Reviewerly/exHarmony huggingface dataset:
DSOARS: a swarm engineering and verification environment

Advances in unmanned systems enable smaller, less expensive platforms that can be deployed in high numbers or swarms providing superior intelligence and overwhelming effects over a widely dispersed battlefield greatly multiplying their effectiveness. But swarming system remain a laboratory curiosity with only a few live demonstrations of limited scope. Swarms are complex and their behavior is difficult to predict requiring skilled engineers and hand-tuning for each mission. Based on 20 years of experience designing swarms for military missions, the Design of Self-Organizing Adaptive Robotic Swarms (DSOARS) is an engineering environment which addresses the three core challenges of swarm design: (1) decomposing mission tasks into the behaviors of the swarm entities, (2) configuring the size of the swarm to a specific mission, and (3) verifying that the resulting swarm behavior consistently achieves the mission goals with a high level of confidence. DSOARS addresses these challenges through two primary innovations: (1) a means to create verified swarm design patterns that decompose high level mission tasks into individual behaviors and (2) a constructive test environment that simultaneously optimizes and characterizes the swarm performance against a range of possible mission conditions. Users with no swarm expertise can specify the requirements and constraints of their mission and DSOARS will configure a swarm that can meet those objectives with performance guarantees. This paper describes the approach and reports experimental results building and configuring a suite of swarm tactics for an urban mission.
A Prediction Model for Determining the Number of Teaching Hours in Courses Based on the Contribution of Program Learning Outcomes

The courses and their learning outcomes, in any study plans are designed to ensure achieving the Program Learning Outcomes (PLOs). Currently, the numbers of hours for courses' topics are distributed on ad-hoc basics, based on the expertise and experience of teachers. This paper studies how to effectively determine the required number of teaching hours for the topics in each course to ensure achieving the Course-Learning Outcomes (CLOs). It proposes a model that assigns a weight for each CLO based on its contribution to PLOs. Some preliminary experiments have been conducted on the proposed model show the effectiveness of the model and encourages for more studies and investigations for the proposed model.
A Study on Visual Programming Platform Design for VR/AR SW Education

In recent years, interest in VR and AR has been growing, and games that incorporate AR technology called Pokemon have attracted worldwide attention. In recent years, it has also become a familiar term for general users, such as supporting AR functions in smartphones. This phenomenon will continue and is accepted as a new technology that will change our future. In this paper, we provide a Visual Programming development environment that enables 3D editing and interaction editing in In-VR environment so that VR/AR contents can be prototyped easily for general users and children. We implemented the platform to study whether it is valid or not.
A Proposed Mobile Based Payment System (Quickpay) for Nigerian Universities

Objectives:In this research work, we proposed and designed a QR code, 2FA authentication based mobile payment system (Quickpay) for Nigerian universities with focus on Ebonyi State University, Abakaliki.Quickpay mobile payment system will aide student in the payment of all the necessary fees required by the university.Methods and Analysis: We deployed object oriented analysis and methodology where we utilized Unified Modelling Language (UML) in analyzing and modelling the new system.Then, the methodology facilitated the use of competent high level programming tool (PHP programming language) for the development of the software (Quickpay mobile app) after detailed system analysis and design.Findings: When using the system proposed in this study, the different security model used in design of mobile payment solution which was seamlessly integrated into the system that will ensure authorized access.This model deploys 2FA authentication system at the initial stage of verification and an SMS token (request for SMS code and User ID) at the second stage of verification to guarantee user access to the system.On successful authentication, the user uses phone to scan the QR code and send an SMS using USSD code and payment code user will click on generate invoice in the form QR code, the user will use a mobile generated form of the invoice to complete the payment transaction.Application Improvements: Despite the QR code system as deployed in most mobile payment system, Quickpay is designed to seamlessly interface with the university portal while utilizing several layers of possession factor authentication methods.This includes an SMS module to generate a security token for authentication and the 2FA authentication system.
Heuristic Theme Clustering for Online Social Media

In this paper, we propose heuristic theme clustering based on Word2Vec. Through the classification standard of ODP (Open Directory Project), the category center words are set, then the priori information of the ODP is used to guide the theme clustering. In this way, it can avoid the problem that the clustering label of the traditional unsupervised learning is not clear enough, and difficult to understand, and it can avoid the requirement of a large amount of annotation data for the classification method of supervised learning. So that heuristic theme clustering can be applied to the current online social media. The results show that our method has a good amount of theme information, and has a high precision.
Improving performance by combining entanglement, networks and waveform design

