Miracle-dz/UniFAR-FTUs huggingface dataset:
The Bus Bunching Problem: Empirical Findings from Spatial Analytics

Service regularity is one of the most significant performance indicators for public transport routes, typically measured through headway adherence. When headway deviations become too large and corresponding headways very small, bus bunching typically occurs. In these cases, passengers experience larger waiting times and overcrowding and an overall poor level of service. This paper aims to gain insight on frequent patterns of bus bunching using spatial analytics. Local and global spatial autocorrelation tests are performed on real world Automatic Vehicle Location (AVL) data to investigate spatial structures in the data. The spatio-temporal variations of bus bunching patterns throughout the day are further modeled using the ST-DBSCAN algorithm. Results show that the last few stops of each route exhibit statistically significant spatial autocorrelation with respect to the frequency of bunching, while the duration of bunching events is longer for route segments located in the central business district. Spatio-temporal clustering indicates that bunching is observed at a higher number of stops during peak traffic periods.
ADS-B degarbling and jamming mitigation by the use of Blind Source Separation

Automatic Dependent Surveillance- Broadcast system is a pillar of the future air traffic system. Equipped aircraft utilize the on-board navigation system (based on the GPS unit) to calculate their position and velocity and then broadcast this information on a common RF channel using an on board emitter. Ground-based sensors receive these messages that are used by the ATC centers to produce an image of the traffic on the controller's display. This system uses a data-link protocol called “1090 Extended Squitter (1090ES)”: each aircraft periodically transmits messages PPM modulated on L-band (1090 MHz) with random access to the channel with a transmitter called transponder. This protocol is affected by two important limitations: the throughput of the channel is limited by the garbling effect (i.e. the reception of superimposed messages in dense scenario) and the fact that the 1090 MHz RF channel can be prone to jamming (that is the transmission of high power signals superimposing the aircraft messages). A method to mitigate these two effects using a multichannel ADS-B receiver and a Principal Component Analysis (PCA) based Blind Source Separation (BSS) technique is proposed in this work. The proposed method is evaluated using real signals both in jamming and garbling cases.
IoT-based Bus Location System Using LoRaWAN

This paper introduces an IoT-based bus location system that can improve the service quality of the bus network and improve the efficiency of operation management. The proposed system provides the bus approach information by utilizing the location and delay information of the bus, and can be realized with low operational cost. The principle of LoRaWAN is used to collect the location information of all buses and deliver the calculated delay information to smart bus stops using electronic paper. A prototype system was implemented, a basic evaluation experiment was conducted and trial calculations for the cost were performed. The corresponding results confirmed that the system can be realized at a lower cost than the bus location system using 3G/LTE.
Discovering Co-Author Relationship In Wocad Data Using Ontology Visualization

Nowadays, ontologies become more and more important as a tool to organize and structure information, and they are considered powerful strategies for designing more easy and precise to use information searching. Ontology visualizations can help in this regard by supporting in the exploration and verification of ontologies. [1] In this research, we decide to build the ontology of WOCAD publications-authors relational database, and further visualize it with VOWL. We build a web system that users can choose which classes of the WOCAD ontology they want to focus on. Graph visualizations are useful only up to a specific graph size, at which point the overview is lost and the graph is difficult to use. [2] With this system, members of CAADRIA who is less familiar with ontologies can understand the WOCAD ontology context quickly. It helps WOCAD ontology user takes less time to learn the knowledge about ontology theory.
Decentralised Energy Market for Implementation into the Intergrid Concept - Part I: Isolated System

Power systems world-wide are currently undergoing unprecedented change as the global energy mix transitions from primarily fossil-fuel based synchronous generation towards utility-scale and distributed renewable energy generation. This paper explores the rationale for the pursuit of alternative network architectures to facilitate the transition towards high penetration levels of renewable energies. Furthermore, the emerging research field of transactive energy markets and their potential for energy price reductions is presented. The paper examines the intergrid architecture coupled with a decentralised transactive energy market as a mechanism for permitting higher levels of distributed generation and storage and for energy price reductions, with the Australian energy market used as a case study. The primary contributions of this paper are the development of a decentralised, peer-to-peer (P2P) transactive energy market (TEM) and the derivation of an autonomous agent algorithm capable of monitoring the internal states of agents to place bids and offers to the market accordingly. This paper (part 1 of 2) investigates the behaviour of the TEM and agent algorithm when applied to an isolated intergrid architecture level. The simulation results contained here-within demonstrate that the decentralised TEM and distributed agent algorithm operate in accordance with the desired fundamental principles of economics (supply vs demand), with the potential for significant retail energy price reductions and higher levels of renewable energies.
Routing in VANETs Based on Intersection Using SDN and Fog Computing

