Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-task graph neural network for truck speed prediction under extreme weather conditions

Truck speed prediction plays a key role in truck transportation management. However, it is a very challenging task since the truck traffic usually shows complex patterns. Most of the existing traffic prediction methods lack the ability to model the dynamic spatial-temporal correlations of truck traffic or ignore contributing contextual factors that impact traffic. Also, truck traffic data is typically sparse and noisy, which makes the truck speed prediction an even more challenging task. How to improve the truck speed prediction by taking advantage of other relevant truck traffic information (such as the truck flow) has not been investigated in depth. Additionally, traffic congestions and poor driving conditions caused by extreme weather conditions can make sudden changes in the general pattern of the truck speed. In this paper, we propose a novel Multi-Task Context Based Gated Recurrent Unit Graph Convolutional Network (MT-C2G) to predict the truck speed under extreme weather conditions. MT-C2G includes four major components: The spatial dependence learning component captures the spatial dependencies shaped by the topological structure of the road network. Truck traffic feature temporal dependence modeling component is built to acquire the temporal dependencies involved in the truck traffic features, and contextual feature temporal dependence modeling component employs a layer of GRU units to capture the temporal dependencies of contextual factors. The multi-task learning component then leverages the information between the truck speed and flow prediction tasks through attention mechanism for improving the performance. Moreover, a data augmentation method SMOTE is utilized to balance the data with the extreme weather conditions. Experiments on two real datasets demonstrate that the proposed MT-C2G fairly outperforms six state-of-the-art traffic prediction methods.
Research on Application of Named Entity Recognition of Electronic Medical Records Based on BERT-IDCNN-CRF Model

Bi-LSTM-CRF (Bi-Directional Long Short-Term Memory Conditional Random Field) model have good performance in Chinese medical Electronic Medical Records (EMRS) Named Entity Recognition (NER), However, Bi-LSTM-CRF model cannot make full use of the parallelism of GPU (Graphics Processing Unit) in massive medical records, and the neglect of word order features and semantic information in IDCNN(Iterated Dilated Convolutional Neural Networks) model leads to poor NER effect. Therefore, this paper proposes a BERT-IDCNN-CRF model. In this model, the two-way transformer pre training model BERT is used to fine tune the model parameters in the manual annotated corpus conforming to the BIOES (Begin Inside Outside End Single) standard. The text is learned in an unsupervised manner, and the semantic information of words is represented by word vectors, which can well represent the context semantics in the sentences of EMRS; The state characteristics of character sequences are learned through BERT model, and the sequence state scores obtained are input to the CRF layer. The CRF layer makes constraint optimization on the sequence state transition, and IDCNN has better recognition effect on convolutional coding of local entities. Experimental test results: the average accuracy, recall and F1 value of the BERT-IDCNN-CRF model are 94.5%, 93.8% and 94.1% respectively, which are increased by 4.8%, 4.3% and 3.6% respectively compared with the baseline model Word2Vec-BiLSTM-CRF. The experiment proves that the BERT-IDCNN-CRF model can better identify medical entities in electronic medical records.
TStream: a framework for real-time and scalable trajectory stream processing and analysis

Recent advances in location-aware devices have resulted in an exponential increase in the trajectory data streams. A number of applications require real-time processing and analysis of massive moving objects' trajectories. For instance, route guidance in emergency evacuation, patients tracking, etc. Existing scalable trajectory management systems lack support for real-time processing, while the real-time systems do not natively support spatial trajectory processing. This work presents TStream, a real-time and scalable trajectory stream processing and analysis framework. TStream utilizes grid index to support efficient processing of continuous range, kNN and join queries.
An investigation of tilted aspect‐sensitive scatterers in the lower atmosphere using the MU and Aberystwyth VHF radars

This study investigates subtle variations of the zenith and azimuth dependence of VHF‐radar echo power in the troposphere and lower stratosphere. Using the middle and upper atmosphere (MU) and Aberystwyth radars, we reanalyze data from two areas of the literature on tilted aspect‐sensitive scatterers, linking results from the spatial interferometry (SI) and Doppler beam‐swinging (DBS) techniques. Whereas wind profilers commonly use three or five radar beams, we examine a MU radar data set with 64 beam positions, so that maps of echo power distribution can be plotted as far as 5° from zenith. The power distribution pattern is often skewed, with the azimuth of maximum power being closely related to the wind shear caused by, for example, inertia‐gravity waves in the lower stratosphere. The results imply that inertia‐gravity wave motions are closely coupled to the smaller‐scale wind field, causing patches of Kelvin‐Helmholtz instability and/or steepening of other shorter‐period gravity waves. These effects can alter the distribution of the tilts of aspect‐sensitive scatterers and explain the skewed echo power patterns. The deviations of vertical‐beam incidence angle measured by SI are found to be inappropriate for off‐vertical beams, and it also appears impossible for basic DBS systems to be used for measuring vertical‐beam incidence angles. Further tests of mountain wave data are consistent with the tilted layer model and help to confirm that the azimuth of gravity waves may be calculated using radar echo‐power imbalances.
TrafficAdaptor: an adaptive obfuscation strategy for vehicle location privacy against traffic flow aware attacks

