Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-driven analyses of future electric personal mobility

Personal mobility is moving towards the era of electrification. Adopting electric vehicles (EV) is widely regarded as an effective solution to energy crisis and air pollution. Many automakers have announced their roadmap to electrification in the next 1-2 decades. At the same time, limited electric range and insufficient charging infrastructure are still obstacles to EV large-scale adoption. However, with the emerging technologies of ride-hailing, connected vehicles, and autonomous vehicles, these obstacles are being solved effectively, and the EV market penetration is expected to increase significantly. Among the many kinds of electric mobility, electric taxis and personal battery electric vehicles (BEV) especially are gaining increasing popularity and acceptance among customers. This dissertation studies the future challenges of electric taxis and personal BEVs. First, this dissertation examines the BEV feasibility from the spatial-temporal travel patterns of taxis. The BEV feasibility of a taxi is quantified as the percentage of occupied trips that can be completed by BEVs during a year. It is found that taxis with certain characteristics are more suitable for switching to BEVs, such as fewer daily shifts, shorter daily driving distance, and higher likelihood to dwell at the borough of Manhattan. Second, we model and simulate the operations of electric autonomous vehicle (EAV) taxis. EAV taxis are dispatched by the optimization-based model and the neural network-based model. The neural network dispatch model is able to learn the optimal dispatch strategies and runs much faster. The EAV taxis dispatched by the neural network-based model can improve operational efficiency in term of less empty travel distance and smaller fleet size. Third, this dissertation proposes a cumulative prospect
Iterative Adversarial Inference with Re-Inference Chain for Deep Graphical Models

SUMMARY Deep Graphical Model (DGM) based on Generative Ad-versarial Nets (GANs) has shown promise in image generation and latent variable inference. One of the typical models is the Iterative Adversarial Inference model (GibbsNet), which learns the joint distribution between the data and its latent variable. We present RGNet (Re-inference GibbsNet) which introduces a re-inference chain in GibbsNet to improve the quality of generated samples and inferred latent variables. RGNet consists of the generative, inference, and discriminative networks. An adversarial game is cast between the generative and inference networks and the discriminative network. The discriminative network is trained to distinguish between (i) the joint inference-latent / data-space pairs and re-inference-latent / data-space pairs and (ii) the joint sampled-latent / generated-data-space pairs. We show empirically that RGNet surpasses GibbsNet in the quality of inferred latent variables and achieves comparable performance on image generation and inpainting tasks.
Machine Learning and Network Analysis using Mathematical Optimisation in Mass Spectrometry Bioinformatics

In this work we develop and present a set of novel computational techniques which address the problems of ICR-FT-MS data mining. The described methodology focuses mainly on two scopes of biogeochemical research, namely the analysis of natural organic matter (NOM) and metabolomics. The development of our approaches focuses on the fields of graph theory, machine learning, and combinatorics. Detailed results of our methods are produced, presented, and evaluated within their corresponding sections.
Auto-Encoding Graphical Inductive Bias for Descriptive Image Captioning

We propose Scene Graph Auto-Encoder (SGAE) that incorporates the language inductive bias into the encoder-decoder image captioning framework for more human-like captions. Intuitively, we humans use the inductive bias to compose collocations and contextual inference in discourse. For example, when we see the relation `person on bike', it is natural to replace `on' with `ride' and infer `person riding bike on a road' even the `road' is not evident. Therefore, exploiting such bias as a language prior is expected to help the conventional encoder-decoder models less likely overfit to the dataset bias and focus on reasoning. Specifically, we use the scene graph --- a directed graph ($\mathcal{G}$) where an object node is connected by adjective nodes and relationship nodes --- to represent the complex structural layout of both image ($\mathcal{I}$) and sentence ($\mathcal{S}$). In the textual domain, we use SGAE to learn a dictionary ($\mathcal{D}$) that helps to reconstruct sentences in the $\mathcal{S}\rightarrow \mathcal{G} \rightarrow \mathcal{D} \rightarrow \mathcal{S}$ pipeline, where $\mathcal{D}$ encodes the desired language prior; in the vision-language domain, we use the shared $\mathcal{D}$ to guide the encoder-decoder in the $\mathcal{I}\rightarrow \mathcal{G}\rightarrow \mathcal{D} \rightarrow \mathcal{S}$ pipeline. Thanks to the scene graph representation and shared dictionary, the inductive bias is transferred across domains in principle. We validate the effectiveness of SGAE on the challenging MS-COCO image captioning benchmark, e.g., our SGAE-based single-model achieves a new state-of-the-art $127.8$ CIDEr-D on the Karpathy split, and a competitive $125.5$ CIDEr-D (c40) on the official server even compared to other ensemble models.
Toward Better Design-Related Decision Making: A Proposal of an Advanced OODA Loop

