title: Bilateral Control with Time Domain Passivity Approach Under Time-varying Communication Delay

abstract: Recently, two-port time-domain passivity approach was modified for time-varying communication delay. The newly proposed approach could achieve stable teleoperation even under the serious time-varying delay and packet loss communication condition. However, after some operation hour, the accumulated energy difference between the input energy from one port and the output energy at the other port caused unstable behavior until the passivity controller is activated. Resetting scheme is introduced for solving this problem, and stable bilateral teleoperation can be guaranteed without worrying about the accumulated energy difference.


A Review of Image Processing Techniques Common in Human and Plant Disease Diagnosis


title: BI-RADS Lexicon for US and Mammography: Interobserver Variability and Positive Predictive Value

abstract: Purpose: To retrospectively evaluate interobserver variability between breast radiologists by using terminology of the fourth edition of the Breast Imaging Reporting and Data System (BI-RADS) to categorize lesions on mammograms and sonograms and to retrospectively determine the positive predictive value (PPV) of BI-RADS categories 4a, 4b, and 4c. Materials and Methods: Institutional review board approval was obtained; informed consent was not required. This study was HIPAA compliant. Ninety-four consecutive lesions in 91 women who underwent image-guided biopsy comprised 59 masses, 32 calcifications, and three masses with calcification. Five radiologists retrospectively reviewed these lesions. Each observer described each lesion with BI-RADS terminology and assigned a final BI-RADS category. Interobserver variability was assessed with the Cohen κ statistic. A pathologic diagnosis was available for all 94 lesions; 30 (32%) were malignant and 64 (68%) were benign. Pathologic analysis of benign lesions was pe...


title: Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces

abstract: While recent advances in deep reinforcement learning have allowed autonomous learning agents to succeed at a variety of complex tasks, existing algorithms generally require a lot of training data. One way to increase the speed at which agents are able to learn to perform tasks is by leveraging the input of human trainers. Although such input can take many forms, real-time, scalar-valued feedback is especially useful in situations where it proves difficult or impossible for humans to provide expert demonstrations. Previous approaches have shown the usefulness of human input provided in this fashion (e.g., the TAMER framework), but they have thus far not considered high-dimensional state spaces or employed the use of deep learning. In this paper, we do both: we propose Deep TAMER, an extension of the TAMER framework that leverages the representational power of deep neural networks in order to learn complex tasks in just a short amount of time with a human trainer. We demonstrate Deep TAMER's success by using it and just 15 minutes of human-provided feedback to train an agent that performs better than humans on the Atari game of Bowling - a task that has proven difficult for even state-of-the-art reinforcement learning methods.


title: Histone modifications in the brain

abstract: Chromatin remodelling, including histone modifications has been recognized to play a central role in the regulation of gene expression. Histone modifications are mostly based on studies in cell culture systems in vitro. Recent evidence suggests that histone modifications are actively involved in activity-dependent neural plasticity via regulation of critical gene transcription necessary for the biological process in vivo. We have reviewed here the recent works studied on long-term memory formation, visual cortical plasticity during the critical period and drug-induced status epilepticus to elucidate a role for histone modifications in these biological processes. All of the studies indicate that chromatin structure, including histone modifications is highly dynamic within the nervous system and suggest the possibility that chromatin structure itself might be recruited as a target of plasticity-associated signal transduction mechanisms.


title: Social network, social trust and shared goals in organizational knowledge sharing

abstract: The aim of our study was to further develop an understanding of social capital in organizational-knowledge-sharing. We first developed a measurement tool and then a theoretical framework in which three social capital factors (social network, social trust, and shared goals) were combined with the theory of reasoned action; their relationships were then examined using confirmatory factoring analysis. We then surveyed of 190 managers from Hong Kong firms, we confirm that a social network and shared goals significantly contributed to a person's volition to share knowledge, and directly contributed to the perceived social pressure of the organization. The social trust has however showed no direct effect on the attitude and subjective norm of sharing knowledge.


title: Methods for Open Information Extraction and Sense Disambiguation on Natural Language Text

abstract: Natural language text has been the main and most comprehensive way of expressing and storing knowledge. A long standing goal in computer science is to develop systems that automatically understand textual data, making this knowledge accessible to computers and humans alike. We conceive automatic text understanding as a bottom-up approach, in which a series of interleaved tasks build upon each other. Each task achieves more understanding over the text than the previous one. In this regard, we present three methods that aim to contribute to the primary stages of this setting. Our first contribution, ClausIE, is an open information extraction method intended to recognize textual expressions of potential facts in text (e.g. “Dante wrote the Divine Comedy”) and represent them with an amenable structure for computers [(“Dante”, “wrote”, “the Divine Comedy”)]. Unlike previous approaches, ClausIE separates the recognition of the information from its representation, a process that understands the former as universal (i.e., domain-independent) and the later as application-dependent. ClausIE is a principled method that relies on properties of the English language and thereby avoids the use of manually or automatically generated training data. Once the information in text has been correctly identified, probably the most important element in a structured fact is the relation which links its arguments, a relation whose main component is usually a verbal phrase. Our second contribution, Werdy, is a word entry recognition and disambiguation method. It aims to recognize words or multi-word expressions (e.g., “Divine Comedy” is a multi-word expression) in a fact and disambiguate verbs (e.g., what does “write” mean?). Werdy is also an unsupervised approach, mainly relying on the syntactic and semantic relation established between a verb sense and its arguments. The other key components in a structured fact are the named entities (e.g., “Dante”) that often appear in the arguments. FINET, our last contribution, is a named entity typing method. It aims to understand the types or classes of those names entities (e.g., “Dante” refers to a writer). FINET is focused on typing named entities in short inputs (like facts). Unlike previous systems, it is designed to find the types that match the entity mention context (e.g., the fact in which it appears). It uses the most comprehensive type system of any entity typing method to date with more than 16k classes for persons, organizations and locations.


title: Fast Visual Odometry Using Intensity-Assisted Iterative Closest Point

abstract: This letter presents a novel method for visual odometry estimation from a RGB-D camera. The camera motion is estimated by aligning a source to a target RGB-D frame using an intensity-assisted iterative closest point (ICP) algorithm. The proposed method differs from the conventional ICP in following aspects. 1) To reduce the computational cost, salient point selection is performed on the source frame, where only points that contain valuable information for registration are used. 2) To reduce the influence of outliers and noises, robust weighting function is proposed to weight corresponding pairs based on statistics of their spatial distances and intensity differences. 3) The obtained robust weighting function from 2) is used for correspondence estimation of the following ICP iteration. The proposed method runs in real-time with a single core CPU thread, hence it is suitable for robots with limited computation resources. The evaluation on TUM RGB-D benchmark shows that in the majority of the tested sequences, our proposed method outperforms state-of-the-art accuracy in terms of translational drift per second with a computation speed of 78 Hz.


title: Visual-Inertial Monocular SLAM with Map Reuse

abstract: In recent years there have been excellent results in visual-inertial odometry techniques, which aim to compute the incremental motion of the sensor with high accuracy and robustness. However, these approaches lack the capability to close loops and trajectory estimation accumulates drift even if the sensor is continually revisiting the same place. In this letter, we present a novel tightly coupled visual-inertial simultaneous localization and mapping system that is able to close loops and reuse its map to achieve zero-drift localization in already mapped areas. While our approach can be applied to any camera configuration, we address here the most general problem of a monocular camera, with its well-known scale ambiguity. We also propose a novel IMU initialization method, which computes the scale, the gravity direction, the velocity, and gyroscope and accelerometer biases, in a few seconds with high accuracy. We test our system in the 11 sequences of a recent micro-aerial vehicle public dataset achieving a typical scale factor error of 1% and centimeter precision. We compare to the state-of-the-art in visual-inertial odometry in sequences with revisiting, proving the better accuracy of our method due to map reuse and no drift accumulation.


title: Smart Cities: an IoT-centric Approach

abstract: A number of recent Smart City testbeds and deployments have focused on the use of the Internet of Things (IoT) paradigm and technologies for improving the efficiency of city infrastructures. Building on this work, we have explored the use of IoT hubs as easy-to-use aggregators and focal points for access to emerging data infrastructures of smart cities. A hub can support not only access to infrastructure data, but also participatory sensing and crowd sourced data where city employees and citizens contribute directly to the data infrastructure of a city. In this way, smart cities can realize a variety of new applications created by local entrepreneurs and community groups without the need for ongoing coordination by governments. In this paper, we outline the growing interest in a hub-centric approach to the IoT and discuss our own experiences in building an IoT hub for two Smart City projects, one in the UK and the other in Canada.


title: The Power of Decision Tables

abstract: We evaluate the power of decision tables as a hypothesis space for supervised learning algorithms. Decision tables are one of the simplest hypothesis spaces possible, and usually they are easy to understand. Experimental results show that on artificial and real-world domains containing only discrete features, IDTM, an algorithm inducing decision tables, can sometimes outperform state-of-the-art algorithms such as C4.5. Surprisingly, performance is quite good on some datasets with continuous features, indicating that many datasets used in machine learning either do not require these features, or that these features have few values. We also describe an incremental method for performing cross-validation that is applicable to incremental learning algorithms including IDTM. Using incremental cross-validation, it is possible to cross-validate a given dataset and IDTM in time that is linear in the number of instances, the number of features, and the number of label values. The time for incremental cross-validation is independent of the number of folds chosen, hence leave-one-out cross-validation and ten-fold cross-validation take the same time.


