Miracle-dz/UniFAR-FTUs huggingface dataset:
The wavelet trie: maintaining an indexed sequence of strings in compressed space

An indexed sequence of strings is a data structure for storing a string sequence that supports random access, searching, range counting and analytics operations, both for exact matches and prefix search. String sequences lie at the core of column-oriented databases, log processing, and other storage and query tasks. In these applications each string can appear several times and the order of the strings in the sequence is relevant. The prefix structure of the strings is relevant as well: common prefixes are sought in strings to extract interesting features from the sequence. Moreover, space-efficiency is highly desirable as it translates directly into higher performance, since more data can fit in fast memory. We introduce and study the problem of compressed indexed sequence of strings, representing indexed sequences of strings in nearly-optimal compressed space, both in the static and dynamic settings, while preserving provably good performance for the supported operations. We present a new data structure for this problem, the Wavelet Trie, which combines the classical Patricia Trie with the Wavelet Tree, a succinct data structure for storing a compressed sequence. The resulting Wavelet Trie smoothly adapts to a sequence of strings that changes over time. It improves on the state-of-the-art compressed data structures by supporting a dynamic alphabet (i.e. the set of distinct strings) and prefix queries, both crucial requirements in the aforementioned applications, and on traditional indexes by reducing space occupancy to close to the entropy of the sequence.
pvSOAR: detecting similar surface patterns of pocket and void surfaces of amino acid residues on proteins

Detecting similar protein surfaces provides an important route for discovering unrecognized or novel functional relationship between proteins. The web server pvSOAR (pocket and void Surfaces Of Amino acid Residues) provides an online resource to identify similar protein surface regions. pvSOAR can take a structure either uploaded by a user or obtained from the Protein Data Bank, and identifies similar surface patterns based on geometrically defined pockets and voids. It provides several search modes to compare protein surfaces by similarity in local sequence, local shape and local orientation. Statistically significant search results are reported for visualization and interactive exploration. pvSOAR can be used to predict biological functions of proteins with known three-dimensional structures but unknown biological roles. It can also be used to study functional relationship between proteins and for exploration of the evolutionary origins of structural elements important for protein function. We present an example using pvSOAR to explore the biological roles of a protein whose structure was solved by the structural genomics project. The pvSOAR web server is available at http://pvsoar.bioengr. uic.edu/.
Privacy Preserving Data Mining-An Overview

In recent years, ample amount of available personal data has made privacy preserving data mining issue an important one. An overview of new and quickly rising research field of privacy preserving data mining and a few exist problems provided in this paper. Also made a classification for the privacy preserving data mining and analyze some works in this field. But sometimes these patterns can reveal sensitive information about the data holder or individuals whose information are the subject of the patterns. The notion of privacy-preserving data mining is to identify and disallow such revelations as evident in the kinds of patterns learned using traditional data mining techniques Data distortion method for achieving privacy protection association rule mining and privacy protection data release were focused on discussion. Detailed evaluation criteria of privacy preserving algorithm was illustrated, which include algorithm performance, data utility, privacy security degree and data mining difficulty. Finally, the development of privacy preserving data mining for further directions is prospected. Keywords-PPDM, randomization , k-anonymity, secure multipart computation
Measuring Group Cohesion in Document Collections

Exploring document collections remains a focus of research. This task can be tackled using various techniques, typically ranking documents according to a relevance index or grouping documents based on various clustering algorithms. The task complexity produces results of varying quality that inevitably carry noise. Users must be careful when interpreting document relevance or groupings. We address this problem by computing cohesion measures for a group of documents confirming/infirming whether it can be trusted to form a semantically cohesive unit. The index is inspired from past work in social network analysis (SNA) and illustrates how document exploration can benefit from SNA techniques.
Attractiveness of Facial Averageness and Symmetry in Non-Western Cultures: In Search of Biologically Based Standards of Beauty

Averageness and symmetry are attractive in Western faces and are good candidates for biologically based standards of beauty. A hallmark of such standards is that they are shared across cultures. We examined whether facial averageness and symmetry are attractive in non-Western cultures. Increasing the averageness of individual faces, by warping those faces towards an averaged composite of the same race and sex, increased the attractiveness of both Chinese (experiment 1) and Japanese (experiment 2) faces, for Chinese and Japanese participants, respectively. Decreasing averageness by moving the faces away from an average shape decreased attractiveness. We also manipulated the symmetry of Japanese faces by blending each original face with its mirror image to create perfectly symmetric versions. Japanese raters preferred the perfectly symmetric versions to the original faces (experiment 2). These findings show that preferences for facial averageness and symmetry are not restricted to Western cultures, consistent with the view that they are biologically based. Interestingly, it made little difference whether averageness was manipulated by using own-race or other-race averaged composites and there was no preference for own-race averaged composites over other-race or mixed-race composites (experiment 1). We discuss the implications of these results for understanding what makes average faces attractive. We also discuss some limitations of our studies, and consider other lines of converging evidence that may help determine whether preferences for average and symmetric faces are biologically based.
Melting, Solidification, and Crystallization of a Thermoplastic Polyurethane as a Function of Hard Segment Content

