Miracle-dz/UniFAR-FTUs huggingface dataset:
On managing geospatial big-data in emergency management: some perspectives

With the rapid growth of mobile devices and applications, geo-tagged data is becoming increasingly important in emergency management and has become a major workload for big data storage systems. Traditional methods that storing geospatial data in centralized databases suffer from inevitable limitations such like scaling out with the growing size of geospatial data. In order to achieve scalability, a number of solutions on big geospatial data management are proposed in recent years. We can simply classify them into two kinds: extending on distributed databases, or migrating to big-data storage systems. For previous, they mostly adopt the massive parallel processing (MPP) based architecture, in which data are stored and retrieved in a set of independent nodes. Each node can be treated as a traditional databases instance with geospatial extension. For the latter, existing solutions tend to build an additional index layer above general-purpose distributed data stores, e.g., HBASE, CASSANDRA, MangoDB, etc., to support geospatial data while integrating the big-data lineage. However, there are no absolutely perfect data management systems on the earth. Some approaches are desired for execution efficiency while some others are better on fulfilling the programming level need for big data scenarios. In this paper, we analysis the requirements and challenges on geospatial big data storage in emergency management, succeed with discussion with individual perspective from practical cases. The purpose of this paper is not only focused on how to program a geospatial data storage platform but also on how to approve the rationality of geospatial big data system that we plan to build.
Stochastic Model Predictive Control for Building HVAC Systems: Complexity and Conservatism

This paper presents a stochastic model predictive control (SMPC) approach to building heating, ventilation, and air conditioning (HVAC) systems. The building HVAC system is modeled as a network of thermal zones controlled by a central air handling unit and local variable air volume boxes. In the first part of this paper, simplified nonlinear models are presented for thermal zones and HVAC system components. The uncertain load forecast in each thermal zone is modeled by finitely supported probability density functions (pdfs). These pdfs are initialized using historical data and updated as new data becomes available. In the second part of this paper, we present a SMPC design that minimizes expected energy cost and bounds the probability of thermal comfort violations. SMPC uses predictive knowledge of uncertain loads in each zone during the design stage. The complexity of a commercial building requires special handling of system nonlinearities and chance constraints to enable real-time implementation, minimize energy cost, and guarantee thermal comfort. This paper focuses on the tradeoff between computational tractability and conservatism of the resulting SMPC scheme. The proposed SMPC scheme is compared with alternative SMPC designs, and the effectiveness of the proposed approach is demonstrated by simulation and experimental tests.
Stranding Events of Kogia Whales along the Brazilian Coast

The genus Kogia, which comprises only two extant species, Kogia sima and Kogia breviceps, represents one of the least known groups of cetaceans in the global ocean. In some coastal regions, however, stranding events of these species have been relatively common over the last decades. Stranding provides the opportunity to investigate the biology of these cetaceans and to explore the epidemiological aspects associated with the mortality of the organisms found on the beach. A number of disturbances (including pelagic fisheries, chemical pollution, boat strikes, and noise pollution) have been confirmed to pose a particular threat to the Kogia species. However, no study has yet investigated potential relationships between environmental conditions and stranding events. Here we analyse how a collection of environmental, physical, and biological variables, such as wind, sea surface temperature (SST), water depth, and chlorophyll-a, correlate to Kogia stranding events along the Brazilian coast. The results of our statistical analyses suggest that K. sima is more likely found in warm tropical waters, which provide an explanation for the high frequency of stranding in northeastern Brazilian coast. In contrast, K. breviceps appears to have a preference for temperate and productive waters. Wind speed results to be also an important factor for predicting Kogia strandings in Brazilian coast. Additionally, literature information in combination with our own data and analyses of stomach contents confirms that oceanic cephalopods constitute the primary nutritional source of both Kogia species. By using the available information as a qualitative proxy for habitat preference and feeding ecology, our study provides a novel and comprehensive assessment of Kogia stranding data in relation to environmental conditions along the Brazilian coast.
Can't Do or Won't Do?: Social Attributions in Human-Agent Cooperation (Extended Abstract)

We highlight how human--agent cooperation is linked to the attribution of critical qualities like trustworthiness and competence to the cooperation partner. To investigate these aspects in a systematic fashion, we devise a novel paradigm of an interactive cooperation game that goes beyond commonly adopted economic scenarios. Our results indicate how a less skillful agent that shows little appreciation for its human partner's suggestions regarding the next action is ascribed negative trustworthiness, but also positive competence. This suggests that perceived competence and cooperativeness interact with each other in important ways that can be studied jointly in an interactive cooperation game scenario.
Pricing of Options with STochastic Volatilities: Application to Agricultural Commodity Contracts

