Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring group-level human mobility from location-based social media check-in data

Location based social network services like Facebook and Twitter have supported billions of social media users to share their check-ins all over the world. The massive check-in data is regarded as a kind of novel data resource to explore human mobility. In this paper, we study the human mobility characteristics and differences presented in Sina Weibo (a Chinese equivalent of Twitter) check-in data for different groups. First, we identified different groups based on their spatial distribution characters. Then, we selected two urban resident groups and two college student groups as our study objects. The four groups are consisted by more than 12,000 Sina Weibo users who contributed over 80,000 geo-tagged Weibo messages in Wuhan city from 2015-2016. We analyzed the four groups’ mobility characters and patterns through spatiotemporal statistics and calculation. The quantitative analysis methods help us to find out that:(i) the mobility differences among communities can be observed through their check-ins;(ii) human dynamics and mobilities are largely affected by the distance (iii) similar social structure directed similar behavior patterns.
High Efficiency Wide Input Voltage Range LCLC Resonant Converter Using Nonlinear Frequency Controller

In server and telecommunication applications, LLC resonant converter featuring high efficiency and high power density becomes an excellent candidate for the frontend DC/DC stage. But, for conventional LLC converter, the performance will be severely deteriorated once the converter is designed for accommodating regulation over wide input voltage range. A modified LLC topology with changeable magnetizing inductance, LCLC resonant converter, has been successfully proved to extend the input voltage range without sacrificing the normal efficiency. Moreover, due to the strong nonlinear feature of the LCLC resonant converter, conventional linear control strategies such as PI controller are very hard to optimize. This paper proposes a nonlinear frequency control method for LCLC converter to achieve better output voltage regulation over wide input voltage range. In the controller, the switching frequency is calculated from a quadratic function, which expresses the opposite variation of the voltage gain. Finally, a 250–400V input 12V/500W output LCLC prototype is built and tested. Experimental results demonstrate the effectiveness of the proposed nonlinear frequency control strategy.
Radial Calendar of Consumption

In the analysis of time-series, it is common to focus on the identification of changing behaviours and patterns that repeat over time. In this article, we propose a visualisation model based on a radial calendar to analyse the Portuguese's consumption data. The model is intended to assist the analysts within a Portuguese Retail Company in the identification of periodic patterns and deviations from the normal consumption values. Our main contributions are: (i) the representation and characterisation of the Portuguese's consumption behaviour over time; (ii) a radial visualisation model to identify consumption patterns and their deviations; and (iii) a user case study to compare this visualisation model to a regular calendar layout. Our model has as main requirement the representation of the maximum amount of data in one single space. As such, it is ideal for analysts without prior knowledge of the data, since it provides an effective and efficient qualitative overview of the Portuguese's consumption.
Momentum limiting velocity controls for robotic manipulators

Robotic tasks in space require manipulation of massive objects capable of attaining large momentum. The momentum can pose hazardous conditions and introduce destabilizing effects on a space platform. Consequently, a technique for limiting the momentum applied to objects under manipulation subject to arbitrary velocity input commands is proposed. The algorithm does not require mass position or inertia information about the object, and it takes actuator limitations into account in forming the momentum limits. To evaluate the probability that a velocity trajectory will fall within the momentum bounds, reliability theory is employed. This enables autonomously generated trajectories to be validated for compliance with momentum limits. <<ETX>>
Constraint reformulation in a hierarchical path planner

Robot path planning consists of approximating the set of free configurations of the robot by a collection of rectangloid cells at successive levels of approximation and, at each level, searching the graph representing the adjacency relation among these cells. A set of algorithms which specifically address the main two computational issues underlying this approach, cell decomposition and graph searching, has been developed. The authors have implemented a planner which incorporates these algorithms and have experimented with it on examples. The experiments show that the planner is significantly faster than previous planners based on the same general approach. The authors focus on the planner's cell-decomposition algorithms. They use a constraint reformulation technique that consists of approximating the obstacles intersecting the cell to be decomposed by a collection of rectangloids and computing their complements in the cell. Two types of approximations are used, bounding and bounded approximations. This technique produces much less cells than previously proposed techniques, resulting in smaller search graphs. Experimental results are presented. <<ETX>>
Affective Content Analysis of Online Video Clips with Live Comments in Chinese

