title: Adversarial Learning for Fine-Grained Image Search

abstract: Fine-grained image search is still a challenging problem due to the difficulty in capturing subtle differences regardless of pose variations of objects from fine-grained categories. In practice, a dynamic inventory with new fine-grained categories adds another dimension to this challenge. In this work, we propose an end-to-end network, called FGGAN, that learns discriminative representations by implicitly learning a geometric transformation from multi-view images for fine-grained rigid object retrieval. We integrate a generative adversarial network (GAN) that can automatically handle complex view and pose variations by converting them to a canonical view without any predefined transformations. Moreover, in an open-set scenario, our network is able to better match rigid objects from unseen and unknown fine-grained categories. Extensive experiments on the public CompCars dataset and a newly collected dataset have demonstrated the effectiveness of the proposed method in both closed-set and open-set scenarios.


title: Affective EEG-Based Person Identification Using the Deep Learning Approach

abstract: There are several reports available on affective electroencephalography-based personal identification (affective EEG-based PI), one of which uses a small dataset and another reaching less than 90% of the mean correct recognition rate \emph{CRR},. Thus, the aim of this paper is to improve and evaluate the performance of affective EEG-based PI using a deep learning approach. The state-of-the-art EEG dataset DEAP was used as the standard for affective recognition. Thirty-two healthy participants participated in the experiment. They were asked to watch affective elicited music videos and score subjective ratings for forty video clips during the EEG measurement. An EEG amplifier with thirty-two electrodes was used to record affective EEG measurements from the participants. To identify personal EEG, a cascade of deep learning architectures was proposed, using a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). CNNs are used to handle the spatial information from the EEG while RNNs extract the temporal information. There has been a cascade of CNNs, with recurrent models known as Long Short-Term Memory (CNN-LSTM) and Gate Recurrent Unit (CNN-GRU) for comparison. Experimental results indicate that CNN-GRU and CNN-LSTM can deal with an EEG (4--40 Hz) rom different affective states and reach up to 99.90--100% mean \emph{CRR}. On the other hand, a traditional machine learning approach such as a support vector machine (SVM) using power spectral density (PSD) as a feature does not reach 50% mean \emph{CRR}. To reduce the number of EEG electrodes from thirty-two to five for more practical application, $F_{3}$, $F_{4}$, $F_{z}$, $F_{7}$ and $F_{8}$ were found to be the best five electrodes for application in similar scenarios to those in this study. CNN-GRU and CNN-LSTM reached up to 99.17% and 98.23% mean \emph{CRR}, respectively.


title: Distributed access control on IoT ledger-based architecture

abstract: Due to increased number of attacks on the Internet of Things (IoT) devices, the security of IoT networks became critical. Some recent researches proposed the adoption of blockchain in IoT networks without a thorough discussion on the impact of the solution on the devices performance. Furthermore, blockchain employment in the context of IoT can be challenging due to the devices hardware limitations. To fill this gap, this paper proposes an IoT ledger-based architecture to ensure access control on heterogeneous scenarios. This research applies conventional devices used on IoT networks, such as Arduino, Raspberry and Orange Pi boards. Finally, we perform performance evaluation focused on access control of IoT devices and on information propagation through peers on a private IoT network scenario.


title: Learning to Index for Nearest Neighbor Search

abstract: In this study, we present a novel ranking model based on learning neighborhood relationships embedded in the index space. Given a query point, conventional approximate nearest neighbor search calculates the distances to the cluster centroids, before ranking the clusters from near to far based on the distances. The data indexed in the top-ranked clusters are retrieved and treated as the nearest neighbor candidates for the query. However, the loss of quantization between the data and cluster centroids will inevitably harm the search accuracy. To address this problem, the proposed model ranks clusters based on their nearest neighbor probabilities rather than the query-centroid distances. The nearest neighbor probabilities are estimated by employing neural networks to characterize the neighborhood relationships, i.e., the density function of nearest neighbors with respect to the query. The proposed probability-based ranking can replace the conventional distance-based ranking for finding candidate clusters, and the predicted probability can be used to determine the data quantity to be retrieved from the candidate cluster. Our experimental results demonstrated that the proposed ranking model could boost the search performance effectively in billion-scale datasets.


title: Vicious and virtuous cycles in ERP implementation : a case study of interrelations between critical success factors

abstract: ERP implementations are complex undertakings. Recent research has provided us with plausible critical success factors (CSFs) for such implementations. This article describes how one list of CSFs (Somers & Nelson, 2001) was used to analyse and explain project performance in one ERP implementation in the aviation industry. In this particular case, poor project performance led to a serious project crisis but this situation was turned around into a success. The list of CSFs employed was found to be helpful and appropriate in explaining both the initial failure and the eventual success of the implementation. CSFs in this case appeared to be highly correlated, ie changes in any one of them would influence most of the others as well. The reversal in performance after the project crisis was caused by substantial changes in attitudes with most of the stakeholders involved, such as top management, project management, project champion and software vendor.


title: Evidence from a Consumer Credit Marketing Field Experiment *

abstract: This brief summarizes what's advertising content worth? Evidence from a consumer credit marketing field experiment in South Africa. The bank offered loans with repayment periods ranging from 4 to 18 months. Deadlines for response were randomly allocated from 2 weeks to 6 weeks. Firms spend billions of dollars developing advertising content, yet there is little field evidence on how much or how it affects demand. The author analyze a direct mail field experiment in South Africa implemented by a consumer lender that randomized advertising content, loan price, and loan offer deadlines simultaneously. The author fined that advertising content significantly affects demand. Although it was difficult to predict ex ante which specific advertising features will matter most in this context, the features that do matter have large effects. Showing fewer example loans, not suggesting a particular use for the loan, or including a photo of an attractive woman increases loan demand by about as much as a 25 percent reduction in the interest rate. The evidence also suggests that advertising content persuades by appealing 'peripherally' to intuition rather than reason. Although the advertising content effects point to an important role for persuasion and related psychology, our deadline results do not support the psychological prediction that shorter deadlines may help overcome time-management problems; instead, demand strongly increases with longer deadlines.


title: A flexible 2.4 GHz microstrip patch antenna using a 3-D printed tile array design

abstract: This paper presents the design of a flexible 3-D printed microstrip patch antenna (MPA) which shows stable RF performance with curvature. The substrate's unique design is based on tiles consisting of Acrylonitrile Butadiene Styrene (ABS) that are surrounded with flexible NinjaFlex. When these tiles are assembled together in an array, a flexible substrate with improved RF properties is realized. The resonant frequency achieved is 2.305 GHz with a bandwidth of 0.12 GHz (5.21%). When the antenna is flexed downwards, there is minimal impact on the RF performance, the resonant frequency shifts slightly upwards to 2.365 GHz (60 MHz) and the bandwidth is 0.135 GHz (5.71%). This flexible substrate design approach using an array of puzzle tiles is scalable and can be implemented in a variety of RF design applications for flexibility.


title: Grace: safe multithreaded programming for C/C++

abstract: The shift from single to multiple core architectures means that programmers must write concurrent, multithreaded programs in order to increase application performance. Unfortunately, multithreaded applications are susceptible to numerous errors, including deadlocks, race conditions, atomicity violations, and order violations. These errors are notoriously difficult for programmers to debug. ::: This paper presents Grace, a software-only runtime system that eliminates concurrency errors for a class of multithreaded programs: those based on fork-join parallelism. By turning threads into processes, leveraging virtual memory protection, and imposing a sequential commit protocol, Grace provides programmers with the appearance of deterministic, sequential execution, while taking advantage of available processing cores to run code concurrently and efficiently. Experimental results demonstrate Grace's effectiveness: with modest code changes across a suite of computationally-intensive benchmarks (1-16 lines), Grace can achieve high scalability and performance while preventing concurrency errors.


title: Finite Element Analysis and Failure Mode Characterization of Pyramidal Fin Arrays Produced by Masked Cold Gas Dynamic Spray

abstract: This work evaluates the shear strength of pyramidal fin arrays made from various feedstock materials (cylindrical aluminum, spherical nickel, and cylindrical stainless steel 304 powders) deposited on an Al6061-T6 substrate. Higher shear strength was measured for the nickel fin array followed by the stainless steel 304 and the aluminum arrays. Different failure modes were observed by inspecting the fracture surfaces under Scanning Electron Microscope. Deposition between the cold sprayed nickel and stainless fins was detected whereas dimples were noticed on the substrate between the fins when aluminum is used as the feedstock material. A numerical simulation of normal and angled impacts using the high strain rate Preston-Tonks-Wallace model was carried out in order to have a better understanding of the experimental results. The equivalent plastic strain (PEEQ) obtained from the finite element analysis at normal impact correlates with the different shear strengths measured experimentally. Furthermore, even if a higher PEEQ was observed for angled impacts compared to its normal collision counterpart, it is suggested that the particles may not bond because of the rotational restitution momentum caused by the tangential friction generated during angled impacts. This rotational restitution momentum was not detected for particle impacts normal to the substrate surface.


title: Text summarization for Malayalam documents — An experience

abstract: The amount of data available in the internet is increasing at a very high speed. Text summarization has helped in making a better use of the information available online. Various methods were adopted to automate text summarization. However there is no existing system for summarizing Malayalam documents. In this paper we have investigated on developing efficient and effective methods to summarize Malayalam documents. This paper explains a statistical sentence scoring technique and a semantic graph based technique for text summarization.


