Miracle-dz/UniFAR-FTUs huggingface dataset:
Smooth Optimization Approach for Covariance Selection ∗

In this paper we study a smooth optimization approach for solving a class of nonsmooth strongly concave maximization problems. In particular, we apply Nesterov’s smooth optimization technique [17, 18] to their dual counterparts that are smooth convex problems. It is shown that the resulting approach has O(1/√ǫ) iteration complexity for finding an ǫ-optimal solution to both primal and dual problems. We then discuss the application of this approach to covariance selection that is approximately solved as a L1-norm penalized maximum likelihood estimation problem, and also propose a variant of this approach which has substantially outperformed the latter one in our computational experiments. We finally compare the performance of these approaches with two other first-order methods studied in [9], that is, Nesterov’s O(1/ǫ) smooth approximation scheme, and block-coordinate descent method for covariance selection on a set of randomly generated instances. It shows that our smooth optimization approach substantially outperforms their first method, and moreover, its variant tremendously outperforms their both methods.
Absorbing the Concept of Absorptive Capacity: How to Realize Its Potential in the Organization Field

The purpose of this Perspective Paper is to advance understanding of absorptive capacity, its underlying dimensions, its multi-level antecedents, its impact on firm performance and the contextual factors that affect absorptive capacity. Nineteen years after the Cohen and Levinthal 1990 paper, the field is characterized by a wide array of theoretical perspectives and a wealth of empirical evidence. In this paper, we first review these underlying theories and empirical studies of absorptive capacity. Given the size and diversity of the absorptive capacity literature, we subsequently map the existing terrain of research through a bibliometric analysis. The resulting bibliometric cartography shows the major discrepancies in the organization field, namely that (1) most attention so far has been focused on the tangible outcomes of absorptive capacity; (2) organizational design and individual level antecedents have been relatively neglected in the absorptive capacity literature; and (3) the emergence of absorptive capacity from the actions and interactions of individual, organizational and inter-organizational antecedents remains unclear. Building on the bibliometric analysis, we develop an integrative model that identifies the multi-level antecedents, process dimensions, and outcomes of absorptive capacity as well as the contextual factors that affect absorptive capacity. We argue that realizing the potential of the absorptive capacity concept requires more research that shows how “micro antecedents” and “macro- antecedents” influence future outcomes such as competitive advantage, innovation, and firm performance. In particular, we identify conceptual gaps that may guide future research to fully exploit the absorptive capacity concept in the organization field and to explore future fruitful extensions of the concept.
Improvement on Blind Symbol Rate Detection under Unfavorable Conditions

The performance of the blind symbol rate detection through non-linearity degrades under some conditions, including small excess bandwidth, limited symbol length and low signal-to-noise ratio. The improved algorithm here considers the main disturbance-continuous part of the power spectral of non-linearity as its envelope layer and eliminates it by an envelope sifting process, thus greatly improve the performance of the detectors, especially under one or more unfavorable factors. Simulation results show that the modified method outperforms the original methods in all cases, especially under unfavorable conditions.
Edge-preserving block compressive sensing with projected landweber

Compressive Sensing (CS) is an emerging new sampling technique which helps to break through the Nyquist sampling frequency for sparse signals. This paper addresses improving one of its recovery algorithms known as the Block Compressive Sensing with Smooth Projected Landweber (BCS-SPL). For reducing the blocking artifacts in BCS-SPL, the Wiener filter has been implemented as a classic way to smooth image at the beginning of each iteration, but it is quite sensitive to image edges and blurs the image. In this paper, we propose a modified method which separates image signal into its low and high frequency components, and then independently processes each of the two components. Subsequently, a smoothness enhancing operation is implemented to improve reduction of high frequency oscillatory artifacts after hard thresholding. Experimental results show that the proposed method improves reconstructed image quality by more than 3dB compared to the conventional BCS-SPL.
Predicting the Popularity of Online Content with Group-specific Models

