HF , VHF , and UHF Systems and Technology

A wide variety of unique systems and components inhabits the HF, VHF, and UHF bands. Many communication systems (ionospheric, meteor-burst, and troposcatter) provide beyond-line-of-sight coverage and operate independently of external infrastructure. Broadcasting and over-the-horizon radar also operate in these bands. Magnetic-resonance imaging uses HF/VHF signals to see the interior of a human body, and RF heating is used in a variety of medical and industrial applications. Receivers typically employ a mix of analog and digital-signal-processing techniques. Systems for these frequencies make use of RF-power MOSFETs, p-i-n diodes, and ferrite-loaded transmission-line transformers.


Experimental study of desalination using direct contact membrane distillation : a new approach to flux enhancement

New membrane distillation configurations and a new membrane module were investigated to improve water desalination. The performances of three hydrophobic microporous membranes were evaluated under vacuum enhanced direct contact membrane distillation (DCMD) with a turbulent flow regime and with a feed water temperature of only 40 ◦C. The new configurations provide reduced temperature polarization effects due to better mixing and increased mass transport of water due to higher permeability through the membrane and due to a total pressure gradient across the membrane. Comparison with previously reported results in the literature reveals that mass transport of water vapors is substantially improved with the new approach. The performance of the new configuration was investigated with both NaCl and synthetic sea salt feed solutions. Salt rejection was greater than 99.9% in almost all cases. Salt concentrations in the feed stream had only a minor effect on water flux. The economic aspects of the enhanced DCMD process are briefly discussed and comparisons are made with the reverse osmosis (RO) process for desalination. © 2003 Elsevier B.V. All rights reserved.


Dynamic hand gesture recognition using hidden Markov models

Hand gesture has become a powerful means for human-computer interaction. Traditional gesture recognition just consider hand trajectory. For some specific applications, such as virtual reality, more natural gestures are needed, which are complex and contain movement in 3-D space. In this paper, we introduce an HMM-based method to recognize complex single hand gestures. Gesture images are gained by a common web camera. Skin color is used to segment hand area from the image to form a hand image sequence. Then we put forward a state-based spotting algorithm to split continuous gestures. After that, feature extraction is executed on each gesture. Features used in the system contain hand position, velocity, size, and shape. We raise a data aligning algorithm to align feature vector sequences for training. Then an HMM is trained alone for each gesture. The recognition results demonstrate that our methods are effective and accurate.


Human Action Recognition Using Multi-Velocity STIPs and Motion Energy Orientation Histogram

Local image features in space-time or spatio-temporal interest points provide compact and abstract representations of patterns in a video sequence. In this paper, we present a novel human action recognition method based on multi-velocity spatio-temporal interest points (MVSTIPs) and a novel local descriptor called motion energy (ME) orientation histogram (MEOH). The MVSTIP detection includes three steps: first, filtering video frames with multi-direction ME filters at different speeds to detect significant changes at the pixel level; thereafter, a surround suppression model is employed to rectify the ME deviation caused by the camera motion and complicated backgrounds (e.g., dynamic texture); finally, MVSTIPs are obtained with local maximum filters at multispeeds. After detection, we develop MEOH descriptor to capture the motion features in local regions around interest points. The performance of the proposed method is evaluated on KTH, Weizmann, and UCF sports human action datasets. Results show that our method is robust to both simple and complex backgrounds and the method is superior to other methods that are based on local features.


Pixelwise View Selection for Unstructured Multi-View Stereo

This work presents a Multi-View Stereo system for robust and efficient dense modeling from unstructured image collections. Our core contributions are the joint estimation of depth and normal information, pixelwise view selection using photometric and geometric priors, and a multi-view geometric consistency term for the simultaneous refinement and image-based depth and normal fusion. Experiments on benchmarks and large-scale Internet photo collections demonstrate stateof-the-art performance in terms of accuracy, completeness, and efficiency. Fig. 1. Reconstructions for Louvre, Todai-ji, Paris Opera, and Astronomical Clock.


Hybrid Forest: A Concept Drift Aware Data Stream Mining Algorithm

Nowadays with a growing number of online controlling systems in the organization and also a high demand of monitoring and stats facilities that uses data streams to log and control their subsystems, data stream mining becomes more and more vital. Hoeffding Trees (also called Very Fast Decision Trees a.k.a. VFDT) as a Big Data approach in dealing with the data stream for classification and regression problems showed good performance in handling facing challenges and making the possibility of any-time prediction. Although these methods outperform other methods e.g. Artificial Neural Networks (ANN) and Support Vector Regression (SVR), they suffer from high latency in adapting with new concepts when the statistical distribution of incoming data changes. In this article, we introduced a new algorithm that can detect and handle concept drift phenomenon properly. This algorithms also benefits from fast startup ability which helps systems to be able to predict faster than other algorithms at the beginning of data stream arrival. We also have shown that our approach will overperform other controversial approaches for classification and regression tasks.


An induction furnace employing with half bridge series resonant inverter

In this paper, an induction furnace employing with half bridge series resonant inverter built around IGBT as its switching devices suitable for melting 500 grams of brass is presented. Melting times of 10 minutes were achieved at a power level of approximately 4 kW. The operating frequency is automatically tracked to maintain a small constant lagging phase angle using dual phase lock loop when brass is melted. The coil voltage is controlled to protect the resonant capacitors. The experimental results are presented.


A Review of Several Optimization Problems Related to Security in Networked System

Security issues are becoming more and more important to acti vities of individuals, organizations and the society in our modern ne tworked computerized world. In this chapter we survey a few optimization framewor ks for problems related to security of various networked system such as the int erne or the power grid system.


Improving deep learning performance using random forest HTM cortical learning algorithm

Deep Learning is an artificial intelligence function that imitates the mechanisms of the human mind in processing records and developing shapes to be used in selection construction. The objective of the paper is to improve the performance of the deep learning using a proposed algorithm called RFHTMC. This proposed algorithm is a merged version from Random Forest and HTM Cortical Learning Algorithm. The methodology for improving the performance of Deep Learning depends on the concept of minimizing the mean absolute percentage error which is an indication of the high performance of the forecast procedure. In addition to the overlap duty cycle which its high percentage is an indication of the speed of the processing operation of the classifier. The outcomes depict that the proposed set of rules reduces the absolute percent errors by using half of the value. And increase the percentage of the overlap duty cycle with 15%.


Scientific Paper Summarization Using Citation Summary Networks

Quickly moving to a new area of research is painful for researchers due to the vast amount of scientific literature in each field of study. One possible way to overcome this problem is to summarize a scientific topic. In this paper, we propose a model of summarizing a single article, which can be further used to summarize an entire topic. Our model is based on analyzing others’ viewpoint of the target article’s contributions and the study of its citation summary network using a clustering approach.


Finding Deceptive Opinion Spam by Any Stretch of the Imagination

Consumers increasingly rate, review and research products online (Jansen, 2010; Litvin et al., 2008). Consequently, websites containing consumer reviews are becoming targets of opinion spam. While recent work has focused primarily on manually identifiable instances of opinion spam, in this work we study deceptive opinion spam—fictitious opinions that have been deliberately written to sound authentic. Integrating work from psychology and computational linguistics, we develop and compare three approaches to detecting deceptive opinion spam, and ultimately develop a classifier that is nearly 90% accurate on our gold-standard opinion spam dataset. Based on feature analysis of our learned models, we additionally make several theoretical contributions, including revealing a relationship between deceptive opinions and imaginative writing.


Transductive Inference for Text Classification using Support Vector Machines

This paper introduces Transductive Support Vector Machines (TSVMs) for text classi cation. While regular Support Vector Machines (SVMs) try to induce a general decision function for a learning task, Transductive Support Vector Machines take into account a particular test set and try to minimize misclassi cations of just those particular examples. The paper presents an analysis of why TSVMs are well suited for text classi cation. These theoretical ndings are supported by experiments on three test collections. The experiments show substantial improvements over inductive methods, especially for small training sets, cutting the number of labeled training examples down to a twentieth on some tasks. This work also proposes an algorithm for training TSVMs e ciently, handling 10,000 examples and more.


Thumbs up? Sentiment Classification using Machine Learning Techniques

We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classification, and support vector machines) do not perform as well on sentiment classification as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classification problem more challenging. Publication info: Proceedings of EMNLP 2002, pp. 79–86.


Judging Grammaticality with Tree Substitution Grammar Derivations

In this paper, we show that local features computed from the derivations of tree substitution grammars — such as the identify of particular fragments, and a count of large and small fragments — are useful in binary grammatical classification tasks. Such features outperform n-gram features and various model scores by a wide margin. Although they fall short of the performance of the hand-crafted feature set of Charniak and Johnson (2005) developed for parse tree reranking, they do so with an order of magnitude fewer features. Furthermore, since the TSGs employed are learned in a Bayesian setting, the use of their derivations can be viewed as the automatic discovery of tree patterns useful for classification. On the BLLIP dataset, we achieve an accuracy of 89.9% in discriminating between grammatical text and samples from an n-gram language model.


Code Review Quality: How Developers See It

In a large, long-lived project, an effective code review process is key to ensuring the long-term quality of the code base. In this work, we study code review practices of a large, open source project, and we investigate how the developers themselves perceive code review quality. We present a qualitative study that summarizes the results from a survey of 88 Mozilla core developers. The results provide developer insights into how they define review quality, what factors contribute to how they evaluate submitted code, and what challenges they face when performing review tasks. We found that the review quality is primarily associated with the thoroughness of the feedback, the reviewer's familiarity with the code, and the perceived quality of the code itself. Also, we found that while different factors are perceived to contribute to the review quality, reviewers often find it difficult to keep their technical skills up-to-date, manage personal priorities, and mitigate context switching.


