Miracle-dz/UniFAR-FTUs huggingface dataset:
Deployment of Electricity-theft Detection Infrastructure in Chadian Smart Grid Networks

The Chadian electricity company (SNE) wants to modernize the electricity grid’s infrastructure towards a smart grid by using intelligent sensors and considering the architectural aspects of the network and communication. This article presents an Advanced Metering Infrastructure (AMI) deployment in N’Djamena to replace traditional analog devices. The objective is to detect electricity theft by analyzing the network in near-real-time collected by the smart devices. Energy consumption equations at different points are also presented.
A secure algorithm for Random Linear Network Coding

Random Linear Network Coding has been first introduced as a tool to maximize information flow across a network. This paradigm has been later proven to possess inherent weak security that arises from packet mixing. Motivated by this observation, an algorithm for secure data transmission using Random Linear Network Coding is proposed in this paper. The idea is to obscure the global encoding matrix via the use of a predefined matrix followed by a permutation step applied on its rows. A comparative analysis with similar algorithms in the literature is provided as a tool to evaluate the performance of the proposed algorithm.
A Big Data based Decision Framework for Public Management and Service in Tourism

In view of the characteristics of the complexity of public management and service content, the universality of service objects and the diversity of demands faced by current tourism industry development into a new stage of popularisation and industrialisation, this paper proposes a big data driven decision-making model to innovate tourism public management and service, and discusses the connotation, decision-making and implementation process under this decision-making mode. Through the construction of tourism public management and service framework based on big data, this paper discusses the elements, environment characteristics and promotion mode of the framework operation. The mode of tourism public management and service are reformed with decision-making and management based on big data. The problems solution efficiency, quality and services in current tourism industry are improved. Further, tourism public service in the sustainable development in tourism industry worldwide is promoted.
A Concentric Double-Ring Resonator Based Plasmonic Refractive Index Sensor with Glucose Sensing Capability

This paper presents a refractive index sensor that includes two horizontal MIM (Metal-Insulator-Metal) waveguides coupled with a rectangular ohmic cavity and a Concentric Double-Ring Resonator (CDRR). The cavities contain the Material Under Sensing (MUS), while the surrounding area is filled with silver. The designed model is simulated using the Finite Element Method (FEM). Results from simulation show a linear shift of the resonant peaks for variations in the MUS refractive index. Therefore, unknown MUS is identified through peak shifts. The sensor achieves a maximum sensitivity of 1070 nm/RIU with the most sensitive geometry obtained from the simulations. Moreover, the ability to identify glucose concentration levels in the human blood samples makes the proposed sensor an attractive choice for bio-sensing applications.
A Markov Model to Detect Sensor Failure in IoT Environments

The internet of things is a rapidly expanding paradigm which is fundamentally altering the way in which we interact with technology. A range of new services are enabled by this technological revolution, one of which is the task of activity recognition from performing classification on sensed information, which is an area of research related to assisted living. The ability to reliably sense the environment is a crucial aspect of this application area, which has, to date, been prone to error and poor data quality. Therefore, it is essential that we are able to identify potentially anomalous data before it can have a serious effect in the application domain and evoke dangerous consequences. This work presents a novel Markov-based technique for detecting anomalous sensor events in constrained Internet of Things environments. Results from the experiment found a recall score of 97.9% and an F-measure of 92.0%, which represents a promising step forward in this research direction.
Assessment of psychological traits in medalists and non-medalists junior Indian boxers

The purpose of the present study was to assess the psychological traits in medalists and non-medalist junior Indian boxers. A total of (N=60); 16 medalists and 44 non-medalists Junior Indian Boxers with age ranging between 15 – 20 years were selected as subjects for a comparative study. Psychological variables selected for the present study were aggression, motivation and trait and state anxiety. Motivation was measured by questionnaire constructed by Dr. M.L. Kamlesh (1992), anxiety i.e. trait and state anxiety were measured by the questionnaire constructed by Dr. Speilberger, (1966). To measure the aggression questionnaire developed by Buss & Perry (1992) was used. Independent T-Test was employed for the analysis of data and the level of significance was set at 0.05. The results indicated that Concentration (P=0.05) and Self Confidence (P= 0.00) have a significant difference between the Medalist and non-medalists boxers. All other variables found insignificant.
Multistage Centrifugal Pump Fault Diagnosis by Selecting Fault Characteristic Modes of Vibration and Using Pearson Linear Discriminant Analysis

