Miracle-dz/UniFAR-FTUs huggingface dataset:
FFPred: an integrated feature-based function prediction server for vertebrate proteomes

One of the challenges of the post-genomic era is to provide accurate function annotations for large volumes of data resulting from genome sequencing projects. Most function prediction servers utilize methods that transfer existing database annotations between orthologous sequences. In contrast, there are few methods that are independent of homology and can annotate distant and orphan protein sequences. The FFPred server adopts a machine-learning approach to perform function prediction in protein feature space using feature characteristics predicted from amino acid sequence. The features are scanned against a library of support vector machines representing over 300 Gene Ontology (GO) classes and probabilistic confidence scores returned for each annotation term. The GO term library has been modelled on human protein annotations; however, benchmark performance testing showed robust performance across higher eukaryotes. FFPred offers important advantages over traditional function prediction servers in its ability to annotate distant homologues and orphan protein sequences, and achieves greater coverage and classification accuracy than other feature-based prediction servers. A user may upload an amino acid and receive annotation predictions via email. Feature information is provided as easy to interpret graphics displayed on the sequence of interest, allowing for back-interpretation of the associations between features and function classes.
An Open Source Tesseract based Tool for Extracting Text from Images with Application in Braille Translation for the Visually Impaired

Many valuable paper documents are usually scanned and kept as images for backup. Extracting text from the images is quite helpful and thus a need for some tool for this extraction is always there. One of the important applications of this tool is its use in Braille Translation. Braille has been the primary writing and reading system used by the visually impaired since the 19 th century. This application that extracts text from images and then converts it to Braille will prove to be quite useful for converting old valuable documents or books into Braille format. In this paper the complete methodology used for the extraction of texts from scanned images and for the translation of texts to Braille is presented. The scanned images are initially pre-processed and converted to grayscale and then passed through an adaptive threshold function for conversion to binary image. Then it is sent for Recognition using Google’s powerful Tesseract recognition engine which is considered to be the best Open Source OCR Engine currently available. The generated text is then post-processed using a spell checking API JOrtho for removing the errors in the previous step. The final corrected text is then translated to a six dot cell Braille format using a set of rules provided by www.iceb. org. The translation to Braille includes conversion of numbers, alphabets, symbols and compound letters. The translated text can then be saved for printing the document later or for sending it to a Refreshable Braille Display.
Learning Temporal Rules from State Sequences

In this paper we consider the problem of learning rules about temporal relationships between labeled time intervals. We learn these rules from a single series of such labeled intervals, which might be obtained from (multivariate) time series by extracting various features of interest, for instance segments of increasing and decreasing local trends. We seek for the identification of frequent local patterns in this state series . A temporal pattern is defined as a set of states together with their interval relationships described in terms of Allen’s interval logic, for instance “A before B, A overlaps C, C overlaps B” or equivalently “state A ends before state B starts, the gap is covered by state C”. In the spirit of association rule mining we propose an algorithm to discover frequent temporal patterns and to generate temporal rules. As an application we consider the problem of deriving local weather forecasting rules that allow us to conclude from the qualitative behaviour of the air-pressure curve to the windstrength.
ABC Based Multi-Objective Approach for NoC Design Space Exploration

Network-on-Chip (NoC) has recently emerged as an efficient communication solution for the System-on-Chip (SoC) design. Design space exploration and performance evaluation are the most essential task in NoC design. In this paper, an ABC based design space exploration framework for the NoC design is proposed. The objective of the design space exploration is to minimize the total energy consumption and maximum link bandwidth. This research has considered many-many mapping between router and IPs instead of one-one mapping. The results show that the proposed framework saves around 40-50% of energy and around 10% of link bandwidth in compare to other contemporary techniques.
Morphing ensemble Kalman filters

Anewtype of ensemble filter is proposed, which combines an ensemble Kalman filter (EnKF) with the ideas of morphing and registration from image processing. This results in filters suitable for non-linear problems whose solutions exhibit moving coherent features, such as thin interfaces in wildfire modelling. The ensemble members are represented as the composition of one common state with a spatial transformation, called registration mapping, plus a residual. A fully automatic registration method is used that requires only gridded data, so the features in the model state do not need to be identified by the user. The morphing EnKF operates on a transformed state consisting of the registration mapping and the residual. Essentially, the morphing EnKF uses intermediate states obtained by morphing instead of linear combinations of the states.
The effects of indocyanine green and endoillumination on rabbit retina: an electroretinographic and histological study

