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[en] Energy is a critical issue in vehicle routing. UAV's or Drones have been the fundamental way in activities where a human has their limited skills to do. Drones save too much time in accomplishing such tasks. The growing usage of drones by commercial firms has contributed to developing a new Vehicle Routing Problem with Drones (VRPD). Self-driving cars and aircraft can be used to transfer packages from one place to another to customers. Vehicles and drones could have dependent or independent deliveries. A drone takes off from a vehicle for package delivery and then returns to the same vehicle after the delivery as long as the drone's energy restrictions are met. This paper tries to model the UAV's routing process to understand the problem clearly. Besides, the paper proposes a modified version of the Brainstorming algorithm to find the best routing for drones. Experiments are carried out in numerous environments and with various scenarios. The findings indicate the suggested algorithm to solve the drone routing problem is much effective with 70% enhancement than the random routing. (author)
[en] Cyber security is one of the major concerns of today’s connected world. For all the platforms of today’s communication technology such as wired, wireless, local and remote access, the hackers are present to corrupt the system functionalities, circumvent the security measures and steal sensitive information. Amongst many techniques of hackers, port scanning and Distributed Denial of Service (DDoS) attacks are very common. In this paper, the benefits of machine learning are taken into consideration for classification of port scanning and DDoS attacks in a mix of normal and attack traffic. Different machine learning algorithms are trained and tested on a recently published benchmark dataset (CICIDS2017) to identify the best performing algorithms on the data which contains more recent vectors of port scanning and DDoS attacks. The classification results show that all the variants of discriminant analysis and Support Vector Machine (SVM) provide good testing accuracy i.e. more than 90%. According to a subjective rating criterion mentioned in this paper, 9 algorithms from a set of machine learning experiments receive the highest rating (good) as they provide more than 85% classification (testing) accuracy out of 22 total algorithms. This comparative analysis is further extended to observe training performance of machine learning models through k-fold cross validation, Area Under Curve (AUC) analysis of the Receiver Operating Characteristic (ROC) curves, and dimensionality reduction using the Principal Component Analysis (PCA). To the best of our knowledge, a comprehensive comparison of various machine learning algorithms on CICIDS2017 dataset is found to be deficient for port scanning and DDoS attacks while considering such recent features of attack. (author)
[en] One EMRAS II WG9 participant used the information from the modelling exercise (given in Appendix I) to estimate the source term from surface activity measurements. This was done through application of inverse fitting of a Gaussian dispersion model; using measurements for surface activity (Bq/m2), in particular, data from Test 2, for which all measurements were available at the time of this work. An optimization routine was written based on MINPACK to inversely determine the source term by means of the Levenberg-Marquardt algorithm and the Gaussian dispersion model, as implemented in HotSpot 2.07.1. The same empirical formulas used in HotSpot 2.07.01 were applied to estimate dispersion coefficients, wind velocity at a reference height, and other parameters; however, unlike HotSpot 2.07.01, integration of the depletion factor was carried out using a Gaussian integration, instead of the less precise trapezoidal rule.
[en] Wireless communication system requires a higher data rate and a more robust transmission link between source and destination against noise and multipath fading effect while keeping a good quality of service. A cooperative communication system utilizing OFDM is considered an effective technology to enhance system performance. By taking advantages of cooperative diversity utilizing relays, this paper investigates the performance of the cooperative system with coded OFDM over two types of a cooperative protocol such as Decode and Forward (DF) and Amplify and Forward (AF),) utilizing Hierarchical modulation. The main goal of this work is to integrate the hierarchical modulation (HM) with cooperative communication focusing on unequal degradation of the two data streams transmitted on the direct link between source and destination and in-direct link through the relay. The main idea is to use different constellations order at the relay and destination according to the difference of SNR values. Using different constellations enhances system flexibility and manipulate this difference to improve the system performance For a relay network with one source, one relay, and one destination the BER performance is investigated when coded cooperative system employs HM over Rayleigh fading channel with OFDM system with distributed convolutional code. Simulation results showing that the proposed cooperative Coded OFDM system with Hierarchical modulation outperforms the reference system (Cooperative DF coded OFDM with MRC) with 3dB at . Also, the proposed system outperforms (Cooperative coded OFDM with EGC) with 5 dB at BER. Moreover, simulation results show that the proposed system outperforms (Cooperative AF coded OFDM with MRC) with about 4dB at BER. (author)
[en] In this paper we advance into a generalized spinor classification, based on the so-called Lounesto’s classification. The program developed here is based on an existing freedom on the spinorial dual structures definition, which, in certain simple physical and mathematical limit, allows us to recover the usual Lounesto’s classification. The protocol to be accomplished here gives full consideration in the understanding of the underlying mathematical structure, in order to satisfy the quadratic algebraic relations known as Fierz-Pauli-Kofink identities, and also to provide physical observables. As we will see, such identities impose restrictions on the number of possible spinorial classes allowed in the classification. We also expose a subsidiary mathematical device - a slight modification on the Clifford algebra basis - which ensures real spinorial densities and holds the Fierz-Pauli-Kofink quadratic relations.
