Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Traffic Congestion Propagation Patterns: A Propagation Graph Approach

A traffic congestion in a road network may propagate to upstream road segments. Such a congestion propagation may make a series of connected road segments congested in the near future. Given a spatial-temporal network and congested road segments in current time, the aim of predicting traffic congestion propagation pattern is to predict where those congestion will propagate to. This can provide users (e.g. city officials) with valuable information on how congestion will propagate in the near future to help mitigating emerging congestions. However, it is challenging to predict in real-time due to complex propagation process between roads and high computational intensity caused by large dataset. Recent studies have been focusing on finding frequent or most likely congestion propagation patterns in historical data. In contrast, this research will address the problem of predicting congestion propagation patterns in the near future. We predict the footprint of congestion propagation as Propagation Graphs (Pro-Graphs) where the root of each Pro-Graph is a set of congested roads propagating congestion to nearby roads. We propose an efficient algorithm called PPI_Fast to achieve this prediction. Our experiments on real-word dataset from Shenzhen, China shows that the PPI_Fast is able to predict near future propagations with AUC of 0.75 and improves the running time of the baseline algorithm. Two case studies have been done to show our work can find meaningful patterns.
American Sign Language Recognition Using Leap Motion Controller with Machine Learning Approach

Sign language is intentionally designed to allow deaf and dumb communities to convey messages and to connect with society. Unfortunately, learning and practicing sign language is not common among society; hence, this study developed a sign language recognition prototype using the Leap Motion Controller (LMC). Many existing studies have proposed methods for incomplete sign language recognition, whereas this study aimed for full American Sign Language (ASL) recognition, which consists of 26 letters and 10 digits. Most of the ASL letters are static (no movement), but certain ASL letters are dynamic (they require certain movements). Thus, this study also aimed to extract features from finger and hand motions to differentiate between the static and dynamic gestures. The experimental results revealed that the sign language recognition rates for the 26 letters using a support vector machine (SVM) and a deep neural network (DNN) are 80.30% and 93.81%, respectively. Meanwhile, the recognition rates for a combination of 26 letters and 10 digits are slightly lower, approximately 72.79% for the SVM and 88.79% for the DNN. As a result, the sign language recognition system has great potential for reducing the gap between deaf and dumb communities and others. The proposed prototype could also serve as an interpreter for the deaf and dumb in everyday life in service sectors, such as at the bank or post office.
Modeling Consumer Buying Decision for Recommendation Based on Multi-Task Deep Learning

Although marketing researchers and sociologists have recognized the importance of buying decision process and its significant influence on consumer's purchasing behaviors, existing recommender systems do not explicitly model the consumer buying decision process or capture the sequential regularities of what happens before and after each purchase. In this paper, we try to bridge the gap and improve recommendation systems by explicitly modeling consumer buying decision process and corresponding stages. In particular, we propose a multi-task learning model with long short-term memory networks (LSTM) to learn consumer buying decision process. It maps items, users, product categories, and the behavior sequences into real valued vectors, with which the probability of purchasing a product can be estimated. In this way, the model can capture user intentions and preferences, predicts the conversion rate of each candidate product, and makes recommendations accordingly. Experiments on real world data demonstrate the effectiveness of the proposed approach.
Nonnative plants reduce population growth of an insectivorous bird

Significance Nonnative plants lack an evolutionary history with native fauna and support insect communities that are less abundant and diverse, and this may change food availability for vertebrate consumers. Most insectivorous birds are absent or declining in urban areas, yet no study has tested whether nonnative plants impact bird populations via food limitation. We monitored reproduction and survival of Carolina chickadees within residential yards and found that when nonnative plants increased, both insect availability and chickadee population growth declined. We also found that populations could only be sustained if nonnative plants constituted <30% of plant biomass. Our results demonstrate that nonnative plants reduce habitat quality for insectivorous birds and restoration of human-dominated areas should prioritize native plants to support local food webs. Human-dominated landscapes represent one of the most rapidly expanding and least-understood ecosystems on earth. Yet, we know little about which features in these landscapes promote sustainable wildlife populations. Historically, in urban areas, landowners have converted native plant communities into habitats dominated by nonnative species that are not susceptible to pest damage and require little maintenance. However, nonnative plants are also poor at supporting insects that are critical food resources for higher order consumers. Despite the logical connection, no study has examined the impact of nonnative plants on subsequent population responses of vertebrate consumers. Here, we demonstrate that residential yards dominated by nonnative plants have lower arthropod abundance, forcing resident Carolina chickadees (Poecile carolinensis) to switch diets to less preferred prey and produce fewer young, or forgo reproduction in nonnative sites altogether. This leads to lower reproductive success and unsustainable population growth in these yards compared with those with >70% native plant biomass. Our results reveal that properties landscaped with nonnative plants function as population sinks for insectivorous birds. To promote sustainable food webs, urban planners and private landowners should prioritize native plant species.
Vehicle Positioning Method Based on RFID in VANETs

