Miracle-dz/UniFAR-FTUs huggingface dataset:
Fine-grained Opinion Extraction with Mixed Network Model

With the development of opinion mining, the relevant areas are being valued more than before. As the foundation of opinion mining, the performance of opinion extraction is significant for the opinion mining results. Among the present methods of opinion extraction, many of them take advantages of the relation features. Unlike other models which use relations in an independent way, in this paper we propose a mixed network model which interactively uses two relations as well as other lexical and syntactic features to solve the extraction problem. A series of experiments show that our model outperforms other baseline models and has a further potential of improvement. Introduction The extraction task is fundamental for opinion mining, also called sentiment analysis. The trend of opinion extraction is generally from coarse-grained document-level to sentence-level, and then to fine-grained entity-level. Standard machine learning methods like SVM are used in [1,2] to analyze reviews on a document-level analysis. The researches such as [3,4] study about the subjectivity feature on sentence-level analysis. And in [5,6] bring up the joint model to solve extraction task on entity-level. The critical task in entity-level is to extract entities from a whole sentence. Rule-based methods once were the main approaches for opinion extracting [8]. Recently there has been a growing interest in the statistical-based methods and statistical-rule-mixed methods. In these methods, the opinion relations between entities are proven to be a kind of valuable feature for extraction [6]. In this paper, we adopt the ways of Y&C [5] to define two kinds of relations. Here is an example from MPQA corpus [9]: Sally (holder) is satisfied (opinion) with her new camera (target). In the above example, the opinion, the target of the opinion and the holder of the opinion annotated by subscripts are the three categories of entities. The two kinds of relations are IS-ABOUT and IS-FROM, the former one indicates that an opinion is about a target, the latter one indicates that an opinion is from a holder. In this paper, we use relations between entities to create a mixed network model as Figure1 shown, it combines several features to extract opinion entities, with a preliminary input from a CRFs sequence labeling process. In Figure1, the vertexes such as T1 to T3 are words in sentences. Each cluster circled by dotted line indicates an opinion expression, which can contain one or more words. The edges between clusters are either the IS-FROM or IS-ABOUT opinion relations, edges within clusters indicate that the words can form an entity. Since the model uses the two relations interactively, we call it relation based mutual extraction model (RMEM). We perform experiments on MPQA 3.0 dataset, results of serials experiments show that our model outperforms the preliminary extraction method which considered no relations.
Additive Manufacturing for Custom Design: Process and Evaluation and Cost Management

Custom devices are challenging in design and manufacturing cost and require more time to be made. Using Additive Manufacturing (AM) proved to be more suitable in reducing the cost and time regardless of the geometric complexity of the part to be made. AM transforms the way some products are made. These aspects made AM gain lots of momentum in the last decade. In this communication, we will show how to introduce students to advanced design concepts using AM and real life scenarios to make custom parts. Most of these custom products will address the challenges of the medical industry which is the largest one in the USA. Students will be exposed to different software packages and different design and fabrication techniques. The goal of this project is to illustrate the future of additive manufacturing technology, how to prepare students to become the future experts in this field, and to provide a model that can be adopted by academic institution. Survey for some of the main activities were conducted and evaluated.
Automatic License Plate Recognition Using Neural Network and Signal Processing

Author(s): Fu, Yuanxi | Advisor(s): Hua, Yingbo | Abstract: Automatic Plate Recognition plays an important roll in intelligent transportation systems. However, most license plate recognition methods work under restricted conditions like slow speed and good illumination. That is a restriction on industrial application. In this thesis, the constraints are relaxed by vanished points distortion-recovery method and denoising method. This thesis implements a license plate recognition method by morphological edge detection method and convolution neural network recognition method. The thesis is constructed contributes to several papers optimization methods. The proposed approach can be trained for recognition of country-specific license plates. More than 500 images are collected for training and over 300 images are collected for recognition test. This paper achieves 97.05% on license plate recognition for detecting total characters and numbers of the license plates.License plate recognition consists three parts, pre-processing image, locating license plate and identifying license numbers and characters. License plate location is important to obtain license images and plays a key role in identifying plates. The plate recognition has two major steps, character separation and identification. In this paper, machine learning method is applied for license plate recognition.
Fault Diagnosis of Train Plug Door Based on a Hybrid Criterion for IMFs Selection and Fractional Wavelet Package Energy Entropy

