Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing Transportation Dynamics at a City-Scale Using a Reinforcement Learning Framework

Urban planners, authorities, and numerous additional players have to deal with challenges related to the rapid urbanization process and its effect on human mobility and transport dynamics. Hence, optimize transportation systems represents a unique occasion for municipalities. Indeed, the quality of transport is linked to economic growth, and by decreasing traffic congestion, the life quality of the inhabitants is drastically enhanced. Most state-of-the-art solutions optimize traffic in specific and small zones of cities (e.g., single intersections) and cannot be used to gather insights for an entire city. Moreover, evaluating such optimized policies in a realistic way that is convincing for policy-makers can be extremely expensive. In our work, we propose a reinforcement learning frameworks to overtake these two limitations. In particular, we use human mobility data to optimize the transport dynamics of three real-world cities (i.e., Berlin, Santiago de Chile, Dakar) and a synthesized one (i.e., SynthTown). To this end, we transform the transportation dynamics’ simulator MATSim into a realistic reinforcement learning environment able to optimize and evaluate transportation policies using agents that perform realistic daily activities and trips. In this way, we can assess transportation policies in a manner that is convincing for policy-makers. Finally, we develop a model-based reinforcement learning algorithm that approximates MATSim dynamics with a Partially Observable Discrete Event Decision Process (PODEDP) and, with respect to other state-of-art policy optimization techniques, can scale on big transportation data and find optimal policies also on a city-scale.
Click to Success? The Temporal Effects of Facebook Likes on Crowdfunding

Small and medium-sized businesses as well as individuals are increasingly using online crowdfunding platforms to raise funds in the fintech world. Creators of crowdfunding projects depend heavily on social networks like Facebook to publicize their projects. Social media activities such as “liking” on Facebook bring massive traffic to crowdfunding projects and attract contributions. Using data collected from Facebook and Kickstarter, our empirical tests demonstrate that social media activities significantly and positively impact the likely success of crowdfunding. Our duration model analysis reveals that the impact of social media activities on crowdfunding outcomes follows a J-curve in the temporal space. We explain the J-curve by identifying two important effects of social media activities throughout the crowdfunding process: a quality-signaling effect in the opening period and a herding effect in the closing period. Especially in the “last mile,” there is a strong herding effect that helps crowdfunding projects reach their respective fundraising goals. Our results offer useful contributions to the literature and suggestions for practitioners.
Grouping Capacitor Voltage Estimation and Fault Diagnosis With Capacitance Self-Updating in Modular Multilevel Converters

Modular multilevel converter (MMC) with reduced number of voltage sensors is conducive to the reduction of equipment cost and hardware complexity, however, usually resulting in the performance degradation. For example, when only one voltage sensor is equipped for the measurement of arm voltage, estimation of capacitor voltages may be not accurate enough, moreover, condition monitoring and fault diagnosis of submodules (SMs) are hard to implement. Thus, capacitor voltage estimation with capacitance self-updating based on grouping measurement (GM) is proposed in this article. By grouping and updating SM capacitance periodically, the accuracy of voltage estimation is greatly increased, meanwhile, severely aging or faulty capacitors can be detected, convenient for prompt replacement. Furthermore, GM-based fault diagnosis and fault disposal are proposed. Utilizing the measured and estimated group voltages, single or multiple SM open-circuit failures can be located in several sampling cycles and the isolating time of faulty SMs can be easily obtained even considering the time delay of bypass switch. Therefore, capacitor overvoltage and current distortion during SM failures are restrained effectively, which enhances the reliability and achieves seamless operation of MMC. Finally, the effectiveness of the proposed scheme is confirmed by simulation and experimental results.
Active Queue Management Supporting TCP Flows Using Disturbance Observer and Smith Predictor