Thermoplastic polyurethanes (TPU) with varying hard segment contents (HSC) are monitored during melting and solidifying (20 K/min , Tmax = 220 ° C) by small-angle and wide-angle X-ray scattering (WAXS and SAXS). Hard segments: MDI/BD. Soft segments: PTHF1000. The neat materials are injection-molded, having small amorphous hard domains (chord length d⎯⎯h ∼ 35% show sharp Bragg peaks and larger hard domains ( d⎯⎯h > 7 nm ). When heated, small domains melt, but crystallization in the remaining large domains is not detected. Upon cooling, large agglomerates segregate first, which crystallize immediately. Segregation starts for HSC = 42% at 160 °C and for HSC = 75% at 210 °C. When HSC ≤ 30%, the morphologies before and after are similar, but afterward, many hard blocks are dissolved in the soft phase at the expense of the hard domain fraction. In heating and cooling the melts, multiple homogenization and segregation processes are observed, which are explained by the agglomeration of hard blocks of different lengths in the colloidal fluid.
Learning Dynamic Hybrid Markov Random Field for Image Labeling

Using shape information has gained increasing concerns in the task of image labeling. In this paper, we present a dynamic hybrid Markov random field (DHMRF), which explicitly captures middle-level object shape and low-level visual appearance (e.g., texture and color) for image labeling. Each node in DHMRF is described by either a deformable template or an appearance model as visual prototype. On the other hand, the edges encode two types of intersections: co-occurrence and spatial layered context, with respect to the labels and prototypes of connected nodes. To learn the DHMRF model, an iterative algorithm is designed to automatically select the most informative features and estimate model parameters. The algorithm achieves high computational efficiency since a branch-and-bound schema is introduced to estimate model parameters. Compared with previous methods, which usually employ implicit shape cues, our DHMRF model seamlessly integrates color, texture, and shape cues to inference labeling output, and thus produces more accurate and reliable results. Extensive experiments validate its superiority over other state-of-the-art methods in terms of recognition accuracy and implementation efficiency on: the MSRC 21-class dataset, and the lotus hill institute 15-class dataset.
Heterosubtypic Antiviral Activity of Hemagglutinin-Specific Antibodies Induced by Intranasal Immunization with Inactivated Influenza Viruses in Mice

Influenza A virus subtypes are classified on the basis of the antigenicity of their envelope glycoproteins, hemagglutinin (HA; H1–H17) and neuraminidase. Since HA-specific neutralizing antibodies are predominantly specific for a single HA subtype, the contribution of antibodies to the heterosubtypic immunity is not fully understood. In this study, mice were immunized intranasally or subcutaneously with viruses having the H1, H3, H5, H7, H9, or H13 HA subtype, and cross-reactivities of induced IgG and IgA antibodies to recombinant HAs of the H1–H16 subtypes were analyzed. We found that both subcutaneous and intranasal immunizations induced antibody responses to multiple HAs of different subtypes, whereas IgA was not detected remarkably in mice immunized subcutaneously. Using serum, nasal wash, and trachea-lung wash samples of H9 virus-immunized mice, neutralizing activities of cross-reactive antibodies were then evaluated by plaque-reduction assays. As expected, no heterosubtypic neutralizing activity was detected by a standard neutralization test in which viruses were mixed with antibodies prior to inoculation into cultured cells. Interestingly, however, a remarkable reduction of plaque formation and extracellular release of the H12 virus, which was bound by the H9-induced cross-reactive antibodies, was observed when infected cells were subsequently cultured with the samples containing HA-specific cross-reactive IgA. This heterosubtypic plaque reduction was interfered when the samples were pretreated with anti-mouse IgA polyclonal serum. These results suggest that the majority of HA-specific cross-reactive IgG and IgA antibodies produced by immunization do not block cellular entry of viruses, but cross-reactive IgA may have the potential to inhibit viral egress from infected cells and thus to play a role in heterosubtypic immunity against influenza A viruses.
Fuzzy clustering ensemble based on mutual information

