title: Multiresolution-based image fusion with additive wavelet decomposition

abstract: The standard data fusion methods may not be satisfactory to merge a high-resolution panchromatic image and a low-resolution multispectral image because they can distort the spectral characteristics of the multispectral data. The authors developed a technique, based on multiresolution wavelet decomposition, for the merging and data fusion of such images. The method presented consists of adding the wavelet coefficients of the high-resolution image to the multispectral (low-resolution) data. They have studied several possibilities concluding that the method which produces the best results consists in adding the high order coefficients of the wavelet transform of the panchromatic image to the intensity component (defined as L=(R+G+B)/3) of the multispectral image. The method is, thus, an improvement on standard intensity-hue-saturation (IHS or LHS) mergers. They used the "a trous" algorithm which allows the use of a dyadic wavelet to merge nondyadic data in a simple and efficient scheme. They used the method to merge SPOT and LANDSAT/sup TM/ images. The technique presented is clearly better than the IHS and LHS mergers in preserving both spectral and spatial information.


title: Wideband high power high efficiency linear GaN power amplifier for cognitive radio application

abstract: This paper describes a linear, high efficient, high power push-pull GaN amplifier in the VHF-UHF band. This PA shows an efficiency of 60-55% from 10 MHz to 650 MHz, for an output power of 100W and a compressed gain of 19,5 +/-1.5 dB. By reducing the drain voltage to 30V, the efficiency is 70-60 with an output power of 47 dBm and a power gain of 18,5 +/-1.5 dB. Moreover, the measured EVM is less than 2% for an efficiency about 40% and an output power from 0 dBm to 45 dBm using a 64-QAM modulation at 50V. Nevertheless, the record efficiency of 60% have been measured on a 3pi/8 PSK (EDGE) digital modulation with an EVM <; 2.5%, for a tunable output power from 42 to 48 dBm.


title: Improving the Reproducibility of PAN's Shared Tasks: - Plagiarism Detection, Author Identification, and Author Profiling.

abstract: This paper reports on the PAN 2014 evaluation lab which hosts three shared tasks on plagiarism detection, author identification, and author profiling. To improve the reproducibility of shared tasks in general, and PAN’s tasks in particular, the Webis group developed a new web service called TIRA, which facilitates software submissions. Unlike many other labs, PAN asks participants to submit running softwares instead of their run output. To deal with the organizational overhead involved in handling software submissions, the TIRA experimentation platform helps to significantly reduce the workload for both participants and organizers, whereas the submitted softwares are kept in a running state. This year, we addressed the matter of responsibility of successful execution of submitted softwares in order to put participants back in charge of executing their software at our site. In sum, 57 softwares have been submitted to our lab; together with the 58 software submissions of last year, this forms the largest collection of softwares for our three tasks to date, all of which are readily available for further analysis. The report concludes with a brief summary of each task.


title: Fuzzy logic controller based maximum power point tracking of photovoltaic system using boost converter

abstract: Today, the energy crisis in the world has led to the rise in use of renewable energy sources. With the advancement in power electronic technology, the solar photovoltaic energy has been recognized as an important renewable energy resource because it is clean, abundant and pollution free. The efficiency of the photovoltaic system may be increased by using Maximum Power Point Tracker (MPPT). A number of algorithms are developed to track the maximum power point efficiently. Most of the existing MPPT algorithms suffer from the drawback of being slow or wrong tracking. Introduction of intelligent MPPTs in PV systems is very promising. This paper proposes an intelligent control method for the maximum power point tracking (MPPT) of a photovoltaic system under variable insolation conditions. Here in this paper, intelligent control method uses a Fuzzy Logic Controller applied to a DC-DC converter device. The result is compared with the results obtained by using P&O method. The effectiveness of proposed algorithm is validated by simulation using Matlab/Simulink and is compared to those obtained by the conventional methods. The result shows that the fuzzy logic controller exhibits a better performance compared to that of conventional method.


title: A provable SVD-based algorithm for learning topics in dominant admixture corpus

abstract: Topic models, such as Latent Dirichlet Allocation (LDA), posit that documents are drawn from admixtures of distributions over words, known as topics. The inference problem of recovering topics from such a collection of documents drawn from admixtures, is NP-hard. Making a strong assumption called separability, [4] gave the first provable algorithm for inference. For the widely used LDA model, [6] gave a provable algorithm using clever tensor-methods. But [4, 6] do not learn topic vectors with bounded l1 error (a natural measure for probability vectors). ::: ::: Our aim is to develop a model which makes intuitive and empirically supported assumptions and to design an algorithm with natural, simple components such as SVD, which provably solves the inference problem for the model with bounded l1 error. A topic in LDA and other models is essentially characterized by a group of co-occurring words. Motivated by this, we introduce topic specific Catchwords, a group of words which occur with strictly greater frequency in a topic than any other topic individually and are required to have high frequency together rather than individually. A major contribution of the paper is to show that under this more realistic assumption, which is empirically verified on real corpora, a singular value decomposition (SVD) based algorithm with a crucial pre-processing step of thresholding, can provably recover the topics from a collection of documents drawn from Dominant admixtures. Dominant admixtures are convex combination of distributions in which one distribution has a significantly higher contribution than the others. Apart from the simplicity of the algorithm, the sample complexity has near optimal dependence on w0, the lowest probability that a topic is dominant, and is better than [4]. Empirical evidence shows that on several real world corpora, both Catchwords and Dominant admixture assumptions hold and the proposed algorithm substantially outperforms the state of the art [5].


title: Conditional Prior Networks for Optical Flow

abstract: Classical computation of optical flow involves generic priors (regularizers) that capture rudimentary statistics of images, but not long-range correlations or semantics. On the other hand, fully supervised methods learn the regularity in the annotated data, without explicit regularization and with the risk of overfitting. We seek to learn richer priors on the set of possible flows that are statistically compatible with an image. Once the prior is learned in a supervised fashion, one can easily learn the full map to infer optical flow directly from two or more images, without any need for (additional) supervision. We introduce a novel architecture, called Conditional Prior Network (CPN), and show how to train it to yield a conditional prior. When used in conjunction with a simple optical flow architecture, the CPN beats all variational methods and all unsupervised learning-based ones using the same data term. It performs comparably to fully supervised ones, that however are fine-tuned to a particular dataset. Our method, on the other hand, performs well even when transferred between datasets. Code is available at: https://github.com/YanchaoYang/Conditional-Prior-Networks.


title: YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles

abstract: Ahstract- With one billion monthly viewers, and millions of users discussing and sharing opinions, comments below YouTube videos are rich sources of data for opinion mining and sentiment analysis. We introduce the YouTube AV 50K dataset, a freely-available collections of more than 50,000 YouTube comments and metadata below autonomous vehicle (AV)-related videos. We describe its creation process, its content and data format, and discuss its possible usages. Especially, we do a case study of the first self-driving car fatality to evaluate the dataset, and show how we can use this dataset to better understand public attitudes toward self-driving cars and public reactions to the accident. Future developments of the dataset are also discussed.


title: Predicting the popularity of online articles based on user comments

abstract: Understanding user participation is fundamental in anticipating the popularity of online content. In this paper, we explore how the number of users' comments during a short observation period after publication can be used to predict the expected popularity of articles published by a countrywide online newspaper. We evaluate a simple linear prediction model on a real dataset of hundreds of thousands of articles and several millions of comments collected over a period of four years. Analyzing the accuracy of our proposed model for different values of its basic parameters we provide valuable insights on the potentials and limitations for predicting content popularity based on early user activity.


title: Kinematic Control of Redundant Manipulators Using Neural Networks

abstract: Redundancy resolution is a critical problem in the control of robotic manipulators. Recurrent neural networks (RNNs), as inherently parallel processing models for time-sequence processing, are potentially applicable for the motion control of manipulators. However, the development of neural models for high-accuracy and real-time control is a challenging problem. This paper identifies two limitations of the existing RNN solutions for manipulator control, i.e., position error accumulation and the convex restriction on the projection set, and overcomes them by proposing two modified neural network models. Our method allows nonconvex sets for projection operations, and control error does not accumulate over time in the presence of noise. Unlike most works in which RNNs are used to process time sequences, the proposed approach is model-based and training-free, which makes it possible to achieve fast tracking of reference signals with superior robustness and accuracy. Theoretical analysis reveals the global stability of a system under the control of the proposed neural networks. Simulation results confirm the effectiveness of the proposed control method in both the position regulation and tracking control of redundant PUMA 560 manipulators.


