Miracle-dz/UniFAR-FTUs huggingface dataset:
T-Reduce: Route-Aware Mobile Trajectory Data Reduction in Transportation Management Systems

Due to the increasingly popular e-commerce and rapidly growing global market of delivery services, transportation management system (TMS) has been playing a critical role in minimizing transportation delay and cost as well as improving reliability. However, how to efficiently keep track of running vehicles and judiciously store their corresponding trajectories become a key issue in the presence of scarce wireless bandwidth and limited storage space. In this paper, we propose a novel mobile trajectory reduction scheme, called T-Reduce, to reduce the size of trajectory data stored in a TMS server. We first develop a path refinement operation to adjust raw location update data transmitted from vehicles to a given road topology as close as possible to improve their location accuracy. Second, we develop a route matching operation consisting of three sub-operations, route extraction, route recognition, and trajectory generation, for the server to identify and extract a set of routes by decomposing the received location update data. Then, the server can store each trajectory as a set of corresponding route $ids$ instead of storing entire location update data. We set up a small-scale testbed, implement the proposed scheme with a real-world trace data collected from a logistic company, and conduct extensive experiments for performance evaluation and comparison. The experimental results show that the proposed scheme can significantly reduce the average trajectory error and route information up to 26.4% and 88%, respectively, compared to that of two prior trajectory-based and corner-based approaches. The proposed approach can also achieve up to 5.72 times cost efficiency compared to the prior approaches.
High-Precision Design of Pedestrian Mobility for Smart City Simulators

The unprecedented growth of the population living in urban environments calls for a rational and sustainable urban development. Smart cities can fill this gap by providing the citizens with high-quality services through efficient use of Information and Communication Technology (ICT). To this end, active citizen participation with mobile crowdsensing (MCS) techniques is a becoming common practice. As MCS systems require wide participation, the development of large scale real testbeds is often not feasible and simulations are the only alternative solution. Modeling the urban environment with high precision is a key ingredient to obtain effective results. However, currently existing tools like OpenStreetMap (OSM) fail to provide sufficient levels of details. In this paper, we apply a procedure to augment the precision (AOP) of the graph describing the street network provided by OSM. Additionally, we compare different mobility models that are synthetic and based on a realistic dataset originated from a well known MCS data collection campaign (ParticipAct). For the dataset, we propose two arrival models that determine the users' arrivals and match the experimental contact distribution. Finally, we assess the scalability of AOP for different cities, verify popular metrics for human mobility and the precision of different arrival models.
Scalable Coverage Path Planning for Cleaning Robots Using Rectangular Map Decomposition on Large Environments

The goal of coverage path planning is to create a path that covers the entire free space in a given environment. Coverage path planning is the most important component of cleaning robot technology, because it determines the cleaning robot’s movement. When the environment covered by a cleaning robot is extremely large and contains many obstacles, the computation for coverage path planning can be complicated. This can result in significant degradation of the execution time for coverage path planning. Not many studies have focused on the scalability of coverage path planning methods. In this paper, we propose a scalable coverage path planning method based on rectangular map decomposition. The experimental results demonstrate that the proposed method reduces the execution time for coverage path planning up to 14 times when compared with conventional methods.
Proposal of an Internet of Things Platform Using Social Graphs

This paper proposes a new internet of things platform to share function of devices to users that meet flexible conditions. Internet of things is an architecture to retrieve information of content and uses functions of devices. In previous studies, internet of things' platforms focus mainly on sensing environments by connecting sensors and can only set the sharing condition from either private or public. However, most devices belong to person, enterprise, industry and infrastructure require a platform to set sharing conditions by owner relationship and device characteristics. In this study, this requirement for the internet of things platform is considered. To meet the requirement, a new platform using social network information is implemented.
A Framework for Compact and Improved Panoramic VR Dissemination

Panoramic capture devices in Cultural Heritage are becoming widely available to consumer market, also due to comfortable interactive online dissemination and to the growth of VR segment. VR fruition through an HMD although, requires a virtual 3D representation to provide consistency in terms of experience, scale and spatial perception, overcoming limitations of standard approaches in orientation+positional HMD tracking model. However, modeling of 3D scenes and especially optimization of acquired dataset, are often time-consuming tasks: these are further stressed when dealing with latency-free demands of latest HMDs. In this paper, we propose a novel framework for panoramic acquisition and an improved data model for VR dissemination: spherical panoramas, omnidirectional depth-maps and semantic annotations are encoded into a compact, coherent representation that suits modern HMD needs and low-cost VR devices. We describe advantages of our approach in terms of acquisition pipeline, presence and depth perception in HMDs fruition, discussing also visualization efficiency in online contexts. We present a few case studies where we applied the methodology and the workflow we adopted, comparing results. We discuss integration of existing desktop toolkits into the pipeline, dissemination capabilities through recent WebVR API and framework advantages for immersive VR panoramic video streaming.
Laktatazidose – Update 2018

