Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a reputation model applied to geosocial networks: a case study on crowd4city

Geosocial networks gather large amounts of voluntarily generated information that can be explored among many different contexts, including urban areas. In this sense, we developed the Crowd4City system, which put together city authorities and citizens focusing on the improvement of their urban spaces. A challenge in this context is concerning the information reliability on crowdsourced data, by automatically identifying misinformation or spam from malicious users. For such, this paper proposes a reputation model to be applied to geosocial network users aiming at ensuring a better information reliability. A case study evaluates Crowd4City proposal in a real scenario. The promising results enhance information reliability in the geosocial network.
PSA-CMA-ES: CMA-ES with population size adaptation

The population size, i.e., the number of candidate solutions generated at each iteration, is the most critical strategy parameter in the covariance matrix adaptation evolution strategy, CMA-ES, which is one of the state-of-the-art search algorithms for black-box continuous optimization. The population size is required to be larger than its default value when the objective function is well-structured multimodal and/or noisy, while we want to keep it as small as possible for optimization speed. However, the strategy parameter tuning based on trial and error is, in general, prohibitively expensive in black-box optimization scenario. This paper proposes a novel strategy to adapt the population size for CMA-ES. The population size is adapted based on the estimated accuracy of the update of the normal distribution parameters. The CMA-ES with the proposed population size adaptation mechanism, PSA-CMA-ES, is tested both on noiseless and noisy benchmark functions, and compared with existing strategies. The results revealed that the PSA-CMA-ES works well on well-structured multimodal and/or noisy functions, but causes inefficient increase of the population size on unimodal functions. Furthermore, it is shown that the PSA-CMA-ES can tackle noise and multimodality at the same time.
Exploring the Benefits of Utilizing Conceptual Information in Test-to-Code Traceability

Striving for reliability of software systems often results in immense numbers of tests. Due to the lack of a generally used annotation, finding the parts of code these tests were meant to assess can be a demanding task. This is a valid problem of software engineering called test-to-code traceability. Recent research on the subject has attempted to cope with this problem applying various approaches and their combinations, achieving profound results. These approaches have involved the use of naming conventions during development processes and also have utilized various information retrieval (IR) methods often referred to as conceptual information. In this work we investigate the benefits of textual information located in software code and its value for aiding traceability. We evaluated the capabilities of the natural language processing technique called Latent Semantic Indexing (LSI) in the view of the results of the naming conventions technique on five real, medium sized software systems. Although LSI is already used for this purpose, we extend the viewpoint of one-to-one traceability approach to the more versatile view of LSI as a recommendation system. We found that considering the top 5 elements in the ranked list increases the results by 30% on average and makes LSI a viable alternative in projects where naming conventions are not followed systematically.
Improving the detection of evolutionary coupling: an approach considering sliding verification

During the evolution of software systems, change tends to make software designs erode over time. Modifications to a system that violate its architectural principles can result in evolutionary coupling between classes. While techniques have been proposed to detect evolutionary coupling, they are difficult to confirm if, indeed, it is a logical dependency acquired. This occurs due to the fact that, normally, the architect uses a single snapshot of the system architecture in the analysis, instead of making sense of the continuous architectural evolution. To address this problem, we argue for using a set of consecutive releases. We consider that using a single unattached release of the system is an inherent obstacle in establishing true evolutionary coupling between architectural elements - the limited view of the evolution process may hinder such analysis. In this paper, we present an approach that considers the concept of sliding verification to calculate the probability two architectural elements change together, improving the detection of evolutionary coupling. A continuous subset of versions are used in such analysis. We also describe the application of the proposed approach in one large Military Command and Control System evolution scenario.
Surrogate-assisted evolutionary biobjective optimization for objectives with non-uniform latencies

We consider multiobjective optimization problems where objective functions have different (or heterogeneous) evaluation times or latencies. This is of great relevance for (computationally) expensive multiobjective optimization as there is no reason to assume that all objective functions should take an equal amount of time to be evaluated (particularly when objectives are evaluated separately). To cope with such problems, we propose a variation of the Kriging-assisted reference vector guided evolutionary algorithm (K-RVEA) called heterogeneous K-RVEA (short HK-RVEA). This algorithm is a merger of two main concepts designed to account for different latencies: A single-objective evolutionary algorithm for selecting training data to train surrogates and K-RVEA's approach for updating the surrogates. HK-RVEA is validated on a set of biobjective benchmark problems varying in terms of latencies and correlations between the objectives. The results are also compared to those obtained by previously proposed strategies for such problems, which were embedded in a non-surrogate-assisted evolutionary algorithm. Our experimental study shows that, under certain conditions, such as short latencies between the two objectives, HK-RVEA can outperform the existing strategies as well as an optimizer operating in an environment without latencies.
Effect for Inhomogeneity by Pb Substitution on (Bi 1-y Pb y ) 2 Sr 2 Ca 1-x Y x Cu 2 O 8+δ Single Crystals