title: A review of RF and microwave techniques for dielectric measurements on polar liquids

abstract: The requirements for dielectric measurements on polar liquids lie largely in two areas. First there is scientific interest in revealing the structure of and interactions between the molecules - this can be studied through dielectric spectroscopy. Secondly, polar liquids are widely used as dielectric reference and tissue equivalent materials for biomedical studies and for mobile telecommunications, health and safety related measurements. This review discusses these roles for polar liquids and surveys the techniques available for the measurement of their complex permittivity at RF and Microwave frequencies. One aim of the review is to guide researchers and metrologists in the choice of measurement methods and in their optimization. Particular emphasis is placed on the importance of traceability in these measurements to international standards


title: How meta-analysis increases statistical power.

abstract: One of the most frequently cited reasons for conducting a meta-analysis is the increase in statistical power that it affords a reviewer. This article demonstrates that fixed-effects meta-analysis increases statistical power by reducing the standard error of the weighted average effect size (T.) and, in so doing, shrinks the confidence interval around T.. Small confidence intervals make it more likely for reviewers to detect nonzero population effects, thereby increasing statistical power. Smaller confidence intervals also represent increased precision of the estimated population effect size. Computational examples are provided for 3 effect-size indices: d (standardized mean difference), Pearson's r, and odds ratios. Random-effects meta-analyses also may show increased statistical power and a smaller standard error of the weighted average effect size. However, the authors demonstrate that increasing the number of studies in a random-effects meta-analysis does not always increase statistical power.


title: Visual tracking of human visitors under variable-lighting conditions for a responsive audio art installation

abstract: For a responsive audio art installation in a skylit atrium, we introduce a single-camera statistical segmentation and tracking algorithm. The algorithm combines statistical background image estimation, per-pixel Bayesian segmentation, and an approximate solution to the multi-target tracking problem using a bank of Kalman filters and Gale-Shapley matching. A heuristic confidence model enables selective filtering of tracks based on dynamic data. We demonstrate that our algorithm has improved recall and F 2 -score over existing methods in OpenCV 2.1 in a variety of situations. We further demonstrate that feedback between the tracking and the segmentation systems improves recall and F 2 -score. The system described operated effectively for 5–8 hours per day for 4 months; algorithms are evaluated on video from the camera installed in the atrium. Source code and sample data is open source and available in OpenCV.


title: UsingWord Embeddings for Query Translation for Hindi to English Cross Language Information Retrieval

abstract: Cross-Language Information Retrieval (CLIR) has become an important problem to solve in the recent years due to the growth of content in multiple languages in the Web. One of the standard methods is to use query translation from source to target language. In this paper, we propose an approach based on word embeddings, a method that captures contextual clues for a particular word in the source language and gives those words as translations that occur in a similar context in the target language. Once we obtain the word embeddings of the source and target language pairs, we learn a projection from source to target word embeddings, making use of a dictionary with word translation pairs. We then propose various methods of query translation and aggregation. The advantage of this approach is that it does not require the corpora to be aligned (which is difficult to obtain for resource-scarce languages), a dictionary with word translation pairs is enough to train the word vectors for translation. We experiment with Forum for Information Retrieval and Evaluation (FIRE) 2008 and 2012 datasets for Hindi to English CLIR. The proposed word embedding based approach outperforms the basic dictionary based approach by 70% and when the word embeddings are combined with the dictionary, the hybrid approach beats the baseline dictionary based method by 77%. It outperforms the English monolingual baseline by 15%, when combined with the translations obtained from Google Translate and Dictionary.


title: Learning Gaze Transitions from Depth to Improve Video Saliency Estimation

abstract: In this paper we introduce a novel Depth-Aware Video Saliency approach to predict human focus of attention when viewing RGBD videos on regular 2D screens. We train a generative convolutional neural network which predicts a saliency map for a frame, given the fixation map of the previous frame. Saliency estimation in this scenario is highly important since in the near future 3D video content will be easily acquired and yet hard to display. This can be explained, on the one hand, by the dramatic improvement of 3D-capable acquisition equipment. On the other hand, despite the considerable progress in 3D display technologies, most of the 3D displays are still expensive and require wearing special glasses. To evaluate the performance of our approach, we present a new comprehensive database of eye-fixation ground-truth for RGBD videos. Our experiments indicate that integrating depth into video saliency calculation is beneficial. We demonstrate that our approach outperforms state-of-the-art methods for video saliency, achieving 15% relative improvement.


title: Visualizing Business Model Evolution with the Business Model Canvas: Concept and Tool

abstract: The Business Model Canvas (BMC) assists in the design of companies' business models. As strategies evolve so too does the business model. Unfortunately, each BMC is a standalone representation. Thus, there is a need to be able to describe transformation from one version of a business model to the next as well as to visualize these operations. To address this issue, and to contribute to computer-assisted business model design, we propose a set of design principles for business model evolution. We also demonstrate a tool that can assist in the creation and navigation of business model versions in a visual and user-friendly way.


title: Crowd-GPS-Sec: Leveraging Crowdsourcing to Detect and Localize GPS Spoofing Attacks

abstract: The aviation industry's increasing reliance on GPS to facilitate navigation and air traffic monitoring opens new attack vectors with the purpose of hijacking UAVs or interfering with air safety. We propose Crowd-GPS-Sec to detect and localize GPS spoofing attacks on moving airborne targets such as UAVs or commercial airliners. Unlike previous attempts to secure GPS, Crowd-GPS-Sec neither requires any updates of the GPS infrastructure nor of the airborne GPS receivers, which are both unlikely to happen in the near future. In contrast, Crowd-GPS-Sec leverages crowdsourcing to monitor the air traffic from GPS-derived position advertisements that aircraft periodically broadcast for air traffic control purposes. Spoofing attacks are detected and localized by an independent infrastructure on the ground which continuously analyzes the contents and the times of arrival of these advertisements. We evaluate our system with real-world data from a crowdsourced air traffic monitoring sensor network and by simulations. We show that Crowd-GPS-Sec is able to globally detect GPS spoofing attacks in less than two seconds and to localize the attacker up to an accuracy of 150 meters after 15 minutes of monitoring time.


title: Embedded sensor fusion and moving-average filter for Inertial Measurement Unit (IMU) on the microcontroller-based stabilized platform

abstract: Sensor Fusion (Complementary and Kalman filters) and Moving Average filter are implemented on an Arduino microcontroller based data acquisition of rotation degree from Inertial Measurement Unit (IMU) sensor for stabilized platform application. Stable platform prototype is designed to have two degrees of freedom, roll and pitch rotation. Output data from gyro and accelerometer were combined to take the advantage of each sensor. Digital filter algorithm was embedded into microcontroller programming. This paper analyzes overshoot percentage, rise time, and data series smoothness of Sensor Fusion (Complementary and Kalman filter) and Moving Average filter response in IMU data acquisition from step input of 20-degreerotation. Moving-average filter resulted in the smallest overshoot percentage of 0% but produce theslowest responsewith 0.42 second rise time. Overall best results are obtained using Complementary filter (alpha value 0.95) by overshoot percentage of 14.17%, 0.24 second rise time, and 0.18 data series smoothness.


title: Frontal fibrosing alopecia: clinical presentations and prognosis.

abstract: BACKGROUND ::: Frontal fibrosing alopecia is an uncommon condition characterized by progressive frontotemporal recession due to inflammatory destruction of hair follicles. Little is known about the natural history of this disease. ::: ::: ::: OBJECTIVES ::: To determine the clinical features and natural history of frontal fibrosing alopecia. ::: ::: ::: METHODS ::: We studied the cases notes of patients diagnosed with frontal fibrosing alopecia from 1993 to 2008 at the Royal Hallamshire Hospital, Sheffield. ::: ::: ::: RESULTS ::: There were 18 patients aged between 34 and 71 years. Three were premenopausal. All had frontotemporal recession with scarring. This was associated with partial or complete loss of eyebrows in 15 patients while four had hair loss at other sites. One had keratosis pilaris-like papules on the face, and one had follicular erythema on the cheeks. Three patients had oral lichen planus, of whom two also had cutaneous lichen planus affecting other sites of the body. Treatments given included intralesional triamcinolone acetonide, 0.1% tacrolimus ointment and oral hydroxychloroquine. Progression of frontotemporal recession was seen in some patients, but not all. In one patient the hair line receded by 30 mm over 72 months, whereas in another patient there was no positional change in the hair line after 15 years. ::: ::: ::: CONCLUSIONS ::: Frontal fibrosing alopecia is more common in postmenopausal women, but it can occur in younger women. It may be associated with mucocutaneous lichen planus. Recession of the hair line may progress inexorably over many years but this is not inevitable. It is not clear whether or not treatment alters the natural history of the disease - the disease stabilized with time in most of the patients with or without continuing treatment.


