Miracle-dz/UniFAR-FTUs huggingface dataset:
Inferring Inter-City Trip Purpose From the Perspective of the Group

Although trip purpose inference based on passively collected data has long been investigated, less attention has been paid to inter-city trips. The reason is, except using ticket sales data, only limited trips can be extracted due to the lower frequency of inter-city trips during daily life. However, for ticket sales data, only limited features can be explored due to the lower spatial resolution of trajectories. Therefore, this paper endeavoured to exploit the potential of ticket sales data from the perspective of the group. Theoretically, by introducing concepts of text mining, the trip purpose of a group can be viewed as analogous to the topics of a document. Trip purpose was characterized by a time topic model (TTM) that incorporates start time, in contrast to latent Dirichlet allocation (LDA). This approach was implemented via a three-step method. First, groups were reconstructed from tickets. Second, three types of features, i.e., demographic, experience and co-travel network features, were extracted as a series of words to describe passengers. Third, trip purposes were automatically clustered based on the co-occurrence of words in the same group using a TTM. This paper presents comparison experiments to evaluate feature sets and the model performance based on a web-based travel survey, including the ground truth. Moreover, this paper highlights the practical use of a TTM to detect anomalies beyond anticipated trip purpose based on large-scale ticket sales data collected from Beijing, China. The full feature set was found to be preferable since both precision and recall increased when demographic and co-travel network features were considered. Meanwhile, the TTM produced robust and balanced predictions and exhibited additional power to recognize personal business compared with baseline methods.
Flow-Mixup: Classifying Multi-labeled Medical Images with Corrupted Labels

In clinical practice, medical image interpretation often involves multi-labeled classification, since the affected parts of a patient tend to present multiple symptoms or comorbidities. Recently, deep learning based frameworks have attained expertlevel performance on medical image interpretation, which can be attributed partially to large amounts of accurate annotations. However, manually annotating massive amounts of medical images is impractical, while automatic annotation is fast but imprecise (possibly introducing corrupted labels). In this work, we propose a new regularization approach, called Flow-Mixup, for multi-labeled medical image classification with corrupted labels. Flow-Mixup guides the models to capture robust features for each abnormality, thus helping handle corrupted labels effectively and making it possible to apply automatic annotation. Specifically, Flow-Mixup decouples the extracted features by adding constraints to the hidden states of the models. Also, FlowMixup is more stable and effective comparing to other known regularization methods, as shown by theoretical and empirical analyses. Experiments on two electrocardiogram datasets and a chest X-ray dataset containing corrupted labels verify that FlowMixup is effective and insensitive to corrupted labels.
Robust Event-triggered Distributed Average Tracking for Double-integrator Agents Without Velocity Measurements

This paper investigates the distributed average tracking problem for a group of double-integrator agents. In some practical applications, velocity measurements may be unavailable due to technology and space limitations, and it is also usually less accurate and more expensive to implement. To this end, a distributed average tracking algorithm without using velocity measurements and correct initialization is established. It is worth noting that no global information is needed for parameter design. Then, in order to remove the requirement on continuous interaction, and reduce the communication cost and improve the energy efficiency, an event-triggered distributed average tracking algorithm is designed by incorporating an event-triggered communication strategy without using velocity measurements. To be exact, the idea of a dynamic event-triggered strategy is used to construct a triggering condition for each agent to guarantee the exclusion of Zeno behavior. Finally, simulations are provided to illustrate the obtained results.
Improving Classification Attacks in IOT Intrusion Detection System using Bayesian Hyperparameter Optimization

The growth of the Internet of Things (IoT) presents challenges in the field of security. The Intrusion Detection System is an alternative to protecting the internet of things. In this study, we propose an intrusion detection system model that combines unsupervised algorithm and a deep neural network. Autoencoder as unsupervised learning algorithm has a function as a feature extractor that speeds up the learning process on a deep neural network. The performance of a deep learning model depends heavily on the selection of hyperparameters of neural network architecture. In this case, we used Bayesian Hyperparameter Optimization to perform hyperparameter tuning of deep learning models with various activation and weight initialization techniques. The accumulation result is useful to help determine the correct activation function and weight initialization and the hyperparameters that most influence the deep learning model. The results of this study show that Bayesian hyperparameter optimization can improve classification results significantly. Evaluation using the BoT-IoT dataset, the classification accuracy results in deep learning model can reach 99.99%.
An Efficient Gait Recognition Method for Known and Unknown Covariate Conditions