Vehicular ad-hoc networks (VANET), as one of the main components of intelligent transport systems (ITS), face many issues including architecture inflexibility, instability of wireless communications, limitation in transmission range, repetitive topological variations due to high vehicle mobility, etc. One of the current challenges in VANETs in establishing communication and data forwarding arises in areas without Road Side Unit (RSU) coverage, called communication coverage holes. In these areas, vehicles are responsible for data forwarding through vehicle-to-vehicle (V2V) communication. Also given the issues of large data production and big data, communicative capability of vehicles can be used for data forwarding and establishing efficient communication, eliminating unnecessary bandwidth usage through the Internet and its infrastructures. The purpose of this article is proposing a method to improve data forwarding in V2V communication so as to cover communication coverage holes. This can help cover communication coverage holes. To this end, the advantages of software-defined network (SDN) and fog computing are used to improve V2V communication, and propose an intersection-based routing based on SDN and fog computing called SFIR. Finally the method is simulated and compared with the current protocols. Results indicate that SFIR significantly improves packet delivery ratio, packet loss ratio, and delay time.
Get Your Head Out of the Clouds: The Illusion of Confidentiality & Privacy

The cloud has been leveraged for many applications across different industries. Despite its popularity, the cloud technologies are still immature. The security implications of cloud computing also dominate the research space. Many confidentiality-and integrity-based (C-I) security controls concerning data-at-rest and data-in-transit are focused on encryption. In the world where social-media platforms transparently gather data about user behaviors and user interests, the need for user privacy and data protection is of the utmost importance. However, how can a user know that his data is safe, that her data is secure, that his data's integrity is upheld; to be confident that her communications only reach the intended recipients? We propose: they can't. Many threats have been hypothesized in the shared-service arena, with many solutions formulated to avert those threats; however, we illustrate that many technologies and standards supporting C-I controls may be ineffective, not just against the adversarial actors, but also against trusted entities. Service providers and malicious insiders can intercept and decrypt network-and host-based data without any guest or user knowledge.
Genetic-Based Susceptibility of a Foundation Tree to Herbivory Interacts With Climate to Influence Arthropod Community Composition, Diversity, and Resilience

Understanding how genetic-based traits of plants interact with climate to affect associated communities will help improve predictions of climate change impacts on biodiversity. However, few community-level studies have addressed such interactions. Pinyon pine (Pinus edulis) in the southwestern U.S. shows genetic-based resistance and susceptibility to pinyon needle scale (Matsucoccus acalyptus). We sought to determine if susceptibility to scale herbivory influenced the diversity and composition of the extended community of 250+ arthropod species, and if this influence would be consistent across consecutive years, an extreme drought year followed by a moderate drought year. Because scale insects alter the architecture of susceptible trees, it is difficult to separate the direct influences of susceptibility on arthropod communities from the indirect influences of scale-altered tree architecture. To separate these influences, scales were experimentally excluded from susceptible trees for 15 years creating susceptible trees with the architecture of resistant trees, hereafter referred to as scale-excluded trees. Five patterns emerged. (1) In both years, arthropod abundance was 3-4X lower on susceptible trees compared to resistant and scale-excluded trees. (2) Species accumulation curves show that alpha and gamma diversity were 2-3X lower on susceptible trees compared to resistant and scale-excluded trees. (3) Reaction norms of arthropod richness and abundance on individual tree genotypes across years showed genotypic variation in the community response to changes in climate. (4) The genetic-based influence of susceptibility on arthropod community composition is climate dependent. During extreme drought, community composition on scale-excluded trees resembled susceptible trees indicating composition was strongly influenced by tree genetics independent of tree architecture. However, under moderate drought, community composition on scale-excluded trees resembled resistant trees indicating traits associated with tree architecture became more important. (5) One year after extreme drought, the arthropod community rebounded sharply. However, there was a much greater rebound in richness and abundance on resistant compared to susceptible trees suggesting that reduced resiliency in the arthropod community is associated with susceptibility. These results argue that individual genetic-based plant-herbivore interactions can directly and indirectly impact community-level diversity, which is modulated by climate. Understanding such interactions is important for assessing the impacts of climate change on biodiversity.
Searchable Encryption for Healthcare Clouds: A Survey