Predicting the popularity of online content is highly valuable in many applications and has been studied for several years. However, existing models either work in population level---all messages are assumed to follow similar popularity dynamics, lacking flexibility to capture the intrinsic complexity of popularity dynamics, or work in individual level---the popularity dynamics of messages are independent of each other, failing to leverage other messages to improve prediction accuracy. In this paper, we propose a divide and conquer framework for popularity prediction. We first divide messages into groups, anticipating each group of messages follow similar popularity dynamics, and then, we train a group-specific model for the messages of each group. Experiments demonstrate that group-specific models improve the population-level models by about 30% and outperform state-of-the-art individual-level model.
Video deblurring in complex wavelet domain using local Laplace prior for enhancement and anisotropic spatially adaptive denoising for PSF detection

This paper presents a new algorithm for video deblurring using frames before and after each scene as a multiframe observation from that scene. For this reason we develop the recently proposed algorithms that try to benefit from advantages of advanced denoising methods. At first the data is transformed to discrete complex wavelet transform (DCWT) and an initial estimate of clean data and point spread function (PSF) is obtained based on minimization of the energy criterion in gradient projection algorithm. In the next stage we improve the estimated clean data using a denoising method employing local Laplace prior and the estimated PSF is enhanced using an anisotropic spatially adaptive denoising procedure based on the local polynomial approximation (LPA) of blur operator and the intersection of confidence intervals (ICI) used for selection of window sizes of LPA. The mentioned procedure is repeated (in gradient projection algorithm) to obtain the appropriate estimations of PSF and clean data. Applying this technique for deblurring of video sequences produces better results in comparison with other methods.
Supervised Ranking in Open-Domain Text Summarization

The paper proposes and empirically motivates an integration of supervised learning with unsupervised learning to deal with human biases in summarization. In particular, we explore the use of probabilistic decision tree within the clustering framework to account for the variation as well as regularity in human created summaries. The corpus of human created extracts is created from a newspaper corpus and used as a test set. We build probabilistic decision trees of different flavors and integrate each of them with the clustering framework. Experiments with the corpus demonstrate that the mixture of the two paradigms generally gives a significant boost in performance compared to cases where either of the two is considered alone.
Weakly Connected Dominating Set Algorithm Based on Learning Automata and Its Application to Clustering the Sensor Networks

اّ ِکبض ي رگسح يب نيس اراد ي داذؼت دايز ي ُرگ رگسح ىازرا تويق شرًا اب ي دٍذحه يه ِکذٌضاب کيرد ِقطٌه دٍذحه يايفارغج ي ِب ترَص نکارته ُذٌکارپ ذًا ُذض . ّ کير ا زا ي اّ ُرگ ي ياًاَت ي غوج رٍآ ي تاػلاطا طيحهزا ار اراد يه ذضاب ٍ ُداد اّ ي غوج يرٍآ ُذض ار ارب ي ِب شزادرپ سکره ُرگ ي لاسرا يه ٌک ٌ ذ . ي ک ي زا ييرت ُذوػ اْطلاچ رد ييا عًَ ،اّ ِکبض تيدٍذحه فرصه يشرًا تسا ِک ًاويقتسه لَط روػ ِکبض رگسح ار تحت ريثأت رارق يه ذّد . ِضَخ يذٌب ىاٌَؼب يکي زا اْضٍر ي ِتخاٌض ا ُذض ي تسا ِک رَطب ا ُدرتسگ ي تْج ِجاَه ىذض اب ييا صلاچ درَه ُدافتسا رارق يه دريگ . رد ا ي ِلاقه ي ي ارب اراک شٍر ک ي ِضَخ ذٌب ي ٌکت زا ُدافتسا اب ي اتاهَتا ک ي ي گدا ي تسا ُذض ِئارا ر فذّ لصا ي ا ي شٍر ي دٍذحه يتفرگ رظً رد ي شرًا ت ي ارب ي اسفا ي تسا ِکبض روػ لَط ص . ارب ي ً ي ا ِب ل ي ٌکت زا فذّ ي ي ؼض لاصتا اب نکاح ِػَوجه ک ي ٍ ف اتاهَتا ي ي گدا ي تسا ُذض ُدافتسا ر . بض رد ي زاس ِ ي اّ ي ُذض ماجًا , اراک يي پ شٍر ي داٌْط ي اقه رد ي شٍر اب ِس leach و heed ِب بَخ ي ه نطچ ِب ي درَخ . تاملك ديلك ي اتاهَتآ ي ،ريگداي ِکبض ياّ رگسح ب ي ،نيس ِضَخ ذٌب ي ، ؼض لاصتا اب نکاح ِػَوجه ي ف
Image Decomposition Model OSV with L0 Sparse Regularization