Gait is a unique non-invasive biometric form that can be utilized to effectively recognize persons, even when they prove to be uncooperative. Computer-aided gait recognition systems usually use image sequences without considering covariates like clothing and possessions of carrier bags whilst on the move. Similarly, in gait recognition, there may exist unknown covariate conditions that may affect the training and testing conditions for a given individual. Consequently, common techniques for gait recognition and measurement require a degree of intervention leading to the introduction of unknown covariate conditions, and hence this significantly limits the practical use of the present gait recognition and analysis systems. To overcome these key issues, we propose a method of gait analysis accounting for both known and unknown covariate conditions. For this purpose, we propose two methods, i.e., a Convolutional Neural Network (CNN) based gait recognition and a discriminative features-based classification method for unknown covariate conditions. The first method can handle known covariate conditions efficiently while the second method focuses on identifying and selecting unique covariate invariant features from the gallery and probe sequences. The feature set utilized here includes Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), and Haralick texture features. Furthermore, we utilize the Fisher Linear Discriminant Analysis for dimensionality reduction and selecting the most discriminant features. Three classifiers, namely Random Forest, Support Vector Machine (SVM), and Multilayer Perceptron are used for gait recognition under strict unknown covariate conditions. We evaluated our results using CASIA and OUR-ISIR datasets for both clothing and speed variations. As a result, we report that on average we obtain an accuracy of 90.32% for the CASIA dataset with unknown covariates and similarly performed excellently on the ISIR dataset. Therefore, our proposed method outperforms existing methods for gait recognition under known and unknown covariate conditions.
Thermomechanical Stress and Strain Distribution and Thermal Resistivity Correlation to Bondline Thickness of Ag Sinter

Stress and strain energy distribution and thermal resistivity of diebonded Si dies were measured and correlated with varying bondline thickness of hybrid Ag sinter to define and optimize workable bondline thickness window. Thermomechanical simulations were done using carrier device with known PPF-plated Cu flange, Si die, epoxy mold compound and Ag sinter material properties. Principal stress, total strain energy density and interfacial stress distribution were obtained to check susceptibility to die crack, cohesive and adhesive failure respectively using Finite Element Analysis. On the other hand, thermal resistivity (Rth) per die section (Final Peak, Final Carrier, Driver Peak, and Driver Carrier) were measured across varying dry bondline thickness of 20, 40 and $60\mu \mathrm{m}$. Results reveal that maximum principal stress, strain energy and interfacial stress experienced on the die, die-attach, and die backside - die-attach interface are inversely proportional to the bondline thickness of Ag sinter which means lower bondline thickness are more susceptible to reliability failures. Moreover, thermal resistivity on all die sections dramatically increases with increasing bondline thickness and should be controlled within 20 to $60\mu \mathrm{m}$ thickness range.
MISSION: Multimodal-Information-Aided Similar Disease Detection Based on Disease Information Network

To detect similar diseases is meaningful for revealing pathogenesis, and predicting therapeutic drugs. Previous methods measure disease similarity almost according to the semantic on biomedical ontology or the function of disease-causing molecules. However, such methods mostly describe diseases from single information, which may lead to a biased description of the relationships among diseases. In this paper, we propose a novel approach, called MISSION, for measuring the disease similarity based on multimodal-information. MISSION enhances similar disease detection based on disease information network from three aspects, including disease ontology, attribute, and literature, therefore providing a comprehensive evaluation for disease similarity. Through experiments on real-world datasets, we demonstrate that MISSION is effective, efficient, and potentially useful. Further analysis shows that MISSION has the ability to detect similar diseases with varying degrees of rich information.
Network Attack Detection System Using Filter-based Feature Selection and SVM

The selection of features plays a big role in improving the results of a computer network attack detection system. This research used a model of feature selection to find the best combination of network traffic features to identify network attacks while retaining power explanations. This research also used filter-based feature selection, namely Information Gain (IG) and Gain Ratio (GR). Training and testing can be carried out after sigma value of SVM parameter has been determined. From sigma value testing, we chose sigma value of 5000. After SVM training, it is found that Gain Ratio with 30 features perform best for most measurement and classes. Nevertheless, full 41 features outperform IG and GR for probe class. Also, model that integrating feature selection has possibility to converge faster. It is recommended that further analysis and examination is needed to understand features combination result. Additionally, further research is needed to determine the effectiveness of features combinations to improve model performance and to try different approaches besides the filter-based method.
Multi-ion Occupancy but No Anomalous Mole-Fraction Effect between Ba 2+ and Ca 2 +