title: Ubiquitous Commerce: Theories, Technologies, and Applications

abstract: With the rapid development of ubiquitous computing and mobile communication technologies, the traditional business model will change drastically. As a logical extension of e-commerce and m-commerce, ubiquitous commerce (u-commerce) research and application are currently under transition with a history of numerous tried and failed solutions, and a future of promising but yet uncertain possibilities with potential new technology innovations. At this point of the development, we propose a suitable framework and organize the u-commerce research under the proposed classification scheme. The current situation outlined by the scheme has been addressed by exploratory and early phase studies. We hope the findings of this research will provide useful insights for anyone who is interested in u-commerce. The paper also provides some future directions for research.


title: A Fast Self-Tuning Background Subtraction Algorithm

abstract: In this paper, a fast pixel-level adapting background detection algorithm is presented. The proposed background model records not only each pixel's historical background values, but also estimates the efficacies of these values, based on the occurrence statistics. It is therefore capable of removing the least useful background values from the background model, selectively adapting to background changes with different timescales, and restraining the generation of ghosts. A further control process adjusts the individual decision threshold for each pixel, and reduces high frequency temporal noise, based on a measure of classification uncertainty in each pixel. Evaluation results based on the ChangeDetection.net database are presented in this paper. The results indicate that the proposed algorithm outperforms the majority of earlier state-of-the-art algorithms not only in terms of accuracy, but also in terms of processing speed.


title: A Mixed Approach for Data Warehouse Conceptual Design with MDA

abstract: This paper is developed in the context of conceptual design of Data Warehoses (DWs) with Model Driven Architecture (MDA) (a methodology for DW conceptual design). The method is made up of a set of transformation rules as a mechanism to extract multidimensional schemas from the conceptual description of the operational database. Next the generation of multidimensional schemas, we apply user requirements to guide the selection of the multidimensional schema that is supported by the operational database and most likely satisfy user requirements.


title: Multi-label learning with millions of labels: recommending advertiser bid phrases for web pages

abstract: Recommending phrases from web pages for advertisers to bid on against search engine queries is an important research problem with direct commercial impact. Most approaches have found it infeasible to determine the relevance of all possible queries to a given ad landing page and have focussed on making recommendations from a small set of phrases extracted (and expanded) from the page using NLP and ranking based techniques. In this paper, we eschew this paradigm, and demonstrate that it is possible to efficiently predict the relevant subset of queries from a large set of monetizable ones by posing the problem as a multi-label learning task with each query being represented by a separate label. We develop Multi-label Random Forests to tackle problems with millions of labels. Our proposed classifier has prediction costs that are logarithmic in the number of labels and can make predictions in a few milliseconds using 10 Gb of RAM. We demonstrate that it is possible to generate training data for our classifier automatically from click logs without any human annotation or intervention. We train our classifier on tens of millions of labels, features and training points in less than two days on a thousand node cluster. We develop a sparse semi-supervised multi-label learning formulation to deal with training set biases and noisy labels harvested automatically from the click logs. This formulation is used to infer a belief in the state of each label for each training ad and the random forest classifier is extended to train on these beliefs rather than the given labels. Experiments reveal significant gains over ranking and NLP based techniques on a large test set of 5 million ads using multiple metrics.


title: A review of the potential for competitive cereal cultivars as a tool in integrated weed management

abstract: Competitive crop cultivars offer a potentially cheap option to include in integrated weed management strategies (IWM). Although cultivars with high competitive potential have been identified amongst cereal crops, competitiveness has not traditionally been considered a priority for breeding or farmer cultivar choice. The challenge of managing herbicide-resistant weed populations has, however, renewed interest in cultural weed control options, including competitive cultivars. We evaluated the current understanding of the traits that explain variability in competitive ability between cultivars, the relationship between suppression of weed neighbours and tolerance of their presence and the existence of trade-offs between competitive ability and yield in weed-free scenarios. A large number of relationships between competitive ability and plant traits have been reported in the literature, including plant height, speed of development, canopy architecture and partitioning of resources. There is uncertainty over the relationship between suppressive ability and tolerance, although tolerance is a less stable trait over seasons and locations. To realise the potential of competitive crop cultivars as a tool in IWM, a quick and simple-to-use protocol for assessing the competitive potential of new cultivars is required; it is likely that this will not be based on a single trait, but will need to capture the combined effect of multiple traits. A way needs to be found to make this information accessible to farmers, so that competitive cultivars can be better integrated into their weed control programmes.


Neural Networks for 2 D to 3 D Human Pose Estimation


title: Environmental conditions at arrival to the wintering grounds and during spring migration affect population dynamics of barn swallows Hirundo rustica breeding in Northern Italy

abstract: Several populations of long-distance migratory birds are currently suffering steep demographic declines. The identification of the causes of such declines is difficult because population changes may be driven by events occurring in distant geographical areas during different phases of the annual life-cycle of migrants. Furthermore, wintering areas and migration routes of populations of small-sized species are still largely unknown, with few exceptions. In this paper we identified the critical phases of the annual life-cycle that most influence the population dynamics of a small passerine, the Barn Swallow Hirundo rustica. We used information on temporal dynamics of a population breeding in Northern Italy, whose wintering range and timing of migration have been recently described by miniaturised tracking dataloggers. Our results indicated that primary productivity in the wintering grounds in the month when most individuals arrive from autumn migration and primary productivity in an area that is probably a stopover site during spring migration, influenced population dynamics more than habitat conditions at the breeding grounds. By using annual variation in primary productivity at the wintering grounds and stopover sites as predictors, we replicated the observed interannual population changes with great accuracy. However, the steep decline recently suffered by the population could be replicated only by including a constant annual decline in the model, suggesting that changes in primary productivity only predicted the interannual variation around the long-term trend. Our study therefore suggests the existence of critical periods during wintering and migration that may have large impact on population fluctuations of migrant birds.


Usability Measurement : A Roadmap for a Consolidated Model


title: Sensing as a Service and Big Data

abstract: Internet of Things (IoT) will comprise billions of devices that can sense, communicate, compute and potentially actuate. Data streams coming from these devices will challenge the traditional approaches to data management and contribute to the emerging paradigm of big data. This paper discusses emerging Internet of Things (IoT) architecture, large scale sensor network applications, federating sensor networks, sensor data and related context capturing techniques, challenges in cloud-based management, storing, archiving and processing of sensor data.


title: Cultural competence: a conceptual framework for teaching and learning

abstract: Objectives The need to address cultural and ethnic diversity issues in medical education as a means to improve the quality of care for all has been widely emphasised. Cultural competence has been suggested as an instrument with which to deal with diversity issues. However, the implementation of culturally competent curricula appears to be difficult. We believe the development of curricula would profit from a framework that provides a practical translation of abstract educational objectives and that is related to competencies underlying the medical curriculum in general. This paper proposes such a framework. ::: ::: Methods The framework illustrates the following cultural competencies: knowledge of epidemiology and the differential effects of treatment in various ethnic groups; awareness of how culture shapes individual behaviour and thinking; awareness of the social context in which specific ethnic groups live; awareness of one’s own prejudices and tendency to stereotype; ability to transfer information in a way the patient can understand and to use external help (e.g. interpreters) when needed, and ability to adapt to new situations flexibly and creatively. ::: ::: Discussion The framework indicates important aspects in taking care of an ethnically diverse patient population. It shows that there are more dimensions to delivering high-quality care than merely the cultural. Most cultural competencies emphasise a specific aspect of a generic competency that is of extra importance when dealing with patients from different ethnic groups. We hope our framework contributes to the further development of cultural competency in medical curricula.


title: Exploiting Process Variations and Programming Sensitivity of Phase Change Memory for Reconfigurable Physical Unclonable Functions

abstract: Physical unclonable function (PUF) leverages the immensely complex and irreproducible nature of physical structures to achieve device authentication and secret information storage. To enhance the security and robustness of conventional PUFs, reconfigurable physical unclonable functions (RPUFs) with dynamically refreshable challenge-response pairs (CRPs) have emerged recently. In this paper, we propose two novel physically reconfigurable PUF (P-RPUF) schemes that exploit the process parameter variability and programming sensitivity of phase change memory (PCM) for CRP reconfiguration and evaluation. The first proposed PCM-based P-RPUF scheme extracts its CRPs from the measurable differences of the PCM cell resistances programmed by randomly varying pulses. An imprecisely controlled regulator is used to protect the privacy of the CRP in case the configuration state of the RPUF is divulged. The second proposed PCM-based RPUF scheme produces the random response by counting the number of programming pulses required to make the cell resistance converge to a predetermined target value. The merging of CRP reconfiguration and evaluation overcomes the inherent vulnerability of P-RPUF devices to malicious prediction attacks by limiting the number of accessible CRPs between two consecutive reconfigurations to only one. Both schemes were experimentally evaluated on 180-nm PCM chips. The obtained results demonstrated their quality for refreshable key generation when appropriate fuzzy extractor algorithms are incorporated.


title: Optimizing queries using materialized views: a practical, scalable solution

abstract: Materialized views can provide massive improvements in query processing time, especially for aggregation queries over large tables. To realize this potential, the query optimizer must know how and when to exploit materialized views. This paper presents a fast and scalable algorithm for determining whether part or all of a query can be computed from materialized views and describes how it can be incorporated in transformation-based optimizers. The current version handles views composed of selections, joins and a final group-by. Optimization remains fully cost based, that is, a single “best” rewrite is not selected by heuristic rules but multiple rewrites are generated and the optimizer chooses the best alternative in the normal way. Experimental results based on an implementation in Microsoft SQL Server show outstanding performance and scalability. Optimization time increases slowly with the number of views but remains low even up to a thousand.


