Query: Performance Investigation of Feature Selection Methods

Relevant passages:
Survey on mining subjective data on the web <=SEP=> In the past years we have witnessed Sentiment Analysis and Opinion Mining becoming increasingly popular topics in Information Retrieval and Web data analysis. With the rapid growth of the user-generated content represented in blogs, wikis and Web forums, such an analysis became a useful tool for mining the Web, since it allowed us to capture sentiments and opinions at a large scale. Opinion retrieval has established itself as an important part of search engines. Ratings, opinion trends and representative opinions enrich the search experience of users when combined with traditional document retrieval, by revealing more insights about a subject. Opinion aggregation over product reviews can be very useful for product marketing and positioning, exposing the customers’ attitude towards a product and its features along different dimensions, such as time, geographical location, and experience. Tracking how opinions or discussions evolve over time can help us identify interesting trends and patterns and better understand the ways that information is propagated in the Internet. In this study, we review the development of Sentiment Analysis and Opinion Mining during the last years, and also discuss the evolution of a relatively new research direction, namely, Contradiction Analysis. We give an overview of the proposed methods and recent advances in these areas, and we try to layout the future research directions in the field.

Measuring praise and criticism: Inference of semantic orientation from association <=SEP=> The evaluative character of a word is called its semantic orientation. Positive semantic orientation indicates praise (e.g., "honest", "intrepid") and negative semantic orientation indicates criticism (e.g., "disturbing", "superfluous"). Semantic orientation varies in both direction (positive or negative) and degree (mild to strong). An automated system for measuring semantic orientation would have application in text classification, text filtering, tracking opinions in online discussions, analysis of survey responses, and automated chat systems (chatbots). This article introduces a method for inferring the semantic orientation of a word from its statistical association with a set of positive and negative paradigm words. Two instances of this approach are evaluated, based on two different statistical measures of word association: pointwise mutual information (PMI) and latent semantic analysis (LSA). The method is experimentally tested with 3,596 words (including adjectives, adverbs, nouns, and verbs) that have been manually labeled positive (1,614 words) and negative (1,982 words). The method attains an accuracy of 82.8% on the full test set, but the accuracy rises above 95% when the algorithm is allowed to abstain from classifying mild words.

Estimating Attributes: Analysis and Extensions of RELIEF <=SEP=> In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies among them. Kira and Rendell (1992a,b) developed an algorithm called RELIEF, which was shown to be very eecient in estimating attributes. Original RELIEF can deal with discrete and continuous attributes and is limited to only two-class problems. In this paper RELIEF is analysed and extended to deal with noisy, incomplete, and multi-class data sets. The extensions are veriied on various artiicial and one well known real-world problem.

Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews <=SEP=> This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g., “subtle nuances”) and a negative semantic orientation when it has bad associations (e.g., “very cavalier”). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word “excellent” minus the mutual information between the given phrase and the word “poor”. A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.

Automatic Retrieval and Clustering of Similar Words <=SEP=> Bootstrapping semantics from text is one of the greatest challenges in natural language learning. We first define a word similarity measure based on the distributional pattern of words. The similarity measure allows us to construct a thesaurus using a parsed corpus. We then present a new evaluation methodology for the automatically constructed thesaurus. The evaluation results show that the thesaurus is significantly closer to WordNet than Roget Thesaurus is.

Dynamic program slicing methods <=SEP=> A dynamic program slice is that part of a program that ‘‘affects’’ the computation of a variable of interest during program execution on a specific program input. Dynamic program slicing refers to a collection of program slicing methods that are based on program execution and may significantly reduce the size of a program slice because run-time information, collected during program execution, is used to compute program slices. Dynamic program slicing was originally proposed only for program debugging, but its application has been extended to program comprehension, software testing, and software maintenance. Different types of dynamic program slices, together with algorithms to compute them, have been proposed in the literature. In this paper we present a classification of existing dynamic slicing methods and discuss the algorithms to compute dynamic slices. In the second part of the paper, we compare the existing methods of dynamic slice computation.  1998 Elsevier Science B.V. All rights reserved.