We investigated the mechanism whereby ions cross dihydropyridine- sensitive (L-type) Ca channels in guinea pig ventricular myocytes. At the single- channel level, we found no evidence of an anomalous mole-fraction effect like that reported previously for whole-cell currents in mixtures of Ba and Ca. With the total concentration of Ba + Ca kept constant at 10 (or 110) mM, neither conduc- tance nor absolute unitary current exhibits a paradoxical decrease when Ba and Ca are mixed, thereby weakening the evidence for a multi-ion permeation scheme. We therefore sought independent evidence to support or reject the multi-ion nature of the L-type Ca channel by measuring conductance at various permeant ion con- centrations. Contrary to the predictions of models with only one binding site in the permeation pathway, single-channel conductance does not follow Michaelis- Menten kinetics as Ba activity is increased over three orders of magnitude. Two- fold variation in the Debye length of permeant ion solutions has litde effect on conductance, making it unlikely that local surface charge effects could account for these results. Instead, the marked deviation from Michaelis-Menten behavior was best explained by supposing that the permeation pathway contains three or more binding sites that can be occupied simultaneously. The presence of three sites helps explain both a continued rise in conductance as (Ba 2+ ) is increased above 1 10 mM, and the high single-channel conductance (-7 pS) with 1 mM (Ba ~§ ) as the charge carrier; the latter feature enables the L-type channel to carry surpris- ingly large currents at physiological divalent cation concentrations. Thus, despite the absence of an anomalous mole-fraction effect between Ba and Ca, we suggest that the L-type Ca channel in heart cells supports ion flux by a single-file, multi-ion permeation mechanism.
Active Voltage Balancing of Series-Connected 1.7 kV/325 A SiC MOSFETs Enabling Continuous Operation at Medium Voltage

This article presents a medium voltage half-bridge circuit where 3.3 kV power switches are built with two 1.7 kV/325 A SiC MOSFETs series-connected inside power modules. Essence of this work is an active balancing method eliminating the voltage imbalances between the devices by means of gate signals modification. The advantage over commonly-used passive snubbers is low cost and limited amount of additional losses. Performance of the half-bridge is illustrated with a model-based Saber simulation at first, then, an experimental full-scale model is designed with on-the-shelf power modules capable operating at power level above 150 kVA. A series of laboratory tests at 1.5 kV DC confirmed reduction of voltage differences across devices in the stack to an acceptable level – the designed system is working correctly despite EMI caused by fast switching transients. Moreover, the observed current and voltage waveforms prove the positive impact of the method as not only voltages but also switching losses are better balanced among the devices. Experimental comparison to a 3.3 kV counterpart shows two times lower turn-off energy, therefore, the presented design with two 1.7 kV SiC MOSFET power modules can beneficently compete not only in terms of lower cost but also switching performance.
DDaNet: Dual-Path Depth-Aware Attention Network for Fingerspelling Recognition Using RGB-D Images

Automatic fingerspelling recognition aims to overcome communication barriers between people who are deaf and those who can hear. RGB-D cameras are widely used to handle finger occlusion, which usually hinders fingerspelling recognition. However, color-depth misalignment, which is an intrinsic property of RGB-D cameras, hinders the simultaneous processing of color and depth images in the absence of intrinsic parameters of the camera. Furthermore, fine-grained hand gestures performed by various persons and captured from multiple views render the discriminative feature extraction difficult, due to intra-class variability and inter-class similarity. Inspired by the human visual mechanism, we propose a network to learn discriminative features related to fine-grained hand gestures while suppressing the effect of color–depth misalignment. Unlike existing approaches that independently process RGB-D images, a dual-path depth-aware attention network that learns a fingerspelling representation in separate RGB and depth paths, and progressively fuses the features learned from the two paths is proposed. As the hand is usually the closest object to the camera, depth information can contribute to emphasize the key fingers related to a letter sign. Thus, we develop a depth-aware attention module (DAM) to exploit spatial relations in the depth feature maps, refining the RGB and depth feature maps across a bottleneck structure. The module establishes a lateral connection of the RGB and depth paths and provides a depth-aware salient map to both paths. The experimental results demonstrated that the proposed network improved the accuracy (+0.83%) and $F$ score (+1.55%) compared to state-of-the-art methods on a publicly available fingerspelling dataset. The visualization of the network processes demonstrates that the DAM facilitates the selection of representative hand regions from the RGB-D images. Furthermore, the number of parameters and computational overhead of the DAM are negligible in the network. The code is available at https://github.com/cweizen/cweizen-DDaNet_model_master.
Substrate Integrated Transmission Lines: Review and Applications

