Miracle-dz/UniFAR-FTUs huggingface dataset:
PromoterExplorer : an effective promoter identification method based on the AdaBoost algorithm

Motivation: Promoter prediction is important for the analysis of gene regulations. Although a number of promoter prediction algorithms have been reported in literature, significant improvement in prediction accuracy remains a challenge. In this paper, an effective promoter identification algorithm, which is called PromoterExplorer, is proposed. In our approach, we analyze the different roles of various features, that is, local distribution of pentamers, positional CpG island features and digitized DNA sequence, and then combine them to build a highdimensional input vector. A cascade AdaBoost-based learning procedure is adopted to select the most ‘informative’ or ‘discriminating’ features to build a sequence of weak classifiers, which are combined to form a strong classifier so as to achieve a better performance. The cascade structure used for identification can also reduce the false
REDUCER – An Architectural Design Pattern for Reducing Large and Noisy Data Sets

To relieve the burden of reasoning on a point to point basis, in many domains there is a need to reduce large and noisy data sets into trends for qualitative reasoning. In this paper we propose and describe a new architectural design pattern called reducing large and noisy data sets that can be tailored for particular situations. REDUCER consists of 2 consecutive process which takes the original data and removes outliers, inconsistencies or noise; and Compression which takes the filtered data and trends in the data. In this seminal article we also show how REDUCER has successfully been applied to 3 Keywords—Design Pattern, filtering, compression
A Flexible Framework for Integrating Annotations from Different Tools and Tagsets

Tools for linguistic annotation employ different data models and accompanying visualization metaphors, depending on the particular type of annotation envisaged. When a corpus is to be annotated on multiple layers, and the annotations are to be related to one another, the output formats of the annotation tools need to be unified. We describe an implemented framework for this step: reading the output of a variety of tools into a single database, where the data can be visualized, queried and evaluated across the layers. Then, besides the integration of resources at format level, we also seek compatibility between annotation tagsets: We describe how ontologies can be used to mediate between competing tagsets intended to cover the same class of linguistic phenomena.
Existence of Traveling Waves in a Neural Model

In 1992 G. B. Ermentrout and J. B. McLeod published a landmark study of traveling wave fronts for a differential-integral equation modeling a neural network. Since then a number of authors have extended the model by adding an additional equation for a "recovery variable", thus allowing the possibility of traveling pulse type solutions. In a recent paper G. Faye gave perhaps the first rigorous proof of the existence (and stability) of a traveling pulse solution for such a model. The excitatory weight function J used in this work allowed the system to be reduced to a set of four coupled ODEs, and a specific firing rate function S, with parameters, was considered. The method of geometric singular perturbation was employed, together with blow-ups. In this paper, while keeping the same J, we extend Faye's work by obtaining both the fast and slow waves and by removing Faye's hypothesis that the wave passes "under the knee". We also consider a more general set of functions S, and we use only standard ode methods.
International Journal of Advanced Research in Computer Science and Software Engineering

:-Data is an important part of every industry, organization, economy and individual. The term Big data is used to define huge amount of data. The data is so huge that the traditional database management systems are unable to store, process and analyze. It introduces many challenges, opportunities and research topics which require new tools and techniques for processing and analyzing of Big Data. This paper presents review on the term Big Data Mining and presents various challenges on analyzing Big Data.
Geosimulations of urban growth, dasymetric mapping and population dynamics in northwest Florida, 1974 - 2025

