to variations in nonlinearities, as well as the steady-state gain of the plant. We illustrate the effectiveness of our scheme In MATLAB / SMULINK simulation environment.
From Use Case to Use Case Slices

A use case has been widely adopted for requirements specification, which can be created on a high abstraction level or a very detailed level. A use case with a very detailed level can be a lot more useful for requirements elicitation, but such a model may become too complex and difficult to understand the whole context. A use case slice is introduced in order to simplify the requirements analysis and ensure the correctness of software incremental development process (e.g., the agile development process). However, a use case is usually divided into multiple use case slices in a manual way. Some errors may occur during this manual process. In this article, the authors present an automated approach for dividing a use case into use case slices. The approach first decomposes a use case into multiple use case stories, and then these stories can be composed into different use case slices according to different requirements. These use case slices cover all the functionality of the original use case. The authors give a decomposition theory and propose the corresponding algorithm. A case study demonstrates these results.
Two-point Joint CPA Attacks against SM4 Algorithm

On the basis of the vulnerability of single point attack in the power analysis and the characteristics of energy leakage and cryptographic algorithm, a joint attack based on multiple points is proposed in this paper. To find two or more points that can be attacked in the cryptographic algorithm, the joint relationship between the attack points are established, with which the correct key in the algorithm are deciphered. The validation of the proposed method was demonstrated by using the Xor output of the SM4 cipher algorithm and the Sbox output to establish the intermediate function of the joint attack. The effectiveness of the multi-point joint attack is proved by the CPA analysis. In the experiment, the multi-point joint attack always outperforms the single point attack. For the multi-point joint attack, a 49.86% power consumption time decrease was observed compared with the two single point attack. For the attack success rate, the multi-point joint attack presents the optimal performance compared with the single point attack and the two single point attack.
Environment Recognition for Path Generation in Autonomous Mobile Robots

An efficient algorithm for path generation in autonomous mobile robots using a visual recognition approach is presented. The proposal includes image filtering techniques by employing an inspecting camera to sense a cluttered environment. Template matching filters are used to detect several environment elements, such as obstacles, feasible terrain, the target location, and the mobile robot. The proposed algorithm includes the parallel evolutionary artificial potential field to perform the path planning for autonomous navigation of the mobile robot. Our problem to be solved for autonomous navigation is to safely take a mobile robot from the starting point to the target point employing the path with the shortest distance and which also contains the safest route. To find the path that satisfies this condition, the proposed algorithm chooses the best candidate solution from a vast number of different paths calculated concurrently. For achieving efficient autonomous navigation, the proposal employs a parallel computation approach for the evolutionary artificial potential field algorithm for path generation and optimization. Experimental results yield accuracy in environment recognition in terms of quantitative metrics. The proposed algorithm demonstrates efficiency in path generation and optimization.
Entanglement and atomic Fisher information of a two qubits and optical field in squeezed thermal state

Quantum information technology depends on an invaluable and tenuous resource, named quantum correlation, that shows strong significant manifestation of the coherent overlap of states of a combined quantum systems. So the quantifier of this resource under factual conditions that is, whereas corrupte d via generalized optical radiation field states is still limited, and common statements on entanglement dynamics. In this article, we describe quantitatively the nonlocal correlation between a two-qubit and squeezed thermal field. Especially, considering the effect thermal photons and number of photons transition between the two qubits and squeezed thermal field. Also, the Mandel parameter, entanglement and atomic Fisher information over the time evolution as a function of implicated parameters in the system are investigated. We have shown that the squeeze parameter and thermal photons have a potential effect of the dynamical properties of the atomic Fisher information, Mandel parameter and entanglement. Also, the AFI is very sensitive to the field parameters rather than the number of photons transition. Furthermore, the results interpreted in the case of thermal environment considering the effect of thermal photons and squeeze parameter on the evolution of the system under consideration.
Multistage Fuzzy Inference System for Solving Problems in Performance Appraisal

