Corrected version of 19-12-10 (YR-Month-Day) of Part I 2D IPO’s for constant speed Lines, Curves, NURBS. Mainly corrected typos and some clearer formulations. The manuscript (44 pages) “Constant Speed Lines – Curves – NURBS Reference Pulse IPOs (Part I)” of Valere Huypens has been accepted for publication in “The International Journal of Advanced Manufacturing Technology” of Springer

Empty space skipping can be efficiently implemented with hierarchical data structures such as k-d trees and bounding volume hierarchies. This paper compares several recently published hierarchical data structures with regard to construction and rendering performance. The papers that form our prior work have primarily focused on interactively building the data structures and only showed that rendering performance is superior to using simple acceleration data structures such as uniform grids with macro cells. In the area of surface ray tracing, there exists a trade-off between construction and rendering performance of hierarchical data structures. In this paper we present performance comparisons for several empty space skipping data structures in order to determine if such a trade-off also exists for volume rendering with uniform data topologies.

This short paper presents a case report on detecting hijacked journals. Towards identification of a fake journal website and preventing a hijacked paper, we can use different tools including Google Scholar (an altmetric tool), Web of Science (WoS) and Scopus (both as scientometric databases) to distinguish a fake website from a legal journal website. Our evaluation shows that analysis of a doubtful website for a targeted journal based on Google Scholar is not reliable. In fact, the use of scientometric tools for tracking prior publications of the targeted journal is compulsory. Another result of this case study is that in some uncommon cases, fake websites may sometimes convince a scientometric database in order to be partially indexed along with an abstracting of their hijacked papers while these websites steal identity of the legal journals. Therefore as a results, we should check both of WoS and Scopus for verifying a fake website at the same time to obtain more reliability.

A document by Ying Han . Click on the document to view its contents.

Noise and decoherence are two major obstacles to the implementation of large-scale quantum computing. Because of the no-cloning theorem, which says we cannot make an exact copy of an arbitrary quantum state, simple redundancy will not work in a quantum context, and unwanted interactions with the environment can destroy coherence and thus the quantum nature of the computation. Because of the parallel and distributed nature of classical neural networks, they have long been successfully used to deal with incomplete or damaged data. In this work, we show that our model of a quantum neural network (QNN) is similarly robust to noise, and that, in addition, it is robust to decoherence. Moreover, robustness to noise and decoherence is not only maintained but improved as the size of the system is increased. Noise and decoherence may even be of advantage in training, as it helps correct for overfitting. We demonstrate the robustness using entanglement as a means for pattern storage in a qubit array. Our results provide evidence that machine learning approaches can obviate otherwise recalcitrant problems in quantum computing.

In this paper, a new hypervolume-based evolutionary multi-objective optimization algorithm (EMOA), namely R2HCA-EMOA (R2-based Hypervolume Contribution Approximation EMOA), is proposed for many-objective optimization. The core idea of the algorithm is to use an R2 indicator variant to approximate the hypervolume contribution. The basic framework of the proposed algorithm is the same as SMS- EMOA. In order to make the algorithm computationally efficient, a utility tensor structure is introduced for the calculation of the R2 indicator variant. Moreover, a normalization mechanism is incorporated into R2HCA-EMOA to enhance the performance of the algorithm. Through experimental studies, R2HCA-EMOA is compared with three hypervolume-based EMOAs and several other state-of-the-art EMOAs on 5-, 10- and 15-objective DTLZ, WFG problems and their minus versions. Our results show that R2HCA-EMOA is more efficient than the other hypervolume-based EMOAs, and is superior to all the compared state-of-the-art EMOAs.

In this paper, we propose quantum circuits to enable dynamic assertions for classical values, entanglement, and superposition. This enables a dynamic debugging primitive, driven by a programmer’s understanding of the correct behavior of the quantum program. We show that besides generating assertion errors, the assertion logic may also force the qubits under test to be into the desired state. Besides debugging, our proposed assertion logic can also be used in noisy intermediate scale quantum (NISQ) systems to filter out erroneous results, as demonstrated on a 20-qubit IBM Q quantum computer. Our proposed assertion circuits have been implemented as functions in the open-source Qiskit tool.