Was ist neu? Schwere Hyperlaktatämie bei Intensivpatienten Die Ursachen dafür sind vielfältig, am häufigsten liegen eine Sepsis bzw. ein septischer, kardiogener oder hämorrhagischer Schock vor. Eine Hyperlaktatämie ist ein unabhängiger Prädiktor für Versterben bei verschiedenen Gruppen kritisch kranker Patienten. Bei Serum-Laktatwerten > 10 mmol/l sterben 80 % der Patienten auf der Intensivstation. Persistiert die schwere Laktatazidose über 48 Stunden, so sterben alle Patienten. Erhöhte Laktatwerte erfordern eine umgehende diagnostische Abklärung und Einordnung. Sepsis und septischer Schock Die neue Definition der Sepsis erfordert für den septischen Schock ein Serum-Laktat von > 2 mmol/l bei adäquater Volumensubstitution sowie eine Vasopressoren-Gabe, um bei persistierender Hypotonie einen mittleren arteriellen Druck ≥ 65 mmHg zu erzielen. Das 2018 publizierte „1-Stunden-Bündel“ der Surviving-Sepsis-Campaign empfiehlt als erste Maßnahme die Bestimmung des Laktatwertes – erhöhte Laktatwerte müssen engmaschig kontrolliert werden. Zudem werden die Abnahme von Blutkulturen, die Gabe eines Breitspektrum-Antibiotikums sowie die Gabe von Flüssigkeit und Vasopressoren in der ersten Stunde empfohlen. Die Gabe von Kristalloiden wird bei einem erhöhten Laktatwert (Laktat ≥ 4 mmol/l) oder bestehender Hypotension empfohlen, die Flüssigkeitsgabe kann anhand der Laktat-Clearance ausgerichtet werden. Laktat bei Lebererkrankungen Bei Patienten mit einer Leberfunktionseinschränkung ist der Laktatmetabolismus verzögert. Der Laktatwert bei Aufnahme auf die Intensivstation ist bei Leberzirrhose signifikant mit der Anzahl an versagenden Organen sowie der Mortalität assoziiert. Die 12-Stunden-Laktat-Clearance hat bei Ausgangslaktatwerten von über 5 mmol/l eine starke prädiktive Aussage für das Überleben, welche auch nach Korrektur für die Schwere der zugrunde liegenden Erkrankung ein unabhängiger Prädiktor bleibt. Je stärker der Abfall des Laktats innerhalb der initialen Therapie war, desto besser ist das Outcome.
NMFGO: Gene Function Prediction via Nonnegative Matrix Factorization with Gene Ontology

Gene Ontology (GO) is a controlled vocabulary of terms that describe molecule function, biological roles, and cellular locations of gene products (i.e., proteins and RNAs), it hierarchically organizes more than 43,000 GO terms via the direct acyclic graph. A gene is generally annotated with several of these GO terms. Therefore, accurately predicting the association between genes and massive terms is a difficult challenge. To combat with this challenge, we propose an matrix factorization based approach called NMFGO. NMFGO stores the available GO annotations of genes in a gene-term association matrix and adopts an ontological structure based taxonomic similarity measure to capture the GO hierarchy. Next, it factorizes the association matrix into two low-rank matrices via nonnegative matrix factorization regularized with the GO hierarchy. After that, it employs a semantic similarity based <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq1-2861379.gif"/></alternatives></inline-formula> nearest neighbor classifier in the low-rank matrices approximated subspace to predict gene functions. Empirical study on three model species (<italic>S. cerevisiae</italic>, <italic>H. sapiens</italic>, and <italic>A. thaliana</italic>) shows that NMFGO is robust to the input parameters and achieves significantly better prediction performance than GIC, TO, dRW-<inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq2-2861379.gif"/></alternatives></inline-formula>NN, and NtN, which were re-implemented based on the instructions of the original papers. The supplementary file and demo codes of NMFGO are available at <uri>http://mlda.swu. edu.cn/codes.php? name=NMFGO</uri>.
PhraseCTM: Correlated Topic Modeling on Phrases within Markov Random Fields

Recent emerged phrase-level topic models are able to provide topics of phrases, which are easy to read for humans. But these models are lack of the ability to capture the correlation structure among the discovered numerous topics. We propose a novel topic model PhraseCTM and a two-stage method to find out the correlated topics at phrase level. In the first stage, we train PhraseCTM, which models the generation of words and phrases simultaneously by linking the phrases and component words within Markov Random Fields when they are semantically coherent. In the second stage, we generate the correlation of topics from PhraseCTM. We evaluate our method by a quantitative experiment and a human study, showing the correlated topic modeling on phrases is a good and practical way to interpret the underlying themes of a corpus.
ASAPP 2.0: Advancing the state-of-the-art of semantic textual similarity for Portuguese