This1 paper proposes a new vehicle positioning method based on RFID (Radio Frequency Identification). Vehicles obtain two data from different position: the GPS coordinated from the GPS receiver as well as the accurate physical location gained by RFID communication. And the error of GPS can be calculated by the two data, then inaccurate GPS coordinates of adjacent Vehicles can be corrected using the GPS error. The vehicle can be served as a base station, and the adjacent vehicles can take advantage of the GPS error from base station to calculate accurate position. But with the distance between vehicles is increasing, vehicles position accuracy is increase. For the sake of reducing the loss of accuracy caused by SNR(Signal to Noise Ratio) dropping, kalman filter well be used to correcte the offset error.
Multiple Manifold Regularized Sparse Coding for Multi-View Image Clustering

Multi-view clustering has received an increasing attention in many applications, where different views of objects can provide complementary information to each other. Existing approaches on multi-view clustering mainly focus on extending Non-negative Matrix Factorization (NMF) by enforcing the constraint over the coefficient matrices from different views in order to preserve their consensus. In this paper, we argue that it is more reasonable to utilize the high-level manifold consensus rather than the low-level coefficient matrix consensus to better capture the underlying clustering structure of the data. Moreover, it is also effective to utilize the sparse coding framework, instead of the NMF framework, to deal with the sparsity issue. To this end, we propose a novel approach, named Multiple Manifold Regularized Sparse Coding (MMRSC). Experimental results on two publicly available real-world image datasets demonstrate that our proposed approach can significantly outperform the state-of-the-art approaches for the multi-view image clustering task.
A Two-Layer Algorithmic Framework for Service Provider Configuration and Planning with Optimal Spatial Matching

Industrial telecommunication applications prefer to run at the optimal capacity configuration to achieve the required Quality of Service (QoS) at the minimum cost. The optimal capacity configuration is usually achieved through the selection of cell towers capacities and locations. Given a set of service providers (e.g., cell towers) and a set of customers (e.g., major residential areas), where each customer has an amount of demand and each provider has multiple candidate capacities and corresponding costs, the optimal capacity selection is configured through spatial matching to satisfy the demand of each customer at the minimum cost. However, existing solutions developed for spatial matching, in which each provider's capacity is fixed, cannot be directly applied to the capacity configuration problem with multiple capacities and location selections. In this paper, we are the first to study Service Provider Configuration and Planning with Optimal Spatial Matching (SPC-POSM) problem, in which the objectives are 1) to select the proper capacity for each provider at the minimum total cost and 2) to assign providers' service to satisfy the demand of each customer on a condition that the matching distance is no more than service quality requirement. We prove that SPC-POSM problem is NP-hard and design an efficient two-layer meta-heuristic framework to solve the problem. Unsupervised learning technique is designed to accelerate the calculation and a novel local search mechanism is embedded to further improve solution quality. Extensive experimental results on both real and synthetic datasets verify the effectiveness and efficiency of the proposed framework.
Hotspot Interaction in Omnidirectional Videos Using Head-Mounted Displays

Omnidirectional videos (ODVs) -- or 360° videos -- are often viewed using a head-mounted display (HMD), allowing users to look around the scene naturally by turning their head. These videos may be populated with visual markers, hotspots, that can contain additional information of points of interest or provide additional functionality. However, optimal ways for interacting and presenting these hotspots remain unclear. We conducted a 16-participant user study where participants interacted with hotspots with different properties while exploring ODVs. We tested 1) two hotspot activation types, dwell time and fade-in, 2) small and large hotspot icon sizes, and 3) centered and left-edge alignments for the appearing content. We found that a combination of fade-in activation, centered alignment, and large icons generally performs the best, but also that user preferences are diverse. Based on our results, we provide recommendations for optimizing the designs for systems utilizing hotspot interactions with HMDs.
BitStream: Efficient Computing Architecture for Real-Time Low-Power Inference of Binary Neural Networks on CPUs