Recent years have witnessed an increasing interest in online video affective content analysis, since having a better understanding of the emotions of videos can facilitate many applications including video retrieval and classification. Research in video affective computing requires ground truth data for training and evaluation. The live commentary (also known as 'danmaku', 'barrage', or 'bullet comment') is quite popular in recent years, but few researchers have paid attention to the commentary information in the affective analysis of online videos. In this paper, we build a dataset of online video clips, namely DaLC (Dataset with Live Comments), for affective content analysis and related applications. In contrast to existing datasets with only video clips, DaLC consists of not only 204 good quality online video excerpts but also their live comments with a large content diversity. In this paper, we first introduce a multi-dimensional emotional descriptor, which can accommodate the presence of multiple and even ambivalent emotions in a video clip, and is used as the labeling model for the dataset. Moreover, we extract effective features for online video affective content analysis with live comments in Chinese. In addition, to highlight the importance of live comments for online video sentiment analysis, we further conduct several experiments for emotion prediction on the DaLC dataset using different methods. Experimental results show that live commentary features have significantly improved the effectiveness of online video affective analysis.
Personalized Search with Secure Updates in Educational Resources

Under the environment of the large educational data, the search results couldn't meet the requirements of the resource access if the users' intentions are unclear. Therefore, it is urgent to solve the accuracy problem for educational resource search. At the same time, since people pay more attention to privacy issues, it becomes a stumbling block to the development of personalized search. In order to solve this problem, we propose a Personalized Search with Secure Updates (PSSU) in educational resources. The PSSU scheme is able to obtain large educational resources data from resources contributed by the general public by the introduction of Trusted Third-Party (TTP) server, and evaluate the resources by analyzing the user's usage, then the users can obtain high-quality resources that meet their own needs. Since the user's search and index update are performed in an encrypted environment, the user's privacy will not be revealed.
Design of Cruciform Directional Coupler with Capacitive Slots Based on SIW

In this paper, a kind of cruciform directional coupler with slots besides coupling holes is proposed. Coupling holes are located on the diagonal of SIW in order to make structure compact. The slots on top metal which can enhance coupling are equivalent to capacitance. Compared with other kinds of coupler, this structure has a higher space efficiency, and it is flexible to adjust coupling. Finally, the measurement indicates a good performance of 10 dB coupler over 36–41 GHz, return loss is lower than 12 dB, isolation is greater than 20 dB, and coupling fluctuation in pass band is within $\pmb{\pm 0.6}\mathbf{dB}$.
Datenschutzkonforme Anonymisierung von Datenverkehr auf einem Vermittlungssystem

Aufzeichnungen von Datenverkehr – so genannte Netzwerktraces – werden in hohem Maße für Forschung und Industrie benötigt. Mit Hilfe von mitgeschnittenem Datenverkehr ist es möglich, Eigenschaften und Verhalten von Netzen sowie von deren Komponenten, Diensten und Benutzern zu analysieren. Auch die Entwicklung spezieller Anwendungen – wie beispielsweise eines Systems zur Angriffserkennung – benötigt Datenverkehr zur Analyse und Evaluierung des konzipierten Systems. Die Unvorhersehbarkeit von Nutzerverhalten, nicht protokollkonforme Pakete, Angriffe und die allgemein sehr große Diversität in realen Netzdaten legen hierbei deren Einsatz nahe. Künstlich erzeugter Verkehr hingegen kann weder im Hinblick auf Benutzerverhalten noch auf Verkehrscharakteristiken die Realität nachbilden. Reale Daten garantieren daher, dass neu entwickelte Systeme unter realistischen und nicht an bestimmte Tests angepassten Bedingungen getestet werden können bevor sie tatsächlich eingesetzt werden.
A Clustering Algorithm Based on FDP and DBSCAN

In order to solve the problem that the density peak clustering algorithm (FDP) needs to manually selected the center on the decision graph, an integration algorithm based on FDP and DBSCAN is proposed. Inspired by the FDP algorithm, that is, the data with the largest local density is the first center, and we use DBSCAN algorithm to cluster from the data with the largest local density to get the first category. Then the maximum local density data is found out from the data that has not been clustered, and use the DBSCAN algorithm to find the second category. Repeat this until all data is divided. The proposed algorithm optimizes the DBSCAN algorithm, that is, every iteration starts from the current best point (the point with the largest local density, this is the center of every cluster). Compared with the classical algorithm FDP, FCM, K-means, the proposed algorithm can obtain higher efficiency.
Filtering Out Infrequent Events by Expectation from Business Process Event Logs

Process discovery, one of the key steps in process management, aims at discovering process models from process execution data stored in event logs. Most discovery algorithms assume that all data in an event log fully comply with the process execution specification. However, in real event logs, noise and irrelevant infrequent behaviour are often present. In this paper, we propose a novel filtering method that the removal of infrequent behavior from event logs. The method has been evaluated in detail and it is shown that its application in existing process discovery algorithms significantly improves the quality of the discovered process models and that it scales well to large datasets.
Activity Trackers for Raising Guide Dogs: Challenges and Opportunities