This paper proposes a three-stage fault diagnosis strategy for multistage centrifugal pumps. First, the proposed method identifies and selects fault characteristic modes of vibration to overcome the substantial noise produced by other unrelated macro-structural vibrations. In the second stage, raw hybrid statistical features are extracted from the fault characteristic modes of vibration in time, frequency, and the time-frequency domain. These extracted features result in a high-dimensional feature space. However, in general, not all of the features are best to characterize the ongoing processes in a centrifugal pump, and some of the extracted features might be irrelevant or even redundant, which can affect the fault classification capabilities of the classification algorithm. In the third stage, a novel dimensionality reduction technique, called Pearson Linear Discriminant Analysis (PLDA), is introduced. PLDA assesses the helpfulness of the feature parameters. This technique selects highly interclass-correlated features and adds them to a helpful feature pool. To achieve maximum intraclass separation while maintaining the original class information, linear discriminant analysis is then applied to the helpful feature pool. This combination of helpful feature pool formation and linear discriminant analysis forms the proposed application of PLDA. The reduced discriminant feature set obtained from PLDA is then classified using the k-nearest neighbor classification algorithm. The proposed method outperforms the previously presented methods in terms of classification accuracy.
Feature Selection for Stock forecasting using Multivariate Convolution Neural Network

Predicting stock prices are difficult as they are affected by diverse and complex factors. Therefore, among the various indicators that affect stock prices, key indicators must be selected. Hence, we present a new feature selection method using a multivariate convolutional neural network model to select key indicators that affect stock prices. In addition, we used data of daily net buying and net selling amounts based on investor type, unlike technical indicators or financial data used in other studies. The proposed feature selection method is validated by comparing the predicted accuracy of the stock price using selected and overall indicators. Furthermore, we compare the data and verify the sector using the more efficient data by analyzing industrial sectors.
Machine Learning Algorithm Performance on the Lucata Computer

A new parallel computing paradigm has recently become available, one that combines a PIM (processor in memory) architecture with the use of many lightweight threads, where each thread migrates automatically to the memory used by that thread. Our effort focuses on producing performance gains on this architecture for a key machine learning algorithm, Random Forest, that are at least linear in proportion to the number of cores. Beyond that, we show that a data distribution that groups test samples and trees by feature improves run times by a factor more than double the number of cores in the machine.
Towards Multicarrier Waveforms Beyond OFDM: Performance Analysis of GFDM Modulation for Underwater Acoustic Channels

In this paper, we assess the performance of a generalized frequency division multiplexing (GFDM) transceiver for underwater acoustic high data-rate transmission. We also present a comprehensive overview of the work done in the field of next generation multicarrier techniques beyond orthogonal frequency division multiplexing (OFDM) for underwater acoustic (UWA) channels. Since very little work has been done in determining the feasibility of acoustic GFDM, we evaluate the performance of proposed GFDM transceiver in terms of spectral efficiency, peak to average power ratio, error rates and complexity using simulations. We employ two types of receivers – matched filter and zero forcing and evaluate their performances for several system configurations. Results presented show that the zero forcing receiver outperforms the matched filter receiver at high SNR values for almost all the configurations; exceptions being the cases where 1st and 4th Xia pulse shapes are employed with higher values of the roll off factor. We also compare the performance of the proposed GFDM transceiver with that of OFDM and show that GFDM outperforms OFDM by almost 4 dB for a symbol error rate of 10−2. It is concluded that while GFDM is computationally more demanding than OFDM, it is still an appealing technique for future UWA communications due to its higher spectral efficiency, flexibility, and better error performance.
Biokraftstoffe – Eine Übersicht

