Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward efficient authentication for space-air-ground integrated Internet of things

Space-air-ground integrated Internet of things can improve the scope of Internet of things applications significantly by offering truly global coverage all over the world. While space-air-ground integrated Internet of things is promising to be very useful in many aspects, its deployment and application should overcome severe security threats, for example, interceptions, identity forgery, data tampering, and so on. Authentication is an essential step to protect the Internet of things security, and mutual authentication (i.e. two-way authentication) is especially important to ensure the security of both communication parties simultaneously. However, the intrinsical properties of network dynamics and wide coverage make the authentication concern in space-air-ground integrated Internet of things extremely challenging than traditional Internet of things networks. In this article, we propose MASIT, an identity-based efficient and lightweight mutual authentication scheme for space-air-ground integrated Internet of things. MASIT exploits the natural broadcast property of space-air-ground integrated Internet of things to speed up authentication process, and leverage the distinguished feature of IPv6 to support concurrent numerous nodes. Theoretically, we prove that MASIT is existential unforgeable secure under adaptively chosen message and identity Attacks. We also implement MASIT and other existing typical identity-based encryption schemes and evaluate their performance in real platforms. Experimental results showed that, MASIT outperforms the existing identity-based encryption schemes significantly, that is, the signature verification time can be reduced by 50% to 60%, and the user signature size can be reduced by 13% to 50%.
Different plot selection strategies for field training data in ALS-assisted forest

Summary The aim of this paper was to examine different plot selection strategies of field training plots in forest inventory using airborne laser scanner (ALS) data. The applied plot selection strategies were random selection, random selection within pre-stratification according to forest type, selection of plots according to geographical location and selection of plots based on properties of the ALS data given as a priori information. The study was conducted by means of simulation utilizing existing and independent training and validation plot data and the performance was evaluated by assessing bias and the root mean square error (RMSE). The accuracy of simultaneously derived biophysical stand properties, i.e. volume, number of stems and Lorey’s mean height, was examined using non-parametric modelling. The use of ALS data as a priori information provided the most accurate results in the case of stand volume and number of stems the RMSE being less than 15 and 30 per cent, respectively. For the mean height, also the other selection strategies were as good but the most accurate alternative varied according to number of training plots used. In most cases, the RMSE values for the mean height were between 8 and 9 per cent. The bias of the different strategies followed the same patterns as the corresponding RMSE values.
Optical MIMO-OFDM With Fully Generalized Index-Spatial LED Modulation

In this letter, we propose a novel, fully generalized index-spatial (FGIS) light emitting diode (LED) modulation scheme for the optical MIMO-orthogonal frequency division multiplexing (OFDM) system. The fully generalized index-spatial (FGIS) is a spectrally efficient (SE) visible light communication (VLC) modulation scheme in which the LED indices are exploited in an innovative way to address, not only the OFDM signal time/frequency domain shaping problem but also to provide an additional spatial modulation (SM) domain. Compared to recent OFDM-LED index modulation schemes, FGIS achieves higher SE with lower number of LEDs. The simulation results surpass the proposed FGIS in giving superior improvement in both bit error rate (BER) and achievable rate ( $R$ ) compared with the state-of-the-art OFDM-LED index modulation schemes.
Analysis of Hospitalizing Behaviors Based on Big Trajectory Data

With the improvement of living standards, people pay more attention to health, which is significant to analyze people’s hospitalizing behaviors. The wide use of mobile devices generates a great deal of data, which contains a lot of travel information about residents. Many people would like to see a doctor through calling an online car hailing for its convenience. Thus, based on big trajectory data generated by the online car hailing, the hospitalizing behaviors of residents are analyzed in this paper. The hospitalizing behaviors are analyzed from two aspects. One is performed from the temporal aspect, in which the daily numbers of trips of hospitalizing behaviors under different modes are analyzed. The other one is performed from the spatial aspect, in which the hot hospitals, popularity, and gravity distribution of hospitals are analyzed. Based on the spatial analysis, the network constructed by the hot hospitals is also analyzed. The results show that the hospitalizing behavior analysis can reflect the hospitalizing behaviors in detail, which can make contributions to the decision-making of infrastructure configuration for institutions, such as urban planning departments and hospitals.
TOPIC MODEL IMPLEMENTATION TO FIND RELATED DOCUMENTS IN CORPORATE ARCHIVES IN REAL LIFE: “A CASE SCENARIO ON KNOWLEDGE RETRIEVAL”