Cross-Class Sample Synthesis for Zero-shot Learning


title: A Bayesian Algorithm of Wireless Sensor Network Link Selection under Asymmetric Loss Function

abstract: Traditional link selection algorithms of wireless sensor networks needs lots of data packages as testing samples and the nodes of wireless sensor networks are battery-powered. Then it is a shortcoming for the limited energy of wireless sensor networks. The aim of this study is to propose new link selection algorithms to overcome this shortcoming based the concept of Bayesian approach. The new Bayesian link selection algorithms are derived under an asymmetric loss function. Finally, simulations are performed to compare the performance of the new method with other methods. The simulations show that the new algorithm has a good adaptability.


title: Multiparty Access Control for Online Social Networks: Model and Mechanisms

abstract: Online social networks (OSNs) have experienced tremendous growth in recent years and become a de facto portal for hundreds of millions of Internet users. These OSNs offer attractive means for digital social interactions and information sharing, but also raise a number of security and privacy issues. While OSNs allow users to restrict access to shared data, they currently do not provide any mechanism to enforce privacy concerns over data associated with multiple users. To this end, we propose an approach to enable the protection of shared data associated with multiple users in OSNs. We formulate an access control model to capture the essence of multiparty authorization requirements, along with a multiparty policy specification scheme and a policy enforcement mechanism. Besides, we present a logical representation of our access control model that allows us to leverage the features of existing logic solvers to perform various analysis tasks on our model. We also discuss a proof-of-concept prototype of our approach as part of an application in Facebook and provide usability study and system evaluation of our method.


title: PSYCHOLOGICAL SCIENCE Research Article Required Sample Size to Detect the Mediated Effect

abstract: Mediation models are widely used, and there are many tests of the mediated effect. One of the most common questions that researchers have when planning mediation studies is, “How many subjects do I need to achieve adequate power when testing for mediation?” This article presents the necessary sample sizes for six of the most common and the most recommended tests of mediation for various combinations of parameters, to provide a guide for researchers when designing studies or applying for grants.


title: Performance studies of a separately excited DC motor speed control fed by a buck converter using optimized PIλDμ controller

abstract: In this paper, fractional order proportional integral derivative controller (FOPID) has been designed to get a desired performance form a separately excited Direct Current (DC) motor fed by a buck converter. The robustness of the DC motor speed control is increased using this FOPID controller due to the effect of increased iso-damping response. This increased iso-damping occurs due to the addition of two new variables i.e. integro-differential operators λ and μ in FOPID when compared with conventional PID controller. We have also discussed the rule of taking this integro-differential operator which can be used for further improvement of iso-damping response. The five variables (Kp, Ki, Kd, λ and μ) of the FOPID controller are optimized first considering frequency domain design criteria and secondly using time domain performance index. Mathematical model of the plant has been used in order to utilize the present scheme. Genetic Algorithm from MATLAB optimization toolbox has been used for solving the time and frequency based design criteria.


title: A head-mounted operating binocular for augmented reality visualization in medicine - design and initial evaluation

abstract: Computer-aided surgery (CAS), the intraoperative application of biomedical visualization techniques, appears to be one of the most promising fields of application for augmented reality (AR), the display of additional computer-generated graphics over a real-world scene. Typically a device such as a head-mounted display (HMD) is used for AR. However, considerable technical problems connected with AR have limited the intraoperative application of HMDs up to now. One of the difficulties in using HMDs is the requirement for a common optical focal plane for both the realworld scene and the computer-generated image, and acceptance of the HMD by the user in a surgical environment. In order to increase the clinical acceptance of AR, we have adapted the Varioscope (Life Optics, Vienna), a miniature, cost-effective head-mounted operating binocular, for AR. In this paper, we present the basic design of the modified HMD, and the method and results of an extensive laboratory study for photogrammetric calibration of the Varioscope's computer displays to a real-world scene. In a series of 16 calibrations with varying zoom factors and object distances, mean calibration error was found to be 1.24 /spl plusmn/ 0.38 pixels or 0.12 /spl plusmn/ 0.05 mm for a 640 /spl times/ 480 display. Maximum error accounted for 3.33 /spl plusmn/ 1.04 pixels or 0.33 /spl plusmn/ 0.12 mm. The location of a position measurement probe of an optical tracking system was transformed to the display with an error of less than 1 mm in the real world in 56% of all cases. For the remaining cases, error was below 2 mm. We conclude that the accuracy achieved in our experiments is sufficient for a wide range of CAS applications.


title: Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input

abstract: Non-autoregressive translation (NAT) models, which remove the dependence on previous target tokens from the inputs of the decoder, achieve significantly inference speedup but at the cost of inferior accuracy compared to autoregressive translation (AT) models. Previous work shows that the quality of the inputs of the decoder is important and largely impacts the model accuracy. In this paper, we propose two methods to enhance the decoder inputs so as to improve NAT models. The first one directly leverages a phrase table generated by conventional SMT approaches to translate source tokens to target tokens, which are then fed into the decoder as inputs. The second one transforms source-side word embeddings to target-side word embeddings through sentence-level alignment and word-level adversary learning, and then feeds the transformed word embeddings into the decoder as inputs. Experimental results show our method largely outperforms the NAT baseline~\citep{gu2017non} by $5.11$ BLEU scores on WMT14 English-German task and $4.72$ BLEU scores on WMT16 English-Romanian task.


title: Red blood cell segmentation using masking and watershed algorithm: A preliminary study

abstract: Image segmentation is the most important step and a key technology in image processing which directly affect the next processing. In human blood cell segmentation cases, many methods were applied for obtaining better results. It is basically an improved visualization to observe blood cell under blood smear process. This paper will present an approach for red blood cell (RBC) segmentation which is a part of study to perform automated counting for RBC. The methods involve are Ycbcr color conversion, masking, morphological operators and watershed algorithm. The combination of Ycbcr color conversion and morphological operator produce segmented white blood cell (WBC) nucleus. Then it is being used as a mask to remove WBC from the blood cell image. Morphological operators involve binary erosion diminish small object like platelet. The resulted RBC segmentation is passing through marker controlled watershed algorithm which handles overlapping cells. The improvement need to be done for both segmentation and overlapped cell handling to obtain better result in the future.


title: The Nuss Procedure After Breast Augmentation for Female Pectus Excavatum

abstract: Pectus excavatum, the most common congenital chest wall malformation, has a higher incidence among men. Since 1987, when Donald Nuss performed his technique for the first time, the minimally invasive approach has become the most widely used technique for treating pectus excavatum. Few reported studies have focused on the repair of female pectus excavatum. Women with pectus excavatum often present with breast asymmetry that may require breast augmentation, either before or after pectus excavatum repair. To the authors' knowledge, no reports on the Nuss procedure after breast implant surgery have been published. This report describes the case of a 26-year-old woman who underwent minimally invasive repair after breast implant surgery. The authors believe that for women with severe pectus excavatum, the Nuss procedure should be the first choice for surgical correction. Moreover, for breast implant patients, this technique is absolutely feasible without major complications.


title: Surface plasmon polariton mode validation using commercially available finite element method ANSYS HFSS

abstract: Numerical computation using HFSS finite element analysis was conducted in order to determine the dispersion relation (complex index) of the surface plasmon polariton (SPP) mode. SPP result from the interaction or coupling between electromagnetic fields in the form of light and delocalised electrons on a metal interface. SPPs has the effect of confining electromagnetic radiation on the order that is smaller than the wavelength. In this work, the correspondence by H.P Hsu (IEEE Trans. Microw. Theory Techn., 11(4), (1963).) was used in the study of SPP modes comparing both analytical calculations and numerical calculation from ANSYS HFSS to ascertain the veracity of the correspondence for SPP surface waves, and calculating the attenuation coefficient using quality factor, Q, obtained from HFSS eigenmode calculations. Silicon dioxide and silver with the use of measured complex permittivity for silver is used to conduct this study. Numerically-obtained results for propagation length and SPP effective index are compared to analytical calculations from the well-known SPP dispersion relationship.


title: Drone to the Rescue: Relay-Resilient Authentication using Ambient Multi-sensing

abstract: Many mobile and wireless authentication systems are prone to relay attacks whereby two non co-presence colluding entities can subvert the authentication functionality by simply relaying the data between a legitimate prover (\({\mathcal {P}}\)) and verifier (\({\mathcal {V}}\)). Examples include payment systems involving NFC and RFID devices, and zero-interaction token-based authentication approaches. Utilizing the contextual information to determine \({\mathcal {P}}\)-\({\mathcal {V}}\) proximity, or lack thereof, is a recently proposed approach to defend against relay attacks. Prior work considered WiFi, Bluetooth, GPS and Audio as different contextual modalities for the purpose of relay-resistant authentication.


title: A file system abstraction for sense and respond systems

abstract: The heterogeneity and resource constraints of sense-and-respond systems significantly challenge system and application development. In this paper, we present a flexible, intuitive file system abstraction for organizing and managing sense-and-respond systems based on the Plan 9 design principles. A key feature is the support of multiple views of the system via filesystem namespaces. Constructed logical views present an application-specific representation of the network, thus enabling high-level programming. Concurrently, structural views of the network enable resource-efficient planning and execution of tasks. We present and motivate the design using several examples, outline research challenges and our plan to address them, and describe the current implementation state.


title: Decentralized frequency control of a DDG-PV Microgrid in islanded mode

abstract: This paper presents an innovative approach to control the frequency in Diesel-Driven Generator (DDG)-Photovoltaic (PV) Microgrid (MG) in islanded mode. The common approach is based on hierarchical control: primary control and secondary control. The conventional primary controller is a P-controller. Compared to this, we propose a novel primary controller for PV to mimic the dynamic behavior of the conventional synchronous generator (SG) so that more inertia in the MG can be obtained. The proposed secondary control is accomplished decentralized, too, so that no communication links for the real-time control are required and the single point of failure can be avoided. Although in this approach every controller works locally, the controllers and their parameters are designed centrally. This means, the controllers can be appropriately adjusted, when the parameters or the topology of the system change. The parameters of the controllers are optimized by minimizing the H-inf-norm using a known non-smooth method.


title: Scalable training of Markov logic networks using approximate counting

abstract: In this paper, we propose principled weight learning algorithms for Markov logic networks that can easily scale to much larger datasets and application domains than existing algorithms. The main idea in our approach is to use approximate counting techniques to substantially reduce the complexity of the most computation intensive sub-step in weight learning: computing the number of groundings of a first-order formula that evaluate to true given a truth assignment to all the random variables. We derive theoretical bounds on the performance of our new algorithms and demonstrate experimentally that they are orders of magnitude faster and achieve the same accuracy or better than existing approaches.


