Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors affecting assigned information problem ordering during Web search : An exploratory study

Multitasking is the human ability to handle the demands of multiple tasks. Multitasking behavior involves the ordering of multiple tasks and switching between tasks. People often multitask when using information retrieval (IR) technologies as they seek information on more than one information problem over single or multiple search episodes. However, limited studies have examined how people order their information problems, especially during their Web search engine interaction. The aim of our exploratory study was to investigate assigned information problem ordering by forty (40) study participants engaged in Web search. Findings suggest that assigned information problem ordering was influenced by the following factors, including personal interest, problem knowledge, perceived level of information available on the Web, ease of finding information, level of importance and seeking information on information problems in order from general to specific. Personal interest and problem knowledge were the major factors during assigned information problem ordering. Implications of the findings and further research are discussed. The relationship between information problem ordering and gratification theory is an important area for further exploration. 2006 Published by Elsevier Ltd.
Intelligent Agents IV : Proceedings of the Fourth International Workshop on Agent Theories , Architectures and Languages ,

The Procedural Reasoning System (PRS) is the best established agent architecture currently available. It has been deployed in many major industrial applications, ranging from fault diagnosis on the space shuttle to air traffic management and business process control. The theory of PRS-like systems has also been widely studied: within the intelligent agents research community, the beliefdesire-intention (BDI) model of practical reasoning that underpins PRS is arguably the dominant force in the theoretical foundations of rational agency. Despite the interest in PRS and BDI agents, no complete attempt has yet been made to precisely specify the behaviour of real PRS systems. This has led to the development of a range of systems that claim to conform to the PRS model, but which differ from it in many important respects. Our aim in this paper is to rectify this omission. We provide an abstract formal model of an idealised dMARS system (the most recent implementation of the PRS architecture), which precisely defines the key data structures present within the architecture and the operations that manipulate these structures. We focus in particular on dMARS plans, since these are the key tool for programming dMARS agents. The specification we present will enable other implementations of PRS to be easily developed, and will serve as a benchmark against which future architectural enhancements can be evaluated.
THE BATTLE OF THE ATTACK DETECTION ALGORITHMS: 1 DISCLOSING CYBER ATTACKS ON WATER DISTRIBUTION 2 NETWORKS

Systems Analysis Symposium. The goal of the battle was to compare the performance of 8 algorithms for the detection of cyber-physical attacks, whose frequency increased in the past 9 few years along with the adoption of smart water technologies. The design challenge was set 10 for C-Town network, a real-world, medium-sized water distribution system operated through 11 Programmable Logic Controllers and a Supervisory Control And Data Acquisition (SCADA) 12 system. Participants were provided with datasets containing (simulated) SCADA observa- 13 tions, and challenged with the design of an attack detection algorithm. The eﬀectiveness of 14 all submitted algorithms was evaluated in terms of time-to-detection and classiﬁcation accu- 15 racy. Seven teams participated in the battle and proposed a variety of successful approaches 16 leveraging data analysis, model-based detection mechanisms, and rule checking. Results were 17 presented at the Water Distribution Systems Analysis Symposium (World Environmental & 18 Water Resources Congress), in Sacramento, on May 21-25, 2017. This paper summarizes the 19 BATADAL problem, proposed algorithms, results, and future research directions. 20
Infernal 1.0: inference of RNA alignments

SUMMARY INFERNAL builds consensus RNA secondary structure profiles called covariance models (CMs), and uses them to search nucleic acid sequence databases for homologous RNAs, or to create new sequence- and structure-based multiple sequence alignments. AVAILABILITY Source code, documentation and benchmark downloadable from http://infernal.janelia. org. INFERNAL is freely licensed under the GNU GPLv3 and should be portable to any POSIX-compliant operating system, including Linux and Mac OS/X.
A hybrid downscaling model for winter temperature over northeast China