title: A Reconfigurable 16/32 Gb/s Dual-Mode NRZ/PAM4 SerDes in 65-nm CMOS

abstract: While four-level pulse amplitude modulation (PAM4) standards are emerging to increase bandwidth density, the majority of standards use simple binary non-return-to-zero (NRZ) signaling. This paper presents a dual-mode NRZ/PAM4 serial I/O SerDes which can support both modulations with minimum power and hardware overhead relative to a dedicated PAM4 link. A source-series-terminated transmitter achieves 1.2- $\text{V}_{\mathrm {{pp}}}$ output swing and employs lookup table control of a 31-segment output digital-to-analog converter (DAC) to implement 4/2-tap feed-forward equalization in NRZ/PAM4 modes, respectively. Transmitter power is improved with low-overhead analog impedance control in the DAC cells and a quarter-rate serializer based on a tri-state inverter-based mux with dynamic pre-driver gates. The receiver implements an NRZ/PAM4 decision feedback equalizer that employs one finite impulse response and two infinite impulse response taps for first post-cursor and long-tail inter-symbol interference (ISI) cancellation, respectively. First post-cursor ISI cancellation is performed in these comparators to optimize the design’s timing, while the remaining ISI taps are subtracted in a preceding current integration summer for improved sensitivity. Fabricated in GP 65-nm CMOS, the transceiver occupies 0.074 mm2 area and achieves 16 Gb/s NRZ and 32 Gb/s PAM4 operation at 10.9 and 5.5 mW/Gb/s while operating over channels with 27.6 and 13.5 dB loss at Nyquist, respectively.


title: An Artificial Agent for Robust Image Registration

abstract: 3-D image registration, which involves aligning two or more images, is a critical step in a variety of medical applications from diagnosis to therapy. Image registration is commonly performed by optimizing an image matching metric as a cost function. However, this task is challenging due to the non-convex nature of the matching metric over the plausible registration parameter space and insufficient approaches for a robust optimization. As a result, current approaches are often customized to a specific problem and sensitive to image quality and artifacts. In this paper, we propose a completely different approach to image registration, inspired by how experts perform the task. We first cast the image registration problem as a "strategy learning" process, where the goal is to find the best sequence of motion actions (e.g. up, down, etc.) that yields image alignment. Within this approach, an artificial agent is learned, modeled using deep convolutional neural networks, with 3D raw image data as the input, and the next optimal action as the output. To cope with the dimensionality of the problem, we propose a greedy supervised approach for an end-to-end training, coupled with attention-driven hierarchical strategy. The resulting registration approach inherently encodes both a data-driven matching metric and an optimal registration strategy (policy). We demonstrate, on two 3-D/3-D medical image registration examples with drastically different nature of challenges, that the artificial agent outperforms several state-of-art registration methods by a large margin in terms of both accuracy and robustness.


title: Models of facial aging and implications for treatment.

abstract: In this article, which focuses on concepts rather than techniques, the author emphasizes that the best predictor of a good facelift outcome is an already attractive face that has good enough tissue quality to maintain a result past the swelling stage. The author notes that too often, surgeons gravitate toward a particular facial support technique and use it all the time, to often unsatisfactory results. He singles out different areas (the brows, the tear trough, the cheeks, and so forth) and shows how the addition of volume may give results better than traditional methods. As he points out, a less limited and ritualistic approach to the face seems to be how cosmetic surgery is evolving; all factors that might make a face better are reasonable to entertain.


Building a Terrorism Ontology


title: Anxiolytic effects of nicotine in zebrafish

abstract: Anxiolytic effects of nicotine have been documented in studies with rodents and humans. Understanding the neural basis of nicotine-induced anxiolysis can help both with developing better aids for smoking cessation as well as with the potential development of novel nicotinic ligands for treating anxiety. Complementary non-mammalian models may be useful for determining the molecular bases of nicotine effects on neurobehavioral function. The current project examined whether a zebrafish model of anxiety would be sensitive to nicotine. When zebrafish are placed in a novel environment, they dive to the bottom of the tank. In the wild, diving could help to escape predation. We tested the anxiolytic effect of nicotine on the novelty-elicited diving response and subsequent habituation. Zebrafish placed in a novel tank spent the majority of time at the bottom third of the tank during the first minute of a 5-min session and then show a gradual decrease in time spent at the tank bottom. Nicotine treatment at 100 mg/l for 3 min by immersion before testing caused a significant decrease in diving throughout the session, while 50 mg/l was effective during the first minute when the greatest bottom dwelling was seen in controls. Nicotine effects were reversed by the nicotinic antagonist mecamylamine given together with nicotine, but not when administered shortly before the test session after prior nicotine dosing. This implies that the effect of nicotine on diving was due to net stimulation at nicotinic receptors, an effect that is blocked by mecamylamine; and that once invoked, this effect is no longer dependent on continuing activation of nicotinic receptors. The effect of nicotine on diving did not seem to be the result of a general disorientation of the fish. The 100 mg/ml nicotine dose was shown in our earlier study to significantly improve spatial-discrimination learning in zebrafish. Nicotine-induced anxiolytic effects can be modeled in the zebrafish. This preparation will help in the investigation of the molecular bases of this effect.


title: The revised Conners' Parent Rating Scale (CPRS-R): factor structure, reliability, and criterion validity.

abstract: The Conners' Parent Rating Scale (CPRS) is a popular research and clinical tool for obtaining parental reports of childhood behavior problems. The present study introduces a revised CPRS (CPRS-R) which has norms derived from a large, representative sample of North American children, uses confirmatory factor analysis to develop a definitive factor structure, and has an updated item content to reflect recent knowledge and developments concerning childhood behavior problems. Exploratory and confirmatory factor-analytic results revealed a seven-factor model including the following factors: Cognitive Problems, Oppositional, Hyperactivity-Impulsivity, Anxious-Shy, Perfectionism, Social Problems, and Psychosomatic. The psychometric properties of the revised scale appear adequate as demonstrated by good internal reliability coefficients, high test-retest reliability, and effective discriminatory power. Advantages of the CPRS-R include a corresponding factor structure with the Conners' Teacher Rating Scale—Revised and comprehensive symptom coverage for attention deficit hyperactivity disorder (ADHD) and related disorders. Factor congruence with the original CPRS as well as similarities with other parent rating scales are discussed.


title: An Overview of Interactive Medical Image Segmentation

abstract: Image segmentation is often described as partitioning an image into a finite number of semantically non-overlapping regions. In medical applications, it is a fundamental process in most systems that support medical diagnosis, surgical planning and treatments. Generally, this process is done manually by clinicians, which may be time-consuming and tedious. To alleviate the problem, a number of interactive segmentation methods have been proposed in the literature. These techniques take advantage of automatic segmentation and allow users to intervene the segmentation process by incorporating prior-knowledge, validating results and correcting errors, thus potentially lead to accurate segmentation results. In this paper, we present an overview on interactive segmentation techniques for medical images.


title: An introduction to image-based 3D surface reconstruction and a survey of photometric stereo methods

abstract: This paper provides an introduction to photometric methods for image-based 3D shape reconstruction and a survey of photometric stereo techniques. We begin with taxonomy of active and passive shape acquisition techniques. Then we describe the methodical background of photometric 3D reconstruction, define the canonical setting of photometric stereo (Lambertian surface reflectance, parallel incident light, known illumination direction, known surface albedo, absence of cast shadows), discuss the 3D reconstruction of surfaces from local gradients, summarize the concept of the bidirectional reflectance distribution function (BRDF), and outline several important empirically and physically motivated reflectance models. We provide a detailed treatment of several generalizations of the canonical setting of photometric stereo, namely non-distant light sources, unknown illumination directions, and, in some detail, non-Lambertian surface reflectance functions. An important special case is purely specular reflections, where an extended light source allows capturing a surface that consists of perfectly specular surface patches. Linear combinations of purely Lambertian and purely specular reflectance components are favorably used for reconstructing smooth surfaces and also human skin. Nonuniform surface reflectance properties are estimated based on a simultaneous 3D reconstruction and determination of the locally variable parameters of the reflectance function based on a multitude of images. Assuming faceted surfaces, the effective resolution of the 3D reconstruction result can be increased to some extent beyond that of the underlying images. Other approaches separate specular and diffuse reflectance components based on polarization data or color information. The specular reflections can be used additionally to estimate the direction from which the surface is illuminated. Finally, we describe methods to combine photometric 3D reconstruction techniques with active and passive triangulation-based approaches.


title: Cloud Computing Technologies and Applications

abstract: In a nutshell, the existing Internet provides to us content in the forms of videos, emails and information served up in web pages. With Cloud Computing, the next generation of Internet will allow us to “buy” IT services from a web portal, drastic expanding the types of merchandise available beyond those on e-commerce sites such as eBay and Taobao. We would be able to rent from a virtual storefront the basic necessities to build a virtual data center: such as CPU, memory, storage, and add on top of that the middleware necessary: web application servers, databases, enterprise server bus, etc. as the platform(s) to support the applications we would like to either rent from an Independent Software Vendor (ISV) or develop ourselves. Together this is what we call as “IT as a Service,” or ITaaS, bundled to us the end users as a virtual data center.


