Miracle-dz/UniFAR-FTUs huggingface dataset:
Engagement detection based on mutli-party cues for human robot interaction

In this paper, we address the problematic of automatic detection of engagement in multi-party Human-Robot Interaction scenarios. The aim is to investigate to what extent are we able to infer the engagement of one of the entities of a group based solely on the cues of the other entities present in the interaction. In a scenario featuring 3 entities: 2 participants and a robot, we extract behavioural cues that concern each of the entities, we then build models based solely on each of these entities' cues and on combinations of them to predict the engagement level of each of the participants. Person-level cross validation shows that we are capable of detecting the engagement of the participant in question using solely the behavioural cues of the robot with a high accuracy compared to using the participant's cues himself (75.91% vs. 74.32%). Moreover using the behavioural cues of the other participant is also informative where it permits the detection of the engagement of the participant in question at an accuracy of 62.15% on average. The correlation between the features of the other participant with the engagement labels of the participant in question suggests a high cohesion between the two participants. In addition, the similarity of the most significantly correlated features among the two participants suggests a high synchrony between the two parties.
Hydrolase Activities in the Rat Aorta: IV. Relation between Clearance Rates of Circulating 125I‐Labeled Low‐Density Lipoproteins and Levels of Tissue Hydrolase Activity

We studied the relationships among levels of acid cholesteryl esterase (ACE) activity in kidney, liver, and aorta and clearance of125I-labeled human low density lipoprotein (LDL) from the circulation. The premise was that levels of ACE (which is involved in the hydrolysis of the cholesteryl esters of LDL) might correspond to the rate at which LDL was removed from the circulation, or fractional catabolic rate (FCR). The models used were rats with streptozotocin diabetes and^ exercised (swimming) rats. In the diabetic rat, ACE specific activity was increased by 46% in kidney and 30% in liver and decreased by 25% in aorta when compared to controls. The FCR of LDL increased by 25% in the diabetic animal over control values. Insulin treatment restored all these alterations to normal. Swimming rats showed no change in ACE-specific activity of kidney or liver, but aortic ACE increased by approximately 25% over levels in sedentary rats. The FCR of radioiodinated LDL in swimmers was unchanged from normal. Return of swimmers to a sedentary existence resulted in persistence of normal ACE activity in kidney and liver and in FCR of LDL; however, previously elevated aortic ACE activity returned to normal. Thus, dynamic, irreversible removal of LDL from the circulation (FCR) is changed in the same direction as, and may be linked to, ACE activity in non-vascular tissues. Aortic ACE activity, on the other hand, is not so related to either ACE in those tissues or to FCR of LDL. One implication of this finding is that the general pattern of LDL catabolism by the rat is distinct from the local problem of LDL catabolism by the blood vessel wall. Circ Res 47: 433-442, 1980
Image quality assessment using Histograms of Oriented Gradients

Since it is commonly believed that human visual perception is highly adapted for extracting structural information from the scene, many gradient-based image quality assessment (IQA) metrics were proposed. The main research focus in this theme is about designing the computational models of gradient similarity to measure the changes of image quality. In this paper, we turn our attention to a different question: how to estimate the visual importance of different regions in one image using the gradient changes to improve the performance of existing IQA metrics. A novel gradient-based full reference IQA is proposed based on combining Histograms of Oriented Gradients (HOG) with the structural similarity (SSIM) index. Extensive experiments conducted on the LIVE image database show that the proposed HOGM approach achieves much higher consistency with the subjective evaluations than a number of competitive IQA algorithms.
Echo State Network with Bayesian Regularization for Forecasting Short-Term Power Production of Small Hydropower Plants

As a novel recurrent neural network (RNN), an echo state network (ESN) that utilizes a reservoir with many randomly connected internal units and only trains the readout, avoids increased complexity of training procedures faced by traditional RNN. The ESN can cope with complex nonlinear systems because of its dynamical properties and has been applied in hydrological forecasting and load forecasting. Due to the linear regression algorithm usually adopted by generic ESN to train the output weights, an ill-conditioned solution might occur, degrading the generalization ability of the ESN. In this study, the ESN with Bayesian regularization (BESN) is proposed for short-term power production forecasting of small hydropower (SHP) plants. According to the Bayesian theory, the weights distribution in space is considered and the optimal output weights are obtained by maximizing the posterior probabilistic distribution. The evidence procedure is employed to gain optimal hyperparameters for the BESN model. The recorded data obtained from the SHP plants in two different counties, located in Yunnan Province, China, are utilized to validate the proposed model. For comparison, the feed-forward neural networks with Levenberg-Marquardt algorithm (LM-FNN) and the generic ESN are also employed. The results indicate that BESN outperforms both LM-FNN and ESN.
Enhancing personalized web search re-ranking algorithm by incorporating user profile