Aim: To evaluate the functional and morphological retinal toxicity associated with intravitreal injection of indocyanine green (ICG) dye in rabbit eyes during vitrectomy with endoillumination. Methods: 20 eyes of 10 New Zealand pigmented rabbits were used in the study. All eyes underwent pars plana vitrectomy and removal of posterior vitreous cortex under endoillumination. In one eye of each rabbit, intravitreal injection of 0.1 ml of 2.5 mg/ml ICG was applied for 30 seconds followed by 10 minutes of endoillumination. The control eye had endoillumination only without ICG injection. Dark adapted and light adapted electroretinograms (ERGs) were performed before the surgery and 1 week after surgery for serial comparisons. Rabbits were killed 1 week after surgery and eyes were enucleated for histological examination. Results: Serial ERG comparisons showed significant reduction in the light adapted a-wave amplitude (p = 0.037) and significant delays in the dark adapted and light adapted b-wave latencies (p = 0.020 and p = 0.038, respectively) in the ICG treated eyes. Histological examinations demonstrated loss of photoreceptor outer segments with focal absence of photoreceptors in some areas in the ICG injected eyes. Conclusions: Vitrectomy followed by intravitreal injection of 2.5 mg/ml ICG for 30 seconds with endoillumination may result in retinal toxicity causing functional and morphological retinal damages in rabbit eyes. The lowest concentration of ICG should be used if necessary for intraocular use to prevent potential retinal toxicity.
Tele Assistance: A Self-Adaptive Service-Based System Exemplar

Research on adaptive and self-managing systems is hindered by a lack of prototypical applications that researchers could use to evaluate and compare new methods, techniques and tools. To address this limitation, we introduce a reference implementation of a Tele Assistance System (TAS) for research on self-adaptation in the domain of service-based systems. Our TAS exemplar of service-based systems comes with pre-defined scenarios for comparing the effectiveness of different self-adaptation solutions. Other researchers can easily exploit the underlying service platform, reusable components and development method we devised for TAS to speed up the engineering of additional research exemplars for service-based systems.
A Visualization Tool to Improve the Performance of a Classifier Based on Hidden Markov Models

This paper presents a visualization tool to improve the performance of a classifier based on the hidden Markov Model. A specific recognition system for which the visualization tool is designed is an on-line handwritten Japanese character recognition system. The recognition system was built from already estimated parameter values, which leads to some difficulties when trying to adjust the system. To tackle this problem we describe how visual information can be helpful to interpret the results and how it can be used to build a set of viewers for helping the tuning task. These viewers were used to examine the data structure and internal procedures of the recognition engine allowing to detect and correct errors in the first implementation. We conclude the paper comparing the two implemented versions of the classifier by showing the increase we achieved in recognition accuracy.
Establishment of irrigation schedule for rice cropping seasons in the Long Xuyen Quadrangle, Vietnam using Cropwat model

This study is conducted to determine the appropriate irrigation water amount for three main cropping seasons (TMCS) of rice in the Long Xuyen Quadrangle, Vietnam (LXQ). In the work, the Cropwat crop model was used to calculate irrigation water amount for winter-spring (WS), summer-autumn (SA) and autumn-winter (AW) crops based on meteorological data collected in period from 1998 to 2017. Simulation results carried out that WS crop was needed more irrigation water than SA and AW crops. The highest irrigation water requirement (IWR) of WS, SA and AW crops occurred on development stage with approximately values 207.1 mm, 205.8 mm and 102.3 mm respectively. The results showed that the proposed model is successfully applied to define crop water requirement (CWR) and irrigation requirement in the context of climate change leading to irrigation water scarcity.
The Effect of Problem Perturbations on Nonlinear Dynamical Systems and their Reduced-Order Models

Reduced-order models are used extensively in many areas of science and engineering for simulation, design, and control. Reduction techniques for nonlinear dynamical systems produce models that depend strongly on the nominal set of parameters for which the reduction is carried out. In this paper we address the following two questions: “What is the effect of perturbations in the problem parameters on the output functional of a nonlinear dynamical system? ” and “to what extent does the reduced-order model capture this effect? ”
Managing Cloud Computing - A Life Cycle Approach

C loud computing has the promise of significant benefits that include reduced costs, improved service provisioning, and a move to a pay - per - use model. However , there also are many challenges to successfully delivering cloud - based services ; including security, data ownership, interoperability, service maturity and return on investment . These challenges need to be understood and managed before attempting to take advantage of what the clo ud has to offer. In this paper we introduce a nine - step cloud life cycle that can be used for both the migration and the ongoing management of public , cloud - based services. A consortium of organi z ation s using an open - innovation app roach developed the life cycle. This paper describes each step of the life cycle in terms of the key challenges faced, and the recommend ed activities, with resultant outputs , needed to overcome them.
Missing rate for gastric cancer during upper gastrointestinal endoscopy: a systematic review and meta-analysis