One of the most popular location privacy-preserving mechanisms applied in location-based services (LBS) is location obfuscation, where mobile users are allowed to report obfuscated locations instead of their real locations to services. Many existing obfuscation approaches consider mobile users that can move freely over a region. However, this is inadequate for protecting the location privacy of vehicles, as their mobility is restricted by external factors, such as road networks and traffic flows. This auxiliary information about external factors helps an attacker to shrink the search range of vehicles' locations, increasing the risk of location exposure. In this paper, we propose a vehicle traffic flow aware attack that leverages public traffic flow information to recover a vehicle's real location from obfuscated location. As a countermeasure, we then develop an adaptive strategy to obfuscate a vehicle's location by a "fake" trajectory that follows a realistic traffic flow. The fake trajectory is designed to not only hide the vehicle's real location but also guarantee the quality of service (QoS) of LBS. Our experimental results demonstrate that 1) the new threat model can accurately track vehicles' real locations, which have been obfuscated by two state-of-the-art algorithms, and 2) the proposed obfuscation method can effectively protect vehicles' location privacy under the new threat model without compromising QoS.
Vapnik-Chervonenkis bounds for generalization

The authors review the Vapnik and Chervonenkis theorem as applied to the problem of generalization. By combining some of the technical modifications proposed in the literature they derive tighter bounds and a new version of the theorem bounding the accuracy in the estimation of generalization probabilities from finite samples. A critical discussion and comparison with the results from statistical mechanics is given.
Enhance decoding of functional lower-limb movements by combining sensory motor rhythm and movement-related cortical potential features

In previous studies, Sensory Motor Rhythm (SMR) and Movement-Related Cortical Potential (MRCP) have been proved to be complementary in decoding a variety of motion information. However, no studies have reported whether they are complementary when subjects perform functional lower limb movements. In this work, we investigate the effect of two features or their combination on classifying three functional lower limb movements (standing, walking, sitting) and rest. MRCP features are extracted by Locality Preserving Projection (LPP) and SMR features are extracted by selecting the best frequency-channel pairs through the Bhattacharyya distance. A Support Vector Machine (SVM) classifier was employed to assess the performance of different features or their combination in six binary classification tasks, where three types of lower limb movements are compared with each other or with rest. The combination of two features achieved the highest accuracy in most classification task. In the classification of standing and walking, the combination of these two features has shown significantly better performance (both p < 0.05) than the classifiers using either MRCP or SMR. Our results suggest that MRCP and SMR features are complementary for decoding the functional lower limb movements, which would benefit the Brain-computer Interface (BCI) system for lower limb rehabilitation.
Concerning Trends in Likert Scale Usage in Human-robot Interaction: Towards Improving Best Practices

As robots become more prevalent, the importance of the field of human-robot interaction (HRI) grows accordingly. As such, we should endeavor to employ the best statistical practices in HRI research. Likert scales are commonly used metrics in HRI to measure perceptions and attitudes. Due to misinformation or honest mistakes, many HRI researchers do not adopt best practices when analyzing Likert data. We conduct a review of psychometric literature to determine the current standard for Likert scale design and analysis. Next, we conduct a survey of five years of the International Conference on Human-Robot Interaction (HRIc) (2016 through 2020) and report on incorrect statistical practices and design of Likert scales [1, 2, 3, 5, 7]. During these years, only 4 of the 144 papers applied proper statistical testing to correctly designed Likert scales. We additionally conduct a survey of best practices across several venues and provide a comparative analysis to determine how Likert practices differ across the field of Human-robot Interaction. We find that a venue’s impact score negatively correlates with number of Likert-related errors and acceptance rate, and total number of papers accepted per venue positively correlates with the number of errors. We also find statistically significant differences between venues for the frequency of misnomer and design errors. Our analysis suggests there are areas for meaningful improvement in the design and testing of Likert scales. Based on our findings, we provide guidelines and a tutorial for researchers for developing and analyzing Likert scales and associated data. We also detail a list of recommendations to improve the accuracy of conclusions drawn from Likert data.
Hidden supernova remnants in the large magellanic cloud H II complex N44