Performance assessment can be used as a basis for the development of human resources and other decisions. This study discusses issues in the performance appraisal method, particularly how to design more objective methods to promote employees with trainee status as permanent employees. In the decision making method that is being used, there are many situation that should be better describe in qualitative description hence create some uncertainty and subjectivity in the assessment process. The existence of the nature of uncertainty and subjectivity in human judgment requires a method that can process it more objectively. This paper proposes a combination of QFD and pairwise comparison methods along with fuzzy inference system for redesigning the performance appraisal method that based on fuzzy logic approach. Simulation shows that at a certain value range, the proposed method decides ‘promotion canceled’ while current one decides ‘fail’. On the contrary, two ‘pass’ results change to ‘promotion canceled’. Possibly these are a better decision.
Producing Parameterized Value Functions Through Modulation for Cognitive Developmental Robots

Parameterizing value functions as a representation of robotic tasks in different domains allows for their generalization, and can provide a way to transfer knowledge to new situations. To this end, in this paper we propose a modulation based mechanism embedded within a cognitive architecture for robots. It makes use of the combined operation of the long-term memory and the motivational system in order to select candidate primitive value functions for transfer. These are then adapted to the new situation through the addition of modulatory ANNs to progressively conform new parameterized value functions able to address more complex situations in a developmental manner. The proposed method is tested in a Baxter robot, which must solve different tasks in a cooking setup.
Hard Isogeny Problems over RSA Moduli and Groups with Infeasible Inversion

We initiate the study of computational problems on elliptic curve isogeny graphs defined over RSA moduli. We conjecture that several variants of the neighbor-search problem over these graphs are hard, and provide a comprehensive list of cryptanalytic attempts on these problems. Moreover, based on the hardness of these problems, we provide a construction of groups with infeasible inversion, where the underlying groups are the ideal class groups of imaginary quadratic orders. Recall that in a group with infeasible inversion, computing the inverse of a group element is required to be hard, while performing the group operation is easy. Motivated by the potential cryptographic application of building a directed transitive signature scheme, the search for a group with infeasible inversion was initiated in the theses of Hohenberger and Molnar (2003). Later it was also shown to provide a broadcast encryption scheme by Irrer et al. (2004). However, to date the only case of a group with infeasible inversion is implied by the much stronger primitive of self-bilinear map constructed by Yamakawa et al. (2014) based on the hardness of factoring and indistinguishability obfuscation (iO). Our construction gives a candidate without using iO.
Computer Assisted Learning on Aridity Disaster Learning Using SIMIA (Satellite Imagery for Modelling Index of Aridity)

Abstract The purpose of this study is to develop Computer Assisted Learning (CAL) to identify aridity using Satellite Imagery Modelling Index of Aridity (SIMIA) media. SIMIA is a system used to model area of aridity by using of rainfall data from observation of rain and vegetation index extracted by satellite image of LANDSAT 8 OLI and Google Satellite. SIMIA learning planning is performed using procedures of Analyzed, Standardized, Strategy, Utility, Require, and Evaluate (ASSURE). The results show that the ASSURE learning plan is more systematic, it attracts the attention of the participants; it can illustrate the goal to be achieved as determined by the learning achievements. The SIMIA CAL learning plan using ASSURE allows for the selection and delivery of appropriate, timely (pedagogical) content, the use of technology is appropriate to the needs of learners (technology) and the interaction is created between learners and instructors.
Improved Wavelet Denoising by Penalty Function and Regularization Parameter

Abstract Wavelet-based convex optimization in sparse signal processing has attracted extensive research interest in recent years. Research demonstrates that the penalty function promotes the sparsity and improves the accuracy better than L1 norm method in dealing with convex and sparse signal reconstruction issues. This paper presents an improved denoising model which utilizes a penalty function to induce stronger sparsity in wavelet domain. In this paper, we have redefined the relationship model between signal noise levels and the regularization parameter to enhance sparsity caused by penalty functions in a wavelet domain. And three penalty functions are employed for wavelet coefficients to induce strong sparse wavelet coefficients under the wavelet domain. We then apply the improved wavelet denoising algorithm for image denoising to get the values of the PSNR are analysed under different noise level conditions. Experimental results clearly show that the improved wavelet denoising model is strength in terms of both quantitative measure and structural similarity quality, and outperforms many widely used denoising algorithms.
Audio encryption through synchronization of chaotic oscillator circuits: Teaching non-linear dynamics through simple electrical circuits