title: Cannabidiol induces rapid-acting antidepressant-like effects and enhances cortical 5-HT/glutamate neurotransmission: role of 5-HT1A receptors

abstract: Cannabidiol (CBD), the main non-psychotomimetic component of marihuana, exhibits anxiolytic-like properties in many behavioural tests, although its potential for treating major depression has been poorly explored. Moreover, the mechanism of action of CBD remains unclear. Herein, we have evaluated the effects of CBD following acute and chronic administration in the olfactory bulbectomy mouse model of depression (OBX), and investigated the underlying mechanism. For this purpose, we conducted behavioural (open field and sucrose preference tests) and neurochemical (microdialysis and autoradiography of 5-HT1A receptor functionality) studies following treatment with CBD. We also assayed the pharmacological antagonism of the effects of CBD to dissect out the mechanism of action. Our results demonstrate that CBD exerts fast and maintained antidepressant-like effects as evidenced by the reversal of the OBX-induced hyperactivity and anhedonia. In vivo microdialysis revealed that the administration of CBD significantly enhanced serotonin and glutamate levels in vmPFCx in a different manner depending on the emotional state and the duration of the treatment. The potentiating effect upon neurotransmitters levels occurring immediately after the first injection of CBD might underlie the fast antidepressant-like actions in OBX mice. Both antidepressant-like effect and enhanced cortical 5-HT/glutamate neurotransmission induced by CBD were prevented by 5-HT1A receptor blockade. Moreover, adaptive changes in pre- and post-synaptic 5-HT1A receptor functionality were also found after chronic CBD. In conclusion, our findings indicate that CBD could represent a novel fast antidepressant drug, via enhancing both serotonergic and glutamate cortical signalling through a 5-HT1A receptor-dependent mechanism.


title: Near- and Far- Infrared Imaging for Vein Pattern Biometrics

abstract: This paper investigates two infrared imaging technologies, far-infrared thermography and near-infrared imaging, to acquire hand vein pattern images for biometric purposes. The imaging principles for both technologies are studied in depth. Experiments involving data acquisition from various parts of hand, including the back of the hand, palm, and wrist are described using a population of 150 participants using both near and far infrared imaging techniques. Comparison and analysis of the data collected show that far-infrared thermography has difficulties in capturing vein images in the palm, and wrist. However, while it is more suitable for capturing the large veins in the back of the hand, it is sensitive to ambient conditions and human body condition and does not provide a stable image quality. On the other hand, near-infrared imaging produces good quality images when capturing vein patterns in the back of the hand, palm, and wrist. It is more tolerant to changes in environmental and body condition, but it also faces the problem of disruption due to skin features such as hairs and line patterns. An initial vein pattern biometric system is implemented. The results show that all the test subjects can be correctly identified.


title: Capture-replay vs. programmable web testing: An empirical assessment during test case evolution

abstract: There are several approaches for automated functional web testing and the choice among them depends on a number of factors, including the tools used for web testing and the costs associated with their adoption. In this paper, we present an empirical cost/benefit analysis of two different categories of automated functional web testing approaches: (1) capture-replay web testing (in particular, using Selenium IDE); and, (2) programmable web testing (using Selenium WebDriver). On a set of six web applications, we evaluated the costs of applying these testing approaches both when developing the initial test suites from scratch and when the test suites are maintained, upon the release of a new software version. Results indicate that, on the one hand, the development of the test suites is more expensive in terms of time required (between 32% and 112%) when the programmable web testing approach is adopted, but on the other hand, test suite maintenance is less expensive when this approach is used (with a saving between 16% and 51%). We found that, in the majority of the cases, after a small number of releases (from one to three), the cumulative cost of programmable web testing becomes lower than the cost involved with capture-replay web testing and the cost saving gets amplified over the successive releases.


title: Deep Cross Polarimetric Thermal-to-Visible Face Recognition

abstract: In this paper, we present a deep coupled learning frame- work to address the problem of matching polarimetric ther- mal face photos against a gallery of visible faces. Polariza- tion state information of thermal faces provides the miss- ing textural and geometrics details in the thermal face im- agery which exist in visible spectrum. we propose a coupled deep neural network architecture which leverages relatively large visible and thermal datasets to overcome the problem of overfitting and eventually we train it by a polarimetric thermal face dataset which is the first of its kind. The pro- posed architecture is able to make full use of the polari- metric thermal information to train a deep model compared to the conventional shallow thermal-to-visible face recogni- tion methods. Proposed coupled deep neural network also finds global discriminative features in a nonlinear embed- ding space to relate the polarimetric thermal faces to their corresponding visible faces. The results show the superior- ity of our method compared to the state-of-the-art models in cross thermal-to-visible face recognition algorithms.


title: Electronic textiles: a platform for pervasive computing

abstract: The invention of the Jacquard weaving machine led to the concept of a stored "program" and "mechanized" binary information processing. This development served as the inspiration for C. Babbage's analytical engine-the precursor to the modern-day computer. Today, more than 200 years later, the link between textiles and computing is more realistic than ever. In this paper, we look at the synergistic relationship between textiles and computing and identify the need for their "integration" using tools provided by an emerging new field of research that combines the strengths and capabilities of electronics and textiles into one: electronic textiles, or e-textiles. E-textiles, also called smart fabrics, have not only "wearable" capabilities like any other garment, but also have local monitoring and computation, as well as wireless communication capabilities. Sensors and simple computational elements are embedded in e-textiles, as well as built into yarns, with the goal of gathering sensitive information, monitoring vital statistics, and sending them remotely (possibly over a wireless channel) for further processing. The paper provides an overview of existing efforts and associated challenges in this area, while describing possible venues and opportunities for future research.


title: Coin Betting and Parameter-Free Online Learning

abstract: In the recent years, a number of parameter-free algorithms have been developed for online linear optimization over Hilbert spaces and for learning with expert advice. These algorithms achieve optimal regret bounds that depend on the unknown competitors, without having to tune the learning rates with oracle choices. ::: We present a new intuitive framework to design parameter-free algorithms for \emph{both} online linear optimization over Hilbert spaces and for learning with expert advice, based on reductions to betting on outcomes of adversarial coins. We instantiate it using a betting algorithm based on the Krichevsky-Trofimov estimator. The resulting algorithms are simple, with no parameters to be tuned, and they improve or match previous results in terms of regret guarantee and per-round complexity.


title: FACE RECOGNITION TECHNIQUES AND APPROACHES: A SURVEY

abstract: Face recognition is a necessity of the modern age as the need for identification of individual has increased with the globalization of the world. Personal authentication through face has been under research since last two decades. The performance of the face recognition system has been enhanced using various algorithms. A generic facial authentication method contains three major steps i.e. face detection, facial features segmentation and face recognition. There are many commonly used algorithms used for this purpose. This paper provides an overview of different face recognition approaches. These approaches are categorized into four classes in this paper. These are holistic based approach, model based approach, hybrid based approach and feature based approach. Various techniques introduced in each of these categories are discussed.


title: Over Expression of Minichromosome Maintenance Genes is Clinically Correlated to Cervical Carcinogenesis

abstract: Minichromosome Maintenance (MCM) proteins play important roles in cell cycle progression by mediating DNA replication initiation and elongation. Among 10 MCM homologues MCM 2-7 form a hexamer and assemble to the pre-replication complex acting as replication licensing factors. Binding and function of MCM2-7 to pre-replication complex is regulated by MCM10 mediated binding of RECQL4 with MCM2-7. The purpose of this study is to explore the role of MCMs in cervical cancer and their correlation with the clinical parameters of cervical cancer. We have investigated sixty primary cervical cancer tissue samples, eight cervical cancer cell lines and thirty hysterectomised normal cervical tissue. The expression profiling of MCMs was done using semi-quantitative RT-PCR, immunoblotting and immunohistochemistry. MCM2, 4, 5, 6, 7, 10 and RECQL4 are significantly over-expressed in cervical cancer. Among these, MCM4, 6 and 10 show increased frequency of over expression along with advancement of tumor stages. MCM4, 5 and 6 also show differential expression in different types of lesion, while MCM2 and MCM10 are over expressed in cervical cancer irrespective of clinico-pathological parameters. Our data indicates the role of MCM4, MCM5, MCM6, MCM10 and RECQL4 in the progression of cervical cancer.


title: Anti-inflammatory and anti-apoptotic effects of strawberry and mulberry fruit polysaccharides on lipopolysaccharide-stimulated macrophages through modulating pro-/anti-inflammatory cytokines secretion and Bcl-2/Bak protein ratio.

abstract: This study is the first to isolate strawberry (SP) and mulberry fruit polysaccharides (MP) and assess their anti-inflammatory and anti-apoptotic activities using lipopolysaccharide (LPS)-stimulated mouse primary macrophages. Pro-/anti-inflammatory cytokine levels secreted by LPS-stimulated macrophages cultured with SP and MP for 48 h were determined using ELISA method to evaluate anti-inflammatory effects of SP and MP. The Bcl-2/Bak (anti-/pro-apoptotic) protein levels in the cells were determined using Western blotting method to evaluate anti-apoptotic effects of SP and MP. The results showed that the maximum absorption peak of SP and MP appeared at 240 nm with a small shoulder around 280∼310 nm, suggesting that SP and MP might be glycoproteins. SP- and MP-treatment significantly (P<0.05) decreased pro-inflammatory cytokines including interleukin (IL)-1β and IL-6, whereas the anti-inflammatory cytokine IL-10 was markedly increased, suggesting that SP and MP have anti-inflammation potential via modulating pro-/anti-inflammatory cytokine secretion profiles. Both SP and MP modulated Bak and Bcl-2 protein levels in the cells, suggesting that the SP and MP protected LPS-stimulated macrophages from apoptotic cell death. A negative correlation between cytokine secretion levels and Bcl-2 protein levels suggested that pro-inflammatory IL-1β and IL-6 cytokines decreased Bcl-2 levels in the LPS-stimulated macrophages.


title: Elliptic Curve Cryptography in Practice

abstract: In this paper we perform a review of elliptic curve cryptography (ECC) as it is used in practice today in order to reveal unique mistakes and vulnerabilities that arise in implementations of ECC. We study four popular protocols that make use of this type of public-key cryptography: Bitcoin, secure shell (SSH), transport layer security (TLS), and the Austrian e-ID card. We are pleased to observe that about 1 in 10 systems support ECC across the TLS and SSH protocols. However, we find that despite the high stakes of money, access and resources protected by ECC, implementations suffer from vulnerabilities similar to those that plague previous cryptographic systems.