title: Control Improvisation with Application to Music

abstract: Abstract : We introduce the concept of control improvisation, the process of generating a random sequence of control events guided by a reference sequence and satisfying a given specification. We propose a formal definition of the control improvisation problem and an empirical solution applied to the domain of music. More specifically, we consider the scenario of generating a monophonic Jazz melody (solo) on a given song harmonization. The music is encoded symbolically, with the improviser generating a sequence of note symbols consisting of pairs of pitches (frequencies) and discrete durations. Our approach has three phases. The first phase, generalization, learns from the given melody a nondeterministic automaton generating a set of melodies containing the original. We implement this phase using factor oracles. The second phase, safety supervision, enforces rules on the generalized automaton so that it plays in harmony with the accompaniment. The rules are analogous to safety properties that a control system must always obey. The third and final phase, divergence supervision, ensures that sequences produced by the improviser automaton lie, with high probability, within a specified similarity divergence from the original. This phase is implemented by replacing nondeterminism in the improviser automaton with probabilities, which are based on a given divergence measure. For music, several divergence measures have been proposed; amongst these, Normalized Compression Distances (NCDs) have been effectively used, and so we employ a variant of an NCD in this paper. An empirical evaluation is presented on a sample set of Jazz music.


title: Rapid Graph Layout Using Space Filling Curves

abstract: Network data frequently arises in a wide variety of fields, and node-link diagrams are a very natural and intuitive representation of such data. In order for a node-link diagram to be effective, the nodes must be arranged well on the screen. While many graph layout algorithms exist for this purpose, they often have limitations such as high computational complexity or node colocation. This paper proposes a new approach to graph layout through the use of space filling curves which is very fast and guarantees that there will be no nodes that are colocated. The resulting layout is also aesthetic and satisfies several criteria for graph layout effectiveness.


Facial Expression Recognition Based on Facial Components Detection and HOG Features


title: Computationally efficient link prediction in a variety of social networks

abstract: Online social networking sites have become increasingly popular over the last few years. As a result, new interdisciplinary research directions have emerged in which social network analysis methods are applied to networks containing hundreds of millions of users. Unfortunately, links between individuals may be missing either due to an imperfect acquirement process or because they are not yet reflected in the online network (i.e., friends in the real world did not form a virtual connection). The primary bottleneck in link prediction techniques is extracting the structural features required for classifying links. In this article, we propose a set of simple, easy-to-compute structural features that can be analyzed to identify missing links. We show that by using simple structural features, a machine learning classifier can successfully identify missing links, even when applied to a predicament of classifying links between individuals with at least one common friend. We also present a method for calculating the amount of data needed in order to build more accurate classifiers. The new Friends measure and Same community features we developed are shown to be good predictors for missing links. An evaluation experiment was performed on ten large social networks datasets: Academia.edu, DBLP, Facebook, Flickr, Flixster, Googlep, Gowalla, TheMarker, Twitter, and YouTube. Our methods can provide social network site operators with the capability of helping users to find known, offline contacts and to discover new friends online. They may also be used for exposing hidden links in online social networks.


title: Haptic stylus with inertial and vibro-tactile feedback

abstract: In this paper, we introduce a novel stylus capable of displaying two haptic effects to the user. The first effect is a tactile flow effect up and down along the pen, and the other is a rotation effect about the long axis of the pen. The flow effect is based on the haptic illusion of “apparent tactile motion”, while the rotation effect comes from the reaction torque created by an electric motor placed along the stylus shaft. The stylus is embedded with two vibration actuators at the ends, and a DC motor with a rotating balanced mass in the middle. We show that, it is possible to create flow and rotation effects on the stylus by driving the actuators on the stylus. Furthermore, we show that the timing and the actuation patterns of the vibration actuators and DC motor on the stylus significantly affect the discernibility of the synthesized perceptions; hence these parameters should be selected carefully. Two psychophysical experiments, each performed with 10 subjects, shed light on the discernability of the two haptic effects as a function of various actuation parameters. Our results show that, with carefully selected parameters, the subjects can successfully identify the flow of motion and the direction of rotation with high accuracies.


title: Analysis of Cockcroft-Walton Voltage Multiplier

abstract: In this paper a high D.C. voltage is based on Cockcroft-Walton cascade rectifier, without using transformer. The purpose of this paper is to get an output of a high dc voltage by applying input as an low dc voltage. It provides continuous input current to load with low ripple voltage and current. Moreover, based on the n-stage CW voltage multiplier, the proposed converter can provide a suitable dc source for an n + 1-level multilevel inverter. In this paper, control strategy employs two independent frequencies, one of which operates at high frequency while the other one operates at relatively low frequency.


title: A design approach for inset-fed rectangular microstrip antennas

abstract: The goal of this paper is to study the dependence of resonant input resistance of the inset-fed patch on the notch and feed-line geometry. Different geometries, substrate materials, and feed lines were considered. A simple CAD formula for the input resistance is obtained. The effect of the notch on the radiation pattern is also studied to see how it influences cross polarization.


title: Expert Finding Systems for Organizations: Problem and Domain Analysis and the DEMOIR Approach

abstract: Computer systems that augment the process of finding, in an organization or worldwide, the appropriate expert for a given problem are becoming more feasible than ever as a result of the prevalence of corporate intranets and the Internet. In this article, we investigate such systems in 2 parts. First, we explore the expert-finding problem in depth, review and analyze existing systems in this domain, and suggest a domain model that can serve as a framework for design and development decisions. Second, on the basis of our analyses of the problem and solution spaces, we bring to light the gaps that remain to be addressed. Finally, after this two-part investigation, we present our approach, called DEMOIR (dynamic expertise modeling from organizational information resources), which is a modular architecture for expert-finding systems that is based on a centralized expertise-modeling server but also incorporates decentralized components for expertise information gathering and exploitation.


title: Sonophotocatalytic treatment of rhodamine B using visible-light-driven CeO2/Ag2CrO4 composite in a batch mode based on ribbon-like CeO2 nanofibers via electrospinning

abstract: CeO2/Ag2CrO4 composite photocatalyst was successfully fabricated using electrospinning and calcination and chemical precipitation method based on CeO2 ribbon-like fibers and characterized by field-emission scanning electron microscopy (FESEM), energy dispersive X-ray (EDX), X-ray diffraction (XRD), UV–Vis diffuse reflectance spectroscopy (DRS) and Fourier-transform infrared spectroscopy (FT-IR). The as-obtained CeO2/Ag2CrO4 composite used photocatalytic performance in the sonophotodegradation of rhodamine B in aqueous solution under visible-light (LED) irradiation. DRS analysis illustrates that CeO2/Ag2CrO4 composite exhibited enhanced absorption in the visible region-attributed CeO2 nanofibers. The effect of four effective parameters including initial concentration of rhodamine B (RhB), photocatalyst dosage, pH, and irradiation time was studied and optimized using central composite design. The kinetic studies confirmed ability of pseudo first-order reaction based on the Langmuir–Hinshelwood model for fitting empirical data, while its rate constant (kobs), L–H rate constants (kr), and L–H adsorption constants (KA) were 0.0449 min−1, 11.66 mg L−1 min−1 and 1.09E−3 mg L−1, respectively. The enhanced photocatalytic activity could be ascribed to the ultrasound field and formation of a heterojunction system among CeO2 and Ag2CrO4, which lead to a better mass transfer and higher efficiency of charge electron–hole separation, respectively.


title: A research for identifying study anxiety sources among university students

abstract: University students suffer to some levels of study anxiety, such as; have new experiences, environment and situation. Study anxiety is a real phenomenon. Campus environment has universal access to increase study anxiety among students. The prevalence of study anxiety has been acknowledged by students and educators. However, no current research exists to identify the study anxiety sources among university students. In this research, we present a survey aimed to identify of study anxiety sources among university students. The survey involves 770 students of Universiti Malaysia Pahang. The survey asks students about experiences, feelings and thoughts related with anxieties during study process. The results identify seven sources. But, there are five potential sources of study anxiety; exam anxiety, class presentation anxiety, mathematic anxiety, language anxiety and social anxiety. Descriptive statistics were used to analyze the data which was run in SPSS 16.0. The results will be used to address study anxiety sources among university students.


title: Ergonomic assessment of the French and American position for laparoscopic cholecystectomy in the MIS Suite

abstract: BACKGROUND ::: Cholecystectomy was one of the first surgical procedures to be performed with laparoscopy in the 1980s. Currently, two operation setups generally are used to perform a laparoscopic cholecystectomy: the French and the American position. In the French position, the patient lies in the lithotomy position, whereas in the American position, the patient lies supine with the left arm in abduction. To find an ergonomic difference between the two operation setups the movements of the surgeon's vertebral column were analyzed in a crossover study. ::: ::: ::: METHODS ::: The posture of the surgeon's vertebral column was recorded intraoperatively using an electromagnetic motion-tracking system with three sensors attached to the head and to the trunk at the levels of Th1 and S1. A three-dimensional posture analysis of the cervical and thoracolumbar spine was performed to evaluate four surgeons removing a gallbladder in the French and American position. The body angles assessed were flexion/extension of the cervical and thoracolumbar spine, axial rotation of the cervical and thoracolumbar spine, lateroflexion of the cervical and thoracolumbar spine, and the orientation of the head in the sagittal plane. For each body angle, the mean, the percentage of operation time within an ergonomic acceptable range, and the relative frequencies were calculated and compared. ::: ::: ::: RESULTS ::: No statistical difference was observed in the mean body angles or in the percentages of operation time within an acceptable range between the French and the American position. The relative frequencies of the body angles might indicate a trend toward slight thoracolumbar flexion in the French position. ::: ::: ::: CONCLUSION ::: In a modern dedicated minimally invasive surgery suite, the body posture of the neck and trunk and the orientation of the head did not differ significantly between the French and American position.


title: Genome Sequencing and Analysis of the Tasmanian Devil and Its Transmissible Cancer

abstract: The Tasmanian devil (Sarcophilus harrisii), the largest marsupial carnivore, is endangered due to a transmissible facial cancer spread by direct transfer of living cancer cells through biting. Here we describe the sequencing, assembly, and annotation of the Tasmanian devil genome and whole-genome sequences for two geographically distant subclones of the cancer. Genomic analysis suggests that the cancer first arose from a female Tasmanian devil and that the clone has subsequently genetically diverged during its spread across Tasmania. The devil cancer genome contains more than 17,000 somatic base substitution mutations and bears the imprint of a distinct mutational process. Genotyping of somatic mutations in 104 geographically and temporally distributed Tasmanian devil tumors reveals the pattern of evolution and spread of this parasitic clonal lineage, with evidence of a selective sweep in one geographical area and persistence of parallel lineages in other populations.