EPOCHS: a platform for agent-based electric power and communication simulation built from commercial off-the-shelf components

This paper reports on the development and subsequent use of the electric power and communication synchronizing simulator (EPOCHS), a distributed simulation environment. Existing electric power simulation tools accurately model power systems of the past, which were controlled as large regional power pools without significant communication elements. However, as power systems increasingly turn to protection and control systems that make use of computer networks, these simulators are less and less capable of predicting the likely behavior of the resulting power grids. Similarly, the tools used to evaluate new communication protocols and systems have been developed without attention to the roles they might play in power scenarios. EPOCHS integrates multiple research and commercial off-the-shelf systems to bridge the gap.


Closed-form Solution for IMU based LSD-SLAM Point Cloud Conversion into the Scaled 3D World Environment

SLAM is a very popular research stream in computer vision and robotics nowadays. For more effective SLAM implementation it is necessary to have reliable information about the environment, also the data should be aligned and scaled according to the real world coordinate system. Monocular SLAM research is an attractive sub-stream, because of the low equipment cost, size and weight. In this paper we present a way to build a conversion from LSD-SLAM coordinate space to the real world coordinates using a true metric scale with IMU sensor data implementation. The causes of differences between the real and calculated spaces are explained and the possibility of conversions between the spaces is proved. Additionally, a closed-form solution for inter space transformation calculation is presented. The synthetic method of generating high level accurate and well controlled input data for the LSD-SLAM algorithm is presented. Finally, the reconstructed 3D environment representation is delivered as an output of the implemented conversion.


Scalable Iterative Classification for Sanitizing Large-Scale Datasets

Cheap ubiquitous computing enables the collection of massive amounts of personal data in a wide variety of domains. Many organizations aim to share such data while obscuring features that could disclose personally identifiable information. Much of this data exhibits weak structure (e.g., text), such that machine learning approaches have been developed to detect and remove identifiers from it. While learning is never perfect, and relying on such approaches to sanitize data can leak sensitive information, a small risk is often acceptable. Our goal is to balance the value of published data and the risk of an adversary discovering leaked identifiers. We model data sanitization as a game between 1) a publisher who chooses a set of classifiers to apply to data and publishes only instances predicted as non-sensitive and 2) an attacker who combines machine learning and manual inspection to uncover leaked identifying information. We introduce a fast iterative greedy algorithm for the publisher that ensures a low utility for a resource-limited adversary. Moreover, using five text data sets we illustrate that our algorithm leaves virtually no automatically identifiable sensitive instances for a state-of-the-art learning algorithm, while sharing over 93 percent of the original data, and completes after at most five iterations.


The diverse CB1 and CB2 receptor pharmacology of three plant cannabinoids: delta9-tetrahydrocannabinol, cannabidiol and delta9-tetrahydrocannabivarin.

Cannabis sativa is the source of a unique set of compounds known collectively as plant cannabinoids or phytocannabinoids. This review focuses on the manner with which three of these compounds, (-)-trans-delta9-tetrahydrocannabinol (delta9-THC), (-)-cannabidiol (CBD) and (-)-trans-delta9-tetrahydrocannabivarin (delta9-THCV), interact with cannabinoid CB1 and CB2 receptors. Delta9-THC, the main psychotropic constituent of cannabis, is a CB1 and CB2 receptor partial agonist and in line with classical pharmacology, the responses it elicits appear to be strongly influenced both by the expression level and signalling efficiency of cannabinoid receptors and by ongoing endogenous cannabinoid release. CBD displays unexpectedly high potency as an antagonist of CB1/CB2 receptor agonists in CB1- and CB2-expressing cells or tissues, the manner with which it interacts with CB2 receptors providing a possible explanation for its ability to inhibit evoked immune cell migration. Delta9-THCV behaves as a potent CB2 receptor partial agonist in vitro. In contrast, it antagonizes cannabinoid receptor agonists in CB1-expressing tissues. This it does with relatively high potency and in a manner that is both tissue and ligand dependent. Delta9-THCV also interacts with CB1 receptors when administered in vivo, behaving either as a CB1 antagonist or, at higher doses, as a CB1 receptor agonist. Brief mention is also made in this review, first of the production by delta9-THC of pharmacodynamic tolerance, second of current knowledge about the extent to which delta9-THC, CBD and delta9-THCV interact with pharmacological targets other than CB1 or CB2 receptors, and third of actual and potential therapeutic applications for each of these cannabinoids.


Design of an IGBT-based LCL-Resonant Inverter for High-Frequency Induction Heating

A power electronic inverter is developed for a high-frequency induction heating application. The application requires up to 160kW of power at a frequency of 1OOkHz. This power-frequency product represents a significant challenge for today's power semiconductor technology. Voltage source and current source inverters both using ZCS or ZVS are analyzed and compared. To attain the level of performance required, an LCL loadresonant topology is selected to enable ZVS close to the zero current crossing of the load. This mode of softswitching is suitable to greatly reduce the IGBT losses. Inverter control is achieved via a Phase Locked Loop (PLL). This paper presents the circuit design, modeling and control considerations.


Parents with doubts about vaccines: which vaccines and reasons why.

OBJECTIVES
The goals were (1) to obtain national estimates of the proportions of parents with indicators of vaccine doubt, (2) to identify factors associated with those parents, compared with parents reporting no vaccine doubt indicators, (3) to identify the specific vaccines that prompted doubt and the reasons why, and (4) to describe the main reasons parents changed their minds about delaying or refusing a vaccine for their child.

METHODS
Data were from the National Immunization Survey (2003-2004). Groups included parents who ever got a vaccination for their child although they were not sure it was the best thing to do ("unsure"), delayed a vaccination for their child ("delayed"), or decided not to have their child get a vaccination ("refused").

RESULTS
A total of 3924 interviews were completed. Response rates were 57.9% in 2003 and 65.0% in 2004. Twenty-eight percent of parents responded yes to ever experiencing >or=1 of the outcome measures listed above. In separate analyses for each outcome measure, vaccine safety concern was a predictor for unsure, refused, and delayed parents. The largest proportions of unsure and refused parents chose varicella vaccine as the vaccine prompting their concern, whereas delayed parents most often reported "not a specific vaccine" as the vaccine prompting their concern. Most parents who delayed vaccines for their child did so for reasons related to their child's illness, unlike the unsure and refused parents. The largest proportion of parents who changed their minds about delaying or not getting a vaccination for their child listed "information or assurances from health care provider" as the main reason.

CONCLUSIONS
Parents who exhibit doubts about immunizations are not all the same. This research suggests encouraging children's health care providers to solicit questions about vaccines, to establish a trusting relationship, and to provide appropriate educational materials to parents.


A Review on Comparative analysis of different clustering and Decision Tree for Synthesized Data Mining Algorithm

Web mining is the sub category or application of data mining techniques to extract knowledge from Web data. With the advent of new advancements in technology the rapid use of new algorithms has been increased in the market. A data mining is one of the fast growing research field which is used in a wide areas of applications. The data mining consists of classification algorithms, association algorithms and searching algorithms. Different classification and clustering algorithm are used for the synthetic datasets. In this paper various techniques that are based on clustering and decision tree for synthesized data mining are discussed.


Poor Man's Methadone: A Case Report of Loperamide Toxicity.

Loperamide, a common over-the-counter antidiarrheal drug and opioid derivative, is formulated to act upon intestinal opioid receptors. However, at high doses, loperamide crosses the blood-brain barrier and reaches central opioid receptors in the brain, leading to central opiate effects including euphoria and respiratory depression. We report the case of a young man found dead in his residence with a known history of drug abuse. At autopsy, the only significant findings were a distended bladder and bloody oral purge. Drug screening found nontoxic levels of alprazolam, fluoxetine, and marijuana metabolites. Liquid chromatography time-of-flight mass spectrometry found an unusual set of split isotope peaks consistent with chlorine. On the basis of autopsy and toxicological findings, loperamide toxicity was suspected because of its opioid properties and molecular formula containing chlorine. A sample of loperamide was analyzed by liquid chromatography time-of-flight mass spectrometry, resulting in a matching mass and retention time to the decedent's sample. Subsequently, quantitative testing detected 63 ng/mL of loperamide or more than 6 times of therapeutic peak concentration. Cause of death was determined as "toxic effects of loperamide with fluoxetine and alprazolam." Because of its opioid effects and easy accessibility, loperamide is known as "poor man's methadone" and may go undetected at medical and forensic drug screening.