To be successful in innovation, organisations need to be dynamically adaptable to novel situations to avoid getting ‘left behind’. Yet, they face vast uncertainties stemming from unforeseeable technological shifts or future user and market behaviour, making strategic decision-making on innovation an extremely difficult task. Decision-makers thus increasingly try to control or shape the future, rather than foresee it. This includes thinking ahead and generating potential pathways that will make an innovation viable. This captures the essence of designerly ways of thinking in reasoning toward ‘what might be’. Extant literature has been reviewed that discusses alternative strategies how this future-oriented thinking can be applied to become better at selecting novel ideas for development. We observe parallels between divergent thinking, abductive reasoning, analogising and lateral thinking suggested by different authors in this process. The paper continues to propose how these key mechanisms can be embedded within an existing framework for decision-making under uncertainty, the ‘OODA Loop’, which has seen increasing uptake in such decision-making scenarios.
Decentralized Control Mechanism for Determination of Moving Direction in Brittle Stars With Penta-Radially Symmetric Body

A brittle star, an echinoderm with penta-radially symmetric body, can make decisions about its moving direction and move adapting to various circumstances despite lacking a central nervous system and instead possessing a rather simple distributed nervous system. In this study, we aimed to elucidate the essential control mechanism underlying the determination of moving direction in brittle stars. Based on behavioral findings on brittle stars whose nervous systems were lesioned in various ways, we propose a phenomenological mathematical model. We demonstrate via simulations that the proposed model can well reproduce the behavioral findings. Our findings not only provide insights into the mechanism for the determination of moving direction in brittle stars, but also help understand the essential mechanism underlying autonomous behaviors of animals. Moreover, they will pave the way for developing fully autonomous robots that can make decisions by themselves and move adaptively under various circumstances.
Experimental Evaluation of Floating Car Data Collection Protocols in Vehicular Networks

The main objectives of the Intelligent Transportation Systems (ITS) vision is to improve road safety, traffic management, and mobility by enabling cooperative communication among participants. This vision requires the knowledge of the current state of the road traffic, which can be obtained by collecting Floating Car Data (FCD) information using Dedicated Short-Range Communication (DSRC) based on the IEEE 802.11p standard. Most of the existing FCD collection protocols have been evaluated via simulations and mathematical models, while the real-world implications have not been thoroughly investigated. This paper presents an open-source implementation of two state-of-the-art FCD collection algorithms, namely BASELINE and DISCOVER. These algorithms are implemented in an open-source vehicular prototyping platform and validated in a real-world experimental setup.
Malware Executables Analysis Using Static Analysis Technique for Android Devices

Malware is a worldwide epidemic. Recently, the threat of Android malware is spreading rapidly, especially due to third-party Android application developers. The growing amount and variety of these applications cannot take conventional defences, if taken, but they are largely ineffective, and thus, Android smartphones often remain unprotected from malwares. So, a huge need for static malware analysis is felt to overcome these problems and to look into these malware executables deeply. In this study, a static analysis technique using SandDroid Sandbox to detect the Android malware has been proposed. Sandboxes are used to run untested code that contain viruses or untrusted programs from third parties. This analysis technique considers the static information including permissions, certification, code features, advertisement modules and sensitive API calls which can characterize the behaviour of Android applications. SandDroid extracts the information (e.g., requested permissions, certificates and code features etc.) from each application's manifest file, and respective components (Events, Services, Broadcast Receivers) as entry points moving towards sensitive API Calls related to dangerous permissions. SandDroid is efficient since it takes only half of time than other sandboxes to analyse Android applications for malicious patterns and gives better insight.
Ranking of Multi-Word Terms

s “awards_1990\awd_1990_00” documents. Figure 4: Results ranked by frequency of the term. It is interesting to see that multi word terms appear in the top 10 terms based on the frequency of the term. The term, sub term “estimated“ appears a lot in the corpus. This is because each document has these terms in their headers.
Autonomous driving: Model Predictive Control for overtaking manoeuvres

In these last years, the autonomous driving is becoming a very popular trend in the automotive field. Started as a research topic, it has quickly turned into a concern of several companies around the world. Among the considerable technical problems this technology has to face, there’s the path planning, key function for an autonomous vehicle. Thus the main objective of this thesis work is to investigate the path planning techniques for autonomous driving vehicles in an overtaking scenario. As first step, a preliminary research on motion planning methods is carried, in order to have an overview of the problem, in particular focusing on the most suitable techniques for automotive purposes. Then, a focus on the Model Predictive Control was done, highlighting its advantages and showing some applications in the automotive field. In order to compare the MPC performance as path planner, a RRT* path planner was set in a preliminary phase of the work. In a second phase, the MPC was tested both as path planner and as controller, working with a kinematic model and a barrier as obstacle. Lastly, the path planning function of the MPC was isolated, resulting in a better performance in comparison with the RRT* planner. An overtaking of a moving vehicle was achieved at high speeds, denoting the already underlined advantages of the MPC. Further improvements can be done using more accurate vehicle models,i.e. dynamic one and a sensor system can be simulated to gather all environmental data.
Fast Coflow Scheduling via Traffic Compression and Stage Pipelining in Datacenter Networks