title: Consistency-Driven Automatic Methodology to Set Interval Numerical Scales of 2-Tuple Linguistic Term Sets and Its Use in the Linguistic GDM With Preference Relation

abstract: The 2-tuple linguistic modeling is a popular tool for computing with words in decision making. In order to deal with the linguistic term sets that are not uniformly and symmetrically distributed, the numerical scale model has been developed to generalize the 2-tuple linguistic modeling. In the numerical scale model, the key task of the 2-tuple based models is the definition of a numerical scale function that establishes a one to one mapping between the linguistic information and numerical values. In this paper, we propose a consistency-driven automatic methodology to set interval numerical scales of 2-tuple linguistic term sets in the decision making problems with linguistic preference relations. This consistency-driven methodology is based on a natural premise regarding the consistency of preference relations. If linguistic preference relations provided by experts are of acceptable consistency, the corresponding transformed numerical preference relations by the established interval numerical scale are also consistent. Compared with the existing approach based on canonical characteristic values, the consistency-driven methodology provides a new way to set the interval numerical scale without the need of the semantics defined by interval type-2 fuzzy sets. Meanwhile, interval multiplicative preference relations are used in the pairwise comparisons method and the presented theory can be utilized in the pairwise comparisons method as it provides a novel approach to automatic construct interval multiplicative preference relations. Finally, we present the framework for the use of the consistency-driven automatic methodology in linguistic group decision making problems and two numerical examples are given to illustrate the feasibility and validity of this proposal.


title: Sentiment analysis in medical settings

abstract: HighlightsMedical sentiment concerns patient health status, medical conditions or treatments.It occurs in multiple facets.Medical sentiment analysis has many applications even for clinical documents.Linguistic and content peculiarities require a domain-specific sentiment source.Implicit sentiment needs to be considered. ObjectiveClinical documents reflect a patient's health status in terms of observations and contain objective information such as descriptions of examination results, diagnoses and interventions. To evaluate this information properly, assessing positive or negative clinical outcomes or judging the impact of a medical condition on patient's well being are essential. Although methods of sentiment analysis have been developed to address these tasks, they have not yet found broad application in the medical domain. Methods and materialIn this work, we characterize the facets of sentiment in the medical sphere and identify potential use cases. Through a literature review, we summarize the state of the art in healthcare settings. To determine the linguistic peculiarities of sentiment in medical texts and to collect open research questions of sentiment analysis in medicine, we perform a quantitative assessment with respect to word usage and sentiment distribution of a dataset of clinical narratives and medical social media derived from six different sources. ResultsWord usage in clinical narratives differs from that in medical social media: Nouns predominate. Even though adjectives are also frequently used, they mainly describe body locations. Between 12% and 15% of sentiment terms are determined in medical social media datasets when applying existing sentiment lexicons. In contrast, in clinical narratives only between 5% and 11% opinionated terms were identified. This proves the less subjective use of language in clinical narratives, requiring adaptations to existing methods for sentiment analysis. ConclusionsMedical sentiment concerns the patient's health status, medical conditions and treatment. Its analysis and extraction from texts has multiple applications, even for clinical narratives that remained so far unconsidered. Given the varying usage and meanings of terms, sentiment analysis from medical documents requires a domain-specific sentiment source and complementary context-dependent features to be able to correctly interpret the implicit sentiment.


title: Multiple SVM-RFE for gene selection in cancer classification with expression data

abstract: This paper proposes a new feature selection method that uses a backward elimination procedure similar to that implemented in support vector machine recursive feature elimination (SVM-RFE). Unlike the SVM-RFE method, at each step, the proposed approach computes the feature ranking score from a statistical analysis of weight vectors of multiple linear SVMs trained on subsamples of the original training data. We tested the proposed method on four gene expression datasets for cancer classification. The results show that the proposed feature selection method selects better gene subsets than the original SVM-RFE and improves the classification accuracy. A Gene Ontology-based similarity assessment indicates that the selected subsets are functionally diverse, further validating our gene selection method. This investigation also suggests that, for gene expression-based cancer classification, average test error from multiple partitions of training and test sets can be recommended as a reference of performance quality.


Text Mining with Information Extraction


title: A Critical Evaluation of User Participation Research: Gaps and Future Directions.

abstract: In this theoretical synthesis, we juxtapose three traditions of prior research on user participation and involvement: the survey and experimental literature on the relationship between user participation and IS success, the normative literature on alternative development approaches, and qualitative studies that examine user participation from a variety of theoretical perspectives. We also assess progress made in the three bodies of literature, and identify gaps and directions of future research for improving user participation.


Design Time Reliability Predictions for Supporting Runtime Security Measuring and Adaptation


title: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Human Action Recognition Using a Temporal Hierarchy of Covariance Descriptors on 3D Joint Locations

abstract: Human action recognition from videos is a challenging machine vision task with multiple important application domains, such as human-robot/machine interaction, interactive entertainment, multimedia information retrieval, and surveillance. In this paper, we present a novel approach to human action recognition from 3D skeleton sequences extracted from depth data. We use the covariance matrix for skeleton joint locations over time as a discriminative descriptor for a sequence. To encode the relationship between joint movement and time, we deploy multiple covariance matrices over sub-sequences in a hierarchical fashion. The descriptor has a fixed length that is independent from the length of the described sequence. Our experiments show that using the covariance descriptor with an off-the-shelf classification algorithm outperforms the state of the art in action recognition on multiple datasets, captured either via a Kinect-type sensor or a sophisticated motion capture system. We also include an evaluation on a novel large dataset using our own annotation.


title: Development of a Wearable Controller for Gesture-Recognition-Based Applications Using Polyvinylidene Fluoride

abstract: This paper reports on a wearable gesture-based controller fabricated using the sensing capabilities of the flexible thin-film piezoelectric polymer polyvinylidene fluoride (PVDF) which is shown to repeatedly and accurately discern, in real time, between right and left hand gestures. The PVDF is affixed to a compression sleeve worn on the forearm to create a wearable device that is flexible, adaptable, and highly shape conforming. Forearm muscle movements, which drive hand motions, are detected by the PVDF which outputs its voltage signal to a developed microcontroller-based board and processed by an artificial neural network that was trained to recognize the generated voltage profile of right and left hand gestures. The PVDF has been spatially shaded (etched) in such a way as to increase sensitivity to expected deformations caused by the specific muscles employed in making the targeted right and left gestures. The device proves to be exceptionally accurate both when positioned as intended and when rotated and translated on the forearm.


title: Gene Expression Omnibus: NCBI gene expression and hybridization array data repository

abstract: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.


title: Unsupervised POS Induction with Word Embeddings

abstract: Unsupervised word embeddings have been shown to be valuable as features in supervised learning problems; however, their role in unsupervised problems has been less thoroughly explored. In this paper, we show that embeddings can likewise add value to the problem of unsupervised POS induction. In two representative models of POS induction, we replace multinomial distributions over the vocabulary with multivariate Gaussian distributions over word embeddings and observe consistent improvements in eight languages. We also analyze the effect of various choices while inducing word embeddings on "downstream" POS induction results.


title: Automatic Determination of Neighborhood Size in SMOTE

abstract: In order to handle the class imbalance problem, synthetic data generation methods such as SMOTE, ADASYN, and Borderline-SMOTE have been developed. These methods use a common parameter k, the number of nearest neighbors. Nonetheless the most effective k value depends on the given dataset, there is no guideline to determine k. Moreover, if a dataset has noises, small sub-clusters, or complex patterns, the existing SMOTE and its variants show poor classification performance. Our method that we named Automatic Neighborhood size Determination(AND) restricts the size of neighborhood in SMOTE to maintain the original distribution of data, and helps SMOTE for its best performance. Defining and examining a minority region including a minority class object and its neighbors assure safety for generating synthetic samples. The proposed AND-method determines the value of k for each minority objects. By independently generating synthetic minority samples with the automatically predefined k values, we aim at achieving a better classification than the existing methods. Numerical experiments showed that the proposed method outperformed SMOTE, ADASYN, or Borderline-SMOTE.


title: Feature Selection Model for Diagnosis, Electronic Medical Records and Geographical Data Correlation

abstract: Electronic Medical Records (EMRs) collect and describe events and patient health history, related to his interaction with a healthcare facility or clinical trials. Raw data in EMRs are voluminous and heterogeneous. They need to be collected and stored to allow clinical management, treatment and to apply prevention protocols. Using informatics techniques (e.g., data mining models) allows to automatize the process of information extraction and to support health data management. We focus on biological data present in EMRs starting from blind data gathered from University Hospital of Catanzaro. In collaboration with Biochemical Laboratory of the University Hospital, we designed a workflow based system to analyze biological values. The system is able to relate biological data to diagnosis codes and with additional information integrated and correlated to EMRs data. Prediction models have been used and tested on 3 specific diagnosis, proving that system is able to: (i) identify blood test features that are important to detect a pathology and (ii) finding correlations among patients features.


title: Welcome To The Experience Economy

abstract: Health care is no longer just about healing: patients want a "personal transformation," a way to be made whole again. How can your organization think about making the play from services to experiences to transformations, but without your organization's dropping the ball?


