Miracle-dz/UniFAR-FTUs huggingface dataset:
AI-BPO: Adaptive incremental BLE beacon placement optimization for crowd density monitoring applications

With the pandemic of COVID-19, indoor crowd density monitoring has become one of the most critical responsibilities of public space managers. Beacon placement optimization has been tackled as fundamental research work as the performance of crowd density monitoring highly depends on how BLE beacons are allocated. In this research, we propose a novel beacon placement optimization approach to incrementally place the beacon on the updated detection status adaptively in favor of Bayesian optimization, which can help to provide the optimal beacon placement. Our proposed method can optimize the beacon placement effectively to improve the signal coverage quality in the given environment and minimize human workload.
Collaboratively inspect large-area sewer pipe networks using pipe robotic capsules

Sewer pipe is an essential infrastructure in the city as it undertakes the transportation and circulation of wastewater resources. But sewer pipe it is easy to have faults and cause serious secondary urban accidents, such as road holes and road collapse. Because of the complex underground circumstance, inspecting large-area sewer pipes using closed-circuit television or periscope television is difficult. In this study, we proposed a collaborative sewer pipe inspection approach by using novel low-cost pipe robotic capsules, which capture the images of the pipeline inner walls when floating with the water flow. A set of workers collaboratively drop and salvage capsules to cover a large-area pipe network. The routes of workers and pipe capsules are optimized by a meta-heuristic algorithm integrating local search and simulated annealing. The deep neural network is used to recognize faults from raw captured images. A field experiment in Shenzhen was conducted to evaluate the performance of the proposed approach. The results demonstrate that it outperforms the naive inspection method with a shorter travel distance and less waiting time. It is also effective for inspecting the large-area sewer pipe networks with an overall precision of 0.92. It will help us to eliminate the potential safety risk of the public and promote the level of urban governance.
Virtual Machine Introspection in Virtualization: A Security Perspective

Virtualization technology has gained enough attention in several fields such as Cloud Computing, the Internet of Things (IoT), and software defined networking (SDN), etc. However, security issues in virtualization impose several questions on the adoption of this technology and raise strong security concerns. Most of the researchers have employed traditional security approaches in virtualization. However, these approaches are not effective enough for the modern environment. Instead, Introspection-based approaches such as Virtual Machine Introspection (VMI) are more useful to protect the virtualized environment. VMI approaches provide robust solutions in identifying the user and kernel-level processes-based attacks by positioning the security tool outside the VM. The successful implementation of these solutions is still challenging due to having heterogeneous design architectures of hypervisors. In this paper, a comprehensive study of VMI approaches is provided with the perspective of facilitating secure attack detection solutions in the virtualization environment. Various open research challenges are identified and discussed in detail. A brief discussion on the various VMI libraries is provided to give some practical insights to readers. We hope that our work will motivate researchers to work in this direction more actively.
A Method for scenario-movie scene generation using modified MirrorGAN

Text-to-image is a technology for creating an image based on visual information contained in a text. Text-to-image uses a deep learning model to train texts and images and creates images based on text input. Conventional text-to-image research is focused on creating images using short sentences that represent images. Scenario, which are texts that include stage directions and dialogue, have large amounts of information about the movie scenes and the flow of time. This study proposes a method for classifying screenplays and movie scenes based on actions, training them using an modified MirrorGAN model, and creating movie scenes when a screenplay is entered after the learning is complete.
AutoEncoder Based Feature Extraction for Multi-Malicious Traffic Classification

In recent years, research is being activated to classify deep learning-based malicious network traffic. Malicious network traffic classification has a problem of wasting time by learning meaningless features due to a large number of traffic and high-dimensional features. In this paper, we propose a technique for feature extraction based on AutoEncoder and classifying malicious network traffic through a random forest classifier. This technique reduces the time and spatial complexity required in the intrusion detection system by extracting features from high-dimensional data. To evaluate this technique, the performance of AE-RF and Single-RF classifiers is measured for Accuracy, Precision, Recall and F-Score using the CICIDS 2017 data set. The evaluation showed that AE-RF has an accuracy of 98% or more, which shows excellent performance and detection speed.
First Responder Positioning and Localization based on Optimal Anchor Access Point Selection using Minimum Uncertainty Propagation for Disaster Scenarios