Semantic Textual Similarity (STS) aims at computing the proximity of meaning transmitted by two sentences. In 2016, the ASSIN shared task targeted STS in Portuguese and released training and test collections. This paper describes the development of ASAPP, a system that participated in ASSIN, but has been improved since then, and now achieves the best results in this task. ASAPP learns a STS function from a broad range of lexical, syntactic, semantic and distributional features. This paper describes the features used in the current version of ASAPP, and how they are exploited in a regression algorithm to achieve the best published results for ASSIN to date, in both European and Brazilian Portuguese.
Energy Efficiency Maximization for C-RANs: Discrete Monotonic Optimization, Penalty, and  $\ell _{0}$-Approximation Methods

We study downlink of multiantenna cloud radio access networks with finite-capacity fronthaul links. The aim is to propose joint designs of beamforming and remote radio head (RRH)-user association, subject to constraints on users’ quality-of-service, limited capacity of fronthaul links and transmit power, to maximize the system energy efficiency. To cope with the limited-capacity fronthaul we consider the problem of RRH-user association to select a subset of users that can be served by each RRH. Moreover, different to the conventional power consumption models, we take into account the dependence of the baseband signal processing power on the data rate, as well as the dynamics of the efficiency of power amplifiers. The considered problem leads to a mixed binary integer program which is difficult to solve. Our first contribution is to derive a globally optimal solution for the considered problem by customizing a discrete branch-reduce-and-bound approach. Since the global optimization method requires a high computational effort, we further propose two suboptimal solutions able to achieve the near optimal performance but with much reduced complexity. To this end, we transform the design problem into continuous (but inherently nonconvex) programs by two approaches: penalty and $\ell _{0}$ -approximation methods. These resulting continuous nonconvex problems are then solved by the successive convex approximation framework. Numerical results are provided to evaluate the effectiveness of the proposed approaches.
Extracting Information Types from Android Layout Code Using Sequence to Sequence Learning

Mobile apps offer users with functionalities and services by collecting information in various ways. Android app manifest ﬁle and privacy policy are documents that provide users with guidelines about what information type is being collected. However, the information types mentioned in these ﬁles are often abstract and do not include ﬁne-grained details about information collected through user input ﬁelds in apps. Existing approaches only focus on Android API method calls which can reveal collected information types from a general category of well-deﬁned names. However, these approaches are unable to identify the information types based on direct user input as a major source of private information. These information types contain more sensitive data compared to API retrieved information types. Moreover, developers can design user input ﬁelds that refer to any kind of information which can also vary among different apps. To address these problems, we propose to apply natural language processing techniques to Android layout code to extract information types associated with user input ﬁelds.
Deep Multi-View Concept Learning

Multi-view data is common in real-world datasets, where different views describe distinct perspectives. To better summarize the consistent and complementary information in multi-view data, researchers have proposed various multi-view representation learning algorithms, typically based on factorization models. However, most previous methods were focused on shallow factorization models which cannot capture the complex hierarchical information. Although a deep multi-view factorization model has been proposed recently, it fails to explicitly discern consistent and complementary information in multi-view data and does not consider conceptual labels. In this work we present a semi-supervised deep multi-view factorization method, named Deep Multi-view Concept Learning (DMCL). DMCL performs nonnegative factorization of the data hierarchically, and tries to capture semantic structures and explicitly model consistent and complementary information in multi-view data at the highest abstraction level. We develop a block coordinate descent algorithm for DMCL. Experiments conducted on image and document datasets show that DMCL performs well and outperforms baseline methods.
A Social Network to Increase Collaboration and Coordination in Distributed Teams

Trust is one of the key factors involved in determining the success or failure of any project. However, achieving and maintaining trust in distributed projects when team members are geographically, temporally and culturally distant from each other is a considerable challenge. In this paper, we present Trusty, a tool designed to help develop trust in Virtual Teams. The tool is explained by using a schema of trustworthiness, and an indication of how the tool supports some features of these schema in order to foster the development of trust is therefore provided. The tool has also been evaluated by users, and the results of this evaluation are included in this paper.
Knowledge-driven practice for the capstone project course of data science

