Miracle-dz/UniFAR-FTUs huggingface dataset:
Kindness is Contagious: Study into Exploring Engagement and Adapting Persuasive Games for Wellbeing

Intentional engagement in positive activities, such as practicing kindness, showing generosity or expressing gratitude, can help people increase their happiness levels and improve their wellbeing. In this paper we explore how a gamified digital behaviour change intervention can be adapted to encourage people of different personality types to engage in simple acts of kindness. Participants were assigned 5 daily activities for 7 days, and asked to complete as many as possible by the end of each day. Participants received different persuasive notifications every day to encourage them to complete a higher number of activities. We investigated how participation levels are influenced by different personality types, different persuasive message types and different categories of activities. Furthermore, we analysed the influence of the intervention on participant behaviour and the effect on behavioural intention, by comparing pre-intention and post-intention to perform different kinds of positive activities. The findings from this study have implications for future work on personalising persuasive interventions to improve wellbeing and prevent mental health problems.
Relationship Between Foveal Cone Structure and Visual Acuity Measured With Adaptive Optics Scanning Laser Ophthalmoscopy in Retinal Degeneration

Purpose To evaluate foveal function in patients with inherited retinal degenerations (IRD) by measuring visual acuity (VA) after correction of higher-order aberrations. Methods Adaptive optics scanning laser ophthalmoscopy (AOSLO) was used to image cones in 4 healthy subjects and 15 patients with IRD. The 840-nm scanning laser delivered an “E” optotype to measure AOSLO-mediated VA (AOSLO-VA). Cone spacing was measured at the preferred retinal locus by two independent graders and the percentage of cones below the average density of 47 age-similar healthy subjects was computed. Cone spacing was correlated with best-corrected VA measured with the Early Treatment of Diabetic Retinopathy Study protocol (ETDRS-VA), AOSLO-VA, and foveal sensitivity. Results ETDRS-VA significantly correlated with AOSLO-VA (ρ = 0.79, 95% confidence interval [CI] 0.5–0.9). Cone spacing correlated with AOSLO-VA (ρ = 0.54, 95% CI 0.02–0.7), and negatively correlated with ETDRS letters read (ρ = −0.64, 95% CI −0.8 to −0.2). AOSLO-VA remained ≥20/20 until cones decreased to 40.2% (CI 31.1–45.5) below normal. Similarly, ETDRS-VA remained ≥20/20 until cones were 42.0% (95% CI 36.5–46.1) below normal. Cone spacing z scores negatively correlated with foveal sensitivity (ρ = −0.79, 95% CI −0.9 to −0.4) and foveal sensitivity was ≥35 dB until cones were 43.1% (95% CI 39.3–46.6) below average. Conclusions VA and foveal cone spacing were weakly correlated until cones were reduced by 40% to 43% below normal. The relationship suggests that VA is an insensitive measure of foveal cone survival; cone spacing may be a more sensitive measure of cone loss.
Intra‐Sectoral Differences in Climate Change Strategies: Evidence from the Global Automotive Industry

Abstract Companies are increasingly challenged for action on climate change. Most studies on business responses to climate change focus on cross‐sector comparisons and neglect intra‐sectoral dynamics. This paper investigates the influence of supply chain position and regional affiliation on climate change strategies within a particular industry. We present a generic framework integrating both market and non‐market responses to climate change. We argue that climate change strategies comprise several corporate activities that have different foci of interaction and four main objectives: governance, innovation, compensation and legitimation. Using a global sample of 116 automotive companies, we conduct a cluster analysis and identify four types of strategy. We find that the sophistication of automobile manufacturers' strategies significantly differs from that of suppliers. Regional affiliation and firm size prove to be determinants of the strategy type pursued. We cannot find evidence for a relationship between financial performance and a company's strategic approach to climate change. © 2017 The Authors. Business Strategy and the Environment published by ERP Environment and John Wiley & Sons Ltd
Co-embeddings for Student Modeling in Virtual Learning Environments