Feature Extraction and Classification for Automatic Speaker Recognition System – A Review

Automatic speaker recognition (ASR) has found immense applications in the industries like banking, security, forensics etc. for its advantages such as easy implementation, more secure, more user friendly. To have a good recognition rate is a pre-requisite for any ASR system which can be achieved by making an optimal choice among the available techniques for ASR. In this paper, different techniques for the system have been discussed such as MFCC, LPCC, LPC, Wavelet decomposition for feature extraction and VQ, GMM, SVM, DTW, HMM for feature classification. All these techniques are also compared with each other to find out best suitable candidate among them. On the basis of the comparison done, MFCC has upper edge over other techniques for feature extraction as it is more consistent with human hearing. GMM comes out to be the best among classification models due to its good classification accuracy and less memory usage. Keywords— Automatic Speaker Recognition, Mel Frequency Cepstral Coefficients (MFCC), Linear Predictive Cepstral Coefficients, Gaussian Mixture Model ( GMM), Vector Quantization ( VQ), Dynamic Time Warping (DTW), Hidden Markov Model ( HMM), Wavelet decomposition


Learning to Identify Review Spam

In the past few years, sentiment analysis and opinion mining becomes a popular and important task. These studies all assume that their opinion resources are real and trustful. However, they may encounter the faked opinion or opinion spam problem. In this paper, we study this issue in the context of our product review mining system. On product review site, people may write faked reviews, called review spam, to promote their products, or defame their competitors’ products. It is important to identify and filter out the review spam. Previous work only focuses on some heuristic rules, such as helpfulness voting, or rating deviation, which limits the performance of this task. In this paper, we exploit machine learning methods to identify review spam. Toward the end, we manually build a spam collection from our crawled reviews. We first analyze the effect of various features in spam identification. We also observe that the review spammer consistently writes spam. This provides us another view to identify review spam: we can identify if the author of the review is spammer. Based on this observation, we provide a twoview semi-supervised method, co-training, to exploit the large amount of unlabeled data. The experiment results show that our proposed method is effective. Our designed machine learning methods achieve significant improvements in comparison to the heuristic baselines.


Enhanced cardiac perception is associated with benefits in decision-making.

In the present study we provide the first empirical evidence that viscero-sensory feedback from an internal organ is associated with decision-making processes. Participants with accurate vs. poor perception of their heart activity were compared with regard to their performance in the Iowa Gambling Task. During this task, participants have to choose between four card decks. Decks A and B yield high gains and high losses, and if played continuously, result in net loss. In contrast, decks C and D yield small gains and also small losses, but result in net profit if they are selected continuously. Accordingly, participants have to learn to avoid the net loss options in favor of the net gain options. In our study, participants with good cardiac perception chose significantly more of the net gain and fewer of the net loss options. Our findings document the substantial role of visceral feedback in decision-making processes in complex situations.


Novel blood pressure estimation method using single photoplethysmography feature

Continuous blood pressure (BP) monitoring has a significant meaning to the prevention and early diagnosis of cardiovascular disease. However, existing continuous BP monitoring approaches, especially cuff-less BP monitoring approaches, are all contraptions which complex and huge computation required. For example, for the most sophisticated cuff-less BP monitoring method using pulse transit time (PTT), the simultaneous record of photoplethysmography (PPG) signal and electrocardiography (ECG) are required, and various measurement of characteristic points are needed. These issues hindered widely application of cuff less BP measurement in the wearable devices. In this study, a novel BP estimation method using single PPG signal feature was proposed and its performance in BP estimation was also tested. The results showed that the new approach proposed in this study has a mean error −0.91 ± 3.84 mmHg for SBP estimation and −0.36 ± 3.36 mmHg for DBP estimation respectively. This approach performed better than the traditional PTT based BP estimation, which mean error for SBP estimation was −0.31 ± 4.78 mmHg, and for DBP estimation was −0.18 ± 4.32 mmHg. Further investigation revealed that this new BP estimation approach only required measurement of one characteristic point, reducing much computation when implementing. These results demonstrated that this new approach might be more suitable implemented in the wearable BP monitoring devices.


Wideband printed rectangular monopole antenna for circularly polarization

In this paper, a printed monopole antenna for circular polarization has been proposed. The relationship between the geometry of the antenna and the axial ratio is investigated. A wideband circularly polarized antenna is designed according to the parametric studies. The simulated bandwidth of 3dB-axial ratio is from 2.06GHz to 6.04GHz (98%).


An Artificial Neural Network based Intrusion Detection System and Classification of Attacks

Network security is becoming an issue of paramount importance in the information technology era. Nowadays with the dramatic growth of communication and computer networks, security has become a critical subject for computer system. Intrusion detection is the art of detecting computer abuse and any attempt to break the networks. Intrusion detection system is an effective security tool that helps to prevent unauthorized access to network resources by analyzing the network traffic. Different algorithms, methods and applications are created and implemented to solve the problem of detecting the attacks in intrusion detection systems. Most methods detect attacks and categorize in two groups, normal or threat. One of the most promising areas of research in the area of Intrusion Detection deals with the applications of the Artificial Intelligence (AI) techniques. This proposed system presents a new approach of intrusion detection system based on artificial neural network. Multi Layer Percepron (MLP) architecture is used for Intrusion Detection System. The performance and evaluations are performed by using the set of benchmark data from a KDD (Knowledge discovery in Database) dataset. The proposed system detects the attacks and classifies them in six groups.


What can quantum theory bring to information retrieval

The probabilistic formalism of quantum physics is said to provide a sound basis for building a principled information retrieval framework. Such a framework can be based on the notion of information need vector spaces where events, such as document relevance or observed user interactions, correspond to subspaces. As in quantum theory, a probability distribution over these subspaces is defined through weighted sets of state vectors (density operators), and used to represent the current view of the retrieval system on the user information need. Tensor spaces can be used to capture different aspects of information needs. Our evaluation shows that the framework can lead to acceptable performance in an ad-hoc retrieval task. Going beyond this, we discuss the potential of the framework for three active challenges in information retrieval, namely, interaction, novelty and diversity.


The Formulation of Parameters for Type Design of Indian Script Based on Calligraphic Studies

A number of parameters were formulated for better analysing the anatomy of Indic letterforms. This methodology contributes to the understanding of the intricacies of type design of complex Indian scripts.


Using association rules to guide a search for best fitting transfer models of student learning

We say a transfer model is a mapping between the questions in an intelligent tutoring system and the knowledge components (i.e., skills, strategies, declarative knowledge, etc) needed to answer a question correctly. [JKT00] showed how you could take advantage of 1) the Power Law Of Learning, 2) an existing transfer model, and 3) set of tutorial log files, to learn a function (using logistic regression) that will predict when a student will get a question correct. In the main conference proceeding [CHK2004] give an example of using this technique for transfer model selection. Koedinger and Junker [KJ99] also conceptualized a search space where each state is a new transfer model. The operators in this search space split, add or merge knowledge components based upon factors that are tagged to questions. Koedinger and Junker called this method learning factors analysis emphasizing that this method can be used to study learning. Unfortunately, the search space is huge and searching for good fitting transfer models is exponential. The main goal of this paper is show a technique that will make searching for transfer models more efficient. Our procedure implements a search method using association rules as a means of guiding the search. The association rules are mined from a dataset derived from student-tutor interaction logs. The association rules found in the mining process determine what operations to perform on the current transfer model. We report on the speed up achieved. Being able to find good transfer models quicker will help intelligent tutor system builders as well as cognitive science researchers better assess what makes certain problems hard and other problems easy for students.


A Survey of In-Band Full-Duplex Transmission: From the Perspective of PHY and MAC Layers

In-band full-duplex (IBFD) transmission represents an attractive option for increasing the throughput of wireless communication systems. A key challenge for IBFD transmission is reducing self-interference. Fortunately, the power associated with residual self-interference can be effectively canceled for feasible IBFD transmission with combinations of various advanced passive, analog, and digital self-interference cancellation schemes. In this survey paper, we first review the basic concepts of IBFD transmission with shared and separated antennas and advanced self-interference cancellation schemes. Furthermore, we also discuss the effects of IBFD transmission on system performance in various networks such as bidirectional, relay, and cellular topology networks. This survey covers a wide array of technologies that have been proposed in the literature as feasible for IBFD transmission and evaluates the performance of the IBFD systems compared to conventional half-duplex transmission in connection with theoretical aspects such as the achievable sum rate, network capacity, system reliability, and so on. We also discuss the research challenges and opportunities associated with the design and analysis of IBFD systems in a variety of network topologies. This work also explores the development of MAC protocols for an IBFD system in both infrastructure-based and ad hoc networks. Finally, we conclude our survey by reviewing the advantages of IBFD transmission when applied for different purposes, such as spectrum sensing, network secrecy, and wireless power transfer.


KP-Miner: A keyphrase extraction system for English and Arabic documents

Automatic key phrase extraction has many important applications including but not limited to summarization, cataloging/indexing, feature extraction for clustering and classification, and data mining. This paper presents the KP-Miner system, and demonstrates through experimentation and comparison with existing systems that the it is effective in extracting keyphrases from both English and Arabic documents of varied length. Unlike other existing keyphrase extraction systems, the KP-Miner system has the advantage of being configurable as the rules and heuristics adopted by the system are related to the general nature of documents and keyphrase. This implies that the users of this system can use their understanding of the document(s) being input into the system, to fine tune it to their particular needs.


Docker Cluster Management for the Cloud - Survey Results and Own Solution

Docker provides a good basis to run composite applications in the cloud, especially if those are not cloud-aware, or cloud-native. However, Docker concentrates on managing containers on one host, but SaaS providers need a container management solution for multiple hosts. Therefore, a number of tools emerged that claim to solve the problem. This paper classifies the solutions, maps them to requirements from a case study and identifies gaps and integration requirements. We close some of these gaps with our own integration components and tool enhancements, resulting in the currently most complete management suite.


