Miracle-dz/UniFAR-FTUs huggingface dataset:
A deep learning approach to trespassing detection using video surveillance data

Railroad trespassing is a dangerous activity with significant security and safety risks. However, regular patrolling of potential trespassing sites is infeasible due to exceedingly high resource demands and personnel costs. This raises the need to design automated trespass detection and early warning prediction techniques leveraging state-of-the-art machine learning. To meet this need, we propose a novel framework for Automated Railroad Trespassing detection System using video surveillance data called ARTS. As the core of our solution, we adopt a CNN-based deep learning architecture capable of video processing. However, these deep learning-based methods, while effective, are known to be computationally expensive and time consuming, especially when applied to a large volume of surveillance data. Leveraging the sparsity of railroad trespassing activity, ARTS corresponds to a dual-stage deep learning architecture composed of an inexpensive pre-filtering stage for activity detection, followed by a high fidelity trespass classification stage employing deep neural network. The resulting dual-stage ARTS architecture represents a flexible solution capable of trading-off accuracy with computational time. We demonstrate the efficacy of our approach on public domain surveillance data achieving 0.87 f1 score while keeping up with the enormous video volume, achieving a practical time and accuracy trade-off.
DSSLP: A Distributed Framework for Semi-supervised Link Prediction

Link prediction is widely used in a variety of industrial applications, such as merchant recommendation, fraudulent transaction detection, and so on. However, it’s a great challenge to train and deploy a link prediction model on industrial-scale graphs with billions of nodes and edges. In this work, we present a scalable and distributed framework for semi-supervised link prediction problem (named DSSLP), which is able to handle industrial-scale graphs. Instead of training model on the whole graph, DSSLP is proposed to train on the k-hops neighborhood of nodes in a mini-batch setting, which helps reduce the scale of the input graph and distribute the training procedure. In order to generate negative examples effectively, DSSLP contains a distributed batched runtime sampling module. It implements uniform and dynamic sampling approaches, and is able to adaptively construct positive and negative examples to guide the training process. Moreover, DSSLP proposes a model-split strategy to accelerate the speed of inference process of the link prediction task. Experimental results demonstrate that the effectiveness and efficiency of DSSLP in serval public datasets as well as real-world datasets of industrial-scale graphs.
Fiber-Optic Ultrasound Transmitter Based on Multi-Mode Interference in Curved Adhesive Waveguide

Fiber-optic ultrasound transmitters can be used in structural health monitoring, material characterization, and biomedical imaging. However, the bandwidth of current fiber-optic ultrasound transmitters is tens of MHz which prevents the ultrasound image on cells with resolution of $\mu \text{m}$ level. Here, ultra-compact in-fiber ultrasound transmitter is proposed by using the multi-mode interference in the curved UV-cured adhesive waveguide, which is fabricated between two fiber endfaces over a sandwiched core-offset spliced fiber segment. The curved adhesive waveguide can improve the contrast of device reflection spectrum from 2.7 dB to 21.7 dB. The ultrasound is generated by optical pulse excitation via photoabsorption and thermal expansion of UV-cured adhesive, and then detected via the multi-mode interference of the same waveguide with hundreds of Hz to 306 MHz broad transverse acoustic waves. This ultra-compact and cost-effective device offers new opportunities to the advanced biomedical and ultrasound-based applications.
Suspicious Location Detection Using Trajectory Analysis & Location Backfilling – A Scalable Approach

The increasing availability of GPS-embedded devices has introduced a new dimension in digital market especially location-based services. In practice, the location data is used to understand and predict consumer mobility behavior and trend for various purposes. In this paper, we propose two methodologies to first identify suspicious location from consumer location data and to infer location at both individual device and device to device level based on systematic solution. Using stay-point clustering and suspicious patterns we identified from extensive analysis, 20-30% of records with location were observed to be suspicious. After removing inaccurate location data, we have employed scalable heuristic approach to backfill records with location even for devices that originally had no available location. Our model showed the accuracy within 50 meters at 95th percentile across different countries, including Japan, Indonesia, India, and the United States with 10-15% increase in the number of records with location and 5-10% increase in new number of devices with location.
A Bidirectional Hybrid Switched-Capacitor DC-DC Converter with a High Voltage Gain

This paper presents a new non-isolated bidirectional hybrid switched-capacitor DC-DC converter (BHSC) which achieves a high voltage conversion ratio. The hybrid characteristic is due to the switched capacitor cell that is inserted into a conventional buck/boost bidirectional converter, which also helps achieve the high conversion ratio. Apart from the high conversion ratio, the switched cell also helps reduce all passive components and the stress on the active switches, all without interrupting the input to output ground path. The stability of the converter is also addressed by using the state space averaging method (SSA), prior to the actual construction of the converter, in order to design the passive components so that possible instabilities that might occur are eliminated. The simulation results are used to confirm the initial theoretical considerations.
MGL-CNN: A Hierarchical Posts Representations Model for Identifying Depressed Individuals in Online Forums