Today’s organizations were mostly built over their documents. These documents are very crucial sources of knowledge. Even they know the existence of these documents, most of the time, it is nearly impossible to extract captive knowledge inside. In these conditions, organizations choose re-prepare same document again rather than finding proper documents in the archives. On the other hand, finding these documents would save precious time and decrease redundancy of the work. Topic model idea basically focuses on extraction of knowledge from these types of documents. In this study, our aim is to give a summary of Topic Model research and try to explain latest model concept over an imaginary case scenario
Automatic processing of code-mixed social media content

Code-mixing or language-mixing is a linguistic phenomenon where multiple language mix together during conversation. Standard natural language processing (NLP) tools such as part-of-speech (POS) tagger and parsers perform poorly because such tools are generally trained with monolingual content. Thus there is a need for code-mixed NLP. This research focuses on creating a code-mixed corpus in English-Hindi-Bengali and using it to develop a world-level language identifier and a POS tagger for such code-mixed content. The first target of this research is word-level language identification. A data set of romanised and code-mixed content written in English, Hindi and Bengali was created and annotated. Word-level language identification (LID) was performed on this data using dictionaries and machine learn- ing techniques. We find that among a dictionary-based system, a character-n-gram based linear model, a character-n-gram based first order Conditional Random Fields (CRF) and a recurrent neural network in the form of a Long Short Term Memory (LSTM) that consider words as well as characters, LSTM outperformed the other methods. We also took part in the First Workshop of Computational Approaches to Code-Switching, EMNLP, 2014 where we achieved the highest token-level accuracy in the word-level language identification task of Nepali-English. The second target of this research is part-of-speech (POS) tagging. POS tagging methods for code- mixed data (e.g. pipeline and stacked systems and LSTM-based neural models) have been implemented, among them, neural approach outperformed the other approach. Further, we investigate building a joint model to perform language identification and POS tagging jointly. We compare between a factorial CRF (FCRF) based joint model and three LSTM-based multi-task models for word-level language identification and POS tagging. The neural models achieve good accuracy in language identification and POS tagging by outperforming the FCRF approach. Further- more, we found that it is better to go for a multi-task learning approach than to perform individual task (e.g. language identification and POS tagging) using neural approach. Comparison between the three neural approaches revealed that without using task-specific recurrent layers, it is possible to achieve good accuracy by careful handling of output layers for these two tasks e.g. LID and POS tagging.
An architecture for concurrent, peer-to-peer components

Software components are increasingly seen as the preferred technique for the construction of flexible, large-scale systems. Forces such as economic globalization and the Internet explosion mean that today's software must interoperate in a dynamic, heterogeneous, distributed environment. Components are only one aspect of the solution, but the ability to partition a system into granular, minimally-dependent pieces is an important facilitator. A number of component middleware designs have emerged, primarily from the business and scientific communities. These standard architectures generally derive from distributed object architectures. While this approach has the obvious benefit of using established techniques such as remote method invocation, the introduction of different interaction concepts can simplify the development of some applications. This dissertation presents new paradigms for enhancing the performance and ease-of-development of tightly-coupled, peer-to-peer components. Particular emphasis is placed on enabling pipelined concurrency, which is important for maintaining frame rate in such applications as scientific visualization. The dissertation also presents a “pluggable” architecture for implementing layered protocols. As part of the dissertation, a reference implementation was developed and tested. The results confirm that the new paradigms can be implemented efficiently. A sample application was also developed using the reference implementation. The sample application shows that the new paradigms can simplify the development of component software.
Yaw and lateral stability control for four-wheel-independent steering and four-wheel-independent driving electric vehicle

Based on the traditional four-wheel steering system, this paper combines the steer-by-wire system and four-wheel-independent driving technology to develop a new steer-by-wire four-wheel-independent steering and four-wheel-independent driving electric vehicle, which can realize four-wheel-independent steering and four-wheel-independent driving. Aiming at the steering actuator redundancy characteristics of the four-wheel-independent steering and four-wheel-independent driving vehicle, a hierarchical control method is proposed in this paper. In view of parameters perturbation of vehicle speed and tire cornering stiffness and the model uncertainty problem, the structure singular value μ is used to study the stability control of four-wheel-independent steering and four-wheel-independent driving vehicle under multiple perturbations in the upper layer, and verifies the advantages of μ controller by comparing it with H∞ control and proportional-integral-derivative control. The lower layer is aimed at the redundancy of vehicle steering actuator, an on-line reconfigurable steering angle and driving force allocation control method based on tire force optimal allocation is proposed. This method can not only optimize the tire longitudinal force and lateral force distribution under normal conditions but also reconstruct the distribution control strategy on-line under fault conditions, realizing active fault tolerance. Finally, simulations by MATLAB/Simulink and hardware-in-the-loop experiments are conducted to verify the proposed control method. The simulation results show that the designed controller can maintain good stability under the conditions of model perturbation, separation pavement, and actuator failure.
A Supervised Intrusion Detection System for Smart Home IoT Devices

