title: Beyond Self-Tracking and Reminders: Designing Smartphone Apps That Support Habit Formation

abstract: Habit formation is an important part of behavior change interventions: to ensure an intervention has long-term effects, the new behavior has to turn into a habit and become automatic. Smartphone apps could help with this process by supporting habit formation. To better understand how, we conducted a 4-week study exploring the influence of different types of cues and positive reinforcement on habit formation and reviewed the functionality of 115 habit formation apps. We discovered that relying on reminders supported repetition but hindered habit development, while the use of event-based cues led to increased automaticity; positive reinforcement was ineffective. The functionality review revealed that existing apps focus on self-tracking and reminders, and do not support event-based cues. We argue that apps, and technology-based interventions in general, have the potential to provide real habit support, and present design guidelines for interventions that could support habit formation through contextual cues and implementation intentions.


title: Motivating IS security compliance: Insights from Habit and Protection Motivation Theory

abstract: Employees' failure to comply with IS security procedures is a key concern for organizations today. A number of socio-cognitive theories have been used to explain this. However, prior studies have not examined the influence of past and automatic behavior on employee decisions to comply. This is an important omission because past behavior has been assumed to strongly affect decision-making. To address this gap, we integrated habit (a routinized form of past behavior) with Protection Motivation Theory (PMT), to explain compliance. An empirical test showed that habitual IS security compliance strongly reinforced the cognitive processes theorized by PMT, as well as employee intention for future compliance. We also found that nearly all components of PMT significantly impacted employee intention to comply with IS security policies. Together, these results highlighted the importance of addressing employees' past and automatic behavior in order to improve compliance.


title: Repeated evolution of queen parthenogenesis and social hybridogenesis in Cataglyphis desert ants

abstract: Over the last decade, genetic studies on social insects have revealed a remarkable diversity of unusual reproductive strategies, such as male clonality, female clonality, and social hybridogenesis. In this context, Cataglyphis desert ants are useful models because of their unique reproductive systems. In several species, queens conditionally use sexual reproduction and parthenogenesis to produce sterile workers and reproductive queens, respectively. In social hybridogenesis, two distinct genetic lineages coexist within a population, and workers result from mating between partners of different lineages; in contrast, queens and males are both produced asexually by parthenogenesis. Consequently, nonreproductive workers are all interlineage hybrids, whereas reproductives are all pure lineage individuals. Here, we characterized the reproductive systems of 11 species to investigate the distribution of the conditional use of sex and social hybridogenesis in Cataglyphis. We identified one new case in which sexual reproduction was conditionally used in the absence of dependent-lineage reproduction. We also discovered five new instances of social hybridogenesis. Based on our phylogenetic analyses, we inferred that both the conditional use of sex and social hybridogenesis independently evolved multiple times in the genus Cataglyphis.


title: Deep Underwater Image Enhancement

abstract: In an underwater scene, wavelength-dependent light absorption and scattering degrade the visibility of images, causing low contrast and distorted color casts. To address this problem, we propose a convolutional neural network based image enhancement model, i.e., UWCNN, which is trained efficiently using a synthetic underwater image database. Unlike the existing works that require the parameters of underwater imaging model estimation or impose inflexible frameworks applicable only for specific scenes, our model directly reconstructs the clear latent underwater image by leveraging on an automatic end-to-end and data-driven training mechanism. Compliant with underwater imaging models and optical properties of underwater scenes, we first synthesize ten different marine image databases. Then, we separately train multiple UWCNN models for each underwater image formation type. Experimental results on real-world and synthetic underwater images demonstrate that the presented method generalizes well on different underwater scenes and outperforms the existing methods both qualitatively and quantitatively. Besides, we conduct an ablation study to demonstrate the effect of each component in our network.


title: Battery management system in the Bayesian paradigm: Part I: SOC estimation

abstract: Accurate State-of-Charge (SOC) estimation of Li-ion batteries is essential for effective battery control and energy management of electric and hybrid electric vehicles. To this end, first, the battery is modelled by an OCV-R-RC equivalent circuit. Then, a dual Bayesian estimation scheme is developed—The battery model parameters are identified online and fed to the SOC estimator, the output of which is then fed back to the parameter identifier. Both parameter identification and SOC estimation are treated in a Bayesian framework. The square-root recursive least-squares estimator and the extended Kalman-Bucy filter are systematically paired up for the first time in the battery management literature to tackle the SOC estimation problem. The proposed method is finally compared with the convectional Coulomb counting method. The results indicate that the proposed method significantly outperforms the Coulomb counting method in terms of accuracy and robustness.


title: Network Flow Integer Programming to Track Elliptical Cells in Time-Lapse Sequences

abstract: We propose a novel approach to automatically tracking elliptical cell populations in time-lapse image sequences. Given an initial segmentation, we account for partial occlusions and overlaps by generating an over-complete set of competing detection hypotheses. To this end, we fit ellipses to portions of the initial regions and build a hierarchy of ellipses, which are then treated as cell candidates. We then select temporally consistent ones by solving to optimality an integer program with only one type of flow variables. This eliminates the need for heuristics to handle missed detections due to partial occlusions and complex morphology. We demonstrate the effectiveness of our approach on a range of challenging sequences consisting of clumped cells and show that it outperforms state-of-the-art techniques.


title: Finger surface as a biometric identifier

abstract: We present a novel approach for personal identification and identity verification which utilizes 3D finger surface features as a biometric identifier. Using 3D range images of the hand, a surface representation for the index, middle, and ring finger is calculated and used for comparison to determine subject similarity. We use the curvature based shape index to represent the fingers' surface. Gallery and probe shape index signatures are compared using the normalized correlation coefficient to compute a match score. A large unique database of hand images supports the research. We use data sets obtained over time to examine the performance of each individual finger surface as a biometric identifier as well as the performance obtained when combining them. Both identification and verification experiments are conducted. In addition, probe and gallery sets sizes are increased to further improve recognition performance in our experiments. Our approach yields good results for a first-of-its-kind biometric technique, indicating that this approach warrants further research.


title: Fluorescence Lifetime Imaging and FRET-Induced Intracellular Redistribution of Tat-Conjugated Quantum Dot Nanoparticles through Interaction with a Phthalocyanine Photosensitiser

abstract: The interaction of Tat-conjugated PEGylated CdSe/ZnS quantum dots (QD) with the amphiphilic disulfonated aluminium phthalocyanine photosensitiser is investigated in aqueous solution and in a human breast cancer cell line. In aqueous solution, the QDs and phthalocyanine form stable nanocomposites. Using steady-state and time-resolved fluorescence measurements combined with singlet oxygen detection, efficient Förster resonance energy transfer (FRET) is observed with the QDs acting as donors, and the phthalocyanine photosensitiser, which mediates production of singlet oxygen, as acceptors. In cells, the Tat-conjugated QDs localise in lysosomes and the QD fluorescence lifetimes are close to values observed in aqueous solution. Strong FRET-induced quenching of the QD lifetime is observed in cells incubated with the nanocomposites using fluorescence lifetime imaging microscopy (FLIM). Using excitation of the QDs at wavelengths where phthalocyanine absorption is negligible, FRET-induced release of QDs from endo/lysosomes is confirmed using confocal imaging and FLIM, which is attributed to photooxidative damage to the endo/lysosomal membranes mediated by the phthalocyanine acceptor.