We present a neural architecture to model student behavior in virtual educational environments using purely unsupervised information. A crucial part of this architecture is the optimization of a joint embedding function to represent both students and course elements into a single shared space. This joint representation is more adequate than disjoint representations because it elicits insights on the relations between students and contents. Moreover, the model is trained only with interactions of the student with online learning platforms, without requiring any additional manual labeling by experts. We obtain state-of-the-art results using this approach in two types of task: first, dropout prediction in online courses (MOOCs), and second Knowledge Tracing in Intelligent Tutoring Systems (ITS). We explore how the deep architecture is flexible enough to capture variables related to different phenomena, such as engagement or skill mastery.
Cyber-Physical System for Energy-Efficient Stadium Operation

The environmental impacts of medium to large-scale buildings receive substantial attention in research, industry, and media. This article studies the energy savings potential of a commercial soccer stadium during day-to-day operation. Buildings of this kind are characterized by special purpose system installations like grass heating systems and by event-driven usage patterns. This work presents a methodology to holistically analyze the stadium’s characteristics and integrate its existing instrumentation into a Cyber-Physical System, enabling to deploy different control strategies flexibly. In total, seven different strategies for controlling the studied stadium’s grass heating system are developed and tested in operation. Experiments in winter season 2014/2015 validated the strategies’ impacts within the real operational setup of the Commerzbank Arena, Frankfurt, Germany. With 95% confidence, these experiments saved up to 66% of median daily weather-normalized energy consumption. Extrapolated to an average heating season, this corresponds to savings of 775MWh and 148t of CO2 emissions. In winter 2015/2016 an additional predictive nighttime heating experiment targeted lower temperatures, which increased the savings to up to 85%, equivalent to 1GWh (197t CO2) in an average winter. Beyond achieving significant energy savings, the different control strategies also met the target temperature levels to the satisfaction of the stadium’s operational staff. While the case study constitutes a significant part, the discussions dedicated to the transferability of this work to other stadiums and other building types show that the concepts and the approach are of general nature. Furthermore, this work demonstrates the first successful application of Deep Belief Networks to regress and predict the thermal evolution of building systems.
Text Analysis for Decision Making under Adversarial Environments

Sentiment analysis and other practices for text analytics on social media rely on publicly available and editable collections of data for training and evaluation. These data collections are subject to poisoning and data contamination attacks by adversaries having an interest in misleading the results of the performed analysis. We present the problem of adversarial text mining with a focus on decision making and we suggest cross-discipline, cross-application and cross-model strategies for more robust analyses. Our approach is practitioner-centric and is based on broadly-used interpretable models with applications in decision making.
Investigating benchmark correlations when comparing algorithms with parameter tuning

Benchmarks are important for comparing performance of optimisation algorithms, but we can select instances that present our algorithm favourably, and dismiss those on which our algorithm under-performs. Also related are automated design of algorithms, which use problem instances (benchmarks) to train an algorithm: careful choice of instances is needed for the algorithm to generalise. We sweep parameter settings of differential evolution to applied to the BBOB benchmarks. Several benchmark functions are highly correlated. This may lead to the false conclusion that an algorithm performs well in general, when it performs poorly on a few key instances. These correlations vary with the number of evaluations.
Optimization based adaptive tagged visual cryptography

The Tagged Visual Cryptography Scheme (TVCS)1 adds tag images to the noise-like shares generated by the traditional VCS to improve the shares management of the traditional VCS. However, the existing TVCSs suffers visual quality of the recovered secret image may be degraded and there may be pixel expansion. This study proposes a Threshold Adaptive Tagged Visual Cryptography Scheme ((k, n)-ATVCS) to solve the above-mentioned problems. The ATVCS encryption problem is formulated in a mathematical optimization model, and an evolutionary algorithm is developed to find the optimal solution to the problem. The proposed (k, n)-ATVCS enables the encryptor to adjust the visual quality between the tag image and the secret image by tuning parameters. Experimental results show the correctness and effectiveness of this study.
Predicting Emotions From Multimodal Users' Data