Instagram’s ‘Fitspiration’ Trend and Its Effect on Young Women’s Self-Esteem


title: Integrated Cyber Physical Assessment and Response for Improved Resiliency

abstract: Cyber-physical systems (CPS) are control systems that facilitate the integration of physical systems and computer-based algorithms. These systems are commonly used in control system and critical infrastructure for control and monitoring applications. The internet-of-things (IoT) is a subset of CPS in which multiple physical embedded devices and sensors are connected via a distributed network to communicate and transfer data while being driven by computational algorithms for data delivery and decision-making tasks.


Efficient Implementations of Apriori and Eclat


title: Impact of Biofield Energy Treated Herbomineral Formulation (The Trivedi Effect®) on Mouse Dendritic and Splenocyte Cells for Modulation of Pro-inflammatory Cytokines

abstract: The use of herbomineral formulation in the healthcare sector for different chronic diseases is gaining popularity due to its fewer side effects, high safety profile, and cost effectiveness. A new proprietary herbomineral formulation was formulated, consisting of four essential ingredients viz. herbal root extract (ashwagandha), and minerals (zinc, magnesium, and selenium). The study aims to evaluate the in vitro effect of Biofield Energy Healing (The Trivedi Effect®) on the test formulation using murine dendritic (DCs) and splenocyte cells. The herbomineral formulation was divided into two parts; one was represented as control, while the other part was treated with the Biofield Energy Healing Treatment remotely by eighteen renowned Biofield Energy Healers (The Trivedi Effect®) and defined as the Biofield Treated formulation. The effect of the test formulation on these cells were monitored by an estimation of pro-inflammatory cytokines level such as tumor necrosis factor (TNF-α), macrophage inflammatory protein (MIP-1α), and interleukin (IL-1β) in cell culture supernatants at the non-cytotoxic concentrations of the test formulation using MTT assay. The DCs were treated with the Biofield Energy Treated test formulation at different concentrations (i.e. 1.05 to 1052.5 µg/mL) for 24 hours, and the results showed significant (p≤0.001) suppression of TNF-α levels at all the tested concentrations with a maximum percentage decrease by 43.64% at 5.2 µg/mL concentration in the Biofield Treated formulation as compared with the untreated test formulation. Further, the Biofield Treated formulation also demonstrated inhibition of MIP-1α and IL-1β at a concentration range of 0.0000105 to 10.5 µg/mL in LPS stimulated splenocyte cells. There was a significant (p≤0.001) inhibition of MIP-1α (26.52%) and IL-1β (35.28%) in the Biofield Treated test formulation at concentration 0.000105 µg/mL and 0.0000105 µg/mL, respectively in comparison to the untreated test formulation. Overall, these results suggest that the Biofield Energy Healing based herbomineral formulation (The Trivedi Effect®) significantly down-regulated the tested cytokines expression in DCs and splenocyte cells as compared to the untreated formulation. Therefore, the Biofield Healing based formulation might be useful as a better anti-inflammatory product for many chronic and acute inflammatory disease conditions and autoimmune disorders. The Biofield Energy Treatment based formulation can also be effectively applied in cases of organ transplants, stress management and anti-aging by improving overall health.


title: Improving the Expressiveness of Deep Learning Frameworks with Recursion

abstract: Recursive neural networks have widely been used by researchers to handle applications with recursively or hierarchically structured data. However, embedded control flow deep learning frameworks such as TensorFlow, Theano, Caffe2, and MXNet fail to efficiently represent and execute such neural networks, due to lack of support for recursion. In this paper, we add recursion to the programming model of existing frameworks by complementing their design with recursive execution of dataflow graphs as well as additional APIs for recursive definitions. Unlike iterative implementations, which can only understand the topological index of each node in recursive data structures, our recursive implementation is able to exploit the recursive relationships between nodes for efficient execution based on parallel computation. We present an implementation on TensorFlow and evaluation results with various recursive neural network models, showing that our recursive implementation not only conveys the recursive nature of recursive neural networks better than other implementations, but also uses given resources more effectively to reduce training and inference time.


title: Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Classification

abstract: We present highly efficient algorithms for performing forward and backward propagation of Convolutional Neural Network (CNN) for pixelwise classification on images. For pixelwise classification tasks, such as image segmentation and object detection, surrounding image patches are fed into CNN for predicting the classes of centered pixels via forward propagation and for updating CNN parameters via backward propagation. However, forward and backward propagation was originally designed for whole-image classification. Directly applying it to pixelwise classification in a patch-by-patch scanning manner is extremely inefficient, because surrounding patches of pixels have large overlaps, which lead to a lot of redundant computation. ::: The proposed algorithms eliminate all the redundant computation in convolution and pooling on images by introducing novel d-regularly sparse kernels. It generates exactly the same results as those by patch-by-patch scanning. Convolution and pooling operations with such kernels are able to continuously access memory and can run efficiently on GPUs. A fraction of patches of interest can be chosen from each training image for backward propagation by applying a mask to the error map at the last CNN layer. Its computation complexity is constant with respect to the number of patches sampled from the image. Experiments have shown that our proposed algorithms speed up commonly used patch-by-patch scanning over 1500 times in both forward and backward propagation. The speedup increases with the sizes of images and patches.


title: Experimental Dependability Evaluation of Memory Manager in the Real-time Operating System

abstract: The paper presents results of experimental dependability evaluation of the Phoenix-RTOS operating system. Experiments are conducted using a self-developed testing environment and a kernel fault injector. Dependability evaluation is the last stage of a system development process. Results will be used in the future research to propose enhanced error detection techniques embedded into the operating system kernel.


title: Displets: Resolving stereo ambiguities using object knowledge

abstract: Stereo techniques have witnessed tremendous progress over the last decades, yet some aspects of the problem still remain challenging today. Striking examples are reflecting and textureless surfaces which cannot easily be recovered using traditional local regularizers. In this paper, we therefore propose to regularize over larger distances using object-category specific disparity proposals (displets) which we sample using inverse graphics techniques based on a sparse disparity estimate and a semantic segmentation of the image. The proposed displets encode the fact that objects of certain categories are not arbitrarily shaped but typically exhibit regular structures. We integrate them as non-local regularizer for the challenging object class ‘car’ into a superpixel based CRF framework and demonstrate its benefits on the KITTI stereo evaluation. At time of submission, our approach ranks first across all KITTI stereo leaderboards.


title: Real-Time Detection of Task Switches of Desktop Users

abstract: Many methods, including supervised and unsupervised algorithms, have been developed for extractive document summarization. Most supervised methods consider the summarization task as a two-class classification problem and classify each sentence individually without leveraging the relationship among sentences. The unsupervised methods use heuristic rules to select the most informative sentences into a summary directly, which are hard to generalize. In this paper, we present a Conditional Random Fields (CRF) based framework to keep the merits of the above two kinds of approaches while avoiding their disadvantages. What is more, the proposed framework can take the outcomes of previous methods as features and seamlessly integrate them. The key idea of our approach is to treat the summarization task as a sequence labeling problem. In this view, each document is a sequence of sentences and the summarization procedure labels the sentences by 1 and 0. The label of a sentence depends on the assignment of labels of others. We compared our proposed approach with eight existing methods on an open benchmark data set. The results show that our approach can improve the performance by more than 7.1% and 12.1% over the best supervised baseline and unsupervised baseline respectively in terms of two popular metrics F1 and ROUGE-2. Detailed analysis of the improvement is presented as well.


title: Shape from shading for the digitization of curved documents

abstract: Document digitization is faster and more affordable using digital cameras than scanners. On the other hand, if we aim at extending the basic digital camera functionalities for such a purpose, post-processings will be of first importance, at least to improve the text legibility. In this paper, we address the specific problem of the virtual flattening of curved documents, as for example the pages of an opened book lying on its spine. In order to compute the document shape, we use the shape from shading technique and discuss why, in some cases, it is more suitable than other 3D single-view reconstruction techniques. We extend the seminal work by Wada et al. (Proceedings of the IAPR Workshop on machine vision and applications, Tokyo, Japan, pp. 591–594, 1992) and consecutive papers, reformulating the problem in terms of perspective shape from shading. Finally, we design a complete post-processing algorithm and test it on real images. Even if the documents are much curved, it is shown that the restored images are almost identical to scanned images of the flattened documents.


title: Provably Bounded-Optimal Agents

abstract: Since its inception, artificial intelligence has relied upon a theoretical foundation centred around perfect rationality as the desired property of intelligent systems. We argue, as others have done, that this foundation is inadequate because it imposes fundamentally unsatisfiable requirements. As a result, there has arisen a wide gap between theory and practice in AI, hindering progress in the field. We propose instead a property called bounded optimality. Roughly speaking, an agent is bounded-optimal if its program is a solution to the constrained optimization problem presented by its architecture and the task environment. We show how to construct agents with this property for a simple class of machine architectures in a broad class of real-time environments. We illustrate these results using a simple model of an automated mail sorting facility. We also define a weaker property, asymptotic bounded optimality (ABO), that generalizes the notion of optimality in classical complexity theory. We then construct universal ABO programs, i.e., programs that are ABO no matter what real-time constraints are applied. Universal ABO programs can be used as building blocks for more complex systems. We conclude with a discussion of the prospects for bounded optimality as a theoretical basis for AI, and relate it to similar trends in philosophy, economics, and game theory.


title: AZIMUT, a leg-track-wheel robot

abstract: AZIMUT is a mobile robotic platform that combines wheels, legs and tracks to move in three-dimensional environments. The robot is symmetrical and is made of four independent leg-track-wheel articulations. It can move with its articulations up, down or straight, or to move sideways without changing the robot's orientation. To validate the concept, the first prototype developed measures 70.5 cm/spl times/70.5 cm with the articulations up. It has a body clearance of 8.4 cm to 40.6 cm depending on the position of the articulations. The design of the robot is highly modular, with distributed embedded systems to control the different components of the robot.