THE BØIFOT ORTHOMODE JUNCTION


title: Pre-Processing-Free Gear Fault Diagnosis Using Small Datasets with Deep Convolutional Neural Network-Based Transfer Learning

abstract: Early diagnosis of gear transmission has been a significant challenge, because gear faults occur primarily at microstructure or even material level but their effects can only be observed indirectly at a system level. The performance of a gear fault diagnosis system depends significantly on the features extracted and the classifier subsequently applied. Traditionally, fault-related features are extracted and identified based on domain expertise through data preprocessing which are system-specific and may not be easily generalized. On the other hand, although recently the deep neural networks based approaches featuring adaptive feature extractions and inherent classifications have attracted attention, they usually require a substantial set of training data. Aiming at tackling these issues, this paper presents a deep convolutional neural network-based transfer learning approach. The proposed transfer learning architecture consists of two parts; the first part is constructed with a pre-trained deep neural network that serves to extract the features automatically from the input, and the second part is a fully connected stage to classify the features that needs to be trained using gear fault experimental data. Case analyses using experimental data from a benchmark gear system indicate that the proposed approach not only entertains preprocessing free adaptive feature extractions, but also requires only a small set of training data.


title: Five-factor model personality traits as predictors of perceived and actual usage of technology

abstract: Understanding the adoption and use of technology is extremely important in the field of information systems. Not surprisingly, there are several conceptual models that attempt to explain how and why individuals use technology. Until recently, however, the role of personality in general, and the five-factor model (FFM) of personality in particular, had remained largely unexplored. Our study takes an interactional psychology perspective, linking components of the FFM to the use of technology within the conceptual framework of the Unified Theory of Acceptance and Use of Technology (UTAUT). After empirically confirming previous research findings linking performance expectancy, effort expectancy, and social influence to technology use, we test direct relationships between FFM personality traits and technology use in the context of a web-based classroom technological system, utilizing measures of perceived and actual use of technology. Consistent with expectations, conscientiousness and neuroticism are associated with perceived and actual use of technology, with conscientiousness demonstrating a positive association with both perceived and actual use and neuroticism, a negative association. Extraversion was also significantly associated with actual use, although not in the positive direction expected. Further, the significant relationships between the personality traits and the actual use of technology were direct and not mediated by expressed intentions to use the system.


Injuries in Fatal Aircraft Accidents


HeLI, a Word-Based Backoff Method for Language Identification


title: Tumor necrosis factor alpha -C850T polymorphism is significantly associated with endometriosis in Asian Indian women.

abstract: OBJECTIVE ::: To establish the association of tumor necrosis factor alpha (TNFA) promoter C850T polymorphism in Asian Indian women with endometriosis. ::: ::: ::: DESIGN ::: Case control study, from multiple gynecological centers from Hyderabad, a cosmopolitan city in southern India. The study included 245 women who comprised 110 surgically confirmed cases of endometriosis, 50 ultrasonographically confirmed cases of fibroid tumors, and 85 healthy female volunteers. ::: ::: ::: SETTING ::: Academic hospital. ::: ::: ::: PATIENT(S) ::: The cases were 245 women, 160 patients and 85 controls. ::: ::: ::: INTERVENTION(S) ::: None. ::: ::: ::: MAIN OUTCOME MEASURE(S) ::: Tumor necrosis factor alpha -C850T polymorphism may be used as a molecular marker for endometriosis in our population. ::: ::: ::: RESULT(S) ::: In this study we have demonstrated an association between TNFA -C850T polymorphism and endometriosis. The T allele is significantly associated with endometriosis when compared to women with fibroids as well as healthy controls. Our data imply that the T allele is associated with endometriosis (OR = 1.9594: 95% CI, 1.3833-2.7753; in our population. The TT genotype increases the risk of endometriosis by fourfold (4.5542: 95% CI, 2.0388-10.1701. ::: ::: ::: CONCLUSION(S) ::: This study suggests that -C850T TNFA gene polymorphism could be used as a relevant molecular marker to identify women with risk of developing endometriosis in our population.


title: Community detection in social network: Literature review and research perspectives

abstract: Social network community detection has occupied an important place in many scientific fields like biology, sociology, or computer science. This problem still attracted a lot of work. The challenge is how to identify inside these networks, groups of persons strongly linked and sharing the same preferences. As in the literature, there are many works trying to detect communities we tried in this paper to do a review of the most important ones and we tried to present some tips for improvement of obtained results.


title: Expectation disconfirmation and technology adoption: polynomial modeling and response surface analysis

abstract: Individual-level information systems adoption research has recently seen the introduction of expectation-disconfirmation theory (EDT) to explain how and why user reactions change over time. This prior research has produced valuable insights into the phenomenon of technology adoption beyond traditional models, such as the technology acceptance model. First, we identify gaps in EDT research that present potential opportunities for advances-specifically, we discuss methodological and analytical limitations in EDT research in information systems and present polynomial modeling and response surface methodology as solutions. Second, we draw from research on cognitive dissonance, realistic job preview, and prospect theory to present a polynomial model of expectation-disconfirmation in information systems. Finally, we test our model using data gathered over a period of 6 months among 1,143 employees being introduced to a new technology. The results confirmed our hypotheses that disconfirmation in general was bad, as evidenced by low behavioral intention to continue using a system for both positive and negative disconfirmation, thus supporting the need for a polynomial model to understand expectation disconfirmation in information systems.


title: Design patterns: Abstraction and reuse of object-oriented design

abstract: We propose design patterns as a new mechanism for expressing object-oriented design experience. Design patterns identify, name, and abstract common themes in object-oriented design. They capture the intent behind a design by identifying objects, their collaborations, and the distribution of responsibilities. Design patterns play many roles in the object-oriented development process: they provide a common vocabulary for design, they reduce system complexity by naming and defining abstractions, they constitute a base of experience for building reusable software, and they act as building blocks from which more complex designs can be built. Design patterns can be considered reusable micro-architectures that contribute to an overall system architecture. We describe how to express and organize design patterns and introduce a catalog of design patterns. We also describe our experience in applying design patterns to the design of object-oriented systems.


