Miracle-dz/UniFAR-FTUs huggingface dataset:
Computing the Value of Computation for Planning

An intelligent agent performs actions in order to achieve its goals. Such actions can either be externally directed, such as opening a door, or internally directed, such as writing data to a memory location or strengthening a synaptic connection. Some internal actions, to which we refer as computations, potentially help the agent choose better actions. Considering that (external) actions and computations might draw upon the same resources, such as time and energy, deciding when to act or compute, as well as what to compute, are detrimental to the performance of an agent. In an environment that provides rewards depending on an agent's behavior, an action's value is typically defined as the sum of expected long-term rewards succeeding the action (itself a complex quantity that depends on what the agent goes on to do after the action in question). However, defining the value of a computation is not as straightforward, as computations are only valuable in a higher order way, through the alteration of actions. This thesis offers a principled way of computing the value of a computation in a planning setting formalized as a Markov decision process. We present two different definitions of computation values: static and dynamic. They address two extreme cases of the computation budget: affording calculation of zero or infinitely many steps in the future. We show that these values have desirable properties, such as temporal consistency and asymptotic convergence. Furthermore, we propose methods for efficiently computing and approximating the static and dynamic computation values. We describe a sense in which the policies that greedily maximize these values can be optimal. We utilize these principles to construct Monte Carlo tree search algorithms that outperform most of the state-of-the-art in terms of finding higher quality actions given the same simulation resources.
Avoiding contact allergens: from basic research to the in vitro identification of contact allergens

Allergic contact dermatitis (ACD) is a chemical-induced inflammatory skin disease. Contact allergens are low-molecular-weight chemicals that must react with proteins in order to become immunogenic. This interaction leads to the activation of innate immune and stress responses and to the formation of antigenic epitopes for T cells which are the effector cells of ACD. Due to the multitude of chemicals that surround us in our daily life and their potential sensitizing capacity, it is crucial to identify contact sensitizers before these chemicals are used in consumer products. Appropriate in vitro assays for hazard identification are urgently needed to replace animal-based assays. The EU-wide ban on sensitization testing of cosmetic ingredients in animals is in effect since March 2009 and the necessity to test more than 30,000 already marketed chemicals for their sensitizing potential under the EU regulation REACh has intensified the worldwide efforts to replace animal testing. We summarize here the current strategies to develop a battery of assays which allows the identification of contact allergens by in vitro alternatives to animal testing. Our main focus lies on the test systems recently developed within the EU project Sens-it-iv in which we participate.
Differentiating Watching and Posting Behaviors in Danmaku Video Websites: The Effects of Perceived Value and Sense of Virtual Community

Generally, in Danmaku video websites, users have two types of behaviors: watching and posting Danmaku comments. However, current literature only focused on the adoption of Danmaku and seldom discussed the differences between watching and posting behaviors. To fill this gap, we design our model based on S-O-R framework and posit that a sense of virtual community (SOVC) and perceived value composed of information value and emotional value affected by information and system qualities determine the two behaviors. Data collected from 175 respondents was used to test the model. We found that 1) intention to post largely comes from SOVC and emotional value, 2) intention to watch only depends on emotional value, and 3) interactivity and information quality are related to two perceived value significantly while visual appeal only correlates with emotional value. This study provides a new perspective for further understanding of users’ participation behaviors in Danmaku websites.
Modeling and Simulation of Multi-tier Enterprise IT System

This paper discusses modelling and simulation of multi-tier enterprise IT system. The layers in multi-tier architecture consist of web layer, application layer and database layer. Entities in the multi-tier system have been abstracted out into 3 categories- consumer, resource and router. Existing modelling and simulation frameworks for multi-tier systems focus on power management or performance of load balancing algorithms. Our framework enables seamless modelling, simulation, and experimentation of a wide range of what-if scenarios in multi-tier systems while encapsulating all the variations that arise due to configuration, composition, design and deployment. As an illustration, we discuss and simulate prediction of bottleneck scenario with results.
Use of Guided Regularized Random Forest for Biophysical Parameter Retrieval