This paper describes the author's vision in developing a flexible workflow infrastructure for enforcing the pragmatic interoperability in industries like manufacturing and healthcare. This vision is based on business continuity planning, web services interoperability, Node-Red and IFTTT workflow technologies.

Approximate circuit design has gained significance in recent years targeting error tolerant applications. In this paper, we consider the problem of minimizing the power for a given accuracy, in a signal processing application with accurate adders replaced by low-power approximate adders. We first demonstrate that the commonly used assumption that the inputs to the adder are uniformly distributed results in an inaccurate prediction of error statistics for multi-level circuits. To overcome this problem, we propose the use of parameterized error models for adders, with input static probabilities as parameters. The static probability computation in our work considers not just the functionality of the adder but also its position in the circuit, functionality of its parents and the number of approximate bits in the parent blocks. This parameterized error model can be incorporated in any optimization framework. We demonstrate up to 6.5 dB improvement in the accuracy of noise power prediction when the proposed model is used to optimize an 8x8 DCT.

Current constant speed IPO’s, usually, use Sampled-data IPO’s and constant speed lines use the wrong initialized software DDA-ipo’s, which make these IPO’s unusable. The Bresenham- and midpoint IPO’s are non-constant speed reference pulse IPO’s with bounded inaccuracy. By adding an ultra-fast 3-lines algorithm “PRM-cs” to the actual midpoint or Bresenham algorithms, we convert these midpoint-ipo’s to very fast, constant speed, reference pulse IPO’s. This applies to 2D-lines, 3D-lines, 2D-curves and 2D-NURBS. The PRM-cs measures, in real-time, the length of the discrete curve and the PRM-cs is completely new. We define the best IPO, the major axis principle and the LSD-priority. The major axis principle holds for the actual 3D-line IPO’s. These IPO’s are, generally, inaccurate, but they can be updated to constant speed 3D-line IPO’s, when the production manager agrees. The Digital Geometric Geometry (DAG) defines the discrete lines globally, but this global definition of a discrete 3D-line, gives discrete 3D-lines whose accuracy is much less than the accuracy of the best discrete 3D-lines (e.g. 37% worse). We describe the three causes of the inaccurate (imperfect) discrete 3D-lines. All existing pulse-rate or PRM-ipo’s use a wrong initialization, which deteriorates the accuracy. We determine the right initialization for the new PRM-cs and the updated PRM-ipo. We propose the benchmark-ipo “listSIM-ipo”. This constant speed IPO can, also, be used in real- time for every 2D- and 3D-curve. The 3rd-degree Trident NURB shows that the constant speed reference pulse method is much better than the existing sampled-data methods.

In this paper, internet of things (IoT) connectivity in rural areas is investigated. Both fronthaul and backhaul considerations are studied. First, intelligent radio resource management (RRM) and network planning techniques are discussed for IoT access/fronthaul networks. The proposed RRM scheduling approach was shown to lead to good performance in scheduling IoT devices. Then, several backhauling techniques for providing connectivity to rural areas are investigated and their cost efficiency is analyzed. Techniques based on free space optics with solar powered devices are found to be a suitable backhaul solution.

Despite the continued evolution of information systems methodologies for more than three decades, the rates of software rejection and failure are still high. This paper investigates the technological environment as a major cause of such disruptions. Additionally, the paper evaluates Agile and DevOps as remedial methodologies for managing the adverse impact of technological disruptions. The main findings affirm both Agile and DevOps as methodologies that emanated from improvements or re-engineering of earlier methodologies. Further findings discern most methodologies; including agile and DevOps; as not strategically focused but appraise DevOps as the most progressive methodology towards this respect. Rather than re-invent the wheel and come up with a new methodology, a framework that aligns DevOps for use in strategic information systems development is proposed. Besides, a more realistic definition of operations is postulated to bolster the alignment.  Keywords software