Wegen steigender Olpreise und wachsender Klimabesorgnis haben Biokraftstoffe an Bedeutung gewonnen. Offen ist weiterhin die Frage, welche Kraftstoffe aus Biomasse mit welchen Mengenpotenzialen, charakteristischen Eigenschaften und Umweltauswirkungen die grosten Beitrage liefern sollen. Neben dem Qualitatsaspekt ist wegen der gefragten grosen Mengen stets der Mengenaspekt wichtig, d. h. erzielbare Ertrage von Biomasse-Rohstoffen sowie Wirkungsgrade/Ausbeuten der technischen Umwandlungsverfahren zum Kraftstoff. Wichtigste Vertreter sind derzeit Fettsauremethylester, Ethanol sowie fur die Zukunft synthetische Kohlenwasserstoffe und Wasserstoff, hergestellt uber biotechnologische oder chemische Verfahren. Ursachen der Mengenbegrenzung sind die begrenzte Nettoproduktion der Photosynthese, Anbauflachen, Umwandlungsverluste und insbesondere die Wasserverfugbarkeit. Als Folge konnen Biokraftstoffe fur sich allein nur einen (kleineren) Teil zur Losung der Rohstoffsicherheit beitragen. Erforderlich sind zusatzlich Effizienzsteigerung, Einsparung im Bereich der Mobilitat und Anpassung der Konsumgewohnheiten.
A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

Pervasive growth and usage of the Internet and mobile applications have expanded cyberspace. The cyberspace has become more vulnerable to automated and prolonged cyberattacks. Cyber security techniques provide enhancements in security measures to detect and react against cyberattacks. The previously used security systems are no longer sufficient because cybercriminals are smart enough to evade conventional security systems. Conventional security systems lack efficiency in detecting previously unseen and polymorphic security attacks. Machine learning (ML) techniques are playing a vital role in numerous applications of cyber security. However, despite the ongoing success, there are significant challenges in ensuring the trustworthiness of ML systems. There are incentivized malicious adversaries present in the cyberspace that are willing to game and exploit such ML vulnerabilities. This paper aims to provide a comprehensive overview of the challenges that ML techniques face in protecting cyberspace against attacks, by presenting a literature on ML techniques for cyber security including intrusion detection, spam detection, and malware detection on computer networks and mobile networks in the last decade. It also provides brief descriptions of each ML method, frequently used security datasets, essential ML tools, and evaluation metrics to evaluate a classification model. It finally discusses the challenges of using ML techniques in cyber security. This paper provides the latest extensive bibliography and the current trends of ML in cyber security.
Soft Sensor for VFA Concentration in Anaerobic Digestion Process for Treating Kitchen Waste Based on DSTHELM

Kitchen waste is both a pollutant and an available resource. The anaerobic digestion (AD) technology can effectively treat waste and generate biogas. The concentration of volatile fatty acids (VFA) is one of the essential quality monitoring indicators in the anaerobic digestion process of kitchen waste and is difficult to measure online. In this paper, the hierarchical extreme learning machine (HELM) is applied to the soft sensor for the concentration of VFA considering the shortcomings of traditional soft sensor. The combined feature information is extracted by the multi-layer extreme learning machine-autoencoder (ELM-AE) structure in the HELM. Besides, the last layer is the combined manifold regularization extreme learning machine (ELM) and used to predict and mine the data structure of labeled data and unlabeled data. Considering that the characteristics of system operating conditions are prone to change in the actual production process of anaerobic digestion, the domain space transfer hierarchical extreme learning machine (DSTHELM) is used to solve the data drift problem caused by different initial conditions in the actual data; this is different from the traditional hierarchical extreme learning machine. This method increases the accuracy by 14.31% and 9.67% in training set and test set compared with HELM, indicating that the model possesses higher prediction accuracy and better generalization performance.
Artificial Neural Networks for detecting Intrusions: A survey