Clustering ensemble is a new topic in machine learning. It can find a combined clustering with better quality from multiple partitions. But how to find the combined clustering is a difficult problem. In this paper, we extend the object function proposed by Strehl & Ghosh which is based on mutual information and we present a new algorithm similar to information bottleneck to solve the object function. This algorithm can combine "soft" partitions and need not establish label correspondence between different partitions. We conducted experiments on four real-world data sets to compare our algorithm with other five ensemble algorithms, including CSPA, HGPA, MCLA, QMI. The results indicate that our algorithm provides solutions of improved quality.
Irregular repetition slotted ALOHA with multiuser detection

Irregular repetition slotted ALOHA is a random access scheme where users transmit multiple copies of a packet to the receiver to provide time-domain diversity; then, the receiver attempts to iteratively decode packets and cancel their interference contribution in other time slots, increasing the chance of resolving collisions and decoding more packets. This scheme was shown to support larger system loads compared to conventional slotted ALOHA. In the original scheme, collision resolution between l colliding packets is possible only when l - 1 of those packets are decoded in prior iterations, which is probable because of the other copies of each packet transmitted in other slots. However, although asymptotic analysis promises high efficiency of this scheme, the analysis relies on the large number of slots participating in the iterative collision resolution, which requires large receiver complexity and introduces delay. In this paper, we study this scheme for receivers capable of decoding multiple colliding packets jointly, which increases the chance of decoding more packets under large system loads. Asymptotic analysis for this generalized model is provided and it is shown by simulations that a multiuser detector supports larger loads for low number of slots jointly processed.
Adaptive Logging: Optimizing Logging and Recovery Costs in Distributed In-memory Databases

By maintaining the data in main memory, in-memory databases dramatically reduce the I/O cost of transaction processing. However, for recovery purposes, in-memory systems still need to flush the log to disk, which incurs a substantial number of I/Os. Recently, command logging has been proposed to replace the traditional data log (e.g., ARIES logging) in in-memory databases. Instead of recording how the tuples are updated, command logging only tracks the transactions that are being executed, thereby effectively reducing the size of the log and improving the performance. However, when a failure occurs, all the transactions in the log after the last checkpoint must be redone sequentially and this significantly increases the cost of recovery. In this paper, we first extend the command logging technique to a distributed system, where all the nodes can perform their recovery in parallel. We show that in a distributed system, the only bottleneck of recovery caused by command logging is the synchronization process that attempts to resolve the data dependency among the transactions. We then propose an adaptive logging approach by combining data logging and command logging. The percentage of data logging versus command logging becomes a tuning knob between the performance of transaction processing and recovery to meet different OLTP requirements, and a model is proposed to guide such tuning. Our experimental study compares the performance of our proposed adaptive logging, ARIES-style data logging and command logging on top of H-Store. The results show that adaptive logging can achieve a 10x boost for recovery and a transaction throughput that is comparable to that of command logging.
An adaptive hold-off algorithm in WiMAX mesh networks with coordinated distributed scheduling

IEEE 802.16 mesh mode is positioned to be used as a wireless backbone network. In coordinated distributed scheduling (C-DSCH), when nodes transmit control messages to compete for channel access, they need to wait for some period of time called hold-off time to compete again. Network performance is sharply affected by the hold-off mechanism in the bandwidth reservation procedure. In this paper, a new approach to adaptively adjust the hold-off time is developed considering nodes' condition in every transmission opportunity. The proposed algorithm takes both the number of nodes in contention and the buffer sizes of the nodes into account. Delay, throughput and control messages overhead are analyzed through simulation.
Event-Based Time Label Propagation for Automatic Dating of News Articles

Since many applications such as timeline summaries and temporal IR involving temporal analysis rely on document timestamps, the task of automatic dating of documents has been increasingly important. Instead of using feature-based methods as conventional models, our method attempts to date documents in a year level by exploiting relative temporal relations between documents and events, which are very effective for dating documents. Based on this intuition, we proposed an eventbased time label propagation model called confidence boosting in which time label information can be propagated between documents and events on a bipartite graph. The experiments show that our event-based propagation model can predict document timestamps in high accuracy and the model combined with a MaxEnt classifier outperforms the state-ofthe-art method for this task especially when the size of the training set is small.
Large-scale Topological Environmental Model Based Particle Filters for Mobile Robot Indoor Localization

