Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding of human behaviors from videos in nursing care monitoring systems

This paper addresses the issue in scenario-based understanding of human behavior from videos in a nursing care monitoring system. The analysis is carried out based on experiments consisting of single-state scenarios and multi-state scenarios where the former monitors activities under contextual contexts for elementary behavior reasoning, while the latter dictating the elementary behavior order for behavior reasoning, with a priori knowledge in system profile for normality detection. By integrating the activities, situation context, and profile knowledge we can have a better understanding of patients in a monitoring system. In activity recognition, a Negation-Selection mechanism is developed. which employs a divide-and-conquer concept with the Negation using posture transition to preclude the negative set from the activities. The Selection that follows the Negation uses a moving history trace for activity recognition. Such a history trace composes not only the pose from single frame, but also history trajectory information. As a result, the activity can be more accurately identified. The developed approach has been established into a nursing care monitoring system for elder's daily life behaviors. Results have shown the promise of the approach which can accurately interpret 85% of the regular daily behavior. In addition, the approach is also applied to accident detection which was found to have 90% accuracy with 0% false alarm.
Spatial and environmental correlates of species richness and turnover patterns in European cryptocephaline and chrysomeline beetles

Abstract Despite some general concordant patterns (i.e. the latitudinal richness gradient), species richness and composition of different European beetle taxa varies in different ways according to their dispersal and ecological traits. Here, the patterns of variation in species richness, composition and spatial turnover are analysed in European cryptocephaline and chrysomeline leaf beetles, assessing their environmental and spatial correlates. The underlying rationale to use environmental and spatial variables of diversity patterns is to assess the relative support for niche- and dispersal-driven hypotheses. Our results show that despite a broad congruence in the factors correlated with cryptocephaline and chrysomeline richness, environmental variables (particularly temperature) were more relevant in cryptocephalines, whereas spatial variables were more relevant in chrysomelines (that showed a significant longitudinal gradient besides the latitudinal one), in line with the higher proportion of flightless species within chrysomelines. The variation in species composition was also related to environmental and spatial factors, but this pattern was better predicted by spatial variables in both groups, suggesting that species composition is more linked to dispersal and historical contingencies than species richness, which would be more controlled by environmental limitations. Among historical factors, Pleistocene glaciations appear as the most plausible explanation for the steeper decay in assemblage similarity with spatial distance, both in cryptocephalines and chrysomelines.
Semantic Tags for Lecture Videos

In an effort to develop effective multi-media learning objects (MLO), we propose a framework to extract and associate semantic tags to temporally segmented instructional videos. These tags serve for the purpose of efficient indexing and retrieval system. We create these semantic tags from potential keywords extracted from the lecture transcript. The keywords undergo a series of refinement process to select few but meaningful set of tags. We use word similarity measure using visual ness and word sense disambiguation to select the tags from candidate keywords. These tags are finally associated with video segments in which they appear based on timestamp. Each video segment represents a key idea or a topic. We also evaluated the objective keyword selection criteria to subjective test with some interesting results.
Learning all optimal policies with multiple criteria

We describe an algorithm for learning in the presence of multiple criteria. Our technique generalizes previous approaches in that it can learn optimal policies for all linear preference assignments over the multiple reward criteria at once. The algorithm can be viewed as an extension to standard reinforcement learning for MDPs where instead of repeatedly backing up maximal expected rewards, we back up the set of expected rewards that are maximal for some set of linear preferences (given by a weight vector, w). We present the algorithm along with a proof of correctness showing that our solution gives the optimal policy for any linear preference function. The solution reduces to the standard value iteration algorithm for a specific weight vector, w.
EFFICIENT SPECTRUM UTILIZATION IN COGNITIVE RADIO THROUGH REINFORCEMENT LEARNING

Machine learning schemes can be employed in cognitive radio systems to intelligently locate the spectrum holes with some knowledge about the operating environment. In this paper, we formulate a variation of Actor Critic Learning algorithm known as Continuous Actor Critic Learning Automaton (CACLA) and compare this scheme with Actor Critic Learning scheme and existing Q-learning scheme. Simulation results show that our CACLA scheme has lesser execution time and achieves higher throughput compared to other two schemes.
Sensory prediction for autonomous robots

For a complex autonomous robotic system such as a humanoid robot, the learning-based sensory prediction is considered effective to develop a perceptual environment model by itself. We developed a learning system for an autonomous robot to predict the next sensory information from the current sensory information and the expected action. The system we consider contains a learning procedure and a behavior generation procedure. The learning procedure uses a multi layer perceptron minimizing the error between a given sensory input and its predicted value. The behavior generation procedure is based on a uniform probablistic density function to sample the learning data randomly, which is the effective strategy when the system does not have any assumption or knowledge of the environment. We also investigated sensory blind prediction which should allow action plannning as well as offer a reliable forecast for a safe evolution of the robot in the environment. The simulation and experimental results show that the system learns interaction between the robot and the environment in high fidelity.
Performance measurements of the saturation throughput in IEEE 802.11 access points