Big data analytics in datacenters often involve scheduling of data-parallel jobs. Traditional scheduling techniques based on improving network resource utilization are subject to limited bandwidth in datacenter networks. To alleviate the shortage of bandwidth, some cluster frameworks employ techniques of traffic compression to reduce transmission consumption. However, they tackle scheduling in a coarse-grained manner at task level and do not perform well in terms of flow-level metrics due to high complexity. Fortunately, the abstraction of coflow pioneers a new perspective to facilitate scheduling efficiency. In this paper, we introduce a coflow compression mechanism to minimize the completion time in data-intensive applications. Due to the NP-hardness, we propose a heuristic algorithm called <italic>Fastest-Volume-Disposal-First</italic> (FVDF) to solve this problem. For online applicability, FVDF supports stage pipelining to accelerate scheduling and exploits recurrent neural networks (RNNs) to predict compression speed. Meanwhile, we build <inline-formula><tex-math notation="LaTeX">${\sf Swallow}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">Swallow</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq1-2931716.gif"/></alternatives></inline-formula>, an efficient scheduling system that implements our proposed algorithms. It minimizes coflow completion time (CCT) while guaranteeing resource conservation and starvation freedom. The results of both trace-driven simulations and real experiments show the superiority of our algorithm, over existing one. Specifically, <inline-formula><tex-math notation="LaTeX">${\sf Swallow}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">Swallow</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq2-2931716.gif"/></alternatives></inline-formula> speeds up CCT and job completion time (JCT) by up to <inline-formula><tex-math notation="LaTeX">$1.47 \times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mo>. </mml:mo><mml:mn>47</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq3-2931716.gif"/></alternatives></inline-formula> and <inline-formula><tex-math notation="LaTeX">$1.66 \times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mo>. </mml:mo><mml:mn>66</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq4-2931716.gif"/></alternatives></inline-formula> on average, respectively, over the SEBF in Varys, one of the most efficient coflow scheduling algorithms so far. Moreover, with coflow compression, <inline-formula><tex-math notation="LaTeX">${\sf Swallow}$</tex-math><alternatives><mml:math><mml:mi mathvariant="sans-serif">Swallow</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq5-2931716.gif"/></alternatives></inline-formula> reduces data traffic by up to 48.41 percent on average.
Unsupervised Machine Learning Applications for Seismic Facies Classification

The objective of the present study was to apply several unsupervised machine learning (ML) methods such as K-means, principal component analysis (PCA), self-organizing mapping (SOM) and generative topographic mapping (GTM) techniques to seismic data volumes from the Barents Sea, the STACK area in Oklahoma
Multiple Chromosomes in Bacteria: Low Level of Evolutionary Constraint Drives the Rapid Genetic Divergence of Chromosome II

Multiple chromosomes in bacteria are designated as a larger primary chromosome (CI) and smaller accessory chromosomes (CII and CIII). Although previous studies examined multiple chromosomes in several bacterial species, the evolutionary mechanisms for the origin of CIIs still remain unclear. In this study, the four following hypotheses were tested. 1) CIIs exhibit lower sequence conservation and sequence divergence compared to their corresponding CIs across species of Proteobacteria. 2) The differential sequence divergence of CI and CII depends on pathogenic and non-pathogenic lifestyles. 3) CIIs harbor a higher level of horizontal gene transfers (HGTs) than CIs. 4) Orthologs located on CIIs experience less purifying selection than their corresponding orthologs on CIs. Results reveal a higher level of sequence conservation of CIs than the sequence conservation of CIIs. There is no significant difference in HGT estimates between CIs and CIIs. A majority of orthologous genes of CIs and CIIs experience purifying selection; however, genes on CIIs were significantly less constrained than the corresponding ones on CIs. This finding is true for both pathogenic and non-pathogenic bacteria, but the selective constraints for non-pathogenic bacteria are relatively less constrained. It was concluded that the differential selective constraint is a potent driving force for the rapid evolution of CII. Therefore, gene expression analysis at the transcriptome and proteome levels may shed light on the gene regulation mechanisms that might affect the sequence divergence between CI and CII.
Analysis of Security Imaging Method for Walking Human Screening With Single Channel Synthetic Aperture Radar

