Miracle-dz/UniFAR-FTUs huggingface dataset:
Extending Hub Labels for Reverse k-Nearest Neighbor Queries on Large-Scale networks

Quite recently, the algorithmic community has focused on solving multiple shortest-path query problems beyond simple vertex-to-vertex queries, especially in the context of road networks. Unfortunately, this research cannot be generalized for large-scale graphs, e.g., social or collaboration networks, or to efficiently answer Reverse k-Nearest Neighbor (RkNN) queries, which are of practical relevance to a wide range of applications. To remedy this, we propose ReHub, a novel mainmemory algorithm that extends the Hub Labeling technique to efficiently answer RkNN queries on large-scale networks. Our experimentation will show that ReHub is the best overall solution for this type of queries, requiring only minimal preprocessing and providing very fast query times.
Cross-language transfer of semantic annotation via targeted crowdsourcing

The development of a natural language speech application requires the process of semantic annotation. Moreover multilingual porting of speech applications increases the cost and complexity of the annotation task. In this paper we address the problem of transferring the semantic annotation of the source language corpus to a low-resource target language via crowdsourcing. The current crowdsourcing approach faces several problems. First, the available crowdsourcing platforms have skewed distribution of language speakers. Second, speech applications require domain-speciﬁc knowledge. Third, the lack of reference target language annotation, makes crowdsourcing worker con-trol very difﬁcult. In this paper we address these issues on the task of cross-language transfer of domain-speciﬁc semantic annotation from an Italian spoken language corpus to Greek, via targeted crowdsourcing. The issue of domain knowledge transfer is addressed by priming the workers with the source language concepts. The lack of reference annotation is coped with a consensus-based annotation algorithm. The quality of annotation transfer is assessed using source language references and inter-annotator agreement. We demonstrate that the proposed computational methodology is viable and achieves acceptable annotation quality.
Modeling neuron–glia interactions with the Brian 2 simulator

Despite compelling evidence that glial cells could crucially regulate neural network activity, the vast majority of available neural simulators ignores the possible contribution of glia to neuronal physiology. Here, we show how to model glial physiology and neuron-glia interactions in the Brian 2 simulator. Brian 2 offers facilities to explicitly describe any model in mathematical terms with limited and simple simulator-specific syntax, automatically generating high-performance code from the user-provided descriptions. The flexibility of this approach allows us to model not only networks of neurons, but also individual glial cells, electrical coupling of glial cells, and the interaction between glial cells and synapses. We therefore conclude that Brian 2 provides an ideal platform to efficiently simulate glial physiology, and specifically, the influence of astrocytes on neural activity.
System Description of BJTU-NLP MT for NTCIR-10 PatentMT.

例如|v ,|wd 用|p 具有|v 广谱抗|n 微生物|n 活性|b 的|ude1 聚腈基丙烯酸酯|n 膜覆盖 |n 皮肤|n 表面|n 的|ude1 不可|v 缝合|v 性|ng小|a 伤口|n 将|d 会|v 减弱|v 伤口|n 感染|v 的|ude1 可能|n 。 |wj on|IN the|DT other|JJ hand|NN ,|, a|DT cable|NN 324|CD is|VBZ connected|VBN to|TO the|DT movable|JJ plate|NN 321|CD . |. 一方|接続詞、|特殊可動|名詞プレート|名詞 321|名詞に|助詞は|助詞ケーブル|名 詞 324|名詞が|助詞接続|名詞さ|動詞れて|接尾辞いる|接尾辞。 |特殊 Preprocessing: • English sentence: tokenizer.perl, lowercase.perl, Stanford POS Tagger. • Chinese Sentence: ICTCLAS2011. • Japanese sentence: Mecab. • Before building the translation model, long sentences with more than 90 words are removed. Training: • The GIZA++ is applied to align words. • Parameter of phrase alignment heuristic is “grow-diag-final”. • Parameter of reordering model is “msd-bidirectional-fe”. • The SRILM toolkit is used to build trigram models with Kneser-Ney smoothing. Tuning: • MERT Decode: • Moses Post-Processing: • Japanese output: Remove the spaces. • English outputs: detokenizer.perl, recaser in Moses toolkit. Chinese Unknown Word Prediction Using SVM According to the characteristics of the patent documents, we used a SVM based method that predicted the unknown words for the result of word segmentation and tagging by ICTCLAS2011. First of all, we manually marked unknown words boundary to construct training corpus. Then, we used SVM tool to train the model. And finally, we predicted the unknown words of input sentence. Example Training Data: +1 优选/v/O 的/ude1/O 亚/b/B-S 乙烯/n/B-I 基芳/n/B-I 族/ng/B-E 单体/n/O 包括/v/O -1 优选/v/O 的/ude1/B-S 亚/b/B-I 乙烯/n/B-I 基芳/n/B-I 族/ng/B-E 单体/n/O 包括/v/O -1 优选/v/O 的/ude1/O 亚/b/B-S 乙烯/n/B-I 基芳/n/B-I 族/ng/B-I 单体/n/B-E 包括/v/O Test Data: 0 自/p/O C1-C10/x/O 亚/b/B-S 烷基/n/B-E ,/wd/O C6-C12/x/O 0 自/p/O C1-C10/x/B-S 亚/b/B-I 烷基/n/B-E ,/wd/O C6-C12/x/O 0 自/p/O C1-C10/x/O 亚/b/B-S 烷基/n/B-I ,/wd/B-E C6-C12/x/O Results: 0.86 自/p/O C1-C10/x/O 亚/b/B-S 烷基/n/B-E ,/wd/O C6-C12/x/O -0.75 自/p/O C1-C10/x/B-S 亚/b/B-I 烷基/n/B-E ,/wd/O C6-C12/x/O -0.59 自/p/O C1-C10/x/O 亚/b/B-S 烷基/n/B-I ,/wd/B-E C6-C12/x/O
A Memory-Based Approach to Anti-Spam Filtering

