Miracle-dz/UniFAR-FTUs huggingface dataset:
EnjoyPhoto: a vertical image search engine for enjoying high-quality photos

In this paper, we propose building a vertical image search engine called EnjoyPhoto that leverages rich metadata from various photo forum web sites to meet users' requirements for enjoying high-quality photos, which is virtually impossible in traditional image search engines. To solve the ranking problem when aggregating multiple photo forums, we propose a novel rank fusion algorithm that uses duplicate photos to normalize rating scores. To further improve user experiences in enjoying photos, we design an in-place image browsing interface, and compare it with several other interfaces in a user study. With rich metadata and rating information, more attractive user interfaces are enabled, including slideshow authoring and photo recommendations. We conducted experiments and user studies on a 2.5-million image database to evaluate the proposed rank fusion algorithm, investigate the rationale behind building a vertical image search engine, and study user interfaces and preferences for the purpose of enjoying high-quality photos. The experimental results demonstrate the effectiveness of the proposed ranking algorithm. The results also show that the 2.5-million high-quality image database in EnjoyPhoto performs comparably with Google's 1- billion image database for queries related to location, nature, and daily life categories. Finally, our results show that the in-place browsing interface-called Force-Transfer view-is much more convenient for users than traditional interfaces.
Modeling Virtual Hands with Haptic Interface Device

For direct manipulation environment with force feedback, we propose a kind of string-based two-handed multi-fingers haptic interface device. The system can sense the positions of three fingertips and wrist on each of the user's hand. Force feedback can be perceived when the position of the finger comes into contact with the virtual object. We explain in detail about the algorithms used in the force feedback generation and the calculation of the positions of the user's hands. Then, we have modeled images of virtual hands from positions on the user's real hand measured by the system. Method of hand shape modeling is presented in this paper.
A flexible class of regenerating codes for distributed storage

In the distributed storage setting introduced by Dimakis et al., B units of data are stored across n nodes in the network in such a way that the data can be recovered by connecting to any k nodes. Additionally one can repair a failed node by connecting to any d nodes while downloading at most β units of data from each node. In this paper, we introduce a flexible framework in which the data can be recovered by connecting to any number of nodes as long as the total amount of data downloaded is at least B. Similarly, regeneration of a failed node is possible if the new node connects to the network using links whose individual capacity is bounded above by βmax and whose sum capacity equals or exceeds a predetermined parameter γ. In this flexible setting, we obtain the cut-set lower bound on the repair bandwidth along with a constructive proof for the existence of codes meeting this bound for all values of the parameters. An explicit code construction is provided which is optimal in certain parameter regimes.
An Automated Testing Platform for Mobile Applications

With the growing popularity and complexity of mobile apps, quality assurance becomes more and more important in mobile app development. Unfortunately, Android is also suffering from the notorious fragmentation problem. To tackle these problems, we introduce an automated testing platform, BugRocket. BugRocket combines a distributed testing system with automated testing techniques equipped on mobile devices. In this paper, we developed a toolset for automated testing, and set up such a testing platform with 40 of the most popular Android devices. An case study is conducted and the results show that BugRocket can work for functional testing and compatibility testing. Besides, BugRocket can record a failed run as long as annotated GUI model and system logs to alleviate locating bugs and bug fixing.
Class-Based N-Gram Language Model for New Words Using Out-of-Vocabulary to In-Vocabulary Similarity

Out-of-vocabulary (OOV) words create serious problems for automatic speech recognition (ASR) systems. Not only are they missrecognized as in-vocabulary (IV) words with similar phonetics, but the error also causes further errors in nearby words. Language models (LMs) for most open vocabulary ASR systems treat OOV words as a single entity, ignoring the linguistic information. In this paper we present a class-based n-gram LM that is able to deal with OOV words by treating each of them individually without retraining all the LM parameters. OOV words are assigned to IV classes consisting of similar semantic meanings for IV words. The World Wide Web is used to acquire additional data for finding the relation between the OOV and IV words. An evaluation based on adjusted perplexity and word-error-rate was carried out on the Wall Street Journal corpus. The result suggests the preference of the use of multiple classes for OOV words, instead of one unknown class. key words: out-of-vocabulary, class-based n-gram, language model, adjusted perplexity, speech recognition
Five Centuries of Monarchy in Korea: Mining the Text of the Annals of the Joseon Dynasty

We present a quantitative study of the Annals of the Joseon Dynasty, the daily written records of the five hundred years of a monarchy in Korea. We first introduce the corpus, which is a series of books describing the historical facts during the Joseon dynasty. We then define three categories of the monarchial ruling styles based on the written records and compare the twentyfive kings in the monarchy. Finally, we investigate how kings show different ruling styles for various topics within the corpus. Through this study, we introduce a very unique corpus of monarchial records that span an entire monarchy of five hundred years and illustrate how text mining can be applied to answer important historical questions.
Experience Retrieval in Learning Software Organizations : Do you find what you are looking for ?