In order to solve the problems of information overload on the web when searching for concrete information, the current information retrieval algorithms need to be improved. It is difficult to find satisfactory information that accurately matches user information needs even if it is present in the web database. Hence, much more "intelligence" should be embedded to retrieval algorithms to provide better search, retrieval, filtering and present relevant information according to individual needs. This paper presents an analysis and overview of the next generation search and retrieval in the context of personalization which is going to bring a new dimension in the field of information technology by giving a better search and retrieval facility on the web. Current web search engines are designed in such a way that the needs of any individual user are not understood and it satisfy users without considering the particular needs. Personalization of web search enables the users to obtain relevant information by incorporating individual interests and preferences. An enhanced web search re-ranking algorithm is proposed by incorporating user information obtained from various sources. The proposed algorithm is implemented in java and experimented with real time datasets and proved that the retrieval quality is improved and individual needs are addressed.
Improving FolkRank With Item-Based Collaborative Filtering

Collaborative tagging applications allow users to annotate online resources. The result is a complex tapestry of interrelated users, resources and tags often called a folksonomy. Folksonomies present an attractive target for data mining applications such as tag recommenders. A challenge of tag recommendation remains the adaptation of traditional recommendation techniques originally designed to work with two dimensional data. To date the most successful recommenders have been graph based approaches which explicitly connects all three components of the folksonomy. In this paper we speculate that graph based tag recommendation can be improved by coupling it with item-based collaborative filtering. We motive this hypothesis with a discussion of informational channels in folksonomies and provide a theoretical explanation of the additive potential for item-based collaborative filtering. We then provided experimental results on hybrid tag recommenders built from graph models and other techniques based on popularity, user-based collaborative filtering and item-based collaborative filtering. We demonstrate that a hybrid recommender built from a graph based model and item-based collaborative filtering outperforms its constituent recommenders. Furthermore the inability of the other recommenders to improve upon the graph-based approach suggests that they offer information already included in the graph based model. These results confirm our conjecture. We provide extensive evaluation of the hybrids using data collected from three real world collaborative tagging applications.
Low-Energy Standby-Sparing for Hard Real-Time Systems

Time-redundancy techniques are commonly used in real-time systems to achieve fault tolerance without incurring high energy overhead. However, reliability requirements of hard real-time systems that are used in safety-critical applications are so stringent that time-redundancy techniques are sometimes unable to achieve them. Standby sparing as a hardware-redundancy technique can be used to meet high reliability requirements of safety-critical applications. However, conventional standby-sparing techniques are not suitable for low-energy hard real-time systems as they either impose considerable energy overheads or are not proper for hard timing constraints. In this paper we provide a technique to use standby sparing for hard real-time systems with limited energy budgets. The principal contribution of this paper is an online energy-management technique which is specifically developed for standby-sparing systems that are used in hard real-time applications. This technique operates at runtime and exploits dynamic slacks to reduce the energy consumption while guaranteeing hard deadlines. We compared the low-energy standby-sparing (LESS) system with a low-energy time-redundancy system (from a previous work). The results show that for relaxed time constraints, the LESS system is more reliable and provides about 26% energy saving as compared to the time-redundancy system. For tight deadlines when the time-redundancy system is not sufficiently reliable (for safety-critical application), the LESS system preserves its reliability but with about 49% more energy consumption.
SIRUP: Serendipity In Recommendations via User Perceptions

In this paper, we propose a model to operationalise serendipity in content-based recommender systems. The model, called SIRUP, is inspired by the Silvia's curiosity theory, based on the fundamental theory of Berlyne, aims at (1) measuring the novelty of an item with respect to the user profile, and (2) assessing whether the user is able to manage such level of novelty (coping potential). The novelty of items is calculated with cosine similarities between items, using Linked Open Data paths. The coping potential of users is estimated by measuring the diversity of the items in the user profile. We deployed and evaluated the SIRUP model in a use case with TV recommender using BBC programs dataset. Results show that the SIRUP model allows us to identify serendipitous recommendations, and, at the same time, to have 71% precision.
25-Hydroxyvitamin D insufficiency and deficiency is associated with HIV disease progression and virological failure post-antiretroviral therapy initiation in diverse multinational settings.