Active queue management (AQM) is a technique to avoid serious congestion of the transmission control protocol (TCP) flows at a router. AQM based on control theory, which utilizes congestion controllers such as proportional-derivative (PD) controller or proportional-integral-derivative (PID) controller, has been previously proposed. In addition, disturbance observer (DOB) has been utilized to compensate for modeling error of a TCP/AQM congestion control system. However, the DOB-based controllers cannot cope with a large time delay in TCP/AQM networks. Although one of the effective time delay compensators is Smith predictor (SP), the implementation of the DOB and SP in an integrated manner has not been accomplished, because of saturation due to the input limit of packet drop probability. In this paper, a novel TCP/AQM congestion control system with the DOB and SP considering the saturation function is proposed to compensate for the modeling error and time delay simultaneously. Simulation results show that the proposed controller provides better throughput and goodput than conventional controllers. One of the simulations assume that the propagation delay and bottleneck link capacity are set to 100 ms and 100 Mbps, respectively. Under this assumption, it is confirmed that the proposed controller achieves a goodput of 99.55 Mbps whereas the classical PID controller and PD controller with DOB achieve 99.23 Mbps and 99.24 Mbps, respectively.
A Review of Electrical Motor Topologies for Aircraft Propulsion

This paper provides a review of the state-of-the-art in aircraft electrical propulsion (AEP). Initially, the limitations of on-board energy storage devices are highlighted and contextualised. The definitions of useful measures for determining the suitability of motor design, namely specific power and motor torque per unit rotor volume (TRW), are discussed and relevant examples are provided. The classifications of motors used for terrestrial vehicle applications are reviewed and their limitations highlighted regarding their suitability to AEP applications. A discussion on motor configurations for aerospace applications is provided which includes: synchronous motor stator winding configurations; axial flux motor configurations and the causes of energy losses. Additionally, the topologies and performance characteristics of existing aerospace motor technologies are examined. It was concluded that electrical motors provide an ideal means for achieving aircraft propulsion and that higher motor speeds are likely to be required for future commercial aircraft motor designs.
The Influence of Mechanical Pressure on Solid/Solid Interface Under DC Voltages

The increased penetration of renewable energy sources in power systems is leading to an extensive use of HVDC cable systems. The weak points of cable systems, HVDC or HVAC, are the accessories, and the solid/solid interfaces. In this paper, experiments conducted to investigate the electric characteristic (DC breakdown voltage, DC leakage current and DC partial discharge monitoring) of solid/solid interfaces are reported. Emphasis is given to the influence of the mating pressure. The results seem to indicate that mechanisms not related to partial discharges are not the root cause for breakdown.
Utilizing Machine Learning Models to Predict the Car Crash Injury Severity among Elderly Drivers

Car crash can cause serious and severe injuries that impact people every day. Those injuries could be especially damaging for elderly drivers of age 60 or more. The goal of this research is to investigate the risk factors that contribute to crash injury severity among elderly drivers. This is accomplished by designing accurate machine learning based predictive models. Naïve Bayesian (NB), Decision Tree (DT), Logistic Regression (LR), Light-GBM, and Random Forest (RF) model are proposed. A set of influential factors are selected to build the five predictive models to classify the severity of injuries as severe injury or non-severe injury. Michigan traffic data of the elderly population is used in this paper. Data normalization and Synthetic Minority Oversampling Technique (SMOTE) as injury classes balancing technique are used in the pre-processing phase. Results show that the Light-GBM achieved the highest accuracy among the five tested models with 87%. According to the Light-GBM model, the three most important factors that impact the severity of injuries are the driver's age, traffic volume, and car's age.
Using Machine Learning to Predict the Future Development of Disease

