Reviewerly/exHarmony huggingface dataset:
Reliability analysis method of aerospace equipment system based on big data

Aerospace equipment is developing in the direction of high speed, accuracy, stability and long life. However, the reliability and life expectancy of some equipment systems are still far from the world advanced level. The main reason is that the data volume is not complete enough, and the data format is single. In order to ensure the healthy operation of these equipment systems, it is necessary to use a large amount of data to diagnose and predict faults. Therefore, the research from simple data collection to the whole process analysis of big data is of great significance for equipment life assessment and reliability analysis. Based on the analysis of big data, this paper proposes a processing method for equipment fault diagnosis and prediction, which is carried out from the aspects of aerospace big data characteristics, data acquisition, data storage, algorithm model and prediction, from complex equipment operation. The fault information is discovered and analyzed to ensure the stable and safe operation of the entire system. Finally, we use the cloud service platform method to simulate the life of the equipment. Compared with the traditional method, the long-term memory network model has been added to predict the full life cycle of the equipment by more than 10%.
Data Center Clustering for Geographically Distributed Cloud Deployments

Geographically-distributed application deployments are critical for a variety of cloud applications, such as those employed in the Internet-of-Things (IoT), edge computing, and multimedia. However, selecting appropriate cloud data centers for the applications, from a large number available locations, is a difficult task. The users need to consider several different aspects in the data center selection, such as inter-data center network performance, data transfer costs, and the application requirements with respect to the network performance. This paper proposes a data center clustering mechanism to group befitting cloud data centers together in order to automate data center selection task as governed by the application needs. Employing our clustering mechanism, we present four different types of clustering schemes, with different importance given to available bandwidth, latency, and cloud costs between pair of data centers. The proposed clustering schemes are evaluated using a large number of data centers from two major public clouds, Amazon Web Services, and Google Cloud Platform. The results, based on a comprehensive empirical evaluation of the quality of obtained clusters, show that the proposed clustering schemes are very effective in optimizing data center selection as per the application requirements.
Improved cuckoo search load distribution (ICS‐LD) and attack detection in cloud environment

Summary For the purpose of obtaining an answer to a spoofing IP issue and related problems in the cloud computing (CC) environment, a novel path identifier is recommended, in which a new packet approach with a new identifier (ID) is produced to every user or client system. For the purpose of identification of the spoofing attack, any watermark image pattern is introduced. Following this, the hypervisor is employed to ascertain plausible trust relationships among the VMs by considering personal sources that can be trusted along with utilizing empirical Bayesian inference (EBI) for the purpose of combining the VMs. With a restricted budget of resources for the maximized detection of attacks, an optimal detection load distribution strategy by improved cuckoo search (ICS) over VMs can be used, which thus provides the intended solution to the hypervisor. The ICS algorithm directs a hypervisor so as to assert among the VMs the optimal load distribution, taking into consideration real time, which causes maximization of the DDoS attacks and detection of transmission control protocol (TCP) flood attacks. Lastly, a fuzzy extreme learning machine (FELM) classifier is proposed in order to detect the attacks.
A Matrix Theoretic Derivation of the Kalman Filter

The Kalman filter is a data analysis method used in a wide range of engineering and applied mathematics problems. This paper presents a matrix-theoretic derivation of the method in the linear model, Gaussian measurement error case. Standard derivations of the Kalman filter make use of probabilistic notation and arguments, whereas we make use, primarily, of methods from numerical linear algebra. In addition to the standard Kalman filter, we derive an equivalent variational (optimization-based) formulation, as well as the extended Kalman filter for nonlinear problems.
ManDri: A New Proposal of Manus VR Facility Integration in Everyday Car Driving

The purpose of this paper is to analyze all the possible uses of the Manus VR in everyday driving, to make it easier and safer. With these technological gloves, equipped with eleven sensors for each hand, able to precisely pick up every movement of the hand and fingers, it is possible to associate various gestures to specific actions. In particular, they allow to turn, accelerate, brake, activate the car turn signals, the air conditioning, even alarm, everything just using simple gestures. But ManDri is not only framed in today’s guide: indeed, a glove, being equipped with two IMUs (inertial measurement units), can be integrated with GPS (global position system). In fact, progresses in these years allow the production of IMU-enabled GPS devices, that allow a GPS receiver to work even when GPS-signals are unavailable, such as inside buildings, tunnels, or places characterized by electronic interference. Moreover, it is also possible to create the ManDri app for all the devices so that, after connecting them to the gloves, dedicated accounts can be created for chosen people allowed to use them, making easier the control, the management, and the gloves gestures customization. This device unavoidably becomes essential and difficult to replace after trying it. These positive premises make us understand how advantageous the choice to use these gloves in everyday driving to improve it, make it safer, and undoubtedly facilitate it.
Tracking Method-Level Clones and a Case Study