This paper presents a general overview of substrate integrated transmission lines, from the perspective of historical background and progress of guided-wave structures and their impacts on the development of microwave circuits and integration solutions. This is highlighted through a technology roadmap involving the categorized five generations of microwave circuits. In particular, the substrate integration technologies are reviewed and discussed with focus on technical features, design highlights, component developments, structures evolution, and systems integration. A number of examples are presented to showcase some of the selected milestone research and development activities and accomplishments in connection with substrate integrated transmission line technologies, with particular focus on substrate integrated waveguide (SIW) techniques. Practical applications and industrial interests are also presented with key references and technical results, which show more and more product developments in the end-user sectors. It can be found that the popularity of SIW techniques is closely related to the achieved seamless integration of planar and non-planar structures into a unified design space, thereby allowing the possibility of combining major advantages of all the structures while alleviating their potential drawbacks. The future perspectives of the substrate integration technology are discussed through five major research directions, which suggest potential impacts in the development of future generations of circuits and systems such as system-on-substrate (SoS).
Learning Pure Nash Equilibrium in Smart Charging Games

Reinforcement Learning Algorithms (RLA) are useful machine learning tools to understand how decision makers react to signals. It is known that RLA converge towards the pure Nash Equilibria (NE) of finite congestion games and more generally, finite potential games. For finite congestion games, only separable cost functions are considered. However, non-separable costs, which depend on the choices of all players instead of only those choosing the same resource, may be relevant in some circumstances, like in smart charging games. In this paper, finite congestion games with non-separable costs are shown to have an ordinal potential function, leading to the existence of an action-dependent continuous potential function. The convergence of a synchronous RLA towards the pure NE is then extended to this more general class of congestion games. Finally, a smart charging game is designed for illustrating convergence of such learning algorithms.
The Effect of Geographical Factor on the Use of Combinable Containers

This paper analyzed the use of combinable containers which can be de/coupled and used as a 20ft and 40ft size container. Based on a model by [6], our model considered a geographical factor in a trade zone with the huge hinterland. The experimental result showed that the ratio of combinable in use was higher among all the container types for distant owners to facilitate an efficient use of a whole container fleet to minimize the transportation cost.
Performance Comparison of Data Mining Techniques for Rain Prediction Models in Indonesia

Rain prediction is a crucial topic that continues to gain interest across the globe. Rain has a massive impact on various aspects of human life such as in agriculture, health, transportation, etc, and also some natural disasters. Various impacts of rain on human life prompts us to build a model to understand and predict rain to provide early warning for various use cases in various fields. Previous research on rain modeling using Data Mining (DM) techniques had suffered from low accuracy caused by the limited availability of the training data and their meteorological attributes. This research aims to address those issues by building the rain model using a richer and more abundant rain data in Indonesia. Four DM techniques are used and compared in this research i.e. the C4.5/J48, Random Forest (RF), Naïve Bayes (NB), and Multilayer Perceptron (MLP). The experimental results showed that the MLP and J48 algorithm can provide the best accuracy (up to 78,4%), which is better than previous research. Other key findings in this research include: (a) the selection of DM techniques has little effect on the model accuracy; (b) a larger training dataset generally improves model accuracy and a larger test dataset is necessary to get a representative realworld test accuracy, and (c) the two most influential attributes in rain modeling are the relative humidity and the minimum temperature, and we suggest to include cloud condensation nuclei in the next research to complete the model.
Resilient asynchronous rendezvous of second-order agents under communication noise

In this paper, we investigate the problem of rendezvous of second-order agents considering two issues: a) misbehaving agents that make update abnormally and can send arbitrary information to neighbors; b) unknown but bounded communication noise. Both issues can deteriorate the rendezvous performance and even destabilize the systems. We propose a self-triggered resilient rendezvous algorithm, which guarantees that the operation of normal agents is unaffected by the disruption of misbehaving agents and ensures practical rendezvous of normal agents. The position and velocity of normal agents always evolve boundedly, and the agent-to-agent position error converges to be bounded by a value scaling with the magnitude of communication noise. By the proposed algorithm, each agent can sample its neighbors and update its control sporadically and fully asynchronously.
Firefly Algorithm-based Optimization of Base Transceiver Station Placement

Long-Term Evolution (LTE), also known as the 4th generation (4G) is a system currently seen as a development system of UMTS/HSPA, which is one of the 3rd generation (3G) evolutions. One of the supporting components of the LTE network is ENodeB. An ENodeB can be analogous to a Base Transceiver Station (BTS). As customer needs are increasing, the need for BTS (ENodeB) is also increasing. It causes a large number of base stations. On the other hand, the construction of new base stations will certainly cost a lot and cause temper the city's landscape. Therefore, it is necessary to optimize the BTS placement. Hence, in this research, we design a simulation of BTS placement optimization using the firefly algorithm (FA) with the parameter to be optimized is the coverage area and traffic. The fitness value obtained from this study reached 97.73% and reduced existing BTS by 50%. This fitness is comparable with the hybrid evolutionary FA (HEFA)-based model, which produces an average fitness of 98.62%.