Miracle-dz/UniFAR-FTUs huggingface dataset:
GIS Approach to Flood Risk Mapping

Flood is becoming the most environmental challenge menacing Ado-Ekiti in recent times. The aim of this study is to evaluate and map flood risk in the study area, using GIS techniques, to achieve this aim, the study has the following objectives, assess the extent and magnitude of flooding in the study area, also it assesses elements at risk in the study area, hence integrate the outcome of these analysis in a GIS environment. Primary data was sourced with the use of structured questionnaires, while the secondary data sources are from Google earth map and literatures. Simple statistics was use to analyse the data to help develop indicators of vulnerability. A hazard and vulnerability maps were produced based on the perception the population living in the area, the map was then combined with the hazard map, and thus to produce an integrated risk map for the study area. Results reveal that from Vulnerability, Hazard and Risk maps showed that buildings closer to rivers and stream all have higher index. The impact of flood on households sampled revealed that some of the household believed that flood occurrence in the study area have some of impacts on their properties and expenditure. The methodology has proven to be a suitable tool to provide a first overview of spatial distribution of risk which is considered by the households
Families of Alpha- Beta- and Gamma- Divergences: Flexible and Robust Measures of Similarities

In this paper, we extend and overview wide families of Alpha-, Beta- and Gamma-divergences and discuss their fundamental properties. In literature usually only one single asymmetric (Alpha, Beta or Gamma) divergence is considered. We show in this paper that there exist families of such divergences with the same consistent properties. Moreover, we establish links and correspondences among these divergences by applying suitable nonlinear transformations. For example, we can generate the Beta-divergences directly from Alpha-divergences and vice versa. Furthermore, we show that a new wide class of Gamma-divergences can be generated not only from the family of Beta-divergences but also from a family of Alpha-divergences. The paper bridges these divergences and shows also their links to Tsallis and Renyi entropies. Most of these divergences have a natural information theoretic interpretation.
How Many Iterations in the Gibbs Sampler

Abstract : When the Gibbs sampler is used to estimate posterior distributions (Gelfand and Smith, 1990) the question of how many iterations are required is central to its implementation. When interest focuses on quantiles of functionals of the posterior distribution, we describe an easily-implemented method for determining the total number of iterations required, and also the number of initial iterations that should be discarded to allow for burn-in. The method uses only the Gibbs iterates themselves, and does not, for example, require external specification of characteristics of the posterior density. Here the method is described for the situation where one long run is generated, but it can also be easily applied if there are several runs from different starting points. It also applies more generally to Markov chain Monte Carlo schemes other than the quantities of interest are probabilities rather than full posterior distributions, and when the draws from the posterior distribution are required to be approximately independent. The method is applied to several different posterior distributions. These include a multivariate normal posterior distribution with independent parameters, a bimodal distribution, a cigar-shaped multivariate normal distribution in ten dimensions, and a highly complex 190-dimensional posterior distribution arising in spatial statistics. In each case the method appears to give satisfactory results.
A taxonomy of parallel techniques for intrusion detection

Intrusion detection systems (IDS) have become a key component in ensuring the safety of systems and networks. These systems enforce a security policy by inspecting arriving packets for known signatures (patterns). This process actually involves several tasks that collectively incur a significant delay. As network line speeds continue to increase, it is crucial that efficient scalable approaches, such as parallelization, are developed for IDS. In this paper we develop a framework which may be used to classify various approaches to parallelizing intrusion detection systems. Parallelization of IDS can occur at three general levels: node (entire system), component (specific task), and sub-component (function within a specific task). We categorize existing and proposed parallel solutions using our framework, discuss the advantages and disadvantages of each, and provide empirical evaluation of one form of parallelism. Additionally, we introduce the notion of functional parallelism for intrusion detection.
An assistive tool for monitoring physical activities in older adults

Active living in older adults reduces disabilities and increases quality of life. Adherence to physical activity is low in older adults, therefore new possibilities to motivate to exercise must be explored. One of this new possibilities is to use exergaming. In this paper, we present an assistive tool for monitoring daily, physical activities in older adults with access to exergaming in their home environments. The proposed system employs the Kinect device and allows one to analyse the body movements. From a pilot study, we proved that the system can detect movements through exergaming. In the future we plan to implement the developed monitoring tool as well as exergaming in nursing homes.
Review of Security Issues in Industrial Networks