Strategies and struggles with privacy in an online social networking community


title: Improved Recurrent Neural Networks for Session-based Recommendations

abstract: Recurrent neural networks (RNNs) were recently proposed for the session-based recommendation task. The models showed promising improvements over traditional recommendation approaches. In this work, we further study RNN-based models for session-based recommendations. We propose the application of two techniques to improve model performance, namely, data augmentation, and a method to account for shifts in the input data distribution. We also empirically study the use of generalised distillation, and a novel alternative model that directly predicts item embeddings. Experiments on the RecSys Challenge 2015 dataset demonstrate relative improvements of 12.8% and 14.8% over previously reported results on the Recall@20 and Mean Reciprocal Rank@20 metrics respectively.


title: Mapping how local perturbations influence systems-level brain dynamics

abstract: The human brain exhibits a distinct spatiotemporal organization that supports brain function and can be manipulated via local brain stimulation. Such perturbations to local cortical dynamics are globally integrated by distinct neural systems. However, it remains unclear how local changes in neural activity affect large-scale system dynamics. Here, we briefly review empirical and computational studies addressing how localized perturbations affect brain activity. We then systematically analyze a model of large-scale brain dynamics, assessing how localized changes in brain activity at the different sites affect whole-brain dynamics. We find that local stimulation induces changes in brain activity that can be summarized by relatively smooth tuning curves, which relate a region's effectiveness as a stimulation site to its position within the cortical hierarchy. Our results also support the notion that brain hubs, operating in a slower regime, are more resilient to focal perturbations and critically contribute to maintain stability in global brain dynamics. In contrast, perturbations of peripheral regions, characterized by faster activity, have greater impact on functional connectivity. As a parallel with this region-level result, we also find that peripheral systems such as the visual and sensorimotor networks were more affected by local perturbations than high-level systems such as the cingulo-opercular network. Our findings highlight the importance of a periphery-to-core hierarchy to determine the effect of local stimulation on the brain network. This study also provides novel resources to orient empirical work aiming at manipulating functional connectivity using non-invasive brain stimulation.


title: The Web SSO Standard OpenID Connect: In-depth Formal Security Analysis and Security Guidelines

abstract: Web-based single sign-on (SSO) services such as Google Sign-In and Log In with Paypal are based on the OpenID Connect protocol. This protocol enables so-called relying parties to delegate user authentication to so-called identity providers. OpenID Connect is one of the newest and most widely deployed single sign-on protocols on the web. Despite its importance, it has not received much attention from security researchers so far, and in particular, has not undergone any rigorous security analysis. ::: In this paper, we carry out the first in-depth security analysis of OpenID Connect. To this end, we use a comprehensive generic model of the web to develop a detailed formal model of OpenID Connect. Based on this model, we then precisely formalize and prove central security properties for OpenID Connect, including authentication, authorization, and session integrity properties. ::: In our modeling of OpenID Connect, we employ security measures in order to avoid attacks on OpenID Connect that have been discovered previously and new attack variants that we document for the first time in this paper. Based on these security measures, we propose security guidelines for implementors of OpenID Connect. Our formal analysis demonstrates that these guidelines are in fact effective and sufficient.


title: The trouble with memes: Inference versus imitation in cultural creation

abstract: Memes are hypothetical cultural units passed on by imitation; although nonbiological, they undergo Darwinian selection like genes. Cognitive study of multimodular human minds undermines memetics: unlike in genetic replication, high-fidelity transmission of cultural information is the exception, not the rule. Constant, rapid "mutation" of information during communication generates endlessly varied creations that nevertheless adhere to modular input conditions. The sort of cultural information most susceptible to modular processing is that most readily acquired by children, most easily transmitted across individuals, most apt to survive within a culture, most likely to recur in different cultures, and most disposed to cultural variation and elaboration.


title: Now Dasher! Dash away!: longitudinal study of fast text entry by Eye Gaze

abstract: Dasher is one of the best known inventions in the area of text entry in recent years. It can be used with many input devices, but studies on user performance with it are still scarce. We ran a longitudinal study where 12 participants transcribed Finnish text with Dasher in ten 15-minute sessions using a Tobii 1750 eye tracker as a pointing device. The mean text entry rate was 2.5 wpm during the first session and 17.3 wpm during the tenth session. Our results show that very high text entry rates can be achieved with eye-operated Dasher, but only after several hours of training.


title: Detection of Malicious Transaction in Database Using Log Mining Approach

abstract: Data mining is the process of finding correlations in the relational databases. There are different techniques for identifying malicious database transactions. Many existing approaches which profile is SQL query structures and database user activities to detect intrusion, the log mining approach is the automatic discovery for identifying anomalous database transactions. Mining of the Data is very helpful to end users for extracting useful business information from large database. Multi-level and multi-dimensional data mining are employed to discover data item dependency rules, data sequence rules, domain dependency rules, and domain sequence rules from the database log containing legitimate transactions. Database transactions that do not comply with the rules are identified as malicious transactions. The log mining approach can achieve desired true and false positive rates when the confidence and support are set up appropriately. The implemented system incrementally maintain the data dependency rule sets and optimize the performance of the intrusion detection process.


title: SUPPLEMENTARY MATERIAL Similarity matrices and clustering algorithms for population identification using genetic data

abstract: S1 Simulated Data Similarity Matrices . . . . . . . . . . . . . . . . . . 9 S2 Correlation Between Similarity Measures . . . . . . . . . . . . . . . 10 S3 Simulated Data Clustering Performance . . . . . . . . . . . . . . . 11 S4 HGDP PCA plots . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 S5 HGDP results for CPU . . . . . . . . . . . . . . . . . . . . . . . . . 13 S6 HGDP results for Spectral approaches . . . . . . . . . . . . . . . . 14 S7 HGDP IBD Spectral TW results . . . . . . . . . . . . . . . . . . . 15 S8 HGDP IBD Spectral PA results . . . . . . . . . . . . . . . . . . . . 16 S9 HGDP Spectral Reconstruction . . . . . . . . . . . . . . . . . . . . 17 S10 Determining the number of Eigenvalues . . . . . . . . . . . . . . . 18 ∗Heilbronn Institute, Department of Mathematics, University of Bristol, Bristol, BS8 1TW, UK †Max Planck Institute for Evolutionary Anthropology, 04103 Leipzig Germany


title: Volume of Fluid (VOF) Method for the Dynamics of Free Boundaries,” Los Alamos Scientific Laboratory report

abstract: Several methods have been previously used to approximate free boundaries in finite-difference numerical simulations. A simple, but powerful, method is described that is based on the concept of a fractional volume of fluid (VOF). This method is shown to be more flexible and efficient than other methods for treating complicated free boundary configurations. To illustrate the method, a description is given for an incompressible hydrodynamics code, SOLA-VOF, that uses the VOF technique to track free fluid surfaces.


title: L2 French Learners’ Processing of Object Clitics: Data from the Classroom

abstract: The purpose of this study was to assess whether the well-documented paucity of object clitics in L2 French production reflects difficulties learners have comprehending these forms in classroom input. To this end, an aural French-English translation task was used to determine the extent to which university-level L2 learners of French (N=152) were able to process and encode the meaning of the object clitics me, te, la, l’, les, lui, leur, y and en. An analysis of the translations revealed variation in performance across clitic types (19-75% accuracy) and as a function of learners’ proficiency level and educational background. There was a positive relationship between L2 proficiency and clitic processing. Post-French immersion learners were better able to process and encode clitics than their post-core French peers. As a group, the learners were only 54% accurate, with their mistranslations of object clitics indicating incomplete use of gender, number, animacy and case markings to link these forms to their co-referents. An under-reliance on animacy and agreement cues by these L2 learners suggests the need for explicit instruction on the importance of syntactic and discourse-pragmatic information in clitic comprehension.


title: Evolution of NAND Flash Memory: From 2D to 3D as a Storage Market Leader

abstract: NAND flash memory is evolving from 2D to 3D structure since 2D NAND flash faced its limitation of size reduction. In this paper, the limitations of 2D planar technology and the development history of 3D NAND flash will be reviewed. Finally, we will discuss the problems to overcome in order to succeed the development of 3D NAND flash.


title: Optimal Filtered Backprojection for Fast and Accurate Tomography Reconstruction

abstract: Tomographic reconstruction is a method of reconstructing a high dimensional image with a series of its low dimensional projections, and the filtered backprojection is one of very popular analytical techniques for the reconstruction due to its computational efficiency and easy of implementation. The accuracy of the filtered backprojection method deteriorates when input data are noisy or input data are available for only a limited number of projection angles. For the case, some algebraic approaches perform better, but they are based on computationally slow iterations. We propose an improvement of the filtered backprojection method which is as fast as the existing filtered backprojection and is as accurate as the algebraic approaches under heavy observation noises and limited availability of projection data. The new approach optimizes the filter of the backprojection operator to minimize a regularized reconstruction error. We compare the new approach with the state-of-the-art in the filtered backprojection and algebraic approaches using four simulated datasets to show its competitive accuracy and computing speed.