This paper presents an extensive empirical evaluation of memory-based learning in the context of anti-spam filtering, a novel cost-sensitive application of text categorization. Unsolicited commercial e-mail, also known as “spam”, floods the mailboxes of users, causing frustration, wasting bandwidth and money, and exposing minors to unsuitable content. Using a recently introduced publicly available corpus, a thorough investigation of the effectiveness of a memory-based anti-spam filter is performed, including different attribute and distance weighting schemes, and studies on the effect of the neighborhood size, the size of the attribute set, and the size of the training corpus. Three different cost scenarios are identified, and suitable cost-sensitive evaluation functions are employed. We conclude that memory-based anti-spam filtering is practically feasible, especially when combined with additional safety nets. Compared to a previously tested Naive Bays filter, the memory-based filter performs on average better, particularly when the misclassification cost for non-spam messages is high.
Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals

Interpretability of deep neural networks is a recently emerging area of machine learning research targeting a better understanding of how models perform feature selection and derive their classification decisions. This paper explores the interpretability of neural networks in the audio domain by using the previously proposed technique of layer-wise relevance propagation (LRP). We present a novel audio dataset of English spoken digits which we use for classification tasks on spoken digits and speaker's gender. We use LRP to identify relevant features for two neural network architectures that process either waveform or spectrogram representations of the data. Based on the relevance scores obtained from LRP, hypotheses about the neural networks' feature selection are derived and subsequently tested through systematic manipulations of the input data. The results confirm that the networks are highly reliant on features marked as relevant by LRP.
MFCompress: a compression tool for FASTA and multi-FASTA data

Motivation: The data deluge phenomenon is becoming a serious problem in most genomic centers. To alleviate it, general purpose tools, such as gzip, are used to compress the data. However, although pervasive and easy to use, these tools fall short when the intention is to reduce as much as possible the data, for example, for medium- and long-term storage. A number of algorithms have been proposed for the compression of genomics data, but unfortunately only a few of them have been made available as usable and reliable compression tools. Results: In this article, we describe one such tool, MFCompress, specially designed for the compression of FASTA and multi-FASTA files. In comparison to gzip and applied to multi-FASTA files, MFCompress can provide additional average compression gains of almost 50%, i.e. it potentially doubles the available storage, although at the cost of some more computation time. On highly redundant datasets, and in comparison with gzip, 8-fold size reductions have been obtained. Availability: Both source code and binaries for several operating systems are freely available for non-commercial use at http://bioinformatics.ua.pt/software/mfcompress/. Contact: ap@ua.pt Supplementary information: Supplementary data are available at Bioinformatics online.
Faults detection and diagnoses of permanent magnet synchronous motor based on neuro-fuzzy network

Faults in electrical machine are very important in order to improve the machine expensive maintenance, efficiency, life time, and reliability at real time, therefore this study deals with Simulink model response for healthy and neuro-fuzzy network (ANFIS), this intelligent technique consist of two parts, the first part include electrical and demagnetization faults while another part deals with mechanical faults. Simulation results obtain record activation, high performance, and efficiency of this network.
The Online Journal of Distance Learning Administration Reaches the 10-Year Mark: A Look Back at Its Collaboration Network Using Social Network Theory

Happy 10th birthday to the Online Journal of Distance Learning Administration! Who would have ever imagined that this online journal situated on the Web site of a smaller university in Carrollton, Georgia, U.S.A. , (population about 20,000 and listed in 2002 as one of the 50 best small southern towns in the United States), and without the financial backing of a large national association, would emerge 10 years later as one of the field’s premier journals? Not even the founding editor, Dr. Melanie Clay, quite envisioned the number of contributors and readers who would seek out her journal from all over the world to entrust their academic knowledge and to read the latest “practical distance education management ideas as well as more theoretical works” (Clay, 2008). Dr. Clay briefly recounted how the journal originated and later surpassed her own expectations.
Fast Inference for Interactive Models of Text