The empirical evidence in this paper supports the existence of seasonality, time-to-maturity, and long-memory effects in the volatility of prices, but not in the returns themselves, in corn and soybean futures markets. This volatility is modeled as an Orenstein-Ulenbeck process driven by fractional Brownian motion. The inclusion of long-memory stochastic volatility is found to have a significant impact upon the term structure of implied volatilities, and should be able to provide better estimates of inand out-of-the money options’ prices. Acknowledgement. We gratefully acknowledge the insights and suggestions provided by Robert Jarrow. At the time this paper was written, Lordkipanidze was a PhD candidate and Tomek an emeritus professor in the Department of Agricultural, Resource and Managerial Economics, now the Dyson School of Applied Economics and Management, at Cornell University.
Hyperspectral Target Detection via Local Background Suppression

This paper focuses on detection of targets in hyperspectral images via local background estimation and suppression. Conventional global approaches are likely to overestimate the actual background interference affecting a given target, thus providing a low target residual energy after the suppression step, which impairs detection performance. In this work, a local scheme for background estimation and suppression is proposed that adaptively estimates the local background over a local neighborhood of each pixel. By acting locally, the method is capable of adapting to the local background complexity without overestimating it, and hence is expected to provide a higher target residual energy, with a consequent improvement in detection performance. The benefits of applying the proposed approach with respect to a global one are evaluated by means of real hyperspectral data.
A class of complementary IIR filters

A class of complementary IIR filters is introduced. One of these filters can be realized as a tapped cascaded interconnection of identical allpass subfilters. The complementary filter is then obtained automatically by adding some extra allpass subfilters and a subtractor. The proposed filters are always allpass complementary and, under certain conditions, also magnitude complementary. Further, in terms of the frequency selectivity, they exhibit properties similar to those of complementary linear-phase FIR filters. These properties are retained also after coefficient quantization. The design of the filters is divided into the design of a prototype FIR filter and a prototype IIR filter being realizable as a parallel connection of two allpass filters. Design examples are included for demonstration.
Discovering inappropriate billings with local density based outlier detection method

This paper presents an application of a local density based outlier detection method in compliance in the context of public health service management. Public health systems have consumed a significant portion of many governments' expenditure. Thus, it is important to ensure the money is spent appropriately. In this research, we studied the potentials of applying an outlier detection method to medical specialist groups to discover inappropriate billings. The results were validated by specialist compliance history and direct domain expert evaluation. It shows that the local density based outlier detection method significantly outperforms basic benchmarking method and is at least comparable, in term of performance, to a domain knowledge based method. The results suggest that the density based outlier detection method is an effective method of identifying inappropriate billing patterns and therefore is a valuable tool in monitoring medical practitioner billing compliance in the provision of health services.
An Actionable Approach to Understand Group Experience in Complex, Multi-surface Spaces

There is a steadily growing interest in the design of spaces in which multiple interactive surfaces are present and, in turn, in understanding their role in group activity. However, authentic activities in these multi-surface spaces can be complex. Groups commonly use digital and non-digital artefacts, tools and resources, in varied ways depending on their specific social and epistemic goals. Thus, designing for collaboration in such spaces can be very challenging. Importantly, there is still a lack of agreement on how to approach the analysis of groups' experiences in these heterogeneous spaces. This paper presents an actionable approach that aims to address the complexity of understanding multi-user multi-surface systems. We provide a structure for applying different analytical tools in terms of four closely related dimensions of user activity: the setting, the tasks, the people and the runtime co-configuration. The applicability of our approach is illustrated with six types of analysis of group activity in a multi-surface design studio.
Underwriting Syndicate, IPO Placement Risk and Lead manager Reputation : An Empirical Study on European Stock Markets