More users suffering from depression turn to online forums to express their problems and seek help. In such forums, there is often a large volume of posts with sensitive content, indicating that the user has a risk of suicide and self-harm. Early detection of depression using appropriate deep learning models and social media data can prevent potential self-harm. However, existing depression detection models are not powerful enough to capture critical sentiment information from the large volume of posts published by each user, which makes the performance of these models not satisfying. To address this problem, we propose a hierarchical posts representations model named Multi-Gated LeakyReLU CNN (MGL-CNN) for identifying depressed individuals in online forums. The model consists of two parts: the first one is a post-level operation, which is used to learn the representation of each post of the user, and the second one is a user-level operation, which is used to obtain the overall representation of the user’s emotional state. Besides, we propose another depression detection model by changing the number of gated units in the MGL-CNN, which is named Single-Gated LeakyReLU CNN (SGL-CNN). We show how to use our models to identify depressed users through a lot of posted content. Experimental results showed that our models performed better than the previous state-of-the-art models on the Reddit Self-reported Depression Diagnosis dataset, and also performed well on the Early Detection of Depression dataset.
Joint Offloading and Charge Cost Minimization in Mobile Edge Computing

Mobile edge computing (MEC) brings a breakthrough for Internet of Things (IoT) for its ability of offloading tasks from user equipments (UEs) to nearby servers which have rich computation resource. 5G network brings a huge breakthrough on transmission rate. Together with MEC and 5G, both execution delay of tasks and time delay from downloading would be shorter and the quality of experience (QoE) of UEs can be improved. Considering practical conditions, the computation resource of an MEC server is finite to some extent. Therefore, how to prevent the abuse of MEC resource and further allocate the resource reasonably becomes a key point for an MEC system. In this paper, an MEC system with multi-user is considered where a base station (BS) with an MEC server, which can not only provide computation offloading service but also data cache service. Especially, we take the charge for both data transmission and task computation as one part of total cost of UEs, and then explore a joint optimization for downlink resource allocation, offloading decision and computation resource allocation to minimize the total cost in terms of the time delay and the charge to UEs. The proposed problem is formulated as a mixed integer programming (MIP) one which is NP-hard. Therefore, we decouple the original problem into two subproblems which are downlink resource allocation problem and joint offloading decision and computation resource allocation problem. Then we address these two subproblems by using convex and nonconvex optimization techniques, respectively. An iterative algorithm is proposed to obtain a suboptimal solution in polynomial time. Simulation results show that our proposed algorithm performs better than benchmark algorithms.
Enterprise-wide Machine Learning using Teradata Vantage: An Integrated Analytics Platform

Big data characterized by variety can be divided into 3 principal categories: numeric structured data, semi-structured data, and unstructured multimedia data involving audio, video, and text. Decision making requires multiple analytical engines suitable for each type of data, programming languages, algorithms, visualization tools, and user interfaces. More often than not, industrial analytics is conducted ad hoc by lashing together analytics components such as distributed data sources, analytics engines, and algorithms. This kind of piecemeal approach ignores scale, security, governance, reliability, model management and fault tolerance that are paramount for industrial strength analytics. A unified, versatile, and robust architecture that combines various components in a single integrated platform is the need of the hour. Teradata Vantage (TD Vantage) is such a platform for delivering production quality enterprise analytics at scale. In this paper, we outline the proposed TD Vantage (available in the market and under continuous development) that unifies data, engines, and algorithms operating in a seamless symphony. We will demonstrate its capabilities through three proofs of concept biz: image data using TensorFlow, text data using Spark, and transaction data using Aster (now renamed Machine Learning Engine or MLE), with Teradata orchestrating interactions among the various components.
An Apriori-based Data Analysis on Suspicious Network Event Recognition

Apriori-based rule generators, which are powered by the DIS-Apriori algorithm and the NIS-Apriori algorithm, are applied to analyze the data sets available in the IEEE BigData 2019 Cup: Suspicious Network Event Recognition. Then, each missing value in the test data set is decided by using the obtained rules. The advantage of our rule-based model is that the obtained rules are very easy to understand in comparison with other ”black-box” machine learning models. Furthermore, two algorithms preserve the logical property ”completeness,” so they generate rules without excess and deficiency. In evaluation, the AUC measure seems unfavorable to our model, so we employed 3-fold cross-validation for the training data set, and we obtained a 94% mean score. This result ensures the validity of our model. We report several meaningful results in this experiment, as well as the estimation of missing values.
Geometric Primitives in LiDAR Point Clouds: A Review