Fast Inference for Interactive Models of Text Jeffrey A. Lund Department of Computer Science, BYU Master of Science Probabilistic models of text are a useful tool for enabling the analysis of large collections of digital text. For example, Latent Dirichlet Allocation can quickly produce topical summaries of large collections of text documents. Many important uses cases of such models include human interaction during the inference process for these models of text. For example, the Interactive Topic Model extends Latent Dirichlet Allocation to incorporate human expertise during inference in order to produce topics which are better suited to individual user needs. However, interactive use cases of probabilistic models of text introduce new constraints on inference the inference procedure must not only be accurate, but also fast enough to facilitate human interaction. If the inference is too slow, then the human interaction will be harmed, and the interactive aspect of the probabilistic model will be less useful. Unfortunately, the most popular inference algorithms in use today either require strong approximations which can degrade the quality of some models, or require time-consuming sampling. We explore the use of Iterated Conditional Modes, an algorithm which is able to obtain locally optimal maximum a posteriori estimates, as an alternative to popular inference algorithms such as Gibbs sampling or mean field variational inference. Iterated Conditional Modes algorithm is not only fast enough to facilitate human interaction, but can produce better maximum a posteriori estimates than sampling. We demonstrate the superior performance of Iterated Conditional Modes on a wide variety of models. First we use a DP Mixture of Multinomials model applied to the problem of web search result cluster, and show that not only can we outperform previous methods in clustering quality, but we can achieve interactive runtimes when performing inference with Iterated Conditional Modes. We then apply Iterated Conditional Modes to the Interactive Topic Model. Not only is Iterated Conditional Modes much faster than the previous published Gibbs sampler, but we are better able to incorporate human feedback during inference, as measured by accuracy on a classification task using the resultant topic model. Finally, we utilize Iterated Conditional Modes with MomResp, a model used to aggregate multiple noisy crowdsourced data. Compared with Gibbs sampling, Iterated Conditional Modes is better able to recover ground truth labels from simulated noisy annotations, and runs orders of magnitude faster.
Guided interactive image segmentation using machine learning and color-based image set clustering

Abstract Motivation Over the last decades, image processing and analysis have become one of the key technologies in systems biology and medicine. The quantification of anatomical structures and dynamic processes in living systems is essential for understanding the complex underlying mechanisms and allows, i.e. the construction of spatio-temporal models that illuminate the interplay between architecture and function. Recently, deep learning significantly improved the performance of traditional image analysis in cases where imaging techniques provide large amounts of data. However, if only a few images are available or qualified annotations are expensive to produce, the applicability of deep learning is still limited. Results We present a novel approach that combines machine learning-based interactive image segmentation using supervoxels with a clustering method for the automated identification of similarly colored images in large image sets which enables a guided reuse of interactively trained classifiers. Our approach solves the problem of deteriorated segmentation and quantification accuracy when reusing trained classifiers which is due to significant color variability prevalent and often unavoidable in biological and medical images. This increase in efficiency improves the suitability of interactive segmentation for larger image sets, enabling efficient quantification or the rapid generation of training data for deep learning with minimal effort. The presented methods are applicable for almost any image type and represent a useful tool for image analysis tasks in general. Availability and implementation The presented methods are implemented in our image processing software TiQuant which is freely available at tiquant.hoehme. com. Supplementary information Supplementary data are available at Bioinformatics online.
Predicting students drop out : a case study

In the emerging field of educational data mining, a strong bias towards data-rich digital learning environments is the current state of affairs [2, table 2]. However, in many educational institutes a lot of regular course data will probably be more readily available. This data may also be used to support and advise students in various ways, for the better of the student as well as the institute. In this study, the situation at the Electrical Engineering department of Eindhoven University of Technology is considered. Based on experience, the department claims to be able to distinguish the potentially successful students from amongst the first year influx before the end of the first semester. To do this in an early stage is important for the student as well as for the university, but the selection is only loosely based on assumed student similarities over the years. There is no thorough analysis. Data mining techniques may corroborate and improve the accuracy of this prediction. Furthermore, data mining techniques may point out indicators of academic success that are missed until now. This study explores if and how data mining techniques can be applied in this practical situation. The techniques are applied on data that is readily available in the institution’s database. In contrast to some other studies [3], no additional data is collected to make the results of the applied techniques easily applicable. 1This is the extended technical report, accompanying the paper under the same title [1]. The report describes the data mining process in detail, including preprocessing data-related issues, and is intended to be accessible for a general public. The paper concentrates more on the results obtained and is written for domain experts. This report is the final result of an internship by the author at the Department of Computer Science, Eindhoven University of Technology, carried out as part of the Master’s program Electrical Engineering. The intership was supervised by Mykola Pechenizkiy (Assistant Professor at the Department of Computer Science) and Jan Vleeshouwers (Student Counsellor at the Department of Electrical Engineering).
Growing Rocks: The Effects of Calcium Carbonate Deposition on Phosphorus Availability in Streams

