Reviewerly/exHarmony huggingface dataset:
Managing Construction Projects: Developing Complexity into Complicatedness

Construction projects are understood as being complex and therefore in need of support through adequate tools to be successfully managed. Among others, the Building Information Model (BIM) is expected to provide a modern and powerful toolset allowing for reliable prediction of the respective development and behaviour. Based on Theory of Systems, the term of complexity in fact matches the principal capabilities of an object-oriented information system where e.g. the Building Information Modelling rests upon. However, any complex system tends to instable behaviour allowing principally for no reliable prediction, in particular in the single run required for unique projects with no possibility to rearrange processes without major losses. Correspondingly, experienced Construction Managers are judging the complexity of projects as a crucial obstacle to efficient execution but declare complexity as not measureable, thus as degree of unmanageability. Therefore, the inherent complexity of interdisciplinary projects needs to be reduced, i.e. transformed into complicatedness, not reducing the effort of elaboration but allowing for stable solutions. In order to achieve such a transfer, the inherent heterogeneity is utilized tracking down the strictness and linearity of the internal and external system borders, thus, investigating the separability of the adjacency matrices. These mainly topological considerations lead to criteria forming substructures finally allowing for predictable behaviour of the project structures with limited uncertainty. Therefrom, we expect some significantly improved understanding of the cybernetics of projects and consequently advanced possibilities in shaping and establishing activity-based risk management, which is crucial to nowadays construction and real estate projects.
Outlier Detection Algorithm based on Mahalanobis Distance for Wireless Sensor Networks

The wireless sensor networks (WSNs) suffer from several constraints such as harsh areas of deployment, the bad medium of communication and energy limitations. So, to guarantee the reliability and to enhance the quality of the measurements in this kind of networks, it is very crucial that the nodes of the network can detect and clean the sensed data which do not conform to an expected pattern. To reach this goal, we present in this paper a new algorithm, called Outlier Detection Algorithm based on Mahalanobis Distance (ODA-MD). In this algorithm, Mahalanobis theorem is used to compute and compare sensed data in a distributed fashion. The efficiency of this proposal that can detect outliers online even several data types exist in the network. The functionalities of the proposed scheme were performed by simulation using data sets from the Berkeley Intel Laboratory. The obtained results demonstrate the effectiveness of the solution compared to other algorithms in terms of energy consumption, false alarm rate and detection accuracy.
Behavior-Based Outlier Detection for Network Access Control Systems

Network Access Control (NAC) systems manage the access of new devices into enterprise networks to prevent unauthorised devices from attacking network services. The main difficulty with this approach is that NAC cannot detect abnormal behaviour of devices connected to an enterprise network. These abnormal devices can be detected using outlier detection techniques. Existing outlier detection techniques focus on specific application domains such as fraud, event or system health monitoring. In this paper, we review attacks on Bring Your Own Device (BYOD) enterprise networks as well as existing clustering-based outlier detection algorithms along with their limitations. Importantly, existing techniques can detect outliers, but cannot detect where or which device is causing the abnormal behaviour. We develop a novel behaviour-based outlier detection technique which detects abnormal behaviour according to a device type profile. Based on data analysis with K-means clustering, we build device type profiles using Clustering-based Multivariate Gaussian Outlier Score (CMGOS) and filter out abnormal devices from the device type profile. The experimental results show the applicability of our approach as we can obtain a device type profile for five dell-netbooks, three iPads, two iPhone 3G, two iPhones 4G and Nokia Phones and detect outlying devices within the device type profile.
Human action recognition using double discriminative sparsity preserving projections and discriminant ridge-based classifier based on the GDWL-l1 graph

Human action recognition is defined as determining the actions of humans happening in video sequences. Human action recognition is one of the interesting topics which can play important role in intelligence, surveillance and health protection systems' performance; the performance of human action recognition methods is a vital issue that has been focused on many recent papers. Subspace learning and classification steps can impress the performance of the human action recognition methods. Accordingly, in this paper, new subspace learning and classification algorithms are proposed for human action recognition. Notably, graphs play important role to describe the relationship of data but most of the graph based methods used Euclidean distance metric. To overcome it, a geodesic distance based weighted LASSO l1-graph (GDWL-l1 graph) is proposed to extract the between-class and within-class graphs. Then, double discriminative sparsity preserving projections (DDSPP) algorithm is introduced to map the high-dimensional data to a new discriminant low-dimensional space using these graphs in order to have better discrimination besides sparsity and locality preserving in the mapped space. Subspace learning using DDSPP algorithm leads to a discriminative and sparse low-dimensional space. At the end, a discriminant ridge-based classifier (DRC) is introduced to inherit the grouping effect of the ridge regression besides incorporating the geodesic distance by defining a criterion of the classification. Experimental results show the suitable performance of the proposed method on HMDB51 and UCF101 datasets which are as 66.41% and 92.46%, respectively.
Multi-Atlas Segmentation of Anatomical Brain Structures Using Hierarchical Hypergraph Learning