The objective of this research is to develop a longterm risk model for the development of cardiovascular disease (CVD) because of type-2 diabetes (T2D). We use the support vector machine (SVM) and the K-nearest neighbours algorithms on the dataset collected from a longitudinal study called Framingham Heart Study, to develop the prediction models. The dataset was first balanced by the Synthetic Minority Oversampling Technique algorithm. The SVM algorithm was then used to train the model, and after tuning the parameters and training for 1000 times, the average accuracy to correctly predict the prevalence of CVD due to T2D came out as 96.5% and the average recall rate was 89.8%. Similarly, we also applied the KNN algorithm to train the dataset, and the recall rate even reaches 92.9%. The advantages of our model are: 1) it can predict with high accuracy both the risk of development of T2D and CVD simultaneously; 2) it can be used without the expensive and tedious oral glucose tolerance test. The model yielded high-performance results after training on the Framingham Heart Study dataset.
Adaptive Spatio-Temporal Graph Convolutional Neural Network for Remaining Useful Life Estimation

Accurate remaining useful life (RUL) estimation is of crucial importance to numerous industrial applications where safety and reliability are among primary concerns. Recently, deep learning based prognostics methods have been emerging as an effective method to improve RUL prediction results. However, these methods, e.g. recurrent neural networks (RNNs), convolutional neural networks (CNNs), only capture temporal information of the sensory data while ignoring intrinsic spatial relations between sensors. To solve this problem, in this work, we propose a framework, namely, adaptive spatio-temporal graph convolutional neural network (ASTGCNN). The proposed framework consists of two parts. In the spatial domain, since the intrinsic graph structure of sensors is not provided in most situations, a dynamic graph neural network is proposed to learn the sensors’ spatial relation. In the temporal domain, a stacked dilated ID CNN is utilized to capture long range dependency of input sensor signals. These two parts are integrated in a unified framework and can be trained in an end-to-end manner. The performance of ASTGCNN is investigated on the turbofan engine dataset Experimental results show that the proposed framework can improve the RUL prediction performance of the current deep learning methods, and learn the intrinsic spatial information of sensors.
Development of Hard Hat Wearing Monitoring System Using Deep Neural Networks with High Inference Speed

Personal protection equipment is essential for safe working conditions on a construction sites and other industrial enterprises. However, there is a number of employee who violate safety precautions, thus, taking additional risks. To avoid accidents strict supervision should be applied. In this work an automatic hard hat wearing detection system from surveillance cameras is developed. To save computational resources small neural networks with high inference speed are applied. For the training of neural networks, a dataset consisting of several industrial scenes and 1478 images was composed. Frame based precision of SqueezeDet neural network for object detection was increased by 9% with the use of additional MobileNets classifier without significant decrease in operating speed. The overall system achieved 0.75 F1-score on a test dataset. Therefore, the developed hard hat wearing monitoring systems is suitable for enhancing on-site safety of workers.
Human operator cognitive availability aware Mixed-Initiative control

This paper presents a Cognitive Availability Aware Mixed-Initiative Controller for remotely operated mobile robots. The controller enables dynamic switching between different levels of autonomy (LOA), initiated by either the AI and/or the human operator. The controller leverages a state-of-the-art computer vision method and an off-the-shelf web camera to infer the cognitive availability of the operator and inform the AI-initiated LOA switching. This constitutes a qualitative advancement over previous Mixed-Initiative (MI) controllers. The controller is evaluated in a disaster response experiment, in which human operators have to conduct an exploration task with a remote robot. MI systems are shown to effectively assist the operators, as demonstrated by quantitative and qualitative results in performance and workload. Additionally, some insights into the experimental difficulties of evaluating complex MI controllers are presented.
A Non-iterative Radial Basis Function Based Quick Convolutional Neural Network

In the past few years, Convolutional Neural Networks (CNNs) have achieved surprisingly good results for objects classification in real world images. However, training a CNN from scratch for large datasets is still a nightmare, when it comes to time and resources. The main reason for this problem is long iterative training process used in CNN’s fully connected layer which is also called a classification layer. Therefore, in this paper we propose a novel approach to make the convolutional neural network quicker and more efficient for image classification tasks. The proposed approach consists of a convolutional feature extraction layer and a non-iterative radial basis function-based classification layer. The proposed approach has been evaluated on three benchmark datasets such as CIFAR-10, MNIST and Digit. The experimental results have demonstrated that the proposed approach can achieve same or higher accuracy in lesser time than the standard CNN.
Requirements for Prescriptive Recommender Systems Extending the Lifetime of EV Batteries