title: Comprehensive analysis of distance and similarity measures for Wi-Fi fingerprinting indoor positioning systems

abstract: We introduce a study in depth of distance/similarity measures for indoor location.Alternative measures provide better results than commonly used Euclidean distance.Choosing an appropriate non-linear representation is crucial for intensity values.Very low intensity values are representative and they should not be filtered.All the experiments are validated with a public database, so they are reproducible. Recent advances in indoor positioning systems led to a business interest in those applications and services where a precise localization is crucial. Wi-Fi fingerprinting based on machine learning and expert systems are commonly used in the literature. They compare a current fingerprint to a database of fingerprints, and then return the most similar one/ones according to: 1) a distance function, 2) a data representation method for received signal strength values, and 3) a thresholding strategy. However, most of the previous works simply use the Euclidean distance with the raw unprocessed data. There is not any previous work that studies which is the best distance function, which is the best way of representing the data and which is the effect of applying thresholding. In this paper, we present a comprehensive study using 51 distance metrics, 4 alternatives to represent the raw data (2 of them proposed by us), a thresholding based on the RSS values and the public UJIIndoorLoc database. The results shown in this paper demonstrate that researchers and developers should take into account the conclusions arisen in this work in order to improve the accuracy of their systems. The IPSs based on k-NN are improved by just selecting the appropriate configuration (mainly distance function and data representation). In the best case, 13-NN with Sorensen distance and the powed data representation, the error in determining the place (building and floor) has been reduced in more than a 50% and the positioning accuracy has been increased in 1.7 m with respect to the 1-NN with Euclidean distance and raw data commonly used in the literature. Moreover, our experiments also demonstrate that thresholding should not be applied in multi-building and multi-floor environments.


title: A prediction model based on artificial neural networks for the diagnosis of obstructive sleep apnea

abstract: Background ::: Recently, artificial neural networks (ANNs) have been widely applied in science, engineering, and medicine. In the present study, we evaluated the ability of artificial neural networks to be used as a computer program and assistant tool in the diagnosis of obstructive sleep apnea (OSA). Our hypothesis was that ANNs could use clinical information to precisely predict cases of OSA.


title: A human parietal face area contains aligned head-centered visual and tactile maps

abstract: Visually guided eating, biting and kissing, and avoiding objects moving toward the face and toward which the face moves require prompt, coordinated processing of spatial visual and somatosensory information in order to protect the face and the brain. Single-cell recordings in parietal cortex have identified multisensory neurons with spatially restricted, aligned visual and somatosensory receptive fields, but so far, there has been no evidence for a topographic map in this area. Here we mapped the organization of a multisensory parietal face area in humans by acquiring functional magnetic resonance images while varying the polar angle of facial air puffs and close-up visual stimuli. We found aligned maps of tactile and near-face visual stimuli at the highest level of human association cortex-namely, in the superior part of the postcentral sulcus. We show that this area may code the location of visual stimuli with respect to the face, not with respect to the retina.


title: Learning Deep Features for DNA Methylation Data Analysis

abstract: Many studies demonstrated that the DNA methylation, which occurs in the context of a CpG, has strong correlation with diseases, including cancer. There is a strong interest in analyzing the DNA methylation data to find how to distinguish different subtypes of the tumor. However, the conventional statistical methods are not suitable for analyzing the highly dimensional DNA methylation data with bounded support. In order to explicitly capture the properties of the data, we design a deep neural network, which composes of several stacked binary restricted Boltzmann machines, to learn the low-dimensional deep features of the DNA methylation data. Experimental results show that these features perform best in breast cancer DNA methylation data cluster analysis, compared with some state-of-the-art methods.


title: Online thevenin equivalent parameter estimation using nonlinear and linear recursive least square algorithm

abstract: This paper proposes method for detection, estimation of Thevenin equivalent parameters to describe power system behavior. Thevenin equivalent estimation is a challenge due to variation in system states caused by power flow in the network. Thevenin equivalent calculation based on changes in system with multiple sources integrated with grid, isolated distributed generator system is analysed and nonlinear least square fit estimation technique for algorithm is adopted. Linear least square fit is used with a linearized model. Performance evaluation of proposed method is carried out through mathematical model, nonlinear and linear least square fit based algorithm technique and simulation through MATLAB/SIMULINK package. Accurate grid and source side impedance estimation technique is applicable for distributed generation sources interfaced with grid to improve dynamic response, stability, reliability when subjected to faults or any other disturbances in network. Algorithm can accurately estimate Thevenin equivalent of multiple sources connected in parallel simultaneously with voltage and current phasor measurements at point of common coupling. Mathematical analysis and simulation results validate the effectiveness of proposed method.


title: An All-in-One Network for Dehazing and Beyond

abstract: This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net). It is designed based on a re-formulated atmospheric scattering model. Instead of estimating the transmission matrix and the atmospheric light separately as most previous models did, AOD-Net directly generates the clean image through a light-weight CNN. Such a novel end-to-end design makes it easy to embed AOD-Net into other deep models, e.g., Faster R-CNN, for improving high-level task performance on hazy images. Experimental results on both synthesized and natural hazy image datasets demonstrate our superior performance than the state-of-the-art in terms of PSNR, SSIM and the subjective visual quality. Furthermore, when concatenating AOD-Net with Faster R-CNN and training the joint pipeline from end to end, we witness a large improvement of the object detection performance on hazy images.


title: The effect of motor action and different sensory modalities on terrain classification in a quadruped robot running with multiple gaits

abstract: Discriminating or classifying different terrains is an important ability for every autonomous mobile robot. A variety of sensors, preprocessing techniques, and algorithms in different robots were applied. However, little attention was paid to the way sensory data was generated and to the contribution of different sensory modalities. In this work, a quadruped robot traversing different grounds using a variety of gaits is used, equipped with a collection of proprioceptive (encoders on active, and passive compliant joints), inertial, and foot pressure sensors. The effect of different gaits on classification performance is assessed and it is demonstrated that separate terrain classifiers for each motor program should be employed. Furthermore, poor performance of randomly generated motor commands confirms the importance of coordinated behavior on sensory information structuring. The collection of sensors sensitive to active, "tactile", terrain exploration proved effective. Among the individual modalities, encoders on passive compliant joints delivered best results. Preconditioning on gait used boosts terrain discrimination in a legged robot.Specific gaits are particularly suited for terrain perception.Inertial, tactile, and proprioceptive sensors are a robust terrain sensing set.Encoders in passive compliant joints performed best from the sensory set.


title: Incremental Stochastic Subgradient Algorithms for Convex Optimization

abstract: In this paper we study the effect of stochastic errors on two constrained incremental sub-gradient algorithms. We view the incremental sub-gradient algorithms as decentralized network optimization algorithms as applied to minimize a sum of functions, when each component function is known only to a particular agent of a distributed network. We first study the standard cyclic incremental sub-gradient algorithm in which the agents form a ring structure and pass the iterate in a cycle. We consider the method with stochastic errors in the sub-gradient evaluations and provide sufficient conditions on the moments of the stochastic errors that guarantee almost sure convergence when a diminishing step-size is used. We also obtain almost sure bounds on the algorithm's performance when a constant step-size is used. We then consider \ram{the} Markov randomized incremental subgradient method, which is a non-cyclic version of the incremental algorithm where the sequence of computing agents is modeled as a time non-homogeneous Markov chain. Such a model is appropriate for mobile networks, as the network topology changes across time in these networks. We establish the convergence results and error bounds for the Markov randomized method in the presence of stochastic errors for diminishing and constant step-sizes, respectively.


title: Airport Traffic Simulation Using Petri Nets

abstract: Airport traffic consists of aircraft performing landing, takeoff and taxi procedures. It is controlled by air traffic controller (ATC). To safely perform this task he/she uses traffic surveillance equipment and voice communication systems to issue control clearances. One of the most important indicators of this process efficiency is practical airport capacity, which refers to the number of aircraft handled and delays which occurred at the same time. This paper presents the concept of airport traffic modelling using coloured, timed, stochastic Petri nets. By the example of the airport with one runway and simultaneous takeoff and landing operations, the applicability of such models in analysis of air traffic processes is shown. Simulation experiments, in which CPN Tools package was used, showed the impact of the initial formation of landing aircraft stream on airside capacity of the airport. They also showed the possibility of its increase by changes in the organisation of takeoff and landing processes.


title: Research on WeChat marketing path of enhancing customer marketing experiences

abstract: With the increasing popularity of WeChat, WeChat marketing becomes a new Popular tool to the enterprises and merchants. They want to use WeChat to get the attention and recognition of the user, and closer the relationship between the gap with the user. After the analysis of consumers purchase motivation and behavior patterns in WeChat marketing, it researched WeChat marketing paths to enhance the customer experience and Put forward effective WeChat marketing strategy of improving customer experience to Make WeChat marketing real utility.


title: From neighborhood to parenthood: the advantages of dependency representation over bigrams in Brown clustering

abstract: We present an effective modification of the popular Brown et al. 1992 word clustering algorithm, using a dependency language model. By leveraging syntax-based context, resulting clusters are better when evaluated against a wordnet for Dutch. The improvements are stable across parameters such as number of clusters, minimum frequency and granularity. Further refinement is possible through dependency relation selection. Our approach achieves a desired clustering quality with less data, resulting in a decrease in cluster creation times.


title: A free but efficient low-voltage class-AB two-stage operational amplifier

abstract: A simple and efficient low-voltage two-stage operational amplifier with Class-AB output stage is introduced. It has a large effective output current boosting factor (/spl sim/50) and close to a factor 2 bandwidth enhancement. This is achieved at the expense of minimum increase in circuit complexity and no additional static power dissipation. Experimental verification of the characteristics of the proposed circuit is provided.