The DIAMOND System for Computing with Abstract Dialectical Frameworks.


title: LASH: Large-Scale Sequence Mining with Hierarchies

abstract: We propose LASH, a scalable, distributed algorithm for mining sequential patterns in the presence of hierarchies. LASH takes as input a collection of sequences, each composed of items from some application-specific vocabulary. In contrast to traditional approaches to sequence mining, the items in the vocabulary are arranged in a hierarchy: both input sequences and sequential patterns may consist of items from different levels of the hierarchy. Such hierarchies naturally occur in a number of applications including mining natural-language text, customer transactions, error logs, or event sequences. LASH is the first parallel algorithm for mining frequent sequences with hierarchies; it is designed to scale to very large datasets. At its heart, LASH partitions the data using a novel, hierarchy-aware variant of item-based partitioning and subsequently mines each partition independently and in parallel using a customized mining algorithm called pivot sequence miner. LASH is amenable to a MapReduce implementation; we propose effective and efficient algorithms for both the construction and the actual mining of partitions. Our experimental study on large real-world datasets suggest good scalability and run-time efficiency.


title: On composition of a federated web search result page: using online users to provide pairwise preference for heterogeneous verticals

abstract: Modern web search engines are federated --- a user query is sent to the numerous specialized search engines called verticals like web (text documents), News, Image, Video, etc. and the results returned by these engines are then aggregated and composed into a search result page (SERP) and presented to the user. For a specific query, multiple verticals could be relevant, which makes the placement of these vertical results within blocks of textual web results challenging: how do we represent, assess, and compare the relevance of these heterogeneous entities? In this paper we present a machine-learning framework for SERP composition in the presence of multiple relevant verticals. First, instead of using the traditional label generation method of human judgment guidelines and trained judges, we use a randomized online auditioning system that allows us to evaluate triples of the form query, web block, vertical>. We use a pairwise click preference to evaluate whether the web block or the vertical block had a better users' engagement. Next, we use a hinged feature vector that contains features from the web block to create a common reference frame and augment it with features representing the specific vertical judged by the user. A gradient boosted decision tree is then learned from the training data. For the final composition of the SERP, we place a vertical result at a slot if the score is higher than a computed threshold. The thresholds are algorithmically determined to guarantee specific coverage for verticals at each slot. We use correlation of clicks as our offline metric and show that click-preference target has a better correlation than human judgments based models. Furthermore, on online tests for News and Image verticals we show higher user engagement for both head and tail queries.


title: Analysis of discovery and access procedure for D2D communication in 5G cellular network

abstract: Device-to-device (D2D) communication, which is defined as a direct communication between two mobile users without traversing the Base Station (BS) or the core network to offload the increasing traffic to the user equipments, is one of the key technologies in the fifth generation (5G) of wireless communication systems. Discovery and communication are the basic two features to fulfill the need for the D2D communication. However, Most of existing D2D studies focused on the communication issues always assume that the discovery is completed. In this paper, we propose two strategies of device discovery and access scheme for the 5G cellular networks. Then the performance analysis based on two dimensional discrete time Markov process model is provided. In addition, we present numerical simulation on the Matlab platform. The simulation results demonstrate the viability of the proposed scheme.


title: Efficient storage of high throughput DNA sequencing data using reference-based compression

abstract: Data storage costs have become an appreciable proportion of total cost in the creation and analysis of DNA sequence data. Of particular concern is that the rate of increase in DNA sequencing is significantly outstripping the rate of increase in disk storage capacity. In this paper we present a new reference-based compression method that efficiently compresses DNA sequences for storage. Our approach works for resequencing experiments that target well-studied genomes. We align new sequences to a reference genome and then encode the differences between the new sequence and the reference genome for storage. Our compression method is most efficient when we allow controlled loss of data in the saving of quality information and unaligned sequences. With this new compression method we observe exponential efficiency gains as read lengths increase, and the magnitude of this efficiency gain can be controlled by changing the amount of quality information stored. Our compression method is tunable: The storage of quality scores and unaligned sequences may be adjusted for different experiments to conserve information or to minimize storage costs, and provides one opportunity to address the threat that increasing DNA sequence volumes will overcome our ability to store the sequences.


title: Nutrient sensing and inflammation in metabolic diseases

abstract: The proper functioning of the pathways that are involved in the sensing and management of nutrients is central to metabolic homeostasis and is therefore among the most fundamental requirements for survival. Metabolic systems are integrated with pathogen-sensing and immune responses, and these pathways are evolutionarily conserved. This close functional and molecular integration of the immune and metabolic systems is emerging as a crucial homeostatic mechanism, the dysfunction of which underlies many chronic metabolic diseases, including type 2 diabetes and atherosclerosis. In this Review we provide an overview of several important networks that sense and manage nutrients and discuss how they integrate with immune and inflammatory pathways to influence the physiological and pathological metabolic states in the body.


title: Automatic test generation: a use case driven approach

abstract: Use cases are believed to be a good basis for system testing. Yet, to automate the test generation process, there is a large gap to bridge between high-level use cases and concrete test cases. We propose a new approach for automating the generation of system test scenarios in the context of object-oriented embedded software, taking into account traceability problems between high-level views and concrete test case execution. Starting from a formalization of the requirements based on use cases extended with contracts, we automatically build a transition system from which we synthesize test cases. Our objective is to cover the system in terms of statement coverage with those generated tests: an empirical evaluation of our approach is given based on this objective and several case studies. We briefly discuss the experimental deployment of our approach in the field at Thales Airborne Systems.


title: [Extracurricular activities of adolescents useful for smoking prevention programs. OCTOPUS team].

abstract: BACKGROUND ::: The cigarette smoking habit continues to be prevalent to a greater degree than would be desirable among teenagers. Innovative prevention programs are needed. This descriptive cross-sectional study sets out the behavior variables related to the cigarette smoking habit and the extracurricular activities in which teenagers are most frequently involved which are useful for setting out extracurricular prevention programs. ::: ::: ::: METHODS ::: The data was collected by means of a questionnaire validated in a representative sample of school age youths (ages 10-11 and 13-14) from Asturias. The variables entailed in cigarette smoking were analyzed using the regression method. ::: ::: ::: RESULTS ::: The starting smoker percentage is 14.5%-42.5%, regular smokers totaling 1.1% and 12.4%, respectively. Two models were constructed with the variables significantly related to smoking behavior, which are properly classified into smoker/non-smoker by 98.85% and 91.39% of the children, by ages. The environmental variables (availability of cigarettes and alcoholic beverages and regular visits to places entailing risk) are the major aspects comprising the model. The most common extracurricular activities are: watching TV, reading and listening to music and watching or playing sports. ::: ::: ::: CONCLUSION ::: The findings provide keys to planning extracurricular activities tailored to fit in with the activities most popular among teens: TV commercials and ads on music media (CD's, tapes, etc.) and printed information mailed directly to teens at their homes, with messages conveyed by opinion-leaders among teens in the fields of sports, music and television.