Convolutional Neural Networks (CNN) have been widely used in many multimedia applications such as image classification, speech recognition, natural language processing and so on. Nevertheless, the high performance of deep CNN models also comes with high consumption of computation and storage resources, making it difficult to run CNN models in real time applications on mobile devices, where computational ability, memory resource and power are largely constrained. Binary network is a recently proposed tech- nique to reduce the computational and memory complexity of CNN, in which the expensive floating-point operations can be replaced by much cheaper bit-wise operations. Despite its obvious advantages, only few works explored the efficient implementation of Binary Neural Networks (BNN). In this work, we present a general architecture for efficient binary convolution referred to as BitStream with the latest computation flow for BNNs instead of the traditional row-major im2col based one. We mainly optimize memory access during computation of BNNs, the proposed calculation flow is cache friendly as well as can largely reduce memory overhead of BNNs, decidedly leading to its memory efficiency and further computational efficiency. Extensive evaluations on various networks demon- strate the efficiency of the proposed method. For instance, memory consumption of BitStream is reduced by 18-32× than original networks and 3× than existing implementations of BNNs during inference. Moreover, our implemented binary Alexnet can achieve 8.07× and 2.84× speedup over floating point precision model and conventional implementations of BNNs on 8 × Cortex A53 CPU s. With 4 × Intel CORE i7 6700 CPUs, the binary vgg-like convolutional network on CIFAR-10 runs even 1.69× faster than floating point precision version on TitanX GPU.
Which eHealth interventions are most effective for smoking cessation? A systematic review

Purpose To synthesize evidence of the effects and potential effect modifiers of different electronic health (eHealth) interventions to help people quit smoking. Methods Four databases (MEDLINE, PsycINFO, Embase, and The Cochrane Library) were searched in March 2017 using terms that included “smoking cessation”, “eHealth/mHealth” and “electronic technology” to find relevant studies. Meta-analysis and meta-regression analyses were performed using Mantel–Haenszel test for fixed-effect risk ratio (RR) and restricted maximum-likelihood technique, respectively. Protocol Registration Number: CRD42017072560. Results The review included 108 studies and 110,372 participants. Compared to nonactive control groups (eg, usual care), smoking cessation interventions using web-based and mobile health (mHealth) platform resulted in significantly greater smoking abstinence, RR 2.03 (95% CI 1.7–2.03), and RR 1.71 (95% CI 1.35–2.16), respectively. Similarly, smoking cessation trials using tailored text messages (RR 1.80, 95% CI 1.54–2.10) and web-based information and conjunctive nicotine replacement therapy (RR 1.29, 95% CI 1.17–1.43) may also increase cessation. In contrast, little or no benefit for smoking abstinence was found for computer-assisted interventions (RR 1.31, 95% CI 1.11–1.53). The magnitude of effect sizes from mHealth smoking cessation interventions was likely to be greater if the trial was conducted in the USA or Europe and when the intervention included individually tailored text messages. In contrast, high frequency of texts (daily) was less effective than weekly texts. Conclusions There was consistent evidence that web-based and mHealth smoking cessation interventions may increase abstinence moderately. Methodologic quality of trials and the intervention characteristics (tailored vs untailored) are critical effect modifiers among eHealth smoking cessation interventions, especially for web-based and text messaging trials. Future smoking cessation intervention should take advantages of web-based and mHealth engagement to improve prolonged abstinence.
Argon plasma improves the tissue integration and angiogenesis of subcutaneous implants by modifying surface chemistry and topography

Background Tissue integration and vessel formation are important criteria for the successful implantation of synthetic biomaterials for subcutaneous implantation. Objective We report the optimization of plasma surface modification (PSM) using argon (Ar), oxygen (O2) and nitrogen (N2) gases of a polyurethane polymer to enhance tissue integration and angiogenesis. Methods The scaffold’s bulk and surface characteristics were compared before and after PSM with either Ar, O2 and N2. The viability and adhesion of human dermal fibroblasts (HDFs) on the modified scaffolds were compared. The formation of extracellular matrix by the HDFs on the modified scaffolds was evaluated. Scaffolds were subcutaneously implanted in a mouse model for 3 months to analyze tissue integration, angiogenesis and capsule formation. Results Surface analysis demonstrated that interfacial modification (chemistry, topography and wettability) achieved by PSM is unique and varies according to the gas used. O2 plasma led to extensive changes in interfacial properties, whereas Ar treatment caused moderate changes. N2 plasma caused the least effect on surface chemistry of the polymer. PSM-treated scaffolds significantly (P<0.05) enhanced HDF activity and growth over 21 days. Among all three gases, Ar modification showed the highest protein adsorption. Ar-modified scaffolds also showed a significant upregulation of adhesion-related proteins (vinculin, focal adhesion kinase, talin and paxillin; P<0.05) and extracellular matrix marker genes (collagen type I, fibronectin, laminin and elastin) and deposition of associated proteins by the HDFs. Subcutaneous implantation after 3 months demonstrated the highest tissue integration and angiogenesis and the lowest capsule formation on Ar-modified scaffolds compared with O2- and N2-modified scaffolds. Conclusion PSM using Ar is a cost-effective and efficient method to improve the tissue integration and angiogenesis of subcutaneous implants.
Can User Behaviour Sequences Reflect Perceived Novelty?