A hybrid downscaling model is established to forecast the winter temperature at 182 stations over northeast China based on the year‐to‐year increment approach (differences in variables between the current and previous year). As winter temperature over China is related to general circulation and oceanic circulation signals, the three most suitable predictors at specific regions are ultimately considered in our model after analysing the physical processes involved. Accordingly, winter sea level pressure (SLP) from version 2 of the NCEP's Climate Forecast System (CFSv2) is used as a current‐year predictor, along with two other previous‐year predictors – sea surface temperature (SST) in August and sea‐ice concentration (SIC) in November, from observation. Four separate downscaling schemes are then proposed, three of which involve just one predictor (i.e. SLP‐scheme, SST‐scheme, and SIC‐scheme), and the fourth involves all of the predictors [i.e. hybrid downscaling (HD)‐scheme]. We evaluate the schemes, through cross‐validation, by examining the spatial and temporal anomaly correlation coefficients (ACCs) and reduction in root‐mean‐square error percentage (RMSEP). Considerable evidence is found that all of the schemes improve the skill in predicting winter temperature over northeast China, especially the HD‐scheme, as compared with real‐time CFSv2 forecasting. The 33‐year spatial ACC average for the HD‐scheme rises approximately from −0.017 to 0.30, which is much larger than the threshold value of 0.19, showing statistical significance at the 99% confidence level. Additionally, the temporal RMSEP (i.e. the difference between the RMSE of the CFSv2 outputs and the downscaling results) decreases by more than 30% for all four schemes. Moreover, the cold winter temperature pattern over northeast China in 2013 is reproduced well by the HD‐scheme.
Extracting Human Body based on Background Estimation in Modified HLS Color Space

computer vision is a very important task, with many potential application. Study of human motion analysis is related to several research areas of computer vision such as the motion capture, detection, tracking and segmentation of people. In this paper, we describe a segmentation method for extracting human body contour in modified HLS color space. To estimate a background, the modified HLS color space is proposed, and the background features are estimated by using the HLS color components. Here, the large amount of human dataset, which was collected from DV cameras, is pre-processed. The human body and its contour is successfully extracted from the image sequences.
Performance analysis of relative service using TCP-aware marking and dynamic WRED

The implementation of successful assured forwarding (AF) services according to the DiffServ framework remains a challenging problem today, despite the numerous proposals for assured forwarding per-hop-behaviour (AF PHB) mechanisms and AF-based service implementations. The interaction of the TCP and UDP traffic under an AF-based service and a number of relative issues such as fairness among flows, achievable bandwidth guarantees and qualitative performance have been taken into consideration in this work in order to address the existing limitations. We propose two modules, the TCP-window aware marker (TWAM) and the dynamic WRED (WRED) mechanism for implementing the differentiated services (DiffServ) AF PHB. We provide analytical models and an experimental evaluation in order to demonstrate how they succeed in enhancing the quality, improving the performance and easing the deployment of a production level AF-based service. Copyright © 2008 John Wiley & Sons, Ltd.
Kinematical analysis and measurement of sports form

This study describes a method for the measurement of sports form. The data obtained can be used for quantitative sports-skill evaluation. Here, we focus on the golf-driver-swing form, which is difficult to measure and also difficult to improve. The measurement method presented was derived by kinematical human-body model analysis. The system was developed using three-dimensional (3-D) rate gyro sensors set at positions on the body that express the 3-D rotations and translations during the golf swing. The system accurately measures the golf-driver-swing form of golfers. Data obtained by this system can be related quantitatively to skill criteria as expressed in respected golf lesson textbooks. Quantitative data for criteria geared toward a novice golfer and a midlevel player are equally useful
Chaos, Solitons and Fractals