A sensor based on quantum multiphoton entanglement (ME) combined with hyper-entanglement (HE) is described. Measures of effectiveness (MOEs) are derived describing the benefits of forming hybrid states between ME and HE. An open systems approach is taken with both environmental noise and loss factors included. Methods of generating the ME and HE states are described with schematics provided. Networks are introduced yielding additional performance improvements. Waveforms for effective atmospheric propagation are discussed. A new parameterized mode is derived using Lie algebra techniques. The waveform is shown to be a scaled and translated form of the Airy wave. The additional scaling and translation parameters derived from Lie algebra theory show promise for selecting the best performing waveform or linear combination of such waveforms. Alternatively, parameters for single waveforms or linear combinations of parameterized Airy waves can be selected through optimization. An MOE, the Holevo bound, is maximized ultimately yielding the Holevo-Shumacher-Westmoreland capacity. Analysis of the translated and scaled Airy function show that its asymptotic form yields much less loss during atmospheric propagation. It is robust under turbulence and atmospheric inhomogeneities. Additional parameters in the argument of the Airy function that occur as a natural result of the derivation show promise for using the waveform to facilitate imaging around corners. Second quantization is applied resulting in a version of the waveform when only one signal photon is present for entangled or non-entangled systems. Even when only one signal photon is propagating the waveform is shown to have advantages for sensing and imaging.
Simulating human vehicle identification performance with infrared imagery and augmented reality assistance

The U.S. Army CCDC C5ISR Night Vision and Electronic Sensors Directorate is researching the use of augmented reality (AR) technologies to improve the situational awareness and decision-making capabilities of EO/IR sensor operators. A major research requirement for such technologies involves defining the accuracy of AR information required to improve human performance for tasks related to EO/IR sensors, as AR systems may unintentionally provide inaccurate information to operators, which may be worse than providing no information at all. We designed a simulation to assess human performance during a vehicle identification task (using infrared imagery) when the operator receives aid via an AR system. U.S. Soldiers, trained to identify vehicles using infrared sensors, viewed images of the vehicles at different ranges in blocks while a simulated AR system attempted to identify each vehicle. Each block of images possessed an inherent level of AR accuracy (either 100%, 75%, or 50%). Performance with AR was compared to baseline performance (i.e., completing the task with no AR assistance). We further explored human performance by examining time-constrained decisions with AR. While perfect AR information was generally used effectively by participants, AR mistakes progressively increased human errors and slowed response times. Human performance varied as the range to the target increased, indicating greater dependency on the AR system as the task became more difficult. Time constraints reduced identification accuracy and usually affected unaided and aided performance similarly. Our work demonstrates the importance of simulation as a tool for understanding the effects of AR on military task performance.
Study on the Learning Material, Facilities and Equipment, and Learning Platform in Children's Language Training Service Regarding the E-business

Nowadays, children's language training services have developed at an unprecedented speed. In the era of e-commerce, the various service resources that constitute services have developed rapidly and varied. This paper attempts to analyze and optimize the service resources of children's language training services from the aspects of the learning material, the facilities and equipment, and the learning platform, and establish an excellent paradigm.
Investigation of cookie vulnerabilities

Cookies have emerged as one of the most convenient solutions to keep track of browsers. They continue to raise both security and privacy concerns due to their continuous evolution. There is limited support for confidentiality, integrity and authentication in the way cookies are used. In this respect, the possibilities for misusing cookies are very real and are being exploited. This paper will first discuss the pros and cons of cookies. Also, discuss the step by step process of how cookies work. This paper will discuss the preferences of users based on survey results. This paper will discuss cookie vulnerabilities, ways to exploit them, and ways to mitigate them.
The Estimation of Uncertain Gates: An Application to Educational Indicators

Uncertain gates in possibility theory correspond to noisy gates in probability theory. They allow us to reduce the number of parameters which must be elicited to define conditional possibility tables in possibilistic networks. Usually the choice of the connector and its parameters is made by experts but sometimes if there is an existing CPT or if data are available, it can be interesting to perform an estimation of uncertain connectors. This estimation allows us to better understand how information is combined. Furthermore, it is possible to match one of three sorts of behaviour (indulgent, compromise, severe) with the corresponding information. This point is important for knowledge engineering. If the data are available, the estimation can also be useful to verify if the uncertain connector fits with the data, because expert knowledge is often imprecise and uncertain. In this paper, we will show how to perform the estimation of uncertain gates and we will illustrate our approach with several examples of results in the domain of education.
Fitting contrast by least square method for phase-shifting interferometry of unknown and arbitrary phase-steps under high non-uniform illumination