The proliferation in Internet of Things (IoT) devices, which routinely collect sensitive information, is demonstrated by their prominence in our daily lives. Although such devices simplify and automate every day tasks, they also introduce tremendous security flaws. Current insufficient security measures employed to defend smart devices make IoT the “weakest” link to breaking into a secure infrastructure, and therefore an attractive target to attackers. This paper proposes a three layer intrusion detection system (IDS) that uses a supervised approach to detect a range of popular network based cyber-attacks on IoT networks. The system consists of three main functions: 1) classify the type and profile the normal behavior of each IoT device connected to the network; 2) identifies malicious packets on the network when an attack is occurring; and 3) classifies the type of the attack that has been deployed. The system is evaluated within a smart home testbed consisting of eight popular commercially available devices. The effectiveness of the proposed IDS architecture is evaluated by deploying 12 attacks from 4 main network based attack categories, such as denial of service (DoS), man-in-the-middle (MITM)/spoofing, reconnaissance, and replay. Additionally, the system is also evaluated against four scenarios of multistage attacks with complex chains of events. The performance of the system’s three core functions result in an ${F}$ -measure of: 1) 96.2%; 2) 90.0%; and 3) 98.0%. This demonstrates that the proposed architecture can automatically distinguish between IoT devices on the network, whether network activity is malicious or benign, and detect which attack was deployed on which device connected to the network successfully.
Improving scalability of task-based programs

In a multi-core era, parallel programming allows further performance improvements, but with an important programmability cost. We envision that the best approach to parallel programming that can exceed the programability, parallelism, power, memory and reliability walls in Computer Architecture is a run-time approach. Many traditional computer architecture concepts can be revisited and applied at the runtime layer in a completely transparent way to the programmer. The goal of this work is taking the computer architecture value prediction and data-prefetching concepts inside a runtime environment like OmpSs.
On the Effectiveness of Fingerprinting Mobile Devices Investigating Modern Web-Tracking Mechanisms

Client fingerprinting techniques enhance classical cookie-based user tracking to increase the robustness of tracking techniques, e.g., in cases where a user regularly deletes cookies. A unique identifier is created based on characteristic attributes of the client device and then used for deployment of personalized advertisements or similar use cases. Whereas fingerprinting performs well for highly customized devices (especially desktop computers), these methods often lack in precision for highly standardized devices like mobile phones and tablets. In this work, we show that widely used techniques do not perform well for mobile devices yet, but that it is possible to build a fingerprinting system for precise recognition and identification. We evaluate our proposed system in an online study and verify its robustness against misclassification. Fingerprinting of web clients is often seen as an offence to web users’ privacy as it usually takes place without the users’ knowledge, awareness, and consent. Thus, we also analyze whether it is possible to outrun fingerprinting of mobile devices. We investigate different scenarios in which users are able to circumvent a fingerprinting system and evade our newly created methods.
Recognition and Description of Unknown Everyday Objects by Using an Image Based Meta-Search Engine for Service Robots

In future applications service robots will operate in complex and unstructured environments. While performing daily tasks such systems will have to recognize many different unknown objects in order to be able to conduct continuative processes. To support enhanced functionalities of a service robot system an image based meta-search engine that is accessible for a machine can be used. In this contribution the realization of an approach to determine reasonable identification results for single and multiple everyday objects by using the Google reverse image search system is described. In order to deliver convenient results for the communication of a robotic system with a human user the generated search results are post-processed and prepared in order to provide reasonable object descriptions. To enable an intuitive application, control a gesture based approach allows potential users to initialize a search procedure by pointing at an object of interest while being observed by a camera system. The performed experiments show a quite reliable detection behavior for a reasonably broad variety of everyday objects.
Purity checking for reference attribute grammars

JastAdd is a meta compilation tool. It supports Reference Attribute Grammars (RAGs) and other declarative features for easy computation of abstract syntax tree properties in compilers. The attributes values are computed automatically on demand, and are defined by relations, called equations. JastAdd provides caching and optionally parallel evaluation for evaluation of attributes. The control flow is managed by JastAdd and thus not specified by the programmer. Therefore, the result must be invariant of exact order of execution. This requires that the equations are without side effects, i.e., pure otherwise unexpected behaviour might occur. This is not checked by JastAdd and upto the programmer to guarantee. For large programs keeping the code side effect free can be hard and side effect might find their way into the code and may cause subtle errors under specific situations. I explored the possibility to use existing purity checkers for JastAdd. However, no existing tool was directly applicable, and I have therefore implemented a prototype purity checking tool for JastAdd and Java programs. The solution is then tested on several programs and the performance evaluated. Keyword side effect, Reference attribute grammar, Abstract syntax tree, purity checker, declarative programming, JastAdd
Numerical Experimental Validation of a Proposed MPPT Algorithm with Dynamic Hysteresis for PV Systems