This paper describes an improved particle filters for mobile robot indoor localization based on a large-scale topological environmental model. The model is composed of multiple pivoted nodes and the connecting relations between them. We only choose the crossings as the nodes in the model so that the indoor environment can be presented by simplest structure. For localization, the traditional particle filters uses large number of samples when applied in large-scale environment, which greatly increase the computational cost and the memory space. To solve this problem, we improve the particle filters by selective distributing. We estimate the possible nodes which the robot is adjacent to in the large-scale topological environment model by laser scan-matching, then distribute the samples around the candidate nodes so that the number will decrease remarkably comparing with the traditional particle filters. Experiments result implemented in real mobile robot platform and further localization result analysis show the validity and practicability of this method.
A Visual Approach for Spatio-Temporal Data Mining

In this paper, we propose a system for mining very large spatio-temporal datasets. The system comprises new techniques to efficiently support the data-mining process, address the spatial and temporal dimensions of the dataset, and visualize and interpret results. In particular, we have developed an advanced visualization tool for flexible and intuitive interaction with the dataset, including functionality for displaying association rules and variable distributions
Compiling Plan Operators from Domains Expressed in Qualitative Process Theory

The study of Qualitative Physics has concentrated on expressing qualitatively how the physical world behaves. Qualitative Physics systems accept partial descriptions of the world and output the possible changes that can occur. These systems currently assume that the world is left untouched by human or robot agents, limiting them to certain types of problem solving. For instance, a stateof-the-art qualitative physics system can diagnose faulty electrical circuits but can not construct plans to rewire circuits to change their behavior. This paper describes an approach to planning in physical domains and a working implementation which integrates Forbus' Qualitative Process Engine (QPE) with a temporal interval-based planner. The approach involves compiling QPE expressions describing a physical domain into a set of operators and rules of the planner. The planner can then construct plans involving processes, existence of individuals, and changes in quantities. We describe how the compilation is performed, the types derivable plans, and current limitations in our approach.
A quaternion phase-only correlation algorithm for color images

Due to mathematical limitations, conventional phase-only correlation (POC) technique can only be applied to grayscale images or at most complex images. A full color image must be first converted to a grayscale one before performing the POC, during which the chrominance information has been wasted. In this paper, a novel extension of the POC technique to the quaternion field (QPOC) is proposed, which can naturally make full use of the luminance as well as the chrominance information in color images. The effectiveness of the proposed QPOC for color images is demonstrated through its applications in color template matching and subpixel color image registration.
R-Learning and Gaussian Process Regression Algorithm for Cloud Job Access Control

Reinforcement learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Recently reinforcement learning has been given abroad attention, but when it is applied to solve problems with large-scale discrete or contiguous state space environments, the results are likely to be unsatisfactory and even fail to find optimal policies. In order to solve this problem, we establish a new generative model about the value function and use Gaussian Process Regression to approximate the state-action pairs which were never or seldom visited. We testify to the performance of the proposed algorithm by an access-control queuing job in a cloud computing environment. The computational results demonstrate the scheme can balance the exploration and exploitation in the learning process and accelerate the convergence to a certain extent.
Imitation Learning

Imitation learning techniques aim to mimic human behavior in a given task. An agent (a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and actions. The idea of teaching by imitation has been around for many years; however, the field is gaining attention recently due to advances in computing and sensing as well as rising demand for intelligent applications. The paradigm of learning by imitation is gaining popularity because it facilitates teaching complex tasks with minimal expert knowledge of the tasks. Generic imitation learning methods could potentially reduce the problem of teaching a task to that of providing demonstrations, without the need for explicit programming or designing reward functions specific to the task. Modern sensors are able to collect and transmit high volumes of data rapidly, and processors with high computational power allow fast processing that maps the sensory data to actions in a timely manner. This opens the door for many potential AI applications that require real-time perception and reaction such as humanoid robots, self-driving vehicles, human computer interaction, and computer games, to name a few. However, specialized algorithms are needed to effectively and robustly learn models as learning by imitation poses its own set of challenges. In this article, we survey imitation learning methods and present design options in different steps of the learning process. We introduce a background and motivation for the field as well as highlight challenges specific to the imitation problem. Methods for designing and evaluating imitation learning tasks are categorized and reviewed. Special attention is given to learning methods in robotics and games as these domains are the most popular in the literature and provide a wide array of problems and methodologies. We extensively discuss combining imitation learning approaches using different sources and methods, as well as incorporating other motion learning methods to enhance imitation. We also discuss the potential impact on industry, present major applications, and highlight current and future research directions.
Large Scale P2P Distribution of Open-Source Software

Open-source software communities currently face an increasing complexity in managing and distributing software content among their developers and contributors. This is mainly due to the continuously growing size of the software, of the community, the high frequency of updates, and the heterogeneity of the participants. We propose a large scale P2P distribution system that tackles two main issues in software content management: efficient content dissemination and advanced information system capabilities.