On the basis of a sample of 1605 Initial Public Off erings made on German, French and British stock markets, we try to understand the constitutio n of underwriting syndicate taking into consideration two hypotheses made on IPO placement risk and lead manager reputation. We show that the situations are highly contrasted betw e n these three stock markets. The first fundamental result is the existence of various form s of syndication in German, British and French markets. German issues are characterized by large syndicates where the lead manager is joined by other banks. Conversely, British issues a re strongly concentrated on syndicates with single bank. French IPOs present an intermediate si tuation with syndicate size composed of more than two banks. Our second result shows that t he hypothesis of an increase in syndicate size, in the presence of great uncertainty seems we ll-founded on German and French markets. We noted that these two markets were on average mor risky for share placement and that the mechanisms of the syndicate size increase worked to diversify the risk for syndicate members. On the other hand, the British market did not respo nd to the influences of risk variables. It is a market where initial return volatility, proportion of “paper return” and underpricing of previous IPOs were the lowest and the best controlled by ban ks. The third result relates to lead manager reputation. We noted that reputation building is fo unded on two different dimensions. The first is represented in terms of market share as regards IPO gross proceeds, the second as regards number of issues. That represents a certain form of IPO market segmentation, in which certain banks concentrated on large IPOs bringing a large a mount of proceeds and other banks which tried to obtain a high number of small IPO contract s. This different approach of reputation gives opposing results on the French and British ma rkets. The British IPOs underwritten by the more reputable lead managers, in terms of gross pro ceeds, were shared between several banks, whereas the IPOs underwritten by banks with a great reputation in terms of number of issues are rather assumed by only one bank. These relation s are the opposite on the French market. The latter point indicates that it is not possible to have a single vision of lead manager reputation as a factor of IPO concentration.
An adaptive Logluv transform for High Dynamic Range video compression

High Dynamic Range (HDR) imaging represents a wide range of intensity levels found in real scenes ranging from direct sunlight to shadows. However, raw HDR sequences require very huge memory space for storage. In this work, we present an approach of how an existing encoder can be used for encoding HDR video sequence with our proposed adaptive LogLuv transform. The proposed method shows considerable improvement when compared with non-adaptive LogLuv transform.
Controlling a class of non-linear systems on rectangles

In this paper we focus on a particular class of nonlinear affine control systems of the form ẋ = f(x) + Bu, where the drift f is a multi-affine vector field (i.e., affine in each state component), the control distribution B is constant, and the control u is constrained to a convex set. For such a system, we first derive necessary and sufficient conditions for the existence of a multi-affine feedback control law keeping the system in a rectangular invariant. We then derive sufficient conditions for driving all initial states in a rectangle through a desired facet in finite time. If the control constraints are polyhedral, we show that all these conditions translate to checking the feasibility of systems of linear inequalities to be satisfied by the control at the vertices of the state rectangle. This work is motivated by the need to construct discrete abstractions for continuous and hybrid systems, in which analysis and control tasks specified in terms of reachability of sets of states can be reduced to searches on finite graphs. We show the application of our results to the problem of controlling the angular velocity of an aircraft with gas jet actuators.
An empirical study in risk management: estimation of Value at Risk with GARCH family models

In this paper the performance of classical approaches and GARCH family models are evaluated and compared in estimation one-step-ahead VaR. The classical VaR methodology includes historical simulation (HS), RiskMetrics, and unconditional approaches. The classical VaR methods, the four univariate and two multivariate GARCH models with the Student’s t and the normal error distributions have been applied to 5 stock indices and 4 portfolios to determine the best VaR method. We used four evaluation tests to assess the quality of VaR forecasts:- Violation ratio- Kupiec’s test- Christoffersen’s test- Joint testThe results point out that GARCH-based models produce far more accurate forecasts for both individual and portfolio VaR. RiskMetrics gives reliable VaR predictions but it is still substantially inferior to GARCH models. The choice of an optimal GARCH model depends on the individual asset, and the best model can be different based on different empirical data.
Development of a VR Therapy Application for Iraq War Veterans with PTSD

Post Traumatic Stress Disorder (PTSD) is reported to be caused by traumatic events that are outside the range of usual human experiences including (but not limited to) military combat, violent personal assault, being kidnapped or taken hostage and terrorist attacks. Initial data suggests that 1 out of 6 returning Iraq War military personnel are exhibiting symptoms of depression, anxiety and PTSD. Virtual Reality (VR) exposure therapy has been used in previous treatments of PTSD patients with reports of positive outcomes. The aim of the current paper is to specify the rationale, design and development of an Iraq War PTSD VR application that is being created from the virtual assets that were initially developed for the X-Box game entitled Full Spectrum Warrior which was inspired by a combat tactical training simulation, Full Spectrum Command.
Meta-data and Biological Sequence Annotation

