Miracle-dz/UniFAR-FTUs huggingface dataset:
Reducing Cost of Process Modeling through Multi-source Data Transfer Learning

The availability of an accurate model can facilitate tasks such as design or monitoring in chemical industries. A well-constructed model is invaluable but it can be costly to construct due to the expense in acquiring the labeled data. In the effort to reduce cost of building model, transfer of information using available sources has been considered. However, only the linear transfer has been considered for chemical processes in the reported literature. Moreover, the unlabeled data problem for transfer is not considered too. In this work, a new Gaussian process (GP) model based transfer learning approach for modeling is proposed. The aim is to leverage the statistical approach of the GP to transfer the knowledge from some available data of other processes for both labeled and unlabeled data. These data is used to provide information that is lacking to the process of interest. Case studies are presented to demonstrate the features of the proposed method.
Lip Reading Using Committee Networks With Two Different Types of Concatenated Frame Images

This paper proposes a lip-reading method based on convolutional neural networks (CNNs) applied to two different types of concatenated frame images (CFIs), consisting of (a) full-lip images and (b) patches around lip landmarks. In addition, we introduce committee networks with the predictions obtained from the two different types of the CFIs, which provide better performance than single or committee networks using either type of the CFIs. For efficient training using a limited dataset, such as OuluVS2, we propose time-based label-preserving transform and use a quarter VGG-m in which the number of parameters is reduced compared to the VGG-m. The experimental results with the OuluVS2 dataset show that the proposed method using different types of the CFIs in committee networks outperformed the state-of-the-art methods without pre-training using a large-scale dataset.
Constrained-Target Band Selection for Multiple-Target Detection

This paper develops a new approach to band selection for multiple-target detection, called constrained-target band selection (CTBS). Its idea is derived from the concept of constrained energy minimization (CEM) by constraining a target of interest, while minimizing the variance resulting from the background (BKG). By taking advantage of CEM, the variance produced by a target of interest can be further used as a measure of prioritizing bands as well as a means of selecting bands for this particular target. As a result, two CTBS-based band prioritization (BP) criteria, called minimal variance-based BP (MinV-BP) and maximal variance-based BP (MaxV-BP), and two CTBS-based BS methods, called sequential forward CTBS (SF-CTBS) and sequential backward CTBS (SB-CTBS), can be derived for multiple-target detection. Since the bands selected by CTBS vary with targets of interest used to constrain CEM, in order for CTBS to be applied to multiple targets, a new fusion technique, called band fusion selection (BFS), is further developed for CTBS to integrate bands selected by different targets so that CTBS can work for all targets. Unlike most BS methods for target detection which generally simultaneously select a fixed set of bands for all targets of interest, the ideas of constraining multiple-target detection and using BFS are novelty of this paper. Experimental results show that CTBS performs well for multiple-target detection.
Double Auction Based Resource Allocation for Secure Video Caching in Heterogeneous Networks

Recently, caching techniques have been regarded as efficient approaches to alleviate the data traffic loaded over backhaul channels, which can reduce the transmission delay and improve the quality and experience of video services. This work investigates a small-cell based caching system composed of one mobile network operator (MNO) and multiple video service providers (VSPs). In this system, different VSPs have their caching requirements, and the MNO, who manages and operates its small base stations (SBSs), will assign these SBSs’ storage to VSPs for placing videos. Considering different video popularities and MUs’ preferences of VSPs, the caching service brings different utilities to VSPs, as well as that providing caching service to different VSPs causes distinct costs to the MNO. However, such privacy information of utility and cost cannot be aware of among VSPs and the MNO. In addition, malicious VSPs may break the fairness of caching systems by requesting undeserved caching resource. Concerning these problems above, this paper designs a secure caching mechanism based on double auction, which can encourage both the MNO and VSPs to truthfully report their acceptances and requirements of caching resource, respectively. Moreover, the proposed caching mechanism ensures the efficient operation of market by maximizing the social welfare. The performance and economic properties of the designed caching mechanism are validated with simulation results.
Determination of the Signal-to-Noise Ratio for Noisy Electrocardiogram Using Lossless Data Compression

A numerical method for determining the signal-to-noise ratio (SNR) in electrocardiograms (ECG), distorted by various types of additive noise, is proposed. It is based on the calculation of the compression ratio (CRn) of an ECG fragment of 10 seconds duration. The functional dependences of SNR on CRn for ECG distorted by electrode motion, muscle artefacts and mixed noise, were found, and they are exponential. The values of root mean square error (RMSE) for such dependencies were 4.24 dB, 3.57 dB и 3.33 dB accordingly for SNR in range −10…45 dB. It is proved that the inclusion of heart rate (HR) in the model reduces the error by 0.6-0.8 dB. The present approach is based on the standard library zlib and is recommended for use in portative mobile systems.
Intrusion and Anomaly Detection in Wireless Networks