title: Double JPEG compression forensics based on a convolutional neural network

abstract: Double JPEG compression detection has received considerable attention in blind image forensics. However, only few techniques can provide automatic localization. To address this challenge, this paper proposes a double JPEG compression detection algorithm based on a convolutional neural network (CNN). The CNN is designed to classify histograms of discrete cosine transform (DCT) coefficients, which differ between single-compressed areas (tampered areas) and double-compressed areas (untampered areas). The localization result is obtained according to the classification results. Experimental results show that the proposed algorithm performs well in double JPEG compression detection and forgery localization, especially when the first compression quality factor is higher than the second.


title: Allocating Decision-Making Powers Among Creditor Classes: The Ups and Downs of Battling Claims Heterogeneity in Czech Corporate Insolvency Law

abstract: The problem of claims heterogeneity makes allocating decision-making powers an arduous task for any insolvency law that aims to vest control over corporate insolvency proceedings in the creditors.


title: Reward functions of the basal ganglia

abstract: Besides their fundamental movement function evidenced by Parkinsonian deficits, the basal ganglia are involved in processing closely linked non-motor, cognitive and reward information. This review describes the reward functions of three brain structures that are major components of the basal ganglia or are closely associated with the basal ganglia, namely midbrain dopamine neurons, pedunculopontine nucleus, and striatum (caudate nucleus, putamen, nucleus accumbens). Rewards are involved in learning (positive reinforcement), approach behavior, economic choices and positive emotions. The response of dopamine neurons to rewards consists of an early detection component and a subsequent reward component that reflects a prediction error in economic utility, but is unrelated to movement. Dopamine activations to non-rewarded or aversive stimuli reflect physical impact, but not punishment. Neurons in pedunculopontine nucleus project their axons to dopamine neurons and process sensory stimuli, movements and rewards and reward-predicting stimuli without coding outright reward prediction errors. Neurons in striatum, besides their pronounced movement relationships, process rewards irrespective of sensory and motor aspects, integrate reward information into movement activity, code the reward value of individual actions, change their reward-related activity during learning, and code own reward in social situations depending on whose action produces the reward. These data demonstrate a variety of well-characterized reward processes in specific basal ganglia nuclei consistent with an important function in non-motor aspects of motivated behavior.


The futility of DNSSec


title: Deep Learning for Target Classification from SAR Imagery: Data Augmentation and Translation Invariance

abstract: This report deals with translation invariance of convolutional neural networks (CNNs) for automatic target recognition (ATR) from synthetic aperture radar (SAR) imagery. In particular, the translation invariance of CNNs for SAR ATR represents the robustness against misalignment of target chips extracted from SAR images. To understand the translation invariance of the CNNs, we trained CNNs which classify the target chips from the MSTAR into the ten classes under the condition of with and without data augmentation, and then visualized the translation invariance of the CNNs. According to our results, even if we use a deep residual network, the translation invariance of the CNN without data augmentation using the aligned images such as the MSTAR target chips is not so large. A more important factor of translation invariance is the use of augmented training data. Furthermore, our CNN using augmented training data achieved a state-of-the-art classification accuracy of 99.6%. These results show an importance of domain-specific data augmentation.


title: Propranolol treatment of infantile hemangioma: clinical and radiologic evaluations.

abstract: BACKGROUND ::: There is no way to predict the size that proliferative infantile hemangiomas (IHs) can reach and to expect the occurrence of complications. Moreover, there are no well-known characteristics that can affect the rate of involution of IHs and to predict its completion. Accordingly, intervention is frequently indicated. Different modalities have been reported for treatment of IHs. The possible mechanisms of action of propranolol on IHs are complex. ::: ::: ::: METHODS ::: Fifty infants presented with 80 IHs treated by oral propranolol at a dose of 2 mg/kg body weight per day. Treatment outcomes were clinically and radiologically evaluated. ::: ::: ::: RESULTS ::: The first noticeable effects on propranolol treatment were the changes in color and softening of IHs, followed by regression of their sizes. The clinically elicited color changes of superficial IHs and superficial components of compound IHs have been objectively proven by statistically significant color clearance (P ≤ .001) and resisting index (P ≤ .01) (∼50% increase) as a good indicator of lower vascular activity within IHs. Moreover, the softening of lesions followed by the clinically elicited regression of sizes of deep IHs and deep components of compound IHs has been objectively proven by statistically significant changes at lesions' thickness (P ≤ .01) (∼50% regression) and resisting index (P ≤ .01) (∼50% increase). ::: ::: ::: CONCLUSIONS ::: Collectively, high efficacy and tolerance of propranolol treatment have been elicited. However, propranolol treatment of IHs is still an issue suitable for more studies to confirm the safety and efficacy of the drug and to investigate whether there are some hemangiomas that are, perhaps, nonresponsive to propranolol treatment.


title: Multiple Line-based Intra Prediction for High Efficiency Video Coding

abstract: Traditional intra prediction usually utilizes the nearest reference line to generate the prediction block when only considering the strong spatial correlation. However, this kind of single line-based method is not always a good solution because of two reasons. One is that the reconstruction quality of pixels in different positions of a block vary from each other if a block has been quantized in the transform domain. In general, the boundaries (especially block corner) have a worse quality than inner regions for smooth regions. The other is the existence of signal noise and object occlusion. Few of noise or occlusion on the nearest reference line may lead to a large prediction deviation. Because of the two reasons, this paper proposes to utilize multiple reference lines to improve the intra prediction. Besides the nearest reference line, further reference lines are also used to generate the prediction, in where a residue compensation procedure is introduced to suppress the discontinuities along block boundaries. The advantages of the proposed method include that it probably provide more accurate prediction from further reference lines with a higher quality. The other advantage is that the proposed method can reduce the influence of noise or occlusion on the reference line to improve the coding efficiency. In addition, this paper also designs several optional acceleration algorithms when considering the encoding complexity. Experimental results show that for all intra configuration, the bit saving of the proposed fast scheme is 1.9% on average, and up to 3.2%, with increasing the encoding time 115%.


title: Using open educational resources on culture for the creation of MOOCs: A proposal of training for secondary education teachers

abstract: Because of their development in recent years Digital Humanities have been applied to the field of Education on an international level. In the field of Culture especially, a mere search online will reveal the great number of Massive Open Online Courses (MOOCs) that focus on Greek Arts. In Greece, on the contrary, despite the plethora of Greek sites, the digitalized material on culture they contain has not been systematically used in the field of education. In this paper we suggest a way to create Greek MOOCs for Greek Arts making use of digitalized resources of cultural material, mainly DARIA-GR. In this proposal we use University Open Courses so that the described platform will be able to form, on the one hand, a basis for the training of SE teachers who teach the relevant disciplines and on the other hand an area of enriched material aiding them in the teaching process. Finally, we recommend that TraMOOC be used for the creation of complete foreign-language MOOC for Digital Humanities.