Although often simple in design, chaotic systems produce signals of surprising complexity, facilitating several applications in cryptography. In this work, we present an experimental demonstration of one such application—a chaotic oscillator circuit (transmitter) for encryption of audio signals and another chaotic oscillator (receiver) synchronized to the transmitter via a linear feedback controller for signal decryption. All circuits are realized through operational amplifier (op-amp) chips connected on a breadboard, which makes this demonstration suitable for an undergraduate lab experiment that illustrates several interesting concepts, including nonlinear dynamics, chaos, encryption, and feedback control.
Reducing Cloud provisioning Cost Using Spot Instances hopping

The cost reduction is one of the attractive features offered by the cloud. Spot leasing is one way to reduce the cost. Spot leasing is done by leasing the unused excess instances with low price. Spot instances are facing risks that minimize their reliability and desirability. Risks including instances reclaiming and dynamic price changing. Minimizing the risks associated with the spot leasing is going to help increasing the utilization of the spot instances, which in turn is going to attract more users. In this paper, a framework has been proposed to mitigate the instances reclaiming risk while reducing the leasing cost as possible. This is done by monitoring many markets and hopping between instances. The proposed framework has been evaluated through simulating randomly generated data and actual data collected from Amazon web services. The proposed framework recorded 9% to 42% of cost reduction compared with the actual cost.
Individual Privacy Supporting Organisational Security

The large-scale change emergent from the global proliferation of cloud computing, smart homes, the internet of things and machine learning requires a novel view on the flow of confidential information and its classification. The security of an organisation is affected by the privacy enjoyed by its members. Sufficient data on those members can be leveraged in a so-called abduction attack aiming to extract confidential information from the organisation. The intention of this paper is to foster awareness of this effect. To illustrate it we develop a model of actors and data flows and discuss three scenarios in which he confidentiality achievable by an organisation is limited by the privacy of its members.
A visualization approach to performing task analysis of time series event log data from a microworld simulation

This paper presents a novel visualization technique to perform a task analysis using only information from data logs collected from users interacting with the system. Traditional task analysis includes data acquired from system specifications, observations of the user interacting with the system, and interviews with the user. There are however situations, in which this information is not available such as forensic investigations. In situations in which these other data sources are available, the visualization approach provides an additional tool the analysist can use to further their understanding of the system and begin qualitatively and quantitatively describing a user’s behavior. As a demonstration of this technique, data logs from a microworld simulator, the Grid Game, were analyzed using a custom designed python based graphing tool. The Grid Game is a gamified and simplified simulation of an electric microgrid. Teams of players compete by maintaining and optimizing their grid configuration in the presence of cyber-attacks. Several different visualization graphing techniques were examined to identify their suitability for representing the grid event data in a format capable to support an analyst performing a task analysis. The most suitable graphing technique for this particular use case was a discrete event graphing technique including a common time dimension to provide performance based context to the graphical depiction of logged simulation events.
Developing a Digital Forensics Curriculum: Exploring Trends from 2007 to 2017

The young science of digital forensics has made great strides in the last decade, but so, too, has cyber crime. The growing complexity of cyber crime has necessitated that traditional forensics methods be updated to accommodate new technologies, and that further research is carried out to keep up with the rate of technological innovation. The main purpose of this paper is to determine how academic teaching and research can support the needs of the industry in investigating cyber crime. Current digital forensics curricula in higher education are discussed, followed by an analysis of academic research trends for this discipline for the years 2007 to 2017. We conclude by highlighting trends for which more research is required and which could possibly contribute towards shaping future teaching and learning of digital forensics in higher education.
3D reconstruction of carotid artery in B-mode ultrasound image using modified template matching based on ellipse feature

Detection of vascular areas using B-mode ultrasound is required for automated applications such as registration and navigation in medical operations. The limitations of Ultrasound imaging are the requirement of sonographer’s skills, expertise, and also knowledge when making data acquisition. It also influences the quality of the images. Carotid atherosclerosis can be treated with carotid artery stenting. The starting point of needle injection cannot be determined with certainty. The position of the arteries is in the body, therefore, determining the starting point of needle injection is done by estimation only and cannot be certainly determined. To be able to determine it, the first step needed is to determine the location of the carotid artery. We propose a 3D reconstruction of carotid artery using a modified template matching based on ellipse feature to determine it. It is processed using the procedure of data acquisition, preprocessing, segmentation, outlier selection of ellipse parameter fitting, visualisation. The proposed procedure with preprocessing produces the highest accuracy compared to the template matching method and the Hough Circle method with an accuracy value of 99.41% and has the smallest standard deviation value of 1.05. The best polynomial fitting results for all data is the polynomial equation on 22nd order with the mean error value of 0.26303.
Fast and Precise Visual Rear Axle Calibration