Objectives Esophagogastroduodenoscopy (EGD) is considered a very effective method to identify gastric cancer (GC). However, the existence of missed lesions has been frequently discussed. This systematic review and meta-analysis aimed at assessing the magnitude of missing GC diagnosis with EGD and its predictive factors. Methods MEDLINE was searched to identify all studies assessing and reporting the proportion of missed GC diagnosis with EGD. Pooled proportion and negative predictive values were computed using the random-effects model and heterogeneity was assessed using the Cochrane Q-test and I2. Results The studies included (n=22) were grouped by study design. The pooled negative predictive value was 99.7% (95% confidence interval 99.6–99.9%). Missed GCs proportion was 9.4% (95% confidence interval 5.7–13.1%), being 10.0% in studies including patients with negative EGD followed over time, 8.3% in studies including patients with GC, and 23.3% in studies evaluating the proportion of missed synchronous lesions. Mainly, missed cancers were located in the gastric body both in Eastern and in Western studies (39 and 47%, respectively). The majority of missed GCs were adenocarcinomas. Younger age (<55 years), female sex, marked gastric atrophy, gastric adenoma or ulcer, and inadequate number of biopsy fragments were reported as predictive factors for diagnostic failure. Conclusion EGD is a very effective method to rule out GC. However, missing GC with EGD is not uncommon, with one out of 10 cancers being potentially missed. Interestingly, lesions were more often missed in the body and therefore a more rigorous protocol for endoscopy and biopsy should be implemented worldwide.
Conceptualizations of Mental Health Across Europe: Comparing Psychology with Science and Engineering Students

There is a lack of consensus on the conceptualization of mental health, with models emphasizing negative aspects, positive aspects, or both. The models are mainly theory-based and may not fit in with the population’s opinions. The aim of this ongoing study is to investigate the conceptualizations of mental health in Europe from psychology, science, and engineering students’ perspectives in an explorative way through semi-structured interviews. Their conceptualizations of mental health are investigated qualitatively by thematic analysis to compare seven European countries. Results can be used to improve mental health questionnaires and interventions across Europe by enhancing the fit with students’ conceptualizations
A goal-oriented Web browser 4

p0220 Many users are familiar with the interesting but limited functionality of data detector interfaces like Microsoft’s Smart Tags and Google’s AutoLink. In this chapter we significantly expand the breadth and functionality of this type of user interface through the use of large-scale knowledge bases of semantic information. The result is a Web browser that is able to generate personalized semantic hypertext, providing a goal-oriented browsing experience. p0225 We present (1) Creo, a programming-by-example system for the Web that allows users to create a general purpose procedure with a single example; and (2) Miro, a data detector that matches the content of a page to high-level user goals. p0230 An evaluation with 34 subjects found that they were more efficient using our system, and that the subjects would use features like these if they were integrated into their Web browser. s0010 INTRODUCTION p0235 In this chapter we describe a programming-by-example system for the Web named Creo, and a data detector named Miro. Working together, Creo and Miro provide the user with a goal-oriented Web browsing experience. We describe an evaluation of our software based on data from 34 users, and evaluations of our software’s user interface during an iterative design process. p0240 Finally, we conclude with a discussion of how large-scale knowledge bases of semantic information can be leveraged to improve human–computer interaction.
A New Hybrid Distributed Double Guided Genetic Swarm Algorithm for Optimization and Constraint Reasoning: Case of Max-CSPs

In this paper the authors propose a new distributed double guided hybrid algorithm combining the particle swarm optimization PSO with genetic algorithms GA to resolve maximal constraint satisfaction problems Max-CSPs. It consists on a multi-agent approach inspired by a centralized version of hybrid algorithm called Genetical Swarm Optimization GSO. Their approach consists of a set of evolutionary agents dynamically created and cooperating in order to find an optimal solution. Each agent executes its own hybrid algorithm and it is able to compute its own parameters. The authors' approach is compared to the GSO. It demonstrates its superiority. They reached these results thanks to the distribution using multi-agent systems, diversification and intensification mechanisms.
Temporal Summaries: Supporting Temporal Categorical Searching, Aggregation and Comparison

When analyzing thousands of event histories, analysts often want to see the events as an aggregate to detect insights and generate new hypotheses about the data. An analysis tool must emphasize both the prevalence and the temporal ordering of these events. Additionally, the analysis tool must also support flexible comparisons to allow analysts to gather visual evidence. In a previous work, we introduced align, rank, and filter (ARF) to accentuate temporal ordering. In this paper, we present temporal summaries, an interactive visualization technique that highlights the prevalence of event occurrences. Temporal summaries dynamically aggregate events in multiple granularities (year, month, week, day, hour, etc.) for the purpose of spotting trends over time and comparing several groups of records. They provide affordances for analysts to perform temporal range filters. We demonstrate the applicability of this approach in two extensive case studies with analysts who applied temporal summaries to search, filter, and look for patterns in electronic health records and academic records.
Treatment of temporal aliasing effects in the context of next generation satellite gravimetry missions

