Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic multiobjective optimization problems: test cases, approximations, and applications

After demonstrating adequately the usefulness of evolutionary multiobjective optimization (EMO) algorithms in finding multiple Pareto-optimal solutions for static multiobjective optimization problems, there is now a growing need for solving dynamic multiobjective optimization problems in a similar manner. In this paper, we focus on addressing this issue by developing a number of test problems and by suggesting a baseline algorithm. Since in a dynamic multiobjective optimization problem, the resulting Pareto-optimal set is expected to change with time (or, iteration of the optimization process), a suite of five test problems offering different patterns of such changes and different difficulties in tracking the dynamic Pareto-optimal front by a multiobjective optimization algorithm is presented. Moreover, a simple example of a dynamic multiobjective optimization problem arising from a dynamic control loop is presented. An extension to a previously proposed direction-based search method is proposed for solving such problems and tested on the proposed test problems. The test problems introduced in this paper should encourage researchers interested in multiobjective optimization and dynamic optimization problems to develop more efficient algorithms in the near future.
OPPL-Galaxy: enhancing ontology exploitation in galaxy with OPPL

Biomedical ontologies are key to the success of Semantic Web technologies in Life Sciences; therefore, it is important to provide appropriate tools for their development and further exploitation. The Ontology Pre Processor Language (OPPL) can be used for automating the complex manipulation needed to devise biomedical ontologies with richer axiomatic content, which in turn pave the way towards advanced biological data analyses. We present OPPL-Galaxy, an OPPL wrapper for the Galaxy platform, and a series of examples demonstrating its functionality for enriching ontologies. As Galaxy provides an integrated framework to make use of various bioinformatics tools, the functionality delivered by OPPL to manipulate ontologies can be combined along with the tools and workflows devised in Galaxy. As a result, those workflows can be used to perform more thorough analyses of biological information by exploiting extant biological knowledge codified in (enriched) biomedical ontologies.
Impact of routing protocol on performance of wireless ad hoc vehicular network

Vehicular Ad Hoc Network (VANET) is an emerging new technology integrating ad hoc network, wireless LAN (WLAN) and cellular technology to achieve intelligent inter-vehicle communications and improve road traffic safety and efficiency. VANETs are distinguished from other kinds of ad hoc networks by their hybrid network architectures, node movement characteristics, and new application scenarios. Therefore, VANETs pose many unique networking research challenges, and the design of an efficient routing protocol for VANETs is very crucial. In this paper, we discuss the impact of routing protocol on performance of wireless ad hoc vehicular network along with recent routing protocols. Ad hoc network characteristics are explained in brief. We propose a routing framework based on the enhanced cooperation between the medium access layer and network layers. An algorithm is discussed which takes onto account position, direction and speed of vehicles . Then this concept is applied to reactive routing protocol and introduced preliminary simulation results using ns-2.It is observed that network performance gets improved .
A new metaphor for projection-based visual analysis and data exploration

In many important application domains such as Business and Finance, Process Monitoring, and Security, huge and quickly increasing volumes of complex data are collected. Strong efforts are underway developing automatic and interactive analysis tools for mining useful information from these data repositories. Many data analysis algorithms require an appropriate definition of similarity (or distance) between data instances to allow meaningful clustering, classification, and retrieval, among other analysis tasks. Projection-based data visualization is highly interesting (a) for visual discrimination analysis of a data set within a given similarity definition, and (b) for comparative analysis of similarity characteristics of a given data set represented by different similarity definitions. We introduce an intuitive and effective novel approach for projection-based similarity visualization for interactive discrimination analysis, data exploration, and visual evaluation of metric space effectiveness. The approach is based on the convex hull metaphor for visually aggregating sets of points in projected space, and it can be used with a variety of different projection techniques. The effectiveness of the approach is demonstrated by application on two well-known data sets. Statistical evidence supporting the validity of the hull metaphor is presented. We advocate the hull-based approach over the standard symbol-based approach to projection visualization, as it allows a more effective perception of similarity relationships and class distribution characteristics.
Is a Picture Worth Ten Thousand Words in a Review Dataset?