This paper introduces a feature selection method based on random forest -the Guided Regularized Random Forest (GRRF)- which can be used in classification and regression tasks. The method is based on the regularization of the information gain in the random forest nodes to obtain a subset of relevant and non-redundant features. The proposed method is used as a preliminary step In the process of retrieving biophysical parameters from a hyperspectral image. Preliminary experiments show that we can reduce the RMSE of the retrievals by around 7% for the Leaf Area Index and around 8% for the fraction of vegetation cover when compared to the results using random forest features.
Very High Resolution Optical Image Classification Using Watershed Segmentation and a Region-Based Kernel

In this paper, the problem of the spatial-spectral classification of very high-resolution optical images is addressed using a kernel- and region-based approach. A novel method based on integrating region-based or object-based information into a kernel machine is developed. A Gaussian process model is used to characterize each segment in a segmentation map and to define a region-based admissible kernel accordingly. This kernel is combined with a marker-controlled watershed segmentation that incorporates scale adaptivity. Spatial-spectral fusion capabilities are also ensured by combining the resulting classification method with composite kernels.
Joint Optimization of Energy and QoE with Fairness in Cooperative Fog Computing System

Fog Computing as one of Mobile Edge Computing (MEC) paradigms deploys servers to the edge of networks to reduce the transmission latency. However, how to obtain the energy-effective cooperation policy among fog nodes to enhance the users' quality of experience (QoE) under fairness still remains a challenging issue, where the fairness ensures that fog nodes are encouraged to take part in cooperations. Therefore, we first build up a cooperative fog computing system to process offloading workload on the entire Fog layer by data forwarding. Then we propose a joint optimization problem of QoE (average response time) and energy (average energy consumption) in integrated fog computing process with fairness. After that, we prove the convexity of the optimization problem and design a Fairness Cooperation Algorithm to obtain the optimal fairness cooperation policy of all fog nodes. Finally, by comparing with baseline algorithm and Distributed Optimization Algorithm, the numerical results show that our algorithm can effectively reduce response time reduction and energy consumption.
Antenna Polarization Characterization with Vector Network Analyzer Measurements

This paper proposes the identification of polarization characteristics of antennas by using vector network analyzer (VNA) measurements. Parameters such as the axial ratio, phase difference and tilt angle are measured and calculated, which are sufficient for the characterization of the polarization ellipse. The proposed procedure is intended for measuring antennas in laboratory environments, for example antennas with uncertain polarization properties, and for comparing expected and measured parameters. Experiment results are presented, which agree to expected values.
Circular Relevance Feedback for Remote Sensing Image Retrieval

Relevance feedback (RF) is a popular reranking technique, which aims at improving the performance of image retrieval by taking the user's opinions into account. In this paper, we introduce a new RF method, named circular relevance feedback (CRF), to enhance the behavior of remote sensing image retrieval (RSIR). Instead of the manual selection used in the common RF method, we adopt the active learning (AL) algorithm to select the samples from the initial results automatically in each RF iteration. Moreover, to ensure the selected images are representative and informative enough, we choose different AL algorithms to complete the different RF processes. Finally, the contributions of all AL-driven RF methods are integrated using a circular fusion scheme. The encouraging experimental results on the ground truth RS image archive illustrate that our CRF is useful for enhancing the performance of RSIR. In addition, compared with many existing RF methods, our CRF achieves improved behavior.
CoolCloudSim: Integrating Cooling System Models in CloudSim