85 Introduction . 87 Methods . 90 Field Site Description . 90 Environmental Characterization . 91 Experiment 1. Resource Limitation of Microbialites . 92 Experiment 2. Nutrient Limitation of Non-microbialite Associated Photoautotrophic Microbial Communities . 98 Experiment 3. Influence of Lithification on Nutrient Availability . 99 Statistical Analyses . 101
Artificial Intelligence with Stereo Vision Algorithms and its Methods

Stereo vision is the process which is similar to the human being vision i.e. stereopsis. As we all know that stereo vision is field of computer vision and this is related to artificial intelligence field the stereo vision can be used in many application in field of artificial intelligence like in video cameras for security purpose and in robot this requires much things to do like image segmentation and the motion detection and to calculate distance of any object so stereo vision can be made in use for this methods thus this paper contains the algorithm for detecting motion and also smoothing filters to make output of two images smooth and to measure distance of any object i.e. blob detection the stereo ranging method is given i.e. novel algorithm. We will use novel algorithm for efficient motion detection and tracking of blob. It involves dividing video into image frames, converting frames into gray scale images, subtraction and thresholding of image frames, blob detection and combining blobs. As we all know that speed is also the important factor while making the motion detection the paralleling algorithm is explained for it. This paper also gives relationship of human vision with stereo vision and as we use to cameras in stereo vision there are problems like correspondence problem, camera calibration are explained and the feature and correlation based methods to solve this problems is given and the triangulation method is also made in use to compute depth. Keywordsvision; parallel algorithm; correspondence; camera calibration; triangulation method; blob detection
Sales Prediction System Using Machine Learning

: The objective is to perform techniques like Clustering Model and measures for sales predictions. On the basis of a performance evaluation, a best suited predictive model is suggested for the company sales trend forecast. The results are summarized in terms of accuracy of machine learning techniques taken for prediction . The main objective of the system is to analyze the future sales of a particular company and to predict whether a particular sales will increase or decrease by using different machine learning algorithms like – Random Forest just one commodity is meaningless to sellers. A general prediction for all commodities is needed.Data mining is a discipline that can be used to gather information by data classifier, Decision Tree, and Linear Regression.
Finding Protein and Nucleotide Similarities with FASTA

The FASTA package provides a comprehensive set of similarity searching programs, similar to those provided by the BLAST package, and some additional programs that are not provided by BLAST for searching with short peptides and oligonucleotides. The FASTA programs work with a wide variety of database formats, including mySQL sequence databases. FASTA provides very accurate statistical significance estimates, and is more sensitive than BLASTN when comparing DNA sequences. These protocols describe how to use the FASTA programs to characterize protein and DNA sequences, using protein:protein, protein:DNA, and DNA:DNA comparisons.
Expansão canavieira no Centro-Oeste: limites e potencialidades

This paper analyzes the expansion of sugarcane in the Central-West Region of Brazil, for characterization of its main limitations and potentialities. Results demonstrate that the expansion of sugarcane in this region has been happening mainly due to the search for better food safety (sugar production) and sustainable energy (ethanol production); saturation or decline of some traditional producing areas; natural conditions and agro-ecological zoning favorable to the development of sugarcane; and prospects for logistics improvements. The main limitations are the current context of instability in the ethanol market, the inefficient transport infrastructure (based on highway transportation), the incipient tradition of the sugarcane agroindustry in the Central-West Region of Brazil, and a possible aggravation in income concentration.
Spatial transcriptomics identifies novel markers of vulnerable and resistant midbrain dopamine neurons

Defining transcriptional profiles of substantia nigra pars compacta (SNc) and ventral tegmental area (VTA) dopamine neurons is critical to understand their differential vulnerability in Parkinson Disease. However, reported transcriptional profiles for these neuron populations rely on studies in rodents and display extensive variability across small sample sizes. Here, we use laser capture microdissection coupled with RNA sequencing to analyze individually isolated SNc and VTA dopamine neurons in a large human dataset. By applying an iterative random pooling strategy as input to DESeq2, we describe a minimal cohort size that identifies 33 stably differentially expressed dopamine population-specific markers. Among these, we identify three transcripts, ZCCHC12 , CDH13 and SERPINE2, that together faithfully classify SNc or VTA dopamine neurons in both human and mouse. These novel markers will be vital for future studies aiming to distinguish the identity of stem cell-derived dopamine neurons or to induce dopamine neuron resilience.