Prediction of emotions is important for understanding human be-havior and modeling users in learning environments. In this paper,we present a deep multi-modal architecture for emotions predic-tion, which takes advantage of deep learning, user multimodal dataand the hierarchy of human memory. The architecture consists ofthe combination of Long Short-Term memory (LSTMs). One of thenovelty of our approach is that, we enhance the LSTM with anexplicit memory since in brain studies, the memory is often dividedinto two further main types: explicit (or declarative) memory andimplicit (or procedural) memory, the last one being the main pur-pose of LSTMs architectures. The resulting model has been testedon a public multi-modal dataset.
Towards Practical Link Prediction Approaches in Signed Social Networks

The purpose of this research is to design practical link prediction models in signed social networks. Current works focus on the sign prediction, based on the assumption that it is already known whether there is a link between any two users. In other words, the no-relation status is ignored. Meanwhile, the strength of existing links are assumed to be equal, which is also not realistic. In this study, we will redefine the link prediction problem in signed networks and take a deep investigation on no-relation status. Then, we aim to propose a personalized ranking model from the individual's perspective. This research explores link prediction models in a more realistic scenario, and it will contribute to ongoing research in development of link prediction and recommendations in signed networks. Furthermore, our research will provide a better understanding on the link formation mechanism behind signed network evolution.
Acute administration of interleukin‐6 does not increase secretion of glucagon‐like peptide‐1 in mice

Interleukin 6 (IL‐6) is a cytokine secreted from skeletal muscle in response to exercise which, based on animal and cell studies, has been suggested to contribute to glucose metabolism by increasing secretion of the incretin hormone glucagon‐like peptide‐1 (GLP‐1) and affecting secretion of insulin and glucagon from the pancreatic islets. We investigated the effect of IL‐6 on GLP‐1 secretion in GLP‐1 producing cells (GLUTag) and using the perfused mouse small intestine (harboring GLP‐1 producing cells). Furthermore, the direct effect of IL‐6 on insulin and glucagon secretion was studied using isolated perfused mouse pancreas. Incubating GLUTag cells with 1000 ng/mL of IL‐6 for 2 h did not significantly increase secretion of GLP‐1 whereas 10 mmol/L glucose (positive control) did. Similarly, IL‐6 (100 ng/mL) had no effect on GLP‐1 secretion from perfused mouse small intestine whereas bombesin (positive control) increased secretion. Finally, administering IL‐6 (100 ng/mL) to perfused mouse pancreases did not significantly increase insulin or glucagon secretion regardless of perfusate glucose levels (3.5 vs. 12 mmol/L glucose). Acute effects of IL‐6 therefore do not seem to include a stimulatory effect on GLP‐1 secretion in mice.
Computing Kernels in Parallel: Lower and Upper Bounds

Parallel fixed-parameter tractability studies how parameterized problems can be solved in parallel. A surprisingly large number of parameterized problems admit a high level of parallelization, but this does not mean that we can also efficiently compute small problem kernels in parallel: known kernelization algorithms are typically highly sequential. In the present paper, we establish a number of upper and lower bounds concerning the sizes of kernels that can be computed in parallel. An intriguing finding is that there are complex trade-offs between kernel size and the depth of the circuits needed to compute them: For the vertex cover problem, an exponential kernel can be computed by AC$^0$-circuits, a quadratic kernel by TC$^0$-circuits, and a linear kernel by randomized NC-circuits with derandomization being possible only if it is also possible for the matching problem. Other natural problems for which similar (but quantitatively different) effects can be observed include tree decomposition problems parameterized by the vertex cover number, the undirected feedback vertex set problem, the matching problem, or the point line cover problem. We also present natural problems for which computing kernels is inherently sequential.
Activity Recognition with Evolving Data Streams