This paper addresses the problem of Data Centers (DCs) energy efficiency from a thermal perspective by extending the CloudSim framework to allow simulation and testing of thermal aware resource allocation policies aiming to minimize the cooling system energy consumption. The proposed framework, CoolCloudSim, can be used to develop and test new thermal aware Virtual Machine (VM) allocation strategies aiming to optimize the energy consumption of both cooling system and IT resources while meeting Service Level Agreements (SLAs). The default CloudSim architecture is extended by adding classes which contain mathematical models of the thermal processes within the server room. Furthermore, four new VM allocation policies that consider the cooling system energy consumption are developed based on the thermal and cooling system models. Finally, experiments are run to evaluate various metrics on a set of default CloudSim allocation algorithms and the proposed allocation algorithms. The results show that the proposed algorithms outperform the default CloudSim allocation strategy, Power Aware Best-Fit Decreasing (PABFD), in terms of overall energy consumption and the number of VM migrations, and have on average better results than other existing allocation strategies.
Reflexive Laboratory-Based Cryptococcal Antigen Screening and Preemptive Fluconazole Therapy for Cryptococcal Antigenemia in HIV-Infected Individuals With CD4 <100 Cells/µL: A Stepped-Wedge, Cluster-Randomized Trial

Background: HIV-infected persons with cryptococcal antigenemia (CrAg) are at high risk for meningitis or death. We evaluated the effect of CrAg screening and preemptive fluconazole therapy, adjunctive to antiretroviral therapy (ART), on 6-month survival among persons with advanced HIV/AIDS. Methods: We enrolled HIV-infected, ART-naive participants with <100 CD4 cells/µL, in a stepped-wedge, cluster-randomized trial from July 2012 to December 2014 at 17 Ugandan clinics. Clinics participated in a prospective observational phase, followed by an interventional phase with laboratory-based, reflexive CrAg screening of residual CD4 count plasma. Asymptomatic CrAg+ participants received preemptive fluconazole therapy. We assessed 6-month survival using Cox-regression, adjusting for nadir CD4, calendar time, and stepped-wedge steps. Results: We included 1280 observational and 2108 interventional participants, of whom 9.3% (195/2108) were CrAg+. CD4-, time-, and stepped-wedge–adjusted analyses demonstrated no difference in survival in the observational vs the interventional arms (hazard ratio = 1.34; 95% confidence interval: 0.86 to 2.10; P = 0.20). Fewer participants initiated ART in the interventional (73%) versus the observational phase (82%, P < 0.001). When ART initiation was modeled as a time-dependent covariate or confounder, survival did not differ. However, 6-month mortality of participants with CrAg titers <1:160 and CrAg-negative patients did not differ. Patients with CrAg titers ≥1:160 had 2.6-fold higher 6-month mortality than patients with titers <1:160. Conclusions: We observed no overall survival benefit of the CrAg screen-and-treat intervention. However, preemptive antifungal therapy for asymptomatic cryptococcosis seemed to be effective in patients with CrAg titer <1:160. A more aggressive approach is required for persons with CrAg titer ≥1:160.
Urban Vegetation Mapping Using Hyperspectral Imagery and Spectral Library

The development and expansion of urbanized areas around the cities, brings new challenging issues about the organization, the monitoring, and the distribution of green spaces within the cities (e.g. grass, trees, shrubs, etc.). Indeed, these spaces brings better life quality for population and preserve biodiversity. This study, aims to 1) investigate the feasibility of urban vegetation mapping by species using multiband imagery and spectral libraries and to 2) determine at what scale the mapping is reliable (e.g. trees scale, group of trees scale, high/short vegetation scale).
Irisin a Novel Metabolic Biomarker: Present Knowledge and Future Directions

The rising prevalence of chronic diseases such as type 2 diabetes and cardiovascular diseases owing to fat mass excess has been described. In recent years, muscle function/dysfunction has become relevant in metabolic homeostasis. Irisin was described as an exercise-induced myokine. It is the product of type I membrane protein cleavage encoded by the fibronectin type III domain containing 5 (FNDC5) gene. The main beneficial function attributable to irisin is the change of subcutaneous and visceral adipose tissue into brown adipose tissue, with a consequential increase in thermogenesis. Irisin has also been described as a hormone that may have a key role in glucose homeostasis. The way the association of type 2 diabetes with obesity occurs is not fully understood. In recent years, the possible pathways through which irisin could interact with other organs such as the brain or bone have been described. The present paper intends to review the new findings and possible new directions in irisin research.
FlashDB: Dynamic Self-tuning Database for NAND Flash