While textual reviews have become prominent in many recommendation-based systems, automated frameworks to provide relevant visual cues against text reviews where pictures are not available is a new form of task confronted by data mining and machine learning researchers. Suggestions of pictures that are relevant to the content of a review could significantly benefit the users by increasing the effectiveness of a review. We propose a deep learning-based framework to automatically: (1) tag the images available in a review dataset, (2) generate a caption for each image that does not have one, and (3) enhance each review by recommending relevant images that might not be uploaded by the corresponding reviewer. We evaluate the proposed framework using the Yelp Challenge Dataset. While a subset of the images in this particular dataset are correctly captioned, the majority of the pictures do not have any associated text. Moreover, there is no mapping between reviews and images. Each image has a corresponding business-tag where the picture was taken, though. The overall data setting and unavailability of crucial pieces required for a mapping make the problem of recommending images for reviews a major challenge. Qualitative and quantitative evaluations indicate that our proposed framework provides high quality enhancements through automatic captioning, tagging, and recommendation for mapping reviews and images.
CBIR: from low-level features to high-level semantics

The performance of a content-based image retrieval (CBIR) system is inherently constrained by the features adopted to represent the images in the database. Use of low-level features can not give satisfactory retrieval results in many cases; especially when the high-level concepts in the user's mind is not easily expressible in terms of the low-level features. Therefore whenever possible, textual annotations shall be added or extracted and/or processed to improve the retrieval performance. In this paper a hybrid image retrieval system is presented to provide the user with the flexibility of using both the high-level semantic concept/keywords as well as low-level feature content in the retrieval process. The emphasis is put on a statistical algorithm for semantic grouping in the concept space through relevance feedback in the image space. Under this framework, the system can also incrementally learn the user's search habit/preference in terms of semantic relations among concepts; and uses this information to improve the performance of subsequent retrieval tasks. This algorithm can eliminate the need for a stand-alone thesaurus, which may be too large in size and contain too much redundant information to be of practical use. Simulated experiments are designed to test the effectiveness of the algorithm. An intelligent dialogue system, to which this algorithm can be a part of the knowledge acquisition module, is also described as a front end for the CBIR system.
Aplikasi Model Tam (Technology Acceptance Model)pada Perilaku Pengguna Instagram

The purpose of this study was to describe the perception of its influence on attitudes and adoption of Instagram. Respondents in this study were 105 Instagram users from various groups, with the proviso purposive sampling has been used for at least 6 months instagram in buying and selling online. Methods of data collection instruments used were a questionnaire. And data analysis using Structural Equation Model (SEM) with AMOS and SPSS software program. The research model using the base Technology Acceptance Model (TAM) to the original variables, namely perceived ease of use, perceived usefulness, attitude toward using, and actual usage. The results showed that the variables of perceived ease of use and perceived usefulness has a positive and significant impact on the attitude toward using. Perceived ease of use and perceived usefulness also had a positive and significant impact on actual usage. Attitude toward using has a positive and significant impact on actual usage. In this study also found that the actual usage form of direct influence perceived ease of use and perceived usefulness without attitude toward using. For further research suggested that increasing the interest / enthusiasm of people in the use Instagram with more frequent socialization of the audience that uses Instagram is easy and rewarding for the work that is in the market and buy products online.
Situation Recognition Using 3D Positional Information of Ball from Monocular Soccer Image Sequence

In this paper, we propose a system that tracks a ball stably and accurately and detects the events of the game by using the 3D positional information for automatic soccer video production. We use 3D particle filter with the state vector of nine dimensions in the ball tracking. Since the ball tracking by particle filter is a local search, it is difficult to continue tracking when it fails. Thus, we solve this problem by switching the local search to 3D global search, and by interpolating the lost coordinates. As a result, the tracking accuracy was improved by about 19.8%, and the events like the goal or the goal kick was detected with high accuracy.
Is automatic speech recognition ready for non-native speech? A data collection effort and initial experiments in modelling conversational Hispanic English

We describe the protocol used for collecting a corpus of conversational English speech from non-native speakers at several levels of proficiency, and report the results of preliminary automatic speech recognition (ASR) experiments on this corpus using HTK-based ASR systems. The speech corpus contains both read and conversational speech recorded simultaneously on wide-band and telephone channels, and has detailed time aligned transcriptions. The immediate goal of the ASR experiments is to assess the difficulty of the ASR problem in language learning exercises and thus to gauge how current ASR technology may be used in conversational computer assisted language learning (CALL) systems. The long-term goal of this research, of which the data collection and experiments are a first step, is to incorporate ASR into computer-based conversational language instruction systems.
The Problem with Quantitative Studies of International Conflict