Inhomogeneity is one of the most important problems for the investigation of the superconductor-insulator transition. Y and Pb co-doped Bi2Sr2CaCu2O8+δ(PbY-Bi2212) crystals were fabricated by the self-flax method in order to control both carrier density and dimensionality. The Y-content of PbY-Bi2212 crystals was much wide distributed in the same crucible rather than that of only Y doping Bi2212 (Y-Bi2212) crystals. In order to clear the origin of inhomogeneity, crystals were characterized by wavelength-dispersive X-ray spectroscopy (WDS) and X-ray diffraction (XRD). One can estimate the Y-content of cleavage samples from the c-axis lattice parameter obtained by XRD because mainly Y-con- tent affects to c-axis lattice constant for both Y-Bi2212 and PbY-Bi2212 crystals. It found that there are some samples with multi-phases and the ratio of samples with multi-phase for PbY-Bi2212 crystals is larger than that for Y-Bi2212 crystals. The linear relation between Y-content and Pb-content were also found from WDS. Large inhomogeneity of Y-content may be induced from Pb-content inhomogeneity. Transport properties are strongly dependent to Y-content. However they cannot be explained only from the macroscopic chemical compositions of the substitution atom Y and Pb. It may be related to microscopic inhomogeneity.
Complementary PS II quantum yields calculated from simple fluorescence parameters measured by PAM fluorometry and the Saturation Pulse method

Different fluorescence-based parameters are described in the literature for the quantum yields of non-photochemical energy conversion in PS II, φNPQ and φNO, which are complementary with the quantum yield of photochemical energy conversion, φII. With modern PAM fluorometers, like the Dual-PAM-100, these quantum yields are calculated online with every Saturation Pulse and displayed as the parameters Y(II), Y(NPQ) and Y(NO), the sum of which is always unity: Y(II) + Y(NPQ) +Y(NO) = 1. These quantum yields comprehensively describe the fate of excitation energy in PS II and allow deep insights into the plant's capacity to cope with excess excitation energy. While so far calculation of Y(NPQ) and Y(NO) by the PAM software has been based on rather complex expressions derived by Kramer et al. (2004) Photosynth Res 79: 209-218, more simple expressions derived by Genty et al. (1996) (abstract no P28 of Robert Hill Symposium, Sheffield, UK) were advocated by Hendrickson et al. (2004) Photosynth Res 82: 73-81, with Y(NO) and Y(NPQ) being expressed by the fluorescence yields F, Fm and Fm', measured by PAM fluorometry and the Saturation Pulse method: Y(NO) = F/Fm and Y(NPQ) = F/Fm' F/Fm. Notably these expressions do not require knowledge of Fo'. Here we show that the expressions of Kramer et al. (2004) can be transformed into the simple expressions of Genty et al. (1996) and that these are not only valid for a lake model, but also for a puddle model, as already known for Y(II) = (Fm'-F)/Fm'. The meaning of Y(NO) is discussed and an example of characteristic changes of the complementary PS II quantum yields after heat-pretreatment is presented.
Detecting threshold parameters using entropy analysis in density peaks clustering

The density peak clustering algorithm is a data clustering algorithm with clustering by fast search and finds of density peaks (DPC) as clustering centers. Then each remaining point is assigned to the same cluster as its nearest neighbor of higher density. The density peak clustering could classify elements into clusters very well, but the algorithm requires manual intervention and decision making to select clustering centers. Compared with the traditional K-means algorithm, it is not necessary to input the number of clustering thresholds, but it is necessary to input the distance threshold and the value of clustering border thresholds. This means it is necessary for a human to intervene during clustering by selecting some thresholds. This paper proposed a method based on information entropy backtracking enumeration to automatically discover possible distance thresholds. It doesn't need to intervene manually in the clustering process. This idea is mainly based on the fact that the information entropy is relatively stable in a relatively stable clustering state. Then predict and recommend some clustering results automatically. The paper found that it is easier to obtain clustering results as good as the artificial threshold selection method on several test case.
Similarity resonance for improving process model matching accuracy

Comparing and matching process models is a common task in many process engineering applications such as in querying and refactoring large process model repositories. The first and most challenging task in process model matching is to establish a similarity measure between process activities. Existing works use label similarity techniques to compute activities' similarity. In this paper, we propose a contextual similarity measure that, in addition to label similarity, exploits the similarity of the context surrounding activities. We introduce similarity resonance, a recursive algorithm that computes a global contextual similarity between process activities. Our intuition is that two activities are similar if they are executed in similar contexts which are represented by the surrounding activity neighbors. In turn, the activity neighbors are similar if their neighbors are similar, and so on. In this way, the pairwise similarity between all activities is iteratively computed and updated based on the similarity between their neighbors until the similarity scores have been stabilized and have been propagated to the whole graph. The approach has been implemented as a ProM plugin and was evaluated using several real-life datasets.
"Knowledge base recall: detecting and resolving the unknown unknowns" by Simon Razniewski and Gerhard Weikum with Martin Vesely as coordinator