1 Use of NWP for Nowcasting Convective Precipitation : Recent Progress and Challenges


title: Independent Vector Analysis: Definition and Algorithms

abstract: We present a new approach to independent component analysis (ICA) by extending the formulation of univariate source signals to multivariate source signals. The new approach is termed independent vector analysis (IVA). In the model, we assume that linear mixing model exists in each dimension separately, and the latent sources are independent of the others. In contrast to ICA, the sources are random vectors, not just single variables, which means the elements of a random vector are closely related to the others. Thus, we assume the dependency between the elements of a source vector. In this manner, we define dependence between vectors as Kullback-Leibler divergence between the total joint probability of vectors and the product of marginal probabilities of vectors. Then, the model allows independence between multivariate source signals represented as random vectors, and dependence between the source signals within the vector representation. The proposed vector density model can for example capture variance dependencies within a vector source signal. There are several applications of this new formulation. In the separation of acoustic sources, the algorithm mitigates the permutation problem, i.e. the usual ICA algorithms applied in to the frequency domain mixture data suffer from the unknown permutation of the output signals. Although there are several engineering solutions to fix this problem after the ICA stage, the proposed method provides a natural solution to the problem by capturing the inherent dependencies of the acoustic signals. It therefore avoids the permutation problem and allows the separation of sources in very challenging environments for many sound sources.


title: Effects of currently used pesticides in assays for estrogenicity, androgenicity, and aromatase activity in vitro.

abstract: Abstract Twenty-four pesticides were tested for interactions with the estrogen receptor (ER) and the androgen receptor (AR) in transactivation assays. Estrogen-like effects on MCF-7 cell proliferation and effects on CYP19 aromatase activity in human placental microsomes were also investigated. Pesticides (endosulfan, methiocarb, methomyl, pirimicarb, propamocarb, deltamethrin, fenpropathrin, dimethoate, chlorpyriphos, dichlorvos, tolchlofos-methyl, vinclozolin, iprodion, fenarimol, prochloraz, fosetyl-aluminum, chlorothalonil, daminozid, paclobutrazol, chlormequat chlorid, and ethephon) were selected according to their frequent use in Danish greenhouses. In addition, the metabolite mercaptodimethur sulfoxide, the herbicide tribenuron-methyl, and the organochlorine dieldrin, were included. Several of the pesticides, dieldrin, endosulfan, methiocarb, and fenarimol, acted both as estrogen agonists and androgen antagonists. Prochloraz reacted as both an estrogen and an androgen antagonist. Furthermore, fenarimol and prochloraz were potent aromatase inhibitors while endosulfan was a weak inhibitor. Hence, these three pesticides possess at least three different ways to potentially disturb sex hormone actions. In addition, chlorpyrifos, deltamethrin, tolclofos-methyl, and tribenuron-methyl induced weak responses in one or both estrogenicity assays. Upon cotreatment with 17β-estradiol, the response was potentiated by endosulfan in the proliferation assay and by pirimicarb, propamocarb, and daminozid in the ER transactivation assay. Vinclozolin reacted as a potent AR antagonist and dichlorvos as a very weak one. Methomyl, pirimicarb, propamocarb, and iprodion weakly stimulated aromatase activity. Although the potencies of the pesticides to react as hormone agonists or antagonists are low compared to the natural ligands, the integrated response in the organism might be amplified by the ability of the pesticides to act via several mechanism and the frequent simultaneous exposure to several pesticides.


title: A New Dataset and Method for Automatically Grading ESOL Texts

abstract: We demonstrate how supervised discriminative machine learning techniques can be used to automate the assessment of 'English as a Second or Other Language' (ESOL) examination scripts. In particular, we use rank preference learning to explicitly model the grade relationships between scripts. A number of different features are extracted and ablation tests are used to investigate their contribution to overall performance. A comparison between regression and rank preference models further supports our method. Experimental results on the first publically available dataset show that our system can achieve levels of performance close to the upper bound for the task, as defined by the agreement between human examiners on the same corpus. Finally, using a set of 'outlier' texts, we test the validity of our model and identify cases where the model's scores diverge from that of a human examiner.


A 2 . 7-kW , 29-MHz ClassE / Fodd Amplifier with a Distributed Active Transformer


title: Robust Subspace Clustering via Tighter Rank Approximation

abstract: Matrix rank minimization problem is in general NP-hard. The nuclear norm is used to substitute the rank function in many recent studies. Nevertheless, the nuclear norm approximation adds all singular values together and the approximation error may depend heavily on the magnitudes of singular values. This might restrict its capability in dealing with many practical problems. In this paper, an arctangent function is used as a tighter approximation to the rank function. We use it on the challenging subspace clustering problem. For this nonconvex minimization problem, we develop an effective optimization procedure based on a type of augmented Lagrange multipliers (ALM) method. Extensive experiments on face clustering and motion segmentation show that the proposed method is effective for rank approximation.


title: Re-Ranking Recommendations Based on Predicted Short-Term Interests - A Protocol and First Experiment

abstract: The recommendation of additional shopping items that are potentially interesting for the customer has become a standard feature of modern online stores. In academia, research on recommender systems (RS) is mostly centered around approaches that rely on explicit item ratings and long-term user profiles. In practical environments, however, such rating information is often very sparse and for a large fraction of the users very little is known about their preferences. Furthermore, in particular when the shop offers products from a variety of categories, the decision of what should be recommended can strongly depend on the user's current short-term interests and the navigational context. In this paper, we report the results of an initial experimental analysis evaluating the predictive accuracy of different contextualized and non-contextualized recommendation strategies and discuss the question of appropriate experimental designs for such types of evaluations. To that purpose, we introduce a parameterizable protocol that supports session-specific accuracy measurements. Our analysis, which was based on log data obtained from a large online retailer for clothing and lifestyle products, shows that even a comparably simple contextual post-processing approach based on product features can leverage short-term user interests to increase the accuracy of the recommendations.


