Miracle-dz/UniFAR-FTUs huggingface dataset:
Conditional Expectation Propagation

Expectation propagation (EP) is a powerful approximate inference algorithm. However, a critical barrier in applying EP is that the moment matching in message updates can be intractable. Handcrafting approximations is usually tricky, and lacks generalizability. Importance sampling is very expensive. While Laplace propagation provides a good solution, it has to run numerical optimizations to find Laplace approximations in every update, which is still quite inefficient. To overcome these practical barriers, we propose conditional expectation propagation (CEP) that performs conditional moment matching given the variables outside each message, and then takes expectation w.r. t the approximate posterior of these variables. The conditional moments are often analytical and much easier to derive. In the most general case, we can use (fully) factorized messages to represent the conditional moments by quadrature formulas. We then compute the expectation of the conditional moments via Taylor approximations when necessary. In this way, our algorithm can always conduct efficient, analytical fixed point iterations. Experiments on several popular models for which standard EP is available or unavailable demonstrate the advantages of CEP in both inference quality and computational efficiency.
The Linguistic Characteristics of KPG Written Mediation Tasks across Proficiency Levels

Concerned with written mediation performance of Greek speakers of English when they relay information from one language to another, this paper aims at systematically analyzing and describing mediation tasks included in the national exams for foreign language proficiency, known as Kratiko Pistopiitiko Glossomathias (henceforth KPG). The questions it addresses are: what differentiates mediation tasks across proficiency levels and what language is likely to be produced by candidates of different levels. It ultimately presents a set of illustrative descriptors relevant to mediation which define the language to be used at each level.
Loading of the Geographic Pictures for Digital Map

This paper introduces a data structure used for splicing of the digital map based on the Google earth satellite images.The static loading and dynamic pre-loading techniques of the satellite images are also introduced.These techniques can be used for scanning the large-scale geographic information and can also be used to move,zoom out and zoom in the digital map.
UniSec: a unified security framework with SmartNIC acceleration in public cloud

In the public cloud, the software security functions that multitenants deploy in their virtual networks have limited performance. SmartNIC overcomes these limitations by implementing these security functions with hardware acceleration. However, the shared SmartNIC resources are not open for external users with security considerations. Since the security requirements of tenants are diverse, it is tedious for network operators to develop these functions from scratch with low-level APIs. This paper presents UniSec, a unified programming framework for fast security functions development while improving performance with SmartNIC acceleration. UniSec provides modular abstraction for a single function and module sharing among multiple security functions. With the well-defined APIs of UniSec, developers only need to focus on the core logic instead of complex underlying operations including resource management, matching algorithms, etc. Experimental results show that the code has been reduced by 65% on average for each security function with UniSec. UniSec also improves processing performance up to 76%, compared with the software-only implementation.
CLMS-Net: dropout prediction in MOOCs with deep learning

Massive Open Online Courses (MOOCs) have played an increasingly crucial role in education, but the high dropout rate problem is great serious. Predicting whether students will dropout has attracted considerable attention. Current methods mainly depend on handcrafted features. The process is laborious and not scalability, and even difficult to guarantee the final prediction effect. In this paper, we propose a deep neural network model, which is a combination of Convolutional Neural Network, Long Short-Term Memory network and Support Vector Machine. Our model has an effective feature extraction strategy, which automatically extract features from the raw data, and takes into account the impact of the sequential relationship of student behavior and class imbalance on dropout and, most importantly, reinforce the performance of dropout prediction. Extensive experiments on a public dataset have shown that the proposed model can achieve better results comparable to feature engineering based methods and other neural network methods.
A Privacy-Preserving Multi-Keyword Ranked Search Over Encrypted Data in Hybrid Clouds

With the rapid development of cloud computing services, more and more individuals and enterprises prefer to outsource their data or computing to clouds. In order to preserve data privacy, the data should be encrypted before outsourcing and it is a challenge to perform searches over encrypted data. In this paper, we propose a privacy-preserving multi-keyword ranked search scheme over encrypted data in hybrid clouds, which is denoted as MRSE-HC. The keyword dictionary of documents is clustered into balanced partitions by a bisecting $k$ -means clustering based keyword partition algorithm. According to the partitions, the keyword partition based bit vectors are adopted for documents and queries which are utilized as the index of searches. The private cloud filters out the candidate documents by the keyword partition based bit vectors, and then the public cloud uses the trapdoor to determine the result in the candidates. On the basis of the MRSE-HC scheme, an enhancement scheme EMRSE-HC is proposed, which adds complete binary pruning tree to further improve search efficiency. The security analysis and performance evaluation show that MRSE-HC and EMRSE-HC are privacy-preserving multi-keyword ranked search schemes for hybrid clouds and outperforms the existing scheme FMRS in terms of search efficiency.
Sentence Ordering Algorithm with Subject Criterion for Automatic Multi-Document Summarization