title: Disassembling gamification: the effects of points and meaning on user motivation and performance

abstract: Interest in gamification is growing steadily. But as the underlying mechanisms of gamification are not well understood yet, a closer examination of a gamified activity's meaning and individual game design elements may provide more insights. We examine the effects of points -- a basic element of gamification, -- and meaningful framing -- acknowledging participants' contribution to a scientific cause, -- on intrinsic motivation and performance in an online image annotation task. Based on these findings, we discuss implications and opportunities for future research on gamification.


title: Low-voltage class AB buffers with quiescent current control

abstract: This paper presents a simple class AB buffer which is suitable for low-voltage (1.5 V) applications. The proposed buffer uses an adaptive load to reduce the sensitivity of the quiescent current to the process variation. The main feature of this scheme is its simplicity. The circuit was fabricated in a 2.0 /spl mu/m digital CMOS process. Experimental results demonstrate that the buffer can operate with a supply voltage below 2 V, and it has the capability to drive small resistive loads.


title: A Signaling Game Approach to Databases Querying and Interaction

abstract: As most database users cannot precisely express their information needs, it is challenging for database querying and exploration interfaces to understand them. We propose a novel formal framework for representing and understanding information needs in database querying and exploration. Our framework considers querying as a collaboration between the user and the database system to establish a mutual language for representing information needs. We formalize this collaboration as a signaling game, where each mutual language is an equilibrium for the game. A query interface is more effective if it establishes a less ambiguous mutual language faster. We discuss some equilibria, strategies, and the convergence rates in this game. In particular, we propose a reinforcement learning mechanism and analyze it within our framework. We prove that this adaptation mechanism for the query interface improves the effectiveness of answering queries stochastically speaking, and converges almost surely.


title: Fast ConvNets Using Group-Wise Brain Damage

abstract: We revisit the idea of brain damage, i.e. the pruning of the coefficients of a neural network, and suggest how brain damage can be modified and used to speedup convolutional layers in ConvNets. The approach uses the fact that many efficient implementations reduce generalized convolutions to matrix multiplications. The suggested brain damage process prunes the convolutional kernel tensor in a group-wise fashion. After such pruning, convolutions can be reduced to multiplications of thinned dense matrices, which leads to speedup. We investigate different ways to add group-wise prunning to the learning process, and show that severalfold speedups of convolutional layers can be attained using group-sparsity regularizers. Our approach can adjust the shapes of the receptive fields in the convolutional layers, and even prune excessive feature maps from ConvNets, all in data-driven way.


title: On the effectiveness of preprocessing methods when dealing with different levels of class imbalance

abstract: The present paper investigates the influence of both the imbalance ratio and the classifier on the performance of several resampling strategies to deal with imbalanced data sets. The study focuses on evaluating how learning is affected when different resampling algorithms transform the originally imbalanced data into artificially balanced class distributions. Experiments over 17 real data sets using eight different classifiers, four resampling algorithms and four performance evaluation measures show that over-sampling the minority class consistently outperforms under-sampling the majority class when data sets are strongly imbalanced, whereas there are not significant differences for databases with a low imbalance. Results also indicate that the classifier has a very poor influence on the effectiveness of the resampling strategies.


title: Customers' intention to use digital services in retail banking - an information processing perspective

abstract: Service digitization increasingly impacts work and life. A frequent example is Internet banking. While customers act independently from time and space constraints, banks benefit from significantly lower transaction costs compared to branches. However, customers use online channels for distinct transactions and favor physical interactions with bank advisors for others. To understand the underlying drivers for the intention to use digital banking services, we derive a research model that is theoretically grounded in the Information Processing View. It is validated in a quantitative study with 338 evaluations among retail banking customers. The results indicate that customers’ information requirements and process risk negatively impact intended digital process use. In contrast, process experience positively impacts the intended digital process use. This paper is, to our best knowledge, the first to explore the role of information requirements and process-specific characteristics in detail. It guides practitioners in establishing more effective and efficient digital banking services.


title: Survey of nasal carriage of Staphylococcus aureus and intestinal parasites among food handlers working at Gondar University, Northwest Ethiopia

abstract: Food borne disease are major health problems in developing countries like Ethiopia. Food handlers with poor personal hygiene working in food establishments could be potential sources of disease due to pathogenic organisms. However; information on disease prevalence among food handlers working in University of Gondar cafeterias are very scarce. The aim of this study is to assess the prevalence of nasal carriage of Staphylococcus aureus, their drug resistance pattern and prevalence of intestinal parasites among food handlers working in University of Gondar student’s cafeterias. A cross sectional study was conducted among food handlers working in University of Gondar student’s cafeterias. A pretested structured questionnaire was used for collecting data. Nasal swab and stool were investigated for S. aureus and intestinal parasites; respectively as per the standard of the laboratory methods. Among 200 food handlers, females comprised 171(85.5%). The majority (67.5%) of the food-handlers were young adults aged 18–39 years. One hundred ninety four (97%) of the food handlers were not certified as a food handler. Forty one (20.5%) food handlers were positive for nasal carriage of S. aureus, of these 4(9.8%) was resistant for methicilin. Giardia lamblia was the most prevalent parasites 22 (11%), followed by Ascaris lumbricoides 13(6.5%), Entamoeba histolytica 12 (6%), Strongyloides stercolaris (0.5), Taenia species 1(0.5%) and Schistosoma mansoni 1(0.5%). The finding stressed that food handlers with different pathogenic micro organisms may pose significant risk on the consumers. Higher officials should implement food handler’s training on food safety, periodic medical checkup and continuous monitoring of personal hygiene of food handlers.


title: The Ant Colony Metaphor for Searching Continuous Design Spaces

abstract: This paper describes a form of dynamical computational system—the ant colony—and presents an ant colony model for continuous space optimisation problems. The ant colony metaphor is applied to a real world heavily constrained engineering design problem. It is capable of accelerating the search process and finding acceptable solutions which otherwise could not be discovered by a GA. By integrating the Pareto optimality concept within the selection mechanism in GAs and Ant Colony it is possible to treat both hard and soft constraints. Hard constraints participate in a penalty term while soft constraints become part of a multi-criteria formulation of the problem.


title: Is oval flap reconstruction a good modification for treating pilonidal sinuses

abstract: Abstract Background Flap techniques are acceptable for the surgical treatment of pilonidal sinuses. This study assessed a new modification of the rhomboid flap technique. Methods The study included 133 patients with pilonidal disease who were treated between April 2004 and April 2009. The pilonidal sinus was removed with an oval excision, and an oval head rhomboid flap was prepared to reduce flap necrosis. Results The mean age of the patients was 27.4 ± 4.6 years (range, 13–80). The rate of minor postoperative complications was 11.3%. The mean hospital stay was 2.3 ± .8 days (range, 1–6). The rate of recurrence was 1.5%. Regarding cosmetic results, 116 (87%) patients were very pleased, 15 (11.2%) were pleased, and 5 (3%) were displeased. The mean follow-up period was 22.5 ± 12.4 months (range, 5–57). Conclusions The oval flap reconstruction method is a recommended procedure that produces fewer ischemic flaps with a low rate of recurrence and acceptable cosmesis.


title: Recurrent neural network for facial landmark detection

abstract: Facial landmark detection is an important issue in many computer vision applications about faces. It is very challenging as human faces in wild conditions often present large variations in shape due to different poses, occlusions or expressions. Deep neural networks have been applied to learn the map from face images to face shapes. To the best of our knowledge, Recurrent Neural Network (RNN) has not been used in this issue yet. In this paper, we propose a method which utilizes RNN and Deep Neural Network (DNN) to learn the face shape. First, we build a global network using Long Short Term Memory (LSTM) architecture of RNN to get the initial landmark estimation of faces. Then, we use feed-forward neural networks for local search where a component-based searching method is explored. By using LSTM-RNN, the initial estimation is more reliable which makes the following component-based search feasible and accurate. Experiments show that the global network using LSTM-RNN gets better results than previous networks in both videos and single image. Our method outperforms the state-of-the-art algorithms especially in terms of fine estimation of landmarks.


title: Classification of finger activation for use in a robotic prosthesis arm

abstract: Hand amputees would highly benefit from a robotic prosthesis, which would allow the movement of a number of fingers. In this paper we propose using the electromyographic signals recorded by two pairs of electrodes placed over the arm for operating such prosthesis. Multiple features from these signals are extracted whence the most relevant features are selected by a genetic algorithm as inputs for a simple classifier. This method results in a probability of error of less than 2%.


title: Risk and parameter convergence of logistic regression

abstract: The logistic loss is strictly convex and does not attain its infimum; consequently the solutions of logistic regression are in general off at infinity. This work provides a convergence analysis of gradient descent applied to logistic regression under no assumptions on the problem instance. Firstly, the risk is shown to converge at a rate $\mathcal{O}(\ln(t)^2/t)$. Secondly, the parameter convergence is characterized along a unique pair of complementary subspaces defined by the problem instance: one subspace along which strong convexity induces parameters to converge at rate $\mathcal{O}(\ln(t)^2/\sqrt{t})$, and its orthogonal complement along which separability induces parameters to converge in direction at rate $\mathcal{O}(\ln\ln(t) / \ln(t))$.


title: Optimizing HEVC CABAC Decoding with a Context Model Cache and Application-Specific Prefetching

abstract: Context-based Adaptive Binary Arithmetic Coding is the entropy coding module in the most recent JCT-VC video coding standard HEVC/H.265. As in the predecessor H.264/AVC, CABAC is a well-known throughput bottleneck due to its strong data dependencies. Beside other optimizations, the replacement of the context model memory by a smaller cache has been proposed, resulting in an improved clock frequency. However, the effect of potential cache misses has not been properly evaluated. Our work fills this gap and performs an extensive evaluation of different cache configurations. Furthermore, it is demonstrated that application-specific context model prefetching can effectively reduce the miss rate and make it negligible. Best overall performance results were achieved with caches of two and four lines, where each cache line consists of four context models. Four cache lines allow a speed-up of 10% to 12% for all video configurations while two cache lines improve the throughput by 9% to 15% for high bitrate videos and by 1% to 4% for low bitrate videos.