title: Blind Image Quality Assessment Based on High Order Statistics Aggregation

abstract: Blind image quality assessment (BIQA) research aims to develop a perceptual model to evaluate the quality of distorted images automatically and accurately without access to the non-distorted reference images. The state-of-the-art general purpose BIQA methods can be classified into two categories according to the types of features used. The first includes handcrafted features which rely on the statistical regularities of natural images. These, however, are not suitable for images containing text and artificial graphics. The second includes learning-based features which invariably require large codebook or supervised codebook updating procedures to obtain satisfactory performance. These are time-consuming and not applicable in practice. In this paper, we propose a novel general purpose BIQA method based on high order statistics aggregation (HOSA), requiring only a small codebook. HOSA consists of three steps. First, local normalized image patches are extracted as local features through a regular grid, and a codebook containing 100 codewords is constructed by K-means clustering. In addition to the mean of each cluster, the diagonal covariance and coskewness (i.e., dimension-wise variance and skewness) of clusters are also calculated. Second, each local feature is softly assigned to several nearest clusters and the differences of high order statistics (mean, variance and skewness) between local features and corresponding clusters are softly aggregated to build the global quality aware image representation. Finally, support vector regression is adopted to learn the mapping between perceptual features and subjective opinion scores. The proposed method has been extensively evaluated on ten image databases with both simulated and realistic image distortions, and shows highly competitive performance to the state-of-the-art BIQA methods.


title: Analysis of Movement, Orientation and Rotation-Based Sensing for Phone Placement Recognition

abstract: Phone placement, i.e., where the phone is carried/stored, is an important source of information for context-aware applications. Extracting information from the integrated smart phone sensors, such as motion, light and proximity, is a common technique for phone placement detection. In this paper, the efficiency of an accelerometer-only solution is explored, and it is investigated whether the phone position can be detected with high accuracy by analyzing the movement, orientation and rotation changes. The impact of these changes on the performance is analyzed individually and both in combination to explore which features are more efficient, whether they should be fused and, if yes, how they should be fused. Using three different datasets, collected from 35 people from eight different positions, the performance of different classification algorithms is explored. It is shown that while utilizing only motion information can achieve accuracies around 70%, this ratio increases up to 85% by utilizing information also from orientation and rotation changes. The performance of an accelerometer-only solution is compared to solutions where linear acceleration, gyroscope and magnetic field sensors are used, and it is shown that the accelerometer-only solution performs as well as utilizing other sensing information. Hence, it is not necessary to use extra sensing information where battery power consumption may increase. Additionally, I explore the impact of the performed activities on position recognition and show that the accelerometer-only solution can achieve 80% recognition accuracy with stationary activities where movement data are very limited. Finally, other phone placement problems, such as in-pocket and on-body detections, are also investigated, and higher accuracies, ranging from 88% to 93%, are reported, with an accelerometer-only solution.


title: 3D scan conversion of CSG models into distance volumes

abstract: A distance volume is a volume dataset where the value stored at each voxel is the shortest distance to the surface of the object being represented by the volume. Distance volumes are a useful representation in a number of computer graphics applications. We present a technique for generating a distance volume with sub-voxel accuracy from one type of geometric model, a constructive solid geometry (CSG) model consisting of superellipsoid primitives. The distance volume is generated in a two step process. The first step calculates the shortest distance to the CSG model at a set of points within a narrow band around the evaluated surface. Additionally, a second set of points, labeled the zero set, which lies on the CSG model's surface are computed. A point in the zero set is associated with each point in the narrow band. Once the narrow band and zero set are calculated, a fast marching method is employed to propagate the shortest distance and closest point information out to the remaining voxels in the volume. Our technique has been used to scan convert a number of CSG models, producing distance volumes which have been utilized in a variety of computer graphics applications, e.g. CSG surface evaluation, offset surface generation, and 3D model morphing.


title: Document Modeling with Gated Recurrent Neural Network for Sentiment Classification

abstract: Document level sentiment classification remains a challenge: encoding the intrinsic relations between sentences in the semantic meaning of a document. To address this, we introduce a neural network model to learn vector-based document representation in a unified, bottom-up fashion. The model first learns sentence representation with convolutional neural network or long short-term memory. Afterwards, semantics of sentences and their relations are adaptively encoded in document representation with gated recurrent neural network. We conduct document level sentiment classification on four large-scale review datasets from IMDB and Yelp Dataset Challenge. Experimental results show that: (1) our neural model shows superior performances over several state-of-the-art algorithms; (2) gated recurrent neural network dramatically outperforms standard recurrent neural network in document modeling for sentiment classification. 1


Foundations of Statistical Natural Language Processing


title: Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules

abstract: Morphologically rich languages accentuate two properties of distributional vector space models: 1) the difficulty of inducing accurate representations for low-frequency word forms; and 2) insensitivity to distinct lexical relations that have similar distributional signatures. These effects are detrimental for language understanding systems, which may infer that 'inexpensive' is a rephrasing for 'expensive' or may not associate 'acquire' with 'acquires'. In this work, we propose a novel morph-fitting procedure which moves past the use of curated semantic lexicons for improving distributional vector spaces. Instead, our method injects morphological constraints generated using simple language-specific rules, pulling inflectional forms of the same word close together and pushing derivational antonyms far apart. In intrinsic evaluation over four languages, we show that our approach: 1) improves low-frequency word estimates; and 2) boosts the semantic quality of the entire word vector collection. Finally, we show that morph-fitted vectors yield large gains in the downstream task of dialogue state tracking, highlighting the importance of morphology for tackling long-tail phenomena in language understanding tasks.


title: Efficient Learning of Harmonic Priors for Pitch Detection in Polyphonic Music

abstract: Automatic music transcription (AMT) aims to infer a latent symbolic representation of a piece of music (piano-roll), given a corresponding observed audio recording. Transcribing polyphonic music (when multiple notes are played simultaneously) is a challenging problem, due to highly structured overlapping between harmonics. We study whether the introduction of physically inspired Gaussian process (GP) priors into audio content analysis models improves the extraction of patterns required for AMT. Audio signals are described as a linear combination of sources. Each source is decomposed into the product of an amplitude-envelope, and a quasi-periodic component process. We introduce the Mat\'ern spectral mixture (MSM) kernel for describing frequency content of singles notes. We consider two different regression approaches. In the sigmoid model every pitch-activation is independently non-linear transformed. In the softmax model several activation GPs are jointly non-linearly transformed. This introduce cross-correlation between activations. We use variational Bayes for approximate inference. We empirically evaluate how these models work in practice transcribing polyphonic music. We demonstrate that rather than encourage dependency between activations, what is relevant for improving pitch detection is to learnt priors that fit the frequency content of the sound events to detect.


title: Converting and Annotating Quantitative Data Tables

abstract: Companies, governmental agencies and scientists produce a large amount of quantitative (research) data, consisting of measurements ranging from e.g. the surface temperatures of an ocean to the viscosity of a sample of mayonnaise. Such measurements are stored in tables in e.g. spreadsheet files and research reports. To integrate and reuse such data, it is necessary to have a semantic description of the data. However, the notation used is often ambiguous, making automatic interpretation and conversion to RDF or other suitable format difficult. For example, the table header cell "f (Hz)" refers to frequency measured in Hertz, but the symbol "f" can also refer to the unit farad or the quantities force or luminous flux. Current annotation tools for this task either work on less ambiguous data or perform a more limited task. We introduce new disambiguation strategies based on an ontology, which allows to improve performance on "sloppy" datasets not yet targeted by existing systems.