The center of the rear axle represents the reference point for the motion and collision model of autonomous cars. This makes calibration of the rear axle to the sensor rig a fundamental prerequisite for planning and localization. Many commercial calibration systems are available, however they are spacious, difficult to set up and costly. So far, rear axle calibration got little attention in literature. Most approaches are odometry based which require time-consuming calibration drives. In this work we present a simple but effective approach for fast and precise sensor to rear axle calibration by calibrating the sensor rig of the vehicle together with external sensors that are used to determine the center points of the wheels. We show that this approach is not only highly practical but also precise while requiring a minimum of equipment.
Improving Parallelism of Breadth First Search (BFS) Algorithm for Accelerated Performance on GPUs

Breadth-first search (BFS) is a basis for graph search and a core building block for many higher-level graph analysis applications. However, BFS is also a typical example of parallel computation that is inefficient on GPU architectures. In a graph, a small portion of nodes may have a large number of neighbors, which leads to irregular tasks on GPUs. In addition, the number of nodes in each layer of the graph is also irregular. Therefore, the number of active GPU threads is different for each layer of execution. These irregularities limit the parallelism of BFS executing on GPUs.Unlike the previous works focusing on fine-grained task management to address the irregularity, we propose Virtual-BFS (VBFS) to virtually change the graph itself. By adding virtual vertices, the high-degree nodes in the graph are divided into groups that have an equal number of neighbors, which increases the parallelism such that more GPU threads can work concurrently, and the data set also becomes more regular.Our experimental results show that the VBFS achieves significant speedup over the current GPU implementation of BFS from the Rodinia benchmark [4], and the energy efficiency is also improved.
SHANZ Algorithm for QoE Enhancement of HTTP Based Adaptive Video Streaming

In the last decade, there has been an exponential increase in the video traffic over the internet. Social Medias are becoming one of the main sources of live and on-demand video streaming content. With ever-increasing demand of online video streaming services on heterogeneous platforms, new research challenges are arising day by day. Some of the main challenges online video streaming services face are instability of the video, large start-up delay, high video latency, and lesser adaptability of the algorithm. Most of the existing video service providers fail to maintain a balance between stability and efficiency of their algorithm in unstable network conditions. We have proposed a dynamic rate adaptation algorithm with feedback control mechanism and adaptive step up function, which acts as an explicit knob to maintain a balance between stability and efficiency of the algorithm, even in drastic network conditions. Moreover, we have used Randomized download delay to overcome bandwidth overestimation problem occurred in multiple clients. We have simulated our algorithm using ns-3 and compared our results with FESTIVE, PANDA and AAASH algorithms in using multiple test cases. The results demonstrate that our proposed algorithm outperforms other algorithms by achieving higher Quality of Experience.
An ANN-Based Text Mining Approach Over Hash Tag and Blogging Text Data

In the daily life, everybody keeps on using Internet after the waking up that includes the communication among different people of this world. On one side, Internet has made everyone’s life very convenient and provides many facilities that can be used on social networking sites such as chat, messaging, comment, and blogging. This way everyone keeps on sharing the personal data at different places like Web sites, chats, social media. The text mining can be defined as the process of finding useful information from the given text. There exist various methods that remain helpful in analyzing texts and extracting the information but these often suffer from various complexities. Also, theoretically, it is quite difficult to analyze and extract the information from these raw data. This paper presents an Effective feed forward artificial neural network (FP-ANN) for text mining that generates different textual patterns from several resources and provides results very precisely with lesser computation time, lower cost, and overhead. FP-ANN approach calculates the hash label and discovers importance between inputs for text mining.
Design of English Test Software Based on.NET Framework

Abstract With the rapid development of Internet and computer technology, modern education based on the Internet as main means has become the trend of the development of the education today. Although the domestic English testing system gains more and more attention but most universities and colleges’ equipment downtime rate is calculated in manual mode. Even if some of the systems can realize on-time downtime equipment information collection but for the vast majority of the company it is not always possible. In this paper, we design and implement the .NET based software for the English testing tasks. We adopt the state-of-the-art open source library to undertake the task and the result shows that our approach is correct and feasible.