FlashDB is a self-tuning database optimized for sensor networks using NAND flash storage. In practical systems flash is used in different packages such as on-board flash chips, compact flash cards, secure digital cards and related formats. Our experiments reveal non-trivial differences in their access costs. Furthermore, databases may be subject to different types of workloads. We show that existing databases for flash are not optimized for all types of flash devices or for all workloads and their performance is thus subop- timal in many practical systems. FlashDB uses a novel self-tuning index that dynamically adapts its storage structure to workload and underlying storage device. We formalize the self-tuning nature of an index as a two-state task system and propose a 3-competitive online algorithm that achieves the theoretical optimum. We also provide a framework to determine the optimal size of an index node that minimizes energy and latency for a given device. Finally, we propose optimizations to further improve the performance of our index. We prototype and compare different indexing schemes on multiple flash devices and workloads, and show that our indexing scheme outperforms existing schemes under all workloads and flash devices we consider.
Astaxanthin Prevents Human Papillomavirus L1 Protein Binding in Human Sperm Membranes

Astaxanthin (Asta), red pigment of the carotenoid family, is known for its anti-oxidant, anti-cancer, anti-diabetic, and anti-inflammatory properties. In this study, we evaluated the effects of Asta on isolated human sperm in the presence of human papillomavirus (HPV) 16 capsid protein, L1. Sperm, purified by gradient separation, were treated with HPV16-L1 in both a dose and time-dependent manner in the absence or presence of 30 min-Asta pre-incubation. Effects of HPV16-L1 alone after Asta pre-incubation were evaluated by rafts (CTB) and Lyn dislocation, Tyr-phosphorylation (Tyr-P) of the head, percentages of acrosome-reacted cells (ARC) and endogenous reactive oxygen species (ROS) generation. Sperm membranes were also analyzed for the HPV16-L1 content. Results show that HPV16-L1 drastically reduced membrane rearrangement with percentage of sperm showing head CTB and Lyn displacement decreasing from 72% to 15.8%, and from 63.1% to 13.9%, respectively. Accordingly, both Tyr-P of the head and ARC decreased from 68.4% to 10.2%, and from 65.7% to 14.6%, respectively. Asta pre-incubation prevented this drop and restored values of the percentage of ARC up to 40.8%. No alteration was found in either the ROS generation curve or sperm motility. In conclusion, Asta is able to preserve sperm by reducing the amount of HPV16-L1 bound onto membranes.
A spatial co-location mining algorithm based on a spatial continuous field with refined road-network constraints

Urban service facilities reflect the development status of a city from a micro level. Extracting useful spatial patterns from this type of data can assist planners with the reasonable allocation. Co-location pattern mining is valid to solve this problem. Current methods are mainly implemented in a homogenous spatial field with little constraints. However, the urban service facilities are mostly distributed in a manmade spatial field with refined road-network constraints. To address this problem, we improve the traditional methods from two aspects: (1) using a network kernel density model, we replace the Euclidean distance by an accessibility indicator to measure the proximity of two spatial instances. This indicator involves the direction and network constraints in urban space. (2) We introduce a reachability weight into the calculation of the prevalent index to replace the traditional discrete approach. The above two improvements regard the truth that the movement of human in city mainly depends on the road-network in a spatial continues field. The preliminary experiments show that the algorithm is more applicable than the current methods in solving urban facility problems.
Intelligent and Connected Vehicles: Current Situation, Future Directions, and Challenges