title: Face classification using electronic synapses

abstract: Conventional hardware platforms consume huge amount of energy for cognitive learning due to the data movement between the processor and the off-chip memory. Brain-inspired device technologies using analogue weight storage allow to complete cognitive tasks more efficiently. Here we present an analogue non-volatile resistive memory (an electronic synapse) with foundry friendly materials. The device shows bidirectional continuous weight modulation behaviour. Grey-scale face classification is experimentally demonstrated using an integrated 1024-cell array with parallel online training. The energy consumption within the analogue synapses for each iteration is 1,000 × (20 ×) lower compared to an implementation using Intel Xeon Phi processor with off-chip memory (with hypothetical on-chip digital resistive random access memory). The accuracy on test sets is close to the result using a central processing unit. These experimental results consolidate the feasibility of analogue synaptic array and pave the way toward building an energy efficient and large-scale neuromorphic system. Using chips that mimic the human brain to perform cognitive tasks, namely neuromorphic computing, calls for low power and high efficiency hardware. Here, Yaoet al. show on-chip analogue weight storage by integrating non-volatile resistive memory into a CMOS platform and test it in facial recognition.


title: A Single-Stage LED Driver Based on Interleaved Buck–Boost Circuit and LLC Resonant Converter

abstract: A single-stage LED driver based on an interleaved buck–boost circuit and an LLC resonant converter is proposed. The buck–boost circuit and the LLC resonant converter are integrated by sharing switches, which can decrease the system cost and improve the system efficiency. The input voltage of the buck–boost circuit is half of the rectified voltage, and two buck–boost circuits are formed with the two half-bridge switches and corresponding diodes. The two buck–boost circuits work in interleaved mode and the inductor current is in discontinuous conduction mode, both helping to achieve the power factor (PF) correction. A half-bridge LLC resonant converter is adopted here, and the soft switching characteristic of the LLC resonant converter is not changed by the switch integration. The primary-side switches still work in zero voltage switching (ZVS) mode, and the secondary diodes still work in ZCS mode, which both reduce the switching losses and improve the efficiency of the system. The design process is described in detail this paper, and a 100-W LED street lighting prototype is proposed, with a PF of 0.995, a total harmonic distortion of 5.7%, and an efficiency of 91.6% at full load.


title: Website fingerprinting in onion routing based anonymization networks

abstract: Low-latency anonymization networks such as Tor and JAP claim to hide the recipient and the content of communications from a local observer, i.e., an entity that can eavesdrop the traffic between the user and the first anonymization node. Especially users in totalitarian regimes strongly depend on such networks to freely communicate. For these people, anonymity is particularly important and an analysis of the anonymization methods against various attacks is necessary to ensure adequate protection. In this paper we show that anonymity in Tor and JAP is not as strong as expected so far and cannot resist website fingerprinting attacks under certain circumstances. We first define features for website fingerprinting solely based on volume, time, and direction of the traffic. As a result, the subsequent classification becomes much easier. We apply support vector machines with the introduced features. We are able to improve recognition results of existing works on a given state-of-the-art dataset in Tor from 3% to 55% and in JAP from 20% to 80%. The datasets assume a closed-world with 775 websites only. In a next step, we transfer our findings to a more complex and realistic open-world scenario, i.e., recognition of several websites in a set of thousands of random unknown websites. To the best of our knowledge, this work is the first successful attack in the open-world scenario. We achieve a surprisingly high true positive rate of up to 73% for a false positive rate of 0.05%. Finally, we show preliminary results of a proof-of-concept implementation that applies camouflage as a countermeasure to hamper the fingerprinting attack. For JAP, the detection rate decreases from 80% to 4% and for Tor it drops from 55% to about 3%.


title: A linux real-time packet scheduler for reliable static SDN routing

abstract: In a distributed computing environment, guaranteeing the hard deadline for real-time messages is essential to ensure schedulability of real-time tasks. Since capabilities of the shared resources for transmission are limited, e.g., the buffer size is limited on network devices, it becomes a challenge to design an effective and feasible resource sharing policy based on both the demand of real-time packet transmissions and the limitation of resource capabilities. We address this challenge in two cooperative mechanisms. First, we design a static routing algorithm to find forwarding paths for packets to guarantee their hard deadlines. The routing algorithm employs a validation-based backtracking procedure capable of deriving the demand of a set of real-time packets on each shared network device, and it checks whether this demand can be met on the device. Second, we design a packet scheduler that runs on network devices to transmit messages according to our routing requirements. We implement these mechanisms on virtual software-defined network (SDN) switches and evaluate them on real hardware in a local cluster to demonstrate the feasibility and effectiveness of our routing algorithm and packet scheduler.


title: Fusing laser and vision data with a genetic ICP algorithm

abstract: Knowledge about the environment is essential for humanoid and mobile robots to move and act safely. The most intuitive way to perceive information about the environment is through the vision system. However, the accuracy provided by stereo vision is insufficient for many tasks. A more accurate representation is created by a laser range-finder, which delivers no color information. This paper describes a novel approach to merge data obtained by stereo vision and laser range-finders by using a genetic ICP algorithm, which is able to register noisy point clouds with different resolutions and a small overlap. Furthermore, it describes an easy-to-use and robust method to calibrate the extrinsic parameters of two or more laser range-finders.


title: Semantic Document Distance Measures and Unsupervised Document Revision Detection

abstract: In this paper, we model the document revision detection problem as a minimum cost branching problem that relies on computing document distances. Furthermore, we propose two new document distance measures, word vector-based Dynamic Time Warping (wDTW) and word vector-based Tree Edit Distance (wTED). Our revision detection system is designed for a large scale corpus and implemented in Apache Spark. We demonstrate that our system can more precisely detect revisions than state-of-the-art methods by utilizing the Wikipedia revision dumps https://snap.stanford.edu/data/wiki-meta.html and simulated data sets.


title: Mining Patterns of Cyberbullying on Twitter

abstract: Cyberbullying refers to the use of text, images, audio and video to harass or harm individuals or groups on a repetitive and non–stop basis in online social networks. The phenomenon has emerged as a serious societal and public health problem that demands accurate methods for the detection of cyberbullying instances to mitigate the consequences. We perform a detailed analysis of a large–scale real–world dataset to identify online social network topology structure features that are the most prominent in enhancing the accuracy of state–of–the–art classification methods for cyberbullying detection. We derive a small subset of features that are fast to compute while differentiating between "normal" users, cyberbullies and victims. Our findings have important implications for the design of future cyberbullying detection schemes.


title: Human Tracking Using Convolutional Neural Networks

abstract: In this paper, we treat tracking as a learning problem of estimating the location and the scale of an object given its previous location, scale, as well as current and previous image frames. Given a set of examples, we train convolutional neural networks (CNNs) to perform the above estimation task. Different from other learning methods, the CNNs learn both spatial and temporal features jointly from image pairs of two adjacent frames. We introduce multiple path ways in CNN to better fuse local and global information. A creative shift-variant CNN architecture is designed so as to alleviate the drift problem when the distracting objects are similar to the target in cluttered environment. Furthermore, we employ CNNs to estimate the scale through the accurate localization of some key points. These techniques are object-independent so that the proposed method can be applied to track other types of object. The capability of the tracker of handling complex situations is demonstrated in many testing sequences.


title: Effectiveness of online mindfulness-based interventions in improving mental health: A review and meta-analysis of randomised controlled trials

abstract: Mindfulness-based interventions (MBIs) are increasingly being delivered through the Internet. Whereas numerous meta-analyses have investigated the effectiveness of face-to-face MBIs in the context of mental health and well-being, thus far a quantitative synthesis of the effectiveness of online MBIs is lacking. The aim of this meta-analysis was to estimate the overall effects of online MBIs on mental health. Fifteen randomised controlled trials were included in this study. A random effects model was used to compute pre-post between-group effect sizes, and the study quality of each of the included trials was rated. Results showed that online MBIs have a small but significant beneficial impact on depression (g = 0.29), anxiety (g = 0.22), well-being (g = 0.23) and mindfulness (g = 0.32). The largest effect was found for stress, with a moderate effect size (g = 0.51). For stress and mindfulness, exploratory subgroup analyses demonstrated significantly higher effect sizes for guided online MBIs than for unguided online MBIs. In addition, meta-regression analysis showed that effect sizes for stress were significantly moderated by the number of intervention sessions. Effect sizes, however, were not significantly related to study quality. The findings indicate that online MBIs have potential to contribute to improving mental health outcomes, particularly stress. Limitations, directions for future research and practical implications are discussed.


title: Robot-by-voice: Experiments on commanding an industrial robot using the human voice

abstract: Purpose – This paper reports a few results of an ongoing research project that aims to explore ways to command an industrial robot using the human voice. This feature can be interesting with several industrial, laboratory and clean‐room applications, where a close cooperation between robots and humans is desirable.Design/methodology/approach – A demonstration is presented using two industrial robots and a personal computer (PC) equipped with a sound board and a headset microphone. The demonstration was coded using the Microsoft Visual Basic and C#.NET 2003 and associated with two simple robot applications: one capable of picking‐and‐placing objects and going to predefined positions, and the other capable of performing a simple linear weld on a work‐piece. The speech recognition grammar is specified using the grammar builder from the Microsoft Speech SDK 5.1. The paper also introduces the concepts of text‐to‐speech translation and voice recognition, and shows how these features can be used with application...


title: Detection and location of faults in underground cable using Matlab/Simulink/ANN and OrCad

abstract: The paper deals with detection and location of faults in underground cable network. For precision a three step process is followed in this paper, with creation of transmission system model using a Matlab/Simulink and followed by creation of faults in the system. In second step the fourier analyzed fault voltages and currents obtained from the SIMULINK model are fed to the training set of artificial neural network (ANN) in order to detect the type of fault. In the last step, an independent software OrCad is used to locate the fault distance from the either ends using the principle of time domain reflectometry in a simulated practical underground distribution system.