Images decomposition attracts much attention in recent years. Many variational methods were proposed. The most famous variational decomposition model is OSV model because of its concise expression. However, the OSV model sufiers from many drawbacks. One is that the discretization of the EularLagrange equation is hard because the 4th order term was introduced. The other drawback is that the efiect of decomposition is weak. In this paper, we proposed a new decomposition model with L0 sparse regularization. The L0 sparse term is more sparse than traditional L1 norm. It is very useful in many image processing applications. The combination of OSV model and L0 regularization can improve the efiect of traditional decomposition. To improve the e‐ciency of the minimization, the Split Bregman method for the improved OSV model is also designed. Experimental results validate the proposed model.
Studies on Implementation of Wavelet for Denoising Speech Signal

ABSTRACT The wavelet transform has become a powerful tool of signal analysis and is widely used in many applications which include signal detection and denoising. In hands free speech communication environments situation occurs that speech is superposed by background noise. Over the past few decades there is tremendous increase in the level of ambient environmental noise. This has been due to growth of technology. Noise is added by various factors like noisy engines, heavy machines, pumps, vehicles, over noisy telephone channel or using radio communication device in an aircraft cockpit. The wavelet denoising technique is called thresholding; it is a non linear algorithm. It can be decomposed in three steps. This paper is based on wavelet as denoising algorithm. Haar and Daubechies wavelets are implemented on speech signals and performance is evaluated. Categories and Subject Descriptors Speech signal Processing, Wavelets, Denoising.
ANFIS Method for Usability Assessment of Website of an Educational Institute

Today website is considered as an image of an organization. The advent of privatization has lead to a cut throat competition among the various educational institutes thus designing an effective website is very important. For designing an effective website it is required to identify the need of the visitors of the website. In current scenario the objective of websites of an educational institute is to provide the student and other users an information platform where they can retrieve any information and also can perform other tasks like learning and tracking records. In order to increase the usability of a website it is required to identify the important parameters that visitor look in a site. In this paper we have identified the important parameters that affect the usability of an educational websites and then propose a model for usability assessment using the Adaptive Neuro Fuzzy Inference System Approach (ANFIS).
EXPERIMENTS IN INFORMATION RETRIEVAL FROM SPOKEN DOCUMENTS

This paper describes the experiments performed as part of the TREC-97 Spoken Document Retrieval Track. The task was to pick the correct document from 35 hours of recognized speech documents, based on a text query describing exactly one document. Among the experiments we described here are: Vocabulary size experiments to assess the effect of words missing from the speech recognition vocabulary; experiments with speech recognition using a stemmed language model; using confidence annotations that estimate of the correctness of each recognized word; using multiple hypotheses from the recognizer. And finally we also measured the effects of corpus size on the SDR task. Despite fairly high word error rates, information retrieval performance was only slightly degraded for speech recognizer transcribed documents.
Estimating lighting environments based on shadow area in an omni-directional image