Technology adoption: A conjoint analysis of consumers' preference on future online banking services

The importance of service delivery technology and online service adoption and usage in the banking industry has received an increased discussion in the literature in recent years. Owing to the fact that Strong online banking services are important drivers for bank performance and customer service delivery; several studies have been carried out on online banking service adoption or acceptance where services are already deployed and on the factors that influence customers' adoption and use or intention to use those services. However, despite the increasing discussion in the literatures, no attempt has been made to look at consumers' preference in terms of future online banking service adoption. This study used conjoint analysis and stated preference methods with discrete choice model to analyze the technology adoption pattern regarding consumers' preference for potential future online banking services in the Nigerian banking industry. The result revealed that to increase efficiency and strengthen competitiveness, banks need to promote smart and practical branded services especially self-services at the same time promote a universal adoption of e-banking system services that add entertainment or extra convenience to customers such as ease of usage including digital wallet, real-time interaction (video banking), ATMs integrated with smart phones, website customization, biometric services, and digital currency. These services can contribute to an increasing adoption of online services. & 2015 Elsevier Ltd. All rights reserved.


Information overload and the message dynamics of online interaction spaces: a theoretical model and empirical exploration

This publication contains reprint articles for which IEEE does not hold copyright. Full text is not available on IEEE Xplore for these articles.


Decoding actions at different levels of abstraction.

Brain regions that mediate action understanding must contain representations that are action specific and at the same time tolerate a wide range of perceptual variance. Whereas progress has been made in understanding such generalization mechanisms in the object domain, the neural mechanisms to conceptualize actions remain unknown. In particular, there is ongoing dissent between motor-centric and cognitive accounts whether premotor cortex or brain regions in closer relation to perceptual systems, i.e., lateral occipitotemporal cortex, contain neural populations with such mapping properties. To date, it is unclear to which degree action-specific representations in these brain regions generalize from concrete action instantiations to abstract action concepts. However, such information would be crucial to differentiate between motor and cognitive theories. Using ROI-based and searchlight-based fMRI multivoxel pattern decoding, we sought brain regions in human cortex that manage the balancing act between specificity and generality. We investigated a concrete level that distinguishes actions based on perceptual features (e.g., opening vs closing a specific bottle), an intermediate level that generalizes across movement kinematics and specific objects involved in the action (e.g., opening different bottles with cork or screw cap), and an abstract level that additionally generalizes across object category (e.g., opening bottles or boxes). We demonstrate that the inferior parietal and occipitotemporal cortex code actions at abstract levels whereas the premotor cortex codes actions at the concrete level only. Hence, occipitotemporal, but not premotor, regions fulfill the necessary criteria for action understanding. This result is compatible with cognitive theories but strongly undermines motor theories of action understanding.


TRESOR Runs Encryption Securely Outside RAM

Current disk encryption techniques store necessary keys in RAM and are therefore susceptible to attacks that target volatile memory, such as Firewire and cold boot attacks. We present TRESOR, a Linux kernel patch that implements the AES encryption algorithm and its key management solely on the microprocessor. Instead of using RAM, TRESOR ensures that all encryption states as well as the secret key and any part of it are only stored in processor registers throughout the operational time of the system, thereby substantially increasing its security. Our solution takes advantage of Intel’s new AES-NI instruction set and exploits the x86 debug registers in a non-standard way, namely as cryptographic key storage. TRESOR is compatible with all modern Linux distributions, and its performance is on a par with that of standard AES implementations.


Discoverer: Automatic Protocol Reverse Engineering from Network Traces

Application-level protocol specifications are useful for many security applications, including intrusion prevention and detection that performs deep packet inspection and traffic normalization, and penetration testing that generates network inputs to an application to uncover potential vulnerabilities. However, current practice in deriving protocol specifications is mostly manual. In this paper, we present Discoverer, a tool for automatically reverse engineering the protocol message formats of an application from its network trace. A key property of Discoverer is that it operates in a protocol-independent fashion by inferring protocol idioms commonly seen in message formats of many application-level protocols. We evaluated the efficacy of Discoverer over one text protocol (HTTP) and two binary protocols (RPC and CIFS/SMB) by comparing our inferred formats with true formats obtained from Ethereal [5]. For all three protocols, more than 90% of our inferred formats correspond to exactly one true format; one true format is reflected in five inferred formats on average; our inferred formats cover over 95% of messages, which belong to 30-40% of true formats observed in the trace.


Howard: A Dynamic Excavator for Reverse Engineering Data Structures

Even the most advanced reverse engineering techniques and products are weak in recovering data structures in stripped binaries—binaries without symbol tables. Unfortunately, forensics and reverse engineering without data structures is exceedingly hard. We present a new solution, known as Howard, to extract data structures from C binaries without any need for symbol tables. Our results are significantly more accurate than those of previous methods — sufficiently so to allow us to generate our own (partial) symbol tables without access to source code. Thus, debugging such binaries becomes feasible and reverse engineering becomes simpler. Also, we show that we can protect existing binaries from popular memory corruption attacks, without access to source code. Unlike most existing tools, our system uses dynamic analysis (on a QEMU-based emulator) and detects data structures by tracking how a program uses memory.


Transparent Runtime Shadow Stack : Protection against malicious return address modifications

Exploitation of buffer overflow vulnerabilities constitutes a significant portion of security attacks in computer systems. One of the most common types of buffer overflow attacks is the hijacking of the program counter by overwriting function return addresses in the process’ stack so as to redirect the program’s control flow to some malicious code injected into the process’ memory. Previous solutions to this problem are based either on hardware or the compiler. The former requires special hardware while the latter requires the source code of the software. In this paper we introduce the use of a T ransparent RUntime Shadow Stack (TRUSS) to protect against function return address modification. Our proposed scheme is built on top of DynamoRIO, a dynamic binary rewriting framework. DynamoRIO is implemented on both Windows and Linux. Hence, our scheme is able to protect applications on both operating systems. We have successfully tested our implementation on the SPECINT 2000 benchmark programs on both Windows and Linux, John Wilander’s “Dynamic testbed for twenty buffer overflow attacks” as well as Microsoft Access, Powerpoint and Word 2002. This paper will discuss the implementation details of our scheme as well as provide a performance evaluation. The latter shows that TRUSS is able to operate with an average overhead of about 20% to 50% which we believe is acceptable.


Automatic Reverse Engineering of Data Structures from Binary Execution

With only the binary executable of a program, it is useful to discover the program’s data structures and infer their syntactic and semantic definitions. Such knowledge is highly valuable in a variety of security and forensic applications. Although there exist efforts in program data structure inference, the existing solutions are not suitable for our targeted application scenarios. In this paper, we propose a reverse engineering technique to automatically reveal program data structures from binaries. Our technique, called REWARDS, is based on dynamic analysis. More specifically, each memory location accessed by the program is tagged with a timestamped type attribute. Following the program’s runtime data flow, this attribute is propagated to other memory locations and registers that share the same type. During the propagation, a variable’s type gets resolved if it is involved in a type-revealing execution point or “type sink”. More importantly, besides the forward type propagation, REWARDS involves a backward type resolution procedure where the types of some previously accessed variables get recursively resolved starting from a type sink. This procedure is constrained by the timestamps of relevant memory locations to disambiguate variables reusing the same memory location. In addition, REWARDS is able to reconstruct in-memory data structure layout based on the type information derived. We demonstrate that REWARDS provides unique benefits to two applications: memory image forensics and binary fuzzing for vulnerability discovery.


Diagnosis and debugging of programmable logic controller control programs by neural networks

Ladder logic diagram (LLD) as the interfacing programming language of programmable logic controllers (PLCs) is utilized in modern discrete event control systems. However, LLD is hard to debug and maintain in practice. This is due to many factors such as non-structured nature of LLD, the LLD programmers' background, and the huge sizes of real world LLD. In this paper, we introduce a recurrent neural network (RNN) based technique for PLC program diagnosis. A manufacturing control system example has been presented to illustrate the applicability of the proposed algorithm. This method could be very advantageous in reducing the complexity in PLC control programs diagnosis because of the ease of use of the RNN compared to debugging the LLD code.


Support Vector Machine-recursive feature elimination for the diagnosis of Parkinson disease based on speech analysis

Parkinson disease has become a serious problem in the old people. There is no precise method to diagnosis Parkinson disease now. Considering the significance and difficulty of recognizing the Parkinson disease, the measurement of samples' voices is regard as one of the best non-invasive ways to find the real patient. Support Vector Machine is one of the most effective tools to classify in machine learning, and it has been applied successfully in many areas. In this paper, we implement the SVM-recursive feature elimination which has not been used before for selecting the subset including the most important features for classification from the original features. We also implement SVM with PCA for selecting the principle components for diagnosis PD set with 22 features in order to compare. At last, we discuss the relationship between SVM-RFE and SVM with PCA specially in the experiment. The experiment illustrates that the SVM-RFE has the better performance than other methods in general.


Towards efficient content-aware search over encrypted outsourced data in cloud

With the increasing adoption of cloud computing, a growing number of users outsource their datasets into cloud. The datasets usually are encrypted before outsourcing to preserve the privacy. However, the common practice of encryption makes the effective utilization difficult, for example, search the given keywords in the encrypted datasets. Many schemes are proposed to make encrypted data searchable based on keywords. However, keyword-based search schemes ignore the semantic representation information of users retrieval, and cannot completely meet with users search intention. Therefore, how to design a content-based search scheme and make semantic search more effective and context-aware is a difficult challenge. In this paper, we proposed an innovative semantic search scheme based on the concept hierarchy and the semantic relationship between concepts in the encrypted datasets. More specifically, our scheme first indexes the documents and builds trapdoor based on the concept hierarchy. To further improve the search efficiency, we utilize a tree-based index structure to organize all the document index vectors. Our experiment results based on the real world datasets show the scheme is more efficient than previous scheme. We also study the threat model of our approach and prove it does not introduce any security risk.