This work presents a very robust and non-iterative algorithm for phase retrieval in phase-shifting interferometry of three unknown and unequal phase-steps under illumination conditions of high spatial non-uniformity. First, the background light is eliminated by subtraction of two interferograms, thereby two secondary patterns are obtained. Second, the object phase is algebraically eliminated from two secondary patterns to obtain only one equation in three unknowns: the modulation light, and the two phase-steps. Third, the square of modulation light is approximated to a polynomial of degree K, and then we demonstrate that it is possible to rewrite the equation in the form of an error function. Forth, the coefficients for the modulation approximation and the phase-steps are computed by applying the least squares method. Some advantages of this approach are its capacity to support high spatial variations in the illumination and the object phase.
Integration of OpenTap with quantum entanglement testbed for security analysis

OpenTap is a network traffic analysis tool tailored to remotely capture statistics from an Openflow switch. We have modified OpenTap for low latency packet capture of metadata which can be used for remote security analysis. We demonstrate the utility of this tool by capturing metadata from a quantum information testbed. Specifically, the testbed transmits photon data to a desktop using packets which OpenTap captures. We used a servo motor to periodically block the beam to create 20ms dips in the photon intensity, and then measured this intensity using two methods: via the metadata captured by OpenTap, and via the photon timestamps gathered by the quantum information testbed. Every photon intensity dip detected by the quantum information testbed was also detected using just the metadata; this indicates OpenTap can be used for remote real-time security analysis using packet metadata.
Job Allocation on Cloud: A Comparative Study

The rapid growth in the field of cloud computing leads to migration of people to cloud, which makes them feel more tensile and adaptive in the environment. Cloud makes the users give less importance to maintain their hardware and other resources because all these works are done by a service provider. This would restrict the users from spending more money on capital expenditure and so the people are ready to invest more in the cloud. The virtual machine (VM) is a buzz which replaced a traditional physical machine through the method called virtualization. Virtualization is the main objective for establishing cloud services. The core idea of virtualization is to create an instance or virtual machine according to user demands, and the number of servers needed is proportional to the amount need for the resource pool. This paper presents a heuristic algorithm—gravitational search algorithm (GSA)—which formulate an optimal solution for allocating task on the cloud. To analysis the efficiency of the proposed algorithm, a comparative study has been done with other heuristic algorithms like the ant colony and particle swarm optimization which is also used for job allocation.
Low Complexity &amp; Improved Efficiency of Encoded Data Using Peres Gate in BWAR with Testable Feature

In this present era of high speed developing world of VLSI, data comparison is broadly used in computing system. In a computation system the receiving data needed to compare with deposited information to trace the identical entry. Comparison of data is a technique which fetches data together from various sources and compares it. When this matching differ the stored data, the use of proprietary matching algorithms is used to compare and correct the mismatch result. As per the survey, different researches have been done and still going on till date. In this study, it is found that the data matching can be done both in encoder and decoder end. The kept data preserved through error correcting codes (ECC code word) is used to relate with arriving data after decryption. Furthermore, in a Renovated butterfly weighted accumulator (BWAR), a type of reversible logic gate called peres gate is proposed to modify the half adder with disparate algorithm to compute hamming distance with reduced complexity and improved efficiency with testable feature. For a ECC code (16, 11) the proposed BWAR architecture minimizes the hardware complexity by 30.4% approximately with 13% of improved efficiency. In this brief, we have used a Xilinx 13.2 for Verilog coding for data matching algorithm.
An Approach to Defense Dictionary Attack with Message Digest Using Image Salt

Hash algorithms have been widely used for cryptography. It has been impossible to decrypt the ciphertexts generated through hash algorithms, as an operation that damages the original text is performed. However, various methods of attack occurred over time after the algorithm was developed. The vulnerability of SHA1 (an old hash algorithm) has been revealed, and there has been a great deal of data available for dictionary attacks. Although the industry has been gradually refraining from using SHA1, it remains in use in some existing systems for various reasons. For example, when problems resulting from service interruption or mass update are critical, updating the encryption algorithm can be a burden. In this study, we aim to increase the complexity of ciphertexts by postprocessing hash ciphertext. For that, image salting techniques are used using two-dimensional array masking. This will allow the use of hash ciphertexts with increased complexity in some devices that are forced to use old hash algorithms for various reasons.
Fast and accurate target detection in overhead imagery using double convolution neural networks