title: The semantics of clustering: analysis of user-generated spatializations of text documents

abstract: Analyzing complex textual datasets consists of identifying connections and relationships within the data based on users' intuition and domain expertise. In a spatial workspace, users can do so implicitly by spatially arranging documents into clusters to convey similarity or relationships. Algorithms exist that spatialize and cluster such information mathematically based on similarity metrics. However, analysts often find inconsistencies in these generated clusters based on their expertise. Therefore, to support sensemaking, layouts must be co-created by the user and the model. In this paper, we present the results of a study observing individual users performing a sensemaking task in a spatial workspace. We examine the users' interactions during their analytic process, and also the clusters the users manually created. We found that specific interactions can act as valuable indicators of important structure within a dataset. Further, we analyze and characterize the structure of the user-generated clusters to identify useful metrics to guide future algorithms. Through a deeper understanding of how users spatially cluster information, we can inform the design of interactive algorithms to generate more meaningful spatializations for text analysis tasks, to better respond to user interactions during the analytics process, and ultimately to allow analysts to more rapidly gain insight.


title: Real-Time Face Detection and Motion Analysis With Application in “Liveness” Assessment

abstract: A robust face detection technique along with mouth localization, processing every frame in real time (video rate), is presented. Moreover, it is exploited for motion analysis onsite to verify "liveness" as well as to achieve lip reading of digits. A methodological novelty is the suggested quantized angle features ("quangles") being designed for illumination invariance without the need for preprocessing (e.g., histogram equalization). This is achieved by using both the gradient direction and the double angle direction (the structure tensor angle), and by ignoring the magnitude of the gradient. Boosting techniques are applied in a quantized feature space. A major benefit is reduced processing time (i.e., that the training of effective cascaded classifiers is feasible in very short time, less than 1 h for data sets of order 104). Scale invariance is implemented through the use of an image scale pyramid. We propose "liveness" verification barriers as applications for which a significant amount of computation is avoided when estimating motion. Novel strategies to avert advanced spoofing attempts (e.g., replayed videos which include person utterances) are demonstrated. We present favorable results on face detection for the YALE face test set and competitive results for the CMU-MIT frontal face test set as well as on "liveness" verification barriers.


title: Analyzing User Modeling on Twitter for Personalized News Recommendations

abstract: How can micro-blogging activities on Twitter be leveraged for user modeling and personalization? In this paper we investigate this question and introduce a framework for user modeling on Twitter which enriches the semantics of Twitter messages (tweets) and identifies topics and entities (e.g. persons, events, products) mentioned in tweets. We analyze how strategies for constructing hashtag-based, entity-based or topic-based user profiles benefit from semantic enrichment and explore the temporal dynamics of those profiles. We further measure and compare the performance of the user modeling strategies in context of a personalized news recommendation system. Our results reveal how semantic enrichment enhances the variety and quality of the generated user profiles. Further, we see how the different user modeling strategies impact personalization and discover that the consideration of temporal profile patterns can improve recommendation quality.


title: Contribution of Obesity and Abdominal Fat Mass to Risk of Stroke and Transient Ischemic Attacks

abstract: Background and Purpose— Waist circumference has been shown to be a better predictor of cardiovascular risk than body mass index (BMI). Our case-control study aimed to evaluate the contribution of obesity and abdominal fat mass to the risk of stroke and transient ischemic attacks (TIA). Methods— We recruited 1137 participants: 379 cases with stroke/TIA and 758 regional controls matched for age and sex. Associations between different markers of obesity (BMI, waist-to-hip ratio, waist circumference and waist-to-stature ratio) and risk of stroke/TIA were assessed by using conditional logistic regression adjusted for other risk factors. Results— BMI showed a positive association with cerebrovascular risk which became nonsignificant after adjustment for physical inactivity, smoking, hypertension, and diabetes (odds ratio 1.18; 95% CI, 0.77 to 1.79, top tertile versus bottom tertile). Markers of abdominal adiposity were strongly associated with the risk of stroke/TIA. For the waist-to-hip ratio, adjusted odds ra...


title: Topology Optimization for Galvanic Coupled Wireless Intra-body Communication

abstract: Implanted sensors and actuators in the human body promise in-situ health monitoring and rapid advancements in personalized medicine. We propose a new paradigm where such implants may communicate wirelessly through a technique called as galvanic coupling, which uses weak electrical signals and the conduction properties of body tissues. While galvanic coupling overcomes the problem of massive absorption of RF waves in the body, the unique intra-body channel raises several questions on the topology of the implants and the external (i.e., on skin) data collection nodes. This paper makes the first contributions towards (i) building an energy-efficient topology through optimal placement of data collection points/relays using measurement-driven tissue channel models, and (ii) balancing the energy consumption over the entire implant network so that the application needs are met. We achieve this via a two-phase iterative clustering algorithm for the implants and formulate an optimization problem that decides the position of external data-gathering points. Our theoretical results are validated via simulations and experimental studies on real tissues, with demonstrated increase in the network lifetime.


title: Recovering traceability links between source code and fixed bugs via patch analysis

abstract: Traceability links can be recovered using data mined from a revision control system, such as CVS, and an issue tracking system, such as Bugzilla. Existing approaches to recover links between a bug and the methods changed to fix the bug rely on the presence of the bug's identifier in a CVS log message. In this paper we present an approach that relies instead on the presence of a patch in the issue report for the bug. That is, rather than analyzing deltas retrieved from CVS to recover links, our approach analyzes patches retrieved from Bugzilla. We use BugTrace, the tool implementing our approach, to conduct a case study in which we compare the links recovered by our approach to links recovered by manual inspection. The results of the case study support the efficacy of our approach. After describing the limitations of our case study, we conclude by reviewing closely related work and suggesting possible future work.


title: Finding structural similarity in time series data using Bag-of-Patterns representation

abstract: For more than one decade, time series similarity search has been given a great deal of attention by data mining researchers. As a result, many time series representations and distance measures have been proposed. However, most existing work on time series similarity search focuses on finding shape-based similarity. While some of the existing approaches work well for short time series data, they typically fail to produce satisfactory results when the sequence is long. For long sequences, it is more appropriate to consider the similarity based on the higher-level structures. In this work, we present a histogram-based representation for time series data, similar to the "bag of words" approach that is widely accepted by the text mining and information retrieval communities. We show that our approach outperforms the existing methods in clustering, classification, and anomaly detection on several real datasets.


title: 3D High-Efficiency Video Coding for Multi-View Video and Depth Data

abstract: This paper describes an extension of the high efficiency video coding (HEVC) standard for coding of multi-view video and depth data. In addition to the known concept of disparity-compensated prediction, inter-view motion parameter, and inter-view residual prediction for coding of the dependent video views are developed and integrated. Furthermore, for depth coding, new intra coding modes, a modified motion compensation and motion vector coding as well as the concept of motion parameter inheritance are part of the HEVC extension. A novel encoder control uses view synthesis optimization, which guarantees that high quality intermediate views can be generated based on the decoded data. The bitstream format supports the extraction of partial bitstreams, so that conventional 2D video, stereo video, and the full multi-view video plus depth format can be decoded from a single bitstream. Objective and subjective results are presented, demonstrating that the proposed approach provides 50% bit rate savings in comparison with HEVC simulcast and 20% in comparison with a straightforward multi-view extension of HEVC without the newly developed coding tools.


title: Neural Disambiguation of Causal Lexical Markers Based on Context

abstract: We propose a neural network architecture for the task of causality classification. We claim that the encoding of the meaning of a sentence is required for the disambiguation of its causal meaning. Our results show that our claim holds, and we outperform the state-of-the-art.


title: Quality of Communication Experience: Definition, Measurement, and Implications for Intercultural Negotiations

abstract: In an increasingly globalized workplace, the ability to communicate effectively across cultures is critical. We propose that the quality of communication experienced by individuals plays a significant role in the outcomes of intercultural interactions, such as cross-border negotiations. In 4 studies, we developed and validated a multidimensional conceptualization of quality of communication experience (QCE) and examined its consequences in intracultural versus intercultural business negotiations. We proposed and found 3 dimensions of QCE—namely, Clarity, Responsiveness, and Comfort. Findings from intercultural and same-cultural negotiations supported the hypotheses that QCE is lower in intercultural negotiation than in intracultural negotiation and that a higher degree of QCE leads to better negotiation outcomes. Moreover, we found evidence that the beneficial effects of higher QCE on negotiation outcomes are more pronounced in intercultural than in intracultural negotiations. We propose an agenda for future research and identify implications for practice.


title: SenTube: A Corpus for Sentiment Analysis on YouTube Social Media

abstract: In this paper we present SenTube – a dataset of user-generated comments on YouTube videos annotated for information content and sentiment polarity. It contains annotations that allow to develop classifiers for several important NLP tasks: (i) sentiment analysis, (ii) text categorization (relatedness of a comment to video and/or product), (iii) spam detection, and (iv) prediction of comment informativeness. The SenTube corpus favors the development of research on indexing and searching YouTube videos exploiting information derived from comments. The corpus will cover several languages: at the moment, we focus on English and Italian, with Spanish and Dutch parts scheduled for the later stages of the project. For all the languages, we collect videos for the same set of products, thus offering possibilities for multiand cross-lingual experiments. The paper provides annotation guidelines, corpus statistics and annotator agreement details.