Serendipity is highly valued as a process for developing original solutions to problems and for innovation. However, it is difficult to capture and thus difficult to measure, but novelty is a key and critical indicator. In this work, we investigate the relationship between user behavioural actions and perceived novelty in the context of browsing. 180 participants completed an open-ended browsing task, while their behaviour actions were tracked. Each seven-action sequence was analysed with respect to the participant's perception of Novelty. Results showed that 6 of the 7 actions map to a sub-sequence that discriminates between high and low novelty. Notably, switching between exploration and immersion, and checking SERPs about the same request in-depth are indicative of highly perceived novelty. The results show that analysing behavioural action sequences leads to better prediction of novelty, and thus the potential for serendipity, than individual browsing actions.
Effective User Interaction for High-Recall Retrieval: Less is More

High-recall retrieval --- finding all or nearly all relevant documents --- is critical to applications such as electronic discovery, systematic review, and the construction of test collections for information retrieval tasks. The effectiveness of current methods for high-recall information retrieval is limited by their reliance on human input, either to generate queries, or to assess the relevance of documents. Past research has shown that humans can assess the relevance of documents faster and with little loss in accuracy by judging shorter document surrogates, e.g.\ extractive summaries, in place of full documents. To test the hypothesis that short document surrogates can reduce assessment time and effort for high-recall retrieval, we conducted a 50-person, controlled, user study. We designed a high-recall retrieval system using continuous active learning (CAL) that could display either full documents or short document excerpts for relevance assessment. In addition, we tested the value of integrating a search engine with CAL. In the experiment, we asked participants to try to find as many relevant documents as possible within one hour. We observed that our study participants were able to find significantly more relevant documents when they used the system with document excerpts as opposed to full documents. We also found that allowing participants to compose and execute their own search queries did not improve their ability to find relevant documents and, by some measures, impaired performance. These results suggest that for high-recall systems to maximize performance, system designers should think carefully about the amount and nature of user interaction incorporated into the system.
searchrefiner: A Query Visualisation and Understanding Tool for Systematic Reviews

We present an open source tool, searchrefiner, for researchers that conduct medical systematic reviews to assist in formulating, visualising, and understanding Boolean queries. The searchrefiner web interface allows researchers to explore how Boolean queries retrieve citations in existing, popular query syntaxes used in systematic review literature search. The web interface allows researchers to perform tasks such as using validation citations to ensure queries are retrieving a minimum set of known relevant citations, and editing Boolean queries by dragging and dropping clauses in a structured editor. In addition, the tools provided by the searchrefiner interface allow researchers to visualise why the queries they formulate retrieve citations, and ways to understand how to refine queries into more effective ones. searchrefiner is targeted at both experts and novices, as a tool for query formulation and refinement, and as a tool for training users to search for literature to compile systematic reviews. The searchrefiner website located at https://ielab.io/searchrefiner contains information about how to download, install, and use the tool, as well as a link to an online hosted version for demonstration purposes.
Zoom-SVD: Fast and Memory Efficient Method for Extracting Key Patterns in an Arbitrary Time Range

Given multiple time series data, how can we efficiently find latent patterns in an arbitrary time range? Singular value decomposition (SVD) is a crucial tool to discover hidden factors in multiple time series data, and has been used in many data mining applications including dimensionality reduction, principal component analysis, recommender systems, etc. Along with its static version, incremental SVD has been used to deal with multiple semi-infinite time series data and to identify patterns of the data. However, existing SVD methods for the multiple time series data analysis do not provide functionality for detecting patterns of data in an arbitrary time range: standard SVD requires data for all intervals corresponding to a time range query, and incremental SVD does not consider an arbitrary time range. In this paper, we propose Zoom-SVD, a fast and memory efficient method for finding latent factors of time series data in an arbitrary time range. Zoom-SVD incrementally compresses multiple time series data block by block to reduce the space cost in storage phase, and efficiently computes singular value decomposition (SVD) for a given time range query in query phase by carefully stitching stored SVD results. Through extensive experiments, we demonstrate that Zoom-SVD is up to 15x faster, and requires 15x less space than existing methods. Our case study shows that Zoom-SVD is useful for capturing past time ranges whose patterns are similar to a query time range.
More than Threads: Identifying Related Email Messages

In the typical state of an ever growing mailbox, it becomes essential to assist the user to better organize and quickly look up the content of his electronic life. Our work addresses this challenge, by identifying related messages within a user's mailbox. We study the notion of semantic relatedness between email messages and aim to offer the user with a wider context of the message he selects or reads. The context is represented by a small set of messages that are semantically related to the given message. We conduct experiments on a large-scale mail dataset obtained from a major Web mail service and demonstrate the effectiveness of our model in this task.