Nowadays, the networks attacks became very sophisticated and hard to be recognized, The traditional types of intrusion detection systems became inefficient in predicting new types of attacks. As the IDS is an important factor in securing the network in the real time, many new effective IDS approaches have been proposed. In this paper, we intend to discuss different Artificial Neural Networks based IDS approaches, also we are going to categorize them in four categories (normal ANN, DNN, CNN, RNN) and make a comparison between them depending on different performance parameters (accuracy, FNR, FPR, training time, epochs and the learning rate) and other factors like the network structure, the classification type, the used dataset. At the end of the survey, we will mention the merits and demerits of each approach and suggest some enhancements to avoid the noticed drawbacks.
Optimization on Deposition of Aluminum Nitride by Pulsed Direct Current Reactive Magnetron Sputtering

In this study, pulsed dc reactive sputtering of aluminum nitride (AlN) thin films was investigated. The aluminum nitride thin films were deposited on Si (100) using a reactive direct current (DC) unbalanced magnetron sputtering system. The DC reactive sputtering was used in sputtering the aluminum targets in a mixture of argon (Ar) and nitrogen (N2) plasma. Processes of aluminum target sputtering were carried out in an atmosphere of a mixture of Ar and N2. However, pulsed DC reactive sputtering of aluminum targets was carried out at total pressures with N2:Ar ratios from 7:30 to 45:15. In-situ optical emission spectrometry (OES) was applied to obtain the optimal deposition rate and the highest sputtering yield from the effects of flow nitrogen/argon (N2:Ar) ratio and pulse frequency on OES intensity. Thus, we have compared Fourier-transform infrared spectroscopy (FTIR) spectra and X-ray diffraction (XRD) patterns of AlN films deposited on Si (100) by DC reactive sputtering with an Al target in the mixture of Ar and N2. FTIR and XRD investigated the quality of the films and the preferred orientation.
Zero internal concentration polarization FO membrane: functionalized graphene

Reducing internal concentration polarization (ICP) and increasing water flux are essential for forward osmosis (FO) efficiency. Here, we use functionalized porous-single-layer graphene as a FO membrane and study its permeation properties via molecular dynamics simulations. Our results indicate that the ICP decreases to zero during FO processes, and that the water flux of graphene oxide reaches 91.5 L per cm2 per day, which is about 1.7 × 103 times higher than that of typical cellulose triacetate FO membranes. Furthermore, no forward or reverse solutes passing through the membrane are observed when the pore-diameter is smaller than 9.4 A. This work may bring potential opportunities for functionalized porous graphene in water desalination, FO power generation and so forth.
An Empirical Study of Hybrid SSD with Optane and QLC Flash

Emerging non-volatile memory (NVM) technologies provide a new way to solve the I/O bottleneck problem. As one of the widely respected solutions, hybrid storage device performance in the real environment is worth studying. Previously, due to the delayed progress of NVM, most of the studies are proceeded on simulated devices. In this paper, an empirical study is presented on the state-of-the-art hybrid storage device - Intel Optane H10, which is designed with Optane Memory and Quad-Level Cell (QLC) NAND flash. Several interesting findings are concluded with the study, which should be well considered during the employment.
A Preliminary Study: Towards Parallel Garbage Collection for NAND Flash-Based SSDs

NAND Flash-based solid-state drives (SSDs) have been widely used as secondary storage devices due to their faster access speed, lower power consumption, and higher reliability compared with hard disk drives. However, application I/O performance can be significantly affected by garbage collection (GC) inside SSDs. For example, a GC operation usually moves valid pages from a victim block into a clean block in a serialized manner. Thus, it increases the GC time and affects the application I/O performance. To address this issue, this article presents a preliminary study on a parallel GC scheme for flash-based SSDs. In our scheme, we parallelize valid page migrations during a GC operation to reduce the total GC time. To do this, we first propose a new flash chip architecture that enables valid page migrations in parallel. Second, we collect information such as new addresses for the migration of valid pages by considering the restriction of SSD operations. Finally, we employ multiple worker threads to migrate the valid pages using the collected information in a parallel manner. We implement and evaluate our scheme using Disksim with Microsoft SSD extension. The experimental result shows that the proposed scheme reduces the overall GC time by 70% and 57% on average compared with the existing scheme and a state-of-the-art GC scheme IPPBE, respectively.
Semidefinite Relaxation for Millimeter Wave Massive MIMO Detection