Analyzing histories of code clones is important for understanding how they affect software development and developers. For this, many studies have been devoted to the approach of tracking code clones. However, to the best of our knowledge, no existing studies have attempted to track code clones in long-term and fine-grained change histories.In this paper, we report on the analysis of histories of method-level code clones hosted by a fine-grained version control system called historage, which allowed us to track source code entities across commits.We have tracked and analyzed method-level code clones in 10 open source software projects and found out that (1) in many projects, method-level code clones are removed regardless of whether they were changed or how frequently they were changed, and (2) a group of method-level code clones created at the same time tend to survive longer than those created individually. We believe that these findings will provide useful insights for future research on code clones such as determining the priority of code clone management.
Social Sensing for Improving the User Experience in Orienteering

We illustrate an application of social sensing for automatically customizing the solutions to orienteering problems with category constraints. The aim is to improve the user satisfaction in tourist trip planning provided by a previously developed efficient and effective algorithm. The system tries to perceive the possible user interests by sensing their social network profile, induced by computing topics arising from their posts. A set of semantic distance between the user profile and the descriptions of the points of interest (POIs) is then computed, giving an ordered list of POIs according to the user interests. The orienteering algorithm is then run taking into account this list of POIs sorted by the semantic closeness to the user posts in social networks. A preliminary running example has been setup and the results are promising in terms of both accuracy and performance.
Social Activity Measurement by Counting Faces Captured in First-Person View Lifelogging Video

This paper proposes a method to measure the daily face-to-face social activity of a camera wearer by detecting faces captured in first-person view lifelogging videos. This study was inspired by pedometers used to estimate the amount of physical activity by counting the number of steps detected by accelerometers, which is effective for reflecting individual health and facilitating behavior change. We investigated whether we can estimate the amount of social activity by counting the number of faces captured in the first-person view videos like a pedometer. Our system counts not only the number of faces but also weighs in the numbers according to the size of the face (corresponding to a face's closeness) and the amount of time it was shown in the video. By doing so, we confirmed that we can measure the amount of social activity based on the quality of each interaction. For example, if we simply count the number of faces, we overestimate social activities while passing through a crowd of people. Our system, on the other hand, gives a higher score to a social actitivity even when speaking with a single person for a long time, which was also positively evaluated by experiment participants who viewed the lifelogging videos. Through evaluation experiments, many evaluators evaluated the social activity high when the camera wearer speaks. An interesting feature of the proposed system is that it can correctly evaluate such scenes higher as the camera wearer actively engages in conversations with others, even though the system does not measure the camera wearer's utterances. This is because the conversation partners tend to turn their faces towards to the camera wearer, and that increases the number of detected faces as a result. However, the present system fails to correctly estimate the depth of social activity compared to what the camera wearer recalls especially when the conversation partners are standing out of the camera's field of view. The paper briefly descibes how the results can be improved by widening the camera's field of view.
Robust and computationally efficient online image stabilisation framework based on adaptive dual motion vector integration

Image stabilisation aims to compensate and smoothen the effects of undesired trembling motion of cameras mounted on non‐static platforms. It becomes quite a challenging task in the case of moving platforms, such as ground vehicles, unmanned aerial vehicles, and handheld devices. Many satisfactory solutions to the image stabilisation problem are proposed in the recent literature, but most of these methods are not adaptable for handling a wide range of intentional motions with minimum lag, especially in real‐time scenarios. In this study, the authors propose an online two‐dimensional image stabilisation technique based on dual motion vector integration, which is a novel adaptive motion smoothing technique that employs an average length of motion vectors to estimate the intentional motion. The overall computational cost of the proposed system is significantly reduced by employing frame‐shaking judgment that only allows processing of jittering frames. Promising experimental results have been obtained on challenging videos obtained from hand‐held and vehicle‐mounted cameras which demonstrate the robustness and effectiveness of the proposed technique against feature point mismatching and presence of moving objects within the scene at a frame rate of 30 frames per second.
Bubble Talk