A new multiclass SVM algorithm and its application to crowd density analysis using LBP features

Crowd density analysis is a crucial component in visual surveillance for security monitoring. In this paper, we propose to estimate crowd density at patch level, where the size of each patch varies in such way to compensate the effects of perspective distortions. The main contribution of this paper is two-fold: First, we propose to learn a discriminant subspace of the high-dimensional Local Binary Pattern (LBP) instead of using raw LBP feature vector. Second, an alternative algorithm for multiclass SVM based on relevance scores is proposed. The effectiveness of the proposed approach is evaluated on PETS dataset, and the results demonstrate the effect of low-dimensional compact representation of LBP on the classification accuracy. Also, the performance of the proposed multiclass SVM algorithm is compared to other frequently used algorithms for multi-classification problem and the proposed algorithm gives good results while reducing the complexity of the classification.


A geometric view of optimal transportation and generative model

In this work, we show the intrinsic relations between optimal transportation and convex geometry, especially the variational approach to solve Alexandrov problem: constructing a convex polytope with prescribed face normals and volumes. This leads to a geometric interpretation to generative models, and leads to a novel framework for generative models. By using the optimal transportation view of GAN model, we show that the discriminator computes the Kantorovich potential, the generator calculates the transportation map. For a large class of transportation costs, the Kantorovich potential can give the optimal transportation map by a close-form formula. Therefore, it is sufficient to solely optimize the discriminator. This shows the adversarial competition can be avoided, and the computational architecture can be simplified. Preliminary experimental results show the geometric method outperforms WGAN for approximating probability measures with multiple clusters in low dimensional space.


The challenges of SVM optimization using Adaboost on a phoneme recognition problem

The use of digital technology is growing at a very fast pace which led to the emergence of systems based on the cognitive infocommunications. The expansion of this sector impose the use of combining methods in order to ensure the robustness in cognitive systems.


Dry electrodes for electrocardiography.

Patient biopotentials are usually measured with conventional disposable Ag/AgCl electrodes. These electrodes provide excellent signal quality but are irritating for long-term use. Skin preparation is usually required prior to the application of electrodes such as shaving and cleansing with alcohol. To overcome these difficulties, researchers and caregivers seek alternative electrodes that would be acceptable in clinical and research environments. Dry electrodes that operate without gel, adhesive or even skin preparation have been studied for many decades. They are used in research applications, but they have yet to achieve acceptance for medical use. So far, a complete comparison and evaluation of dry electrodes is not well described in the literature. This work compares dry electrodes for biomedical use and physiological research, and reviews some novel systems developed for cardiac monitoring. Lastly, the paper provides suggestions to develop a dry-electrode-based system for mobile and long-term cardiac monitoring applications.


Object Category Understanding via Eye Fixations on Freehand Sketches

The study of eye gaze fixations on photographic images is an active research area. In contrast, the image sub-category of freehand sketches has not received as much attention for such studies. In this paper, we analyze the results of a free-viewing gaze fixation study conducted on 3904 freehand sketches distributed across 160 object categories. Our analysis shows that fixation sequences exhibit marked consistency within a sketch, across sketches of a category and even across suitably grouped sets of categories. This multi-level consistency is remarkable given the variability in depiction and extreme image content sparsity that characterizes hand-drawn object sketches. In this paper, we show that the multi-level consistency in the fixation data can be exploited to 1) predict a test sketch’s category given only its fixation sequence and 2) build a computational model which predicts part-labels underlying fixations on objects. We hope that our findings motivate the community to deem sketch-like representations worthy of gaze-based studies vis-a-vis photographic images.


A rare case of mesh infection 3 years after a laparoscopic totally extraperitoneal (TEP) inguinal hernia repair.

Late complications after a laparoscopic inguinal hernia repair are extremely rare and have only recently entered into the literature. One such late complication is mesh infection, of which there have been a handful of cases reported in the literature. Mesh infections occurring many years after inguinal hernia repairs are not only of significance because they are not well documented in the literature, and the pathogenesis and risk factors contributing to their development are not well understood. This report details a rare case of mesh infection 3 years after a laparoscopic totally extraperitoneal inguinal hernia repair, describes our management of the condition, highlights the current options for management, and attempts to define its pathophysiology.


Visual domain-specific modelling : Benefits and experiences of using metaCASE tools

1 Introduction Jackson (Jackson 95) recognises the vital difference between an application's domain and its code: two different worlds, each with its own language, experts, ways of thinking etc. A finished application forms the intersection between these worlds. The difficult job of the software engineer is to build a bridge between these worlds, at the same time as solving problems in both worlds.


Digital Image Forgeries and Passive Image Authentication Techniques: A Survey

Digital images are present everywhere on magazine covers, in newspapers, in courtrooms as evidences, and all over the Internet signifying one of the major ways for communication nowadays. The trustworthiness of digital images has been questioned, because of the ease with which these images can be manipulated in both its origin & content as a result of tremendous growth of digital image manipulation tools. Digital image forensics is the latest research field which intends to authorize the genuineness of images. This survey attempts to provide an overview of various digital image forgeries and the state of art passive methods to authenticate digital images.


A systematic review of neurobiological and clinical features of mindfulness meditations.

BACKGROUND
Mindfulness meditation (MM) practices constitute an important group of meditative practices that have received growing attention. The aim of the present paper was to systematically review current evidence on the neurobiological changes and clinical benefits related to MM practice in psychiatric disorders, in physical illnesses and in healthy subjects.

METHOD
A literature search was undertaken using Medline, ISI Web of Knowledge, the Cochrane collaboration database and references of retrieved articles. Controlled and cross-sectional studies with controls published in English up to November 2008 were included.

RESULTS
Electroencephalographic (EEG) studies have revealed a significant increase in alpha and theta activity during meditation. Neuroimaging studies showed that MM practice activates the prefrontal cortex (PFC) and the anterior cingulate cortex (ACC) and that long-term meditation practice is associated with an enhancement of cerebral areas related to attention. From a clinical viewpoint, Mindfulness-Based Stress Reduction (MBSR) has shown efficacy for many psychiatric and physical conditions and also for healthy subjects, Mindfulness-Based Cognitive Therapy (MBCT) is mainly efficacious in reducing relapses of depression in patients with three or more episodes, Zen meditation significantly reduces blood pressure and Vipassana meditation shows efficacy in reducing alcohol and substance abuse in prisoners. However, given the low-quality designs of current studies it is difficult to establish whether clinical outcomes are due to specific or non-specific effects of MM.

DISCUSSION
Despite encouraging findings, several limitations affect current studies. Suggestions are given for future research based on better designed methodology and for future directions of investigation.


When D2D meets cloud: Hybrid mobile task offloadings in fog computing

In this paper we propose HyFog, a novel hybrid task offloading framework in fog computing, where device users have the flexibility of choosing among multiple options for task executions, including local mobile execution, Device-to-Device (D2D) offloaded execution, and Cloud offloaded execution. We further develop a novel three-layer graph matching algorithm for efficient hybrid task offloading among the devices. Specifically, we first construct a three-layer graph to capture the choice space enabled by these three execution approaches, and then the problem of minimizing the total task execution cost is recast as a minimum weight matching problem over the constructed three-layer graph, which can be efficiently solved using the Edmonds's Blossom algorithm. Numerical results demonstrate that the proposed three-layer graph matching solution can achieve superior performance, with more than 50% cost reduction over the case of local task executions by all the devices.


Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture

Recent studies have demonstrated the usefulness of optical indices from hyperspectral remote sensing in the assessment of vegetation biophysical variables both in forestry and agriculture. Those indices are, however, the combined response to variations of several vegetation and environmental properties, such as Leaf Area Index (LAI), leaf chlorophyll content, canopy shadows, and background soil reflectance. Of particular significance to precision agriculture is chlorophyll content, an indicator of photosynthesis activity, which is related to the nitrogen concentration in green vegetation and serves as a measure of the crop response to nitrogen application. This paper presents a combined modeling and indices-based approach to predicting the crop chlorophyll content from remote sensing data while minimizing LAI (vegetation parameter) influence and underlying soil (background) effects. This combined method has been developed first using simulated data and followed by evaluation in terms of quantitative predictive capability using real hyperspectral airborne data. Simulations consisted of leaf and canopy reflectance modeling with PROSPECT and SAILH radiative transfer models. In this modeling study, we developed an index that integrates advantages of indices minimizing soil background effects and indices that are sensitive to chlorophyll concentration. Simulated data have shown that the proposed index Transformed Chlorophyll Absorption in Reflectance Index/Optimized Soil-Adjusted Vegetation Index (TCARI/OSAVI) is both very sensitive to chlorophyll content variations and very resistant to the variations of LAI and solar zenith angle. It was therefore possible to generate a predictive equation to estimate leaf chlorophyll content from the combined optical index derived from above-canopy reflectance. This relationship was evaluated by application to hyperspectral CASI imagery collected over corn crops in three experimental farms from Ontario and Quebec, Canada. The results presented here are from the L’Acadie, Quebec, Agriculture and AgriFood Canada research site. Images of predicted leaf chlorophyll content were generated. Evaluation showed chlorophyll variability over crop plots with various levels of nitrogen, and revealed an excellent agreement with ground truth, with a correlation of r = .81 between estimated and field measured chlorophyll content data. D 2002 Elsevier Science Inc. All rights reserved.