In multi-document summarization, order of sentences in summarization result must be coherence and it must represent information in correct steps to make it easy to understand by the reader. Problem arises when some subject of sentences are represented using pronouns. The subject pronoun in an incorrect sentence order will confuse the reader as the pronoun can refer to more than one subject. In this paper, we propose a new subject criterion for sentence ordering strategy to complement the existing ordering strategy. Sentences will be clustered based on its subject and will be ordered with respect to subject levels. We test the system using Document Understanding Conference summarization data and compare it with results from existing algorithm without using subject criterion. The accuracy of ordering with all criterions including subject criterion is 83%. The result shows that there is a slight improvement in ordering accuracy when subject criterion is included.
Gradient Dependent Reconstruction from Scalar Data

Computed Tomography (CT) is widely accepted as an important tool in medicine. Increasingly, CT is finding a wide variety of application in material science and engineering fields. CT is being used for non-destructive inspection and characterization of aeronautic and automobile components. These components have wide variations in geometry and material characteristics, from single solid piece of metals such as aluminum to exotic composite materials and from micro scale engine parts to large scale airplane tail fins. Unlike organic parts, machine parts often have ‘sharp’ features. Consequently, feature sensitive reconstruction from volume data has seen sporadic but critical work in the recent years. A number of these papers present algorithms to construct isosurfaces with sharp edges and corners from Hermite data, i.e. data containing the exact surface normals at the exact intersection of the surface and grid edges. Such surface normals are not available with CT data. In this thesis, we discuss some fundamental problems with the previous algorithms and the difficulties in using these algorithms on real CT (scalar data) and further describe a new approach to feature reconstruction from volume data. Feature sensitive reconstruction is based on the ability to approximate surface normals from scalar field gradients. Change in gradients can also be used to measure local directions of geometric structures in CT data . We describe a method to extract fiber bundle directions from industrial CT of fiber composites using gradients. Specifically this dissertation proposes, 1) a method to reconstruct isosurfaces from scalar data while preserving sharp features (edges and corners) given a scalar grid and
Convergence Analysis of Gradient-Based Learning in Continuous Games

Considering a class of gradient-based multi-agent learning algorithms in non-cooperative settings, we provide convergence guarantees to a neighborhood of a stable Nash equilibrium. In particular, we consider continuous games where agents learn in 1) deterministic settings with oracle access to their individual gradient and 2) stochastic settings with an unbiased estimator of their individual gradient. We also study the effects of non-uniform learning rates, which cause a distortion of the vector ﬁeld that can alter the equilibrium to which the agents converge and the learning path. We support the analysis with numerical examples that provide insight into how games may be synthesized to achieve desirable equilibria.
On the fly generated data for industrial part orientation estimation with deep neural networks

In this paper a method is proposed for estimating the orientation of industrial parts. Classical 2d images of the part are used to train a deep neural network and infer the part pose by computing its quaternion representation. Another innovative point of this work is in the use of synthetic data which are generated on the fly during the network training from a textured cad model placed in a virtual scene. This way overcomes the difficulties to obtain pose ground truth images from real images. At the same time, using the cad model with several lighting conditions and material reflectances allows to anticipate challenging industrial situations. As a first step of the method, the part is separated from the background using a semantic segmentation network. Then a depth image of the part is produced employing an encoder-decoder network with skip connections. Finally, the depth map is associated with the local pixels coordinates to estimate the part orientation with a fully connected network using a SO(3) metric loss function. This method can estimate the part pose from real images with visually convincing results suitable for any pose refinement processes.
Recurrent ensemble boosting based method for detecting small and transparent foreign bodies in semi-opaque containers

In recent years, driven by new standards and brand image, performant quality control has become highly important in the beverage industry. In the absence of reliable and affordable methods for detecting foreign bodies in semi-opaque bottles, many manufacturers still opt for human visual inspection on the production line. Advanced technological systems such as X-ray based systems started to be used but such technologies are expensive and intrusive. The visible domain offers an alternative and advantageous solution. In this work a recurrent boosting method is proposed and extended to dynamic information to detect small transparent objects. The proposed method works on images video acquired by an RGB camera, it considers an ensemble classification strategies that includes temporal and dynamical features in addition to the classical spatial image features such as texture and color. The experimental results show a better performance with time series and dynamic information, the effectiveness of the method is demonstrated in detection of random foreign objects regardless their size or transparency in different semi-opaque bottles.
Analytical solution for mode coupling coefficients at the joint of optical fibers with axial mismatch