Apples to Apples: Learning Semantics of Common Entities Through a Novel Comprehension Task.


title: Nomenclature of CD molecules from the Tenth Human Leucocyte Differentiation Antigen Workshop

abstract: Kohler and Milstein’s1 description of monoclonal antibodies (mAbs) in 1975 revolutionised immunology. With the advent of hybridoma technology, immunologists started to produce an ever increasing number of mAbs directed against leukocyte cell-surface molecules. Inevitably, several mAbs produced by different laboratories and given independent names recognised the same molecule. The First International Human Leucocyte Differentiation Antigen (HLDA) Workshop was organised in the early 1980s, and aimed to identify groups of mAbs reacting with a common human cell-surface antigen and to agree a nomenclature that would facilitate better communication amongst the scientific community.2 The outcomes of the Workshops are the ‘CDs’, an abbreviation for the non-descriptive ‘cluster of differentiation’ number. The CD number is assigned to a group or cluster of mAbs that recognise a molecule expressed on the surface of human leukocytes and other cells relevant to the immune system. The nomenclature has been universally adopted by the scientific community, officially approved by the International Union of Immunological Societies and sanctioned by WHO. Its usage has now evolved and is also commonly used to name the molecules themselves. However, by adding either mAb or molecule/protein it should be made clear whether one means the CDxx mAb or CDxx molecule. Ten HLDA Workshops have been organised to date, with the most recent one held in 2014 in conjunction with the Australasian Society of Immunology in Wollongong, Australia.


title: Optimizing Interactive Systems with Data-Driven Objectives

abstract: Effective optimization is essential for interactive systems to provide a satisfactory user experience. However, it is often challenging to find an objective to optimize for. Generally, such objectives are manually crafted and rarely capture complex user needs accurately. Conversely, we propose an approach that infers the objective directly from observed user interactions. These inferences can be made regardless of prior knowledge and across different types of user behavior. Then we introduce: Interactive System Optimizer (ISO), a novel algorithm that uses these inferred objectives for optimization. Our main contribution is a new general principled approach to optimizing interactive systems using data-driven objectives. We demonstrate the high effectiveness of ISO over several GridWorld simulations.


title: Decision quality and satisfaction: the effects of online information sources and self-efficacy

abstract: Purpose ::: ::: ::: ::: ::: Digital libraries and social media are two sources of online information with different characteristics. The purpose of this paper is to integrate self-efficacy into the analysis of the relationship between information sources and decision making, and to explore the effect of self-efficacy on decision making, as well as the interacting effect of self-efficacy and information sources on decision making. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: Survey data were collected and the partial least squares structural equation modeling was employed to verify the research model. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The effect of digital library usage for acquiring information on perceived decision quality (PDQ) is larger than that of social media usage for acquiring information on PDQ. Self-efficacy in acquiring information (SEAI) stands out as the key determinant for PDQ. The effect of social media usage for acquiring information on PDQ is positively moderated by SEAI. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: Decision making is a fundamental activity for individuals, but human decision making is often subject to biases. The findings of this study provide useful insights into decision quality improvement, highlighting the importance of SEAI in the face of information overload. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: This study integrates self-efficacy into the analysis of the relationship between information sources and decision making, presenting a new perspective for decision-making research and practice alike.


title: Serefind: a social networking website for classifieds

abstract: This paper presents the design and implementation of a social networking website for classifieds, called Serefind. We designed search interfaces with focus on security, privacy, usability, design, ranking, and communications. We deployed this site at the Johns Hopkins University, and the results show it can be used as a self-sustaining classifieds site for public or private communities.


title: An Exploration on Brain Computer Interface and Its Recent Trends

abstract: Detailed exploration on Brain Computer Interface (BCI) and its recent trends has been done in this paper. Work is being done to identify objects, images, videos and their color compositions. Efforts are on the way in understanding speech, words, emotions, feelings and moods. When humans watch the surrounding environment, visual data is processed by the brain, and it is possible to reconstruct the same on the screen with some appreciable accuracy by analyzing the physiological data. This data is acquired by using one of the non-invasive techniques like electroencephalography (EEG) in BCI. The acquired signal is to be translated to produce the image on to the screen. This paper also lays suitable directions for future work.


title: The incentive sensitization theory of addiction: some current issues

abstract: We present a brief overview of the incentive sensitization theory of addiction. This posits that addiction is caused primarily by drug-induced sensitization in the brain mesocorticolimbic systems that attribute incentive salience to reward-associated stimuli. If rendered hypersensitive, these systems cause pathological incentive motivation ('wanting') for drugs. We address some current questions including: what is the role of learning in incentive sensitization and addiction? Does incentive sensitization occur in human addicts? Is the development of addiction-like behaviour in animals associated with sensitization? What is the best way to model addiction symptoms using animal models? And, finally, what are the roles of affective pleasure or withdrawal in addiction?


title: Consensus protocols for networks of dynamic agents

abstract: In this paper, we introduce linear and nonlinear consensus protocols for networks of dynamic agents that allow the ::: agents to agree in a distributed and cooperative fashion. ::: We consider the cases of networks with communication time-delays and channels that have filtering effects. ::: We find a tight upper bound on the maximum fixed time-delay ::: that can be tolerated in the network. It turns out that the ::: connectivity of the network is the key in reaching a ::: consensus. The case of agreement with bounded inputs is ::: considered by analyzing the convergence of a class of nonlinear protocols. A Lyapunov function is introduced that ::: quantifies the total disagreement among the nodes of a ::: network. Simulation results are provided for agreement in networks with communication time-delays and constrained inputs.


title: An innovative methodology for detection and quantification of cracks through incorporation of depth perception

abstract: Visual inspection of structures is a highly qualitative method in which inspectors visually assess a structure’s condition. If a region is inaccessible, binoculars must be used to detect and characterize defects. Although several Non-Destructive Testing methods have been proposed for inspection purposes, they are nonadaptive and cannot quantify crack thickness reliably. In this paper, a contact-less remote-sensing crack detection and quantification methodology based on 3D scene reconstruction (computer vision), image processing, and pattern recognition concepts is introduced. The proposed approach utilizes depth perception to detect cracks and quantify their thickness, thereby giving a robotic inspection system the ability to analyze images captured from any distance and using any focal length or resolution. This unique adaptive feature is especially useful for incorporating mobile systems, such as unmanned aerial vehicles, into structural inspection methods since it would allow inaccessible regions to be properly inspected for cracks. Guidelines are presented for optimizing the acquisition and processing of images, thereby enhancing the quality and reliability of the damage detection approach and allowing the capture of even the slightest cracks (e.g., detection of 0.1 mm cracks from a distance of 20 m), which are routinely encountered in realistic field applications where the camera-object distance and image contrast are not controllable.


title: Integrating predictive analytics and social media

abstract: A key analytical task across many domains is model building and exploration for predictive analysis. Data is collected, parsed and analyzed for relationships, and features are selected and mapped to estimate the response of a system under exploration. As social media data has grown more abundant, data can be captured that may potentially represent behavioral patterns in society. In turn, this unstructured social media data can be parsed and integrated as a key factor for predictive intelligence. In this paper, we present a framework for the development of predictive models utilizing social media data. We combine feature selection mechanisms, similarity comparisons and model cross-validation through a variety of interactive visualizations to support analysts in model building and prediction. In order to explore how predictions might be performed in such a framework, we present results from a user study focusing on social media data as a predictor for movie box-office success.