Digital Nudging <=SEP=> Digital nudging is the use of user-interface design elements to guide people’s behavior in digital choice environments. Digital choice environments are user interfaces – such as web-based forms and ERP screens – that require people to make judgments or decisions. Humans face choices every day, but the outcome of any choice is influenced not only by rational deliberations of the available options but also by the design of the choice environment in which information is presented, which can exert a subconscious influence on the outcome. In other words, ‘‘what is chosen often depends upon how the choice is presented’’ (Johnson et al. 2012, p. 488) such that the ‘‘choice architecture alters people’s behavior in a predictable way’’ (Thaler and Sunstein 2008, p. 6). Even simple modifications of the choice environment in which options are presented can influence people’s choices and ‘‘nudge’’ them into behaving in particular ways. In fact, there is no neutral way to present choices. For example, Johnson and Goldstein (2003) showed that simply changing default options (from opt-in to opt-out) in the context of organ donation nearly doubled the percentage of people who consent to being organ donors. Many choices are made in online environments. As the design of digital choice environments always (either deliberately or accidentally) influences people’s choices, understanding the effects of digital nudges in these environments can help designers lead users to the most desirable choice. For example, the mobile payment app Square nudges people into giving tips by setting the default to ‘‘tipping’’ so that customers must actively select a ‘‘no tipping’’ option if they choose not to give a tip. Using this simple nudge has raised tip amounts, especially where little or no tipping has been common (Carr 2013). These examples show that simply changing the default option affects the outcome.

StyleCheck : An Automated Stylistic Analysis Tool <=SEP=> StyleCheck is a user-friendly tool with multiple functions designed to aid in the production of quality writing. Its features include stylistic analysis (on both documentwide and individual-sentence scales) and spelling and grammar check, as well as generating suggested replacements for all types of errors. In addition, StyleCheck includes the capability to identify the famous author (out of a limited corpus) with the style most similar to the user’s. The source code for StyleCheck is available online at: https://github.com/alexpwelton/StyleCheck Dartmouth Computer Science Technical Report TR2014-754

Perspectives and Limitations of Self-Organizing Maps in Blind Separation of Source Signals <=SEP=> |The capabilities of self-organizing maps (SOMs) in parametrizing data manifolds qualify them as candidates for blind separation algorithms. We study the virtues and problems of the SOM-based approach in a simple example. Also numerical simulations of more general cases have been performed. It shows that the performance is unquestionable in the case of a linear mixture only if the observed data are prewhitened and inhomogeneities in the input data are compensated. The algorithm is robust with respect to deviations from linearity, although may fail for complex non-linearly distorted signals. Due to computational restrictions only mixtures from a few sources can be resolved. Under certain conditions it is possible to separate more sources than sensors using a dimension-increasing map.

Anomaly Detection in Vessel Tracking Using Support Vector Machines (SVMs) <=SEP=> The paper is devoted to supervise method approach to identify the vessel anomaly behaviour in waterways using the Automated Identification System (AIS) vessel reporting data. In this work, we describe the use of SVMs to detect the vessel anomaly behaviour. The SVMs is a supervised method that needs some pre knowledge to extract the maritime movement patterns of AIS raw data into information. This is the basis to remodel information into a meaningful and valuable form. The result of this work shows that the SVMs technique is applicable to be used for the identification of vessel anomaly behaviour. It is proved that the best accuracy result is obtained from dividing raw data into 70% for training and 30% for testing stages.

Volley: Automated Data Placement for Geo-Distributed Cloud Services <=SEP=> As cloud services grow to span more and more globally distributed datacenters, there is an increasingly urgent need for automated mechanisms to place application data across these datacenters. This placement must deal with business constraints such as WAN bandwidth costs and datacenter capacity limits, while also minimizing user-perceived latency. The task of placement is further complicated by the issues of shared data, data inter-dependencies, application changes and user mobility. We document these challenges by analyzing month-long traces from Microsoft's Live Messenger and Live Mesh, two large-scale commercial cloud services.