The COVID-19 pandemic is still ongoing worldwide, and the damage it has caused is unprecedented. For prevention, South Korea has adopted a local quarantine strategy rather than a global lockdown. This approach not only minimizes economic damage but also eﬃciently prevents the spread of the disease. In this work, the spread of COVID-19 under local quarantine measures is modeled using the Susceptible-Exposed-Infected-Recovered model on complex networks. In this network approach, the links connected to infected and so isolated people are disconnected and then reinstated when they are released. These link dynamics leads to time-dependent reproduction number. Numerical simulations are performed on networks with reaction rates estimated from empirical data. The temporal pattern of the accumulated number of conﬁrmed cases is then reproduced. The results show that a large number of asymptomatic infected patients are detected as they are quarantined together with infected patients. Additionally, possible consequences of the breakdowns of local quarantine measures and social distancing are considered.
Differentiation and Weighted Model Integration

Stochastic gradient descent (SGD), especially in combination with auto-differentiation, has been the prime working horse of deep learning and has helped the field to rise to the most prominent spot of machine learning. Gradient based methods have also seen deployment in the field of probabilistic inference. However, optimization by differentiation in the field of probabilistic inference has until now only been targeted towards problems with either discrete or continuous random variables. In this note we show how to perform gradient based optimization on discrete-continuous probabilistic models, expressed as weighted model integration problems, by means of auto-differentiation. This provides a new powerful tool for inference, learning and optimization on (deep) discrete-continuous models.
War is God's Way of Teaching GIS

When people recall the morning of September 11, 2001, most speak of horror, sadness, and shock. My own vivid recollection is of horror and sadness, but not shock. Instead, I felt a sickening realization that the inevitable had finally happened, and I was surprised only by the method. Since 1997, my colleagues and I at Oak Ridge National Laboratory (ORNL) had been helping the United States Government and international organizations anticipate and prepare for threats potentially involving far greater numbers of victims than those that actually occurred that morning in New York, Pennsylvania, and Washington, D.C. I left ORNL for my current position at the University of Kansas (KU) less than one month before the 9-11 attacks, and global threats were still on my mind. The topic scheduled for my graduate seminar that very day was “estimating populations at risk. ”
This paper is included in the Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation

Read-only transactions are critical for consistently reading data spread across a distributed storage system but have worse performance than simple, non-transactional reads. We identify three properties of simple reads that are necessary for read-only transactions to be performance-optimal, i.e., come as close as possible to simple reads. We demonstrate a fundamental tradeoff in the design of read-only transactions by proving that performance optimality is impossible to achieve with strict serializability, the strongest consistency. Guided by this result, we present PORT, a performance-optimal design with the strongest consistency to date. Central to PORT are version clocks, a specialized logical clock that concisely captures the necessary ordering constraints. We show the generality of PORT with two applications. Scylla-PORT provides process-ordered serializability with simple writes and shows performance comparable to its non-transactional base system. Eiger-PORT provides causal consistency with write transactions and signiﬁcantly improves the performance of its transactional base system.
International Journal of Remote Sensing

Accurate high-resolution leaf area index (LAI) reference maps are necessary for the validation of coarser-resolution satellite-derived LAI products. In this article, we propose an efﬁcient method based on the Bayesian Maximum Entropy (BME) paradigm to combine ﬁeld observations and Landsat Enhanced Thematic Mapper Plus (ETM + )-derived LAI surfaces in order to produce more accurate LAI reference maps. This method takes into account the uncertainties associated with ﬁeld observations and with the regression relationship between ETM + -derived LAI and ﬁeld measurements to perform a non-linear prediction of LAI, the variable of interest. In order to demonstrate the difference by soft data and hard data, we estimate the LAI reference maps by three BME interpolation methods, BME1, BME2, and BME3. BME1 and BME2 perform maximum estimation and mean estimation, respectively, by taking the ETM + -derived LAI as interval soft data and the ﬁeld LAI measurements as hard data. BME3 is utilized when ETM + -derived LAI surfaces are processed as uniform probability soft data and ﬁeld measurements are processed as Gaussian probability soft data. Three study sites are selected from the BigFoot project (NASA’s Earth Observing System validation programme) (http://www.fsl.orst.edu/larse/bigfoot/index.html). In regard to the mean and standard deviation of LAI surfaces, standard deviation predicted by BME methods has lower values than
Tools and Algorithms for the Construction and Analysis of Systems