Phytochemical Screening and Antioxidant Activity of Selected Wild Plants in Liliaceae Family Growing Syria


title: Bees algorithm for generalized assignment problem

abstract: Bees algorithm (BA) is a new member of meta-heuristics. BA tries to model natural behavior of honey bees in food foraging. Honey bees use several mechanisms like waggle dance to optimally locate food sources and to search new ones. This makes them a good candidate for developing new algorithms for solving optimization problems. In this paper a brief review of BA is first given, afterwards development of a BA for solving generalized assignment problems (GAP) with an ejection chain neighborhood mechanism is presented. GAP is a NP-hard problem. Many meta-heuristic algorithms were proposed for its solution. So far BA is generally applied to continuous optimization. In order to investigate the performance of BA on a complex integer optimization problem, an attempt is made in this paper. An extensive computational study is carried out and the results are compared with several algorithms from the literature.


title: Dynamic Materialized Views

abstract: A conventional materialized view blindly materializes and maintains all rows of a view, even rows that are never accessed. We propose a more flexible materialization strategy aimed at reducing storage space and view maintenance costs. A dynamic materialized view selectively materializes only a subset of rows, for example, the most frequently accessed rows. One or more control tables are associated with the view and define which rows are currently materialized. The set of materialized rows can be changed dynamically, either manually or automatically by an internal cache manager using a feedback loop. Dynamic execution plans are generated to decide whether the view is applicable at run time. Experimental results in Microsoft SQL Server show that compared with conventional materialized views, dynamic materialized views greatly reduce storage requirements and maintenance costs while achieving better query performance with improved buffer pool efficiency.


title: Opinion Mining in Online Reviews About Distance Education Programs

abstract: The popularity of distance education programs is increasing at a fast pace. En par with this development, online communication in fora, social media and reviewing platforms between students is increasing as well. Exploiting this information to support fellow students or institutions requires to extract the relevant opinions in order to automatically generate reports providing an overview of pros and cons of different distance education programs. We report on an experiment involving distance education experts with the goal to develop a dataset of reviews annotated with relevant categories and aspects in each category discussed in the specific review together with an indication of the sentiment. ::: Based on this experiment, we present an approach to extract general categories and specific aspects under discussion in a review together with their sentiment. We frame this task as a multi-label hierarchical text classification problem and empirically investigate the performance of different classification architectures to couple the prediction of a category with the prediction of particular aspects in this category. We evaluate different architectures and show that a hierarchical approach leads to superior results in comparison to a flat model which makes decisions independently.


title: Optimal Design of Remote Center Compliance Devices of Rotational Symmetry

abstract: Remote Center Compliance (RCC) devices are passive devices used in automated assembly. For round peg-in-hole insertions, initial misalignments of the peg can cause heavy radial loadings on the corresponding hole in arbitrary direction. RCC devices with rotational symmetry property are therefore desirable. This paper discusses how such devices may be designed and shows that circular periodic structures satisfy this property. The elastic part of the RCC device is formulated as a compliant mechanism and a systematic design methodology is proposed based on structural optimization. A smooth optimal design is achieved with distributed compliance and numerical simulation is conducted to illustrate the feasibility. The proposed method is expected to be used to design a novel device for assembling fragile plastic parts, which is a challenge to 3C industry.


title: Topological and geometric beautification of reverse engineered geometric models

abstract: Boundary representation models reverse engineered from 3D range data suffer from various inaccuracies caused by noise in the measured data and the model building software. Beautification aims to improve such models in a post-processing step solely working with the boundary representation model. The improved model should exhibit topological and geometric regularities representing the original, ideal design intent. This paper gives an overview of algorithms for a complete beautification system suitable for improving the topology and the geometry of low to medium complexity reverse engineered models.


title: Determination of thoracic and lumbar spinal processes by their percentage position between C7 and the PSIS level

abstract: BackgroundAccurate measurements of spinal movement require reliable determination of anatomical landmarks. Current methods of identifying these are not sufficiently reliable or valid for this purpose. A reliable and convenient method of placing markers on selected vertebra is needed to compare measurements between different testers, subjects and sessions.FindingsTwo testers palpated T4, T7, T10, L1 and L4 spinal processes according to established criteria. They measured the position of spinal processes between C7 and the Posterior Superior Iliac Spine (PSIS) at the Pelvis independently using a flexible ruler placed on the spine. Subjects with a wide range of body heights but without visible spinal deformities were recruited for measurements. Reliability was calculated using absolute and relative values. Mean percentage position and 95% Confidence Intervals were calculated using the mean of both testers’ measurement for all subjects.Twenty-two subjects participated. The mean distance between C7 and the PSIS level was 50.9 cm (SD: 3.5 cm). Relative reliability for all spinal processes was almost perfect (ICC: > 0.9). Absolute reliability values showed high agreement between testers. Percentage position of T4 was found to be situated 21% along the distance between C7 and the PSIS level, T7 at 39%, T10 at 54.1%, L1 at 70.9% and L4 at 86.1% accordingly. 95% Confidence intervals around mean percentage positions had a maximum at L1 with 2.8% range from upper to lower limit.ConclusionsThe distance of three thoracic and two lumbar spinal processes can be reliably and accurately measured by independent testers, using a flexible ruler. Percentage positions between C7 and PSIS level correspond to spinal processes for subjects without visible deformities in the sagittal and frontal plane.


Approach to TDT-2 : Segmentation , Detection , and Tracking


Advanced Machine Learning Practical 1 : Manifold Learning ( PCA and Kernel PCA ) Professor : Aude Billard Assistants :


title: Signal Synthesis and Receiver Design for MIMO Radar Imaging

abstract: Multiple-input-multiple-output (MIMO) radar is an emerging technology that has significant potential for advancing the state-of-the-art of modern radar. When orthogonal waveforms are transmitted, with M+N (N transmit and M receive) antennas, an MN-element filled virtual array can be obtained. To successfully utilize such an array for high-resolution MIMO radar imaging, constant-modulus transmit signal synthesis and optimal receive filter design play critical roles. We present in this paper a computationally attractive cyclic optimization algorithm for the synthesis of constant-modulus transmit signals with good auto- and cross-correlation properties. Then we go on to discuss the use of an instrumental variables approach to design receive filters that can be used to minimize the impact of scatterers in nearby range bins on the received signals from the range bin of interest (the so-called range compression problem). Finally, we present a number of numerical examples to demonstrate the effectiveness of the proposed approaches.


title: Energy-Efficient Information and Communication Infrastructures in the Smart Grid: A Survey on Interactions and Open Issues

abstract: Smart grid has modernized the way electricity is generated, transported, distributed, and consumed by integrating advanced sensing, communications, and control in the day-to-day operation of the grid. Electricity is a core utility for the functioning of society and for the services provided by information and communication technologies (ICTs). Several concepts of the smart grid, such as dynamic pricing, distributed generation, and demand management, have significantly impacted the operation of ICT services, in particular, communication networks and data centers. Ongoing energy-efficiency and operational expenditures reduction efforts in communication networks and data centers have gained another dimension with those smart grid concepts. In this paper, we provide a comprehensive survey on the smart grid-driven approaches in energy-efficient communications and data centers, and the interaction between smart grid and information and communication infrastructures. Although the studies on smart grid, energy-efficient communications, and green data centers have been separately surveyed in previous studies, to this end, research that falls in the intersection of those fields has not been properly classified and surveyed yet. We start our survey by providing background information on the smart grid and continue with surveying smart grid-driven approaches in energy-efficient communication systems, followed by energy, cost and emission minimizing approaches in data centers, and the corresponding cloud network infrastructure. We discuss the open issues in smart grid-driven approaches in ICTs and point some important research directions such as the distributed renewable energy generation capability-coupled communication infrastructures, optimum energy-efficient network design for the smart grid environment, the impact of green communication techniques on the reliability and latency requirements of smart grid data, workload consolidation with smart grid-awareness, and many more.


title: Learning object repositories as knowledge management systems

abstract: Over the past years, a number of international initiatives that recognize the importance of sharing and reusing digital educational resources among educational communities through the use of Learning Object Repositories (LORs) have emerged. Typically, these initiatives focus on collecting digital educational resources that are offered by their creators for open access and potential reuse. Nevertheless, most of the existing LORs are designed more as digital repositories, rather than as Knowledge Management Systems (KMS). By exploiting KMSs functionalities in LORs would bare the potential to support the organization and sharing of educational communities’ explicit knowledge (depicted in digital educational resources constructed by teachers and/or instructional designers) and tacit knowledge (depicted in teachers’ and students’ experiences and interactions of using digital educational resources available in LORs). Within this context, in this paper we study the design and the implementation of fourteen operating LORs from the KMSs’ perspective, so as to identify additional functionalities that can support the management of educational communities’ explicit and tacit knowledge. Thus, we propose a list of essential LORs’ functionalities, which aim to facilitate the organization and sharing of educational communities’ knowledge. Finally, we present the added value of these functionalities by identifying their importance towards addressing the current demands of web-facilitated educational communities, as well as the knowledge management activities that they execute. https://doi.org/10.34105/j.kmel.2013.05.009


title: Collective nostalgia: A group-level emotion that confers unique benefits on the group.

abstract: This research established collective nostalgia as a group-level emotion and ascertained the benefits it confers on the group. In Study 1, participants who reflected on a nostalgic event they had experienced together with ingroup members (collective nostalgia) evaluated the ingroup more positively and reported stronger intentions to approach (and not avoid) ingroup members than those who recalled a nostalgic event they had experienced individually (personal nostalgia), those who reflected on a lucky event they had experienced together with ingroup members (collective positive), and those who did not recall an event (no recall). In Study 2, collective (vs. personal) nostalgia strengthened behavioral intentions to support the ingroup more so than did recalling an ordinary collective (vs. personal) event. Increased collective self-esteem mediated this effect. In Study 3, collective nostalgia (compared with recall of an ordinary collective event) led participants to sacrifice money in order to punish a transgression perpetrated against an ingroup member. This effect of collective nostalgia was more pronounced when social identification was high (compared with low). Finally, in Study 4, collective nostalgia converged toward the group average (i.e., was socially shared) when participants thought of themselves in terms of their group membership. The findings underscore the viability of studying nostalgia at multiple levels of analysis and highlight the significance of collective nostalgia for understanding group-level attitudes, global action tendencies, specific behavioral intentions, and behavior. (PsycINFO Database Record (c) 2014 APA, all rights reserved)


title: Development of a Spatial Decision Support Framework for IMPETUS project in West Africa

abstract: Spatial Decision Support Systems (SDSS) need to be interdisciplinary and highly related to the target group. Developing systems for different persons in government, university and rural areas in developing regions seems to be impossible, if requirements in scientific reliability with the use of complex models are taken into account.