On Challenges in Evaluating Malware Clustering <=SEP=> Malware clustering and classification are important tools that enable analysts to prioritize their malware analysis efforts. The recent emergence of fully automated methods for malware clustering and classification that report high accuracy suggests that this problem may largely be solved. In this paper, we report the results of our attempt to confirm our conjecture that the method of selecting ground-truth data in prior evaluations biases their results toward high accuracy. To examine this conjecture, we apply clustering algorithms from a different domain (plagiarism detection), first to the dataset used in a prior work’s evaluation and then to a wholly new malware dataset, to see if clustering algorithms developed without attention to subtleties of malware obfuscation are nevertheless successful. While these studies provide conflicting signals as to the correctness of our conjecture, our investigation of possible reasons uncovers, we believe, a cautionary note regarding the significance of highly accurate clustering results, as can be impacted by testing on a dataset with a biased cluster-size distribution.

Gource: visualizing software version control history <=SEP=> This film demonstrates a tool for visualizing the development history of software projects as an interactive animation, showing developers working on the hierarchical file-directory structure of a project over the course of its development.

The Quest for Scalable Blockchain Fabric: Proof-of-Work vs. BFT Replication <=SEP=> Bitcoin cryptocurrency demonstrated the utility of global consensus across thousands of nodes, changing the world of digital transactions forever. In the early days of Bitcoin, the performance of its probabilistic proof-of-work (PoW) based consensus fabric, also known as blockchain, was not a major issue. Bitcoin became a success story, despite its consensus latencies on the order of an hour and the theoretical peak throughput of only up to 7 transactions per second. The situation today is radically different and the poor performance scalability of early PoW blockchains no longer makes sense. Specifically, the trend of modern cryptocurrency platforms, such as Ethereum, is to support execution of arbitrary distributed applications on blockchain fabric, needing much better performance. This approach, however, makes cryptocurrency platforms step away from their original purpose and enter the domain of database-replication protocols, notably, the classical state-machine replication, and in particular its Byzantine fault-tolerant (BFT) variants. In this paper, we contrast PoW-based blockchains to those based on BFT state machine replication, focusing on their scalability limits. We also discuss recent proposals to overcoming these scalability limits and outline key outstanding open problems in the quest for the “ultimate” blockchain fabric(s).

Integrated system of face recognition and sound localization for a smart door phone <=SEP=> This paper proposes a smart system using both face recognition and sound localization techniques to identify foreign faces through a door phone in a more efficient and accurate way. In particular, a visitor's voice is used to localize the proper location of the speaker. The location information is then used to adjust the door phone camera position to frame the face in its field of view. In order to accurately locate the facial position of the visitor, 4 microphones are positioned in a cross configuration. Furthermore, face detection and face recognition algorithms as well as a wireless interface are used to recognize the visitor and send the information to the home owner's phone. The entire control system is built using an FPGA chip and tested for actual use in door phone environments1.

Deep Convolutional Factor Analyser for Multivariate Time Series Modeling <=SEP=> Deep generative models can perform dramatically better than traditional graphical models in a number of machine learning tasks. However, training such models remains challenging because their latent variables typically do not have an analytical posterior distribution, largely due to the nonlinear activation nodes. We present a deep convolutional factor analyser (DCFA) for multivariate time series modeling. Our network is constructed in a way that bottom layer nodes are independent. Through a process of up-sampling and convolution, higher layer nodes gain more temporal dependency. Our model can thus give a time series different representations at different depths. DCFA only consists of linear Gaussian nodes. Therefore, the posterior distributions of latent variables are also Gaussian and can be estimated easily using standard variational Bayes algorithm. We show that even without nonlinearity the proposed deep model can achieve state-of-the-art results in anomaly detection, classification and clustering using both synthetic and real-world datasets.

Conceptual Impact-Based Recommender System for CiteSeerx <=SEP=> CiteSeer is a digital library for scientific publications written by Computer Science researchers. Users are able to retrieve relevant documents from the database by searching by author name and/or keyword queries. Users may also receive recommendations of papers they might want to read provided by an existing conceptual recommender system. This system recommends documents based on an automaticallyconstructed user profile. Unlike traditional content-based recommender systems, the documents and the user profile are represented as concepts vectors rather than keyword vectors and papers are recommended based on conceptual matches rather than keyword matches between the profile and the documents. Although the current system provides recommendations that are on-topic, they are not necessarily high quality papers. In this work, we introduce the Conceptual Impact-Based Recommender (CIBR), a hybrid recommender system that extends the existing conceptual recommender system in CiteSeer by including an explicit quality factor as part of the recommendation criteria. To measure quality, our system considers the impact factor of each paper’s authors as measured by the authors’ h-index. Experiments to evaluate the effectiveness of our hybrid system show that the CIBR system recommends more relevant papers as compared to the conceptual recommender system.