To the best of our knowledge, the most recent light detection and ranging (lidar)-based surveys have been focused only on specific applications such as reconstruction and segmentation, as well as data processing techniques based on a specific platform, e.g., mobile laser. However, in this article, lidar point clouds are understood from a new and universal perspective, i.e., geometric primitives embedded in versatile objects in the physical world. In lidar point clouds, the basic unit is the point coordinate. Geometric primitives that consist of a group of discrete points may be viewed as one kind of abstraction and representation of lidar data at the entity level. We categorize geometric primitives into two classes: shape primitives, e.g., lines, surfaces, and volumetric shapes, and structure primitives, represented by skeletons and edges. In recent years, many efforts from different communities, such as photogrammetry, computer vision, and computer graphics, have been made to finalize geometric primitive detection, regularization, and in-depth applications. Interpretations of geometric primitives from multiple disciplines try to convey the significance of geometric primitives, the latest processing techniques regarding geometric primitives, and their potential possibilities in the context of lidar point clouds. To this end, primitive-based applications are reviewed with an emphasis on object extraction and reconstruction to clearly show the significances of this article. Next, we survey and compare methods for geometric primitive extraction and then review primitive regularization methods that add real-world constrains to initial primitives. Finally, we summarize the challenges, expected applications, and describe possible future for primitive extraction methods that can achieve globally optimal results efficiently, even with disorganized, uneven, noisy, incomplete, and large-scale lidar point clouds.
Future of EV Charging

Charging infrastructure for electric vehicles will be the key factor for ensuring a smooth transition to e-mobility. It is here, that five technologies will play a vital role in the EV charging infrastructure: smart charging (including vehicle-to-grid V2G technology), charging of EVs from photovoltaic panels, (ultra)fast charging, contactless charging and on-road charging of EVs. With the use of smart charging, the EV charging power and direction can be continuously controlled. Smart charging of EVs can provide several benefits to the EV owner and to the providers of the EV charging infrastructure like reduced peak demand on the grid and reduced cost.
Infer Latent Privacy for Attribute Network in Knowledge Graph

The information of the real world is stored as triplets (head entity, relation, tail entity) in knowledge graphs. They are extremely useful resources for many intelligent applications but suffer from incompleteness. This paper proposes a knowledge graph representation model to infer latent privacy based on the existing data in attribute network. In our model, considering the nodes are heterogeneous, we classify the nodes into attribute nodes and entity nodes. In order to protect the privacy of entities, we don’t follow the previous methods to learn and store the feature embedding of each entity in knowledge graph. Our model focuses in capturing the restriction patterns of attribute nodes, which is safe when merging data from various sources. Given a triplet (entity node, relation, attribute node), firstly, we get the embedding of the entity node by using a sophisticated way to utilize all the information of the node, not only the node connections but also the external text information. Then, we infer the attribute node for the entity node in a certain relation. Finally, we calculate the probability that the triplet is exist. In experiments, we evaluate our model on the tasks of triplet classification and link prediction. Evaluation results show that our approach outperforms the state-of-the-art methods with an accuracy rate of 90.0% in the task of triplet classification on the person attribute knowledge graph FB13. Besides, our model reaches promising performance by MeanRank =5.10, Hits@l = 35.14% and Hits@5=64.94% in the task of conference prediction on the academic network DBLP.
Can Machine/Deep Learning Classifiers Detect Zero-Day Malware with High Accuracy?

The detection of zero-day attacks and vulnerabilities is a challenging problem. It is of utmost importance for network administrators to identify them with high accuracy. The higher the accuracy is, the more robust the defense mechanism will be. In an ideal scenario (i.e., 100% accuracy) the system can detect zero-day malware without being concerned about mistakenly tagging benign files as malware or enabling disruptive malicious code running as none-malicious ones. This paper investigates different machine learning algorithms to find out how well they can detect zero-day malware. Through the examination of 34 machine/deep learning classifiers, we found that the random forest classifier offered the best accuracy. The paper poses several research questions regarding the performance of machine and deep learning algorithms when detecting zero-day malware with zero rates for false positive and false negative.
Reconstructing Cardiac Shape via Constrained Voronoi Diagram and Cyclic Dynamic Time Wrapping From CMR

Reconstructing cardiac shapes from CMR short axis parallel contours is a prerequisite task for downstreaming analysis and diagnosis. This task, however, is not trivial when the inflow and outflow tract must be taken into account. These tracts are a difficulty for reconstructing whole cardiac shapes from CMR. We aimed to develop a method to reconstruct either left cardiac structure or right cardiac structure with minimal user assistance in a unified geometric framework. Ventricular, atrial, and arteries contours were delineated on short axis images. Cyclic dynamic time wrapping (DTW) method were utilized to triangulate two parallel contours on neighboring imaging slices. Reconstructing three contours on inflow and outflow tracts were divided into two parts. The outer part was reconstructed from the lower contour and the convex hull of two upper contours. The interior part was parobolic surfaces fit to points not existing in the convex hull and the Voronoi cell centroids. All cases were automatically reconstructed successfully with average computational time less than one minute. Reconstructed shape have anatomically reasonable structures.
Multi-Set Canonical Correlation Analysis for 3D Abnormal Gait Behaviour Recognition Based on Virtual Sample Generation