title: Ethical and Social Aspects of Self-Driving Cars

abstract: As an envisaged future of transportation, self-driving cars are being discussed from various perspectives, including social, economical, engineering, computer science, design, and ethics. On the one hand, self-driving cars present new engineering problems that are being gradually successfully solved. On the other hand, social and ethical problems are typically being presented in the form of an idealized unsolvable decision-making problem, the so-called trolley problem, which is grossly misleading. We argue that an applied engineering ethical approach for the development of new technology is what is needed; the approach should be applied, meaning that it should focus on the analysis of complex real-world engineering problems. Software plays a crucial role for the control of self-driving cars; therefore, software engineering solutions should seriously handle ethical and social considerations. In this paper we take a closer look at the regulative instruments, standards, design, and implementations of components, systems, and services and we present practical social and ethical challenges that have to be met, as well as novel expectations for software engineering.


title: Supervision via competition: Robot adversaries for learning tasks

abstract: There has been a recent paradigm shift in robotics to data-driven learning for planning and control. Due to large number of experiences required for training, most of these approaches use a self-supervised paradigm: using sensors to measure success/failure. However, in most cases, these sensors provide weak supervision at best. In this work, we propose an adversarial learning framework that pits an adversary against the robot learning the task. In an effort to defeat the adversary, the original robot learns to perform the task with more robustness leading to overall improved performance. We show that this adversarial framework forces the the robot to learn a better grasping model in order to overcome the adversary. By grasping 82% of presented novel objects compared to 68% without an adversary, we demonstrate the utility of creating adversaries. We also demonstrate via experiments that having robots in adversarial setting might be a better learning strategy as compared to having collaborative multiple robots.


title: An Online Learning Approach to Improving the Quality of Crowd-Sourcing

abstract: We consider a crowd-sourcing problem where in the process of labeling massive data sets, multiple labelers with unknown annotation quality must be selected to perform the labeling task for each incoming data sample or task, with the results aggregated using for example simple or weighted majority voting rule. In this paper, we approach this labeler selection problem in an online learning framework, whereby the quality of the labeling outcome by a specific set of labelers is estimated so that the learning algorithm over time learns to use the most effective combinations of labelers. This type of online learning in some sense falls under the family of multi-armed bandit (MAB) problems, but with a distinct feature not commonly seen: since the data is unlabeled to begin with and the labelers’ quality is unknown, their labeling outcome (or reward in the MAB context) cannot be readily verified; it can only be estimated against the crowd and be known probabilistically. We design an efficient online algorithm LS_OL using a simple majority voting rule that can differentiate high and low quality labelers over time, and is shown to have a regret (with respect to always using the optimal set of labelers) of $O(\log ^{2} T)$ uniformly in time under mild assumptions on the collective quality of the crowd, thus regret free in the average sense. We discuss further performance improvement by using a more sophisticated majority voting rule, and show how to detect and filter out “bad” (dishonest, malicious or very incompetent) labelers to further enhance the quality of crowd-sourcing. Extension to the case when a labeler’s quality is task-type dependent is also discussed using techniques from the literature on continuous arms. We establish a lower bound on the order of $O(\log T D_{2}(T))$ , where $D_{2}(T)$ is an arbitrary function such that $D_{2}(T) > O(1)$ . We further provide a matching upper bound through a minor modification of the algorithm we proposed and studied earlier on. We present numerical results using both simulation and set of images labeled by amazon mechanic turks.


title: Experiments with small helicopter automated landings at unusual attitudes

abstract: This paper describes a set of experiments involving small helicopters landing automated landing at unusual attitudes. By leveraging the increased agility of small air vehicles, we show that it is possible to automatically land a small helicopter on surfaces pitched at angles up to 60 degrees. Such maneuvers require considerable agility from the vehicle and its avionics system, and they pose significant technical and safety challenges. Our work builds upon previous activities in human-inspired, high-agility flight for small rotorcraft. However, it was not possible to leverage manual flight test data to extract landing maneuvers due to stringent attitude and position control requirements. Availability of low-cost, local navigation systems requiring no on-board instrumentation has proven particularly important for these experiments to be successful.


title: A model of aesthetic appreciation and aesthetic judgments

abstract: Although aesthetic experiences are frequent in modern life, there is as of yet no scientifically comprehensive theory that explains what psychologically constitutes such experiences. These experiences are particularly interesting because of their hedonic properties and the possibility to provide self-rewarding cognitive operations. We shall explain why modern art’s large number of individualized styles, innovativeness and conceptuality offer positive aesthetic experiences. Moreover, the challenge of art is mainly driven by a need for understanding. Cognitive challenges of both abstract art and other conceptual, complex and multidimensional stimuli require an extension of previous approaches to empirical aesthetics. We present an information-processing stage model of aesthetic processing. According to the model, aesthetic experiences involve five stages: perception, explicit classification, implicit classification, cognitive mastering and evaluation. The model differentiates between aesthetic emotion and aesthetic judgments as two types of output. Psychology of aesthetic appreciation Our aim in this article is to explain why people are attracted by art. We give an answer from a psychological perspective with special interest paid to psychologically relevant features of art, especially modern art. We discuss how cognitive processing of art produces affective, often positive and self-rewarding aesthetic experiences. We propose a model that represents different processing stages as well as important variables that are involved in aesthetic experiences. We aim to understand the art-specific cognitive experiences that give art such a prominent position in human culture and thus go beyond perceiving art solely as an interesting perceptual stimulus. Moreover, we show that the often-controversial modern or contemporary art is particularly interesting from such a psychological perspective. Although we mainly focus on visual arts, the


title: Modeling and detecting change in user behavior through his social media posting using cluster analysis

abstract: According to World Health Organization, one of the greatest health hazards of 21st century is mental disorder. Unlike any physical illness, mental illness is not that apparent to be recognized at early stages. Also, especially in India, patients do not come forward to seek help because of the social taboo or inferiority that is associated with these diseases. As per World Health Organization, 11 percent of the world's population suffer from mental disorders but only 1 percent of the population form the community of experts who can treat them, leading to the lack of sufficient man power to treat mental illness, and thus the treatments being very expensive. This calls for a strong need for a technique to automatically identify non-normal behavior of a person, which would serve as an indicator for early detection of mental illness. According to the census report of India 2011, the citizens from the age group of 18 to 30 is the majority having mental health problems, which is incidentally the age group which is very active on social networking sites. Online social networks serve as a valuable source of information about people through their published interests, attributes and social interactions and also a true mirror of their behavior. People who don't share their issues with friends and families then find a place on social media and open up their feelings there. Majority of work in current relevant literature talks about classifying tweets based on the sentiments. Whereas, our approach is to analy se the tweets of a person over a period of time to track the change in his behaviour if any. We have developed a new unsupervised technique for detecting change in behaviour of a person using the difference in the structural and behavioural feature vector and defining a threshold using iterative clustering. With a synthesized data, our models lead us to 92% accuracy and a precision 92 % and recall of 90 %.


title: Functional beta 1- and beta 2-adrenoceptors in the human myocardium.

abstract: Functional beta-adrenoceptor populations in the human heart were studied in vitro in electrically-paced strips of the right auricular and ventricular myocardium. The relative potency of selected agonists in producing inotropic responses (Tmax, T'max) in the presence of blockers for neuronal and extraneuronal uptakes was found to be as follows: isoprenaline greater than noradrenaline = adrenaline = salbutamol greater than dobutamine. Prenalterol had a negative inotropic effect in these preparations. The selective beta 1-(practolol) and beta 2-(H 35/25) blockers reduced inotropic responses to adrenaline (Tmax, T'max) and noradrenaline (T'max) in the auricular strips. These results indicate the participation of beta 2-adrenoceptors in inotropic responses in the human auricular and ventricular myocardium. For comparison, inotropic responses of electrically-paced rat myocardium to beta-adrenergic agonists in the presence of blockers for neuronal and extraneuronal uptakes were likewise studied. The relative potencies for Tmax were: noradrenaline = adrenaline greater than prenalterol greater than dobutamine = salbutamol. Given the high relative potency of salbutamol in the human myocardial strips (analogous to that previous shown in the beta 2-dominated atria of the frog and trout) and the low relative potency of salbutamol in the rat tissue, these findings indicate a greater population of functionally active beta 2-adrenoceptors in the human than in the rat myocardium.


title: Latent Palmprint Matching

abstract: The evidential value of palmprints in forensics is clear as about 30% of the latents recovered from crime scenes are from palms. While palmprint-based personal authentication systems have been developed, they mostly deal with low resolution (about 100 ppi) palmprints and only perform full-to-full matching. We propose a latent-to-full palmprint matching system that is needed in forensics. Our system deals with palmprints captured at 500 ppi and uses minutiae as features. Latent palmprint matching is a challenging problem because latents lifted at crime scenes are of poor quality, cover small area of palms and have complex background. Other difficulties include the presence of many creases and a large number of minutiae in palmprints. A robust algorithm to estimate ridge direction and frequency in palmprints is developed. This facilitates minutiae extraction even in poor quality palmprints. A fixed-length minutia descriptor, MinutiaCode, is utilized to capture distinctive information around each minutia and an alignment-based matching algorithm is used to match palmprints. Two sets of partial palmprints (150 live-scan partial palmprints and 100 latents) are matched to a background database of 10,200 full palmprints to test the proposed system. Rank-1 recognition rates of 78.7% and 69%, respectively, were achieved for live-scan palmprints and latents.