In this paper is presented the analytical formula for calculating the coupling coefficient between a modes of arbitrary radial and azimuthal orders on the joint of optical fibers when the joint has an axial mismatched. The solution is obtained by a known method of Gauss approximation. As an example, the present work shows the calculations results of dependencies of mode coupling coefficients from ratio of radii of mode spot for some low order modes.
Exploring regulatory networks in plants: transcription factors of starch metabolism

Biological networks are complex (non-linear), redundant (cyclic) and compartmentalized at the subcellular level. Rational manipulation of plant metabolism may have failed due to inherent difficulties of a comprehensive understanding of regulatory loops. We first need to identify key factors controlling the regulatory loops of primary metabolism. The paradigms of plant networks are revised in order to highlight the differences between metabolic and transcriptional networks. Comparison between animal and plant transcription factors (TFs) reveal some important differences. Plant transcriptional networks function at a lower hierarchy compared to animal regulatory networks. Plant genomes contain more TFs than animal genomes, but plant proteins are smaller and have less domains as animal proteins which are often multifunctional. We briefly summarize mutant analysis and co-expression results pinpointing some TFs regulating starch enzymes in plants. Detailed information is provided about biochemical reactions, TFs and cis regulatory motifs involved in sucrose-starch metabolism, in both source and sink tissues. Examples about coordinated responses to hormones and environmental cues in different tissues and species are listed. Further advancements require combined data from single-cell transcriptomic and metabolomic approaches. Cell fractionation and subcellular inspection may provide valuable insights. We propose that shuffling of promoter elements might be a promising strategy to improve in the near future starch content, crop yield or food quality.
An Approach to Product Rating based on Aspect Ranking in Opinion Mining

Objectives: The main objective of this paper is product ranking framework that automatically generate from the product review given by own individual user’s opinion by sentiment analysis. The additional work conducts product ranking that is spawned in a source of a URL in a large scale. This aspect ranking from the products is used for analyzing the feedback in online marketing websites and rating of a product. Methods/Analysis: Bag-of-words (BOW) is the popular way to analyze the text and perform sentimental analysis. Our proposed system focuses on unigram, bigram and neutral polarity. Our proposed system classifies the polarity of each opinion words. In this the dependent and independent word will be given as input and stored in the database. We classify the meaning of the word, in some cases these words will give different meaning or contrast polarity. So it is very important to train the polarity of the words accurately. The classifier is used to predict the sentiment on each aspect by analyzing the positive and negative words separately. Finally aspect ranking is performed to obtain the final graphical result. Most of the time, the sentiments are classified as positive or negative. There are also some neutral words which should also be categorized. So, it’s important to train the classifier to predict the positive, negative and neutral words. Finally, aspect ranking of the features is performed to rate the products. Findings: This paper has analyzed and categorized the reviews as positive, negative and neutral. It is found that the neutral reviews which are omitted in most of the websites play an important role in deciding the rating of the product. Ten different products belonging to five different categories are considered from a popular website and the output is presented in the form of a 3D graph. Improvement: Numerous product reviews are available online in the internet. These reviews enable the users to choose between different products. Since, the reviews are many in number, valuable time of the consumer is wasted in going through each of the reviews and finally arriving at a conclusion. This paper aims at providing a snapshot of the overall product rating based on the key aspects which determine the product features. The ranking algorithm is used to rate the products in a scale of ten as an improvement to the star rating provided by most websites.
Sentiment Analysis Main Tasks and Applications: A Survey

The blooming of social media has simulated interest in sentiment analysis. Sentiment analysis aims to determine from a specific piece of content the overall attitude of its author in relation to a specific item, product, brand, or service. In sentiment analysis, the focus is on the subjective sentences. Hence, in order to discover and extract the subjective information from a given text, researchers have applied various methods in computational linguistics, natural language processing, and text analysis. The aim of this paper is to provide an in-depth up-to-date study of the sentiment analysis algorithms in order to familiarize with other works done in the subject. The paper focuses on the main tasks and applications of sentiment analysis. State-of-the-art algorithms, methodologies and techniques have been categorized and summarized to facilitate future research in this field.
Disconnected operation in the Coda file system

Disconnected operation is a mode of operation that enables a client to continue accessing critical data during temporary failures of a shared data repository. An important, though not exclusive, application of disconnected operation is in supporting portable computers. In this paper, we show that disconnected operation is feasible, efficient and usable by describing its design and implementation in the Coda File System. The central idea behind our work is that caching of data, now widely used for performance, can also be exploited to improve availability.
Variational Inference of Penalized Regression with Submodular Functions