Although awareness is constantly rising, that industrial computer networks (in a very broad sense) can be exposed to serious cyber threats, many people still think that the same countermeasures, developed to protect general-purpose computer networks, can be effectively adopted also in those situations where a physical system is managed/controlled through some distributed Information and Communication Technology (ICT) infrastructure. Unfortunately, this is not the case, as several examples of successful attacks carried out in the last decade, and more frequently in the very recent past, have dramatically shown. Experts in this area know very well that often the peculiarities of industrial networks prevent the adoption of classical approaches to their security and, in particular, of those popular solutions that are mainly based on a detect and patch philosophy. This paper is a contribution, from the security point of view, to the assessment of the current situation of a wide class of industrial distributed computing systems. In particular, the analysis presented in this paper takes into account the process of ensuring a satisfactory degree of security for a distributed industrial system, with respect to some key elements such as the system characteristics, the current state of the art of standardization and the adoption of suitable controls (countermeasures) that can help in lowering the security risks below a predefined, acceptable threshold.
Off-policy learning in large-scale POMDP-based dialogue systems

Reinforcement learning (RL) is now part of the state of the art in the domain of spoken dialogue systems (SDS) optimisation. Most performant RL methods, such as those based on Gaussian Processes, require to test small changes in the policy to assess them as improvements or degradations. This process is called on policy learning. Nevertheless, it can result in system behaviours that are not acceptable by users. Learning algorithms should ideally infer an optimal strategy by observing interactions generated by a non-optimal but acceptable strategy, that is learning off-policy. Such methods usually fail to scale up and are thus not suited for real-world systems. In this contribution, a sample-efficient, online and off-policy RL algorithm is proposed to learn an optimal policy. This algorithm is combined to a compact non-linear value function representation (namely a multi-layers perceptron) enabling to handle large scale systems.
Angiogenic and Arthritogenic Properties of the Soluble Form of CD13

Key Points Soluble CD13 (sCD13) is a potent angiogenic factor in vitro and in vivo. sCD13 induces acute inflammatory arthritis in mice and induces MN migration via GPCR. sCD13 increases cytokine secretion and phosphorylation of signaling molecules. Aminopeptidase N/CD13 is expressed by fibroblast-like synoviocytes (FLS) and monocytes (MNs) in inflamed human synovial tissue (ST). This study examined the role of soluble CD13 (sCD13) in angiogenesis, MN migration, phosphorylation of signaling molecules, and induction of arthritis. The contribution of sCD13 was examined in angiogenesis and MN migration using sCD13 and CD13-depleted rheumatoid arthritis (RA) synovial fluids (SFs). An enzymatically inactive mutant CD13 and intact wild-type (WT) CD13 were used to determine whether its enzymatic activity contributes to the arthritis-related functions. CD13-induced phosphorylation of signaling molecules was determined by Western blotting. The effect of sCD13 on cytokine secretion from RA ST and RA FLS was evaluated. sCD13 was injected into C57BL/6 mouse knees to assess its arthritogenicity. sCD13 induced angiogenesis and was a potent chemoattractant for MNs and U937 cells. Inhibitors of Erk1/2, Src, NF-κB, Jnk, and pertussis toxin, a G protein–coupled receptor inhibitor, decreased sCD13-stimulated chemotaxis. CD13-depleted RA SF induced significantly less MN migration than sham-depleted SF, and addition of mutant or WT CD13 to CD13-depleted RA SF equally restored MN migration. sCD13 and recombinant WT or mutant CD13 had similar effects on signaling molecule phosphorylation, indicating that the enzymatic activity of CD13 had no role in these functions. CD13 increased the expression of proinflammatory cytokines by RA FLS, and a CD13 neutralizing Ab inhibited cytokine secretion from RA ST organ culture. Mouse knee joints injected with CD13 exhibited increased circumference and proinflammatory mediator expression. These data support the concept that sCD13 plays a pivotal role in RA and acute inflammatory arthritis.
Power Spectrum of MSK-Type Modulations in the Presence of Data Imbalance

Using the amplitude-modulation pulse (AMP) representation of continuousphase modulation (CPM) introduced more than a decade ago by Laurent, the power spectral density (PSD) of minimum-shift-keying (MSK)-type modulations (modulation index equal to one-half) is computed in the presence of data imbalance. The advantage of this technique is that closed-form expressions can be obtained that clearly elucidate the partitioning of the spectrum into components due to the efiective AMP pulse shapes and those due to the AMP sequence correlations. As such, these expressions give insight into the nature of the PSD distortion produced by the imbalance, i.e., a tilt in the main lobe and a relative unbalance between the upper and lower side-lobe levels caused by the correlation between the in-phase (I) and quadrature-phase (Q) components of the complex AMP data sequences and the correlation between the sequences themselves. It is demonstrated that data imbalance does not change the rate at which the side lobes roll ofi. It also is shown that, for all practical purposes, the PSD can be computed based on a two-pulse stream AMP approximation.
Delay-Dependent Exponential Stability Analysis for Delayed Stochastic Hopfield Neural Networks