Problem: this is the first time that the SLEUTH model has been applied to Escambia, Santa Rosa and Okaloosa counties. Here, dasymetric mapping and censuses from the simulations are performed, generating three scientific questions. First, how will the Cellular Automaton (CA) model depict the different urban and other landscape changes? Second, what results will these simulations produce if dasymetric mapping and censuses from the sky are applied under past, present and future conditions? Third, using this CA model, will it be possible to replicate alternative scenarios such as smart growth and urban sprawl? The answers to these questions are the main contributions of this research to the fields of geographic techniques and demographics in space and time. Methods: imagery classification was applied to Landsat MSS and TM according to Anderson Level I; accuracy classification was performed comparing the classified images against sample points taken from air photos and Digital Orthoimagery Quarter Quadrangles (DOQQs); SLEUTH was implemented in a high-performance computer; dasymetric mapping using Geolytics databases developed in ArcGIS and ERDAS Imagine; and finally, census from the simulations were generated using linear regressions and the allometric growth model. Results: the results were graphical and statistical outputs of all methods previously mentioned plus analyses of these maps and statistics about land cover and demographics. The SLEUTH simulations produced yearly graphical and statistical results from 1975 until 2025. Conclusions: urban expansion principally affects agriculture, rangelands, grasslands and forests; barrenlands, especially the beaches, also suffer from development, showing unprecedented rates of urban growth. Smart growth provided an alternative strategy in which urban growth occurred in a more compact way, increasing its population density and decreasing the open space in the metro areas. Finally, the other scenario called urban sprawl simulated urban growth at a higherrate-than normal, encouraging spontaneous and edge growth and lower population densities. Index Words: Landsat Imagery, Cellular Automaton, SLEUTH, High Performance Computer, Dasymetric Mapping, Linear Regression, Allometric Growth Model, Smart Growth, Urban Sprawl. GEOSIMULATIONS OF URBAN GROWTH, DASYMETRIC MAPPING AND POPULATION DYNAMICS IN NORTHWEST FLORIDA 1974 – 2025
Automatic Short Answer Marking

A connector as disclosed for connecting fluid passages through a flexible sheet material. The connector is appropriate for use in connecting the coolant lines of a personal cooling garment through a waterproof immersion suit or a chemical proof outer garment. The connector includes inside and outside connector blocks with matching faces that sandwich the sheet material between them. Fluid passages through the two blocks meet at respective ports in the matching faces and are joined by coupling sleeves extending through holes in the sheet material. O-rings around the ports seal the holes through the sheet material. A cap screw or the like secures the two blocks to one another. The connector is equipped with valve male fittings and male lock components of a standard connector system that is now used for personal cooling garments. This allows the use of the connector in conjunction with an existing and satisfactory "zero leak" connect and disconnect system.
Software Acquisition Capability Maturity Model (SA-CMM) Version 1.02

Abstract : Government and industry have the need to improve the maturity of their internal software acquisition processes. In order for organizations to make improvements, they must know the ultimate goal and what is required to achieve that goal. Additionally, progress toward achieving the goal must be measurable. A capability maturity model provides the framework needed to facilitate the desired improvement. The Software Acquisition Capability Maturity Model (SA-CMM) has been developed to provide such a framework. This new version incorporates change requests that have been received, as well as the results of lessons learned from conducting appraisals and from the use of Version 1.01.
Analisis Sistem Dinamik Ketersediaan Beras di Merauke Dalam Rangka Menuju Lumbung Padi Bagi Kawasan Timur Indonesia

Dalam rangka menyongsong dicanangkannya kabupaten Merauke Papua, sebagai lumbung padi bagi kawasan timur Indonesia, maka diperlukan suatu kajian kebijakan yang dapat memberikan arah bagi proses perencanaan di masa datang. Tujuan dari penelitian ini adalah menganalisis sistem persediaan beras di kabupaten Merauke di masa datang dan memberikan alternatif kebijakan untuk mendukung ketersediaan beras tersebut. Metode yang digunakan adalah dengan pendekatan sistem dinamik dimana sistem persediaan beras ditentukan oleh dua sub sistem besar yaitu sub-sistem penyediaan dan sub-sistem permintaan. Masing-masing sub sistem diidentifikasi menjadi komponen atau faktor yang spesifik dan berinteraksi secara dinamis berdasarkan waktu dan kondisi. Hasil penelitian menunjukkan bahwa model ketersediaan beras di Merauke yang telah dirancang-bangun telah dapat bekerja dengan baik dengan tingkat ketepatan yang baik pula. Model yang telah dibuat dan kemungkinan pengembangannya dapat dipakai sebagai alat untuk melandasi pengambilan keputusan maupun penentuan kebijakan stok beras Merauke sebagai lumbung padi di masa mendatang secara lebih komprehensif. Dari beberapa skenario yang telah dicoba diatas, dapat disimpulkan bahwa dalam 10 tahun mendatang dengan menerapkan kebijakan peningkatan pendayagunaan lahan dan peningkatan produksi melalui peningkatan IP (Indeks Pertanaman) dengan irigasi teknis, dengan penerapan mekanisasi pertanian, penggunaan bibit unggul, penggunaan pupuk berimbang, penanganan pascapanen dan penggunaan saprodi lainnya, memberikan pengaruh yang nyata terhadap kemampuan Merauke dalam memasok beras wilayah Indonesia bagian timur. Melalui skenario ini kemampuan Merauke dalam memasok beras adalah 83.69% jika persentase masyarakat yang mengkonsumsi beras 30% dan jika terjadi pergeseran konsumsi menjadi 40%, maka kemampuan pasokannya menjadi 62.77%.
Equivalence Checking of Reversible Circuits