To create realistic CG images, the information about the lighting is very important. There are two ways to estimate the information of the light source. One is a direct measurement method using images captured with a fish-eye lens or a spherical mirror[Debevec 1998], and the other is an indirect measurement method to estimate positions and intensities of the light sources from the shadow information of objects[Sato et al. 2003]. In the direct measurement method, by concerning pixels of the captured image as light sources having corresponding intensities, it is possible to estimate the lighting environment densely. However, for a high-intensity light source like the sun, the dynamic range of the camera is insufficient, and the radiant intensity of the light source cannot be accurately estimated. So, we propose a method that combines a direct measurement technique and an indirect measurement method. In our proposed method, the light source information of the high-intensity area in the captured image is estimated by indirect measurement method. In the experiments using real images, even for outdoor scenes that contain the high-intensity light source like the sun, the measurement of the light source environment could be performed by the proposed method. Also, it was confirmed that images including realistic shadows equivalent to real images could be created.
Maximum likelihood estimation of the parameters of fractional Brownian motions

This paper provides convergence analysis for maximum likelihood estimation of the parameters describing a particular multiscale process known as fractional Brownian motion. We concentrate on schemes that 'pre-whiten' the available noise-corrupted data via the fast wavelet transform and show that such schemes are strongly consistent and asymptotically efficient. We also analyse the rate of convergence of the maximum likelihood estimates and show that this rate depends on the memory of the fractional process.
Mechanisms for Efficient Allocation in Divisible Capacity Networks

We propose a mechanism for auctioning bundles of multiple divisible goods. Such a mechanism is very useful for allocation of bandwidth in a network where the buyers want the same amount of bandwidth on each link in their route. We first propose a single-sided VCG-type mechanism. However, instead of reporting types, the players only reveal a two-dimensional bid signal - the maximum quantity that they want and the per unit price they are willing to pay. We show the existence of an efficient Nash equilibrium in the corresponding auction game of the mechanism. We show through an example that not all Nash equilibria are efficient but provide a distributed algorithm that yields the efficient one. Further, we provide a sufficient characterization of all efficient Nash equilibria. We then present a double-sided auction mechanism for multiple divisible goods, and show that there exists a Nash equilibrium of the auction game which yields the efficient allocation
Using web page layout for extraction of sender names

Recently, the credibility of information available on the Web has been regarded as an important issue. Sender name is one of the important indicators of the credibility of the information. In this paper, we propose a new method for extracting sender name. The proposed method use the named entity recognition method, and reducing the DOM node using Web page Layout for preprocessing. Experimental result shows that our proposed method can effectively extract sender names when the preprocessing is successful.
Application of Moving Objects and Spatiotemporal Reasoning

In order to predict future variations of moving objects whic h general attributes, locations, and regions of spatial objects are changed over time, spatiotemporal data , omain knowledge, and spatiotemporal operations are required to process together with temporal and spatial a t ributes of data. However, conventional researches on temporal and spatial reasoning cannot be applied directl y to he inference using moving objects, because they have been studied separately on temporal or spatial attribu te of data. Therefore, in this paper, we not only define spatial objects in time domain but also propose a new type of m oving objects and spatiotemporal reasoning model that has the capability of operation and inference for m ving objects. The proposed model is made up of spatiotemporal database, GIS tool, and inference engine for application of spatiotemporal reasoning using moving objects and they execute operations and inferences f or moving objects. Finally, to show the applicability of the proposed model, a proper domain is established for the battlefield analysis system to support commander’s decision making in the army operational situation and it is e xperimented with this domain.
Network coding optimization based on the genetic algorithm with memory function

Network coding technology brought benefits for us, but also brought us the corresponding expenses. Kim et al. put forward the network coding optimization to reduce cost. In this paper, the problem of network coding optimization is improved, at first we use graph decomposition method constructing the network coding optimization model, then we propose the genetic algorithm with memory function (MGA, Genetic Algorithm with Memory). This paper got the MGA using the orthogonal crossover operator, trust and neighborhood for the simple genetic algorithm. The result of simulation experiment shows that the speed of - MGA getting the solution from network coding optimization model is much faster and the quality of the solution is better (that is to say the average number of the coding node is less in the network coding scheme).
Texture Mapping of Images with Arbitrary Contours