The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations

In this paper, we describe the acquisition and contents of a large-scale Chinese face database: the CAS-PEAL face database. The goals of creating the CAS-PEAL face database include the following: 1) providing the worldwide researchers of face recognition with different sources of variations, particularly pose, expression, accessories, and lighting (PEAL), and exhaustive ground-truth information in one uniform database; 2) advancing the state-of-the-art face recognition technologies aiming at practical applications by using off-the-shelf imaging equipment and by designing normal face variations in the database; and 3) providing a large-scale face database of Mongolian. Currently, the CAS-PEAL face database contains 99 594 images of 1040 individuals (595 males and 445 females). A total of nine cameras are mounted horizontally on an arc arm to simultaneously capture images across different poses. Each subject is asked to look straight ahead, up, and down to obtain 27 images in three shots. Five facial expressions, six accessories, and 15 lighting changes are also included in the database. A selected subset of the database (CAS-PEAL-R1, containing 30 863 images of the 1040 subjects) is available to other researchers now. We discuss the evaluation protocol based on the CAS-PEAL-R1 database and present the performance of four algorithms as a baseline to do the following: 1) elementarily assess the difficulty of the database for face recognition algorithms; 2) preference evaluation results for researchers using the database; and 3) identify the strengths and weaknesses of the commonly used algorithms.


Recovering free space of indoor scenes from a single image

In this paper we consider the problem of recovering the free space of an indoor scene from its single image. We show that exploiting the box like geometric structure of furniture and constraints provided by the scene, allows us to recover the extent of major furniture objects in 3D. Our “boxy” detector localizes box shaped objects oriented parallel to the scene across different scales and object types, and thus blocks out the occupied space in the scene. To localize the objects more accurately in 3D we introduce a set of specially designed features that capture the floor contact points of the objects. Image based metrics are not very indicative of performance in 3D. We make the first attempt to evaluate single view based occupancy estimates for 3D errors and propose several task driven performance measures towards it. On our dataset of 592 indoor images marked with full 3D geometry of the scene, we show that: (a) our detector works well using image based metrics; (b) our refinement method produces significant improvements in localization in 3D; and (c) if one evaluates using 3D metrics, our method offers major improvements over other single view based scene geometry estimation methods.


Educational games for improving the teaching-learning process of a CLIL subject: Physics and chemistry in secondary education

The use of educational games in an academic context seems to be a superb alternative to traditional learning activities, such as drill-type exercises, in order to engage 21st-century students. Consequently, this work tries to raise the following objectives: analyze the effectiveness of game-based learning, characterize game elements that may contribute to create playing experiences, comprehend how different player types interact with games and, finally, design interactive games which may create challenges, set goals and provide feedback on progress while motivating learners to study physics and chemistry in a foreign language in the second cycle of Secondary Education (4th E.S.O. that corresponds to the age of 15-16 years old). Specifically, we have used several Web 2.0 tools (Hot Potatoes, Scratch, What2Learn and SMART Notebook 11) and applications (Microsoft PowerPoint and Microsoft Excel) in order to create the games; and these games are based on the successive contents: laboratory safety, laboratory equipment, stoichiometry, atomic structure, electronic configuration, the periodic table, forces, motion and energy.


Classification and Adaptive Novel Class Detection of Feature-Evolving Data Streams

Data stream classification poses many challenges to the data mining community. In this paper, we address four such major challenges, namely, infinite length, concept-drift, concept-evolution, and feature-evolution. Since a data stream is theoretically infinite in length, it is impractical to store and use all the historical data for training. Concept-drift is a common phenomenon in data streams, which occurs as a result of changes in the underlying concepts. Concept-evolution occurs as a result of new classes evolving in the stream. Feature-evolution is a frequently occurring process in many streams, such as text streams, in which new features (i.e., words or phrases) appear as the stream progresses. Most existing data stream classification techniques address only the first two challenges, and ignore the latter two. In this paper, we propose an ensemble classification framework, where each classifier is equipped with a novel class detector, to address concept-drift and concept-evolution. To address feature-evolution, we propose a feature set homogenization technique. We also enhance the novel class detection module by making it more adaptive to the evolving stream, and enabling it to detect more than one novel class at a time. Comparison with state-of-the-art data stream classification techniques establishes the effectiveness of the proposed approach.


A Driving Right Leg Circuit (DgRL) for Improved Common Mode Rejection in Bio-Potential Acquisition Systems

The paper presents a novel Driving Right Leg (DgRL) circuit designed to mitigate the effect of common mode signals deriving, say, from power line interferences. The DgRL drives the isolated ground of the instrumentation towards a voltage which is fixed with respect to the common mode potential on the subject, therefore minimizing common mode voltage at the input of the front-end. The paper provides an analytical derivation of the common mode rejection performances of DgRL as compared to the usual grounding circuit or Driven Right Leg (DRL) loop. DgRL is integrated in a bio-potential acquisition system to show how it can reduce the common mode signal of more than 70 dB with respect to standard patient grounding. This value is at least 30 dB higher than the reduction achievable with DRL, making DgRL suitable for single-ended front-ends, like those based on active electrodes. EEG signal acquisition is performed to show how the system can successfully cancel power line interference without any need for differential acquisition, signal post-processing or filtering.


Head Nod Detection from a Full 3D Model

As a non-verbal communication mean, head gestures play an important role in face-to-face conversation and recognizing them is therefore of high value for social behavior analysis or Human Robotic Interactions (HRI) modelling. Among the various gestures, head nod is the most common one and can convey agreement or emphasis. In this paper, we propose a novel nod detection approach based on a full 3D face centered rotation model. Compared to previous approaches, we make two contributions. Firstly, the head rotation dynamic is computed within the head coordinate instead of the camera coordinate, leading to pose invariant gesture dynamics. Secondly, besides the rotation parameters, a feature related to the head rotation axis is proposed so that nod-like false positives due to body movements could be eliminated. The experiments on two-party and four-party conversations demonstrate the validity of the approach.


Algorithmic Di erentiation in Python with AlgoPy

Many programs for scienti c computing in Python are based on NumPy and therefore make heavy use of numerical linear algebra (NLA) functions, vectorized operations, slicing and broadcasting. AlgoPy provides the means to compute derivatives of arbitrary order and Taylor approximations of such programs. The approach is based on a combination of univariate Taylor polynomial arithmetic and matrix calculus in the (combined) forward/reverse mode of Algorithmic Di erentiation (AD). In contrast to existing AD tools, vectorized operations and NLA functions are not considered to be a sequence of scalar elementary functions. Instead, dedicated algorithms for the matrix product, matrix inverse and the Cholesky, QR, and symmetric eigenvalue decomposition are implemented in AlgoPy. We discuss the reasons for this alternative approach and explain the underlying idea. Examples illustrate how AlgoPy can be used from a user's point of view.


Energy detection spectrum sensing on DPSK modulation transceiver using GNU radio

Cognitive radio (CR) can facilitate the issue of range shortage by permitting secondary users to coincide with incumbent clients in authorized range groups, while it causes no interference to occupant interchanges. Spectrum sensing is the essential in enabling innovation of CR to detect the presence of primary users (PUs) or authorized user signals and adventure the spectrum holes. Henceforth, this paper addresses the testing of spectrum sensing method for Differential Phase Shift Keying (DPSK) transceiver by utilizing an open source programming, GNU Radio. Moreover, the effect of various reproduction channels, for example, Dynamic channel demonstrate with Rayleigh fading, dynamic channel display with Rician fading, frequency selective faded channel show with Rayleigh and frequency selective faded channel show with Rician fading are exhibited.


Nerve root sedimentation sign: evaluation of a new radiological sign in lumbar spinal stenosis.

STUDY DESIGN
Retrospective case-referent study.

OBJECTIVE
To assess whether the new sedimentation sign discriminates between nonspecific low back pain (LBP) and symptomatic lumbar spinal stenosis (LSS).

SUMMARY OF BACKGROUND DATA
In the diagnosis of LSS, radiologic findings do not always correlate with clinical symptoms, and additional diagnostic signs are needed. In patients without LSS, we observe the sedimentation of lumbar nerve roots to the dorsal part of the dural sac on supine magnetic resonance image scans. In patients with symptomatic and morphologic central LSS, this sedimentation is rarely seen. We named this phenomenon "sedimentation sign" and defined the absence of sedimenting nerve roots as positive sedimentation sign for the diagnosis of LSS.

METHODS
This study included 200 patients. Patients in the LSS group (n = 100) showed claudication with or without LBP and leg pain, a cross-sectional area <80 mm, and a walking distance <200 m; patients in the LBP group (n = 100) had LBP, no leg pain, no claudication, a cross-sectional area of the dural sac >120 mm, and a walking distance >1000 m. The frequency of a positive sedimentation sign was compared between the 2 groups, and intraobserver and interobserver reliability were assessed in a random subsample (n = 20).

RESULTS
A positive sedimentation sign was identified in 94 patients in the LSS group (94%; 95% confidence interval, 90%-99%) but none in the LBP group (0%; 95% confidence interval, 0%-4%). Reliability was kappa = 1.0 (intraobserver) and kappa = 0.93 (interobserver), respectively. There was no difference in the detection of the sign between segmental levels L1-L5 in the LSS group.