title: A SiGe BiCMOS 16-element phased-array transmitter for 60GHz communications

abstract: The demonstration of multi-Gb/s links in the 60GHz band has created new opportunities for wireless communications [1,2]. Due to the directional nature of millimeter-wave (mm-Wave) propagation, beam steering enables longer-range non-line-of-sight (NLOS) links at these frequencies. A phased-array architecture is attractive for an integrated 60GHz transmitter (Tx) since it can attain both beam steering and higher EIRP through spatial combining. An all-RF 16-element 40-to-45GHz Tx for satellite applications [3], a 6-element 60GHz Tx with IF-path phase-shift [4], and a bi-directional 4-element 60GHz Tx/Rx with RF phase shifters [5] have been recently demonstrated in silicon. This work presents a fully-integrated phased-array Tx which supports multi-Gb/s NLOS IEEE 802.15.3c links. In addition to beamsteering, the IC has the following major features: an on-chip power sensor at each element, 3 temperature sensors, LO leakage and I/Q phase and amplitude adjustment, front-end OP 1dB programmability, and an integrated modulator for pi/2-BPSK/MSK signaling (common mode in 802.15.3c). The IC integrates 2240 NPNs, 323,000 FETs and hundreds of transmission lines and is fabricated in the IBM 8HP 0.12µm SiGe BiCMOS process (f T = 200GHz).


title: airVLC: An Application for Visualizing Wind-Sensitive Interpolation of Urban Air Pollution Forecasts

abstract: Air pollution has been identified as a major source of health problems for people living in cities. In this sense, it is important to identify the areas of the city that present high levels of pollutants in order to avoid them. airVLC is an application for predicting and interpolating real-time urban air pollution forecasts for the city of Valencia (Spain). We compare different regression models in order to predict the levels of four pollutants (NO, NO2, SO2, O3) in the six measurement stations of the city. Since wind is a key feature in the dispersion of the pollution, we study different techniques to incorporate this factor in the models. Finally, we are able to interpolate forecasts all around the city. For this goal, we propose a new interpolation method that takes wind direction into account, improving well-known methods like IDW or Kriging. By using these pollution estimates, we are able to generate real-time pollution maps of the city of Valencia and publish them into a public website.


title: Visualizing the Execution of Java Programs

abstract: Jinsight is a tool for exploring a program’s run-time behavior visually. It is helpful for performance analysis, debugging, and any task in which you need to better understand what your Java program is really doing.


title: Can testedness be effectively measured?

abstract: Among the major questions that a practicing tester faces are deciding where to focus additional testing effort, and deciding when to stop testing. Test the least-tested code, and stop when all code is well-tested, is a reasonable answer. Many measures of "testedness" have been proposed; unfortunately, we do not know whether these are truly effective. In this paper we propose a novel evaluation of two of the most important and widely-used measures of test suite quality. The first measure is statement coverage, the simplest and best-known code coverage measure. The second measure is mutation score, a supposedly more powerful, though expensive, measure. We evaluate these measures using the actual criteria of interest: if a program element is (by these measures) well tested at a given point in time, it should require fewer future bug-fixes than a "poorly tested" element. If not, then it seems likely that we are not effectively measuring testedness. Using a large number of open source Java programs from Github and Apache, we show that both statement coverage and mutation score have only a weak negative correlation with bug-fixes. Despite the lack of strong correlation, there are statistically and practically significant differences between program elements for various binary criteria. Program elements (other than classes) covered by any test case see about half as many bug-fixes as those not covered, and a similar line can be drawn for mutation score thresholds. Our results have important implications for both software engineering practice and research evaluation.


title: Priority Based Dynamic Round Robin (PBDRR) Algorithm with Intelligent Time Slice for Soft Real Time Systems

abstract: In this paper, a new variant of Round Robin (RR) algorithm is proposed which is suitable for soft real time systems. RR algorithm performs optimally in timeshared systems, but it is not suitable for soft real time systems. Because it gives more number of context switches, larger waiting time and larger response time. We have proposed a novel algorithm, known as Priority Based Dynamic Round Robin Algorithm(PBDRR),which calculates intelligent time slice for individual processes and changes after every round of execution. The proposed scheduling algorithm is developed by taking dynamic time quantum concept into account. Our experimental results show that our proposed algorithm performs better than algorithm in [8] in terms of reducing the number of context switches, average waiting time and average turnaround time.


title: Inertial and magnetic sensing of human movement near ferromagnetic materials

abstract: This paper describes a Kalman filter design to estimate orientation of human body segments by fusing gyroscope, accelerometer and magnetometer signals. Ferromagnetic materials near the sensor disturb the local magnetic field and therefore the orientation estimation. The magnetic disturbance can be detected by looking at the total magnetic density and a magnetic disturbance vector can be calculated. Results show the capability of this filter to correct for magnetic disturbances.


Loss of Alleles of Loci on the Short Arm of Chromosome 3 in Renal Cell Carcinoma


Natural Language Processing based Automatic Multilingual Code Generation


title: Screening Rules for Convex Problems

abstract: Reference EPFL-REPORT-229236 URL: http://arxiv.org/abs/1609.07478 Record created on 2017-06-21, modified on 2017-07-12


title: Data-driven risk-averse stochastic optimization with Wasserstein metric

abstract: Abstract In this paper, we study a data-driven risk-averse stochastic optimization approach with Wasserstein Metric for the general distribution case. By using the Wasserstein Metric, we can successfully reformulate the risk-averse two-stage stochastic optimization problem with distributional ambiguity to a traditional two-stage robust optimization problem. In addition, we derive the worst-case distribution and perform convergence analysis to show that the risk aversion of the proposed formulation vanishes as the size of historical data grows to infinity.


title: Fast k-Means Based on k-NN Graph

abstract: In the era of big data, k-means clustering has been widely adopted as a basic processing tool in various contexts. However, its computational cost could be prohibitively high as the data size and the cluster number are large. It is well known that the processing bottleneck of k-means lies in the operation of seeking closest centroid in each iteration. In this paper, a novel solution towards the scalability issue of k-means is presented. In the proposal, k-means is supported by an approximate k-nearest neighbors graph. In the k-means iteration, each data sample is only compared to clusters that its nearest neighbors reside. Since the number of nearest neighbors we consider is much less than k, the processing cost in this step becomes minor and irrelevant to k. The processing bottleneck is therefore overcome. The most interesting thing is that k-nearest neighbor graph is constructed by iteratively calling the fast $k$-means itself. Comparing with existing fast k-means variants, the proposed algorithm achieves hundreds to thousands times speed-up while maintaining high clustering quality. As it is tested on 10 million 512-dimensional data, it takes only 5.2 hours to produce 1 million clusters. In contrast, to fulfill the same scale of clustering, it would take 3 years for traditional k-means.


title: Improved Selective Refinement Network for Face Detection

abstract: As a long-standing problem in computer vision, face detection has attracted much attention in recent decades for its practical applications. With the availability of face detection benchmark WIDER FACE dataset, much of the progresses have been made by various algorithms in recent years. Among them, the Selective Refinement Network (SRN) face detector introduces the two-step classification and regression operations selectively into an anchor-based face detector to reduce false positives and improve location accuracy simultaneously. Moreover, it designs a receptive field enhancement block to provide more diverse receptive field. In this report, to further improve the performance of SRN, we exploit some existing techniques via extensive experiments, including new data augmentation strategy, improved backbone network, MS COCO pretraining, decoupled classification module, segmentation branch and Squeeze-and-Excitation block. Some of these techniques bring performance improvements, while few of them do not well adapt to our baseline. As a consequence, we present an improved SRN face detector by combining these useful techniques together and obtain the best performance on widely used face detection benchmark WIDER FACE dataset.


title: A 6.5 to 15.1 GHz ultra-wideband SiGe LC VCO with 80 % continuous tuning range

abstract: This work describes the theoretical analysis, design and measurement of an ultra-wideband LC voltage controlled oscillator (VCO). The VCO was fabricated using a 130 nm SiGe technology and is continuously tunable from 6.5 to 15.1 GHz without any switching behavior and therefore enables frequency chirp generation with high chirp bandwidth for FMCW primary radar application. The combination of a measured low phase noise of −110 dBc/Hz at 1 MHz offset in average over the tuning range together with a continuous tuning range of 80% and a power consumption of 19 mW was never previously reported for an integrated LC VCO.


title: A Survey on Software-Defined Wireless Sensor Networks: Challenges and Design Requirements

abstract: Software defined networking (SDN) brings about innovation, simplicity in network management, and configuration in network computing. Traditional networks often lack the flexibility to bring into effect instant changes because of the rigidity of the network and also the over dependence on proprietary services. SDN decouples the control plane from the data plane, thus moving the control logic from the node to a central controller. A wireless sensor network (WSN) is a great platform for low-rate wireless personal area networks with little resources and short communication ranges. However, as the scale of WSN expands, it faces several challenges, such as network management and heterogeneous-node networks. The SDN approach to WSNs seeks to alleviate most of the challenges and ultimately foster efficiency and sustainability in WSNs. The fusion of these two models gives rise to a new paradigm: Software defined wireless sensor networks (SDWSN). The SDWSN model is also envisioned to play a critical role in the looming Internet of Things paradigm. This paper presents a comprehensive review of the SDWSN literature. Moreover, it delves into some of the challenges facing this paradigm, as well as the major SDWSN design requirements that need to be considered to address these challenges.