Guide puppy raisers play a crucial role in the process of training assistance dogs to support mobility of the visually impaired. Recent technological developments bring about a plethora of IT-based solutions, such as canine activity trackers and mobile apps that could provide novel benefits in supporting the puppy raisers in their important contribution to society. We present an exploratory empirical study of the perceptions and attitudes of puppy raisers and their Guide Dog Center towards such technological solutions focusing in particular on their perceptions towards privacy.
Human Activity Recognition Based on Deep Learning Method

With the increasing demand of security defense. , anti-terrorism investigation and disaster rescue. , human activity classification and recognition have become a hot research topic. When a human is illuminated by electromagnetic waves. , a Doppler signal is generated from his or her moving parts. Indeed. , bodily movements are what make humans micro-Doppler signatures unique. , offering a chance to classify human activities. Classification needs a lot of samples for training. , however. , in the real application. , there is a certain gap between the simulated data and the real data. , and the measured data is often difficult to obtain. Due to the nonstationary characteristic for human radar echoes. , the spectrograms for the human activities show different micro-Doppler signatures. Therefore. , we proposed a method of human activity classification based on spectrograms using deep learning techniques. , including deep convolutional generative adversarial network for expanding and enriching training set and a transfer-learned deep convolutional network (DCNN) for feature extraction and classification. , which is based on a DCNN pre-trained by a large-scale RGB image data set-that is. , ImageNet. Finally. , the simulation results verified the effectiveness of the proposed method.
Trabajar en grupo y aprender haciendo: desarrollo de competencias clave para los estudiantes del siglo XXI.

espanolEn los ultimos tiempos gana relevancia una nueva competencia que conjuga dos verbos: aprender y ensenar, y que sin duda es imprescindible en un mundo de sobreabundancia de informacion, incertidumbre y cambios drasticos. Nuestros alumnos han de prepararse para adaptarse rapidamente a los cambios, y necesitan desarrollar una actitud activa. Tienen que aprender a trabajar en equipo, colaborando para detectar problemas y ensenar a otros como resolverlos, compartiendo conocimientos y comunicando de una manera eficaz. Estas son las habilidades que se pretenden desarrollar en la experiencia que se presenta. El objetivo de la comunicacion es describir la experiencia y valorar el efecto que puede tener el grupo en los resultados del individuo. Los resultados muestran que, descontando el efecto que pudieran tener las habilidades y conocimientos previos, el trabajo en grupo contribuye a mejorar las calificaciones individuales del estudiante, tanto si es valorado en su conjunto, como si se evalua individualmente. EnglishIn recent times, a new competence that combines two verbs: learn and teach, is gaining relevance and is undoubtedly essential in a world of information overflow, uncertainty and drastic changes. Our students have to prepare to adapt quickly to changes and need to develop an active attitude. They have to learn to work as a team, collaborating to detect problems and teach others how to solve them, share knowledge and communicate in an effective way. These are the skills that we intend to expound on in the presented experience. The objective of the communication is to describe the experience and assess the effect that the group may have on the individual's results. The results show that, discounting the effect that previous skills and knowledge may have, group work contributes to improving the individual grades of the student, whether she/he is assessed as a whole, or assessed individually.
How well can familial hypercholesterolemia be identified in an electronic health record database?

Background Familial hypercholesterolemia (FH) is a condition characterized by high cholesterol levels and increased risk for coronary heart disease (CHD) that often goes undiagnosed. The Dutch Lipid Network Criteria (DLNC) are used to identify FH in clinical settings via physical examination, personal and family history of CHD, in addition to the presence of deleterious mutations of the LDLR, ApoB, and PCSK9 genes. Agreement between clinical and genetic diagnosis of FH varies. While an ICD diagnosis code was not available for coding FH until 2016, Systematized Nomenclature of Medicine (SNOMED) clinical concept codes, including genetic diagnoses, for FH have been utilized in electronic health records (EHRs). Objective To evaluate the concordance of identifying FH via SNOMED and ICD-10 CM codes vs the DLNC in an EHR database. Methods Using the Practice Fusion EHR database, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value were calculated comparing an FH cohort identified via SNOMED and ICD-10 CM codes to one identified via the DLNC. Results Among 907,616 patients with hypercholesterolemia, 2,180 were identified as FH via SNOMED code (zero were identified via ICD-10 CM), 259 had a DLNC score 6–8 (probable FH), and 45 had a DLNC score >8 (definite FH). Compared to DLNC score >8, the sensitivity, specificity, and PPV of the FH SNOMED code were 84.4%, 99.4%, and 6.4%, respectively. Compared to DLNC score ≥6, the sensitivity was 36.8% and the specificity was 99.5% with a PPV of 18.7%. Conclusion Compared to the clinical criteria for FH, identification of FH patients via SNOMED diagnosis codes had high sensitivity and specificity, but low PPV. The discordance of these two techniques in identifying FH patients speaks to the challenges in identifying FH patients in large electronic databases such as administrative claims and EHR.
A Novel Frequency Reconfigurable Coupling Microstrip Patch Antenna