title: MicroRNAs in metabolism

abstract: &NA; MicroRNAs (miRNAs) have within the past decade emerged as key regulators of metabolic homoeostasis. Major tissues in intermediary metabolism important during development of the metabolic syndrome, such as β‐cells, liver, skeletal and heart muscle as well as adipose tissue, have all been shown to be affected by miRNAs. In the pancreatic β‐cell, a number of miRNAs are important in maintaining the balance between differentiation and proliferation (miR‐200 and miR‐29 families) and insulin exocytosis in the differentiated state is controlled by miR‐7, miR‐375 and miR‐335. MiR‐33a and MiR‐33b play crucial roles in cholesterol and lipid metabolism, whereas miR‐103 and miR‐107 regulates hepatic insulin sensitivity. In muscle tissue, a defined number of miRNAs (miR‐1, miR‐133, miR‐206) control myofibre type switch and induce myogenic differentiation programmes. Similarly, in adipose tissue, a defined number of miRNAs control white to brown adipocyte conversion or differentiation (miR‐365, miR‐133, miR‐455). The discovery of circulating miRNAs in exosomes emphasizes their importance as both endocrine signalling molecules and potentially disease markers. Their dysregulation in metabolic diseases, such as obesity, type 2 diabetes and atherosclerosis stresses their potential as therapeutic targets. This review emphasizes current ideas and controversies within miRNA research in metabolism.


title: SPSC: Efficient Composition of Semantic Services in Unstructured P2P Networks

abstract: The problem of automated semantic peer-to-peer P2P service composition has been addressed in cross-disciplinary research of semantic web and P2P computing. Solutions for semantic web service composition in structured P2P networks benefit from the underlying distributed global index but at the cost of network traffic overhead for its maintenance. Current solutions to service composition in unstructured P2P networks with selective flooding can be more robust against changes but suffer from redundant messaging, lack of efficient semantics-empowered search heuristics and proven soundness. In this paper, we present a novel approach, called SPSC, for efficient semantic service composition planning in unstructured P2P networks. SPSC peers conduct a guarded heuristics-based composition to jointly plan complex workflows of semantic services in OWL-S. The semantic service query branching method based on local observations by peers about the semantic overlay alleviates the problem of reaching dead-ends in the not fully observable and heuristically pruned search space. We theoretically prove that the SPSC approach is sound and provide a lower bound of its completeness. Finally, our experimental evaluation shows that SPSC achieves high cumulative recall with relatively low traffic overhead.


title: Efficient Round-Trip Time monitoring in OpenFlow networks

abstract: Monitoring Round-Trip Time provides important insights for network troubleshooting and traffic engineering. The common monitoring technique is to actively send probe packets from selected vantage points (hosts or middleboxes). In traditional networks, the control over the network routing is limited, making it impossible to monitor every selected path. The emerging concept of Software Defined Networking simplifies network control. However, OpenFlow, the common SDN protocol, does not support RTT monitoring as part of its specification. In this paper, we leverage the ability of OpenFlow to control the routing, and present GRAMI, the Granular RTT Monitoring Infrastructure. GRAMI uses active probing from selected vantage points for efficient RTT monitoring of all the links and any round-trip path between any two switches in the network. GRAMI was designed to be resource efficient. It requires only four flow entries installed on every switch in order to enable RTT monitoring of all the links. For every round-trip path selected by the user, it requires a maximum of two additional flow entries installed on every switch along the measured path. Moreover, GRAMI uses a minimal number of probe packets, and does not require the involvement of the controller during online RTT monitoring.


title: Improving multiview face detection with multi-task deep convolutional neural networks

abstract: Multiview face detection is a challenging problem due to dramatic appearance changes under various pose, illumination and expression conditions. In this paper, we present a multi-task deep learning scheme to enhance the detection performance. More specifically, we build a deep convolutional neural network that can simultaneously learn the face/nonface decision, the face pose estimation problem, and the facial landmark localization problem. We show that such a multi-task learning scheme can further improve the classifier's accuracy. On the challenging FDDB data set, our detector achieves over 3% improvement in detection rate at the same false positive rate compared with other state-of-the-art methods.


Benchmarking RDF Query Engines: The LDBC Semantic Publishing Benchmark


title: Systems analysis and design: for the small enterprise

abstract: Part I: Introduction. Information Systems in the Small Enterprise. The Systems Development Life Cycle. Part II: Analysis. Problem Identification and Definition. Data Flow Diagrams. System and Data Models. Project Management. Part III: Design. Problem Solving and Design Development Strategies. File and Form Design. Report and Query Design. Process Design. Cost/Benefit Analysis. Part IV: Development. Prototyping. 4GL Programming. System Environment. System Testing. Part V: Implementation. System Documentation and Training. System Conversion. Information System Maintenance and Review.


title: Partnership Models for Climate Compatible Development: Experiences from Zambia

abstract: Partnership working is necessary to allow nations to harness the evolving opportunities presented by climate finance and to progress towards climate compatible development (CCD). However, the new multi-stakeholder partnerships being formed and the factors affecting their outcomes remain poorly understood. This paper aims to identify the characteristics of partnership models that can lead to successful delivery of CCD projects by analyzing case study data from two projects in Zambia. The projects are primarily funded under the umbrella of Corporate Social Responsibility and support activities such as conservation farming which can have carbon storage (mitigation), adaptation and rural development benefits. In each of the case study projects, multiple partnerships have been established between private sector companies, government, Non-Governmental Organizations (NGOs), traditional authorities and community stakeholders to achieve project aims. A new partnership evaluation model is developed and applied to analyze the partnerships formed. Findings show that the rationale behind the partnership, partner-related factors, and process-related factors can all affect achievement of the project's aims. Good practices are identified which can inform future partnerships and projects. For example, when establishing a project, the initiating partner must identify gaps that can be addressed by establishing one or more partnership(s). Careful


title: Smart health: A context-aware health paradigm within smart cities

abstract: The new era of mobile health ushered in by the wide adoption of ubiquitous computing and mobile communications has brought opportunities for governments and companies to rethink their concept of healthcare. Simultaneously, the worldwide urbanization process represents a formidable challenge and attracts attention toward cities that are expected to gather higher populations and provide citizens with services in an efficient and human manner. These two trends have led to the appearance of mobile health and smart cities. In this article we introduce the new concept of smart health, which is the context-aware complement of mobile health within smart cities. We provide an overview of the main fields of knowledge that are involved in the process of building this new concept. Additionally, we discuss the main challenges and opportunities that s-Health would imply and provide a common ground for further research.