Despite immense data collections, prestigious journals, and sophisticated analyses, empirical ndings in the literature on international con ict are frequently unsatisfying. Statistical results appear to change from article to article and speci cation to speci cation. Very few relationships hold up to replication with even minor respeci cation. Accurate forecasts are nonexistent. We provide a simple conjecture about what accounts for this problem, and o er a statistical framework that better matches the substantive issues and types of data in this eld. Our model, a version of a \neural network" model, forecasts substantially better than any previous e ort, and appears to uncover some structural features of international con ict.
Linear-time String Indexing and Analysis in Small Space

The field of succinct data structures has flourished over the past 16 years. Starting from the compressed suffix array by Grossi and Vitter (STOC 2000) and the FM-index by Ferragina and Manzini (FOCS 2000), a number of generalizations and applications of string indexes based on the Burrows-Wheeler transform (BWT) have been developed, all taking an amount of space that is close to the input size in bits. In many large-scale applications, the construction of the index and its usage need to be considered as one unit of computation. For example, one can compare two genomes by building a common index for their concatenation and by detecting common substructures by querying the index. Efficient string indexing and analysis in small space lies also at the core of a number of primitives in the data-intensive field of high-throughput DNA sequencing. We report the following advances in string indexing and analysis: We show that the BWT of a string T ∈ {1,…,σ}n can be built in deterministic O(n) time using just O(n log σ) bits of space, where σ ≤ n. Deterministic linear time is achieved by exploiting a new partial rank data structure that supports queries in constant time and that might have independent interest. Within the same time and space budget, we can build an index based on the BWT that allows one to enumerate all the internal nodes of the suffix tree of T. Many fundamental string analysis problems, such as maximal repeats, maximal unique matches, and string kernels, can be mapped to such enumeration and can thus be solved in deterministic O(n) time and in O(n log σ) bits of space from the input string by tailoring the enumeration algorithm to some problem-specific computations. We also show how to build many of the existing indexes based on the BWT, such as the compressed suffix array, the compressed suffix tree, and the bidirectional BWT index, in randomized O(n) time and in O(n log σ) bits of space. The previously fastest construction algorithms for BWT, compressed suffix array and compressed suffix tree, which used O(n log σ) bits of space, took O(n log log σ) time for the first two structures and O(n log εn) time for the third, where ε is any positive constant smaller than one. Alternatively, the BWT could be previously built in linear time if one was willing to spend O(n log σ log log σ n) bits of space. Contrary to the state-of-the-art, our bidirectional BWT index supports every operation in constant time per element in its output.
An integrated navigation system for a long range AUV

Presents the basic concepts of an integrated navigation system devised for a long range AUV which has to reach distant mission points while the currents can be only roughly predicted and details on the sea bed are locally known. The system includes several basic functions that are under the control of an execution monitor in order to perform re-planning when abnormal conditions are encountered.
Queue management for TFRC-based traffic in 3G networks

We study the performance of TFRC-based traffic running across a 3G network. The goal is to evaluate the impact of three different queue management schemes on the end-to-end performance provided by TFRC. We show that drop-on-full queuing is absolutely inappropriate for TFRC traffic running over 3G links since it leads to burst packet drops and oscillatory behaviour in TFRC. Although, this is mostly avoided with RED (Random Early Discard) queuing, we find large initial probing phases of TFRC when using RED. With a simpler active queue management scheme we propose, PDPC, this probing phase is significantly reduced.
The effect of isolated disparity on depth perception in real and virtual environments

In this paper, we investigated depth perception in real and virtual environments when binocular disparity is the sole distance cue. The observers were asked to estimate the relative depth of spheres verbally in virtual and actual environments. Constant apparent sized stimuli were used to measure the just-noticeable difference in depth perception, thus avoiding providing a size gradient cue. Results of the experiments revealed individual differences in virtual reality in contrast to reality. Specifically a subgroup of observers had difficulty perceiving the depth of virtual spheres in virtual reality, which may indicate that they rely on apparent size for judging depth. Furthermore, the just-noticeable differences were more variable in the virtual environment than with real objects. Our results reveal individual differences when the disparity-driven convergence cue is the only distance cue provided in virtual reality.
Earth Observation Data for Habitat Monitoring ( EODHaM ) system