We have obtained ROSAT PSPC observations of N44, one of the largest H II complexes in the Large Magellanic Cloud. The X-ray emission mostly fails within the ionized shell structures in N44. We find that one faint shell is a classical supernova remnant overlooked by previous surveys. If we model the two largest shells as pressure-driven superbubbles, the predicted X-ray luminosity falls far below the observed value. Instead, we show that off-center supernova remnants hitting superbubble shells can explain the excess X-ray emission.
Efficient network-constrained trajectory queries

The large search companies have very clearly shown that full-text search on very large datasets can be executed efficiently. In this paper, we show how querying spatio-temporal trajectory data can be converted to a full-text search problem. This allows for the reuse of efficient data and index structures from the full-text domain. The core idea is to convert a trajectory into a document consisting of spatial and temporal terms. For example, spatial terms are municipality names, zip codes, or road-network segment numbers. Temporal terms are, for example, morning, weekday, spring, and 2020. Using a dataset consisting of +62 million trajectories (24.9 billion GPS points) we show how to query this dataset efficiently. These queries cover spatial, temporal, and spatio-temporal queries.
The Effect of Adding Nickel Processing Tailings to Concrete Mixtures on a Laboratory Scale

One of the stages in mining is processing. At the processing stage. minerals are processed to produce products with higher mineral concentrations. This process has by-products called tailings. The Indonesian mining industry as one of the world's nickel producers cannot be separated from this issue. The large nickel production also has an impact on the large tailings production. These tailings are generally just stockpiled without further use. This study aims to provide an understanding of the strength of the material resulting from the addition of tailings to the concrete mixture. Five variations of the material were made by mixing tailings with ratios of 0%, 20%, 40%, 50%, and 60% in the concrete mix. Physical and mechanical properties testing was carried out to obtain the rock strength value from the concrete sample. During this research, the parameters of UCS, Young's Modulus, and Poisson's Ratio were used as indicators of rock strength. Based on the tests that have been carried out, the addition of tailings to the concrete mix generally weakens the strength of the concrete. However, there is a trend of increasing rock strength at a mixture ratio of 60% where the UCS value is 1.324 MPa and Young's modulus is 0.584. Furthermore, porosity and degree of saturation are considered to have a role in these rock strength parameters. This is indicated by the degree of saturation of the 60% ratio, which is 18.8 with an increase in Young's Modulus and UCS. Based on these results, it can be concluded that tailings can be further utilized in concrete mixtures so that their availability in the field can be converted and reduced. Finally, other parameters of the mixing ratio can be performed to complement the weaknesses in this study.
Is Interface Quality and Information Quality on Online Review Matters?

The use of e-marketplace in Indonesia has been growing rapidly for the last decade. The practicality has been the main reason for customers to utilize their shopping activity through e-marketplace. This study aimed to identify and investigate the factors influencing customer purchase intention in the context of e-marketplace. Moreover, this study will be divided into two parts. First part is to present a theoretical perspective of literature in the context of customer purchase intention represented by S-O-R theory and Prospect Theory as basic theories to adopting the model. The second part discusses on a conceptual model, which proposed four antecedent factors (interface quality, information quality of online review, enjoyment, and trust) and one moderating variable: risk, that affect customers purchase intention. The result shows that the interface quality of e- marketplace, information quality of the online review, enjoyment and trust have positive effects towards customer purchase intention. Meanwhile, relationship between trust and purchase intention does not moderate by risk. This result also implied that customers start to consider the usefulness of interface quality and information quality of online reviews before making a purchase. This study suggests that e-marketplaces should place greater reinforcement on developing quality factors of interface and online review on their platform to promote a positive attitude and behavior of customer. Furthermore, this study will help managers to understand the main factors in the online shopping environment which drives customers purchase intention to increase e-marketplace productivity and efficiency.
PEDM: A Multi-task Learning Model for Persona-aware Emoji-embedded Dialogue Generation

As a vivid and linguistic symbol, Emojis have become a prevailing medium interspersed in text-based communication (e.g., social media and chit-chat) to express emotions, attitudes, and situations. Generally speaking, a social-oriented chatbot that can generate appropriate Emoji-embedded responses would be much more competitive, making communications more fun, engaging, and human-like. However, the current Emoji-related research is still in its infancy, leading to an awkward situation of data deficiency. How to develop an Emoji-embedded dialogue system while addressing the lack of data will be interesting and meaningful for the application of future AI. To bridge this gap, we propose a multi-task learning method for persona-aware Emoji-embedded dialogue generation in this article. Specifically, as the benchmark of model training and evaluation, which includes 1.2 million Emoji-embedded tweets and 1.1 million post-response pairs, we first construct a dataset named EmojiTweet to handle the data deficiency problem. Then, a Seq2Seq-based model with multi-task learning is designed to simultaneously learn response generation and Emoji embedding from the constructed non-Emoji dialogue and Emoji-embedded monologue data. Afterward, we incorporate persona factors into our model by adopting persona fusion and personalized bias methods to deliver personalized dialogues with more accurately selected Emojis. Finally, we conduct extensive experiments, where the experimental results and evaluations demonstrate that our model has three key benefits: improved dialogue quality, higher user engagement, and not relying on large-scale Emoji-embedded dialogue data representing specific personas. EmojiTweet will be published publicly via https://mea-lab-421.github. io/EmojiTweet/.
Spatiotemporal heterogeneities of the associations between human mobility and close contacts with COVID-19 infections in the United States