The train plug door is the only way for passengers getting on and off. Its failures will make train operation ineffective. Taking the developed digital signal processing technologies into consideration, a data-driven diagnosis method for train plug doors is proposed based on sound recognition. First, a novel preprocessing method based on empirical mode decomposition and hybrid intrinsic mode functions (IMFs) selection criterion is proposed. The selected significant IMFs are used to reconstruct the signals. Inspired by the idea of fractional calculus, novel entropy named fractional wavelet package decomposition energy entropy (FWPDE) is proposed. Finally, multi-class support vector machine is used for classification and validation. Experimental results indicate that the proposed preprocessing method is of great significance to extract effective FWPDE features. In addition, FWPDE is more powerful in comparison with the classical wavelet package decomposition energy entropy. The identification accuracy using the proposed method reaches 96.28%, which demonstrates its effectiveness and superiority.
Approximation Algorithms for Distributionally Robust Stochastic Optimization

Two-stage stochastic optimization is a widely used framework for modeling uncertainty, where we have a probability distribution over possible realizations of the data, called scenarios, and decisions are taken in two stages: we take first-stage actions knowing only the underlying distribution and before a scenario is realized, and may take additional secondstage recourse actions after a scenario is realized. The goal is typically to minimize the total expected cost. A common criticism levied at this model is that the underlying probability distribution is itself often imprecise. To address this, an approach that is quite versatile and has gained popularity in the stochastic-optimization literature is the two-stage distributionally robust stochastic model: given a collection D of probability distributions, our goal now is to minimize the maximum expected total cost with respect to a distribution in D. There has been almost no prior work however on developing approximation algorithms for distributionally robust problems where the underlying scenario collection is discrete, as is the case with discrete-optimization problems. We provide frameworks for designing approximation algorithms in such settings when the collection D is a ball around a central distribution, defined relative to two notions of distance between probability distributions: Wasserstein metrics (which include the L1 metric) and the L∞ metric. Our frameworks yield efficient algorithms even in settings with an exponential number of scenarios, where the central distribution may only be accessed via a sampling oracle. For distributionally robust optimization under a Wasserstein ball, we first show that one can utilize the sample average approximation (SAA) method—solve the distributionally robust problem with an empirical estimate of the central distribution—to reduce the problem to the case where the central distribution has a polynomial-size support, and is represented explicitly. This follows because we argue that a distributionally robust problem can be reduced in a novel way to a standard two-stage stochastic problem with bounded inflation factor, which enables one to use the SAA machinery developed for two-stage stochastic problems. Complementing this, we show how to approximately solve a fractional relaxation of the SAA problem (i.e., the distributionally robust problem obtained by replacing the original central distribution with its empirical estimate). Unlike in two-stage {stochastic, robust} optimization with polynomially many scenarios, this turns out to be quite challenging. We utilize a variant of the ellipsoid method for convex optimization in conjunction with several new ideas to show that the SAA problem can be approximately solved provided that we have an (approximation) algorithm for a certain max-min problem that is akin to, and generalizes, the k-max-min problem—find the worst-case scenario consisting of at most k elements—encountered in two-stage robust optimization. We ob-
Applying LSB Steganography for Disseminating Academic Testimonials in e-Learning and its Authentication aspects

: In the present day scenario, security plays a vital role in e-learning system. In an e-learning system, all kinds of transmissions and communications between the participants of any e-learning system are done via Internet. Since, Internet is publicly accessible; authenticity has a major role to play. Steganography is one of such techniques through which authenticity can be achieved in e-learning. In our proposed system, we have applied Least Significant Bit steganography technique on the color images and also discussed about the compression algorithm. For all the individual applications, we calculate the MSE and PSNR values to check the portability of the proposed system.
Penerapan Metode Waterfall Dalam Perancangan Sistem Informasi Ujian Pada SMK Indonesia Global Berbasis Web

Dalam era digitalisasi pemanfaatan teknologi informasi dapat dirasakan hampir dalam semua bidang seperti bidang kesehatan, perbankan, budaya dan pendidikan. Dalam dunia pendidikan penerapan teknologi informasi sangat diperlukan baik dalam kegiatan belajar mengajar maupun dalam penilaian ujian siswa-siswi. Dalam penilaian ujian pada sekolah menengah kejuruan Indonesia Global Depok masih mengunakan ujian pada umumnya dimana guru membagikan kertas soal ujian dan siswa menjawab soal yang diberikan oleh guru pada selembar kertas yang sudah disediakan. Dengan ujian secara konvensional ini sering menimbulkan permasalahan seperti guru terlambat membuat rapor nilai karena guru harus mengoreksi jawaban siswa satu persatu. Sistem informasi ujian dalam bentuk online berbasis web dapat membantu memberikan solusi terhadap permasalahan yang berhubungan dengan penilaian siswa. Dalam pembuatan sistem informasi ujian berbasis web menggunakan metode waterfall yang terdiri dari analisis, desain, pengkodean dan pengujian serta entity relationship diagram dalam merancang database. Dengan adanya sistem informasi ujian berbasis website dapat mempermudah guru dalam pengolahan data nilai siswanya agar lebih efektif dan efisien dalam pembuatan laporan nilai.
Research on event perception based on geo-tagged social media data