CONCLUSION
A positive sedimentation sign exclusively and reliably occurs in patients with LSS, suggesting its usefulness in clinical practice. Future accuracy studies will address its sensitivity and specificity. If they confirm the sign's high specificity, a positive sedimentation sign can rule in LSS, and, with a high sensitivity, a negative sedimentation sign can rule out LSS.


Combining Gradient Boosting Machines with Collective Inference to Predict Continuous Values

Gradient boosting of regression trees is a competitive procedure for learning predictive models of continuous data that fits the data with an additive non-parametric model. The classic version of gradient boosting assumes that the data is independent and identically distributed. However, relational data with interdependent, linked instances is now common and the dependencies in such data can be exploited to improve predictive performance. Collective inference is one approach to exploit relational correlation patterns and significantly reduce classification error. However, much of the work on collective learning and inference has focused on discrete prediction tasks rather than continuous. In this work, we investigate how to combine these two paradigms together to improve regression in relational domains. Specifically, we propose a boosting algorithm for learning a collective inference model that predicts a continuous target variable. In the algorithm, we learn a basic relational model, collectively infer the target values, and then iteratively learn relational models to predict the residuals. We evaluate our proposed algorithm on a real network dataset and show that it outperforms alternative boosting methods. However, our investigation also revealed that the relational features interact together to produce better predictions.


Multiple Classifier Systems

Ensemble methods are learning algorithms that construct a set of classi ers and then classify new data points by taking a weighted vote of their predictions The original ensemble method is Bayesian aver aging but more recent algorithms include error correcting output coding Bagging and boosting This paper reviews these methods and explains why ensembles can often perform better than any single classi er Some previous studies comparing ensemble methods are reviewed and some new experiments are presented to uncover the reasons that Adaboost does not over t rapidly


Optimizing Java Bytecode Using the Soot Framework: Is It Feasible?

This paper presents Soot, a framework for optimizing Java bytecode. The framework is implemented in Java and supports three intermediate representations for representing Java bytecode: Baf, a streamlined representation of Java's stack-based bytecode; Jimple, a typed three-address intermediate representation suitable for optimization; and Grimp, an aggregated version of Jimple. Our approach to class le optimization is to rst convert the stack-based bytecode into Jimple, a three-address form more amenable to traditional program optimization, and then convert the optimized Jimple back to bytecode. In order to demonstrate that our approach is feasible, we present experimental results showing the e ects of processing class les through our framework. In particular, we study the techniques necessary to effectively translate Jimple back to bytecode, without losing performance. Finally, we demonstrate that class le optimization can be quite e ective by showing the results of some basic optimizations using our framework. Our experiments were done on ten benchmarks, including seven SPECjvm98 benchmarks, and were executed on ve di erent Java virtual machine implementations.


The Java Language Specification

class RawMembers<T> extends NonGeneric implements Collection<String> { static Collection<NonGeneric> cng = new ArrayList<NonGeneric>(); public static void main(String[] args) { RawMembers rw = null; Collection<Number> cn = rw.myNumbers(); // OK Iterator<String> is = rw.iterator(); // Unchecked warning Collection<NonGeneric> cnn = rw.cng; // OK, static member } } In this program (which is not meant to be run), RawMembers<T> inherits the method: Iterator<String> iterator() from the Collection<String> superinterface. The raw type RawMembers inherits iterator() from Collection, the erasure of Collection<String>, which means that the return type of iterator() in RawMembers is Iterator. As a result, the attempt to 4.9 Intersection Types TYPES, VALUES, AND VARIABLES 70 assign rw.iterator() to Iterator<String> requires an unchecked conversion, so a compile-time unchecked warning is issued. In contrast, RawMembers inherits myNumbers() from the NonGeneric class whose erasure is also NonGeneric. Thus, the return type of myNumbers() in RawMembers is not erased, and the attempt to assign rw.myNumbers() to Collection<Number> requires no unchecked conversion, so no compile-time unchecked warning is issued. Similarly, the static member cng retains its parameterized type even when accessed through a object of raw type. Note that access to a static member through an instance is considered bad style and is discouraged. This example reveals that certain members of a raw type are not erased, namely static members whose types are parameterized, and members inherited from a non-generic supertype. Raw types are closely related to wildcards. Both are based on existential types. Raw types can be thought of as wildcards whose type rules are deliberately unsound, to accommodate interaction with legacy code. Historically, raw types preceded wildcards; they were first introduced in GJ, and described in the paper Making the future safe for the past: Adding Genericity to the Java Programming Language by Gilad Bracha, Martin Odersky, David Stoutamire, and Philip Wadler, in Proceedings of the ACM Conference on Object-Oriented Programming, Systems, Languages and Applications (OOPSLA 98), October 1998. 4.9 Intersection Types An intersection type takes the form T1 & ... & Tn (n > 0), where Ti (1 ≤ i ≤ n) are types. Intersection types can be derived from type parameter bounds (§4.4) and cast expressions (§15.16); they also arise in the processes of capture conversion (§5.1.10) and least upper bound computation (§4.10.4). The values of an intersection type are those objects that are values of all of the types Ti for 1 ≤ i ≤ n. Every intersection type T1 & ... & Tn induces a notional class or interface for the purpose of identifying the members of the intersection type, as follows: • For each Ti (1 ≤ i ≤ n), let Ci be the most specific class or array type such that Ti <: Ci. Then there must be some Ck such that Ck <: Ci for any i (1 ≤ i ≤ n), or a compile-time error occurs. • For 1 ≤ j ≤ n, if Tj is a type variable, then let Tj' be an interface whose members are the same as the public members of Tj; otherwise, if Tj is an interface, then let Tj' be Tj. TYPES, VALUES, AND VARIABLES Subtyping 4.10 71 • If Ck is Object, a notional interface is induced; otherwise, a notional class is induced with direct superclass Ck. This class or interface has direct superinterfaces T1', ..., Tn' and is declared in the package in which the intersection type appears. The members of an intersection type are the members of the class or interface it induces. It is worth dwelling upon the distinction between intersection types and the bounds of type variables. Every type variable bound induces an intersection type. This intersection type is often trivial, consisting of a single type. The form of a bound is restricted (only the first element may be a class or type variable, and only one type variable may appear in the bound) to preclude certain awkward situations coming into existence. However, capture conversion can lead to the creation of type variables whose bounds are more general, such as array types).


Reverse Engineering of the Interaction Diagrams from C++ Code

In object oriented programming, the functionalities of a system result from the interactions (message exchanges) among the objects allocated by the system. While designing object interactions is far more complex than designing the object structure in forward engineering, the problem of understanding object interactions during code evolution is even harder, because the related information is spread across the code. In this paper, a technique for the automatic extraction of UML interaction diagrams from C++ code is proposed. The algorithm is based on a static, conservative flow analysis, that approximates the behavior of the system in any execution and for any possible input. Applicability of the approach to large software is achieved by means of two mechanisms: partial analysis and focusing. Usage of our method on a real world, large C++ system confirmed its viability.


Compilers: Principles, Techniques, and Tools


A Fast Algorithm for Finding Dominators in a Flowgraph

A fast algorithm for finding dominators in a flowgraph is presented. The algorithm uses depth-first search and an efficient method of computing functions defined on paths in trees. A simple implementation of the algorithm runs in <italic>O</italic>(<italic>m</italic> log <italic>n</italic>) time, where <italic>m</italic> is the number of edges and <italic>n</italic> is the number of vertices in the problem graph. A more sophisticated implementation runs in <italic>O</italic>(<italic>m</italic>α(<italic>m</italic>, <italic>n</italic>)) time, where α(<italic>m</italic>, <italic>n</italic>) is a functional inverse of Ackermann's function.
Both versions of the algorithm were implemented in Algol W, a Stanford University version of Algol, and tested on an IBM 370/168. The programs were compared with an implementation by Purdom and Moore of a straightforward <italic>O</italic>(<italic>mn</italic>)-time algorithm, and with a bit vector algorithm described by Aho and Ullman. The fast algorithm beat the straightforward algorithm and the bit vector algorithm on all but the smallest graphs tested.


P300 amplitude is determined by target-to-target interval.

P300 event-related brain potential (ERP) measures are affected by target stimulus probability, the number of nontargets preceding the target in the stimulus sequence structure, and interstimulus interval (ISI). Each of these factors contributes to the target-to-target interval (TTI), which also has been found to affect P300. The present study employed a variant of the oddball paradigm and manipulated the number of preceding nontarget stimuli (0, 1, 2, 3) and ISI (1, 2, 4 s) in order to systematically assess TTI effects on P300 values from auditory and visual stimuli. Number of preceding nontargets generally produced stronger effects than ISI in a manner suggesting that TTI determined P300 measures: Amplitude increased as TTI increased for both auditory and visual stimulus conditions, whereas latency tended to decrease with increased TTI. The finding that TTI is a critical determinant of P300 responsivity is discussed within a resource allocation theoretical framework.


Ultra Low Power Wake-Up Radios: A Hardware and Networking Survey

In wireless environments, transmission and reception costs dominate system power consumption, motivating research effort on new technologies capable of reducing the footprint of the radio, paving the way for the Internet of Things. The most important challenge is to reduce power consumption when receivers are idle, the so called idle-listening cost. One approach proposes switching off the main receiver, then introduces new wake-up circuitry capable of detecting an incoming transmission, optionally discriminating the packet destination using addressing, then switching on the main radio only when required. This wake-up receiver technology represents the ultimate frontier in low power radio communication. In this paper, we present a comprehensive literature review of the research progress in wake-up radio (WuR) hardware and relevant networking software. First, we present an overview of the WuR system architecture, including challenges to hardware design and a comparison of solutions presented throughout the last decade. Next, we present various medium access control and routing protocols as well as diverse ways to exploit WuRs, both as an extension of pre-existing systems and as a new concept to manage low-power networking.