Decaling is an intuitive paradigm for texture mapping in an analogy of attaching stickers on an object in the real world. This paradigm enables an artist to put decals directly on a 3D model after interactive manipulations such as modifying their positions, scales and orientations. In this paper, we present a novel method for multipleconstrained decaling. Given a region inside a texture together with a set of feature points in the region and a 3D model, our problem is to map the texture region onto the surface of the model in an intuitive manner, while satisfying the constrained imposed by a user-specified correspondence between a set of feature points in the region and the surface. We propose a solution for this problem. Our approach iteratively determines a portion of the mesh representing the surface while accordingly refining its parameterization, guided by the feature point correspondence.
A Knowledge Based Approach to Detection of Idea Plagiarism in Online Research Publications

— Plagiarism is on the rise because of the easy access to information through World Wide Web. Web pages are growing in the internet on daily basis. Researchers want to be well connected globally to popularize their ideas. Therefore, allowing download of research documents are inevitable. However, this falls prey to those who turn the cake and spoil the issue. Even unknowingly, a researcher ends in verbatim copying of other former researchers’ ideologies or conclusions to quote / use in their own research paper. This paper presents an analysis of NLP based plagiarism detection approaches which leads to proposing of an ontology based solution to detect text plagiarism more meaningfully. We address word-word and paraphrasing techniques and investigate the use of ontology in detecting idea plagiarism. The main objective is to investigate the exclusion of ‘Related Work’ section and the use of WordNet for plagiarism detection in research publications.
iTac: Aspect Based Sentiment Analysis using Sentiment Trees and Dictionaries

This paper describes our approach for the fourth task of the SemEval 2014 challenge: Aspect Based Sentiment Analysis. Our system is designed to solve all four subtasks: (i) identifying aspect terms, (ii) determining the polarity of an aspect term, (iii) detecting aspect categories, and (iv) determining the polarity of a predefined aspect category. Our system is based on the Stanford sentiment tree.
The Formation and Evolution of Massive Black Holes

The past 10 years have witnessed a change of perspective in the way astrophysicists think about massive black holes (MBHs), which are now considered to have a major role in the evolution of galaxies. This appreciation was driven by the realization that black holes of millions of solar masses and above reside in the center of most galaxies, including the Milky Way. MBHs also powered active galactic nuclei known to exist just a few hundred million years after the Big Bang. Here, I summarize the current ideas on the evolution of MBHs through cosmic history, from their formation about 13 billion years ago to their growth within their host galaxies.
The elderly in Trinidad: health, social and economic status and issues of loneliness.

OBJECTIVES To determine the general health and social status of elderly persons surveyed in Trinidad and to explore issues of loneliness. METHOD An island-wide survey of persons aged 65 years and older was conducted in early 2002 in Trinidad Eight hundred and forty-five (845) elderly persons were chosen using systematic random sampling. The main survey instrument for data collection was a questionnaire that included structured as well as open-ended questions. The subjects were chosen in a house-to-house survey conducted in all eight counties in Trinidad. Elderly people who were unable to comprehend the questions were excluded from the survey. RESULTS Those selected ranged in age from 65-102 years and represented all the ethnic groups in Trinidad. These elderly persons lived in a wide range of housing situations. The majority lived in the homes of family members (57%) and 16% lived on their own. A large proportion (80%) had at least one chronic medical problem, although 44% reported their health as "fairly good" or "good". More than a half of the males (53%) and 67% of the females were taking at least one prescribed medicine. The main sources of income were old age pension (85%) and National Insurance (15%). Thirty-three per cent reported feelings of loneliness. This figure includes 28% of those who did not live alone. CONCLUSION The data revealed that across all ethnic groups more than one-third of the sample reported themselves to be in fair to good health. Many of these elders were lonely because their relatives were quite occupied with their own affairs.