In this paper, we consider the application of semidefinite relaxation (SDR) to millimeter wave (mmWave) massive multiple input multiple output (MIMO) systems. In particular, we focus on the use of SDR as a computationally efficient approximation of maximum likelihood (ML) detection in mmWave massive MIMO for high order modulations. The SDR detector is a convex relaxation of the ML problem. To mitigate the high path loss at mmWave frequencies and the poor scattering nature of the mmWave channel, this paper proposes the use of a uniform planar array (UPA) hybrid beamforming technique with large antenna arrays and the SDR detector to improve the performance of 16-quadrature amplitude modulation (16-QAM) mmWave massive MIMO systems. When perfect Channel State Information (CSI) is available at the base station (BS) and mobile station (MS), computer simulations have shown that a gain exceeding 5 dB can be achieved with reasonable complexity by using the SDR algorithm compared to a linear detection system with Zero-forcing (ZF) or minimum mean-square error (MMSE) detection. Moreover, the SDR applied in this paper has fewer constraints and a lower complexity than the other SDRs used for conventional MIMO, and it can be solved in polynomial time using any of the available SDR solvers.
Comparative Study of Magnetically Coupled Coil Used in Dynamic Wireless Battery Charger for Electric Vehicles

Charging performance of dynamic wireless chargers for electric vehicles (EVs) depends on the architecture of magnetically coupled coils. This paper aims to investigate the influence of coil's structures to charging performance. The coupling factor, output power, and system efficiency of different coil's structures are compared. First, the magnetic flux distribution of each structure is studied via finite element analysis (FEA). Then, the experimental measurement of the coupling profile is performed. Key information for designing and developing the coupled coil to improve charging performance is obtained from this paper.
Implementation of an UAV Real-Time Wireless Communication System Using Wi-Fi

As the importance of unmanned aerial vehicles (UAVs) increases, various studies are being conducted worldwide. In the area of using UAV in a disaster situation, various studies are underway for rapid lifesaving. Multiple sensors are employed in the UAV to carry out the purpose of detection, search, and tracking. Unfortunately, the effectiveness of the visual sensors degrades under a disaster situation due to obstacles such as fire, smoke, rainfall. In search and rescue activities, the acoustic information supplements poor visual information. In this paper, we investigate the possibility of implementing a real-time wireless communication system for transmitting acoustic data collected by the UAV to a remote computer. Due to the lack of computation power of the UAV, the remote computer is required for processing the data to estimate the position of the target. Therefore, The requirements of the communication board are investigated to implement the system with Wi-Fi.
Crack Detection With 2D Wall Mapping For Building Safety Inspection

In the Philippines, the number of earthquakes occurring has risen to an alarming rate. ’The Big One’ is one of the biggest expected catastrophes that is undoubtedly going to occur in the next decade as said by various experts. Buildings that were able to withstand the upcoming earthquakes, are to be inspected by engineers without knowing if the safety of the building is compromised. Thus, there is a need for a system that can inspect the cracks on the wall for faster and safer inspection. The objective of this study is to develop a crack detecting system capable of analyzing physical characteristics of cracks and mapping the surface of the wall. The model to be used for classifying and determining what cracks are, was trained with the use of Faster R-CNN machine learning architecture. Trained using the SDNET2018 combined with actual data generated by the proponents, the resulting system can detect cracks with an accuracy of 90% and classify the cracks according to the shape. The system also calculates its physical properties and has a recommender system that provides remarks on what necessary actions can be done.