Temporal aliasing effects have a large impact on the gravity field accuracy of current gravimetry missions and are also expected to dominate the error budget of Next Generation Gravimetry Missions (NGGMs). This paper focuses on aspects concerning their treatment in the context of Low‐Low Satellite‐to‐Satellite Tracking NGGMs. Closed‐loop full‐scale simulations are performed for a two‐pair Bender‐type Satellite Formation Flight (SFF), by taking into account error models of new generation instrument technology. The enhanced spatial sampling and error isotropy enable a further reduction of temporal aliasing errors from the processing perspective. A parameterization technique is adopted where the functional model is augmented by low‐resolution gravity field solutions coestimated at short time intervals, while the remaining higher‐resolution gravity field solution is estimated at a longer time interval. Fine‐tuning the parameterization choices leads to significant reduction of the temporal aliasing effects. The investigations reveal that the parameterization technique in case of a Bender‐type SFF can successfully mitigate aliasing effects caused by undersampling of high‐frequency atmospheric and oceanic signals, since their most significant variations can be captured by daily coestimated solutions. This amounts to a “self‐dealiasing” method that differs significantly from the classical dealiasing approach used nowadays for Gravity Recovery and Climate Experiment processing, enabling NGGMs to retrieve the complete spectrum of Earth's nontidal geophysical processes, including, for the first time, high‐frequency atmospheric and oceanic variations.
Control System Design for the Digitized Grumman F - 14 Benchmark Control Problem

A particular H¿ design approach is coupled with multicriterion optimization to design a pitch axis controller for the F - 14, one of the CSS benchmark control problems. In this two-degree-of-freedom structure, the controller is designed via an H¿ method based on minimizing a weighted quadratic combination of the sensitivity and complementary sensitivity functions of the system. The pre-compensator is used to shape the output responses so as to cause the entire system to achieve the desired performance. The angle-of-attack ¿(t) model following error, constraints on the normalized pitch rate |q(jw)|nom, and the effects of the wind gust and sensor noise on ¿ and q are examined. The robustness of ¿(t) under the aircraft large parameter variations is also studied.
Adaptive Optimization of a Parametric Receiver for Fast Frequency-Hopping

| The reduced fuzzy rank order detector (R-FROD) [3] is a robust diversity combiner for fast frequency fopping multiple access. Its performance is determined by the value of an adaptation parameter which represents the degree of spread exhibited by the demodulated spread spectrum data. EÆciency (time complexity) of this adaptation is critical for a receiver to be practically useful. A study is presented of three powerful methods for optimizing this parameter. The methods presented are: the golden section search (GSS) algorithm, a straightforward random search (RS) algorithm, and the newer simultaneous perturbation stochastic approximation (SPSA) algorithm.
A Hierarchical Name System Based on Hybrid P2P for Data-Centric Networks

Data-centric network plays an important part in the next generation network architecture. And name system is one of basic elements in data-centric network. This paper describes an approach which achieves the name system design criterion. In naming, a combination of three entities is used to achieve security, scalability, and flexibility. In name resolution, a three-layer structure is proposed to achieve precise and semantic resolutions.
A Model Based Framework For Privacy Preserving Clustering Using SOM

Privacy has become an important issue in the progress of data mining techniques. Many laws are being enacted in various countries to protect the privacy of data. This privacy concern has been addressed by developing data mining techniques under a framework called privacy preserving data mining. Presently there are two main approaches popularly used -data perturbation and secure multiparty computation. In this paper we propose a technique for privacy preserving clustering using Principal component Analysis(PCA) based transformation approach. This method is suitable for clustering horizontally partitioned or centralized data sets . The framework was implemented on synthetic datasets and clustering was done using Self organizing Map(SOM). The accuracy of clustering before and after privacy preserving transformation was estimated.
Findings of the 2013 Workshop on Statistical Machine Translation

We present the results of the WMT13 shared tasks, which included a translation task, a task for run-time estimation of machine translation quality, and an unofficial metrics task. This year, 143 machine translation systems were submitted to the ten translation tasks from 23 institutions. An additional 6 anonymized systems were included, and were then evaluated both automatically and manually, in our largest manual evaluation to date. The quality estimation task had four subtasks, with a total of 14 teams, submitting 55 entries.