In this paper, a security screening imaging method for walking human with single channel synthetic aperture radar (SAR) technology is proposed. According to the theory of SAR imaging, the imaging response for each single echo of the three dimensional (3D) moving target is analyzed. Then, the relationships between the accurate degree of imaging result and radar system parameters are presented with the global human walking model, which can be summarized as: positive correlations with radar angular velocity and rotation radius, negative correlations with aperture time and motion velocity, and uncertainty with radar initial angle. Hence, the short synthetic aperture time and high-resolution of terahertz (THz) radar makes it particularly suitable for walking human security imaging. Meanwhile, the main Doppler frequency shift of walking human imaging is corrected by the back-projection (BP) algorithm modified with the estimated velocity detected by the range profile. Besides, the numerical simulation experiments of point targets are provided to verify the conclusion of moving target imaging response. Furthermore, the experiments of walking human are processed with different radar system parameters to demonstrate the performance and effectiveness of our method.
Machine Translation with Image Context from Mandarin Chinese to English

Despite ongoing improvements in machine translation, machine translators still lack the capability of incorporating context from which source text may have been derived. Machine translators use text from a source language to translate it into a target language without observing any visual context. This work aims to produce a neural machine translation model that is capable of accepting both text and image context as a multimodal translator from Mandarin Chinese to English. The model was trained on a small multimodal dataset of 700 images and sentences, and compared to a translator trained only on the text associated with those images. The model was also trained on a larger text only corpus of 21,000 sentences with and without the addition of the small multimodal dataset. Notable differences were produced between the text only and the multimodal translators when trained on the small 700 sentence and image dataset, however no observable discrepancies were found between the translators trained on the larger text corpus. Further research with a larger multimodal dataset could provide more results clarifying the utility of multimodal machine translation.
Secure and Efficient ${K}$ Nearest Neighbor Query Over Encrypted Uncertain Data in Cloud-IoT Ecosystem

Uncertain data pervades many fields, including environmental monitoring, the monitoring of animal migrations, and urban warfare. Such uncertain data collected by field devices, such as Internet of Things (IoT) and Internet of Battlefield Things (IoBT) devices, may also be encrypted and outsourced to an untrustworthy third party for storage and data sharing such as a cloud server. However, the properties of uncertain data and the complication of operating over encrypted data make the searching schemes more ineffective. In this article, we design an efficient and safe ${K}$ nearest neighbor (KNN) query scheme for uncertain data stored in semi-trusted cloud servers. We apply the modified homomorphic encryption, which requires two servers to interact and encrypt the uncertain data, and we use the authorized rank method to compute KNN. We protect the security of the data while simultaneously improving the query efficiency. Our detailed security analysis show that our scheme can realize the goal of concealing both the access and the search patterns. Comprehensive experiments are conducted to demonstrate the scheme’s performance.
Non-STDP based Unsupervised Memristive Neural Networks with High Hardware Compatibility

With the development of research on memristor, memristive neural networks (MNNs) have become a hot research topic recently. Because memristor can mimic the spike timing-dependent plasticity (STDP), the research on STDP based MNNs is rapidly increasing. However, although state-of-the-art works on STDP based MNNs have many applications such as pattern recognition, STDP mechanism brings relatively complex hardware framework and low processing speed, which block MNNs' hardware realization. A non-STDP based unsupervised MNN is constructed in this paper. Through the comparison with STDP method on the basis of two common structures including feedforward and crossbar, non-STDP based MNNs not only remain the same advantages as STDP based MNNs including high accuracy and convergence speed in pattern recognition, but also better hardware performance as few hardware resources and higher processing speed. By virtue of the combination of memristive character and simple mechanism, non-STDP based MNNs have better hardware compatibility, which may give a new viewpoint for memristive neural networks' engineering applications.
Receipt automatic reader

Smartphones bring a new way to scan and digitalize written documents by taking pictures. This enables new document analysis applications to emerge. As a counterpart, unsupervised document capturing brings new challenges mainly related to target document localization and high quality text recognition. In this context, this work addresses automatic sale receipt understanding in an industrial context. It relies on the extraction of accurate and essential consumption data even with low quality receipt captures. We propose a tool chain that combines Deep Neural Networks and traditional image processing to ensure accurate automatic data extraction. The proposed workflow is evaluated globally by the analysis of the quality of the text recognition at the end of the processing.
Development of a 3D digitisation system for hostile sites

Our project represents a contribution to the so-called hostile site management solution such as historic ruins, oil pipelines, earthquake-affected buildings, unexploited mines, sewage pipelines, sites infected by radioactivity. These places have a significant characteristic, the difficulty of access, which makes human intervention very expensive and dangerous. Our approach is to design a robotised autonomous and remote-controlled [1] vehicle [2], [3] on which a mounted remote sensing device LiDAR perform investigative and survey work. The LIDAR works in2D/3D digitisation to collect the data of the studied place and to transmit them then using different telecommunication means [4]. As a result of this operation, We conclude a 2D/3D map following the reception of the encrypted and sent images. Technicians can, therefore, launch interventions within minimised risks [5].