Pedestrian detection is paramount for advanced driver assistance systems (ADAS) and autonomous driving. As a key technology in computer vision, it also finds many other applications, such as security surveillance etc. Generally, pedestrian conducted images visible spectrum, which are not suitable night time detection. Infrared (IR) or thermal imaging often adopted due to its capability of capturing the emitted energy from pedestrians. The process firstly extracts candidate pedestrians...

Dynamic recommendation is essential for modern recommender systems to provide real-time predictions based on sequential data. In real-world scenarios, the popularity of items and interests users change over time. Based this assumption, many previous works focus interaction sequences learn evolutionary embeddings items. However, we argue that sequence-based models are not able capture collaborative information among directly. Here propose Graph Collaborative Filtering (DGCF), a novel...

A critical issue in autonomous vehicle navigation and advanced driver assistance systems (ADAS) is the accurate real-time detection of traffic lights. Typically, vision-based sensors are used to detect light. However, lights using computer vision, image processing, learning algorithms not trivial. The challenges include appearance variations, illumination reduced information low conditions. To address these challenges, we present a visual camera-based light algorithm, where identify...

Mining Electronic Health Records (EHRs) becomes a promising topic because of the rich information they contain. By learning from EHRs, machine models can be built to help human expert make medical decisions and thus improve healthcare quality. Recently, many based on sequential or graph model are proposed achieve this goal. EHRs contain multiple entities relations, viewed as heterogeneous graph. However, previous studies ignore heterogeneity in EHRs. On other hand, current neural networks...

Making fast driving decisions at intersections is a challenging problem for improving safety of autonomous vehicles. Furthermore, representing sensor data in machine understandable format essential to enable vehicles understand traffic situations. Ontologies are used represent knowledge aware In this paper, we introduce decision making system, which utilizes only related part the ontology-based base make intersections. The system performs real-time reasoning using regulations and map...

Graphs are popularly used to model structural relationships between objects. In many application domains such as social networks, sensor networks and telecommunication, graphs evolve over time. this paper, we study a new problem of discovering the subgraphs that exhibit significant changes in evolving graphs. This is challenging since it hard define changing regions closely related actual (i.e., additions/deletions edges/nodes) We formalize problem, design an efficient algorithm able...

American Water Works Association has estimated that, by 2050, the total cost of pipeline system management will exceed $1.7 trillion. Thus, it is important to assess performance water mains in order optimize rehabilitation process. Recently, use machine learning methods pipe line condition prediction increased. However, existing models rely solely on underlying data-generating distributions and do not accommodate different datasets. Hence, a stacking ensemble based method proposed this work...

Recently, Graph Neural Networks (GNNs) have proven their effectiveness for recommender systems. Existing studies applied GNNs to capture collaborative relations in the data. However, real-world scenarios, a recommendation graph can be of various kinds. For example, two movies may associated either by same genre or director/actor. If we use single elaborate all these relations, too complex process. To address this issue, bring idea pre-training process step step. Based on divide-and-conquer,...

Medical triage chatbot is widely used in pre-diagnosis by asking symptom and medical history-related questions. Information collected from patients through an online system often incomplete imprecise, thus it's essentially hard to achieve precise triaging. In this paper, we propose Multi-relational Hyperbolic Diagnosis Predictor (MHDP) --- a novel multi-relational hyperbolic graph neural network-based approach, build disease predictive model. More specifically, MHDP, generate heterogeneous...

Graph patterns are able to represent the complex structural relations among objects in many applications various domains. The objective of graph summarization is obtain a concise representation single large graph, which interpretable and suitable for analysis. A good summary can reveal hidden relationships between nodes graph. key issue how construct high-quality representative super-graph, GS, super-node summarizes collection based on similarity attribute values neighborhood associated with...

Health disparities, or inequalities between different patient demographics, are becoming a crucial issue in medical decision-making, especially Electronic Record (EHR) predictive modeling. In order to ensure the fairness of sensitive attributes, conventional studies mainly adopt calibration re-weighting methods balance performance on among demographic groups. However, we argue that these have some limitations. First, usually mean making trade-off model's and fairness. Second, many attribute...

Session-based Recommendation (SBR) is to predict users' next interested items based on their previous browsing sessions. Existing methods model sessions as graphs or sequences estimate user interests interacted make recommendations. In recent years, graph-based have achieved outstanding performance SBR. However, none of these consider temporal information, which a crucial feature in SBR it indicates timeliness currency. Besides, the session exhibit hierarchical structure and are demonstrated...

Click-through rate (CTR) prediction plays a predominant role in the online advertisements. CTR is problem of binary classification with imbalanced data. Many existing approaches for imbalance learning only focus on over-sampling and under-sampling, but these methods definitely ignore some vital information original In this paper, we first propose weighted output extreme machine (WO-ELM) to learn A hierarchical (H-C-ELM) proposed based WO-ELM (W-ELM). The H-C-ELM has two levels its structure....

Nowadays, the concept of “digital twin” has received great attention from both academia and industry. However, few methodological solutions have been reported in existing studies. This paper presents a life prediction method for aircraft structure, illustrates how this can be embedded into framework. fuse heterogeneous information acquired inspected physic entity, fifinite element software, historical database predictive model, giving an accurate real-time remaining useful (RUL) structure....

Recent studies on Next-basket Recommendation (NBR) have achieved much progress by leveraging Personalized Item Frequency (PIF) as one of the main features, which measures frequency user's interactions with item. However, taking PIF an explicit feature incurs bias towards frequent items. Items that a user purchases frequently are assigned higher weights in PIF-based recommender system and appear more personalized recommendation list. As result, will lose fairness balance between items never...

Service-oriented business process generation is a key activity in the Service-Oriented Architecture (SOA) lifecycle, and most of other activities such as application execution depend on being developed. After requirements are acquired, developer has to use specific programming technologies orchestrate web services generate deployable process. It time-consuming specify all processes from low-level services, especially for an enterprise that focuses series similar businesses. This paper...

Unintentional modulation (UIM), which is unavoidable and unique to individual emitters, can be used reliably realize emitter identification. Previous identification methods extract features from either some parts of the signal, ignoring UIM on other parts, or immediately whole resulting in heavy computational loads. In this paper, we take structure into consideration, propose a new feature extraction scheme. We first analyze mechanism UIM, realizing that jitter frequency intensity...

To address the problem that multipath interference of intercepted radar pulse signals in practical scenarios is obvious, leading to a significant decrease accuracy specific emitter identification. In this paper, new radiation source fingerprint proposed, which takes forward quotient neighboring points signal spectrum as fingerprint, and weakens by improving frequency resolution, with good anti-multipath capability. A fast extraction method using Chirp-Z transform for local high-resolution...