Lithium-ion batteries in electric vehicles are subject to degradation, which is strongly influenced by the actions of vehicle users. Hereby, inexperienced users can cause the battery to reach its end of life state earlier than average. For this reason, this paper proposes the concept of a prescriptive recommender system that supports users in planning their utilization actions. The paper identifies functionalities of decision support systems relevant to extend the lifetime of electric and electronic systems. This allows to determe generic functional and non-functional requirements for prescriptive recommender systems. Further, the derived requirements are discussed in connection to the practicability of a prescriptive recommender system.
Minimizing Coflow Completion Time in Optical Circuit Switched Networks

Nowadays, optical circuit switching is becoming an increasingly favored technology in scaling data center networks for its definitive advantages in data rate, power consumption, and device cost. Concurrently, reducing coflow completion time (CCT) is of great significance for improving application-level performance. However, minimizing CCT in circuit switched networks is totally different from that in traditional packet switched networks due to port constraints and circuit reconfiguration delays. To address this issue, this article proposes Grouped Optimization-based Scheduling (GOS), a CCT minimization algorithm for circuit switched networks integrating circuit and coflow scheduling. We first formalize the CCT minimization problem into a 0-1 programming problem, then relax and solve the problem in 2 steps to obtain the coflow order and flow grouping decisions on each circuit. Thus intra-group reconfiguration delays are saved, and small coflows can be prioritized at the group level. Theoretical analysis proves GOS is a 4-approximation algorithm in average CCT. To reduce computing overheads, we further propose a heuristic approximation algorithm. Extensive simulations show that the heuristic algorithm has satisfactory CCT performance (0.12× Varys, 0.36× Sunflow) as well as high throughput (16.74× Varys, 1.32× Sunflow), and well adapts to a wide range of reconfiguration delays and algorithm decision time.
Deep Learning-Based Autoencoder for m-User Wireless Interference Channel Physical Layer Design

Deep learning (DL) based autoencoder (AE) has been proposed recently as a promising, and potentially disruptive approach to design the physical layer of beyond-5G communication systems. Compared to a traditional communication system with a multiple-block structure, the DL based AE approach provides a new paradigm to physical layer design with a pure data-driven and end-to-end learning based solution. In this article, we address the dynamic interference in a multi-user Gaussian interference channel. We show that standard constellation are not optimal for this context, in particular, for a high interference condition. We propose a novel adaptive DL based AE to overcome this problem. With our approach, dynamic interference can be learned and predicted, which updates the learning processing for the decoder. Compared to other machine learning approaches, our method does not rely on a fixed training function, but is adaptive and applicable to practical systems. In comparison with the conventional system using $n$ -psk or $n$ -QAM modulation schemes with zero force (ZF) and minimum mean square error (MMSE) equalizer, the proposed adaptive deep learning (ADL) based AE demonstrates a significant achievable BER in the presence of interference, especially in strong and very strong interference scenarios. The proposed approach has laid the foundation of enabling adaptable constellation for 5G and beyond communication systems, where dynamic and heterogeneous network conditions are envisaged.
Uniform and High Gain GaN p-i-n Ultraviolet APDs Enabled by Beveled-Mesa Edge Termination

In this letter, a beveled-mesa edge termination technology was developed to improve the performance of GaN p-i-n ultraviolet avalanche photodiodes (APDs). Simulation results showed that the beveled-mesa is effective in reduction of the electric field at the mesa sidewall. With a photoresist thermal-reflow process, 12°-angle beveled-mesa APDs were fabricated from homoepitaxial p-i-n structures on GaN substrate. The most beneficial property of the beveled-mesa termination is the uniform breakdown-characteristic of the beveled-mesa APDs compared to the vertical-mesa APDs. Record-high <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\mathbf {BR}}$ </tex-math></inline-formula> uniformity of 95.4 V ± 0.2 V and average avalanche gain up to <inline-formula> <tex-math notation="LaTeX">$3\times 10^{\mathbf {6}}$ </tex-math></inline-formula> were observed for the beveled-mesa APDs within a linear array across 4 mm side-length. These results indicate the potential of the beveled-mesa GaN APD array for ultraviolet light imaging applications.
Improving Software Fault Localization by Combining Spectrum and Mutation