Determining the equivalence of reversible circuits designed to meet a common specification is considered. The circuits' primary inputs and outputs must be in pure logic states but the circuits may include elementary quantum gates in addition to reversible logic gates. The specification can include don't-cares arising from constant inputs, garbage outputs, and total or partial don't-cares in the underlying target function. The paper explores well-known techniques from irreversible equivalence checking and how they can be applied in the domain of reversible circuits. Two approaches are considered. The first employs decision diagram techniques and the second uses Boolean satisfiability. Experimental results show that for both methods, circuits with up to 27,000 gates, as well as adders with more than 100 inputs and outputs, are handled in under three minutes with reasonable memory requirements.
LIMITS TO THE CONSISTENCY OF EKF-BASED SLAM 1

: This paper analyzes the consistency of the classical extended Kalman ﬁlter (EKF) solution to the simultaneous localization and map building (SLAM) problem. Our results show that in large environments the map quickly becomes inconsistent due to linearization errors. We propose a new EKF-based SLAM algorithm, robocentric mapping , that greatly reduces linearization errors, improving map consistency. We also present results showing that large-scale mapping methods based on building local maps with a local uncertainty representation (Tard´os et al. , 2002) have better consistency than methods that work with global uncertainties.
Erosion Behavior of Sand-silt Mixtures: the Role of Silt Content

ABSTRACT Yao, P.; Hu, Z. ; Su, M. ; Chen, Y.P., and Ou S.Y. , 2018. Erosion behavior of sand-silt mixtures: the role of silt content. In: Shim, J.S. ; Chun, I. , and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 1171–1175. Coconut Creek (Florida), ISSN 0749-0208. It is well known that critical clay content is key factor controlling overall cohesion of the mixtures and increasing erosion threshold. Besides cohesive clay fraction, existing studies show that non-cohesive silt can enhance bed stability of a sandy bed as well. Many studies were conducted with an artificial bimodal mixture with distinct peaks of sand and silt fraction to understand the role of silt particles. However, it is unclear to what extent a bimodal mixture represents natural sand-silt mixtures. In this study, we conducted series of annular flume experiments on sediment samples collected from typical silt dominated tidal flat to study role of the silt on erosion behavior of sand-silt mixtures. Two types of sand-silt mixtures were used: a silt-sized mixture with median grain size of 52 μm, and a very fine sand-sized mixture with median grain size of 82 μm. The results show mixtures with 30% silt content behaves as sand, and a Shields curve of fine sands (van Rijn, 2007) is applicable for prediction of the critical bed shear stress. However, bed stability is enhanced when silt content increases to 60%, since the critical bed shear stress is twice of bed materials with 30% silt. Since the fractional grain size ratio (Dm,sand/Dm,silt) of each mixture is the same, silt content is considered as the important factor on increasing the threshold of motion. More studies should be conducted to understand the critical silt content and the underlying mechanisms.
Robot behavior adaptation for human-robot interaction based on policy gradient reinforcement learning