This paper is concerned with the problem of delay-dependent exponential stability analysis for a class of stochastic Hopfield type neural networks with constant time delays. By employing an augmented Lyapunov-Krasovskii functional, together with the linear matrix inequality approach, a delay-dependent condition guaranteeing the global exponential stability (in the mean square sense) of the considered stochastic neural network is presented. A numerical example is provided to demonstrate the effectiveness of the proposed stability condition.
Effective Gauge degrees of freedom and the (non)existence of the glueball superpotential

We propose an efficient way to obtain a correct Veneziano-Yankielowicz type integration constant of the effective glueball superpotential Weff(S,g,Λ), even for massless theories. Applying our method, we show some = 1 theories do not have such an effective glueball superpotential, even though they have isolated vacua. In these cases, S = 0 typically.
Efficient budget allocation with accuracy guarantees for crowdsourcing classification tasks

In this paper we address the problem of budget allocation for redundantly crowdsourcing a set of classification tasks where a key challenge is to find a trade-off between the total cost and the accuracy of estimation. We propose CrowdBudget, an agent-based budget allocation algorithm, that efficiently divides a given budget among different tasks in order to achieve low estimation error. In particular, we prove that CrowdBudget can achieve at most max{0, K/2- O,(√B)} estimation error with high probability, where K is the number of tasks and B is the budget size. This result significantly outperforms the current best theoretical guarantee from Karger et al,. In addition, we demonstrate that our algorithm outperforms existing methods by up to 40% in experiments based on real-world data from a prominent database of crowdsourced classification responses.
A Review on Transform Domain Adaptive Filters

In this paper, transform domain adaptive filters are studied and reviewed based on previous researches. In transform domain adaptive filters orthogonality properties of transformation such as discrete cosine transform (DCT), discrete sine transform (DST), wavelet transform and discrete fourier transform (DFT) are used to achieve an improved convergence rate as compared to the time domain analysis. Also, it provides better computational speed, fast convolution as compared to time domain algorithms. TDAF are applied when long memory or long durartion impulse response is required since it increases the computational complexity in time domain. A tabular format of review is also given for all transform domain adaptive filters algorithms, characteristics and their application areas. Keywords— Tranform doimain adaptive filter, convergence rate, introduction, discrete cosine transform, discrete sine transform, Fourier Transform.
Adaptive data management for self-protecting objects in cloud computing systems

While Cloud data services are a growing successful business and computing paradigm, data privacy and security are major concerns. One critical problem is to ensure that data owners' policies are honored, regardless of where the data is physically stored and how often it is accessed, and modified. This scenario calls for an important requirement to be satisfied. Data should be managed in accordance to owners' preferences, Cloud providers service agreements, and the local regulations that may apply. In this work we propose innovative policy enforcement techniques for adaptive sharing of users' outsourced data. We introduce the notion of autonomous security-aware objects, that by means of object-oriented programming techniques, encapsulate sensitive resources and assure their protection. Our evaluation demonstrates that our approach is effective.
A heuristic extending the Squarified treemapping algorithm

A heuristic extending the Squarified Treemap technique for the representation of hierarchical information as treemaps is presented. The original technique gives high quality treemap views, since items are laid out with rectangles that approximate squares, allowing easy comparison and selection operations. New key steps, with a low computational impact, have been introduced to yield treemaps with even better aspect ratios and higher homogeneity among items.
Learning Human Behavior from Observation for Gaming Applications

The gaming industry has reached a point where improving graphics has only a small effect on how much a player will enjoy a game. The focus has turned to adding more humanlike characteristics into computer game agents. Machine learning techniques are scarcely being used in games, although they do offer powerful means for creating humanlike behaviors in agents. The first person shooter (FPS), Quake 2, is an open source game that offers a multi-agent environment in which to create game agents (bots). The work described in this paper seeks to combine neural networks with a modeling paradigm known as context based reasoning (CxBR) to create a contextual game observation (CONGO) system that produces humanlike Quake 2 bots. A default level of intelligence is instilled into the bots through contextual scripts to prevent the bot from being trained to be completely useless. The results show that the humanness and entertainment value as compared to a traditional scripted bot have improved, although, CONGO bots usually ranked only slightly above a novice skill level. Overall, CONGO offers the gaming community a mode of game play that has promising entertainment value.
Adaptive denoising filtering for object detection applications