Experimentable Digital Twins—Streamlining Simulation-Based Systems Engineering for Industry 4.0

Digital twins represent real objects or subjects with their data, functions, and communication capabilities in the digital world. As nodes within the internet of things, they enable networking and thus the automation of complex value-added chains. The application of simulation techniques brings digital twins to life and makes them experimentable; digital twins become experimentable digital twins (EDTs). Initially, these EDTs communicate with each other purely in the virtual world. The resulting networks of interacting EDTs model different application scenarios and are simulated in virtual testbeds, providing new foundations for comprehensive simulation-based systems engineering. Its focus is on EDTs, which become more detailed with every single application. Thus, complete digital representations of the respective real assets and their behaviors are created successively. The networking of EDTs with real assets leads to hybrid application scenarios in which EDTs are used in combination with real hardware, thus realizing complex control algorithms, innovative user interfaces, or mental models for intelligent systems.


Design, kinematics and dynamics modeling of a lower-limb walking assistant robot

This paper focuses on a lower limb walking assistant robot. This robot has been designed and modeled for two tasks of facilitating healthy people, and assisting patients for walking. First, basic concepts of the motion mechanism are presented. Next, its kinematics and dynamics equations have been developed. Kinematics analysis has been done using Denavit-Hartenberg scheme to obtain position, velocity and acceleration of center of mass (COM) of all links. Dynamics equations have been developed using recursive Lagrange method and finally inverse dynamics problem has been solved. Obtained simulation results will be discussed which validate the proposed model.


Metrics for text entry research: an evaluation of MSD and KSPC, and a new unified error metric

We describe and identify shortcomings in two statistics recently introduced to measure accuracy in text entry evaluations: the minimum string distance (MSD) error rate and keystrokes per character (KSPC). To overcome the weaknesses, a new framework for error analysis is developed and demonstrated. It combines the analysis of the presented text, input stream (keystrokes), and transcribed text. New statistics include a unified total error rate, combining two constituent error rates: the corrected error rate (errors committed but corrected) and the not corrected error rate (errors left in the transcribed text). The framework includes other measures including error correction efficiency, participant conscientiousness, utilised bandwidth, and wasted bandwidth. A text entry study demonstrating the new methodology is described.


Lyapunov Approach for the stabilization of the Inverted Spherical Pendulum

A nonlinear controller is presented for the stabilization of the spherical inverted pendulum system. The control strategy is based on the Lyapunov approach in conjunction with LaSalle's invariance principle. The proposed controller is able to bring the pendulum to the unstable upright equilibrium point with the position of the movable base at the origin. The obtained closed-loop system has a very large domain of attraction, that can be as large as desired, for any initial position of the pendulum which lies above the horizontal plane


A Data Structure for Dynamic Trees

We propose a data structure to maintain a collection of vertex-disjoint trees under a sequence of two kinds of operations: a link operation that combines two trees into one by adding an edge, and a cut operation that divides one tree into two by deleting an edge. Our data structure requires O(log n) time per operation when the time is amortized over a sequence of operations. Using our data structure, we obtain new fast algorithms for the following problems:
 (1) Computing deepest common ancestors.
 (2) Solving various network flow problems including finding maximum flows, blocking flows, and acyclic flows.
 (3) Computing certain kinds of constrained minimum spanning trees.
 (4) Implementing the network simplex algorithm for the transshipment problem.
 Our most significant application is (2); we obtain an O(mn log n)-time algorithm to find a maximum flow in a network of n vertices and m edges, beating by a factor of log n the fastest algorithm previously known for sparse graphs.


A survey of variability modeling in industrial practice

Over more than two decades, numerous variability modeling techniques have been introduced in academia and industry. However, little is known about the actual use of these techniques. While dozens of experience reports on software product line engineering exist, only very few focus on variability modeling. This lack of empirical data threatens the validity of existing techniques, and hinders their improvement. As part of our effort to improve empirical understanding of variability modeling, we present the results of a survey questionnaire distributed to industrial practitioners. These results provide insights into application scenarios and perceived benefits of variability modeling, the notations and tools used, the scale of industrial models, and experienced challenges and mitigation strategies.


NONCONVEX MODEL FOR FACTORING NONNEGATIVE MATRICES

We study the Nonnegative Matrix Factorization problem which approximates a nonnegative matrix by a low-rank factorization. This problem is particularly important in Machine Learning, and finds itself in a large number of applications. Unfortunately, the original formulation is ill-posed and NPhard. In this paper, we propose a row sparse model based on Row Entropy Minimization to solve the NMF problem under separable assumption which states that each data point is a convex combination of a few distinct data columns. We utilize the concentration of the entropy function and the `∞ norm to concentrate the energy on the least number of latent variables. We prove that under the separability assumption, our proposed model robustly recovers data columns that generate the dataset, even when the data is corrupted by noise. We empirically justify the robustness of the proposed model and show that it is significantly more robust than the state-ofthe-art separable NMF algorithms.


Multi-label classification by exploiting label correlations

a Department of Computer Science and Technology, Tongji University, Shanghai 201804, PR China b Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2G7, Canada c Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, PR China d System Research Institute, Polish Academy of Sciences, Warsaw, Poland e School of Software, Jiangxi Agricultural University, Nanchang 330013, PR China


Variational Bayesian Inference for Big Data Marketing Models 1

Hierarchical Bayesian approaches play a central role in empirical marketing as they yield individual-level parameter estimates that can be used for targeting decisions. MCMC methods have been the methods of choice for estimating hierarchical Bayesian models as they are capable of providing accurate individual-level estimates. However, MCMC methods are computationally prohibitive and do not scale well when applied to massive data sets that have become common in the current era of “Big Data”. We introduce to the marketing literature a new class of Bayesian estimation techniques known as variational Bayesian (VB) inference. These methods tackle the scalability challenge via a deterministic optimization approach to approximate the posterior distribution and yield accurate estimates at a fraction of the computational cost associated with simulation-based MCMC methods. We exploit and extend recent developments in variational Bayesian inference and highlight how two VB estimation approaches – Mean-field VB (that is analogous to Gibbs sampling) for conjugate models and Fixed-form VB (which is analogous to Metropolis-Hasting) for nonconjugate models – can be effectively combined for estimating complex marketing models. We also show how recent advances in parallel computing and in stochastic optimization can be used to further enhance the speed of these VB methods. Using simulated as well as real data sets, we apply the VB approaches to several commonly used marketing models (e.g. mixed linear, logit, selection, and hierarchical ordinal logit models), and demonstrate how the VB inference is widely applicable for marketing problems.


Uplift Modeling with Multiple Treatments and General Response Types

Randomized experiments have been used to assist decisionmaking in many areas. They help people select the optimal treatment for the test population with certain statistical guarantee. However, subjects can show significant heterogeneity in response to treatments. The problem of customizing treatment assignment based on subject characteristics is known as uplift modeling, differential response analysis, or personalized treatment learning in literature. A key feature for uplift modeling is that the data is unlabeled. It is impossible to know whether the chosen treatment is optimal for an individual subject because response under alternative treatments is unobserved. This presents a challenge to both the training and the evaluation of uplift models. In this paper we describe how to obtain an unbiased estimate of the key performance metric of an uplift model, the expected response. We present a new uplift algorithm which creates a forest of randomized trees. The trees are built with a splitting criterion designed to directly optimize their uplift performance based on the proposed evaluation method. Both the evaluation method and the algorithm apply to arbitrary number of treatments and general response types. Experimental results on synthetic data and industry-provided data show that our algorithm leads to significant performance improvement over other applicable methods. Accepted: 2017 SIAM International Conference on Data


Automated Diagnosis of Coronary Artery Disease Based on Data Mining and Fuzzy Modeling

A fuzzy rule-based decision support system (DSS) is presented for the diagnosis of coronary artery disease (CAD). The system is automatically generated from an initial annotated dataset, using a four stage methodology: 1) induction of a decision tree from the data; 2) extraction of a set of rules from the decision tree, in disjunctive normal form and formulation of a crisp model; 3) transformation of the crisp set of rules into a fuzzy model; and 4) optimization of the parameters of the fuzzy model. The dataset used for the DSS generation and evaluation consists of 199 subjects, each one characterized by 19 features, including demographic and history data, as well as laboratory examinations. Tenfold cross validation is employed, and the average sensitivity and specificity obtained is 62% and 54%, respectively, using the set of rules extracted from the decision tree (first and second stages), while the average sensitivity and specificity increase to 80% and 65%, respectively, when the fuzzification and optimization stages are used. The system offers several advantages since it is automatically generated, it provides CAD diagnosis based on easily and noninvasively acquired features, and is able to provide interpretation for the decisions made.


Predicting Source Code Quality with Static Analysis and Machine Learning

This paper is investigating if it is possible to predict source code quality based on static analysis and machine learning. The proposed approach includes a plugin in Eclipse, uses a combination of peer review/human rating, static code analysis, and classification methods. As training data, public data and student hand-ins in programming are used. Based on this training data, new and uninspected source code can be accurately classified as “well written” or “badly written”. This is a step towards feedback in an interactive environment without peer assessment.