Accurate segmentation of anatomical brain structures is crucial for many neuroimaging applications, e.g., early brain development studies and the study of imaging biomarkers of neurodegenerative diseases. Although multi-atlas segmentation (MAS) has achieved many successes in the medical imaging area, this approach encounters limitations in segmenting anatomical structures associated with poor image contrast. To address this issue, we propose a new MAS method that uses a hypergraph learning framework to model the complex subject-within and subject-to-atlas image voxel relationships and propagate the label on the atlas image to the target subject image. To alleviate the low-image contrast issue, we propose two strategies equipped with our hypergraph learning framework. First, we use a hierarchical strategy that exploits high-level context features for hypergraph construction. Because the context features are computed on the tentatively estimated probability maps, we can ultimately turn the hypergraph learning into a hierarchical model. Second, instead of only propagating the labels from the atlas images to the target subject image, we use a dynamic label propagation strategy that can gradually use increasing reliably identified labels from the subject image to aid in predicting the labels on the difficult-to-label subject image voxels. Compared with the state-of-the-art label fusion methods, our results show that the hierarchical hypergraph learning framework can substantially improve the robustness and accuracy in the segmentation of anatomical brain structures with low image contrast from magnetic resonance (MR) images.
Higher Dimensional Baker Map and its Digital Implementation With LSB-Extension Method

Chaotic systems cannot be realized using fixed or floating point arithmetic on a digital platform. Implementations using these methods always yield periodic and erroneous orbits which are not predicted by the original mathematical method. Also, using such implementations in cryptography is not appealing, because classical cryptography mostly deals with integers and bitwise operations. Therefore, new methods are needed for implementing chaotic systems on digital platforms. Integer domain chaotic systems (IDCS) constitute one such method. There is a generalized IDCS which can be realized for all dimensions. Recently, another method called LSB-extension method has been proposed. However, this method can only be used with binary shift chaotic maps (BSCM), but there is not a BSCM defined for all dimensions. In this paper, such a BSCM is defined for all dimensions by generalizing the 2D baker map and the generalized map is called higher-dimensional baker map (HDBM). It is shown that this generalization exhibits Devaney's chaos and a rigorous proof of this statement is given. Moreover, two generic building blocks are introduced for implementing the HDBM, and using these blocks, the HDBM is realized both on software and FPGA (Field Programmable Gate Array) hardware.
Promoting trust in interoperability of systems-of-systems

The ability of constituent systems to collaborate effectively in a System-of-System (SoS) depends on the extent of interoperability achieved among them. However, communication among constituents relies on common agreements made by intermediaries in many levels of interoperability, increasing the cost and barriers of collaboration. In this scenario, this work presents a Trust Interoperability Blockchain Architecture (TIBA) to enable trust in the interoperability of SoS. TIBA will be evaluated though simulations in a large company in the context of Industry 4.0, considering the Product as a Service (PaaS) business model. We expect simulations results reveal that TIBA can contribute towards the assurance of trust interoperability in SoS.
Optimization of Cloud Data Center using CloudSim – A methodology

Cloud computing can be termed as provisioning of computing power, storage, Infrastructure and other resources over the internet as an on-demand pay service. Recent developments in the technological advancement are in the cloud storage, security, fault tolerances, load balancing and optimization. Cloud simulator is a tool that assists in modelling different cloud applications. Cloudism offers a simulation framework for modelling and simulation that emphasis more on exploring design issues without factoring cloud infrastructure and services. Load balancing in cloud is a method of allocation of workload across multiple nodes to obtain resource optimization with minimum response time. In this paper the state of the art of the cloud simulations along with an approach implemented using CloudSim3.0 is illustrated.
Dynamic Interpolation Search revisited

A new dynamic Interpolation Search (IS) data structure is presented that achieves O(log⁡log⁡n) search time with high probability on unknown continuous or even discrete input distributions with measurable probability of element collisions, including power law and Binomial distributions. No such previous result holds for IS when the probability of element collisions is measurable. Moreover, our data structure exhibits O(1) search time with high probability (w.h.p.) for a wide class of input distributions that contains all those for which o(log⁡log⁡n) expected search time was previously known.
Implementation of Bayesian Regulation Algorithm for Estimation of Production Index Level Micro and Small Industry