Activity recognition aims to provide accurate and opportune information on people’s activities by leveraging sensory data available in today’s sensory rich environments. Nowadays, activity recognition has become an emerging field in the areas of pervasive and ubiquitous computing. A typical activity recognition technique processes data streams that evolve from sensing platforms such as mobile sensors, on body sensors, and/or ambient sensors. This article surveys the two overlapped areas of research of activity recognition and data stream mining. The perspective of this article is to review the adaptation capabilities of activity recognition techniques in streaming environment. Categories of techniques are identified based on different features in both data streams and activity recognition. The pros and cons of the algorithms in each category are analysed, and the possible directions of future research are indicated.
Biocontrol Activity of Volatile-Producing Bacillus megaterium and Pseudomonas protegens Against Aspergillus and Penicillium spp. Predominant in Stored Rice Grains: Study II

Abstract In our previous studies, Bacillus megaterium KU143, Microbacterium testaceum KU313, and Pseudomonas protegens AS15 have been shown to be antagonistic to Aspergillus flavus in stored rice grains. In this study, the biocontrol activities of these strains were evaluated against Aspergillus candidus, Aspergillus fumigatus, Penicillium fellutanum, and Penicillium islandicum, which are predominant in stored rice grains. In vitro and in vivo antifungal activities of the bacterial strains were evaluated against the fungi on media and rice grains, respectively. The antifungal activities of the volatiles produced by the strains against fungal development and population were also tested using I-plates. In in vitro tests, the strains produced secondary metabolites capable of reducing conidial germination, germ-tube elongation, and mycelial growth of all the tested fungi. In in vivo tests, the strains significantly inhibited the fungal growth in rice grains. Additionally, in I-plate tests, strains KU143 and AS15 produced volatiles that significantly inhibited not only mycelial growth, sporulation, and conidial germination of the fungi on media but also fungal populations on rice grains. GC-MS analysis of the volatiles by strains KU143 and AS15 identified 12 and 17 compounds, respectively. Among these, the antifungal compound, 5-methyl-2-phenyl-1H-indole, was produced by strain KU143 and the antimicrobial compounds, 2-butyl 1-octanal, dimethyl disulfide, 2-isopropyl-5-methyl-1-heptanol, and 4-trifluoroacetoxyhexadecane, were produced by strain AS15. These results suggest that the tested strains producing extracellular metabolites and/or volatiles may have a broad spectrum of antifungal activities against the grain fungi. In particular, B. megaterium KU143 and P. protegens AS15 may be potential biocontrol agents against Aspergillus and Penicillium spp. during rice grain storage.
Configuring the parameters of artificial neural networks using neuroevoiution and automatic algorithm configuration

NeuroEvolution (NE) is a powerful method that uses Evolutionary Algorithms (EAs) for learning Artificial Neural Networks (ANNs). However, NE's performance is determined by the definition of dozens of parameters that guide the search of the EAs. In this study we apply automatic algorithm configuration for tuning the parameters of a NE method in an offline matter. The tuned NE method is then used to evolve the weights, topology and activation functions of ANNs while performing feature selection and its performance is compared to the case of using default parameters. We show that tuning the parameters results in NE methods able to solve the problems with 100% accuracy in significantly less generations.
Forecasting soybean futures price using dynamic model averaging and particle swarm optimization

We develop a model to forecast Chinese soybean futures price with eighteen predictors by integrating the recently proposed dynamic model averaging (DMA) and particle swarm optimization (PSO). Specifically, three important parameters, i.e., two forgetting factors and a decay factor, of DMA are tuned by PSO. The proposed prediction model, named DMA-PSO, not only allow for coefficients to change over time, but also allow for forecasting model to evolve over time. Experimental results show that the proposed DMA-PSO outperforms four counterparts and the best predictors in DMA-PSO for forecasting soybean futures price vary a lot over time1.
Efficient identification, localization and quantification of grapevine inflorescences in unprepared field images using Fully Convolutional Networks