A Learning Software Organization (LSO) often supports such information retrieval needs by installing an Experience Base (EB) or another Knowledge Management (KM) system. However, even in such environments, searching for the right information can still be a challenge. In this paper, we identify some of the reasons for these challenges. Based on our observations we provide requirements to improve existing EB or KM systems, and to guide developers in implementing better system from scratch. Our observations are based on personal experience with implementing and running EB / KM systems. In addition, a study focusing on search requirements and habits was conducted in a small research center.
A componential model for understanding the effects of composite interface metaphors on web-based task performance and learning

Two experiments describe how a composite metaphor interface (CMI) for performing website tasks can be developed using an iterative and theory-based design approach. The CMI consisted of two semantically unrelated metaphors: the library metaphor and the travel metaphor. A new componential model for predicting the effectiveness of CMIs is described and used for deriving and testing specific hypotheses. Results show that an improved version of the CMI resulted in better declarative knowledge about metaphor-interface mappings than a non-metaphorical control interface (even before the interface was first used), and in less mental effort, as measured by the clicking behavior. It also resulted in faster performance of web-based tasks, but only if differences in pre-existing declarative knowledge were taken into account. The discussion addresses i.a. the question how mismatches between interface functions, and the metaphor they belong to (confusions) can be measured and used for improving the quality of the CMI.
Remote‐sensing constraints on South America fire traits by Bayesian fusion of atmospheric and surface data

Satellite observations reveal substantial burning during the 2007 and 2010 tropical South America fire season, with both years exhibiting similar total burned area. However, 2010 CO fire emissions, based on satellite CO concentration measurements, were substantially lower (−28%), despite the once‐in‐a‐century drought in 2010. We use Bayesian inference with satellite measurements of CH4 and CO concentrations and burned area to quantify shifts in combustion characteristics in 2010 relative to 2007. We find an 88% probability in reduced combusted biomass density associated with the 2010 fires and an 82% probability of lower fire carbon losses in 2010 relative to 2007. Higher combustion efficiency was a smaller contributing factor to the reduced 2010 CO emissions. The reduction in combusted biomass density is consistent with a reduction (4–6%) in Global Ozone Monitoring Experiment 2 solar‐induced fluorescence (a proxy for gross primary production) during the preceding months and a potential reduction in biomass (≤8.3%) due to repeat fires.
Proposal : Effective Type Theory for Modularity

The programming language Standard ML (SML) draws much of its expressive power from advanced concepts in type theory. In addition, unlike other HOT (Higher-Order Typed) languages, SML has the distinction of being formally defined. Unfortunately, the Definition of SML is not type-theoretic, making it difficult to analyze and extend. Much work in the last decade has thus been devoted to better understanding the type-theoretic underpinnings of SML, particularly with respect to its module system. In my thesis work I plan to study several extensions to SML that would enhance its support for modular programming even further. My methodology for formalizing these extensions follows the approach advocated by Harper and Stone, who gave an interpretation of SML that involves elaborating SML programs into type theory. This approach allows me to design my extensions at the level of the underlying type theory, which is the ideal setting for language design, but provides the option to fall back on elaboration techniques when type inference or syntactic sugar is key to making a language feature palatable. I will incorporate my extensions into the TILT compiler for SML, whose front-end is a suitable testbed for implementation as it is based on the Harper-Stone framework. This proposal describes my work thus far on extending SML with higher-order modules, modules as firstclass values, and recursive modules. A running theme in my type theory for modules is the importance of effects. The design of my type system for the first two extensions is driven by the intuition that the creation of abstract data types should be thought of as an effect. My type system is more expressive than previous systems because it distinguishes type abstraction (a compile-time effect) from type generativity (a run-time effect). While my proposal for recursive modules relies more heavily on elaboration techniques, its success hinges critically on the introduction of a type of lazy memoized modules, used to encapsulate term-level computational effects.
The Model of Cultural Competence Through an Evolutionary Concept Analysis

Becoming a culturally competent health professional is a demanding prerequisite in this multicultural society. Cultural competence is explored and abstracted as a conceptual framework through a concept analysis using the evolutionary method. Its model is constructed from a systemic, comprehensive literature review and analysis. Taking into account how cultural competence is viewed by other disciplines (medicine, psychology, education, and social work), a comprehensive definition, antecedents, and consequences of cultural competence in nursing are described and diagrammed. Additionally, two model cases and future implications are discussed. The broader American society is composed of a mosaic of discrete cultural groups existing within the context of their values and identities. Those distinct cultures affect each patient’s ways of thinking and his or her perceptions on health care and health behaviors. The proposed model of cultural competence provides a theoretical guide for developing strategies to achieve culturally competent care in nursing practice and research.
Social networks: intentions and usage