Performance measurements of IEEE 802.11 access points provide important information for networking research and network management. In this paper, we present an active measurement method to determine the maximum saturation throughput of wireless LAN access points. The saturation throughput is achieved when there always is a frame ready to transmit, and it reaches the maximum for optimal transmission conditions. We use the proposed method to measure and analyze the performance of five IEEE 802.11b access points. The results show the strength of our method and identify performance characteristics of commercial access points. The analysis produces the following results. The maximum saturation throughput shows significant differences between models. Increasing the offered load to the access point's Ethernet interface does not always increase the throughput; a few access points present a downlink throughput reduction when the offered load exceeds their bridging capabilities. Some access points exhibit better performance in certain orientations.
Dynamic Pricing of Enterprise Software with Value Uncertainty : Motivation for Selling Software as a Service November 26 , 2011

This paper studies a software vendors decision to o¤er perpetual or lease-based licensing when customers are uncertain about their valuation for the software prior to adoption, and adoption requires an upfront implementation cost. First, in a discrete-time overlapping-generations model, I show that perpetual licensing can be more pro table than lease-based licensing when consumer valuation uncertainty is present. This result sheds light on the historical prevalence of perpetual licensing in the enterprise software market and contrasts with the conventional wisdom of durable-goods theories. Second, I model the new lease-based Software-as-a-Service (SaaS) as a type of leasing that requires a lower implementation cost, and I study the monopolists motivation to o¤er SaaS. O¤ering SaaS is likely to be optimal for applications that feature lower levels of customer valuation uncertainty and require less customer-speci c implementation. These two ndings o¤er insights on the variation of SaaS adoption across di¤erent applications.
Risk of Overweight and Obesity among Migrants in Switzerland

We assessed the risk of overweight and obesity for six large migrant groups in Switzerland. We used population-based survey data form the Swiss Migrant Health Survey 2010 and the Swiss Health Survey 2007. The sample comprised permanent residents aged 17 - 64 years (n = 14,637). Multivariate logistic regressions have been used to estimate odds ratios (OR). Compared with Swiss nationals, the odds of being obese were 1.97 times higher for Turkish nationals (CI-95 1.26, 3.07), 1.73 times higher for Kosovan nationals (CI-95 1.09, 2.74) and 3.42 times higher for Serbs (CI-95 2.28, 5.12). The chance of being obese was not statistically different between Swiss nationals and those from Portugal, Italy, and Germany. The chance of being overweight or obese (BMI ≥ 25) was higher for migrants than for Swiss nationals. The respective odds were 1.54 times higher for nationals from Portugal (CI-95 1.13, 2.11), 2.05 times higher for those from Turkey (CI-95 1.50, 2.78), 2.67 times higher for those from Serbia (CI-95 1.93, 3.68), 2.68 times higher for those from Kosovo (CI-95 2.01, 3.56), and 2.16 times higher for nationals from Italy (CI-95 1.54, 3.02). We suggest that preventive initiatives should take into account the specific needs of migrants as well as the needs of persons with low education from all nationalities.
Efficient Key agreement protocol using Proxy server for Wireless communication

: A key agreement protocol is the most important part to establish a secure cryptographic system and the effort to standardize the key agreement protocols is in rapid progress. Several efficient and secure key agreement protocols have been proposed so far since Diffie-Hellman proposed a public key agreement system in 1976. But, since Diffie-Hellman based key agreement protocols need a lot of computation to establish the session key, they are not proper to apply wireless Internet environment. In this paper, we propose the efficient key agreement protocol using proxy server. The proposed protocol supports the security of the Diffie-Hellman based protocol and the computation work of mobile user can be decreased using proxy server.
Analysis of several scheduling algorithms under the nano-threads programming model

The authors present the analysis, in a dynamic processor allocation environment, of four scheduling algorithms running on top of the nano-threads programming model. Three of them are well-known: uniform-sized chunking, guided self-scheduling and trapezoid self-scheduling. The fourth is their proposal: adaptable size chunking. In that environment, applications are automatically decomposed into tasks by a parallelizing compiler which uses the hierarchical task graph to represent the source application. The parallel code is an executable representation of this graph with the support of a user-level library (the nano-threads library). The execution environment includes a user-level process (CPU manager) which controls the allocation of processors to applications. The analysis of the scheduling algorithms shows it is possible to provide enough information to the library to allow a fast adaptation to dynamic changes in the processors allocated to the application.
Replication Analysis in Exploratory Factor Analysis: What it is and why it makes your analysis better