[en] The Particle Data Group recommends a set of procedures to be applied when discrepant data are to be combined. We introduce an alternative method based on a more general and solid statistical framework, providing a robust way to include possible unknown systematic effects interfering with experimental measurements or their theoretical interpretation. The limit of large data sets and practical cases of interest are discussed in detail.
[en] We propose using high order partial least squares path modeling (PLS-PM) to define a synthetic Italian well-being index merging traditional data, represented by the Quality of Life index proposed by “Il Sole 24 Ore”, and information provided by big data, represented by a Subjective Well-being Index (SWBI) performed extracting moods by Twitter. High order constructs allow to define a more abstract higher-level dimension and its more concrete lower-order sub-dimensions. These layered constructs have gained wide attention in applications of PLS-PM; many contributions in literature proposed their use to build composite indicators. The aim of the paper is to underline some critical issues in the use of these models and to suggest the implementation of a new adapted repeated indicator approach. Furthermore, following some recommendations proposed on the use of PLS-PM in longitudinal studies, we compare the situation in 2016 and 2017.
[en] Progress in Big Data in recent years has grown exponentially, which has allowed the detection and processing of a large amount of data. Until recently, this fact was unattainable by the lack of mechanization of the corporate governance reports. This paper investigates the relationship between corporate governance decisions affect the indebtedness policies of 1,956 industrial companies listed in Europe and the USA over the period 2016–2018 (5,868 observations). To measure corporate governance decisions, we use detailed information on the expertise of audit committees, the proportion of independent directors, board structures and women's presence on corporate boards. Our findings, which are based on a static panel data analysis, show that there is a strong negative relationship between Audit Committees expertise and indebtedness level in European and North American companies. There are also evidence that European and American companies with a onetier board structure and Audit Committees expertise are less likely to have lower level of indebtedness. Our results shed new light on corporate governance in relation to the experience of audit committees and the influence of their characteristics on indebtedness policy
[en] In this paper we use high frequency multidimensional textual news data and propose an index of inflation news. We utilize the power of text mining and its ability to convert large collections of text from unstructured to structured form for in-depth quantitative analysis of online news data. The significant relationship between the household’s infla-tion expectations and news topics is documented and the forecasting performance of news-based indices is evaluated for different horizons and model variations. Results sug-gest that with optimal number of topics a machine learning model is able to forecast the inflation expectations with greater accuracy than the simple autoregressive models. Addi-tional results from forecasting headline inflation indicate that the overall forecasting accu-racy is at a good level. Findings in this paper support the view in the literature that the news are good indicators of inflation and are able to capture inflation expectations well.
[en] Now-a-days, in the field of machine learning the data augmentation techniques are common in use, especially with deep neural networks, where a large amount of data is required to train the network. The effectiveness of the data augmentation technique has been analyzed for many applications; however, it has not been analyzed separately for the multimodal biometrics. This research analyzes the effects of data augmentation on single biometric data and multimodal biometric data. In this research, the features from two biometric modalities: fingerprint and signature, have been fused together at the feature level. The primary motivation for fusing biometric data at feature level is to secure the privacy of the user’s biometric data. The results that have been achieved by using data augmentation are presented in this research. The experimental results for the fingerprint recognition, signature recognition and the feature-level fusion of fingerprint with signature have been presented separately. The results show that the accuracy of the training classifier can be enhanced with data augmentation techniques when the size of real data samples is insufficient. This research study explores that how the effectiveness of data augmentation gradually increases with the number of templates for the fused biometric data by making the number of templates double each time until the classifier achieved the accuracy of 99%. (author)