Abstract. Technological advancement makes information dissemination more convenient. When a notable event occurs, social media serves a popular platform for citizens to share event-related information. Therefore, in the information age, how to effectively observe the event and improve event management ability is an open question worthy of attention. Traditional social survey methods and various automatic sensors have been widely used to monitor the specific event. However, widely used social media service provides a unique approach for the event study with individuals as smart sensors. How to perceive an event through social media data has triggered a series of researches. Currently, we can find when, where what happened and induced impact based on geo-tagged social media data. However, event study based on social media is still in its infancy. This paper provides an overview of event study based on geo-tagged social media data. Firstly, we introduce the event model and the characteristics of social media data. Then, how to detect and trace event, how to analyze event impact and visually express obtained knowledge are discussed respectively. Subsequently, based on the existing researches, we propose further questions and conclude.
Exploring User’s Experience using E-Notification Management System

Technology is believed to give an impact on user’s behaviour. Thus, e-notification management system prototype was developed to improve user’s compliance behaviour towards Information Security Policies (ISPs). The purpose of this study was to evaluate how this prototype can be used to improve the security compliance behaviour among users through their experience of using the propose system. Eighteen (18) users from selected local hospitals in Malaysia were interviewed and the qualitative analysis found that Management Support, Information Security Awareness, SelfEfficacy, Security Barrier and Trust contributed to ISPs compliance behaviour. Furthermore, most of the participants were satisfied with the prototype system. The prototype is hoped to give benefits to organizations in implementing and distributing ISPs systematically, especially in healthcare sector.
On the Importance of Container Image Placement for Service Provisioning in the Edge

Edge computing promises to extend Clouds by moving computation close to data sources to facilitate short-running and low-latency applications and services. Providing fast and predictable service provisioning time presets a new and mounting challenge, as the scale of Edge-servers grows and the heterogeneity of networks between them increases. This paper is driven by a simple question: can we place container images across Edge-servers in such a way that an image can be retrieved to any Edge-server fast and in a predictable time. To this end, we present KCBP and KCBP-WC, two container image placement algorithms which aim to reduce the maximum retrieval time of container images. KCBP and KCBP-WC are based on k-Center optimization. However, KCBP-WC tries to avoid placing large layers of a container image on the same Edge-server. Evaluations using trace-driven simulations show that KCBP and KCBP-WC can be applied to various network configurations and reduce the maximum retrieval time of container images by 1.1x to 4x compared to state-of-the-art placements (i.e., Best-Fit and Random).
Molecular Docking in Modern Drug Discovery: Principles and Recent Applications

The process of hunt of a lead molecule is a long and a tedious process and one is often demoralized by the endless possibilities one has to search through. Fortu-nately, computational tools have come to the rescue and have undoubtedly played a pivotal role in rationalizing the path to drug discovery. Of all techniques, molecular docking has played a crucial role in computer aided drug design and has swiftly gained ranks to secure a valuable position in the modern scenario of structure-based drug design. In this chapter, the principle, sampling algorithms, scoring functions and diverse available software ’ s for molecular docking have been summarized. We demonstrate the interplay of docking, classical techniques of structure-based design and X-ray crystallography in the process of drug discovery. In addition, we dwell upon some of the limitations faced in docking studies. Finally, several success stories of molecular docking approaches in drug discovery have been highlighted, concluding with remarks on molecular docking for the future.
Regular expression matching using associative memory

This paper describes a method for the implementation of regular expression matching based on the use of a form of associative (or content addressable) memory. The regular expression matching is performed by converting the regular expression into a Deterministic Finite Automata, but then using associative memory to hold the state transition information. Rather than try to simplify the resulting automata, this approach starts from the point of view of each next state in the automata being arrived at from some particular area of state-input space. We implement our automata by defining a number of orthogonal regions of state-input space, each of which is compared against our current state and input data. The highest priority region that produces a match will identify a corresponding next state for the automata. This work differs from previous work in that the associative memory used performs a full set membership test, and is hence more selective than systems based on Ternary Content Addressable Memory. This report describes work in progress, which to date has produced the rule processing software and outline designs for the hardware.
Modification biterm topic model input feature for detecting topic in thematic virtual museums