Find the secret to improve the quality of life by reading this tools and algorithms for the construction and analysis of systems proceedings of the 8th international conference tacas 2002 held as part of the joint european conferences on theory and practice of software etaps 2002 grenoble france april 8 12 2. This is a kind of book that you need now. Besides, it can be your favorite book to read after having this book. Do you ask why? Well, this is a book that has different characteristic with others. You may not need to know who the author is, how well-known the work is. As wise word, never judge the words from who speaks, but make the words as your good value to your life.
Efficient Exploitation of Multimedia Documents : Exploring , Indexing and Searching in Very Large Databases

TEXMEX is a common project-team with CNRS, University of Rennes 1 and INSA. It has been created on January the 1 st , 2002 and became an INRIA project on November the 1 st , 2002. With the success of sites like Youtube or DailyMotion, with the development of the Digital Terrestrial TV, it is now obvious that the digital videos have invaded our usual information channels like the web. While such new documents are now available in huge quantities, using them remains difficult. Beyond the storage problem, they are not easy to manipulate, browse, describe, search, summarize, visualize as soon as the simple scenario " 1. search the title by keywords 2. watch the complete document " does not fulfill the user's needs anymore. That is, in most cases. Most usages are linked with the key concept of repurposing. Videos are a raw material that each user recombines in a new way, to offer new views of the content, to adapt it to new devices (ranging from HD TV sets to mobile phones), to mix it with other videos, to answer information queries. Somehow, each use of a video gives raise to a new short-lived document that exists only while it is viewed. Achieving such a repurposing process implies the ability to manipulate videos extracts as easily as words in a text. Many applications exist in both professional and domestic areas. On the professional side, such applications include transforming a TV broadcast program into a web site, a DVD or a mobile phone service, switching from a traditional TV program to an interactive one, better exploiting TV and video archives, constructing new video services (video on demand, video edition.). On the domestic side, video summarizing can be of great help, as can a better management of the videos locally recorded, or simple tools to face the exponential number of TV channels available that increase the quantity of interesting documents available, overall increasing but make them really hard to find. In order to face such new application needs, we propose a multi-field work, gathering in a single team specialists that are able to deal with the various media and aspects of large video collections: image, video, text, sound and speech, but also data analysis, indexing, machine learning. The main goal of this work is to segment, structure, describe, or delinearize the multimedia content in order to be able to recombine …
Estimate of Turbulent Energy Dissipation Rate From the VHF Radar and Radiosonde Observations in the Antarctic

This study estimated the turbulent kinetic energy dissipation rates (TKEDRs) from 1‐year observations of the Program of the Antarctic Syowa Mesosphere‐Stratosphere‐Troposphere/Incoherent Scatter radar (PANSY radar) from October 2015 to September 2016 and compared the results with estimates from radiosonde measurements based on Thorpe's method. The radar‐based estimates showed that the TKEDR at Syowa Station was on the order of 10−5–10−3 m2/s3 in the altitude range of 1.5–19 km. Taking the proportional constant for Thorpe's method (the ratio of the Thorpe scale to Ozmidov scale) as unity, the radiosonde‐based measurements show values of TKEDR larger than radar‐based estimates by a factor of 2–5. The difference in the TKEDR between radiosonde‐ and radar‐based estimates is larger in the middle and upper troposphere than in the stratosphere. According to previous observational and numerical studies, Thorpe's method tends to overestimate the TKEDR for deep overturning layers. It is confirmed that the depth of the overturning layer is negatively correlated with the difference between radiosonde‐ and radar‐based estimates. The seasonal variation was also examined. An analysis using the distance from the local tropopause level showed that the local maximum in the TKEDR around the tropopause is particularly clear in austral summer. This is likely connected to the seasonality in the gravity wave activity in the Antarctic stratosphere.
Personalized tag recommendation based on generalized rules