In this art project, the ephemeral and intangible aspects of human's communication are represented by soap-bubble. The shapeless, intangible, and insubstantial speech - once the speech is shouted out through speaker's mouth it disappears unless someone hears it immediately, or even it is heard, the message will be forgotten as time goes - is transferred to a semi-tangible yet still fleeting bubble. The bubble machine that we created provides person-to-person and person-to-space interaction. The machine has a iris mechanism that varies its outlet size reacting to the participant's speech pattern as if it tries to talk something. Once the participant pauses, the machine blows out various sizes of bubble. The floating bubble represents the subtle state of a message from interpersonal communications that lies in the middle of real and digital world. Also, it creates a certain delay until it pops, which is a metaphor of our behavior that we often delay to send out text-messages through chatting apps. We believe that anyone can be an artist. By open-sourcing the details of fabrication process and materials, we want to encourage people to build the machine, interact with it at any locations, and use and modify it as a art tool for realizing their own ideas whether it is for art or not.
Sequential Bayesian Inference for Dynamical Systems Using the Finite Volume Method

Optimal Bayesian sequential inference, or filtering, for the state of a deterministic dynamical system requires simulation of the Frobenius-Perron operator, that can be formulated as the solution of an initial value problem in the continuity equation on filtering distributions. For low-dimensional, smooth systems the finite-volume method is an effective solver that conserves probability and gives estimates that converge to the optimal continuous-time values. A Courant–Friedrichs–Lewy condition assures that intermediate discretized solutions remain positive density functions. We demonstrate this finite-volume filter (FVF) in a simulated example of filtering for the state of a pendulum, including a case where rank-deficient observations lead to multi-modal probability distributions.
Cross‐layer design of resource allocation and segment adaptation based on CPIQ model for DASH clients over LTE networks

With the rapid development of dynamic adaptive streaming over HTTP (DASH) services, how to satisfy the requirements of DASH clients has attracted more and more attention. This study was carried out to focus on the quality of experience (QoE) modelling and the model-based cross-layer design which consists of segment adaptation and resource allocation, improving the playback experience of the clients. First, the key factors are investigated, which can affect the clients’ subjective satisfaction. A characteristics and playback information of the segments-based QoE (CPIQ) model is established by employing the curve-fitting method based on a large amount of subjective experimental results of these factors. Then with the CPIQ model as the objective function, a cross-layer design CPIQ model-based joint algorithm of segment request and resource allocation (CJRRA) model is formulated to maximise the total QoE of all the clients subject to the network resource and segment representation constraints. Segment adaptation and resource allocation strategy can be determined jointly by the authors developed low complexity solution. Finally, the simulation results show that the overall performance of CPIQ model and CJRRA significantly outperforms other compared models or algorithms in terms of accuracy, linearity and stability.
Asynchronous Quasi-Random Number Generator: Taking Advantage of PVT Variations

Random number generators find application in many fields, including cryptography, digital signatures and network equipment testers, to cite a few. Two main classes of such generators are usually proposed, pseudo-random number generators and true-random number generators. The former are simple to build and use, but cannot be employed in every application, especially in those where randomness is meant to support security. The later can be complicated to build, since they often must rely on hard-to-predict events that are hard to produce in the deterministic world of digital circuits. This work proposes a quasi-random number generator hardware implementation, intended to provide most of the benefits of true-random number generators with costs closer to those of pseudo-random number generators. The quasi-random number generator described here relies on the use of asynchronous circuit design techniques allied to process, voltage and temperature variability to achieve relatively high degrees of randomness. An FPGA prototype demonstrates the feasibility of the approach.
Objects characterization-based approach to enhance detection of degree of malignancy in breast cancer histopathology

Histologic grading from images has become widely accepted as a powerful indicator of prognosis in breast cancer. Automated grading can assist the doctor diagnosing the medical condition. But algorithms still lag behind human experts in this task, as human experts excel in identifying parts, detecting characteristics and relating concepts and semantics. This can be improved by making algorithms distinguish and characterize the most relevant types of objects in the image and characterizing images from that. We propose a three-stage automated approach named OBI (Object-based Identification) with steps: 1. Object-based identification, which identifies the “type of object” of each region and characterizes it; 2. Learn about image, which characterizes distribution of characteristics of those types of objects in image; 3. Determination of degree of malignancy, which assigns a degree of malignancy based on a classifier over object type characteristics (the statistical distribution of characteristics of structures) in the image. Our proof-of-concept prototype uses publicly-available Mytos-Atypia dataset [19] to compare accuracy with alternatives. Results summary: human expert (medical doctor) 84%, classic machine learning 74%, convolution neural networks (CNN), 78%, our approach (OBI) 86%. As future work, we expect to generalize our results to other datasets and problems, explore mimicking knowledge of human concepts further, merge the object-based approach with CNN techniques and adapt it to other medical imaging contexts.
Comparison of spacing detection algorithms for optical straight fringes images