The effectiveness of disaster relief activities is highly reliant on the coordination of the first responders (FR) in the disaster area. However, disaster scenarios commonly entail the destruction of communication infrastructures that hampers the first responder's coordination of activities. Localizing FR positions and enabling coordination among FRs is a necessary capability in every disaster management. Thus, this paper presents a positioning and localization scheme with an optimal anchor access point selection algorithm using minimum uncertainty propagation. Furthermore, the localization is optimized through the Gauss-Newton algorithm that aims to minimize the mean distance error on existing localization and positioning methods.
Barrier-Free Pedestrian Routing with Contraction Hierarchies

We present a holistic approach for pedestrian routing that allows computing shortest paths that may have indoor and outdoor sections. Such routes arise, for example, when the destination is not just an address but a specific store in a large mall, or when one needs to get to a certain track at a large train station. Currently, map services as Google Maps or OSRM do not offer such functionality. We identify and overcome three main challenges for answering such complex route planning queries: (i) Pedestrian routing requires fine-grained data, as the location of stairs and elevators, building dventrances, building footprints, and elevation/level information. A single missing staircase can change the length of the computed path severely. (ii) Indoor routing has to be integrated carefully into classical path planning to allow the computation of sensible routes that may enter and exit buildings. (iii) Given the large amount of data to be considered in a query, acceleration techniques need to be applied in order to achieve interactive query times. Retrieving barrier-free routes for wheelchairs is also our important use case.
RACER: Real-Time Automated Complex Event Recognition in Smart Environments

As smart environments become laden with more and more sensors, there has been a need to develop systems that could derive useful information from these sensors and make the smart environments smarter. Complex Event Processing (CEP) has emerged as a popular strategy to identify crucial events from sensor data. However, the existing CEP strategies overlook the relationship with other sensors in the spatial vicinity and understate the temporal variation of sensor data. In this paper, we develop RACER, which is an end-to-end complex event processing system that takes into consideration both the spatial location of the sensor in observation and the varying impact of temporal changes in the sensor data. Experiments performed for a duration of five months over both collected and live streaming data shows that RACER fares well compared to the other state-of-the-art approaches.
Optimized Test Case Generation for Basis Path Testing using Improved Fitness Function with PSO

The generation of an optimal number of test cases for the Basis path testing is a crucial and challenging optimization problem in the field of software testing. In the literature, a variety of Basis path testing approaches have been proposed. Recently, the search-based optimization approaches for the Basis path testing have been found more effective compared to the traditional analytical based approaches of Basis path testing. Even the existing search-based Basis path testing approaches can generate effective test cases covering most of the paths, still, there are many paths are remained uncovered. In this work, we propose a Particle Swarm Optimization (PSO) based test case selection approach for the Basis path testing. In this contribution, we introduce an improved fitness function namely Improved Fitness Function (IFF) that can guide the PSO based optimization process towards selection of best test case. For this, we use a High Probability of Coverage (HPC) path to define the IFF. To demonstrate our proposed approach, we conducted a detailed case study over the Largest among Three Numbers (LTN) program. The results of our case study show that the proposed approach can produce more better results in terms of all linearly independent paths coverage of Control Flow Graph (CFG).
Dual-Attention Multi-Scale Graph Convolutional Networks for Highway Accident Delay Time Prediction

Traffic-related forecasting plays a critical role in determining transportation policy, unlike traditional approaches, which can only make decisions based on statistical results or historical experience. Through machine learning, we are able to capture the potential interactions between urban dynamics and find their mutual interactions in a spatial context. However, despite a plethora of traffic-related studies, few works have explored predicting the impact of congestion. Therefore, this paper focuses on predicting how a car accident leads to traffic congestion, especially the length of time it takes for the congestion to occur. Accordingly, we propose a novel model named Dual-Attention Multi-Scale Graph Convolutional Networks (DAMGNet) to address this issue. In this proposed model, heterogeneous data such as accident information, urban dynamics, and various highway network characteristics are considered and combined. Next, the context encoder encodes the accident data, and the spatial encoder captures the hidden features between multi-scale Graph Convolutional Networks (GCNs). With our designed dual attention mechanism, the DAMGNet model is able to effectively learn the correlation between features. The evaluations conducted on a real-world dataset prove that our DAMGNet has a significant improvement in RMSE and MAE over other comparative methods.
Detection Model of Heavy Equipment Using YOLOv3 while Driving