BACKGROUND Low 25-hydroxyvitamin D (25(OH)D) has been associated with increased HIV mortality, but prospective studies assessing treatment outcomes after combination antiretroviral therapy (cART) initiation in resource-limited settings are lacking. METHODS A case-cohort study (N = 411) was nested within a randomized cART trial of 1571 cART-naive adults in 8 resource-limited settings and the United States. The primary outcome (WHO stage 3/4 disease or death within 96 weeks of cART initiation) was met by 192 cases, and 152 and 29 cases met secondary outcomes of virologic and immunologic failure. We studied prevalence and risk factors for baseline low 25(OH)D (<32 ng/mL) and examined associated outcomes using proportional hazard models. RESULTS Low 25(OH)D prevalence was 49% and ranged from 27% in Brazil to 78% in Thailand. Low 25(OH)D was associated with high body mass index (BMI), winter/spring season, country-race group, and lower viral load. Baseline low 25(OH)D was associated with increased risk of human immunodeficiency virus (HIV) progression and death (adjusted hazard ratio (aHR) 2.13; 95% confidence interval [CI], 1.09-4.18) and virologic failure (aHR 2.42; 95% CI, 1.33-4.41). CONCLUSIONS Low 25(OH)D is common in diverse HIV-infected populations and is an independent risk factor for clinical and virologic failure. Studies examining the potential benefit of vitamin D supplementation among HIV patients initiating cART are warranted.
Delay extraction based closed-form SPICE compatible passive macromodels for distributed transmission line interconnects

Time-domain macromodeling of highspeed interconnects characterized by distributed transmission lines has generated immense interest during the recent years. It has been demonstrated that, preserving passivity of the macromodel is essential to guarantee a stable global transient simulation. In this paper, a SPICE compatible closed-form passive macromodel for distributed transmission lines is presented. The proposed method enables representation of the distributed stamp in terms of simple delay and resistive elements. The new method while guaranteeing the passivity of the macromodel, provides significant speedup, and enables easy implementation. Necessary formulation and validation examples are given.
A Subband Adaptive Filtering Algorithm Employing Dynamic Selection of Subband Filters

We present a novel normalized subband adaptive filter (NSAF) which dynamically selects subband filters in order to reduce computational complexity while maintaining convergence performance of conventional NSAF. The selection operation is performed to achieve the largest decrease between the successive mean square deviations at every iteration. As a result, an efficient and competent NSAF algorithm is derived. The experimental results show that the proposed NSAF algorithm gains an advantage over the conventional NSAF in that it leads to a similar convergence performance with a substantial saving of overall computational burden.
EDUM: classroom education measurements via large-scale WiFi networks

Behavior in classroom-based courses is hard to measure at large-scale. In this paper, we propose the EDUM (EDUcation Measurement) system to help characterize educational behavior through data collected from WLANs (WiFi networks) on campuses. EDUM characterizes students' punctuality (attendances, late arrivals, and early departures) for lectures using longitudinal WLAN data, and further characterizes the attractiveness of lectures using mobile phone's interactive states at minute-scale granularity. EDUM is easy to deploy and extensible for new types of data. We deploy EDUM at Tsinghua University where ~700 volunteer students' data are measured during a 9-week period by ~2,800 APs and two popular mobile apps. Our results show that EDUM makes it possible to obtain large-scale observations on punctuality, distraction and study performance, and quantitatively confirm or disprove numerous assumptions about educational behavior.
An Affordable Virtual Reality Training Aid for Anaesthetists

The use of virtual reality for medical training applications is increasing rapidly. Until now, the systems have been mostly to aid surgeons, but there exists another group of medical staff that would benefit from virtual reality also the anaesthetists. There is a great need to increase the experience of trainee anaesthetists without endangering patients. An affordable virtual reality training aid for anaesthetists would accomplish this. In the context of some currently available products, the requirements for such a system are examined, including hardware, software, cost and design.
Methodology definition for reliable network experimentation

As researchers in the networking area keep adopting experimental network testing as a valid mechanism to develop, validate, and improve their research, it becomes more apparent that an overall framework supporting and assisting during the experimentation process is necessary. Particularly, this assistance is relevant in processes such as experiment preparation, or results validation. As a consequence, the goal, and thus the contribution, of this paper is twofold, on the one hand we propose a novel set of guidelines which establish the set of requirements any testbed for network experimentation should follow. On the other hand, as the other relevant contribution of this work, we propose a mechanism for generating meta-data information on the experiments that ease the publication of the obtained datasets. Finally, as a use-case, we present a particular implementation of this framework which we deploy in a real scenario to prove the capabilities of the proposed testing procedure.
Geometric modeling of subcellular structures, organelles, and multiprotein complexes