Abstract Small Industry is an industrial company whose workforce consists of 5-19 people. While the Micro Industry is an industrial company whose workforce consists of 1-4 people. The purpose of this study is to estimate the Production Index of Micro and Small Industries according to KBLI for years to come. KBLI is a reference classification used to classify Indonesian economic activities/activities into several business fields/business fields that are distinguished based on the type of economic activity that produces products/outputs in the form of goods and services. In this study, the estimation method used is the Bayesian Regulation Algorithm. This algorithm is one of the Artificial Neural Networks algorithms that can solve many estimation problems by building a trained model and showing good performance. The data to be estimated in this study are data of Micro and Small Industry Production Index according to KBLI processed from the Central Board Statistics of Indonesia. This study uses 3 architectural models, namely: 5-10-20-1, 5-15-30-1 and 5-20-40-1. The best architectural model is 5-10-20-1, resulting in 83% accuracy, MSE 0.0031434897 with a minimum error of 0.001 - 0.05.
PERSWADE-CORE: A Core Ontology for Communicating Socio-Environmental and Sustainability Science

The Centre on Persuasive Systems for Wise Adaptive Living (PERSWADE) aims at developing and applying persuasive technologies and system science for social innovation that can help humanity to move toward sustainable, wise, adaptive living. The PERSWADE collaborative knowledge base needs to be designed with the intent to bring together, enrich and logically relate heterogeneous content, such as datasets, scientific literature and any kind of multimedia and social content, to support a participatory approach and help to translate science into action. PERSWADE-CORE, the foundation ontology described in this paper, plays a critical role in this by providing the backbone semantic infrastructure to enable collaboration through efficient data and knowledge integration, sharing and reuse. It also serves the purpose of clarifying and explaining the goals, functions and operations of the Centre. Because of its purpose, PERSWADE-CORE has been designed to be easy-to-use and easy-to-adapt by allowing generic, as well as more specific, relationships among concepts. The PERSWADE approach prioritizes interoperability and relies on the Semantic Web infrastructure. Furthermore, its design is intrinsically aimed at collaborative environments in which ontologies are expected to evolve as a response to users' activity.
NFC Car Keys By Using RSA Cryptography In WSN Security

NFC is the latest wireless technology which works on the principle of Radio Frequency Identification (RFID). The data is moved over charismatic field within the close closeness of about 4-5 cms. Though RFID has been around for a long time, it does not provide personalized facility s to authorized person according to the random key producer. NFC is as long as facility s which no technology has offered so far. NFC is basically executed into smart phones. Basically we need to identify the key splitting in two shares with the use of mediated RSA algorithm. The Identity based encryption (IBE) is the open key encryption which is used for customization. The new combination of NFC and SIM can enable the realization of many new use cases likely UICC has opened gates to limitless opportunities.
User Preferences and Situational Needs of Mobile User Authentication Methods

As it becomes commonplace to use mobile devices to store personal and sensitive data, mobile user authentication (MUA) methods have witnessed significant advancement to improve data and device security. On the other hand, traditional MUA methods such as password (or passcode) are still being widely deployed. Despite the growing body of knowledge on technical strengths and security vulnerabilities of various MUA methods, the perception of mobile users may be different, which can play a decisive role in MUA adoption. Additionally, user preferences for MUA methods may be subject to the influence of their demographic factors and device types. Furthermore, the pervasive use of mobile devices has generated many situations that create new usability and security needs of MUA methods such as support of one-handed and/or sight-free interaction. This study investigates user perception and situational needs of MUA methods using a survey questionnaire. The research findings can guide the design and selection of MUA methods.
Embedded GPU 3D Panoramic Viewing System Based on Virtual Camera Roaming 3D Environment

The traditional 2D panoramic view has the problem of small imaging range, low utilization of original image and serious stretching of the foreground. This paper designs a 3D panoramic viewing system. The real-time performance of the panoramic system requires a large number of parallel image transformations. The general-purpose processor CPU is incapable. This paper designs an embedded GPU solution. The core computing module is based on NXP's i.MX6Q application processor and builds an embedded Linux system. Using V4L2 to capture images to reduce memory and CPU usage, and use OpenGL ES shaders to process image transformations in parallel at high speed. Because it is in a 3D environment, it is impossible to cover all images through a still viewpoint like a 2D look-around. In order to roam the 3D scene, this paper proposes to design a virtual camera system. Using the virtual camera to roam the 3D environment, realize the virtual viewpoint in the 3D scene, traverse each scene in the grid, eliminate the dead angle of the field of view, and finally realize the 3D panoramic view, and verified on the embedded hardware platform, which can clearly roam every image on the surface. And recorded the video of 3-D looping. Experiments show that the 3D panoramic viewing system proposed in this paper has excellent performance and good effects. It has a good real-time performance on the image display, and the response speed is increased by 5%. The main operation is undertaken by the GPU and only takes a small number of CPU resources. The proposed virtual camera system can better realize the 3D panoramic view.
Source Camera Identification using Photo Response Noise Uniformity