Visual Sentiment Prediction by Merging Hand-Craft and CNN Features <=SEP=> Nowadays, more and more people are getting used to social media such as Instagram, Facebook, Twitter, and Flickr to post images and texts to express their sentiment and emotions on almost all events and subjects. In consequence, analyzing sentiment of the huge number of images and texts on social networks has become more indispensable. Most of current research has focused on analyzing sentiment of textual data, while only few research has focused on sentiment analysis of image data. Some of these research has considered handcraft image features, the others has utilized Convolutional Neural Network (CNN) features. However, no research to our knowledge has considered mixing both hand-craft and CNN features. In this paper, we attempt to merge CNN which has shown remarkable achievements in Computer Vision recently, with handcraft features such as Color Histogram (CH) and Bag-of-Visual Words (BoVW) with some local features such as SURF and SIFT to predict sentiment of images. Furthermore, because it is often the case that the large amount of training data may not be easily obtained in the area of visual sentiment, we employ both data augmentation and transfer learning from a pre-trained CNN such as VGG16 trained with ImageNet dataset. With the handshake of hand-craft and End-to-End features from CNN, we attempt to attain the improvement of the performance of the proposed visual sentiment prediction framework. We conducted experiments on an image dataset from Twitter with polarity labels ("positive" and "negative"). The results of experiments demonstrate that our proposed visual sentimental prediction framework outperforms the current state-of-the-art methods.

A gender identity interview for children. <=SEP=> A 12-item gender identity interview schedule was administered to 85 children referred for concerns regarding their gender identity development and 98 clinical and normal control children. Factor analysis identified two factors, which were labeled Affective Gender Confusion and Cognitive Gender Confusion. The gender-referred group gave significantly more deviant responses than did the controls on both factors. Results were discussed with regard to several diagnostic and assessment issues pertaining to children with gender identity disorder.

Factors Affecting Online Search Intention and Online Purchase Intention <=SEP=> This research focuses on various factors affecting online search intention which has been found to be a key predictor of online purchase intention. Data were collected from a sample consisting of mostly young adults with familiarity of computer use and online shopping experience. A structural equation model was employed to test hypotheses. According to the findings, utilitarian value of Internet information search, hedonic value of Internet information search, perceived benefits of Internet shopping, perceived risk of Internet shopping, and Internet purchase experience predicted online search intention well. The findings also showed that online search intention positively affects * Professor of Marketing, College of Business Administration, Seoul National University(jaekim@snu.ac.kr) ** Marketing, Telephone Service Team, Hanaro Telecom Co., Inc. (heechun@hanaro.com) *** M.B.A. candidate, College of Business Administration, Seoul National University(haejoo80@yahoo.com) This research was supported by the Institute of Management Research, Seoul National University. Seoul Journal of Business Volume 10, Number 2 (December 2004)

Tracing Data Lineage Using Schema Transformation Pathways <=SEP=> With the increasing amount and diversity of information available on the Internet, there has been a huge growth in information systems that need to integrate data from distributed, heterogeneous data sources. Tracing the lineage of the integrated data is one of the current problems being addressed in data warehouse research. In this paper, we propose a new approach for tracing data linage based on schema transformation pathways. We show how the individual transformation steps in a transformation pathway can be used to trace the derivation of the integrated data in a step-wise fashion, thus simplifying the lineage tracing process.

Cyber Security Incidents on Critical Infrastructure and Industrial Networks <=SEP=> National critical infrastructure and industrial processes are heavily reliant on automation, monitoring and control technologies, including the widely used Supervisory Control and Data Acquisition (SCADA) systems. The growing interconnection of these systems with corporate networks exposes them to cyber attacks, with several security incidents reported over the last few decades. This study provides a classification scheme for categorising security incidents related to critical infrastructure and industrial control systems. The classification scheme is applied to analyse 242 security incidents on critical infrastructure and industrial control networks, which were reported between 1982 and 2014. The results show interesting patterns, with key points highlighted for the purpose of improving the way we plan for and direct efforts toward protecting critical infrastructure and industrial networks.