The broadcast nature of wireless networks and the mobility features created new kinds of intrusions and anomalies taking profit of wireless vulnerabilities. Because of the radio links and the mobile equipment features of wireless networks, wireless intrusions are more complex because they add to the intrusions developed for wired networks, a large spectrum of complex attacks targeting wireless environment. These intrusions include rogue or unauthorized access point (AP), AP MAC spoofing, and wireless denial of service and require adding new techniques and mechanisms to those approaches detecting intrusions targeting wired networks. To face this challenge, some researchers focused on extending the deployed approaches for wired networks while others worked to develop techniques suitable for detecting wireless intrusions. The efforts have mainly addressed: (1) the development of theories to allow reasoning about detection, wireless cooperation, and response to incidents; and (2) the development of wireless intrusion and anomaly detection systems that incorporate wireless detection, preventive mechanisms and tolerance functions. This chapter aims at discussing the major theories, models, and mechanisms developed for the protection of wireless networks/systems against threats, intrusions, and anomalous behaviors. The objectives of this chapter are to: (1) discuss security problems in a wireless environment; (2) present the current research activities; (3) study the important results already developed by researchers; and (4) discuss the validation methods proposed for the protection of wireless networks against attacks.
HMP: A Hybrid Monitoring Platform for Wireless Sensor Networks Evaluation

Wireless sensor networks (WSNs), as an essential part of the deployment of the Internet of Things paradigm, require an adequate debugging and monitoring procedures to avoid errors in their operation. One of the best tools for WSN supervision is the so-called Monitoring Platforms that harvest information about the WSN operation in order to detect errors and evaluate performance. Monitoring platforms for the WSN can be hardware or software implemented, and, additionally, they can work in active or passive mode. Each approach has advantages and drawbacks. To benefit from their advantages and compensate their limitations, hybrid platforms combine different approaches. However, very few hybrid tools, with many restrictions, have been proposed. Most of them are designed for a specific implementation of WSN nodes; many of them are lack of a real implementation, and none of them provides an accurate solution to synchronization issues. This paper presents a hybrid monitoring platform for WSN, called HMP. This platform combines both hardware and software, active and passive monitoring approaches. This hybridization provides many interesting capabilities; HMP harvests the information both actively (directly from the sensor nodes) and passively (by means of messages captured from the WSN), causing a very low intrusion in the observed network. In addition, HMP is reusable; it may be applied to almost any WSN and includes a suitable trace synchronism procedure. Finally, HMP follows an open architecture that allows interoperability and layered development.
Scalable and Configurable End-to-End Collection and Analysis of IoT Security Data : Towards End-to-End Security in IoT Systems

In recent years, there is a surge of interest in approaches pertaining to security issues of Internet of Things deployments and applications that leverage machine learning and deep learning techniques. A key prerequisite for enabling such approaches is the development of scalable infrastructures for collecting and processing security-related datasets from IoT systems and devices. This paper introduces such a scalable and configurable data collection infrastructure for data-driven IoT security. It emphasizes the collection of (security) data from different elements of IoT systems, including individual devices and smart objects, edge nodes, IoT platforms, and entire clouds. The scalability of the introduced infrastructure stems from the integration of state of the art technologies for large scale data collection, streaming and storage, while its configurability relies on an extensible approach to modelling security data from a variety of IoT systems and devices. The approach enables the instantiation and deployment of security data collection systems over complex IoT deployments, which is a foundation for applying effective security analytics algorithms towards identifying threats, vulnerabilities and related attack patterns.
A Platform and Methodology Enabling Real-Time Motion Pattern Recognition on Low-Power Smart Devices

This article presents a low-power platform, i.e., NeblinaTM system-on-module, with extensive hardware variants targeting Internet of Things applications. The base hardware utilizes a sensor fusion algorithm for 3D orientation tracking alongside a novel configurable framework for real-time Motion Pattern Recognition (MPR) on ARM Cortex M4F using inertial sensors. The MPR engine consists of configurable blocks performing shock-aware segmentation, histogram feature extraction, and classification using a single-hidden-layer Feedforward Neural Network. The framework can be used for human fitness/daily activity tracking or shock pattern recognition, e.g., in sports, such as tennis, golf, hockey, etc. Our platform can deliver multimodal user feedback as well. Experimental results have evaluated Neblina’s MPR framework in terms of A) accuracy in human fitness activity recognition using some existing datasets, B) memory usage and latency for real-time execution on Cortex M4F and C) low power consumption for a longer lasting battery.
Fractal Analysis on Artificial Profiles and Electroencephalography Signals by Roughness Scaling Extraction Algorithm