The rapid development of data applications poses severe challenges as well as significant opportunities for data science talent cultivation. On the demand for data analysis specialty, in this poster the authors report on some preliminary results of a knowledge-driven practice for the capstone project course of data science. The practice consists of the tier of knowledge generalization and specialization and the tier of data science practice. Three kinds of knowledge, including general knowledge from the perspectives of data science, domain knowledge from real-world applications, and engineering expertise knowledge for conducting data analysis process, are refined to implement data science practice to achieve solutions effectively and efficiently for real-world applications. Preliminary results indicate that knowledge-driven practice promotes students to improve the ability of data analysis efficiently, and students attending the course win world-class data analysis competitions, such as KDD (Knowledge Discovery and Data Mining) Cup and Kaggle.
Comparison of Representations of Named Entities for Document Classification

We explore representations for multi-word names in text classification tasks, on Reuters (RCV1) topic and sector classification. We find that: the best way to treat names is to split them into tokens and use each token as a separate feature; NEs have more impact on sector classification than topic classification; replacing NEs with entity types is not an effective strategy; representing tokens by different embeddings for proper names vs. common nouns does not improve results. We highlight the improvements over state-of-the-art results that our CNN models yield.
Algorithms for Fault-Tolerant Placement of Stateful Virtualized Network Functions

Traditional network functions (NFs) such as firewalls are implemented in costly dedicated hardware. By decoupling NFs from physical devices, network function virtualization enables virtual network functions (VNF) to run in virtual machines (VMs). However, VNFs are vulnerable to various faults such as software and hardware failures. To enhance VNF fault tolerance, the deployment of backup VNFs in stand-by VM instances is necessary. In case of stateful VNFs, stand-by instances require constant state updates from active instances during its operation. This will guarantee a correct and seamless handover from failed instances to stand-by instances after failures. Nevertheless, such state updates to stand-by instances could consume significant network bandwidth resources and lead to potential admission failures for VNF requests. In this paper, we study the fault-tolerant VNF placement problem with the optimization objective of admitting as many requests as possible. In particular, the VNF placement of active/stand-by instances, the request routing paths to active instances, and state transfer paths to stand-by instances are jointly considered. We devise an efficient heuristic algorithm to solve this problem, and propose a bicriteria approximation algorithm with performance guarantees for a special case of the problem. Simulations with realistic settings show that our algorithms can significantly improve the request admission rate compared to conventional approaches.
Evaluation of Radiomics to Predict the Accuracy of Markerless Motion Tracking of Lung Tumors: A Preliminary Study

Template-based matching algorithms are currently being considered for markerless motion tracking of lung tumors. These algorithms use tumor templates derived from the planning CT scan, and track the motion of the tumor on single energy fluoroscopic images obtained at the time of treatment. In cases where bone may obstruct the view of the tumor, dual energy fluoroscopy may be used to enhance soft tissue contrast. The goal of this study is to predict which tumors will have a high degree of accuracy for markerless motion tracking based on radiomic features obtained from the planning CT scan, using peak-to-sidelobe ratio (PSR) as a surrogate of tracking accuracy. In this study, CT imaging data of 8 lung cancer patients were obtained and analyzed through the open source IBEX program to generate 2,287 radiomic features. Agglomerative hierarchical clustering was used to narrow down these features into 145 clusters comprised of the highest correlation to PSR. The features among the clusters with the least inter-correlation were then chosen to limit redundancy in the data. The results of this study demonstrated a number of radiomic features that are positively correlated to PSR. The features with the highest degree of correlation included complexity, orientation and range. This approach may be used to determine patients for whom markerless motion tracking would be beneficial.
Robotic manipulation and sensing of deformable objects in domestic and industrial applications: a survey

We present a survey of recent work on robot manipulation and sensing of deformable objects, a field with relevant applications in diverse industries such as medicine (e.g. surgical assistance), food handling, manufacturing, and domestic chores (e.g. folding clothes). We classify the reviewed approaches into four categories based on the type of object they manipulate. Furthermore, within this object classification, we divide the approaches based on the particular task they perform on the deformable object. Finally, we conclude this survey with a discussion of the current state-of-the-art approaches and propose future directions within the proposed classification.
When Extremists Win: Cultural Transmission Via Iterated Learning When Populations Are Heterogeneous

How does the process of information transmission affect the cultural or linguistic products that emerge? This question is often studied experimentally and computationally via iterated learning, a procedure in which participants learn from previous participants in a chain. Iterated learning is a powerful tool because, when all participants share the same priors, the stationary distributions of the iterated learning chains reveal those priors. In many situations, however, it is unreasonable to assume that all participants share the same prior beliefs. We present four simulation studies and one experiment demonstrating that when the population of learners is heterogeneous, the behavior of an iterated learning chain can be unpredictable and is often systematically distorted by the learners with the most extreme biases. This results in group-level outcomes that reflect neither the behavior of any individuals within the population nor the overall population average. We discuss implications for the use of iterated learning as a methodological tool as well as for the processes that might have shaped cultural and linguistic evolution in the real world.