In this paper, we propose an adaptation mechanism for robot behaviors to make robot-human interactions run more smoothly. We propose such a mechanism based on reinforcement learning, which reads minute body signals from a human partner, and uses this information to adjust interaction distances, gaze meeting, and motion speed and timing in human-robot interaction. We show that this enables autonomous adaptation to individual preferences by an experiment with twelve subjects.
ReplicaDivergenceControl ProtocolBasedon PredictedProfile PDPTA 2002 AhmedJebaliandMesaacMakpangou INRIA

The DivergenceControl Protocol Basedon Predicted Profileallowscooperativeprocessesdeployedona weaklyconnectedenvironmentto conciliatetheoffering of low responsetimeto clientswith theguaranteeof a boundedconsistency , despitecharacteristicsof the underlyingcommunicationinfrastructure. Thispaperpresentshowto decomposea global divergenceboundinto a set of local criteria that are locally checkedat any invocation.We propose a protocol relying on thesecriteria to incrementallyconstructhistoriesthat preserveat any time, theglobal divergenceboundbetweenany replica and the “ideal state” of the replicatedobject. An implementationof the protocol is discussed.This paperreportsa numberof experiments conductedin orderto evaluatetherationaleandtheperformanceof the proposedprotocol. Theseexperimentsshow that theprotocolbenefitsa lot fromtheprovisionof a correctprofileandoutperformsintuitivecooperationprotocols in large-scalesettings.
Dataset Similarity to assess Semi-supervised Learning under Distribution Mismatch between the Labelled and Unlabelled Datasets

—Semi-supervised deep learning (SSDL) is a popular strategy to leverage unlabelled data for training, when labelled data is not readily available. In real-world scenarios, different unlabelled data sources are usually available, with varying degrees of distribution mismatch, with respect to the labelled datasets. However, a quantitative and systematic approach to this selection problem would be preferable. It would enable the selection of unlabelled data for a given SSDL problem in a systematic way. In this work, we ﬁrst test the SSDL MixMatch algorithm under various distribution mismatch conﬁgurations, to later propose a quantitative unlabelled dataset selection heuristic based on dataset dissimilarity measures. These are designed to systematically assess how distribution mismatch between the labelled and unlabelled datasets affects MixMatch performance. We refer to our proposed method as deep dataset dissimilarity measures (DeDiMs), designed to compare labelled and unlabelled datasets. They use the feature space of a generic Wide-ResNet and can be applied prior to learning, are quick to evaluate and model agnostic. The yielded strong correlation between MixMatch accuracy and the proposed DeDiMs in our tests, suggest a good ﬁt for quantitatively ranking different unlabelled datasets ante hoc , according to expected MixMatch accuracy gain. referred to as deep dataset dissimilarity measures (DeDiMs). In our extensive test-bed, the evaluated DeDiMs yielded linear correlation coefﬁcients of up to 96% to the MixMatch accuracy.
Evaluating Audience Engagement of an Immersive Performance Evaluating Audience Engagement of an Immersive Performance on a Virtual Stage on a Virtual Stage

: In this paper, we describe a methodology for determining audience engagement designed specifically for stage performances in a virtual space. We use a combination of galvanic skin response data (GSR), self-reported emotional feedback us ing the positive and negative affect schedule (PANAS), and a think aloud methodology to assess user reaction to the virtual reality experience. We describe a case study that uses the process to explore the role of immersive viewing of a performance by comparing users’ engagement while watching a virtual dance performances on a monitor vs. using an immersive head mounted display (HMD). Results from the study indicate significant differences between the viewing experiences. The process can serve as a potential tool in the development of a VR storytelling experience.
Approximation Algorithms for Min Sum k -Clustering and Balanced k -Median