Yield and its prediction is one of the most important tasks in grapevine breeding purposes and vineyard management. Commonly, this trait is estimated manually right before harvest by extrapolation, which mostly is labor-intensive, destructive and inaccurate. In the present study an automated image-based workflow was developed quantifying inflorescences and single flowers in unprepared field images of grapevines, i.e. no artificial background or light was applied. It is a novel approach for non-invasive, inexpensive and objective phenotyping with high-throughput. First, image regions depicting inflorescences were identified and localized. This was done by segmenting the images into the classes "inflorescence" and "non-inflorescence" using a Fully Convolutional Network (FCN). Efficient image segmentation hereby is the most challenging step regarding the small geometry and dense distribution of flowers (several hundred flowers per inflorescence), similar color of all plant organs in the fore- and background as well as the circumstance that only approximately 5% of an image show inflorescences. The trained FCN achieved a mean Intersection Over Union (IOU) of 87.6% on the test data set. Finally, individual flowers were extracted from the "inflorescence"-areas using Circular Hough Transform. The flower extraction achieved a recall of 80.3% and a precision of 70.7% using the segmentation derived by the trained FCN model. Summarized, the presented approach is a promising strategy in order to predict yield potential automatically in the earliest stage of grapevine development which is applicable for objective monitoring and evaluations of breeding material, genetic repositories or commercial vineyards.
Deep learned features for image retrieval

A novel approach for feature learning using deep learning is presented. More specifically, a Convolutional Neural Network that is trained using feature correspondences learns to map a given image patch to a descriptor. Therefore, descriptors are directly learned from examples instead of being hand-crafted. The proposed approach is evaluated in a challenging image retrieval dataset.
Versatile Genome Engineering Techniques Advance Human Ocular Disease Researches in Zebrafish

Over recent decades, zebrafish has been established as a sophisticated vertebrate model for studying human ocular diseases due to its high fecundity, short generation time and genetic tractability. With the invention of morpholino (MO) technology, it became possible to study the genetic basis and relevant genes of ocular diseases in vivo. Many genes have been shown to be related to ocular diseases. However, the issue of specificity is the major concern in defining gene functions with MO technology. The emergence of the first- and second-generation genetic modification tools zinc-finger nucleases (ZFNs) and TAL effector nucleases (TALENs), respectively, eliminated the potential phenotypic risk induced by MOs. Nevertheless, the efficiency of these nucleases remained relatively low until the third technique, the clustered regularly interspersed short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) system, was discovered. This review highlights the application of multiple genome engineering techniques, especially the CRISPR/Cas9 system, in the study of human ocular diseases in zebrafish.
Network Classification in Temporal Networks Using Motifs

Network classification has a variety of applications, such as detecting communities within networks and finding similarities between those representing different aspects of the real world. However, most existing work in this area focus on examining static undirected networks without considering directed edges or temporality. In this paper, we propose a new methodology that utilizes feature representation for network classification based on the temporal motif distribution of the network and a null model for comparing against random graphs. Experimental results show that our method improves accuracy by up 10% compared to the state-of-the-art embedding method in network classification, for tasks such as classifying network type, identifying communities in email exchange network, and identifying users given their app-switching behaviors.
Stance Classification through Proximity-based Community Detection

Numerous domains have interests in studying the viewpoints expressed online, be it for marketing, cybersecurity, or research purposes with the rise of computational social sciences. Current stance detection models are usually grounded on the specificities of some social platforms. This rigidity is unfortunate since it does not allow the integration of the multitude of signals informing effective stance detection. We propose the SCSD model, or Sequential Community-based Stance Detection model, a semi-supervised ensemble algorithm which considers these signals by modeling them as a multi-layer graph representing proximities between profiles. We use a handful of seed profiles, for whom we know the stance, to classify the rest of the profiles by exploiting like-minded communities. These communities represent profiles close enough to assume they share a similar stance on a given subject. Using datasets from two different social platforms, containing two to five stances, we show that by combining several types of proximity we can achieve excellent results. Moreover, we compare the proximities to find those which convey useful information in term of stance detection.