Small sample dataset and two-dimensional (2D) approach are challenges to vision-based abnormal gait behaviour recognition (AGBR). The lack of three-dimensional (3D) structure of the human body causes 2D based methods to be limited in abnormal gait virtual sample generation (VSG). In this paper, 3D AGBR based on VSG and multi-set canonical correlation analysis (3D-AGRBMCCA) is proposed. First, the unstructured point cloud data of gait are obtained by using a structured light sensor. A 3D parametric body model is then deformed to fit the point cloud data, both in shape and posture. The features of point cloud data are then converted to a high-level structured representation of the body. The parametric body model is used for VSG based on the estimated body pose and shape data. Symmetry virtual samples, pose-perturbation virtual samples and various body-shape virtual samples with multi-views are generated to extend the training samples. The spatial-temporal features of the abnormal gait behaviour from different views, body pose and shape parameters are then extracted by convolutional neural network based Long Short-Term Memory model network. These are projected onto a uniform pattern space using deep learning based multi-set canonical correlation analysis. Experiments on four publicly available datasets show the proposed system performs well under various conditions.
Reducing Waiting Time at an Intersection with Autnomous and Non-autonomous Vehicles

It has been observed that some vehicles do not use inter-vehicle or inter-road communication in the mixed environment of autonomous vehicles and human-operated vehicles. Therefore, the traffic flow in mixed environments may be worse than in the case in which the environment includes only autonomous vehicles. In our previous research, we proposed a protocol for this environment. However, our evaluation was restricted to limited areas. In this paper, we evaluate our protocol for wide areas and discuss our results.
Structural Dictionary Learning based on Non-convex Surrogate of ℓ₂,₁ Norm for Classification

Recently, group sparse representation which is based on a hypothesis about correlation of coefficient variables has attracted much attention due to its effectiveness and robustness in dictionary learning. Traditional group sparse representation methods use $\ell_{2,1}$ norm to enforce the estimation of models with joint sparsity patterns, which often leads to over-punishment phenomenon. To solve this issue, we replace $\ell_{2,1}$ with non-convex surrogate of $\ell_{2,0}$, and give a general solver for the corresponding optimization algorithm. Experimental results confirm the effectiveness of our proposed method.
Non-Invasive Electrophysiological Mapping Entropy Predicts Atrial Fibrillation Ablation Efficacy Better Than Clinical Characteristics

Success rate of atrial fibrillation (AF) ablation remains far from satisfactory. In this study, a 6 months AF freedom predictive model based on Fuzzy Entropy of non-invasive body surface potential maps is compared with clinical predictors.The study included 29 patients referred for pulmonary vein isolated catheter ablation procedure. Non-invasive electrocardiographic mapping with 54 ECG electrodes was recorded for all patients during the ablation procedure. Six months follow up was used to evaluate the efficacy of the ablation procedure.Predictions based on non-invasive electrocardiographic mappings during adenosine infusion (accuracy: 90%, AUC: 0.93) showed a clear improvement over standard-of-care clinical parameter models (accuracy: 62.1%, AUC: 0. 54). Our results indicate that measurements of electrophysiological complexity of AF signals could improve the clinical practice by predicting the efficacy of AF ablation procedures.
Personalized DeepInf: Enhanced Social Influence Prediction with Deep Learning and Transfer Learning

Social influence is referred to as the phenomenon that one’s opinions or behaviors be affected by others. Nowadays, the potential impact of social influence analysis (SIA) is significant. For example, SIA applications can include viral marketing, online content recommendation. Convention social influence analysis uses hand-crafted features and requires domain expert knowledge. Such an approach is not scalable and introduces a high cost. To overcome these disadvantages, deep learning based approaches was introduced. One of the most recent approaches is DeepInf, which is an end-to-end framework for predicting social influence by learning user’s latent features. We extended DeefInf in the current paper by integrating teleport probability $\alpha$ from the domain of page rank into the graph convolution network (GCN) model to enhance the performance. Furthermore, we also propose an algorithm called hybrid personalized propagation of neural predictions (HPPNP), which shows an impressive performance in terms of prediction accuracy compared to existing methods. We reused the datasets from DeepInf and performed extensive experiments on Open Academic Graph, Twitter, DIGG datasets. By optimally sampling the teleport probability $\alpha$, the experimental results show that our model performs the best when compared with existing methods on different datasets. These results demonstrates the effectiveness of our enhanced personalized DeepInf-namely, HPPNP-in social influence prediction via both deep and transfer learning.