title: A secured SDN framework for IoT

abstract: In the last couple of years Software Defined Network (SDN) have come into existence which empowers network operators with more flexibility to manage and program their network. This type of network solves the limitation of legacy networks. Data plane and control planes are separated from each other as a result data plane devices simple act as a packet forwarding device and leaving the decision making part to a centralized system called controller. Though it has a lot of advantages, still security of SDN is an open issue. In modern day living wireless sensor network (WSN) technologies come across all most all areas which creates another research dimension called IoT where sensors and actuators blend in one piece. Application of SDN architecture in the IoT environment is a higher challenge. In this article we will present the security challenges in SDN and a secured architecture for IoT in an SDN based network.


title: Arabic Text Categorization Based on Arabic Wikipedia

abstract: This article describes an algorithm for categorizing Arabic text, relying on highly categorized corpus-based datasets obtained from the Arabic Wikipedia by using manual and automated processes to build and customize categories. The categorization algorithm was built by adopting a simple categorization idea then moving forward to more complex ones. We applied tests and filtration criteria to reach the best and most efficient results that our algorithm can achieve. The categorization depends on the statistical relations between the input (test) text and the reference (training) data supported by well-defined Wikipedia-based categories. Our algorithm supports two levels for categorizing Arabic text; categories are grouped into a hierarchy of main categories and subcategories. This introduces a challenge due to the correlation between certain subcategories and overlap between main categories. We argue that our algorithm achieved good performance compared to other methods reported in the literature.


title: Modeling urban growth with GIS based cellular automata and least squares SVM rules: a case study in Qingpu–Songjiang area of Shanghai, China

abstract: A critical issue in urban cellular automata (CA) modeling concerns the identification of transition rules that generate realistic urban land use patterns. Recent studies have demonstrated that linear methods cannot sufficiently delineate the extraordinary complex boundaries between urban and non-urban areas and as most urban CA models simulate transitions across these boundaries, there is an urgent need for good methods to facilitate such delineations. This paper presents a machine learning CA model (termed MachCA) with nonlinear transition rules based on least squares support vector machines (LS-SVM) to simulate such urban growth. By projecting the input dataset into a high dimensional space using the LS-SVM method, an optimal hyper-plane is constructed to separate the complex boundaries between urban and nonurban land, thus enabling the retrieval of nonlinear CA transition rules. In the MachCA model, the transition rules are yes–no decisions on whether a cell changes its state or not, the rules being dynamically updated for each iteration of the model implementation. The application of the MachCA for simulating urban growth in the Shanghai Qingpu–Songjiang area in China reveals that the spatial configurations of rural–urban patterns can be modeled. A comparison of the MachCA model with a conventional CA model fitted by logarithmic regression (termed LogCA) shows that the MachCA model produces more hits and less misses and false alarms due to its capability for capturing the spatial complexity of urban dynamics. This results in improved simulation accuracies, although with only less than 1 % deviation between the overall errors produced by the MachCA and LogCA models. Nevertheless, the way MachCA model use in retrieving the transition rules provides a new method for simulating the dynamic process of urban growth.


title: Tree Adjoining Grammar Based Parser for a Hindi Text-to-Scene Conversion System

abstract: We have developed Preksha: a Hindi text visualizer. Preksha generates a 3D scene by understanding a given Hindi text. We propose the Earley's parsing algorithm using Tree adjoining grammar (TAG) for Hindi language understanding. A brief overview of the TAG formalism and the Earley's parsing algorithm is presented. Preksha's Hindi language parsing is explainedwith the Lisp notation. The maincontribution of this paper is to apply TAG parsing for morphologically-richfree-order language Hindi for automatic text visualization (ATV).


title: High Accurate Howland Current Source: Output Constraints Analysis

abstract: Howland circuits have been widely used as powerful source for exciting tissue over a wide frequency range. When a Howland source is designed, the components are chosen so that the designed source has the desired characteristics. However, the operational amplifier limitations and resistor tolerances cause undesired behaviors. This work proposes to take into account the influence of the random distribution of the resistors in the modified Howland circuit over the frequency range of 10 Hz to 10 MHz. Both output current and impedance of the circuit are deduced either considering or the operational amplifiers parameters. The probability density function due to small changes in the resistors of the circuit was calculated by using the analytical modeling. Results showed that both output current and impedance are very sensitive to the resistors variations. In order to get higher output impedances, high operational amplifier gains are required. The operational amplifier open-loop gain increases as increasing the sensitivity of the output impedance. The analysis done in this work can be used as a powerful co-adjuvant tool when projecting this type of circuit in Spice simulators. This might improve the implementations of practical current sources used in electrical bioimpedance.


title: To increase data capacity of QR code using multiplexing with color coding: An example of embedding speech signal in QR code

abstract: QR codes have become widely popular along with the increased usage and popularity of smart phones. In many areas, QR codes have overtaken the place of linear barcodes because of the obvious advantage of large data capacity and ease of data retrieval. QR code specifications offers many more advantages like reduced space, durability against soil and damage, high data capacity, supported languages are more than other barcodes, supports 360 degree reading, etc over linear barcodes which makes QR codes worth opting. The structural flexibility of QR code architecture opens many more possibilities to stretch the limits of data capacity further away which includes data hiding techniques, multiplexing techniques, use of color QR codes, use of data compression techniques, etc. Proposed technique attempts to increase data capacity by multiplexing several QR codes and generating a color QR code. Experimental results show that this technique offers increase of the data capacity upto 24 times as compared to a standard QR code of same size. To get quicker results while demultiplexing, multiplexing of 12 or less QR codes is advisable with proposed technique. Due to such large capacity offered by proposed technique, embedding of speech signal into a QR code has made possible.


title: Keyframe-based Learning from Demonstration Method and Evaluation

abstract: We present a framework for learning skills from novel types of demonstrations that have been shown to be desirable from a Human–Robot Interaction perspective. Our approach—Keyframe-based Learning from Demonstration (KLfD)—takes demonstrations that consist of keyframes; a sparse set of points in the state space that produces the intended skill when visited in sequence. The conventional type of trajectory demonstrations or a hybrid of the two are also handled by KLfD through a conversion to keyframes. Our method produces a skill model that consists of an ordered set of keyframe clusters, which we call Sequential Pose Distributions (SPD). The skill is reproduced by splining between clusters. We present results from two domains: mouse gestures in 2D and scooping, pouring and placing skills on a humanoid robot. KLfD has performance similar to existing LfD techniques when applied to conventional trajectory demonstrations. Additionally, we demonstrate that KLfD may be preferable when demonstration type is suited for the skill.


title: Intrinsic representation of tangent vectors and vector transports on matrix manifolds

abstract: The quasi-Newton methods on Riemannian manifolds proposed thus far do not appear to lend themselves to satisfactory convergence analyses unless they resort to an isometric vector transport. This prompts us to propose a computationally tractable isometric vector transport on the Stiefel manifold of orthonormal p-frames in $$\mathbb {R}^n$$Rn. Specifically, it requires $$O(np^2)$$O(np2) flops, which is considerably less expensive than existing alternatives in the frequently encountered case where $$n\gg p$$nźp. We then build on this result to also propose computationally tractable isometric vector transports on other manifolds, namely the Grassmann manifold, the fixed-rank manifold, and the positive-semidefinite fixed-rank manifold. In the process, we also propose a convenient way to represent tangent vectors to these manifolds as elements of $$\mathbb {R}^d$$Rd, where d is the dimension of the manifold. We call this an "intrinsic" representation, as opposed to "extrinsic" representations as elements of $$\mathbb {R}^w$$Rw, where w is the dimension of the embedding space. Finally, we demonstrate the performance of the proposed isometric vector transport in the context of a Riemannian quasi-Newton method applied to minimizing the Brockett cost function.


title: Dual-axis solar tracking system: A combined astronomical estimation and visual feedback

abstract: This paper presents a proposed technique for dual-axis solar tracking system using fusion based approach of an astronomical based estimation and a visual sensor based feedback to locate and track sun position continuously time by time. The astronomical based calculation is used to estimate azimuth and elevation angles of a dual-axis solar power plant with respect to the time and location of the plant. Meanwhile, the visual based correction is used to localize the sun on the captured image when the camera can detect the sun (i.e. at sunny). The main purpose of this designed method is to improve efficiency of a dual-axis solar power plant system in absorbing sun energy continuously time by time with stable performance in various weather conditions. According to the simulation results, it shows that the proposed algorithm outperform to the only astronomical algorithm based. The sum squared error on azimuth and elevation angles resulted from the proposed algorithm along the simulation are 0.3688 and 0.3874 degree, respectively. On the other hand, the astronomical algorithm only method result sum squared error 1.0997 and 1.2877 degree on azimuth and elevation angles, respectively.


title: When do people use query suggestion? A query suggestion log analysis

abstract: Query suggestion, which enables the user to revise a query with a single click, has become one of the most fundamental features of Web search engines. However, it has not been clear what circumstances cause the user to turn to query suggestion. In order to investigate when and how the user uses query suggestion, we analyzed three kinds of data sets obtained from a major commercial Web search engine, comprising approximately 126 million unique queries, 876 million query suggestions and 306 million action patterns of users. Our analysis shows that query suggestions are often used (1) when the original query is a rare query, (2) when the original query is a single-term query, (3) when query suggestions are unambiguous, (4) when query suggestions are generalizations or error corrections of the original query, and (5) after the user has clicked on several URLs in the first search result page. Our results suggest that search engines should provide better assistance especially when rare or single-term queries are input, and that they should dynamically provide query suggestions according to the searcher's current state.