Knowledge bases about entities like people, places and products have become key assets of search and recommender systems. The largest of them contain many millions of entities and billions of facts about them. Nevertheless, they have major gaps and limitations in what they cover, thus posing the challenge of detecting and resolving these "unknown unknowns". This paper provides an overview on the problems in mapping knowledge base recall and the existing approaches to address these issues. Specifically, we discuss i) formalisms and tools for describing incompleteness, ii) rule mining methods to assess recall, iii) text mining methods to this end, and iv) approaches towards relative recall and informativeness.
Role of accelerated aging in limb muscle wasting of patients with COPD

Purpose Skeletal muscle wasting is an independent predictor of health-related quality of life and survival in patients with COPD, but the complexity of molecular mechanisms associated with this process has not been fully elucidated. We aimed to determine whether an impaired ability to repair DNA damage contributes to muscle wasting and the accelerated aging phenotype in patients with COPD. Patients and methods The levels of phosphorylated H2AX (γH2AX), a molecule that promotes DNA repair, were assessed in vastus lateralis biopsies from 10 COPD patients with low fat-free mass index (FFMI; COPDL), 10 with preserved FFMI and 10 age- and gender-matched healthy controls. A panel of selected markers for cellular aging processes (CDKN2A/p16ink4a, SIRT1, SIRT6, and telomere length) were also assessed. Markers of oxidative stress and cell damage and a panel of pro-inflammatory and anti-inflammatory cytokines were evaluated. Markers of muscle regeneration and apoptosis were also measured. Results We observed a decrease in γH2AX expression in COPDL, which occurred in association with a tendency to increase in CDKN2A/p16ink4a, and a significant decrease in SIRT1 and SIRT6 protein levels. Cellular damage and muscle inflammatory markers were also increased in COPDL. Conclusion These data are in keeping with an accelerated aging phenotype as a result of impaired DNA repair and dysregulation of cellular homeostasis in the muscle of COPDL. These data indicate cellular degeneration via stress-induced premature senescence and associated inflammatory responses abetted by the senescence-associated secretory phenotype and reflect an increased expression of markers of oxidative stress and inflammation.
Weakly-Supervised Deep Recurrent Neural Networks for Basic Dance Step Generation

Synthesizing human’s movements such as dancing is a flourishing research field which has several applications in computer graphics. Recent studies have demonstrated the advantages of deep neural networks (DNNs) for achieving remarkable performance in motion and music tasks with little effort for feature pre-processing. However, applying DNNs for generating dance to a piece of music is nevertheless challenging, because of 1) DNNs need to generate large sequences while mapping the music input, 2) the DNN needs to constraint the motion beat to the music, and 3) DNNs require a considerable amount of hand-crafted data. In this study, we propose a weakly supervised deep recurrent method for real-time basic dance generation with audio power spectrum as input. The proposed model employs convolutional layers and a multilayered Long Short-Term memory (LSTM) to process the audio input. Then, another deep LSTM layer decodes the target dance sequence. Notably, this end-to-end approach has 1) an auto-conditioned decode configuration that reduces accumulation of feedback error of large dance sequence, 2) uses a contrastive cost function to regulate the mapping between the music and motion beat, and 3) trains with weak labels generated from the motion beat, reducing the amount of hand-crafted data. We evaluate the proposed network based on i) the similarities between generated and the baseline dancer motion with a cross entropy measure for large dance sequences, and ii) accurate timing between the music and motion beat with an F-measure. Experimental results revealed that, after training using a small dataset, the model generates basic dance steps with low cross entropy and maintains an F-measure score similar to that of a baseline dancer.
Pb-Stress Induced Oxidative Stress Caused Alterations in Antioxidant Efficacy in Two Groundnut (Arachis hypogaea L.) Cultivars

