Miracle-dz/UniFAR-FTUs huggingface dataset:
ReCAP - Information Retrieval and Case-Based Reasoning for Robust Deliberation and Synthesis of Arguments in the Political Discourse

The ReCAP project is a recently started project within the DFG priority programm robust argumentation machines (RATIO). It follows the vision of future argumentation machines that support researchers, journalistic writers, as well as human decision makers to obtain a comprehensive overview of current arguments and opinions related to a certain topic, as well as to develop personal, well-founded opinions justified by convincing arguments. Unlike existing search engines, which primarily operate on the textual level, such argumentation machines will reason on the knowledge level formed by arguments and argumentation structures. The focus of ReCAP is on novel contributions to and confluence of methods from information retrieval and knowledge representation and reasoning, in particular case-based reasoning. The aim is to develop methods that are able to capture arguments in a robust and scalable manner, in particular representing, contextualizing, and aggregating arguments and making them available to a user. Together with experts from the political domain real-world scenarios and use cases are worked out. A corpus of semantically annotated argumentations is being created from relevant text sources and will be made available to the argumentation research community.
Fully-Automatic Inverse Tone Mapping Preserving the Content Creator’s Artistic Intentions

High Dynamic Range (HDR) displays can show images with higher color contrast levels and peak luminosities than the common Low Dynamic Range (LDR) displays. However, most existing video content is recorded and/or graded in LDR format. To show this LDR content on HDR displays, a dynamic range expansion by using an Inverse Tone Mapped Operator (iTMO) is required. In addition to requiring human intervention for tuning, most of the iTMOs don’t consider artistic intentions inherent to the HDR domain. Furthermore, the quality of their results decays with peak brightness above 1000 nits. In this paper, we propose a fully-automatic inverse tone mapping operator based on mid-level mapping. This allows expanding LDR images into HDR with peak brightness over 1000 nits, preserving the artistic intentions inherent to the HDR domain. We assessed our results using full-reference objective quality metrics as HDR- VDP-2.2 and DRIM. Experimental results demonstrate that our proposed method outperforms the current state of the art.
A Multi-User Multi-Task Model For Stress Monitoring From Wearable Sensors

This study presents results on stress monitoring based on peripheral physiological signals acquired from wearable sensors. Using machine learning methods on data collected during laboratory experiment on 6 subjects exposed to 3 kinds of well-known stress tasks (TSST, MST and SECPT), the influence of the inter-subject and the inter-task variability on stress/no stress classification is evaluated. The relevant set of features and the type of classifier (choice between SVM, Decision Tree, and Naïve Bayes) are first selected by using Leave-One-Subject-Out (LOSO) cross validation. The model learnt on the 3 tasks reach 85% of good classification rate over the validation set, using SVMs as classifier and as features: the mean of the heart rate and the sum of all skin response's width-prominence products. After that, different results obtained using several partitioning of learning/validation sets are compared (e.g. learning on one task and on all subjects and validating on the other tasks) in order to evaluate the subjects and tasks influence. We observe that our model is robust to the inter-subject and the inter-task variability with small differences for SECPT which suggests a slightly different physiological reaction to physical stressors.
Energy Efficient Routing Protocols, Classification and Comparison in Wireless Sensor Networks: A Survey

Wireless Sensor Network (WSN) is used to monitor the environmental conditions in the deployed area under sectors like military, health, agriculture, disaster areas, etc. The wireless sensor nodes are very small in size and are powered by a battery having limited lifetime. Hence to achieve longevity and reliability, the foremost concern while designing protocols and applications is minimizing energy consumption and maximizing network lifetime. In this paper we review the popular energy efficient Routing protocols and its strategies to consume the nodes energy efficiently and hence increase the life time of the wireless sensor network.
HiCDB: a sensitive and robust method for detecting contact domain boundaries