Copyright is retained by the first or sole author, who grants right of first publication to the Practical Assessment, Research & Evaluation. Permission is granted to distribute this article for nonprofit, educational purposes if it is copied in its entirety and the journal is credited. PARE has the right to authorize third party reproduction of this article in print, electronic and database forms.
Four experiments on the relational dynamics and prosocial consequences of gratitude

Abstract Four experiments utilized experimental inductions of gratitude and behavioral measures of prosociality to explore the effects of 4 variables on gratitude and grateful prosocial outcomes: benefactor similarity (Study 1), intention (Study 2), future benefits (Study 3), and anonymity (Study 4). We consistently found that the receipt of a favor increased prosocial behavior, and this effect was mediated by gratitude. Benefactor similarity did not meaningfully influence either prosocial behavior or gratitude. Although benefactor intention did meaningfully affect gratitude, the effect was too small to influence the mediational effect of gratitude on prosocial behavior. Neither anonymity nor the possibility of future procurement decreased the role of gratitude in distribution; instead, both of these variables may have enhanced the role of gratitude. These data support gratitude as an important component of prosocial behavior and suggest that gratitude may contain an altruistic component, consistent with its relational function.
Citizens, Criminalization and Violence in Natural Resource Conflicts in Latin America

In Latin America grassroots organizing against megaprojects such as open pit mining, oil extraction, hydro dams and large plantations goes hand in hand with increased criminalization of social protest and violations of the human rights of activists. This results in numerous communities demanding a clean environment, participation, and justice – all at the same time. They not only face foreign companies, but are also caught in the middle of armed and non-armed actors that contest the same territory and its natural resources. Their resistance is considered as a threat to internal security; citizens are increasingly viewed as criminals. This paper suggests new avenues for research that is located at the nexus of local resistance towards megaprojects and the increase of human rights violations and criminalization in natural resource conflicts. It proposes, first, to approach natural resource conflicts as hybrid spaces where citizenship is constructed in relation to multiple actors that engage in processes of providing, protecting and violating citizenship rights, and second, to study such processes by way of slow ethnography. Such an approach to natural resource conflicts paves the way not only for understanding how citizens engage in acts of resistance and experience violations of human rights, but also how such processes shape new subject-positions. Resumen: Ciudadanos como criminales: Ciudadania, criminalizacion y violencia en conflictos sobre recursos naturales En America Latina la resistencia a mega proyectos como la mineria a cielo abierto, la ex-traccion de petroleo, la construccion de hidroelectricas y el monocultivo de grandes extensiones va de la mano con la criminalizacion de la protesta social y violaciones de los derechos humanos de los activistas. Eso resulta en comunidades que demandan un ambiente sano, participacion y justicia social al mismo tiempo. Estas comunidades no solamente enfrentan las companias extranjeras, sino tambien se encuentran atrapadas entre actores armados y no armados que compiten por el control de sus territorios y los recursos naturales asociados. La resistencia de las comunidades es considerado como una amenaza a la seguridad interna; los ciudadanos son vistos cada vez mas como criminales. Este articulo sugiere nuevas vias para investigar el nexo de la resistencia local a los megaproyectos por un lado, y el aumento de las violaciones de los derechos humanos y la criminalizacion de actores sociales en conflictos sobre los recursos naturales por otro. Propone, primero, abordar los conflictos sobre los recursos naturales como espacios hibridos en los cuales se construye la ciudadania en relacion a actores multiples que proveen, protegen y violan los derechos ciudadanos y, segundo, estudiar estos procesos por medio de una 'etnografia lento'. Este enfoque no solamente permite entender por que el ciudadano participan en la protesta social y sufren las violaciones de derechos humanos, sino tambien como estos procesos moldean nuevas posiciones de los sujetos.
ViA: a perceptual visualization assistant

This paper describes an automated visualized assistant called ViA. ViA is designed to help users construct perceptually optical visualizations to represent, explore, and analyze large, complex, multidimensional datasets. We have approached this problem by studying what is known about the control of human visual attention. By harnessing the low-level human visual system, we can support our dual goals of rapid and accurate visualization. Perceptual guidelines that we have built using psychophysical experiments form the basis for ViA. ViA uses modified mixed-initiative planning algorithms from artificial intelligence to search of perceptually optical data attribute to visual feature mappings. Our perceptual guidelines are integrated into evaluation engines that provide evaluation weights for a given data-feature mapping, and hints on how that mapping might be improved. ViA begins by asking users a set of simple questions about their dataset and the analysis tasks they want to perform. Answers to these questions are used in combination with the evaluation engines to identify and intelligently pursue promising data-feature mappings. The result is an automatically-generated set of mappings that are perceptually salient, but that also respect the context of the dataset and users' preferences about how they want to visualize their data.
Double diffractive -production in collisions