The performance of software fault localization techniques is critical to software debugging and the reliability of software. Spectrum-based fault localization (SBFL) and mutation-based fault localization (MBFL) are the two most popular fault localization methods. However, the accuracies of the two methods are still limited. For example, only 10.63% of faults can be detected by inspecting the top 3 suspicious elements reported by Ochiai, which is a famous SBFL technique. Unfortunately, programmers only examine the first few suspicious elements before losing patience. Since the information used in SBFL and MBFL are quite different and complementary, this paper proposes a novel approach by combining spectrum and mutation to improve the fault localization accuracy. First, the faulty program is evaluated by using SBFL, and the potential faulty statements are ranked according to their suspiciousness. Then, mutants of the program are generated and executed by MBFL. Finally, the statements that are ranked in the top tied ${n}$ by SBFL are evaluated and reranked according to their mutation scores. Experiments are carried on the Defects4J benchmark and the results reveal that the accuracy of the proposed approach outperforms those of the SBFL and MBFL techniques. In terms of the faults located by inspecting the top 1 suspicious elements, the SMFL techniques detect at least 2.36 times more faults than two SBFL techniques (DStar and Ochiai) and detect at least 1.86 times more faults than two MBFL techniques (MUSE and Metallaxis).
A Particle-Filter-Based Online Method for Degradation Analysis with Exponential-Dispersion Process

As a flexible stochastic process for degradation mod-eling and analysis, the exponential-dispersion process includes several widely used stochastic processes as its special cases, such as Wiener, gamma and inverse Gaussian processes. The Tweedie exponential-dispersion model is an important class of the exponential-dispersion model, which is flexible and can describe various types of degradation processes. Recently, several offline methods have been introduced for degradation analysis with the Tweedie exponential-dispersion model. However, when dealing with continually updating degradation observations, these offline methods may have the difficulty of storing and recalling historical observations everytime for analyzing newly observed data. Furthermore, to provide real-time results for in-situ decision making, a novel degradation analysis method equipped with a flexible degradation model and online inference method is promising. This paper proposes an online degradation analysis method based on the Tweedie exponential-dispersion model and a particle filter method. The Tweedie exponential-dispersion model is adopted to model the degradation process and the particle filter method is leveraged to develop the online inference method. The particle filter method can update the parameters’ estimators in an iterative process by using each observed data only once in the process. The estimators would be updated after receiving new data without need to recall the historical data. The online method can meet the real-time requirement for in-situ degradation analysis. A simulation study is presented to show the effectiveness of the online inference. A case study with the GaAs lasers data is used to demonstrate the proposed method for degradation modeling and online inference.
EVALUATION OF ANALGESIC AND ANTIDIARRHOEAL PROPERTIES OF THE ETHANOLIC EXTRACT OF CRATAEVA NURVALA BUCH. HAM (CAPPARIDACEAE) LEAVES

The leaves of medicinal plant Crataeva nurvala Buch. Ham (Capparidaceae) was extracted in ethanol to evaluate the peripherally acting analgesic potential using acetic acid induced writhing and antidiarrhoeal activity using intestinal motility test both in mice. The crude extract showed significant (P<0.01) analgesic activity at oral doses of 200 and 400mg/kg body weight with an inhibition of writhing 68.4% and 76.3% compared to 67% for the positive control. In the motility test, the crude extract at same oral doses showed 31.16% and 35.31% inhibition of intestinal propulsion of charcoal marker where as positive control group exhibited 36.25% inhibition of propulsion of charcoal through the intestine.