In the last decades, the renewable energies have had a highly positive effect on the daily life. This due to the necessity of reducing the common fuel energies that have negatively impacted the environment. Among all the available renewable energies, the solar energy is one of the most common. This energy is obtained through Photovoltaic (PV) systems. Thus, in previous years, new techniques of Maximum Power Point Tracking (MPPT) to raise the quality of the energy provided by PV panels have been proposed. Since the energy provided by the PV cells depends on the external environmental conditions, as temperature or sun irradiance, the MPPT methods should be adequate to deal with these external changes by maintaining the desired power level. Hence, this paper proposes a recent hysteretic dynamic technique to extract the maximum power from a PV panel array by employing a Boost DC/DC converter to supply energy to an inductive load. Here, a comparative study between the results obtained with the well known Perturb and Observer (P&O) algorithm and by using our dynamic hysteretic MPPT method is analyzed, specifically when the PV panel is submitted to fast variations in temperature and irradiance. It will be proved through numerical experiments realized in MatLab/Simulink that our hysteretic MPPT algorithm provides a better achievement of the maximum power of the PV panel in comparison to the conventional Perturb and Observer method.
Multicast Power Minimization Algorithm Based on PSO for Multicast Algorithm

MPM problem has been mathematically formulated to a constrained optimization problem by using Hybrid PSO. Considering that the centralized algorithm requiring a global knowledge of the networks is impractical in large wireless networks, the distributed version of the centralized algorithm is employed, which requires multihop neighborhood information under the assumption that limited frequency band is available. A variety of simulations have been conducted to examine the performance of the proposed approach and the results show that our algorithm consistently outperforms existing techniques.
Students as partners in complex number task design

We report on a collaborative project at university level involving students as partners in task design for a bridging mathematics module (known in the UK as a Foundation module) which is part of gaining access to first year degree studies. Three teacher-researchers met regularly with four student partners who developed a set of tasks on matrices and on complex numbers which were trialled with students on this Foundation module. We show the mediational processes by which the tasks developed from ‘static’ designs to more ‘dynamic’ designs using the software Autograph. Our analyses highlighted various tools in the mediation of the learning of mathematics, in the mediation of task development and in the mediation of the engagement of all team members in collaboration.
Color Stability of Phycoerythrin Crude Extract (PECE) from Rhodomonas Salina Toward Physicochemical Factors

Rhodomonas salina produces Cr-phycoerythrin545 as its designated phycoerythrin (PE) with an absorption maximum at 545 nm and a shoulder 564 nm. PE has potential to be applied as colorants, pharmaceutical agents, and fluorescent dye tags. The stability of the PE color is influenced by the physicochemical factors of the solution. This study aimed to analyze the color stability of PECE against chemical (ethanol and pH) and physical (light and temperature) factors. PECE was prepared from freeze-dried biomass of R. salina and was extracted in phosphate buffer solution (pH = 6.0) using a freeze-thaw method in -25 oC (2 hours) and 4 oC (24 hours). The resulting extract was concentrated and dried in a freeze-dryer. Analyses were conducted using UV-visible and fluorescence spectrophotometer. PECE showed color stability against light of white fluorescent lamp exposure up to 8 hours, temperature exposure up to 40 oC, ethanol solution up to concentration of 20 % (v/v), and pH range 3.9-8.42. Results from this study can be useful for extraction, purification, and future application of Cr-PE545.
Structural damage detection using wavelet packet transform combining with principal component analysis

In structural health monitoring (SHM), data-driven is a model free method that mines the internal characteristics of the raw measured responses, does not need considering the structural model. This paper proposes a new data-driven damage detection method based on wavelet packet transform (WPT) combining with principal component analysis (PCA). In this method, the responses are firstly decomposed by WPT, and each layer of wavelet packet energy of each measurement is obtained. Since the wavelet packet components of small energy magnitudes are easily contaminated by noises and should be discarded. PCA is utilised to reduce and eliminate redundant information of wavelet packet energy, and a damage index named the rate of the wavelet packet energy score change (WPESCR) is proposed to locate the damage. A simple beam is employed to demonstrate the effectiveness of this method. The numerical and experimental results show that the method combined with WPT and PCA can successfully locate the damage. As a data-driven method, the new WPESCR indicator is a better candidate for continuous online SHM.