title: LTE Authentication Protocol (EPS-AKA) weaknesses solution

abstract: Extensible Authentication Protocol (EAP) is an authentication framework in Long Term Evolution (LTE) networks. EAP-AKA is one of the methods of EAP which uses the Authentication and Key Agreement (AKA) mechanism based on challenge-response mechanisms, EAP-AKA is used in the 3rd generation mobile networks then modified and inherited to 4th generation mobile networks (LTE) as Evolved Packet System Authentication and Key Agreement (EPS-AKA) mechanism which is used when the user access the network through EUTRAN. EPS-AKA vulnerabilities are disclosure of the user identity, Man in the Middle attack and Denial of Services (DoS) attacks so a robust authentication mechanism must replace EPSAKA to avoid such attacks. In this paper, Modified Evolved Packet System Authentication and Key Agreement (MEPS-AKA) protocol based on Simple Password Exponential Key Exchange (SPEKE) and symmetric key cryptography is proposed to solve these problems by performing a pre-authentication procedure to generate a dynamic key every time user access to the network, also each message send or received is confidentially protected. Scyther tool is used to verify the efficiency of the proposed protocol. EPS-AKA and MEPS-AKA are simulated using C programming language to calculate the execution time for both algorithms. The proposed protocol is simulated using a client-server application program using C# programming language.


Motion Planning : The Essentials


title: CRF based road detection with multi-sensor fusion

abstract: In this paper, we propose to fuse the LIDAR and monocular image in the framework of conditional random field to detect the road robustly in challenging scenarios. LIDAR points are aligned with pixels in image by cross calibration. Then boosted decision tree based classifiers are trained for image and point cloud respectively. The scores of the two kinds of classifiers are treated as the unary potentials of the corresponding pixel nodes of the random field. The fused conditional random field can be solved efficiently with graph cut. Extensive experiments tested on KITTI-Road benchmark show that our method reaches the state-of-the-art.


title: Fuzzy Least Squares Twin Support Vector Machines

abstract: Least Squares Twin Support Vector Machine (LST-SVM) has been shown to be an efficient and fast algorithm for binary classification. It combines the operating principles of Least Squares SVM (LS-SVM) and Twin SVM (T-SVM); it constructs two non-parallel hyperplanes (as in T-SVM) by solving two systems of linear equations (as in LS-SVM). Despite its efficiency, LST-SVM is still unable to cope with two features of real-world problems. First, in many real-world applications, labels of samples are not deterministic; they come naturally with their associated membership degrees. Second, samples in real-world applications may not be equally important and their importance degrees affect the classification. In this paper, we propose Fuzzy LST-SVM (FLST-SVM) to deal with these two characteristics of real-world data. Two models are introduced for FLST-SVM: the first model builds up crisp hyperplanes using training samples and their corresponding membership degrees. The second model, on the other hand, constructs fuzzy hyperplanes using training samples and their membership degrees. Numerical evaluation of the proposed method with synthetic and real datasets demonstrate significant improvement in the classification accuracy of FLST-SVM when compared to well-known existing versions of SVM.


title: The Meaning Factory at SemEval-2016 Task 8: Producing AMRs with Boxer

abstract: We participated in the shared task on meaning representation parsing (Task 8 at SemEval-2016) with the aim of investigating whether we could use Boxer, an existing open-domain semantic parser, for this task. However, the meaning representations produced by Boxer, Discourse Representation Structures, are considerably different from Abstract Meaning Representations, AMRs, the target meaning representations of the shared task. Our hybrid conversion method (involving lexical adaptation as well as post-processing of the output) failed to produce state-of-the-art results. Nonetheless, F-scores of 53% on development and 47% on test data (50% unofficially) were obtained.


title: Differing patterns of genetic instability in mice deficient in the mismatch repair genes Pms2, Mlh1, Msh2, Msh3 and Msh6.

abstract: Defects in genes associated with DNA mismatch repair (MMR) have been linked to hereditary colon cancer. Because the MMR pathway includes multiple factors with both overlapping and divergent functions, we sought to compare the impact of deficiencies in each of several MMR genes on genetic instability using a collection of knock-out mouse models. We investigated mutation frequencies and patterns in MMR-deficient mice using two transgenic reporter genes, supFG1 and cII, in the context of mice deficient for Pms2, Mlh1, Msh2, Msh3 or Msh6 or both Msh2 and Msh3 or both Msh3 and Msh6. We found that the mean mutation frequencies of all of the MMR-deficient mice were significantly higher than the mean mutation frequencies of wild-type mice. Mlh1-deficient mice and Msh2-deficient mice had the highest mutation frequencies in a comparison of the single nullizygous mice. Of all the mice studied, mice nullizygous for both Msh2 and Msh3 and those nullizygous for both Msh3 and Msh6 displayed the greatest overall increases in mutation frequencies compared with wild-type mice. Sequence analysis of the mutated reporter genes revealed significant differences between the individual groups of MMR-deficient mice. Taken together, our results further characterize the functions of the MMR factors in mutation avoidance and provide in vivo correlation to biochemical models of the MMR pathway.


title: Who Did What to Whom? A Contrastive Study of Syntacto-Semantic Dependencies

abstract: We investigate aspects of interoperability between a broad range of common annotation schemes for syntacto-semantic dependencies. With the practical goal of making the LinGO Redwoods Treebank accessible to broader usage, we contrast seven distinct annotation schemes of functor--argument structure, both in terms of syntactic and semantic relations. Drawing examples from a multi-annotated gold standard, we show how abstractly similar information can take quite different forms across frameworks. We further seek to shed light on the representational 'distance' between pure bilexical dependencies, on the one hand, and full-blown logical-form propositional semantics, on the other hand. Furthermore, we propose a fully automated conversion procedure from (logical-form) meaning representation to bilexical semantic dependencies.


title: Humanoid robot HRP-3

abstract: A development of humanoid robot HRP-2 is presented in this paper. HRP-2 is a humanoid robotics platform, which we developed in phase two of HRP. HRP was a humanoid robotics project, which had run by the Ministry of Economy, Trade and Industry (METI) of Japan from 1998FY to 2002FY for five years. The ability of the biped locomotion of HRP-2 is improved so that HRP-2 can cope with uneven surface, can walk at two third level of human speed, and can walk on a narrow path. The ability of whole body motion of HRP-2 is also improved so that HRP-2 can get up by a humanoid robot's own self if HRP-2 tips over safely. In this paper, the appearance design, the mechanisms, the electrical systems, specifications, and features upgraded from its prototype are also introduced.


title: Using A Semantic Concordance For Sense Identification

abstract: This paper proposes benchmarks for systems of automatic sense identification. A textual corpus in which open-class words had been tagged both syntactically and semantically was used to explore three statistical strategies for sense identification: a guessing heuristic, a most-frequent heuristic, and a co-occurrence heuristic. When no information about sense-frequencies was available, the guessing heuristic using the numbers of alternative senses in WordNet was correct 45% of the time. When statistics for sense-frequencies were derived from a semantic concordance, the assumption that each word is used in its most frequently occurring sense was correct 69% of the time; when that figure was calculated for polysemous words alone, it dropped to 58%. And when a co-occurrence heuristic took advantage of prior occurrences of words together in the same sentences, little improvement was observed. The semantic concordance is still too small to estimate the potential limits of a co-occurrence heuristic.


title: Multivariate random forests

abstract: Random forests have emerged as a versatile and highly accurate classiﬁcationand regression methodology, requiring little tuning and providing interpretableoutputs. Here, we brieﬂy outline the genesis of, and motivation for, the randomforest paradigm as an outgrowth from earlier tree-structured techniques. Weelaborate on aspects of prediction error and attendant tuning parameter issues.However,ouremphasisisonextendingtherandomforestschematothemultipleresponse setting. We provide a simple illustrative example from ecology thatshowcases the improved ﬁt and enhanced interpretation afforded by the randomforest framework.


title: An Outer-Rotor Flux-Switching Permanent-Magnet-Machine With Wedge-Shaped Magnets for In-Wheel Light Traction

abstract: This paper proposes a novel outer-rotor flux-switching permanent-magnet (OR-FSPM) machine with specific wedge-shaped magnets for in-wheel light-weight traction applications. First, the geometric topology is introduced. Then, the combination principle of stator slots and rotor poles for OR-FSPM machines is investigated. Furthermore, to demonstrate the relationship between performance specifications (e.g., torque and speed) and key design parameters and dimensions (e.g., rotor outer diameter and stack length) of OR-FSPM machines at preliminary design stage, an analytical torque-sizing equation is proposed and verified by two-dimensional (2-D) finite-element analysis (FEA). Moreover, optimizations of key dimensions are conducted on an initially designed proof-of-principle three-phase 12-stator-slot/22-rotor-pole prototyped machine. Then, based on 2-D-FEA, a comprehensive comparison between a pair of OR-FSPM machines with rectangular- and wedge-shaped magnets and a surface-mounted permanent-magnet (SPM) machine is performed. The results indicate that the proposed OR-FSPM machine with wedge-shaped magnets exhibits better flux-weakening capability, higher efficiency, and wider speed range than the counterparts, especially for torque capability, where the proposed wedge-shaped magnets-based one could produce 40% and 61.5% more torque than the rectangular-shaped magnets-based machine and SPM machine, respectively, with the same rated current density (5 A/mm2). Finally, the predicted performance of the proposed OR-FSPM machine is verified by experiments on a prototyped machine.


title: Twitter as a Corpus for Sentiment Analysis and Opinion Mining

abstract: Microblogging today has become a very popular communication tool among Internet users. Millions of users share opinions on different aspects of life everyday. Therefore microblogging web-sites are rich sources of data for opinion mining and sentiment analysis. Because microblogging has appeared relatively recently, there are a few research works that were devoted to this topic. In our paper, we focus on using Twitter, the most popular microblogging platform, for the task of sentiment analysis. We show how to automatically collect a corpus for sentiment analysis and opinion mining purposes. We perform linguistic analysis of the collected corpus and explain discovered phenomena. Using the corpus, we build a sentiment classifier, that is able to determine positive, negative and neutral sentiments for a document. Experimental evaluations show that our proposed techniques are efficient and performs better than previously proposed methods. In our research, we worked with English, however, the proposed technique can be used with any other language.