This study is intended to develop an artificial intelligence model capable of recognizing and detecting heavy equipment to compensate for visual observation while driving. The total number of data collected was 6,700 images, of which 5,820 were used as training data and the remaining 880 images as a set of testing data. The YOLOv3 Network proposed in this study shows improved performance compared to the existing YOLOv3 and YOLOv2 with performance indicators for F1-Score 84.65%, Precision 86.94%, and Recall 82.47% in the detection of heavy equipment in the testing data set. Since the heavy equipment detection model proposed in the study is designed to enable real-time detection, it is expected to be helpful in patrolling construction sites in areas where gas pipes are buried.
Integration Model for Estimated Time of Arrival

Estimated Time of Arrival (ETA) plays a vital role in many application scenarios. For example, in various scenarios such as online car-hailing order distribution, price estimation, mid-trip estimation, and route decision-making. Accurate arrival time estimation can help the platform improve efficiency. However, accurate arrival time estimation is affected by static information and dynamic information, and the estimated arrival time has high technical difficulties and challenges. The organizers of this competition provided departure time and date, itinerary, road conditions, as well as topological structure data and weather information of the city's road network. At the same time, according to the characteristics of the given data, rich feature processing methods such as statistical features, category features, graph features, embedding features, and sequence features are used to provide massive feature information for model learning. One of the most important points is the application of "future data". Of course, in addition to the features, a lot of work has been done on the model structure and model fusion through the combination of machine learning and deep learning, ensuring the accuracy and stability of the model. The ETA is a typical time series problem. Therefore, In the deep learning section, we choose DCN [3] model and WDR [4] model as the basis, and the model distillation is combined on this as the deep learning part of the integrated model. At the same time, traditional machine learning is also used as a part of the integrated model, through a large number of different dimensions of feature engineering, to make up for the machine learning model's inability to better express the time series problem, and build a machine learning model with higher accuracy. Finally, through the fusion of the deep learning model and the machine learning model, extremely high accuracy is achieved in the ETA problem.
ICFinder: A Ubiquitous Approach to Detecting Illegal Hazardous Chemical Facilities with Truck Trajectories

Chemical materials are useful but sometimes hazardous, which requires strict regulation from the government. However, due to the potential economic benefits, many illegal hazardous chemical facilities are running underground, which poses a significant public safety threat. However, the traditional solutions, e.g., on-field screening and the anonymous tip-offs, involve a lot of human efforts. In this paper, we propose a ubiquitous approach called ICFinder to detecting illegal chemical facilities with chemical transportation trajectories. We first generate candidate locations by clustering stay points extracted from trajectories, and filter out known locations. Then, we rank those locations in suspicion order by modeling whether it has the loading/unloading events. ICFinder is evaluated over the real-world dataset from Nantong in China, and the deployed system identified 20 illegal chemical facilities in 3 months.
CbPIS: Cyberbullying Profile Identification System with Users in Loop

The internet has given us access to vast amounts of information and enabled us to communicate globally. However, posting uncensored comments on internet has raised several concerns. Cyberbullying, provoking and pestering are some of the growing issues on social media. People try to belittle others on public forums for fun. There is no suitable and scalable way to establish the authenticity and reliability of information and users. In this paper, we propose Cyberbullying Profile Identification System (CbPIS), to identify bullying profile involved in cyberbullying. In the proposed work, we first evaluate and compare existing state-of-the-art machine learning and deep learning techniques, then select the suitable technique for the system development. Moreover, CbPIS takes Users’ feedback for validation purpose and based on the feedback, if needed, CbPIS retrains itself to improve the predictions. Experimental results show that CbPIS with user feedback gives effective results.
Dataset Distillation for Core Training Set Construction

Machine learning is a widely adopted solution to complex and non-linear problems, but it takes considerable labor and time to develop an optimal model with high reliability. The costs increase even more as the model deepens and training data grows. This paper presents a method in which, a technique known as dataset distillation, can be implemented in data selection to reduce the training time. We first train the model with distilled images, and then, predict original train data to measure training contribution as sampling weight of selection. Our method enables the fast and easy calculation of weights even in the case of redesigning a network.
KERC 2019: The 1st Korean Emotion Recognition Challenge