Given their large retention number worldwide, vehicles, as a very important part of the emerging Internet of Things, have been stably evolving toward becoming highly connected, intelligent, and autonomous. Since the very first experiment on an American Wonder by Francis P Houdina in 1925, autonomous driving has attracted intensive research interest from standards organizations, industry, as well as academia. In this article, we first present an overview of the current progress and achievements, then envision the future evolution directions of autonomous driving systems. Besides describing the representative functional modules, we also discuss the critical enabling technologies, and identify the security concerns and corresponding countermeasures.
A Monte Carlo Approach to Skill-Based Automated Playtesting

In order to create well-crafted learning progressions, designers guide players as they present game skills and give ample time for the player to master those skills. However, analyzing the quality of learning progressions is challenging, especially during the design phase, as content is ever-changing. This research presents the application of Stratabots-automated player simulations based on models of players with varying sets of skills-to the human computation game Foldit. Stratabot performance analysis coupled with player data reveals a relatively smooth learning progression within tutorial levels, yet still shows evidence for improvement. Leveraging existing general gameplaying algorithms such as Monte Carlo Evaluation can reduce the development time of this approach to automated playtesting without losing predicitive power of the player model.
Random Forests Model Selection

Random Forests (RF) of tree classiﬁers are a popular ensemble method for classiﬁcation. RF have shown to be eﬀective in many diﬀerent real world classiﬁcation problems and nowadays are considered as one of the best learning algorithms in this context. In this paper we discuss the eﬀect of the hyperparameters of the RF over the accuracy of the ﬁnal model, with particular reference to diﬀerent theoretically grounded weighing strategies of the tree in the forest. In this way we go against the common misconception which considers RF as an hyperparameter-free learning algorithm. Results on a series of benchmark datasets show that performing an accurate Model Selection procedure can greatly improve the accuracy of the ﬁnal RF classiﬁer.
The relationship between the physical activity efficacy and physical activity of the middle school students

Purpose: The purpose of this study is to determine the physical activity efficacy and physical activity of the middle school students, according to gender and to analyze the relationship between them. Material: The study group consisted of a total of 701 students from three public secondary schools, 343 of whom were female and 358 were male students. In order to determine the physical activity efficacy of the students, the Physical Activity Efficacy Scale was used, while the Physical Activity Questionnaire for Older Children was used for establishing the level of physical activity. In evaluating the data, descriptive statistics, independent t-test, Pearson correlation tests were used as statistical methods. Results: According to the data obtained by the study, the students’ physical activity efficacy was found to be good, with no difference between the females and the males (p>0.05). The students’ physical activity level was average, while the males were found to be physically more active than the females (p<0.05). A positive correlation was found between the physical activity efficacy and the physical activity of the students (p<0.05). Conclusion: According to the findings of this study, physical activities should be increased during the time of the middle school, which is a part of the period of adolescence. Besides, in order to enhance their physical activity efficacy, the adolescents should be provided with physical activity environments.
A Fluorescence Sensor Capable of Real-Time Herbicide Effect Monitoring in Greenhouses and the Field

Herbicide resistant weeds need to be identified early so that yield loss can be avoided by applying proper field management strategies. A novel chlorophyll-fluorescence-imaging sensor has been developed to conduct real-time herbicide effect evaluation. In this research, greenhouse and field experiments were conducted to calibrate the capability of the sensor in monitoring herbicide effects on different biotypes of two grass weeds (Alopecurus myosuroides, Apera spica-venti) in southwestern Germany. Herbicides with different modes of action were applied for the effect monitoring. Chlorophyll fluorescence yield of the plants was measured 3–15 days after treatment (DAT) using the new fluorescence sensor. Visual assessment of the weeds was carried out on 21 DAT. The results showed that the maximal PS II quantum yield (Fv/Fm) of herbicide sensitive weeds was significantly lower than the values of resistant populations in 5 DAT. The new technology was capable of quickly identifying the herbicide’s effect on plants. It can be used to optimize management strategies to control herbicide resistant weeds.