We present a first estimate of the cross-section for the exclusive process γ L(Q 2 1)γ ∗ L(Q 2 2) → ρLρL, which will be studied in the future high energy e+e−− linear collider. As a first step, we calculate the Born order approximation of the amplitude for longitudinally polarized virtual photons and mesons, in the kinematical region s ≫ −t, Q1 , Q2. This process is completely calculable in the hard region Q1 , Q 2 2 ≫ ΛQCD. We perform most of the steps in an analytical way. The resulting cross-section turns out to be large enough for this process to be measurable with foreseen luminosity and energy, for Q1 and Q 2 2 in the range of a few GeV. Unité mixte C7644 du CNRS Unité mixte 8627 du CNRS
Determining "Grim Reaper" Policies to Prevent Languishing Bugs

Long-lived software products commonly have a large number of reported defects, some of which may not be fixed for a lengthy period of time, if ever. These so-called languishing bugs can incur various costs to project teams, such as wasted time in release planning and in defect analysis and inspection. They also result in an unrealistic view of the number of bugs still to be fixed at a given time. The goal of this work is to help software practitioners mitigate their costs from languishing bugs by providing a technique to predict and pre-emptively close them. We analyze defect fix times from an ABB program and the Apache HTTP server, and find that both contain a substantial number of languishing bugs. We also train decision tree classification models to predict whether a given bug will be fixed within a desired time period. We propose that an organization could use such a model to form a "grim reaper" policy, whereby bugs that are predicted to become languishing will be pre-emptively closed. However, initial results are mixed, with models for the ABB program achieving F-scores of 63-95%, while the Apache program has Fscores of 21-59%.
STATIONARY LEVEL SET METHOD FOR MODELLING SHARP INTERFACES IN GROUNDWATER FLOW

We present a new numerical procedure for modelling steady motion of interfaces in groundwater flow. A modified version of the level set method utilizing a technique from the method of vanishing viscosity is developed and solved together with the time independent Navier-Stokes equation. Simulation for steady non-linear seepage flow through porous media is performed. The discretized-coupled system is solved over a fixed mesh domain. The resulted numerical scheme allows tackling the front motion faster and simply with less computation efforts. The Streamline/Upwind technique is used to stabilize the solved-coupled system of FE-equations over unstructured 2D and 3D FE-meshes. Examples on the application of the technique for evolving sharp interface under a steady state of seepage flow in embankment dam are investigated.
Time dispersion characteristics for wideband channel in 28 GHz millimeter wave band for 5G cellular networks

The demand for high data rate transmission for the future wireless communication technology is increasing rapidly. Due to the congestion in the current bands for cellular network, it may not be able to satisfy these requirements. For the future cellular networks, the millimeter wave (mm-wave) bands are the candidate bands because of the large available bandwidth. The 28 GHz band is the strongest candidate for 5G cellular networks. The large bandwidth at this band is one of the main parameters that make the mm-wave bands promising candidate for the future cellular networks. The channel needs to be characterized based on wideband characteristics to know the wideband channel behavior in mm-wave bands. In this paper, the time dispersion parameters at 28 GHz mm-wave band are presented. The wideband channel is characterized based on the root mean square (RMS) delay spread and the mean excess delay that are the main time dispersion parameters. The cumulative distribution function (CDF) is used to model the RMS delay spread based on different distributions. In this paper, the parameters that are derived from 28 GHZ measurements which were conducted in New York City have been used to modify the COST 2100 model. The modified model is used to model the wideband channel at 28 GHz band in LOS scenario. The results show that the RMS delay spread varies between 129 ns and 247 ns for LOS scenario. The results also show that the correlation between RMS delay spread and transmitter-receiver (TX-RX) separation distance is low. The maximum mean excess delay is 454.8 ns.
Efficient lattice rescoring using recurrent neural network language models

Recurrent neural network language models (RNNLM) have become an increasingly popular choice for state-of-the-art speech recognition systems due to their inherently strong generalization performance. As these models use a vector representation of complete history contexts, RNNLMs are normally used to rescore N-best lists. Motivated by their intrinsic characteristics, two novel lattice rescoring methods for RNNLMs are investigated in this paper. The first uses an n-gram style clustering of history contexts. The second approach directly exploits the distance measure between hidden history vectors. Both methods produced 1-best performance comparable with a 10k-best rescoring baseline RNNLM system on a large vocabulary conversational telephone speech recognition task. Significant lattice size compression of over 70% and consistent improvements after confusion network (CN) decoding were also obtained over the N-best rescoring approach.