KERC is a global challenge for recognizing the emotions of Korean people appearing in movies and TV dramas by using audiovisual data analyses. The KERC committee provides an Audio-Visual dataset comprising video clips 2 to 4 seconds in length which the attendant should classify into one of 7 emotions - Anger, Disgust, Fear, Happiness, Neutral, Sadness, and Surprise. KERC 2019 was held between July to November 2019 and received 102 applications worldwide, of which 15 teams submitted their final results. The KERC benchmarking platform provides researchers with an opportunity to evaluate their emotion recognition methods on Korean people as well as various races. The top three winners of KERC achieved 66%, 60% and 56% accuracy, a large improvement over the baseline (39%). This paper also proposed the tool to support the creation and annotation of emotion video clips. KERC will also be held in 2020 as an annual event and continues to encourage the development of Korean emotion recognition technology.
Speech Emotion Recognition Using MFCC and Wide Residual Network

Emotion recognition from speech has been a topic of research from many years due to its importance in human-computer interaction. While a lot of work has been done upon recognizing emotions through facial expressions, recognition of emotions through speech is still a challenging task in Machine Learning due to the obscure knowledge about the effectiveness of different speech features. In this work, Mel-frequency cepstral coefficients (MFCCs) has been used as a feature extractor for speech files. Further, classification of speech signals has been done using Convolution Neural Network (CNN) in the form of Wide Residual Network (WRN) followed by a Dense Neural Network (DNN). To train and test this approach we used Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and Toronto Emotional Speech Set (TESS) databases together. Results show that the proposed approach is gives an accuracy of 90.09% in recognizing emotions from speech into 8 categories.
Reversing Obfuscated Control Flow Structures in Android Apps using ReDex Optimizer

Code obfuscation is a technique that makes programs harder to understand. Malware writers widely the obfuscation technique to evade detection from anti-malware software, or to deter reverse engineering attempts for their malicious code. If we de-obfuscate the obfuscated code and restore it to the original code before obfuscation was applied, we can analyze the obfuscated malware effectively and efficiently. In this paper, we apply ReDex optimizer for reversing the control-flow obfuscation performed by the Obfuscapk system on open-source Android applications. We then analyze the effectiveness and limitations of ReDex in terms of its deobfuscation ability to reverse the control-flow obfuscation of Android apps. The experimental results show that ReDex can recover 1089 of 1108 apps obfuscated with control-flows obfuscation techniques of Obfuscapk obfuscator. During the process of optimizing bytecode, ReDex reduces the number of methods and fields significantly while it has a limitation in removing dead codes related to both useless goto statements and random nop instructions.
Analysis of Western Australian electric vehicle and charging station trials

An electric vehicle (EV) trial and an EV recharging research project are being simultaneously undertaken in Perth, both the first of their kind in Australia. The EV trials involve 11 locally converted Ford Focus vehicles, while the EV recharging study involves the use of 17 charging outlets (final configuration 23 outlets) from Level 2 AC recharging stations. Data is being logged from both the vehicles and the recharging stations and is transmitted to a server at the University of Western Australiars (UWA) Renewable Energy Vehicle Project (REV), where it is used for statistical evaluation, analysis and modelling.
Research on Airline Operational Control Model Based on Analytic Hierarchy Process

With expansion of airlines fleet capacity in China, airlines pay more attention to airline profit, service evaluation, etc., besides flight safety. Therefore it is important to study on operational control approaches that can promote operational efficiency, guarantee flight on-schedule rate and reduce operational cost. In this paper, an Analytic Hierarchy Process is employed to build a related-factor model mainly influencing dispatchers making decision in the operational control process of airlines. This process makes the complicated decision-making system hierarchic and analyzes the importance of all related factors by comprising their importance in a hierarchical way. Through an algorithmic example and data analysis, the operational control decision-making model of an assisting nature based on hierarchic analysis in this paper can provide quantitative basis for final decision making.
NILR:N-Most Interesting Location-based Recommender System

Location-Based Recommender Systems (LBRSs) have gained popularity in recent years as users tend to make decisions based on what are shared in social medias. Such systems depend on each user's historical behavioral information (or user profile) to determine users’ interests. However, it is impossible for new users to have the profile, making it difficult and challenging to recommend interesting locations (also known as a cold start problem). In order to tackle this issue, we propose an enhanced method, called N-most interesting location-based recommender system (NILR), which effectively recommends the N-most preferred places for each user without leveraging her profile. We also introduce a novel metric (so called interestingness score) to measure locations’ attractiveness. The metric takes into account both check-in frequencies and number of return visits of previous users already in the system. The method ranks the top-N locations based on the combination of the traditional HITS-based model (Hypertext Induced Topic Search) [1] and the proposed NILR. The results of the experiments on Foursquare dataset reveal that our proposed location recommender system and raking method perform effectively and efficiently, and outperform the HITS model in terms of accuracies and rankings.