Outsourcing medical data and their search services to a third party cloud have been a popular trend for many medical practices, because using healthcare cloud services can help cut down the cost of Electronic Health Records (EHR) systems in terms of front-end ownership cost and IT maintenance burdens. Healthcare cloud applications need searchable encryption with the following two capabilities for protecting data privacy and access privacy: (1) the healthcare providers need to share the encrypted data with authorized users and enable querying over encrypted data, and (2) they also need to keep the query keywords and associated search operations private such that healthcare data hosting service providers cannot gain access to unauthorized content or trace and infer sensitive data stored in the healthcare cloud. This survey paper describes the notion of searchable encryption (SE) in the context of healthcare applications and characterize the SE use cases into four scenarios in healthcare. Then we provide a comprehensive overview of the four representative SE techniques: searchable symmetric encryption (SSE), public key encryption with keyword search (PEKS), attribute-based encryption with keyword search (ABKS), and proxy re-encryption with keyword search (PRES) according to different EHR retrieving scenarios and requirements. We categorize and compare the different SE schemes in terms of their security, efficiency, and functionality. The survey is designed to benefit both experienced researchers in the computer science (CS) field and non-specialists who are domain scientists or healthcare professionals with limited CS and information security background. Thus, we are in favor of technological overview of the state of art searchable encryption models and the underlying key techniques, instead of detailed proofs and constructions of the respective SE algorithms. We describe how the existing SE schemes relate to and differ from one another, and point out the connections between the SE techniques and the security and privacy requirements of healthcare applications and the open research problems.
Spatio-Temporal Impact of Hand and Body Blockage for Millimeter-Wave User Equipment Design at 28 GHz

The recently proposed channel model by the Third Generation Partnership Project for fifth generation New Radio (5G-NR) systems at millimeter- wave carrier frequencies in TR38.901 provides a good starting point for understanding the impact of propagation, material penetration, and blockage in cellular access systems. With this backdrop, the focus of this article is on hand and body blockage which have a significant bearing on user equipment (UE) design and in the practical viability of millimeter-wave systems. Building on TR38.901, we first study hand and body blockage loss with experiments using a 28 GHz experimental prototype that emulates a 5G base station and a form-factor UE. These measurement estimates show that while blockage losses can be substantial, they are significantly lower than the agreed model in TR38.901. We then study the implications of these revised estimates on spherical coverage with the UE. For this, we consider two form-factor UE designs: a face design and an edge design. Our studies show that while both designs are generally comparable in terms of spherical coverage, practical design considerations favor the edge design over the face design. We finally report on the timescales at which blockage events occur and show that given the symbol periods in 5G-NR, it is possible to implement mitigation strategies to overcome blockage (e.g., switch beams at the UE from a blocked path to an unblocked path).
User Insights about Using Immersive Virtual Reality in Industrial Design Studio Courses

In recent years, it is observed that immersive headsets such as HTC Vive, Oculus Rift etc., which were developed mainly targeting game and entertainment industries, are being used for different purposes in different fields. Industrial design is one of them. Although this technology has already been used professionally by designers in private sectors, it is not clear whether it will be accepted or not by industrial design students and instructors, and whether will take place or not in industrial design studio courses. For this reason, with the support of Autodesk and PENTA Technology, a study, in which 12 instructors and 26 students from 10 universities in Turkey participated, was conducted with the aim of gaining insights from industrial design students and instructors about using this type of immersive virtual reality (IVR) technology in industrial design courses. First of all, participants were introduced to this particular technology by the representatives of Autodesk and PENTA. Then, they tried it in a training session. After the trial session, participants were asked to fill out an online questionnaire. In the last part of the study, participants discussed the advantages and disadvantages of this technology in terms of industrial design education. This part was recorded with both a video camera and a sound recorder. As a result, both quantitative and qualitative data were collected. By analyzing the data, attitudes of the actors of industrial design education towards IVR technology were tried to be understood.
Open-Switch Fault Detection in Three-Phase Symmetrical Cascaded Multilevel Inverter Using Conducted Disturbances