Physicians and healthcare organizations always collect large amounts of data during patient care. These large and high-dimensional datasets are usually characterized by an inherent sparseness. Hence, the analysis of these datasets to figure out interesting and hidden knowledge is a challenging task. This paper proposes a new data mining framework based on generalized association rules to discover multiple-level correlations among patient data. ∗Corresponding author. Tel. : +39 0110907084 Fax: +39 0110907099. Email addresses: elena.baralis@polito.it (Elena Baralis), luca.cagliero@polito.it (Luca Cagliero), tania.cerquitelli@polito.it (Tania Cerquitelli), silvia.chiusano@polito.it (Silvia Chiusano), paolo.garza@polito.it (Paolo Garza), dario.antonelli@polito.it (Dario Antonelli), alessandro.fiori@ircc.it (Giulia Bruno), naeem.mahoto@faculty.muet. edu.pk (Naeem A. Mahoto) Preprint submitted to Specifically, correlations among prescribed examinations, drugs, and patient profiles are discovered and analyzed at different abstraction levels. The rule extraction process is driven by a taxonomy to generalize examinations and drugs into their corresponding categories. To ease the manual inspection of the result, a worthwhile subset of rules, i.e., the non-redundant generalized rules, is considered. Furthermore, rules are classified according to the involved data features (medical treatments or patient profiles) and then explored in a top-down fashion, i.e., from the small subset of high-level rules a drill-down is performed to target more specific rules. The experiments, performed on a real diabetic patient dataset, demonstrate the effectiveness of the proposed approach in discovering interesting rule groups at different abstraction levels.
Region-of-interest-based rate control scheme for high-efciency video coding

APSIPA Transactions on Signal and Information Processing / Volume 3 / 2014 / e16 DOI: 10.1017/ATSIP.2014. 15, Published online: 17 December 2014 Link to this article: http://journals.cambridge. org/abstract_S2048770314000158 How to cite this article: Marwa Meddeb, Marco Cagnazzo and Béatrice Pesquet-Popescu (2014). Region-of-interest-based rate control scheme for high-efciency video coding. APSIPA Transactions on Signal and Information Processing, 3, e16 doi:10.1017/ATSIP.2014. 15 Request Permissions : Click here
This paper

Content delivery networks (CDNs) cache and deliver hundreds of trillions of user requests each day from hundreds of thousands of servers around the world. The trafﬁc served by CDNs can be partitioned into hundreds of trafﬁc classes, each with different user access patterns, popularity distributions, object sizes, and performance requirements. Midgress is the cache miss trafﬁc between the CDN’s servers and the content provider origins. A major goal of a CDN is to minimize its midgress, since higher midgress translates to higher bandwidth costs and increased user-perceived latency. We propose algorithms that provision trafﬁc classes to servers such that midgress is minimized. Using extensive traces from Akamai’s CDN, we show that our midgress-aware trafﬁc provisioning schemes can reduce midgress by nearly 20% in comparison with the midgress-unaware schemes currently in use. We also propose an efﬁcient heuristic for trafﬁc provisioning that achieves near-optimal midgress and is suitable for use in production settings. Further, we show how our algorithms can be extended to other settings that require minimum caching performance per trafﬁc class and minimum content duplication for fault tolerance. Finally, our paper provides a strong case for implementing midgress-aware trafﬁc provisioning in production CDNs.
A Comparative Study on Classification Algorithms for Sentiment Analysis

— Social media is a very popular way of expressing opinions and integrating with other people in the online world. How to analyze user generated reviews and to classify them into different sentiment classes is gradually becoming a question that people play close attention to. This problem has become a comparison benchmark test for different classification methods. Sentiment analysis focused on social networks, product reviews, stock market, news comments, etc. In this paper, I survey the various algorithms available for sentiment analysis. Different algorithms are use like Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks, etc. Sentiment analysis is used in politics, to detect stock, to add or nix the advertisements.