Various regularizers inducing structured-sparsity are constructed as Lovász extensions of submodular functions. In this paper, we consider a hierarchical probabilistic model of linear regression and its kernel extension with this type of regularization, and develop a variational inference scheme for the posterior estimate on this model. We derive an upper bound on the partition function with an approximation guarantee, and then show that minimizing this bound is equivalent to the minimization of a quadratic function over the polyhedron determined by the corresponding submodular function, which can be solved eﬃciently by the proximal gradient algorithm. Our scheme gives a natural extension of the Bayesian Lasso model for the maximum a posteriori (MAP) estimation to a variety of regularizers inducing structured sparsity, and thus this work provides a principled way to transfer the advantages of the Bayesian formulation into those models. Finally, we investigate the empirical performance of our scheme with several Bayesian variants of widely known models such as Lasso, generalized fused Lasso, and non-overlapping group Lasso.
How to Exploit Structure while Solving Weighted Model Integration Problems

Weighted model counting (WMC) is a state-of-the-art technique for probabilistic inference in discrete domains. WMC has recently been extended towards weighted model integration (WMI) in order to handle discrete and continuous distributions alike. While a number of WMI solvers have been introduced, their relationships, strengths and weaknesses are not yet well understood. WMI solving consists of two sub-problems: 1) ﬁnding convex polytopes; and 2) integrating over them e ﬃ ciently. We formalize the ﬁrst step as λ -SMT and discuss what strategies solvers apply to solve both the λ -SMT and the integration problem. This formalization allows us to compare state-of-the-art solvers and their behaviour across di ﬀ erent types of WMI problems. Moreover, we identify factor-izability of WMI problems as a key property that emerges in the context of probabilistic programming. Problems that can be factorized can be solved more e ﬃ ciently. However, current solvers exploiting this property restrict themselves to WMI problems with univariate conditions and fully factorizable weight functions. We introduce a new algorithm, F-XSDD, that lifts these restrictions and can exploit factoriz-ability in WMI problems with multivariate conditions and partially factorizable weight functions. Through an empirical evaluation, we show the e ﬀ ectiveness of our approach.
Soil carbon mineralization in response to nitrogen enrichment in surface and subsurface layers in two land use types

Atmospheric nitrogen (N) deposition increases N availability in soils, with consequences affecting the decomposition of soil carbon (C). The impacts of increasing N availability on surface soil C dynamics are well studied. However, subsurface soils have been paid less attention although more than 50% soil C stock is present below this depth (below 20 cm). This study was designed to investigate the response of surface (0–20 cm) and subsurface (20–40 cm and 40–60 cm) C dynamics to 0 (0 kg N ha−1), low (70 kg N ha−1) and high (120 kg N ha−1) levels of N enrichment. The soils were sampled from a cropland and a grass lawn and incubated at 25 °C and 60% water holding capacity for 45 days. Results showed that N enrichment significantly decreased soil C mineralization (Rs) in all the three soil layers in the two studied sites (p < 0.05). The mineralization per unit soil organic carbon (SOC) increased with profile depth in both soils, indicating the higher decomposability of soil C down the soil profile. Moreover, high N level exhibited stronger suppression effect on Rs than low N level. Rs was significantly and positively correlated with microbial biomass carbon explaining 80% of variation in Rs. Overall; these results suggest that N enrichment may increase C sequestration both in surface and subsurface layers, by reducing C loss through mineralization.
CLS-PROG: an intelligent tool for classifying programs in online judge systems

With the development of modern information technology, modern education system is changing from informationization to intelligentization. In programming education, there are many programming problems that we can solve with multiple solutions. Classifying programs according to solutions is useful and significant. Since this technology can be used to develop many other intelligent programming education systems: (1). Assessment of multi-solution programs, (2). Personalized knowledge recommendation to students, (3). Analyzing key and difficult points for teaching. We develop a tool named CLS-PROG for classifying programs. Specifically, first, we standardize programs to reduce syntactic variation via program transformation technology. Next, we extract features from each code block of a program individually. Finally, we propose an effective multi-instance multi-task learning machine model to classify programs. Experiments was conducted on 4537 programs from an OJ (Online Judge) system. The results demonstrate the effectiveness of our tool. This tool can be used for teaching programming courses and training programming competition.
Handbook of Natural Language Processing and Machine Translation

The meaning of what Lewis Carroll's Humpty Dumpty says is abundantly clear to him, but to others, it is virtually incomprehensible. Because the rules of his language are entirely of his own devising, he is the only one who knows what he means. Translation of one language into another poses a similar problem; knowing the usual meaning of a word is not enough.