Electroencephalography (EEG) was widely investigated in brain status detection and disease diagnosis, in which the fractal analysis played an important role. In this paper, the roughness scaling extraction (RSE) algorithm proposed in our previous study on surface morphologies was applied to calculate the fractal dimensions (FDs) of artificial profiles and EEG signals. Fractal profiles with ideal FDs ranging from 1.01 to 1.99 were generated through the Weierstrass–Mandelbrot function. The RSE algorithm and the traditional algorithms, including the Higuchi algorithm, the Katz algorithm, and the box counting algorithm, were compared by analyzing the artificial profiles. Based on the mean relative errors and mean square errors, it was found that the RSE algorithm was more accurate than the traditional algorithms. To investigate the influence of noise on FD calculation, noise with different levels was added to the fractal profiles. The RSE and Higuchi algorithms were found reliable at signal-to-noise ratios of 50 and 40 dB, while the accuracy of RSE was also superior to that of the Higuchi. The RSE, Higuchi, and Katz algorithms were utilized to analyze the EEG signals of epilepsy events. The significant FD increasing, which corresponded to the seizure onset, could be detected, and the overlapping between the seizure and non-seizure statuses was small by using the RSE algorithm, indicating its feasibility for the EEG fractal analysis.
Distributed Interference and Power Control based-Game Theory for D2D Communication

Device-to-Device communication underlaying cellular network can improve the spectrum efficiency due to both direct proximity. Unlike previous works, which have dealt with potential and dynamic games etc., we propose in this paper to study and to investigate both pure and mixed games, using two utility functions game of cellular and D2D users, in order to enhance the system spectral efficiency and to increase the SINR. First, we model each kind of users (D2D and Cellular users) as a rational player, aiming at maximizing its own net utility, based on the Quality of Service (QoS), in terms of Signal-to-Interference plus-Noise-Ratio (SINR) threshold. Second, pure and mixed games are investigated in this paper, where existence and uniqueness of the Nash equilibrium (NE) are studied. Finally, simulations are conducted to compare the proposed framework for the two kinds of proposed games.
Analyzing the Impact of New Instruction Method Using Hardware Control Such As Robotics in Learning Programming

This research work aims to find out how students gain knowledge involving in robotics instruction method and learning programing languages with robotics tools. Teaching programming language via hardware control such as robotics is very popular in this time and in the future. We expect this will make students learn programming language with great eagerness within very popular technology ‘robotics. The second aim of this study is to inform computer science teachers about how much their instruction methods based on robotics affects the outcome of their class. We expect this study will have positive effects to the field of computer science education. The computer science class is different from other classes. Computer science teachers have to use new educational methods because of continuously improving technology. The entire world are getting instant information what is new in technology via social medias. Computer science teachers should take the first step to introduce the news to their students in a practical way. It is the way to make students to see the changes on technology in the class.
An NB-IoT-based smart trash can system for improved health in smart cities

The intelligent treatment of urban garbage is an important component of creating a smart city and also solves several problems associated with urban garbage. Many traditional garbage cans are widely distributed, resulting in a waste of human and material resources, untimely government. Therefore, in this paper, we propose an intelligent system based on edge computing and the narrow-band Internet of things (NB-IoT) for monitoring smart trash cans (STCs). The deployed intelligent garbage cans are distributed throughout the city and are equipped with a variety of sensors, including a compression sensor, a location sensor, an infrared sensor, and an alarm sensor. The data sent from the smart bins are preprocessed through edge nodes for data classification and priority transmission, which reduces the required network transmission bandwidth and the computational tasks at the centralized data center. The NB-IoT is a narrow-band communication technology with low power consumption, wide coverage, low cost, and large capacity. The experimental results show that the proposed STC system shows good system performance, and allows for intelligent management of garbage in smart cities.
A Car-Following Model Considering Preceding Vehicle’s Lane-Changing Process

Car following and lane changing are two common driving behaviors in the traffic flow. Preceding vehicle’s lane changing is affected by the surroundings and will have a greater influence on the followers’ driving decision. The existing car-following theory does not fully take it into consideration that the followers’ driving behavior may change during a lane-changing process. In order to reflect the driving decision in a complex traffic flow more precisely, the influence on the following vehicle during the preceding vehicle’s lane-changing process is studied. First, the different types of stimulus during the preceding vehicle’s lane-merging (LM) process and the space gain effect produced by the preceding vehicle’s lane-passing (LP) behavior are analyzed. Then, the LM-FVDM and LP-FVDM are proposed based on the classical car-following model—FVD model. Finally, the linear stability theory, numerical simulation, and NGSIM data sets are used to analyze and validate the performance of the LM-FVDM and LP-FVDM. The numerical simulation results show that the model can reasonably reflect the driving decision of the following vehicle in various scenarios, and verification based on NGSIM shows that the $R$ -squared of vehicles’ speed and distance is significantly better than the FVD model, which can more effectively reflect the speed adjustment process of the following vehicle during the preceding vehicle’s lane-changing process in the real traffic flow.
Adoption of Virtualization by Government Organizations in Kenya