Abstract Contact domains are closely linked to gene regulation and lineage commitment, while current understanding of contact domains and their boundaries is still limited. Here, we present a novel method HiCDB, which is constructively based on local relative insulation metric and multi-scale aggregation approach to detect contact domain boundaries (CDBs) on Hi-C maps. Compared with other ‘state-of-art’ methods, HiCDB shows improved sensitivity and specificity in determining CDBs at various Hi-C resolutions. The superiority of HiCDB enabled us to study the epigenetic features of detected CDBs and showed enrichment of architectural proteins and cell-type-specific transcription factor binding sites at CDBs. The further comparison of GM12878 and IMR90 Hi-C datasets suggested that cell-type-specific CDBs are marked by active regulatory signals and correlate with activation of nearby cell identity genes.
[Activation of aldehyde dehydrogenase 2 alleviates H 2O 2-induced injury in cardiomyocytes].

OBJECTIVE To investigate the changes of aldehyde dehydrogenase 2 (ALDH2) expression in H 2O 2inducedcardiomyocytes oxidative stress injury. METHODS Cultured H9C2 cardiomyocytes were exposed to H 2O 2-inducedoxidative stress and the effects of the ALDH2 agonist Alda-1 and ALDH2 inhibitor Daidzin were tested on the stress level ofthe exposed cells. MTT colorimetric assay was used to assess the cell viability after the treatments. The oxidative stress level inthe myocardial cells was detected using DHE fluorescence staining, and the activity and protein level of ALDH2 were detectedwith spectrophotometry and Western blotting. RESULTS Compared with normal control cells, Alda-1 treatment did notsignificantly affect the cell viability, oxidative stress level, or ALDH2 activity and protein level. H 2O 2 exposure significantlylowered the cell activity and ALDH2 activity and protein expression and increased the oxidative stress level; Alda-1 treatmentobvious antagonized the effects of H 2O 2. Blocking ALDH2 with Daidzin produced similar effects to H 2O 2 exposure on theviability, oxidative stress level, and ALDH2 activity and expression in the myocardial cells. CONCLUSIONS H 2O 2 exposure lowersthe activity and reduces the protein expression of ALDH2 in cardiomyocyte H9C2 cells, and activation of ALDH2 can alleviateH 2O 2-induced oxidative stress in the cells.
Learning Analytics Intervention: A Review of Case Studies

Intervention has been claimed to be the greatest challenge in learning analytics. As the provision of just-in-time and personalised support for learners, intervention has yet to be widely implemented in learning analytics practices. This paper reviews intervention practices in higher education in 23 case studies. The cases were categorised into four types – direct message, actionable feedback, categorisation of students, and course redesign – according to the nature of the methods of intervention; and the intervention methods were summarised. Most of the intervention cases belonged to the first two types. Direct message involves contacting at-risk students via channels such as emails or phone calls to encourage their participation, provide additional learning resources, or remind them of deadlines. Actionable feedback involves the provision of suggestions or information for students to help them understand their performance and possible ways of improving it. Only a few cases were identified for the other two types of intervention, which involve categorisation of students into different groups based on their risk levels for taking specific remedial actions for each group, and redesigning the course structure or contents based on the analysis of data. The results of this review serve to facilitate the formulation of intervention strategies for higher education institutions which practise learning analytics.
Zoonotic Sporotrichosis Related to Cat Contact: First Case Report from Panama in Central America

A previously healthy 34-year-old man presented with a four-week-old upper arm injury that started with a cat scratch and progressed into painless erythematous nodular ascending lesions in the left upper limb. We made the diagnosis of sporotrichosis based on the epidemiological association of the contact with a cat and culture identification. Sporotrichosis is a mycosis implantation caused by the Sporothrix schenckii complex. We present the first Central American case of sporotrichosis associated with contact with a feline.
Reconfigurable S-Band Patch Antenna Radiation Patterns for Satellite Missions