Optical fringe is one of important output information from the optical systems. Some important optical or system parameters can be obtained by analyzing the fringe information from optical system such as interferometer system or diffraction setup. The straight fringe is a kind of optical fringes frequently appearing in Young’s double slit interference and single-slit diffraction and other optical structures. For the information extraction of straight fringes, it is often necessary to calculate the fringe spacing parameters. Popular straight fringes analysis methods include the fringe center method and the Fourier transform method. In addition, some image processing methods realized line detection can also be used to analyze this straight fringes image, which include Hough transform and Radon transform. In this paper, four algorithms for fringe analysis are discussed and compared, which focus on method’s principle, algorithm’s simulation and performance when they be applied to detect the fringes spacing. At the same time, the anti-noise performance of two image processing algorithms including Hough transform and Radon transform are analyzed.
Using experimental approaches to study translation

Abstract In the last few years, the study of the cognitive processes that underlie the translating act is well under way in the field of translation studies (TS). Yet considering the potential of this research area, translation behaviour and how it is affected and modulated by linguistic and psycholinguistic factors is still underinvestigated. The main culprit of this holdback appears to be fuzziness over what experimental methods are expected to deliver and how sometimes they are actually used. The present discussion article outlines these aspects, draws attention to methodological issues commonly encountered when an experimental approach is adopted, and suggests ways to overcome the challenges posed by this promising line of research.
Generating SBVR-XML Representation of a Controlled Natural Language

Semantics of Business Vocabulary and Business Rule (SBVR) were introduced to describe the business process in most formal way. SBVR specify business rules. Semantics of Business Vocabulary and Business Rules is introduced by standard of Object Management Group (OMG) in 2008. Complex business rules are formally defined by Semantics of Business Vocabulary and Business Rules (SBVR). This paper provides a novel approach for translating SBVR specification of software requirements into XML schema. The purpose of this paper is to generate XML from SBVR instead of NL natural language specification because due to informal nature of natural language the generation of XML form NL will be resulted in lesser accuracy. SBVR Bridge the gap between humans and machines as human can understand simple natural language sentences while this natural language has ambiguous nature for machine and IT specialists. The VeTIS tool is used for the transformation purpose. SBVR rules generated as first output and these rules gave as input to transaction editor that extract SBVR vocabulary such as noun concept, fact type etc. In the last step these SBVR elements are replaced by elements that are called tags of XML vocabulary.
Quadrotor Flight Simulator Modeling

It could be argued that flight simulation is the most pervasive and successful area within the simulation arena. Within simulators it is possible to deal with emergency situations and gain familiarity with new aircraft types, especially the quadrotor, which is one of the most active research topics in rotary wing unmanned aerial vehicles due to its mechanical design simplicity and human interaction safe characteristic. Visual simulation techniques have been developed to provide effective simulated real-world visual environment for flight simulators. The purpose of this work is to build a software flight simulator for quadrotor system and to display all the trajectory tracking details. The simulator was built by modeling the dynamic model of quadrotor on a software platform. The paper presents several simulation results of the quadrotor dynamics in three flight directions. The simulator results show the stable performance of the PID (Proportional-Integral-derivative) controller being used.
Optimal Prediction and Update for Box Set-Membership Filter

This paper investigates a box set-membership filter for nonlinear dynamic systems and on-line usage.To the best of our knowledge, although ellipsoid set-membership filter has more freedom degree to optimize a bounding estimation, it is computationally intractable to obtain an optimal prediction and update, and the approximation loss is uncertain in different scenarios.In this paper, we equivalently transform the prediction and update of the box set-membership filter to linear programing problems without loss of performance, respectively.Moreover, for a typical nonlinear dynamic system in target tracking, the remainder bound of the nonlinear function can be obtained analytically on-line.However, the ellipsoid bounding problem of the remainder usually needs to be relaxed to solve a semi-definite programming problem.Thus, the computational complexity of the optimal box set-membership filter is much less than that of the ellipsoid set-membership filter based on the semi-definite programming.Finally, a numerical example in target tracking demonstrates the effectiveness of the box set-membership filter.The proposed box set-membership filter can obtain a better trade-off between the filter accuracy and the computational complexity.