We propose a Double Convolutional Neural Networks (D2CNN) framework for automatic target detection. D2CNN achieved high speed and high positional accuracy on our high-altitude imagery dataset. Translation invariance in convolutional neural network (CNN) is a double-edged sword. A CNN with large translation invariance is fast, but suffers positional accuracy, which is critical for automatic target detection. A CNN with small translation invariance can achieve high positional accuracy at the expense of speed. In a typical target detection case, targets are very sparse. In our D2CNN framework we employ two separate CNN. The first with large translation invariance generates region proposals. The second CNN with small translation invariance detects targets with high positional accuracy in the proposed regions by the first CNN. The two CNN are trained separately using different training strategies. Training examples are shared between the two CNN. However, data augmentation algorithms are very different. For the first CNN data augmentation, we place an object at various locations such as the center, the lower left portion, the up-right portion. For the second CNN data augmentation, an object/target is always centered. We fine tune hyper-parameters of pooling and convolution layers to increase translation invariance for the first CNN, and decrease translation invariance for the second CNN.
Blockchain

Blockchain technology has major relevance for the digitization of services and processes in many different areas of application beyond the financial industry and independent of cryptocurrencies in particular. Whilst for the Internet of Things the potential for automation associated with smart contracts is especially significant, for applications from the supply chain fields or for proofs of origin it is the irreversibility of the transactions conducted. This article describes the functioning of this new technology and the most important resulting qualities. The chapter provides a list of criteria for identifying digitization projects for which blockchain technology is suitable. Because of the extent of blockchain technologies and their applications, developing the basic technologies requires a multidisciplinary approach, as does developing applications, carrying out studies of cost-effectiveness, and designing new governance models. The diverse competencies offered by the various Fraunhofer institutes, put the Fraunhofer-Gesellschaft in a position to make a significant contribution to the ongoing development and application of blockchain technology.
Psychological features of managerial activity of the railway transport manager

Today in modern society there is a rapid technical progress, which, in turn, complicates almost any social activity and significantly increases the requirements for their performers. This trend also applies to the managerial activities of the railway transport manager, which is viewed as a multi-component process, usually covering both human resource management and business information management, rather complex technological processes, etc. [1].
Fault Detection Using Sensors Data Trends for Autonomous Robotic Mapping

Map building of an environment is the foremost need for precise automatic robotic navigation. Moreover, fault detection and recovery are importantly required for every fully automatic system. The presented work describes influence of qualitative trends of the data streams coming from IMU sensor, Odometry senso, and Mapping in order to detect faulty creation of the map of an environment at runtime. It detects qualitative trends (inc, dec, con) within sensors data using a special software entity called Binary Qualitative Observer (BiQObs). BiQObs takes two data streams from robot sensors, extracts their qualitative trends, and finally compares these trends to find out whether they match or not. The work comprises of basically three main activity phases: Observe, Detect Fault and Repair Fault. First phase Observe uses BiQObs for generating and observing trends in sensors data specifically YAW measurement of the robotic pose from three different sources namely IMU, odometry, and pose from mapping. Besides BiQObs, two more observers namely Node Observer (NObs) and General observer (GObs) are also used for monitoring system nodes and their topics. Second phase Detect Fault uses Diagnosis Model Server and Diagnosis Engine to detect the root cause of the fault caused by mismatch between qualitative trends. Last phase Repair Fault uses Diagnosis Repair Engine and action servers for repairing the diagnosed fault. Finally experimental results are evaluated using the scenario of mapping of an unknown environment with Pioneer-3DX robot equipped with SICK laser and IMU sensors. Evaluation process successfully detects fault like incorrect odometry due to slippage of wheels, and brings the case in normal situation in a remarkable time of 13.54 seconds.
Quintiliano e Virgilio

This is an article, published in 1988, from the <italic>Enciclopedia Virgiliana</italic> (translation by Barbara Macleod). Quintilian had the highest regard for Virgil, who in his ‘reading list’ of Latin authors takes first place as Homer does among Greek writers. Indeed he judges him superior in general to Homer, while conceding that he cannot reach Homer’s peaks. Quintilian constantly quotes Virgil, especially to comment on his word usage (especially archaisms), metrical practice and orthography, and to illustrate various figures of speech. Quintilian’s relationship to the later commentator Servius is discussed. He almost never criticizes Virgil, and he assumes his readers know him as well as he does.