Lead (Pb) is an important environmental pollutant extremely toxic to plants and other living organisms including humans. To assess Pb phytotoxicity, a pot culture experiment was carried out using two groundnut cultivars (Arachis hypogaea L. cultivar K6 and cultivar K9) on plant growth, ROS levels, lipid peroxidation, and antioxidant metabolism using biochemical, histochemical methods. Plants were grown in pots for 14 days, in the botanic garden, and subjected to Pb-stress (0, 100, 200, 400 and 800 ppm) by adding Pb (NO3)2 solution and further allowed to grow for 10 days. The results showed that cultivar K6 registered lower Pb accumulation than cultivar K9, however, localization of Pb was greater in roots than leaves in both groundnut cultivars. The Pb-stress results in an increase in free radicals (O2•- and H2O2) generation in both groundnut cultivars, but more significantly in cultivar K9 than K6. Pb-stress also caused significant changes in the rate of peroxidation as shown in the levels of malondialdehyde (MDA) content in roots and leaves of both groundnut cultivars. Free proline, ascorbic acid (AsA) and non-protein thiol (NP-SH) contents were increased in cultivar K6 due to Pb-stress, but less in cultivar K9. Pb treated plants showed increased levels of antioxidant enzymes such as superoxide dismutase (SOD), guaiacol peroxidase (GPX), ascorbate peroxidase (APX) glutathione reductase (GR) and glutathione S-transferase (GST). Isozyme band intensities of SOD, GPX and APX were more consistent with the respective changes in antioxidative enzyme activities. These results indicate that cultivar K6 possesses greater tolerance potential for Pb toxicity than cultivar K9.
BIRD: bio-inspired distributed interest forwarding in vehicular named-data networks

In this work we tackle the problem of congestion and Interest broadcast storm problem in vehicular named data networks (VNDN) and propose a bio-inspired approach which makes the Interest forwarding self-adaptive and autonomous. The properties like scalability, self-adaptiveness, and simplicity are inherently available to the biological species. These properties are desirable in the VNDN environment, who face the daunting issue of Interest flooding and congestion. The proposed Bio-Inspired Distributed (BIRD) Interest forwarding scheme allows the on-road vehicles to make intelligent Interest forwarding decisions based on the simple rules followed birds in nature. The Interest packets are guided through multiple paths in a flock like manner towards the provider. Simulation results show that at the cost of additional packets for multiple paths we achieve an average of 20% higher Content satisfaction ratio from both RUFS and NAIF. Additionally, BIRD incurs 10% less delay as compared to the multi-path NIAF scheme in urban scenarios with varying network density.
Hardened Registration Process for Participatory Sensing

Participatory sensing systems need to gather information from a large number of participants. However, the openness of the system is a double-edged sword: by allowing practically any user to join, the system can be abused by an attacker who introduces a large number of virtual devices. This poster proposes a hardened registration process for Participatory Sensing to raise the bar: registrations are screened through a number of defensive measures, towards rejecting spurious registrations that do not correspond to actual devices. This deprives an adversary from a relatively easy take-over and, at the same time, allows a flexible and open registration process. The defensive measures are incorporated in the participatory sensing application.
A dataset of daily interactive manipulation

Robots that succeed in factories may struggle to complete even the simplest daily task that humans take for granted, because the change of environment makes the task exceedingly difficult. Aiming to teach robots to perform daily interactive manipulation in a changing environment using human demonstrations, we collected our own data of interactive manipulation. The dataset focuses on the position, orientation, force, and torque of objects manipulated in daily tasks. The dataset includes 1,603 trials of 32 types of daily motions and 1,596 trials of pouring alone, as well as helper code. We present our dataset to facilitate the research on task-oriented interactive manipulation.
Improving the classification performance of amnestic MCI using multiple neuropsychological scales

In this paper, MCI diagnostic model based on neuropsychological scales was researched, and the performance of which was improved by combination of multiple neuropsychological scales. The data of 41 subjects includes 15 AD patients, 12 aMCI patients and 14 normal subjects as control group. Each subject was tested on the neuropsychology scales, which include MMSE, ADAS-cog and CCWS. The feature selection was based on Support Vector Machines-Recursive Feature Elimination (SVM-RFE), then use the PCA algorithm for feature compression. Support Vector Machines was utilized to build the classification model, and the classifier performance were evaluated based on accuracy, and using 10-fold cross validation. Finally, three neuropsychology scales were combined, and the result showed that classification accuracy for AD vs NC, AD vs aMCI and aMCI vs NC respectively was 1.0, 1.0 and 0.9, and classification of the three categories is up to 0.93. The results show that combination of multiple neuropsychological scales can improve the diagnostic performance and classifier performance of aMCI.
A topic BiLSTM model for sentiment classification

The Long Short Term Memory (LSTM) network is very effective for capturing sequence information which can help to analyze sentiments. However, it fails to capture the meaning of polysemous word under different contexts. In this paper, we propose topic information-based bidirectional LSTM (BiLSTM) model for sentiment classification. BiLSTM model learns topic information to obtain the sensitive representation of the polysemous word under given circumstance. The topic information is generated through a topic modeling via Latent Dirichlet Allocation (LDA). The topic information-based BiLSTM network allows the model to capture the meaning of the polysemous word and long sequence information automatically. The experimental results on real-world datasets demonstrate that the proposed method outperforms the task of benchmark sentiment classification on SemEval 2013 and IMDB.