How to evaluate technologies for health behavior change in HCI research <=SEP=> New technologies for encouraging physical activity, healthy diet, and other types of health behavior change now frequently appear in the HCI literature. Yet, how such technologies should be evaluated within the context of HCI research remains unclear. In this paper, we argue that the obvious answer to this question - that evaluations should assess whether a technology brought about the intended change in behavior - is too limited. We propose that demonstrating behavior change is often infeasible as well as unnecessary for a meaningful contribution to HCI research, especially when in the early stages of design or when evaluating novel technologies. As an alternative, we suggest that HCI contributions should focus on efficacy evaluations that are tailored to the specific behavior-change intervention strategies (e.g., self-monitoring, conditioning) embodied in the system and studies that help gain a deep understanding of people's experiences with the technology.

Millimeter-Wave MIMO Prototype: Measurements and Experimental Results <=SEP=> Millimeter-wave MIMO systems are one of the candidate schemes for 5G wireless standardization efforts. In this context, the main contributions of this article are three-fold. First, we describe parallel sets of measurements at identical transmit-receive location pairs with 2.9, 29 and 61 GHz carrier frequencies in indoor office, shopping mall, and outdoor settings. These measurements provide insights on propagation, blockage and material penetration losses, and the key elements necessary in system design to make mm-Wave systems viable in practice. Second, one of these elements is hybrid beamforming necessary for better link margins by reaping the array gain with large antenna dimensions. From the class of fully-flexible hybrid beamformers, we describe a robust class of directional beamformers toward meeting the high data-rate requirements of mm-Wave systems. Third, leveraging these design insights, we then describe an experimental prototype system at 28 GHz that realizes high data rates on both the downlink and uplink and robustly maintains these rates in outdoor and indoor mobility scenarios. In addition to maintaining large signal constellation sizes in spite of radio frequency challenges, this prototype leverages the directional nature of the mm-Wave channel to perform seamless beam switching and handover across mm-Wave base stations, thereby overcoming the path losses in non-line-of-sight links and blockages encountered at mm-Wave frequencies.

Classroom Response Systems: A Review of the Literature. <=SEP=> As the frequency with which Classroom Response Systems (CRSs) are used is increasing, it becomes more and more important to define the affordances and limitations of these tools. Currently existing literature is largely either anecdotal or focuses on comparing CRS and non-CRS environments that are unequal in other aspects as well. In addition, the literature primarily describes situations in which the CRS is used to provide an individual as opposed to a group response. This article points to the need for a concerted research effort, one that rigorously explores conditions of use across diverse settings and pedagogies.

A unified protocol stack solution for LTE and WLAN in future mobile converged networks <=SEP=> The interworking of the LTE system and WLAN technologies has drawn much attention lately, due to the growing demands for various multimedia services and large data traffic in hotspot areas. Existing research studies have mostly investigated the coupling architectures for these two wireless communication standards at the network layer. However, in the current architectures, many important coordination functions and joint optimizations cannot be accomplished efficiently. To tackle this problem, a new CBS solution is proposed, which integrates different RATs at layer 2 in the true sense of convergence. We design a unified protocol stack that includes all the original functions of both LTE and WLAN systems. Then we propose a convergence architecture, the RMC sublayer, for joint management of these two RATs. The proposed CBS solution can support seamless offloading through soft handover, guaranteed QoS, forwarding management by a single IP address, and customized bandwidth aggregation service. Finally, our simulation and initial experiment results demonstrate the feasibility and efficiency of the CBS solution in future mobile converged networks.