In this paper, a reconfigurable novel S-band patch antenna system for cubesat satellites is presented. It is shown that, with the same antenna, it is possible to achieve different radiation patterns and polarization, using power divider and digital control phase shifter, thus allowing the use of the designed S-Band patch antenna system for a large number of space mission.
Joint Tracking and Registration in Multi-Target Multi-Sensor Surveillance Using Factor Graphs

This paper describes a new approach for joint tracking and registration in multi-target multi-sensor surveillance in presence of sensor biases. Due to a mutual dependency on target state and sensor bias, measurements cause cross-couplings between target and bias estimates. Therefore, an all-in-one filter (e.g. extended Kalman filter) only is practical for a limited amount of sensors/targets without impairing real-time capability. Since Bayesian filtering historically is the method of choice in target tracking applications, bias estimation is attempted to be decoupled from the tracking filters for the sake of computational efficiency. Current approaches either solve the registration problem first and ignore remaining bias errors during the tracking phase, or make simplifying assumptions about cross-correlations in order to get the tracking filters decoupled from bias estimation. In contrast, we propose a joint estimator for both target states and sensor biases that preserves real-time capability and exhibits a decoupling with all cross-correlations considered. The basic idea is driven by graphical inference methods on factor graphs and entails a departure from the classical filtering framework. We introduce a multiple-model state transition factor to cope with non-cooperative targets. The benefits are threefold. First, conditional independence between target states given the mutual biases is exploited to attain high parallelism scalable in the number of targets. Second, operational delays are brought to a minimum as registration and tracking is performed simultaneously. Third, out-of-sequence measurements are easily integrable. The proposed algorithm is evaluated using a simulated scenario.
Quasi-Color-Free LD-Based Long-Reach 28-GHz MMWoF With 512-QAM OFDM

By using a 512-quadrature amplitude modulating-orthogonal frequency division multiplexing (512-QAM OFDM) data stream with universal filtered multicarrier (UFMC) process to directly encode a quasi-color-free laser diode (QCFLD) with dual-mode carrier, a long-reach 28-GHz millimeter-wave over fiber (MMWoF) link over 50-km single-mode fiber (SMF) is demonstrated for future fiber network with 5G wireless link. At the fiber-wired optical transmission stage, the dual-mode QCFLD can support raw data rates up to 54, 42, and 24 Gbit/s after 0, 25, and 50-km SMF transmissions. The data-rate degradation results from chromatic dispersion induced power fading effect. The 28-GHz MMW carrier optically heterodyned at remote node with a carrier-to-noise ratio of 53.5 dB is generated after 25-km SMF propagation, which achieves 6-m free-space transmission at 16 Gbit/s. Lengthening the SMF to 50 km still allows the MMWoF for wireless transmission at 16-Gbit/s over 5 m and 14-Gbit/s over 10 m. To favor the 5G wireless access with high data capacity, the UFMC processed QAM-OFDM was further adopted for noise suppression via sidelobe filtering operation. By lengthening the cyclic prefix and the filter window for the OFDM, the tradeoff between data quality and net-data-rate ratio without and with UFMC process is discussed. With UFMC, the MMWoF transmission capacity can deliver 3-GHz wide 32-QAM UFMC processed OFDM data at 15 Gbit/s over 50 km in SMF and 10 m in free space. Even within a limited subcarrier bandwidth as narrow as 140 MHz for practical 5G users, the 512-QAM UFMC data can be allocated to provide a raw data rate of 1.26 Gbit/s.
Design of Dual Frequency Antenna Fed by Coplanar Waveguide

A dual frequency antenna fed by coplanar waveguide has been designed in this paper. The antenna consists of a mental ring radiator, and two grounding plane as a fed element located on both sides of the radiator. Using the high frequency simulating software, we found the reasonable size of the antenna operation frequency covered 2.1-2.6GHz and 5.15-8.3GHz. Measurement results show that the antenna with very good symmetrical radiation patterns, and with wonderful impedance bandwidth and radiation efficiency. The voltage standing wave ratio of the antenna is below 1.5 when S11less than −10 dB in working frequency bands. It can be used in the wireless local area networks communication system.
Helicopter Lands on Uneven Terrain by means of Articulated Robotic Legs-Modelling, Simulation and Control Approach