To support decisions relating to the use and conservation of protected areas and surrounds, the EU-funded BIOdiversity multi-SOurce monitoring System: from Space TO Species (BIO SOS) project has developed the Earth Observation Data for HAbitat Monitoring (EODHaM) system for consistent mapping and monitoring of biodiversity. The EODHaM approach has adopted the Food and Agriculture Organization Land Cover Classification System (LCCS) taxonomy and translates mapped classes to General Habitat Categories (GHCs) from which Annex I habitats (EU Habitats Directive) can be defined. The EODHaM system uses a combination of pixel and object-based procedures. The 1st and 2nd stages use earth observation (EO) data alone with expert knowledge to generate classes according to the LCCS taxonomy (Levels 1 to 3 and beyond). The 3rd stage translates the final LCCS classes into GHCs from which Annex I habitat type maps are derived. An additional module quantifies changes in the LCCS classes and their components, indices derived from earth observation, object sizes and dimensions and the translated habitat maps (i.e., GHCs or Annex I). Examples are provided of the application of EODHaM system elements to protected sites and their surrounds in Italy, Wales (UK), the Netherlands, Greece, Portugal and India. ublis © 2014 The Authors. P
The automated generation of Web documents that are tailored to the individual reader

Many studies in communication have shown that presenting information in a manner that is tailored to the characteristics of a particular audience can have a significant effect on an individual. Incorporating this kind of tailoring facility in a system for the management and presentation of Web documents would be a very important enhancement of the Web’s current state of development. Our long-term research goal has been to investigate and develop theories of text composition, in particular, computational models of rhetorical structure and lexical semantics, which are particular problems for incorporating style in natural language systems. Recently, we have begun to apply this research to developing Web-based natural language generation systems that can tailor documents to the individual user on demand.
Porcine and bovine surgical products: Jewish, Muslim, and Hindu perspectives.

OBJECTIVE To determine the acceptability of porcine and bovine surgical implants among persons of Jewish, Muslim, and Hindu faiths whose beliefs prohibit them from consuming porcine and bovine products. DATA SOURCES An evaluation of current literature concerning religious beliefs among persons of Jewish, Muslim, and Hindu faiths was undertaken to determine if animal-derived surgical implants are permitted for use in these religions. STUDY SELECTION Because of the limited published literature about this topic, the opinions of religious leaders in Australia were sought. DATA EXTRACTION Religious and cultural beliefs can conflict with and limit treatment options, especially in surgery. Approximately 81 porcine and bovine surgical implants are regularly used in Australia. DATA SYNTHESIS It is deemed acceptable for members of the Jewish faith to undergo surgery using porcine products. In dire situations and only after all other options have been exhausted, followers of the Muslim faith are permitted to use porcine surgical products. Hindu religious leaders did not accept the use of bovine surgical implants. CONCLUSIONS Australia comprises a multicultural society; therefore, it is necessary to consider religious beliefs of all patients. As part of a surgeon's duty of care, the informed consent process should include a discussion about animal-derived surgical implants to avoid religious distress and possible litigation. A greater understanding of religious views would enhance the medical care of persons of Jewish, Muslim, and Hindu faiths.
An Improved Bootstrap Test of Stochastic Dominance

We propose a new method of testing stochastic dominance that improves on existing tests based on the standard bootstrap or subsampling. The method admits prospects involving infinite as well as finite dimensional unknown parameters, so that the variables are allowed to be residuals from nonparametric and semiparametric models. The proposed bootstrap tests have asymptotic sizes that are less than or equal to the nominal level uniformly over probabilities in the null hypothesis under regularity conditions. This paper also characterizes the set of probabilities that the asymptotic size is exactly equal to the nominal level uniformly. As our simulation results show, these characteristics of our tests lead to an improved power property in general. The improvement stems from the design of the bootstrap test whose limiting behavior mimics the discontinuity of the original test's limiting distribution.
Software Development Worldwide: The State of the Practice

A survey of 104 projects in India, Japan, Europe, and the US is yielding quantitative data about these countries' software development practices and performance, adoption of competing development models, and the practices' impact on performance. The findings are particularly relevant to firms that are considering the potential advantages or a greater use of outsourcing.