Virtualization is an emerging trend that entails turning an object or resource simulated or emulated in software into an identical physically realized object in terms of functions. It uses abstraction to make software look and act like hardware, with significant advantages in flexibility, cost, scalability, overall capabilities, performance and a wide range of applications. Virtualization provides an opportunity for government organizations to share (ICT) resources. These resources include; servers, operating systems, applications and data storage devices. This paper establishes the most relevant issues related to the strengths, weaknesses, opportunities and threats regarding adoption of virtualization and presents a framework of the factors involved in adoption of this technology. The aim is to explore the potential of virtualization adoption in transforming and automating the business processes of government organizations in Kenya. Adoption of this technology is key to policy makers, regulators, service providers, and the industry and research institutions.
An Approximate Circuit Model to Analyze Microstrip Rampart Line in OSB Suppressing

An approximate circuit model to analyze microstrip rampart line in open-stopband (OSB) suppressing is presented. The proposed antenna consists of periodically orthogonally bending microstrip transmission line, and the widths of two transversal lines are adjusted to match the input impedance of the unit cell to the characteristic impedance of the transmission line. The approximate circuit model is presented to analyze the behavior of the PLWA. The prototype antenna shows a seamless frequency scanning from 3.7 to 6.8 GHz with a scanning range of 118° from the backward to forward quadrant. The simulation and measured results have demonstrated that the OSB is successfully suppressed in the improved case.
Smoke-Detection Framework for High-Definition Video Using Fused Spatial- and Frequency-Domain Features

Video-based smoke detection is an effective method for fire alarm systems. Given the widespread use of high-definition cameras, a smoke detection method for high-definition video is needed. This paper proposes a smoke-detection framework for high-definition video, in which the main idea is to use the small smoke image blocks to match the image features of the motion area in the video and to use the support vector machine classifier for smoke recognition. The ViBe algorithm and other methods are used to effectively extract the areas for classification. This detection framework consists of spatial- and frequency-domain features. In the extraction of frequency domain features, we use local phase quantization (LPQ) features. In the local texture features of the spatial domain, we add the compensation of adjacent pixels and consider the gradient of the symmetrical pixels using the center-symmetric local binary pattern feature. To improve results, we also propose the trisection feature fusion scheme for features in the spatial and frequency domains. The experiments show that using the feature extraction and fusion schemes, our smoke-detection framework achieves the good performance in the detection of smoke in the video from different datasets.
A Feasibility Analysis Framework of Time-Sensitive Networking Using Real-Time Calculus

In the field of intelligent connected vehicles, as the rate of in-vehicle communication continues to increase, the importance of real-time and reliability requirements has been more prominent. The time-sensitive networking (TSN) task group is dedicated to the amendments for meeting the urgent needs in the industrial field. In the implementation of the TSN, time latency bounds of critical traffic, memory bounds of nodes and utilization of transmission resources are needed to evaluate the feasibility of the TSNs. In this paper, we propose a framework called component-based analysis (CBA) to analyze these properties. In CBA, we use a data model, a resource model, and a component model to construct an abstract model of the TSNs with the system architecture and analyze the feasibility of this abstract model with real-time calculus. Moreover, we discuss the preemption mechanism mentioned in IEEE 802.1Qbu to handle the conflict of different priority queues, which has a serious influence on the resource model of the TSNs. Finally, we validate this framework by performing an analysis on a synthetic case and compare the obtained properties with different conflict handling mechanisms.
Fast Residential Area Extraction from Remote Sensing Image Based on Log-Gabor Filter

Monitoring urbanization may help government agencies and urban region planners in updating land maps and forming long-term plans accordingly.In this paper,a novel method for fast extracting residential area from remote sensing images based on log-Gabor filter is proposed.The method is divided in three steps.Firstly,the edge-oriented urban characteristics in a remote sensing image are detected using log-Gabor filter.Secondly,with the filtering orientations perpendicular to each other,two log-Gabor filter response images to suppress the noise and acquire a smooth spatial region.Thirdly,a set of smooth regions served as residential areas can be extracted using Otsu's method.The comparison of our method with the classical texture analyzing method of co-occurrence matrix demonstrates its superiority.