In this paper, a robotic landing gear for helicopters is proposed to replace the conventional fixed skid and a mathematical model is used to investigate into a control strategy for landing on irregular terrains. The landing gear consists of four articulated legs connected to the helicopter fuselage. Each leg has two revolute joints governed by PID joint controllers to generate motion with two degrees of freedom (DoF). A combination of Lagrange and Newton-Euler techniques is used to model the system dynamics, its motion and joint torques. A contact model is introduced to simulate ground interaction forces and a level controller that uses a PI algorithm to maintain the main body desired attitude when landing in two-axes slopes. Simulation results are compared to a second model designed using the SimMechanics toolbox for validation purpose.
Modelling the impacts of imperfect maternal transmission on the spread of Wolbachia in Ae. aegypti population

Dengue continues to be a significant health burden for individuals living in its endemic region. One of the strategies to reduce this burden requires targeting the vector agent, Ae. aegypti, in which intracellular bacterium Wolbachia has proven to be attractive in achieving this aim. Using Wolbachia-infected (WI) mosquitoes to wipe out the Wolbachia-uninfected (WU) ones comes with a maternal-inherited advantage known as cytoplasmic incompatibility, but Wolbachia itself exerts some fitness cost to their host. Also, the effect of imperfect maternal transmission from the WI mosquitoes could be a factor. Despite the successful introduction of WI Ae. aegypti mosquitoes at a small scale, how the method works and the likelihood of success at a larger scale is not fully understood. In this study, we analyze the invasive prospects of Wolbachia by developing a model that introduces the WI mosquitoes into a wild-type Ae-aegypti population. We study the interactions between key parameters (host fitness and imperfect maternal transmission) that determine whether WI mosquitoes will be suppressed or will dominate. Our analyses show a reproduction fitness threshold exists above which the WI mosquitoes will always dominate, and below which WI mosquitoes may co-exist with the non-Wolbachia infected mosquitoes. We show that cytoplasmic incompatibility alone is not enough for WI mosquitoes to dominate as both its fitness and the possibility of mixed offspring play a critical role. In the absence of WI mosquitoes with greater fitness than the wild-type mosquitoes, small introductions of WI mosquitoes will not lead to invasion. However, under the same circumstances, stable co-existence of both types of mosquito is possible. Hence direct killing of wild-type mosquitoes combined with Wolbachia introduction might be an effective strategy in the fight against dengue.
Multilayer Compact Grid Antenna Array for 79 GHz Automotive Radar Applications

A novel wideband compact grid antenna array is developed for 79 GHz multiple–input multiple-output radar applications. The key issue is the stable radiation pattern with flat-gain characteristic along the whole frequency bandwidth, which is very advantageous for the targeted radar applications. The antenna is taped out with the new high-resolution multilayer printed circuit board (PCB) technology called “Any-Layer PCB. ” This technology provides the possibility to stack microvias in PCBs and reduce the fabrication cost compared with other multilayer technologies in millimeter-wave bands. With this feature, the width of the array has been compacted with 17.2%, which leads to a higher sidelobe suppression and an enhancement of the field of view in radar applications. A combined impedance and 3 dB gain bandwidth of 9.6% is achieved. The gain and sidelobe level are 13.52 and –14 dB at 79 GHz, respectively.
A Monte Carlo Simulation Approach to Evaluate Service Capacities of EV Charging and Battery Swapping Stations