This paper proposes a fault detection approach for open-circuit faults in a seven-level cascaded H-bridge (CHB) multilevel inverter. This method utilizes a simple three-phase electromagnetic interferences (EMI) filter placed at the inverter's output, to measure the conducted emissions. The measured voltages across the EMI filter resistors are compared to those expected in healthy case, and distortions are explored to locate open-switch fault. Time domain signals, Teager energy operator, and multi-scale wavelet were combined to make robust and effective fault detection method. Simulation results under different faulty cases demonstrate that an open-circuit fault in a CHB multilevel inverter can be detected in less than 20 ms.
A Password Cracking Method Based On Structure Partition and BiLSTM Recurrent Neural Network

Identity authentication is an important line of defense for network security, and passwords are still the mainstream of identity authentication. Password attacking is an important means of password security research. Probabilistic context-free grammar (PCFG) is the most effective password structure partitioning method at present. The string generation method based on neural network has powerful generalization ability. They effectively characterize the passwords on the substructure level and the character level respectively. In this paper, based on the merits of the above two models, we propose a password attacking method based on structure partition and bidirectional long short-term memory (BiLSTM) recurrent neural network, which is denoted as SPRNN model. Firstly, passwords are divided into abstract substructures. Then substrings of characters, digits and symbols in substructures are generated by using BiLSTM model to take account of the accuracy and generalization ability of the model. Finally, the method is verified by experiment on six real Chinese and English password datasets. The results show that in the context of a fixed number of guessing trials, the SPRNN model breaks the password 25% -30% more than Narayanan's method, about 10% than Weir et al.'s method password between the cross datasets.
Active Damping of Capacitor Current Based on Parallel Feedforward Compensation of LCL Grid Connected Inverter

An active damping (AD) of filter capacitor current for LCL single phase grid connected inverter (GCI) is introduced. In suggested method, a damping coefficient is added across the LCL filter system and feedback capacitor current at reference voltage point to form a damping loop. This configuration is known as the Parallel Feedforward Compensation (PFC) method. It has fast dynamic response and relatively convenient tuning procedure as compared to capacitor current proportionally feedback method. The grid current regulation is achieved by Proportional Resonant (PR) control implementation. The effect of damping coefficient and stability characteristics of current control loop under LCL filter parameter variation is analyzed with suggested methodology. Finally, the effectiveness of suggested methodology is examined in MATLAB environment and verified by simulation results.
Research on LTE Positoning Algorithm under Multipath Environment

Long Term Evolution (LTE) is a promising positioning system for its appropriate positioning characteristics and excellent coverage, especially in the harsh environments such as urban canyons and indoor scenarios. However, most of the researches so far on LTE positioning algorithms are based on time parameters. Thus in this paper, an algorithm based on signal parameters estimation via rotational invariance techniques (ESPRIT) to jointly estimate the time-of- arrival (TOA) and the direction-of-arrival (DOA) from received LTE signals in multipath environment is presented. This method estimates the TOA parameters first after modeling the received LTE signals in the frequency domain. Then the DOA parameters via the difference of the TOA are obtained, and thereby the joint TOA and DOA estimation is gained. Simulation results show that this method performs well on different signal-to-noise-ratios, numbers of multipath and transmission bandwidths. Moreover, the simulation results show that the proposed algorithm has much better performance than multiple signal classification (MUSIC) algorithm and propagator method (PM) and its positioning accuracy can achieve centimeter level.
Multi-Class Classification of Turkish Texts with Machine Learning Algotirthms

The problem of text classification is the process of supervised assignment of text documents to one or more predefined categories or classes according to the content of the processed texts with natural language processing methods. Text classification applications are actively used in various fields such as categorization of social interactions, web pages and news texts, optimization of search engines, extracting information, and automatically processing e-mails. In this context, it is aimed to classify Turkish texts with methods based on supervised machine learning. In this context, the classification success of supervised learning models on Turkish texts was analyzed with different parameters. These models have been tested for classification of news texts on five predefined classes (economy, politics, sport, health, and technology) and the system was trained with different number of training documents and the classification process was carried out. In this context, the classification performances of Multinomial Naïve Bayes, Bernoulli Naïve Bayes, Support Vector Machine, K-Nearest Neighbor, and Decision Trees algorithms on Turkish news texts are compared and interpreted in the light of the results obtained with different parameters. As a result of the study, the procedure with the best classification success was the Multinomial Naïve Bayes algorithm with a classification success of about 90%. These results show that the Naïve Bayes probability model can be used as an effective classifier method in classifying Turkish texts compared to other methods. In this context, it is envisaged that the proposed methodology could be applied to Turkish texts on different web platforms (social networks, forums, communication networks, etc.) for different purposes.