The widespread of augmented reality applications, cognitive video surveillance, autonomous or supportive navigation systems, has increased the importance of accurate object detection algorithms. However, the presence of noise depending on the characteristics of the acquisition device, on lighting intensity and directions, and on weather conditions, could severely degrade the performance of such applications. As a matter of fact, effective ad-hoc denoising strategies are required since traditional noise removal algorithms designed to improve the quality of the image, could even worsen the accuracy of detection. This paper presents a low-cost adaptive filtering strategy that adapts the characteristics of the filter depending on the impact of each image region on the feature sets. This solution permits improving the correct detection percentage of approximately 30%with respect to using noisy images. The approach is generally intended for object detection algorithms based on Histogram-of-Oriented-Gradients (HOG) and can run in real time on a limited complexity hardware.
¿Por qué los adolescentes juegan videojuegos? Propuesta de una escala de motivos para jugar videojuegos a partir de la teoría de usos y gratificaciones

Este articulo propone una escala de motivos para jugar videojuegos en adolescentes, adaptada al castellano a partir de los estudios previos de Selnow (1984), Wigand et al. (1985), Raney et al. (2006) y Sherry et al. (2006). Se utilizaron datos obtenidos a partir de 400 cuestionarios aplicados a estudiantes espanoles de entre 14 y 20 anos. Se ha comprobado la fiabilidad del modelo mediante el analisis factorial confirmatorio y de consistencia interna, asi como la validez de criterio y constructo relacionando los cuatro factores del modelo con variables de consumo y rasgos de personalidad, respectivamente.
Prevalence of Arsenic Exposure from Drinking Water and Awareness of Its Health Risks in a Bangladeshi Population: Results from a Large Population-Based Study

We conducted a population-based prevalence survey in Araihazar, Bangladesh, to describe the distribution of arsenic exposure in a rural Bangladeshi population and to assess the population’s awareness to this problem as well as to possible remediation options. Water samples from 5,967 contiguous tube wells in a defined geographic area were tested using laboratory-based methods. Additionally, for each well, the owner/caretaker (or a close relative) was interviewed regarding his or her awareness of the health consequences of As exposure. Arsenic exposure data and demographic characteristics for the 65,876 users of these wells were also collected from the 5,967 respondents. Among the 65,876 residents, more than half (54%) regularly consumed well water with an As concentration ≥ 50 μg/L—above the acceptable government standard in Bangladesh. Respondents were 15–92 years of age, with an average age of 42 years, and 43% were male. Presence of awareness was significantly related to male sex, nonlabor head of household occupation, better housing, and having had the well tested for As concentration. Most respondents (92%) expressed a willingness to take steps to reduce their exposure, with switching to a safe well the most favored option (46.2%). Willingness to reduce exposure was positively related to awareness of the health risks of As. However, the association between awareness and switching to a safe well [odds ratio (OR) = 1.25; 95% confidence interval (CI), 1.01–1.54] was no stronger than the associations between awareness and using surface water (with or without treatments) (OR = 1.54; 95% CI, 1.22–1.95) or using an existing well after treatment or increasing the depth (OR = 1.34; 95% CI, 1.08–1.67). These findings suggest that health education programs may need to target individuals with lower socioeconomic status and that well switching should be encouraged with more appropriate health education. Increasing knowledge of the health consequences of As may be an important element in facilitating remediation.
Towards Less Supervision in Activity Recognition from Wearable Sensors

Activity Recognition has gained a lot of interest in recent years due to its potential and usefulness for context-aware wearable computing. However, most approaches for activity recognition rely on supervised learning techniques lim iting their applicability in real-world scenarios and their scalability to large amounts of activities and training data. State-of-the-art activity recognition algorithms can roughly be divided in two groups concerning the choice of the classifier, one group using generative models and the other discriminative approaches. This paper presents a method for activity recognition which combines a generative model with a discriminative classifier in an integrated approach. The generative part of the algorithm allows to extract and learn structure in activity data without any labeling or supervision. The discriminant part then uses a small but labeled subset of the training data to train a discriminant classifier. In experiments we show that this scheme enables to attain high recognition rates even though only a subset of the training data is used for training. Also the tradeoff between labeling effort and recognition performance is analyzed and discussed.
A review of feedforward control approaches in nanopositioning for high-speed spm

Control can enable high-bandwidth nanopositioning needed to increase the operating speed of scanning probe microscopes (SPMs). High-speed SPMs can substantially impact the throughput of a wide range of emerging nanosciences and nanotechnologies. In particular, inversion-based control can find the feedforward input needed to account for the positioning dynamics and, thus, achieve the required precision and bandwidth. This article reviews inversion-based feedforward approaches used for high-speed SPMs such as optimal inversion that accounts for model uncertainty and inversion-based iterative control for repetitive applications. The article establishes connections to other existing methods such as zero-phase-error-tracking feedforward and robust feedforward. Additionally the article reviews the use of feedforward in emerging applications such as SPM-based nanoscale combinatorial-science studies, image-based control for subnanometer-scale studies, and imaging of large soft biosamples with SPMs.