1 Abstract One of the challenges of this decade is to nd and access relevant information on the World Wide Web (WWW). It is therefore critical to create very well-designed information servers. We focus our attention on servers dedicated to experimental science, i.e. more speciically servers dedicated to biology. Applied science requires tools that innuence and process captured data. A popular example is the various genome sequencing projects. Experimental data (raw DNA sequences) need to be converted into "interpreted" data (annotations), and this process involves various analytical strategies (i.e. gene nding by pattern recognition and sequence comparison , identiication and characterization of regulatory and repetitive elements, .). We came to the conclusion that a major advancement can be achieved by introducing meta-data into these systems. The function of meta-data descriptions is to enable to abstract and capture the essential information in the underlying data independent of representational details 15]. In this paper, we describe an approach that takes meta-data into account, in order to have more reliable data (high rate of correct functional annotations) within the public sequence databases. A rst implementation of this approach has been done and an attempt for the evaluation of the advantages of such an approach is presented. 2 Introduction Large scale genome sequencing projects like the Human Genome Project (HGP) and model organisms sequencing projects (E. coli, Drososphila, C. elegans, .) led to an explosion of biological information that continues today. Currently, the limiting factor of these projects is rather to analyze biological sequences and to assign functions to the majority of the open reading frames than to produce large fragments of DNA. Furthermore, the biological community is faced with various diiculties that are all strongly correlated to identifying and delivering high quality information. For example, it is currently impossible to question the accuracy of an annotation (a description of a sub-region, a particular functional assignment, .) in a public sequence database. This is a crucial fact, considering that methodologies based on sequence comparison are mainly used in computational molecular biology to infer new functional annotations with respect to the annotations previously established. The danger when experts validate putative functional annotations is therefore double: the rst risk is to enter an incorrect functional annotation within a sequence database, the second risk is to use this annotation to deene new functional annotations and then to propagate the mistake within the sequence databases. Due to the …
MLIP: A Multiprocessor Linkage Analysis System

This paper describes MLIP, a multiprocessor two- point genetic linkage analysis system that supports statistical calculations, such as the posterior probability of linkage (PPL), based on the full multidimensional parameter space implicit in the linkage likelihood. We provide empirical results on a wide variety of data showing that MLIP does significantly speed up two-point log-likelihood ratio calculations over a grid space of model parameters. Thanks to MLIP, full multidimensional genome scans are now routinely being completed at our centers with runtimes on the order of days, not months or years.
Search and Navigation in Semantically Integrated Document Collections

The paper presents a novel approach to semantic search and navigation in office-like document collections. The approach is based on a semantic document model that we have developed to enable unique identification, semantic annotation, and semantic linking of document units of officelike documents. In order to semantically annotate document units and to link semantically related document units, we first conceptualize document units’ semantics and represent them by vectors of ontological concepts and their corresponding weight vectors. In the semantic search, we represent a user query by a query’s concept vector, which is generated in the same way as document units’ concept vectors, and then determine the search results by measuring the similarity between the query’s and the document units’ concept weight vectors. After the search, by following the semantic links of a selected document unit, the user can navigate through the document collection and discover semantically related document units. Results of the preliminary evaluation, conducted with a prototype implementation, are promising. We present a brief analysis of these results. Keywords-semantic search, semantic linking and navigation;
Head pose estimation using stereo vision for human-robot interaction

We present a method for estimating a person's head pose with a stereo camera. Our approach focuses on the application of human-robot interaction, where people may be further away from the camera and move freely around in a room. We show that depth information acquired from a stereo camera not only helps improving the accuracy of the pose estimation, but also improves the robustness of the system when the lighting conditions change. The estimation is based on neural networks, which are trained to compute the head pose from grayscale and disparity images of the stereo camera. It can handle pan and tilt rotations from -90/spl deg/ to +90/spl deg/. Our system does not require any manual initialization and does not suffer from drift during an image sequence. Moreover the system is capable of real-time processing.
Identifying Critical Success Factors for ERP in SMEs through a Case Study

ERP systems have been around since 1990s. Many organisations across the world have deployed ERP systems since then. An ERP system is a very complex system and its deployment involves the entire organisation. But not all ERP projects that are started end successfully. In this article, we analyse a failed ERP project in a small organisation in the capital region of India. We use critical success factors (CFSs) that have been identified in literature for analysis. The CSFs that contributed to the failure turned out to be- poor project management, lack of management support, ineffective external consultant, ineffective vendor's team and wrong package selection.