In this paper, a novel frequency reconfigurable coupling microstrip patch antenna is presented. A coplanar waveguide (CPW) feed line integrated on the ground plane is adopted in the structure to provide $\text{TM}_{21}$ mode. Four PIN diodes are mounted on the patch gap to achieve frequency reconfiguration. By controlling the on-off state of PIN diodes electronically, resonant frequency points (5.75,5.84, and 6.06 GHz) can be adjusted. What's more, a wide tunable range is achieved by loading more parasitic patch.
A Novel Soft-Switched Power Converter Architecture with an Integrated Energy Storage Interface Compatible with DC and AC Energy Sources

In conventional renewable energy systems or hybrid renewable energy systems, a centralized energy storage system is used. Typically, the energy storage component is connected with the renewable resources at the common DC link through a bidirectional converter to handle bidirectional power flow. A bidirectional converter requires two or more switches and results higher switching loss and more components compared to the unidirectional converter. This paper proposed a unidirectional integrated storage interface approach for renewable energy systems. In the proposed approach, the energy storage charging interface is integrated with the input side of the AC-DC or DC-DC converter. Therefore, it is compatible with both DC-input (i.e. such as solar) and AC-input (i.e. such as wind energy) sources. The storage discharging interface in the proposed system shares the output filter of the integrated storage charger AC-DC or DC-DC converter. Therefore, the system becomes compact and efficient. In addition, all the switches are able to achieve soft-switching turn-on and turn-off for different load conditions. Operating principle and analysis of the proposed integrated solar-battery soft-switched quasi-resonant Cuk-based circuit is presented. Simulation result for a 500W and experimental results for a 100W are presented to demonstrate the feature of the proposed converter.
A Novel Enhanced Nonnegative Feature Extraction Approach

Nonnegative matrix factorization (NMF) is a promising approach for image-data representation and classification under the nonnegativity constraint. However, the traditional NMF does not exploit the data-label information and its performance will be degraded in classification tasks. To overcome the flaw of NMF, this paper proposes a novel enhanced nonnegative matrix factorization (ENMF) method for learning powerful discriminative feature. The basic idea is that the training data from the same class are forced to embed into their own feature subspaces, where these subspaces are mutually orthogonal. The ENMF update rule is obtained by means of auxiliary function technique and Cardano's formula. We prove and analyze the convergence of the proposed ENMF algorithm theoretically and empirically. Experimental results on face recognition show that the proposed method outperforms the existing state-of-the-art NMF-based algorithms.
Complete Closed Genome Sequences of Three Salmonella enterica subsp. enterica Serovar Dublin Strains Isolated from Cattle at Harvest

Salmonella enterica subsp. enterica serovar Dublin is a host-adapted pathogen for cattle that can cause invasive disease in humans. ABSTRACT Salmonella enterica subsp. enterica serovar Dublin is a host-adapted pathogen for cattle that can cause invasive disease in humans. To facilitate genomic comparisons characterizing virulence determinants of this pathogen, we present the complete genome sequences of three S. Dublin strains isolated from bovine sources at harvest.
Clustering Large-Scale Origin-Destination Pairs: A Case Study for Public Transit in Beijing

With the extensive collection of various trajectories, a lot of trajectory mining methods have been developed and brought into effect in different applications. The same is true for trajectory clustering. It enables the construction of diverse applications (e.g., mobile social networks) and can promote the intelligence of existing services (e.g., optimizing public transit). In the paper, we propose a three-phase clustering strategy ODTC (Origin Destination pair oriented Trajectories Clustering) for the massive trajectories in the form of OD (Origin Destination) pairs and demonstrate the impact of trajectory clustering on evaluating and adjusting public transit operations. In our ODTC strategy, trajectories are partitioned in the first phase by coarsegrained clustering, reflecting an idea of divide and conquer. While during the second phase of fine-grained clustering, we model the relations of OD pairs as a sparse graph where the spatial and temporal features as well as the constraints of road networks are integrated into the similarity of trajectories. Then we apply a spectral clustering algorithm on the graph to capture clusters. In particular, in the third phase, we borrow the idea from text data mining and give a feasible method to mine the semantics of clusters. As a case study, we perform ODTC on the large-scale trajectories from the Beijing Public Transport Group. From the clustering results, we can observe the mobility patterns of bus passengers. Further, we exploit the clustering results to discover the dynamics of bus operations, evaluate the bus lines and provide support for making the decisions on bus operations.