Recently, the structure, function, stability, and dynamics of subcellular structures, organelles, and multiprotein complexes have emerged as a leading interest in structural biology. Geometric modeling not only provides visualizations of shapes for large biomolecular complexes but also fills the gap between structural information and theoretical modeling, and enables the understanding of function, stability, and dynamics. This paper introduces a suite of computational tools for volumetric data processing, information extraction, surface mesh rendering, geometric measurement, and curvature estimation of biomolecular complexes. Particular emphasis is given to the modeling of cryo‐electron microscopy data. Lagrangian‐triangle meshes are employed for the surface presentation. On the basis of this representation, algorithms are developed for surface area and surface‐enclosed volume calculation, and curvature estimation. Methods for volumetric meshing have also been presented. Because the technological development in computer science and mathematics has led to multiple choices at each stage of the geometric modeling, we discuss the rationales in the design and selection of various algorithms. Analytical models are designed to test the computational accuracy and convergence of proposed algorithms. Finally, we select a set of six cryo‐electron microscopy data representing typical subcellular complexes to demonstrate the efficacy of the proposed algorithms in handling biomolecular surfaces and explore their capability of geometric characterization of binding targets. This paper offers a comprehensive protocol for the geometric modeling of subcellular structures, organelles, and multiprotein complexes. Copyright © 2012 John Wiley & Sons, Ltd.
A Foundation for Open Information Environments

Traditionally, information systems were developed within organizations for use by known audiences for known purposes. Advances in information technology have changed this landscape dramatically. The reach of information systems frequently extends beyond organizational boundaries for use by unknown audiences and for purposes not originally anticipated. Individuals and informal communities can generate and use information in ways previously restricted to formal organizations. We term applications with these characteristics open information environments (OIEs). OIEs are marked by diversity of information available, flexibility in accommodating new sources, users and uses, and information management with minimal controls on structure, content, and access. This creates opportunities to generate new information and use it in unexpected ways. However, OIEs also come with challenges in managing the semantic diversity, flexibility of use, and information quality issues arising from the range of users and lack of controls. In this paper, we propose a set of principles for managing OIEs effectively. We outline a research program to examine the potential of OIEs, the challenges they present, and how to design OIEs to realize the benefits while mitigating the challenges. We highlight our ongoing research in this area, and conclude with a call for more research on this important phenomenon.
PRECISE POSITIONING OF PIEZO-ACTUATED STAGES USING HYSTERESIS-OBSERVER BASED CONTROL

Abstract The piezo-actuated stages are composed of the piezo-electric actuator and the positioning mechanism. The positioning accuracy of the piezo-actuated stage is limited due to hysteretic nonlinearity of the PEA and friction behaviour of the positioning mechanism. To compensate this nonlinearity of piezoelectric actuator, a PI feedback control associated with feedforward compensating based on the hysteresis observer is proposed in this paper. To verify the consistency of the proposed method, the experiments are implemented by real-time control to be compared with the numerical simulation.
Solution Radioactivated by Hadron Radiation Can Increase Sister Chromatid Exchanges

When energetic particles irradiate matter, it becomes activated by nuclear reactions. Radioactivation induced cellular effects are not clearly understood, but it could be a part of bystander effects. This investigation is aimed at understanding the biological effects from radioactivation in solution induced by hadron radiation. Water or phosphate buffered saline was activated by being exposed to hadron radiation including protons, carbon- and iron-ions. 1 mL of radioactivated solution was transferred to flasks with Chinese hamster ovary (CHO) cells cultured in 5 mL of complete media. The induction of sister chromatid exchanges (SCE) was used to observe any increase in DNA damage responses. The energy spectrum and the half-lives of the radioactivation were analyzed by NaI scintillation detector in order to identify generated radionuclides. In the radioactivated solution, 511 keV gamma-rays were observed, and their half-lives were approximately 2 min, 10 min, and 20 min. They respectively correspond to the beta+ decay of 15O, 13N, and 11C. The SCE frequencies in CHO cells increased depending on the amount of radioactivation in the solution. These were suppressed with a 2-hour delayed solution transfer or pretreatment with dimethyl sulfoxide (DMSO). Our results suggest that the SCE induction by radioactivated solution was mediated by free radicals produced by the annihilated gamma-rays. Since the SCE induction and DMSO modulation are also reported in radiation-induced bystander effects, our results imply that radioactivation of the solution may have some contribution to the bystander effects from hadron radiation. Further investigations are required to assess if radioactivation effects would attribute an additional level of cancer risk of the hadron radiation therapy itself.