Analyzing video for traffic categorization is an important pillar of Intelligent Transport Systems. However, it is difficult to analyze and predict traffic based on image frames because the representation of each frame may vary significantly within a short time period. This also would inaccurately represent the traffic over a longer period of time such as the case of video. We propose a novel bio-inspired methodology that integrates analysis of the previous image frames of the video to represent the analysis of the current image frame, the same way a human being analyzes the current situation based on past experience. In our proposed methodology, called IRON-MAN (Integrated Rational prediction and Motionless ANalysis), we utilize Bayesian update on top of the individual image frame analysis in the videos and this has resulted in highly accurate prediction of Temporal Motionless Analysis of the Videos (TMAV) for most of the chosen test cases. The proposed approach could be used for TMAV using Convolutional Neural Network (CNN) for applications where the number of objects in an image is the deciding factor for prediction and results also show that our proposed approach outperforms the state-of-the-art for the chosen test case. We also introduce a new metric named, Energy Consumption per Training Image (ECTI). Since, different CNN based models have different training capability and computing resource utilization, some of the models are more suitable for embedded device implementation than the others, and ECTI metric is useful to assess the suitability of using a CNN model in multi-processor systems-on-chips (MPSoCs) with a focus on energy consumption and reliability in terms of lifespan of the embedded device using these MPSoCs.

Malware behavioral graphs provide a rich source of information that can be leveraged for detection and classification tasks. In this paper, we propose a novel behavioral malware detection method based on Deep Graph Convolutional Neural Networks (DGCNNs) to learn directly from API call sequences and their associated behavioral graphs. In order to train and evaluate the models, we created a new public domain dataset of more than 40,000 API call sequences resulting from the execution of malware and goodware instances in a sandboxed environment. Experimental results show that our models achieve similar Area Under the ROC Curve (AUC-ROC) and F1-Score to Long-Short Term Memory (LSTM) networks, widely used as the base architecture for behavioral malware detection methods, thus indicating that the models can effectively learn to distinguish between malicious and benign temporal patterns through convolution operations on graphs. To the best of our knowledge, this is the first paper that investigates the applicability of DGCNN to behavioral malware detection using API call sequences.

In distributed control systems where devices are connected through Wi-Fi, direct access to low-level MAC operations may help applications to meet their timing constraints. In particular, the ability to timely control single transmission attempts on air, by means of software programs running at the user space level, eases the implementation of mechanisms aimed at improving communication timeliness and reliability. Relevant examples are deterministic traffic scheduling, seamless channel redundancy, rate adaptation algorithms, and so on. In this paper, a novel architecture is defined, we call SDMAC, which in its current embodiment relies on conventional Linux PCs equipped with commercial Wi-Fi adapters. Preliminary SDMAC implementation on a real testbed and its experimental evaluation showed that integrating this paradigm in existing protocol stacks constitutes a viable option, whose performance suits a wide range of applications characterized by soft real-time requirements.

THIS PREPRINT IS NOW ISSUED AS IEEE ACCESS https://ieeexplore.ieee.org/document/10231329 The CENELEC TS 50701 is the first encompassing standard aiming at  governing cybersecurity risk management processes within the railway  industry. Although the technical maturity of this framework is  undeniable, its application in practical projects is still an active  field of discussion among practitioners, especially when dealing the  communication-heavy subsystems. Among such subsystems, signaling is  among the most critical ones. Both Communication-based Train Control  (CBTC) and European Railway Traffic Management Systems (ERTMS) heavily  rely on wireless communications for their operation. This paper  describes two cybersecurity attack scenarios regarding wireless  communications for CBTCs that can impact the safety of these systems  using the lens of the framework provided by the novel CENELEC TS 50701.  In doing so, we discuss the implications of using such guidance,  especially concerning the different interpretations found in the  literature regarding zoning communication systems, to assess and  mitigate the cybersecurity risk and improve the posture of CBTC systems  concerning the examined attacks. Experimental tests conducted in  controlled laboratory environments and high fidelity simulations have  been conducted to support the cybersecurity analysis.