This paper describes the method for detecting topic in short text documents developed by the authors. The method called Feature BTM, based on the modiﬁcation of the third step of the generative process of the well-known BTM model. The authors conducted experiments of quality evaluation that have shown the advantage of eﬃciency by the modiﬁed Feature BTM model before the Standard BTM model. The thematic clustering technology of documents necessary for the creation of thematic virtual museums has described. The authors performed a performance evaluation that shows a slight loss of speed (less than 30 seconds), more eﬀective using the Feature-BTM for clustering the virtual museum collection than the Standard BTM model.
TOWARD KNOWLEDGE TECHNOLOGY SYNCRONICITY FRAMEWORK FOR ASYNCHRONOUS ENVIRONMENT.pdf

While distance learning education continues to grow, online instructors face certain asynchronous uncertainties when it comes to knowledge exchange with students. To counteract such uncertainties and minimize teaching deficiencies expected to occur in asynchronous learning environments, this study examines a set of knowledge building blocks that play a role in the online knowledge exchange process. Knowledge technology synchronicity framework for asynchronous environment is proposed that integrates knowledge seeking behavior, knowledge properties, knowledge domains, knowledge types, knowledge tools, and technology synchronicity. A real-life case is provided to integrate the framework in practice.
An analysis of word embedding spaces and regularities

Word embeddings are widely use in several applications due to their ability to capture semantic relationships between words as relations between vectors in high dimensional spaces. One of the main problems to obtain the information is to deal with the phenomena known as the Curse of Dimensionality, the fact that some intuitive results for well known distances are not valid in high dimensional contexts. In this thesis we explore the problem to distinguish between synonyms or antonyms pairs of words and non-related pairs of words attending just to the distance between the words of the pair. We considerer several norms and explore the problem in the two principal kinds of embeddings, GloVe and Word2Vec.
4th Annual PKI R&D Workshop "Multiple Paths to Trust" Proceedings

This paper analyses the simplicity of the trust model adopted by the Shibboleth infrastructure and describes an enhanced distributed trust model and authorisation decision making capability that can be implemented by using X.509 attribute certificates and a Privilege Management Infrastructure such as PERMIS. Several different combinatorial approaches can be taken, depending upon the trust models adopted by the Shibboleth target and origin sites, and each of these are described. The paper also discusses whether user privacy, which is strongly protected by Shibboleth, is bound to be weakened by the use of X.509 attribute certificates rather than simple attributes, and concludes that this does not have to be the case.
Analysis of P2P Online Lending Default Based on Random Forest

This article, based on Renren Loan website, will utilize SMOTE to process the debit and credit data in a balanced way. In addition, the importance for the variable-Random Forest and cross-validation conception will be applied for feature selection before the parameter optimization by grid searching, so that the model of basic Random Forest will be derived. Finally, featured modules of LDA will be added to further explore the reference value of loan description. The research findings show that the model has a satisfactory performance on test set in terms of predicting the results.
Bivariate Copula-based Linear Mixed-effects Models: An Application to Longitudinal Child Growth Data

Multiple longitudinal outcomes are common in public health research and adequate methods are required when there is interest in the joint evolution of response variables over time. However, the main drawback of joint modeling procedures is the requirement to specify the joint density of all outcomes and their correlation structure, as well as numerical difficulties in statistical inference, when the dimension of these outcomes increases. To overcome such difficulty, we present two procedures to deal with multivariate longitudinal data. We first present an univariate approach, for which linear mixed-effects models are considered for each response variable separately. Then, a novel copula-based modeling is presented, in order to characterize relationships among the response variables. Both methodologies are applied to a real Brazilian data set on child growth.
Capacity based deadline aware dynamic load balancing (CPDALB) model in cloud computing environment

ABSTRACT Cloud computing is a promising infrastructure in which many activities are carried out in both the commercial and academic spheres. Non-uniform utilization of virtual machines (VMs) in cloud creates an imbalance of the load, resulting in the violation of Service Level Agreements (SLAs) and tends to decreases the rate of revenue. There is a huge concern in scaling the load balancing algorithm due to the heterogeneous nature of the cloud. In this paper, a Capacity based Deadline Aware Dynamic Load Balancing (CPDALB) algorithm is proposed to address the problem. CPDALB is a novel heuristic based load balancing approach which focuses on the selection of VM for allocation of tasks to ensure customer satisfaction in terms of meeting deadline constraints and cost of running applications. CPDALB uses the concept of deploying requests to VMs based on their processing capacities to minimize load imbalance with satisfying the deadline. Other QoS parameters viz. turnaround time, response time and utilization are also computed for performance evaluation. An extensive simulation study has been carried out for the comparison of CPDALB with other standard scheduling algorithms given so far in the literature. The experimental results reveal that the proposed algorithm outperforms its competitors on account of almost all parameters under study.