Nowadays, technology of digital media is increasing rapidly, as the price of mobile phones or any other digital imaging devices is reducing and functionality is increasing, which is making digital imaging very much cheaper in every-day life. Also extraordinary tools and filters of image processing are easily available in these devices and those are easy-to-use which provides effective impact for manipulating the images and can be misused. As a result, in recent years source camera device identification has received great importance. The Photo Response Noise Uniformity is a unique pattern which can be used for identification of source device from captured data like images or videos. As identification of video acquisition devices is one of the considerable problems faced in multimedia forensics, so this paper presents work related to source device identification using PRNU fingerprint. The proposed method aims to classify videos to the respective source devices using simple statistic based algorithm. Extraction of PRNU is performed using denoising filters.
A STEEPLED Analysis of the SPI Manifesto

Software Process Improvement (SPI) has been founded on the belief that a well-defined and executed process is likely to produce a high-quality pro-duct. Improving a product might provide insights for future improvement efforts. However, this bottom-up approach does not necessarily lead to improvement, considering in particular that software products/artefacts are usually, unique. In contrast, an improved process is a top-down approach, which is applicable to all products (outputs and outcomes). The SPI Manifesto places people, business focus, and organisational change at the core of Software Process Improvement practices. In so doing, certain SPI principles guide the behaviour of individuals, groups, and organisations in their efforts to improve process. The SPI principles form the basis for elaborating on certain values and serve as a foundation for action in software development. In this paper the authors carry out a STEEPLED [Sociocultural, Technical, Economic, Environmental, Political, Legal, Ethical and Demographic] analysis of the SPI Manifesto. STEEPLED is a multidimensional and multi-faceted analysis tool which assists in identifying the strengths, gaps and impact of the SPI Manifesto. Future work for the SPI Manifesto aims at (i) proposing enhancement modifications to its values and principles and (ii) enriching the SPI Manifesto with the addition of a comparative and practical philosophy level.
An Improved Gravitational Search Algorithm for Optimization Problems

Although GSA is an effective optimization algorithm, the best fitness found by GSA cannot be improved in every generation. In order to improve the performance of GSA, this paper proposed an improved gravitational search algorithm (IGSA). In IGSA, a crossover operation is introduced in IGSA so that each solution can inherit some useful information from the global best solution. The exploitation capability of the algorithm can be greatly enhanced. The linearly decreasing weight is used to balance the global and local search abilities. To verify the effectiveness of IGSA, numerical experiments are carried on ten benchmark problems from CEC2014. The experimental results show that IGSA is competitive with respect to other compared algorithms for solving optimization problems.
Image Scaling: How Hard Can it Be?

For centuries, human artists have been scaling images by hand, sketching with varying levels of detail depending on the demands. Image scaling is a fundamental image operation and exists for various devices including mobile phones and computers. It has great commercial values and has attracted great efforts in research. In this article, we suggest a stretch-shrink based framework for image scaling which imitates the work of a human artist. This can be done by firstly representing the image with line segments (sketches) and then stretching or shrinking the length of the representing segments under proper ratios to achieve the desired scaling. Through extensive experiments, this framework exhibits the prominent ability to achieve high quality scaled images efficiently with less storage consumption.
The impact of temporal smoothing on the accuracy of separation models

Passing clouds and the phenomenon of cloud enhancement (CE) can cause severe fluctuations in the incoming solar radiation. The modeling of PV systems’ operation is conditioned by the ability of forecasting and estimation models to capture such phenomena. In time-averaged datasets, these high-frequency events can be completely smeared out. Thus, it is important to study the impact of temporal smoothing on the accuracy of solar irradiance models. This paper focuses on empirical separation models. In the first part, the frequency of CE episodes in the measured data is analyzed, finding an exponential decrease with increasing data timescale. In the second part, the impact of temporal smoothing on the performance of separation models developed on 1-h or 1-min data is assessed. The results show that the aggregated nRMSE of the hourly models decreases from 37.7% to 28.6% between smallest and largest considered timescale. Under the same scenario, the nRMSE of the minute models decreases from 32.1% to 29.7%. Additionally, the hourly models tend to underestimate the measured diffuse fraction, the aggregated nMBE varying between –11.3% and –1.1%, while the minute models tend to overestimate the measured data, the aggregated nMBE ranging between 2.8% and 8.4%, as the averaging interval is increased. The performance of the models is found to have a strong dependence on climate type. No dominating model could be found. The models which perform most consistently are: (a) Yang2 and BRL-min in temperate climates, (b) BRL and PB in tropical climates, and (c) PB and BRL-min in arid climates.