In this thesis, we consider two closely related clustering problems, Min Sum k Clustering (MS k C) and Balanced k -Median (B k M). In Min Sum k -clustering , one is given a graph and a parameter k , and has to partition the vertices in the graph into k clusters to minimize the sum of pairwise distances between vertices in the same cluster. In the Balanced k -Median problem, one is given the same set of input, and has to partition the vertices into k clusters, C 1 ; : : : ; C k , where each cluster C i is centered at a node c i , while minimizing the total assignment costs for the points in the metric; here the cost of assigning a point j to a cluster C i is equal to j C i j times the ( j; c j ) distance. The most important contribution of this thesis, described in Chapter 4, is an O (log n )-approximation for both these problems, where n is the number of nodes in the input graph. This improves over the previous best O ( (cid:15) (cid:0) 1 log 1+ (cid:15) n )-approximation by Bartal, Charikar and Raz [1] by a factor of O ( (cid:15) (cid:0) 1 log (cid:15) n ). As in the work of Bartal et al., our approximation for general metrics uses probabilistic embeddings into Hierarchically Separated Trees (HSTs). Speci(cid:12)cally, we prove that both MS k C and B k M have constant approximation algorithms in HSTs.
Risk minimization and language modeling in text retrieval dissertation abstract

With the dramatic increase in online information in recent years, text retrieval is becoming increasingly important. Although many different text retrieval approaches have been proposed and studied in the past decades, it is still a significant scientific challenge to develop principled retrieval approaches that also perform well empirically; so far, the theoretically well-motivated models have rarely led to good performance directly. It is also a great challenge in retrieval to develop models that may go beyond the traditional notion of topical relevance and capture more user factors, such as topical redundancy and sub-topic diversity. This thesis presents a new text retrieval framework based on Bayesian decision theory. The framework unifies several existing retrieval models, including the language modeling approach proposed recently, within one general probabilistic framework, and facilitates the development of new principled approaches to text retrieval with the potential of going beyond the traditional notion of topical relevance. In this framework, queries and documents are modeled using statistical language models (i.e., probabilistic models of text), user preferences are modeled through loss functions, and retrieval is cast as a risk minimization problem. While traditional retrieval models rely heavily on ad hoc parameter tuning to achieve satisfactory retrieval performance, the use of language models in the risk minimization framework makes it possible to exploit statistical estimation methods to improve retrieval performance and set retrieval parameters automatically. As a special case of the framework, we present a two-stage language model that, according to extensive evaluation, achieves excellent retrieval performance without any ad hoc parameter tuning. Using language models in retrieval also makes it possible to improve retrieval performance through using improved language models and estimation methods. As another special case of the risk minimization framework, we derive a Kullback-Leibler divergence retrieval model that can exploit feedback documents to improve the estimation of query models. Feedback has so far been dealt with heuristically in the language modeling approach to retrieval. The KL-divergence model provides a more natural way of performing feedback by treating it as query model updating. We propose two specific query model updating
Applying Reinforcement Learning for Game AI in a Tank-Battle Game

Reinforcement learning is an unsupervised machine learning method in the area of Artificial Intelligence. It presents well performance in simulation of the thinking ability of human. However, it needs a trial-and-error process to achieve the goal. In the research field of game AI, it is a good approach to allow the non-player-characters (NPCs) of digital games to become more humanity. In this paper, we try to build a Tank-battle computer game and use the methodology of reinforcement learning for the NPCs (tanks). The goal of this paper is to make this game become more interesting from the enhanced interactions with these intelligent NPCs.
BugHint: A Visual Debugger Based on Graph Mining

– Why doesn’t my code work? Instructors for introductory programming courses frequently are asked that question. Often students understand the problem they are trying to solve well enough to specify a variety of input and output scenarios. However, they lack the ability to identify where the bug is occurring in their code. Mastering the use of a full-feature debugger can be difﬁcult at this stage in their computer science education. But simply providing a hint as to where the problem lies may be sufﬁcient to guide the student to add print statements or do a hand-trace focusing on a certain section of the code. Herein we present a software tool which, given a C++ program, some sample inputs, and respective expected outputs, uses graph mining to identify which lines in the program are most likely the source of a bug. The tool includes a visual display of the control ﬂow graph for each test case, allowing the user to step through the statements executed. Experimental results from a group of CS 1 students show that practice with this method: (1) makes students faster at ﬁnding bugs, (2) improves the way students test a program, and (3) improves program comments by students.