The purpose of this research is studying the key dimensions that explain the use of social network systems. In order to achieve this purpose, it was identified a model that explains the use of social systems. The model is an extension of the Technology Acceptance Model, considering enjoyment as construct and the impact of effective use in the intention to use. It was performed an empirical research to identify that ease of use has generically a minimal impact. Results also showed that intention to use is largely influenced by perceived usefulness, and effective use is influenced mainly by enjoyment.
Equilibrium Impact of Value-at-Risk Regulation

We study the partial and general equilibrium implications of value-at-risk (VaR) regulation in continuous-time economies with intermediate expenditure, stochastic opportunity set, and heterogeneous attitudes to risk. Our findings show that because of an anticipatory effect of VaR constraints on the optimal hedging demand, the partial equilibrium incentives of VaR regulation can lead banks to increase their risk exposure in high-volatility states. In general equilibrium, VaR constraints can produce unambiguously lower interest rates and higher equity Sharpe ratios. The VaR impact on equity volatility and equity expected returns is ambiguous.
Developing the Web 100 Based Network Diagnostic Tool ( NDT )

Finding and fixing a network performance or configuration problem is a difficult and complex task. It requires time, energy, and expertise to track down problems and it is difficult for users to know whom to contact when the source and destination computers are located in different administrative domains. This paper describes a novel new way of performing the basic diagnostic function needed to identify that a problem exists and if that problem is in the source computer, the destination computer, or the network infrastructure. Once a problem is identified, users will have detailed data that can be used by their network administrator to find a solution.
Scalable Performance Environments for Parallel

As parallel systems expand in size and complexity, the absence of performance tools for these parallel systems exacerbates the already diicult problems of application program and system software performance tuning. Moreover, given the pace of technological change, we can no longer aaord to develop ad hoc, one-of-a-kind performance instrumentation software; we need scalable, portable performance analysis tools. We describe an environment prototype based on the lessons learned from two previous generations of performance data analysis software. Our environment prototype contains a set of performance data transformation modules that can be interconnected in user-speciied ways. It is the responsibility of the environment infrastructure to hide details of module interconnection and data sharing. The environment is written in C ++ with the graphical displays based on X windows and the Motif toolkit. It allows users to interconnect and conngure modules graphically to form an acyclic, directed data analysis graph. Performance trace data are represented in a self-documenting stream format that includes internal deenitions of data types, sizes, and names. The environment prototype supports the use of head-mounted displays and sonic data presentation in addition to the traditional use of visual techniques .
Modeling of an Automotive Air Conditioning Compressor Based on Experimental Data

The objective of the current study was to develop a model for the steady-state performance of a reciprocating automotive air conditioning compressor. The model equations were composed of analytical equations based on simple energy and mass balances in addition to simple relations developed from experimental data. Since the model equations are simple in form, they can easily be solved sequentially yielding a reasonable solution time. Furthermore, the analytical equations contained physical parameters which can be varied to provide simulations of various compressor geometries. The constant parameters used in the empirical relations were obtained from a least squares analysis of experimental data. The experimental data used for parameter estimation were obtained from a mobile air conditioning system test facility which utilized R -134a as the refrigerant. It is believed that the modeling algorithm presented can easily be extended to other reciprocating compressors with a minimal amount of experimental data. Furthermore, the algorithm is capable of producing a model that is accurate over a broad range of conditions. The model's performance was verified by comparison of simulation results with experimental data. When provided the suction refrigerant state, discharge refrigerant pressure, compressor speed and ambient air temperature, the model proved capable of predicting the discharge refrigerant enthalpy, the required compressor power and the refrigerant mass flow rate with reasonable accuracy.
Content-based Dynaic Routing in Structured Overlays Networks