With the rapid growth of electric vehicle (EV) ownership, attentions have been paid to the foundation of EVs, the electric vehicle supply equipment (EVSE). Different approaches of effort, among which battery swapping and fast charging are the two most well studied, have been made to solve the tradeoff problem between the battery charging speed and battery lifetime. There has been considerable debate over development strategy between charging and battery swapping. In passenger vehicles, the EV charging mode seems to dominate. But, does it mean that the battery swap mode is a dead-end? The answer should be “No”. There are use cases showing that battery swap can have great potentials for some particular uses, such as taxis and buses. This paper uses Monte Carlo simulations of vehicle behaviors to compare the service capacities and earnings of EV charging and battery swapping for both taxi and bus fleets. Stochastic models of taxis, buses, charging stations (CSs) and battery swapping systems are set up. Subsequently, service capacities of the EVSE are compared. The impact of factors on the service capacity, such as the size of the vehicle's battery, vehicle's moving speed, the power of the CS, and the price of the swapping service is investigated. Finally, possible reasons of today's less prevalence of battery swapping stations are discussed. The results of the analysis, which can be helpful to policymakers and industry investors, show that with same service capacity, an EV battery swapping station could provide significantly more financial and social benefits for the vehicle operators and EVSE service providers than that of an EV CS.
A Novel Multi-source Fusion Model for Known and Unknown Attack Scenarios

Nowadays, it is difficult to attract researchers with single data to do network security research. The single data is not comprehensive and can't depict the change of malicious attacks fully, it's reliability and authenticity are also difficult to determine, so the research of multi-source data fusion can help to distinguish the authenticity of data and obtain better results. In this paper, we propose a multi-source fusion model that uses ontologies to represent and store different information resources, employs logic reference rules to remove redundant and reduce false-positives, reconstructs scenario of the known attack and uses a new AOI-FIM algorithm for mining attack patterns of the unknown attack scenarios. The key benefit of the model is that it solves the syntax and semantic expression in the multi-source heterogeneous data and mines some unknown attack patterns. To illustrate and evaluate our approach, we use two separate case studies from the Darpa2000 and the Vast Challenge2012 dataset. The results show that our approach can reduces false positives obviously, builds scenario effectively for known attacks and mines frequent attack patterns exactly for unknown attacks.
Situation Awareness in Telecommunication Networks Using Topic Modeling

For an operator of wireless telecommunication networks to make timely interventions in the network before minor faults escalate into issues that can lead to substandard system performance, good situation awareness is of high importance. Due to the increasing complexity of such networks, as well as the explosion of traffic load, it has become necessary to aid human operators to reach a good level of situation awareness through the use of exploratory data analysis and information fusion techniques. However, to understand the results of such techniques is often cognitively challenging and time consuming. In this paper, we present how telecommunication operators can be aided in their data analysis and sense-making processes through the usage and visualization of topic modeling results. We present how topic modeling can be used to extract knowledge from base station counter readings and make design suggestions for how to visualize the analysis results to a telecommunication operator.
A Privacy-Preserving Principal Component Analysis Outsourcing Framework

In order to sense and analyze potential information from huge digital data, data mining outsourcing schemes as a principle and effective techniques have recently attracted attention. In this paper, we attempt to guarantee the data privacy of a light-weighted data mining method, Principal Component Analysis (PCA). To achieve our goal, we propose a privacy-preserving PCA(PP-PCA) outsourcing framework, which can understand underlying information without simultaneously disclosing the concrete contents of the data, either the training/predicting data or the prediction results. In our framework, we introduce the Asymmetric Orthogonal Random Linear(AorL) transformation to perturb the training data, utilized by the training model. To protect the privacy of the predicting data and the prediction results, we propose a novel similar-Homomorphic(s-H) preserving technique based on homomorphism encryption mechanism to encrypt the transformed data. Furthermore, our framework is effective and scalable, which allows data users to utilize the trained model from data owner in cloud provider to predict their result. Detailed theoretical analysis and extensive experiments based on three real datasets confirm the security and high efficiency of our framework, respectively.