It has been well-established that human mobility has an inseparable relationship with COVID-19 infections. As the COVID-19 pandemic progresses, our knowledge on how human behaviors including mobility and close contact associates with the pandemic also need to stay updated. In this paper, we examine the relationship of the effective reproduction number (Rt) of COVID-19 daily cases with the two indices that provide mobility insights: Mobility Index (CMI) and Contact Index (CCI). Both relationships are evaluated through Maximal Information Coefficient (MIC). Using the Bayesian Change Point Detection and the KShape clustering algorithms, we found significant temporal and spatial heterogeneities among the relationship between two indices and the daily confirmed COVID-19 cases. Although CMI has demonstrated high correlation with COVID-19 cases in 2020, CCI became much more correlated with COVID-19 cases than CMI in 2021. During the first wave in 2020, it is also shown that mobility has a high impact on states outside of Farwest and Southeast than those states within that region.
PATHFINDERVIS

We present PATHFINDERVIS, a tool which is able to efficiently retrieve and visualize massive trajectory data on up to continent-sized networks. PATHFINDERVIS is an extension of the existing PATHFINDER framework which was designed as a pure backend and lacked a visualization. PATHFINDERVIS features many prototypes of different approaches to efficiently transmit and visualize large trajectory data.
Network-aware multi-agent reinforcement learning for the vehicle navigation problem

Traffic congestion is characterized by longer trip times, and increased air pollution. In a static road network, the travel time to a destination is constant and can be computed using the shortest path first algorithm (SPF). However, road network conditions are dynamic, rendering the SPF to perform sub-optimally at times. In addition, in a realistic multiple-vehicle scenario, the SPF routing algorithm can cause congestion by routing all vehicles through the same shortest path. In this paper, we propose a network-aware multi-agent reinforcement learning model for addressing this problem. Our key idea is to assign an RL agent to intersections. Each RL agent operates as a router agent and is responsible for providing routing instructions to approaching vehicles. When a vehicle reaches an intersection, it submits a routing query to the RL agent consisting of its final destination. The RL agent generates a routing response based on (i) the destination, (ii) the current state of the road network, and (iii) routing policies learned by cooperating with other neighboring RL agents. Our experimental evaluation shows that the proposed MARL model outperforms the SPF algorithm by (up to) 20.2% in average travel time.
A new species of Isometrus Ehrenberg, 1828 (Scorpiones: Buthidae) from southeastern plains of Tamil Nadu, India

Euscorpius is the first research publication completely devoted to scorpions (Arachnida: Scorpiones). Euscorpius takes advantage of the rapidly evolving medium of quick online publication, at the same time maintaining high research standards for the burgeoning field of scorpion science (scorpiology). Euscorpius is an expedient and viable medium for the publication of serious papers in scorpiology, including (but not limited to): systematics, evolution, ecology, biogeography, and general biology of scorpions. Review papers, descriptions of new taxa, faunistic surveys, lists of museum collections, and book reviews are welcome. We here describe a new species of Isometrus Ehrenberg, 1828 from the southeastern plains of Tamil Nadu, India, which is known only from its type locality, Chengalpattu. The new species was recovered as sister to Isometrus nakshatra based on a molecular phylogeny inferred from COI and 16S mitochondrial genes. Sequence divergence between the new species and all the Indian congeners was 12.6–15.7 % for COI and 12.2–14.4 % for 16S. The new species can also be differentiated from all the Indian congeners based on the following set of characters: surface of carapace with mixed (coarse and fine) and dense granulation; telson vesicle length to depth ratio in males 3.5–3.7; chela length to width ratio in males 5.0–5.4; metasomal length to carapace length ratio in males 8.2–8.9; coarse granulation on mesosomal tergites V and VI along the margins; lateral supramedian and ventral lateral carinae on metasomal segments II–IV moderately to weakly granular; ventral median carina of telson vesicle weakly granular; spiniform granules of promedian carina of the pedipalp patella strongly developed. This is the ninth species of Isometrus described from India and the first species from southeastern peninsular India. This discovery highlights the need for more surveys from the eastern peninsular region.