Acyclic overlays used for broker-based publish/subscribe systems provide unique paths for content-based routing from a publisher to interested subscribers. Cyclic overlays may provide multiple paths, however, the subscription broadcast process generates one content-based routing path per subscription. This poses serious challenges in offering dynamic routing of notifications when congestion is detected because instantaneous updates in routing tables are required to generate alternative routing paths. This paper introduces the first subscription-based publish/subscribe system, OctopiS, which offers inter-cluster dynamic routing when congestion in the output queues is detected. OctopiS is based on a formally defined Structured Cyclic Overlay Topology (SCOT). SCOT is divided into homogeneous clusters where each cluster has equal number of brokers and connects to other clusters through multiple inter-cluster overlay links. These links are used to provide parallel routing paths between publishers and subscribers connected to brokers in different clusters. While aiming at deployment at data center networks, OctopiS generates subscription-trees of shortest lengths used by Static Notification Routing (SNR) algorithm. Dynamic Notification Routing (DNR) algorithm uses a bit-vector mechanism to exploit the structuredness of a clustered SCOT to offer inter-cluster dynamic routing without making updates in routing tables and minimizing load on overwhelmed brokers and congested links. Experiments on a cluster testbed with real world data show that OctopiS is scalable and reduces the number of inter-broker messages in subscription delivery by 89%, subscription delay by 77%, end-to-end notification delay in static and dynamic routing by 47% and 58% respectively, and the lengths of output queues of brokers in dynamic routing paths by 59%.
JET BREAK TIME – FLUX DENSITY RELATIONSHIP AND CONSTRAINTS ON PHYSICAL PARAMETERS OF GRB AFTERGLOWS

We derive a relation between the flux density Fν,j at the light curve break of a GRB afterglow and the break time tj . The break is due to the transition from the spherical-like t o je -like evolution of the afterglow when the Lorentz factor of the jet equals the inverse of the initial ha lf opening angle, i.e.γ = 1/θ0. We show that this relation indeed behaves as Fν,j ∝ t −p j among GRBs for the slow cooling case, where p is the power-law index of electron distribution. A statistical analysis of the optic al jet breaks of 9 GRBs gives p = 2.10±0.21, which is consistent with the shock acceleration theory. The value of p derived in this way is different from the observed temporal indexα2 (Fν ∝ t−α2) of the late-time light curve after tj , which suffers several uncertainties from the unclear hydrodynamics of the sideways expansion and exhibi ts a large dispersion. Our results not only confirm that the remnants of GRBs are standard candles, but also prov ide the first evidence that the physical parameters of relativistic shocks are universal, with the favored values ǫe ∼ 0.1 andǫB ∼ 10−3. Subject headings: gamma rays: bursts—gamma rays: observations—ISM: jets and outflows—methods: statistical
An Accurate ECG-Based Transportation Safety Drowsiness Detection Scheme

Many traffic injuries and deaths are caused by the drowsiness of drivers during driving. Existing drowsiness detection schemes are not accurate due to various reasons. To resolve this problem, an accurate driver drowsiness classifier (DDC) has been developed using an electrocardiogram genetic algorithm-based support vector machine (ECG GA-SVM). In existing studies, a cross correlation kernel and a convolution kernel have both been applied for performing the classification. The DDC is designed by a Mercer kernel KDDC formed by commuting the cross correlation kernel Kxcorr,ij and the convolution kernel Kconv,ij. Kxcorr,ij, and captures the symmetric information among ECG signals from different classes, while Kconv,ij captures the antisymmetric information among ECG signals from the same class. The final KDDC (a precomputed kernel) is obtained by a genetic mutation using a multiobjective genetic algorithm. This renders an optimal KDDC that confidently serves as the full descriptor of the drowsiness. The performance of KDDC is compared with the most prevailing kernels. The obtained DDC yields an overall accuracy of 97.01%, sensitivity of 97.16%, and specificity of 96.86%. The analysis reveals that the accuracy of KDDC is better than those of both Kxcorr,ij and Kconv,ij by more than 11%, and typical kernels including linear, quadratic, third order polynomial, and Gaussian radial basis function by 17-63%, respectively. Comparing with related works using the image-based method and the biometric signal-based method, KDDC improves the accuracy by 48.4-87.2%. Testing results showed that KDDC has a less than 1% deviation from simulated results. Also, the average delay of DDC was bounded by 0.55 ms. This renders the real time implementation. Thus, the developed ECG GA-SVM provides an accurate and instantaneous warning to the drivers before they fall into sleep. As a result this ensures the public transport safety.
700 MHz HOM-IOT: design and first test results

IOTs (inductive output tubes) are relatively novel vacuum electron devices that have established their position as reliable and highly efficient UHF amplifiers, predominantly in the television transmitter service. In this application, they reach output power levels equivalent to about 50 kW CW. Scientific applications, like proton accelerators, require UHF amplifiers with considerably higher output power capability. Super power klystrons are available for this service; however, IOTs offer some advantages over klystrons in terms of efficiency: higher basic efficiency; power regulation possible at full output power (no saturation); inconsequential drop of efficiency at reduced output power. Higher order mode IOTs (IOM-IOTs) possess the added advantage of lower operational voltage and smaller size. Based on these expectations, a development of a 700 MHz HOM-IOT, sponsored by the Los Alamos National Laboratory, has been carried out by CPI. The development attention was paid to the suppression of both unwanted modes and stray radiation from the space between electron gun and body. Focusing the electron beam was another issue that required high modeling effort.