title: SaCoseg: Object Cosegmentation by Shape Conformability

abstract: In this paper, an object cosegmentation method based on shape conformability is proposed. Different from the previous object cosegmentation methods which are based on the region feature similarity of the common objects in image set, our proposed SaCoseg cosegmentation algorithm focuses on the shape consistency of the foreground objects in image set. In the proposed method, given an image set where the implied foreground objects may be varied in appearance but share similar shape structures, the implied common shape pattern in the image set can be automatically mined and regarded as the shape prior of those unsatisfactorily segmented images. The SaCoseg algorithm mainly consists of four steps: 1) the initial Grabcut segmentation; 2) the shape mapping by coherent point drift registration; 3) the common shape pattern discovery by affinity propagation clustering; and 4) the refinement by Grabcut with common shape constraint. To testify our proposed algorithm and establish a benchmark for future work, we built the CoShape data set to evaluate the shape-based cosegmentation. The experiments on CoShape data set and the comparison with some related cosegmentation algorithms demonstrate the good performance of the proposed SaCoseg algorithm.


title: Variance Reduction in Stochastic Gradient Langevin Dynamics

abstract: Stochastic gradient-based Monte Carlo methods such as stochastic gradient Langevin dynamics are useful tools for posterior inference on large scale datasets in many machine learning applications. These methods scale to large datasets by using noisy gradients calculated using a mini-batch or subset of the dataset. However, the high variance inherent in these noisy gradients degrades performance and leads to slower mixing. In this paper, we present techniques for reducing variance in stochastic gradient Langevin dynamics, yielding novel stochastic Monte Carlo methods that improve performance by reducing the variance in the stochastic gradient. We show that our proposed method has better theoretical guarantees on convergence rate than stochastic Langevin dynamics. This is complemented by impressive empirical results obtained on a variety of real world datasets, and on four different machine learning tasks (regression, classification, independent component analysis and mixture modeling). These theoretical and empirical contributions combine to make a compelling case for using variance reduction in stochastic Monte Carlo methods.


title: Effects of Tyrosine Ring Fluorination on Rates and Equilibria of Formation of Intermediates in the Reactions of Carbon-Carbon Lyases

abstract: The interactions of ring fluorinated analogs of tyrosine with tyrosine phenol-lyase and tryptophan indole-lyase (tryptophanase) were studied by rapid-scanning stopped-flow spectrophotometry. The reaction of L-tyrosine with tyrosine phenol-lyase resulted in rapid formation of a small absorbance peak at 500 nm, attributed to a quinonoid intermediate. The reaction of 3-fluoro-L-tyrosine with tyrosine phenol-lyase resulted in a peak at 500 nm with much higher absorbance, as did the reaction of 3,5-difluoro-L-tyrosine, due to increased accumulation of quinonoid intermediates. In constrast, complexes with 2-fluoro-L-tyrosine, 2,3-difluoro-L-tyrosine, 2,5-difluoro-L-tyrosine, and 2,6-difluoro-L-tyrosine exhibited much lower absorbance intensity at 500 nm. The rate constant for quinonoid intermediate formation from 3-fluoro-L-tyrosine was comparable to that for L-tyrosine. However, 3,5-difluoro-L-tyrosine reacted to form a quinonoid intermediate at about half the rate of L-tyrosine, while 2,3-difluoro-L-tyrosine reacted at twice the rate of L-tyrosine. In addition, the 2-substituted difluorotyrosines exhibited an intermediate, which was formed rapidly, absorbing strongly at about 340 nm, which is likely due to a gem-diamine intermediate. Tyrosine is not a substrate for tryptophan indole-lyase; the reaction of tryptophan indole-lyase with L-tyrosine resulted in formation of external aldimine, which absorbed at 420 nm, and a very small absorbance peak at 500 nm. 3-Fluoro-L-tyrosine reacted with tryptophan indole-lyase to produce a prominent quinonoid absorbance peak at 500 nm, whereas L-tyrosine, 2-fluoro-L-tyrosine, and all difluoro-L-tyrosines, had a much reduced intensity for this peak. Thus, the presence of ring fluorine substituents in L-tyrosine that are remote from the site of the chemical transformation has significant effects on the rates and equilibria of intermediate formation in the reactions with both tyrosine phenol-lyase and tryptophan indole-lyase. Although it is commonly thought that fluorine substitution will not result in any significant steric effects, our results suggest that the effects of fluorine substitution in the reactions of fluorinated tyrosines with tyrosine phenol-lyase and tryptophan indole-lyase are due to a combination of steric and electronic effects.


title: Recurrent Neural Networks for Online Video Popularity Prediction

abstract: In this paper, we address the problem of popularity prediction of online videos shared in social media. We prove that this challenging task can be approached using recently proposed deep neural network architectures. We cast the popularity prediction problem as a classification task and we aim to solve it using only visual cues extracted from videos. To that end, we propose a new method based on a Long-term Recurrent Convolutional Network (LRCN) that incorporates the sequentiality of the information in the model. Results obtained on a dataset of over 37’000 videos published on Facebook show that using our method leads to over 30% improvement in prediction performance over the traditional shallow approaches and can provide valuable insights for content creators.


title: Authentic Leadership Questionnaire: invariance between samples of Brazilian and Portuguese employees

abstract: The Authentic Leadership Questionnaire (ALQ) is used to assess authentic leadership (AL). Although ALQ is often used in empirical research, cross-cultural studies with this measure are scarce. Aiming to contribute to filling this gap, this study assesses the invariance of the ALQ measure between samples of Brazilian (N = 1019) and Portuguese (N = 842) employees. A multi-group confirmatory factor analysis was performed, and the results showed the invariance of the first- and second-order factor models between the Brazilian and Portuguese samples. The results are discussed considering their cultural setting, with the study’s limitations and future research directions being pointed out.


Nudging Users Into Online Verification: The Case of Carsharing Platforms


title: Space division multiplexing (SDM) for OFDM systems

abstract: A promising solution for significant increase of the bandwidth efficiency and transmission capacity is the exploitation of the spatial dimension, by using space division multiplexing (SDM). SDM algorithms exploit the richly scattered (indoor) wireless channel by using multiple transmit and receive antennas. A new SDM technique, called maximum likelihood decoding (MLD) is proposed. The superior SNR performance of MLD compared to other SDM techniques is proven. To obtain an even higher bit rate and make the system more robust against intersymbol interference, (single-carrier) SDM is successfully applied to the spectrum efficient multi-carrier transmission technique orthogonal frequency division multiplexing (OFDM).


title: Penile advancement and lengthening for the management of post-circumcision traumatic short penis in adolescents.

abstract: OBJECTIVES ::: To report on the safety of combing suprapubic fat liposuction, penile suspensory ligament severing, and Z-plasty of penoscrotal webbing for penile lengthening in cases of post-circumcision traumatic short penis in adolescents. ::: ::: ::: METHODS ::: This prospective study was conducted in 9 male patients with a mean age of 15.6 ± 1.4 years. All patients had a short penis because of traumatic amputation of the glans in 7 patients and both glanular and partial distal corporal loss in 2 patients during circumcision. All were initially treated in childhood by penile skin advancement for phallic coverage. Penile length and circumference were measured pre- and post-operatively, both in the flaccid state at maximal stretch and in the erect state. Operative technique included Z-plasty of penoscrotal web, release of suspensory ligament, and suprapubic fat liposuction. ::: ::: ::: RESULTS ::: Average functional penile length measured 3 months post-operatively had significantly increased by 18-30 mm (mean: 23 ± 4 mm) in the provoked, erect state. All patients had normally developed penile corpora cavernosa and wide circumference (mean: 11.7 ± 0.2 cm). No post-operative complications occurred in any of the patients. No patients had sexual partners or were sexually active. ::: ::: ::: CONCLUSIONS ::: The described technique of penile advancement and lengthening can be safely applied to patients with post-circumcision traumatic short penis.


False Negative of Doppler Ultrasonography During Testis Torsion in Animal Model


title: Sentence-level sentiment analysis in Persian

abstract: Sentiment analysis (SA) is a subfield of natural language processing and data mining which concerns the problem of extracting useful information from users' comments on the Web. Although researchers have been studying different problems in SA for more than one decade, most studies concentrate on English and languages like Persian have not received the attention they deserved. Resource scarcity for assessing sentiment analysis studies is the main limiting factor in Persian. This paper addresses the problem of resource scarcity by introducing two new resources; a sentence-level dataset for sentiment analysis in Persian, SPerSent and a new Persian lexicon, CNRC. SPerSent contains 150000 sentences, each associated with two labels; a binary label indicating the polarity of the sentence, and a five-star rating. These labels are obtained automatically using a lexicon-based method. Specifically, three lexicons are used independently to label each sentence. Then, the majority voting and average methods are used to aggregate the results for polarity and five-star labels, respectively. Finally, a well-known machine learning method, Naive Bayes, is used to evaluate the SPerSent.


title: Deterioration of stator winding insulation by vibration sparking

abstract: Since the introduction of epoxy mica stator windings, some turbine generator stator windings have apparently failed due to a mechanism variously referred to as spark erosion or vibration sparking. The mechanism can produce relatively intense sparking between the surface of the stator bar and the core. The intensity of the sparking is such that it may erode the groundwall insulation much more quickly than slot discharges. Unlike the normal loose coil/slot discharge failure process, spark erosion can happen anywhere in the winding, and not just in stator bars that are operating at high voltage. This paper describes the vibration sparking failure process, which seems to require that the partly conductive slot coating must be much more conductive than normal; and that the coil or bar must be loose in the slot and vibrating under the 100/120 Hz magnetic forces.