D-CFPR: D numbers extended consistent fuzzy preference relations <=SEP=> X iv :1 40 3. 57 53 v1 [ cs .A I] 2 3 M ar 2 01 4 D-CFPR: D numbers extended consistent fuzzy preference relations Xinyang Deng, Felix T.S. Chan, Rehan Sadiq, Sankaran Mahadevan, Yong Deng School of Computer and Information Science, Southwest University, Chongqing, 400715, China Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China School of Engineering, University of British Columbia, 1137 Alumni Ave, Kelowna, BC V1V 1V7, Canada School of Engineering, Vanderbilt University, Nashville, TN, 37235, USA

Conditional molecular design with deep generative models <=SEP=> Although machine learning has been successfully used to propose novel molecules that satisfy desired properties, it is still challenging to explore a large chemical space efficiently. In this paper, we present a conditional molecular design method that facilitates generating new molecules with desired properties. The proposed model, which simultaneously performs both property prediction and molecule generation, is built as a semisupervised variational autoencoder trained on a set of existing molecules with only a partial annotation. We generate new molecules with desired properties by sampling from the generative distribution estimated by the model. We demonstrate the effectiveness of the proposed model by evaluating it on drug-like molecules. The model improves the performance of property prediction by exploiting unlabeled molecules and efficiently generates novel molecules fulfilling various target conditions.

Identifying extremism in social media with multi-view context-aware subset optimization <=SEP=> Cyber threat intelligence and security informatics play critical roles in the identification of the principal influencers and threats associated with criminal activity and extremism in web-based communities. This paper presents an effective, dynamic learning framework utilizing domain specific context information to design a novel classification model that can robustly identify malicious social media posts (e.g. enrollment propaganda for extremist groups) with expressions of extremism or criminal intent. Research towards automated identification of extreme online posts and their associated key suspects and threats faces numerous challenges, 1) Online data, particularly social media data, originated from numerous independent and heterogeneous sources are largely unstructured. 2) The tactics, techniques, and procedures (TTPs) of criminal activity and extremism are constantly evolving. 3) There are limited ground truth data to support the development of effective classification technologies. In this paper, we present a human-machine collaborative, semi-supervised learning system that can efficiently and effectively identify malicious social media posts in presence of these challenges. Our system and framework develops an initial classifier from limited annotated data and in an interactive manner evolves dynamically into a sophisticated model using shortlisted relevant samples, identified via a graph-based optimization method, solvable by maximum flow algorithm. This same method also may be used to refine the classifier as TTPs evolve. Under this framework, the classifier performance converges faster using roughly 1–2 orders of magnitude fewer annotated samples, as compared to fully supervised solutions, resulting in a reasonably acceptable accuracy of nearly 80%. We validate our framework using a large collection of English and non-English flagged words extracted from three web-based forums and manually verified by multiple independent annotators.

Effect of different surface pre-treatments and luting materials on shear bond strength to PEEK. <=SEP=> OBJECTIVES
To assess the bonding potential of a universal composite resin cement and an adhesive/composite system to differently pre-treated PEEK surfaces.


METHODS
One hundred and fifty PEEK disks were embedded in epoxy resin, polished (P4000 grit) and treated as follows (n=30/group): (A) no treatment, (B) acid etching with sulfuric acid (98%) for 1 min, (C) sandblasting for 10s with 50 microm alumina, (D) sandblasting for 10s with 110 microm alumina and (E) silica coating using the Rocatec system (3M ESPE). Polished and sandblasted (50 microm alumina) cp titanium (grade 4) served as a control. Acrylic hollow cylinders were either luted with a universal composite resin cement (RelyX Unicem) or an unfilled resin (Heliobond) and a hybrid composite (Tetric) to the specimens. Bond strength was measured in a shear test and failure modes were assessed. Statistic analysis was performed with one-way ANOVA followed by a post hoc Scheffé test and unpaired t-tests.


RESULTS
With the universal composite resin cement, no bond could be established on any PEEK surfaces, except specimens etched with sulfuric acid (19.0+/-3.4MPa). Shear bond strength to titanium was significantly lower (8.7+/-2.8MPa, p<0.05). Applying the adhesive/composite system, shear bond strength values on pre-treated PEEK ranged from 11.5+/-3.2MPa (silica coating) to 18.2+/-5.4MPa (acid etched) with no statistically significant differences (p>0.05). No bond was obtained on the polished surface.


SIGNIFICANCE
Bonding to PEEK is possible when using a bonding system. No adherence can be achieved with the tested universal composite resin cement except on an etched surface. The results strongly encourage further research in PEEK application in dentistry.