IPFS-Content Addressed , Versioned , P 2 P File System ( DRAFT 3 )


A Taxonomy of Botnets


title: Handcrafted features with convolutional neural networks for detection of tumor cells in histology images

abstract: Detection of tumor nuclei in cancer histology images requires sophisticated techniques due to the irregular shape, size and chromatin texture of the tumor nuclei. Some very recently proposed methods employ deep convolutional neural networks (CNNs) to detect cells in H&E stained images. However, all such methods use some form of raw pixel intensities as input and rely on the CNN to learn the deep features. In this work, we extend a recently proposed spatially constrained CNN (SC-CNN) by proposing features that capture texture characteristics and show that although CNN produces good results on automatically learned features, it can perform better if the input consists of a combination of handcrafted features and the raw data. The handcrafted features are computed through the scattering transform which gives non-linear invariant texture features. The combination of handcrafted features with raw data produces sharp proximity maps and better detection results than the results of raw intensities with a similar kind of CNN architecture.


title: ReStore: Reusing Results of MapReduce Jobs

abstract: Analyzing large scale data has emerged as an important activity for many organizations in the past few years. This large scale data analysis is facilitated by the MapReduce programming and execution model and its implementations, most notably Hadoop. Users of MapReduce often have analysis tasks that are too complex to express as individual MapReduce jobs. Instead, they use high-level query languages such as Pig, Hive, or Jaql to express their complex tasks. The compilers of these languages translate queries into workflows of MapReduce jobs. Each job in these workflows reads its input from the distributed file system used by the MapReduce system and produces output that is stored in this distributed file system and read as input by the next job in the workflow. The current practice is to delete these intermediate results from the distributed file system at the end of executing the workflow. One way to improve the performance of workflows of MapReduce jobs is to keep these intermediate results and reuse them for future workflows submitted to the system. In this paper, we present ReStore, a system that manages the storage and reuse of such intermediate results. ReStore can reuse the output of whole MapReduce jobs that are part of a workflow, and it can also create additional reuse opportunities by materializing and storing the output of query execution operators that are executed within a MapReduce job. We have implemented ReStore as an extension to the Pig dataflow system on top of Hadoop, and we experimentally demonstrate significant speedups on queries from the PigMix benchmark.


title: Deep artifact learning for compressed sensing and parallel MRI

abstract: Purpose: Compressed sensing MRI (CS-MRI) from single and parallel coils is one of the powerful ways to reduce the scan time of MR imaging with performance guarantee. However, the computational costs are usually expensive. This paper aims to propose a computationally fast and accurate deep learning algorithm for the reconstruction of MR images from highly down-sampled k-space data. ::: Theory: Based on the topological analysis, we show that the data manifold of the aliasing artifact is easier to learn from a uniform subsampling pattern with additional low-frequency k-space data. Thus, we develop deep aliasing artifact learning networks for the magnitude and phase images to estimate and remove the aliasing artifacts from highly accelerated MR acquisition. ::: Methods: The aliasing artifacts are directly estimated from the distorted magnitude and phase images reconstructed from subsampled k-space data so that we can get an aliasing-free images by subtracting the estimated aliasing artifact from corrupted inputs. Moreover, to deal with the globally distributed aliasing artifact, we develop a multi-scale deep neural network with a large receptive field. ::: Results: The experimental results confirm that the proposed deep artifact learning network effectively estimates and removes the aliasing artifacts. Compared to existing CS methods from single and multi-coli data, the proposed network shows minimal errors by removing the coherent aliasing artifacts. Furthermore, the computational time is by order of magnitude faster. ::: Conclusion: As the proposed deep artifact learning network immediately generates accurate reconstruction, it has great potential for clinical applications.


title: Unsupervised Machine Commenting with Neural Variational Topic Model

abstract: Article comments can provide supplementary opinions and facts for readers, thereby increase the attraction and engagement of articles. Therefore, automatically commenting is helpful in improving the activeness of the community, such as online forums and news websites. Previous work shows that training an automatic commenting system requires large parallel corpora. Although part of articles are naturally paired with the comments on some websites, most articles and comments are unpaired on the Internet. To fully exploit the unpaired data, we completely remove the need for parallel data and propose a novel unsupervised approach to train an automatic article commenting model, relying on nothing but unpaired articles and comments. Our model is based on a retrieval-based commenting framework, which uses news to retrieve comments based on the similarity of their topics. The topic representation is obtained from a neural variational topic model, which is trained in an unsupervised manner. We evaluate our model on a news comment dataset. Experiments show that our proposed topic-based approach significantly outperforms previous lexicon-based models. The model also profits from paired corpora and achieves state-of-the-art performance under semi-supervised scenarios.


title: The Needs–Affordances–Features Perspective for the Use of Social Media

abstract: The paper develops a needs–affordances–features (NAF) perspective on social media use which posits that individuals’ psychological needs motivate their use of social media applications to the extent to which these applications provide affordances that satisfy these needs. Our theoretical development builds upon two psychological theories, namely self-determination and psychological ownership, to identify five psychological needs (needs for autonomy, relatedness, competence, having a place, and self-identity), that we posit are particularly pertinent to social media use. According to NAF, these psychological needs will motivate use of those social media applications that provide salient affordances to fulfill these needs. We identify such affordances through a comprehensive review of the literature and of social media applications and put forth propositions that map the affordances to the psychological needs that they fulfill. Our theory development generates important implications. First, it has implications for social media research in that it provides an overarching comprehensive framework for the affordances of social media as a whole and the related psychological needs that motivate their use. Future studies can leverage NAF to identify psychological needs motivating the use of specific social media sites based on the affordances the sites provide, and design science research can leverage NAF in the design and bundling of specific social media features to engage users. Second, it has implications for technology acceptance research in that NAF can enrich existing models by opening up the mechanisms through which psychological needs influence user perceptions of social media and their use patterns and behaviors. Finally, NAF provides a new lens and common vocabulary for future studies, which we hope can stimulate cumulative research endeavors to develop a comprehensive framework of information systems affordances in general and the psychological needs that information systems satisfy.


title: 16 Separation of Speech by Computational Auditory Scene Analysis

abstract: The term auditory scene analysis (ASA) refers to the ability of human listeners to form perceptual representations of the constituent sources in an acoustic mixture, as in the well-known ‘cocktail party’ effect. Accordingly, computational auditory scene analysis (CASA) is the field of study which attempts to replicate ASA in machines. Some CASA systems are closely modelled on the known stages of auditory processing, whereas others adopt a more functional approach. However, all are broadly based on the principles underlying the perception and organization of sound by human listeners, and in this respect they differ from ICA and other approaches to sound separation. In this chapter, we review the principles underlying ASA and show how they can be implemented in CASA systems. We also consider the link between CASA and automatic speech recognition, and draw distinctions between the CASA and ICA approaches.


title: Aggregated search result diversification

abstract: Search result diversification has been effectively employed to tackle query ambiguity, particularly in the context of web search. However, ambiguity can manifest differently in different search verticals, with ambiguous queries spanning, e.g., multiple place names, content genres, or time periods. In this paper, we empirically investigate the need for diversity across four different verticals of a commercial search engine, including web, image, news, and product search. As a result, we introduce the problem of aggregated search result diversification as the task of satisfying multiple information needs across multiple search verticals. Moreover, we propose a probabilistic approach to tackle this problem, as a natural extension of state-of-the-art diversification approaches. Finally, we generalise standard diversity metrics, such as ERR-IA and α-nDCG, into a framework for evaluating diversity across multiple search verticals.


title: Equation of SVM-rebalancing: the point-normal form of a plane for class imbalance problem

abstract: The task of detecting a rare but important class has extensively been studied in the machine learning community. It is commonly agreed that traditional classifiers are certainly limited to imbalanced datasets and do not perform well. A number of solutions to the problem were proposed at both data and algorithmic levels. We propose the point-normal form of a plane, namely SVM-rebalancing, to be based on the second type. In this learning process, the assumption of pseudo-prior probabilities provides a rebalanced recipe for countering the imbalance inspired by Bayesian decision theory. Thus, we set a rebalancing programming problem by incorporating a rebalanced heuristics into the fitting of model to raise the class separability. In addition, various measures are used to characterize the performance of classifiers. Compared with several popular decision tree splitting criteria and cost-sensitive learning, the proposed method gives comparable separability with minority class to avoid heavy biasing of the majority class.