title: Efficient projections onto the l1-ball for learning in high dimensions

abstract: We describe efficient algorithms for projecting a vector onto the l1-ball. We present two methods for projection. The first performs exact projection in O(n) expected time, where n is the dimension of the space. The second works on vectors k of whose elements are perturbed outside the l1-ball, projecting in O(k log(n)) time. This setting is especially useful for online learning in sparse feature spaces such as text categorization applications. We demonstrate the merits and effectiveness of our algorithms in numerous batch and online learning tasks. We show that variants of stochastic gradient projection methods augmented with our efficient projection procedures outperform interior point methods, which are considered state-of-the-art optimization techniques. We also show that in online settings gradient updates with l1 projections outperform the exponentiated gradient algorithm while obtaining models with high degrees of sparsity.


title: Feature Correlation Hypergraph: Exploiting High-order Potentials for Multimodal Recognition

abstract: In computer vision and multimedia analysis, it is common to use multiple features (or multimodal features) to represent an object. For example, to well characterize a natural scene image, we typically extract a set of visual features to represent its color, texture, and shape. However, it is challenging to integrate multimodal features optimally. Since they are usually high-order correlated, e.g., the histogram of gradient (HOG), bag of scale invariant feature transform descriptors, and wavelets are closely related because they collaboratively reflect the image texture. Nevertheless, the existing algorithms fail to capture the high-order correlation among multimodal features. To solve this problem, we present a new multimodal feature integration framework. Particularly, we first define a new measure to capture the high-order correlation among the multimodal features, which can be deemed as a direct extension of the previous binary correlation. Therefore, we construct a feature correlation hypergraph (FCH) to model the high-order relations among multimodal features. Finally, a clustering algorithm is performed on FCH to group the original multimodal features into a set of partitions. Moreover, a multiclass boosting strategy is developed to obtain a strong classifier by combining the weak classifiers learned from each partition. The experimental results on seven popular datasets show the effectiveness of our approach.


title: A Low Overhead DPA Countermeasure Circuit Based on Ring Oscillators

abstract: Side-channel attacks, particularly differential power analysis (DPA) attacks, are efficient ways to extract secret keys of the attacked devices by leaked physical information. To resist DPA attacks, hiding and masking methods are commonly used, but it usually resulted in high area overhead and performance degradation. In this brief, a DPA countermeasure circuit based on digital controlled ring oscillators is presented to efficiently resist the first-order DPA attack. The implementation of the critical S-box of the advanced encryption standard (AES) algorithm shows that the area overhead of a single S-box is about 19% without any extra delay in the critical path. Moreover, the countermeasure circuit can be mounted onto different S-box implementations based on composite field or look-up table (LUT). Based on our approach, a DPA-resistant AES chip can be proposed to maintain the same throughput with less than 2K extra gates.


title: Indonesian Twitter Cyberbullying Detection using Text Classification and User Credibility

abstract: Cyberbullying is a repeated act that harasses, humiliates, threatens, or hassles other people through electronic devices and online social networking websites. Cyberbullying through the internet is more dangerous than traditional bullying, because it can potentially amplify the humiliation to an unlimited online audience. According to UNICEF and a survey by the Indonesian Ministry of Communication and Information, 58% of 435 adolescents do not understand about cyberbullying. Some of them might even have been the bullies, but since they did not understand about cyberbullying they could not recognise the negative effects of their bullying. The bullies may not recognise the harm of their actions, because they do not see immediate responses from their victims. Our study aimed to detect cyberbullying actors based on texts and the credibility analysis of users and notify them about the harm of cyberbullying. We collected data from Twitter. Since the data were unlabelled, we built a web-based labelling tool to classify tweets into cyberbullying and non-cyberbullying tweets. We obtained 301 cyberbullying tweets, 399 non-cyberbullying tweets, 2,053 negative words and 129 swear words from the tool. Afterwards, we applied SVM and KNN to learn about and detect cyberbullying texts. The results show that SVM results in the highest f1-score, 67%. We also measured the credibility analysis of users and found 257 Normal Users, 45 Harmful Bullying Actors, 53 Bullying Actors and 6 Prospective Bullying Actors.


title: Nonlinear Control for Tracking and Obstacle Avoidance of a Wheeled Mobile Robot With Nonholonomic Constraint

abstract: This brief presents a novel control scheme for some problems on tracking and obstacle avoidance of a wheeled mobile robot with nonholonomic constraint. An extended state observer is introduced to estimate the unknown disturbances and velocity information of the wheeled mobile robot. A nonlinear controller is designed to achieve tracking target and obstacle avoidance in complex environments. Note that tracking errors converge to a residual set outside the obstacle detection region. Moreover, the obstacle avoidance is also guaranteed inside the obstacle detection region. Simulation results are given to verify the effectiveness and robustness of the proposed design scheme.


title: A high-reliable magnetic design method for three-phase coupled inductor used in interleaved multi-phase boost converters

abstract: This paper proposes a high-reliable magnetic design method considering DC-biased magnetization for a Loosely Coupled Inductor (LCI) used in interleaved three-phase boost converters. The interleaved multi-phase boost converters with an LCI are well-known as one of the attractive circuit topologies that can achieve high-power-density of the converters. However, when designing and implementing an LCI in the interleaved converter, DC-biased magnetization in the transformer part of LCI must be taken into account because magnetic saturation of the core may easily occur. This phenomenon is caused by unbalanced inductor average currents in each phase. Inserting air-gaps into magnetic paths of the transformer part of a LCI is considered as one of the solutions to prevent the magnetic saturation. Nonetheless, in this case, the volume and weight of LCI increase, and they become a matter of concern because the characteristics of an LCI goes closer to the characteristics of the independent inductors. Therefore, there is a trade-off between handling the DC-biased magnetization and downsizing the magnetic core. To optimize this trade-off, a novel magnetic design method for LCI is proposed by considering the maximum permissible percentage of unbalanced inductor average currents. This paper focuses on the three-phase coupled inductors used in three-phase interleaved boost converters. The accuracy and the effectiveness of the proposed magnetic design method are discussed from both theoretical and experimental points of view.


title: Wellbeing and resilience: mechanisms of transmission of health and risk in parents with complex mental health problems and their offspring—The WARM Study

abstract: The WARM study is a longitudinal cohort study following infants of mothers with schizophrenia, bipolar disorder, depression and control from pregnancy to infant 1 year of age. Children of parents diagnosed with complex mental health problems including schizophrenia, bipolar disorder and depression, are at increased risk of developing mental health problems compared to the general population. Little is known regarding the early developmental trajectories of infants who are at ultra-high risk and in particular the balance of risk and protective factors expressed in the quality of early caregiver-interaction. We are establishing a cohort of pregnant women with a lifetime diagnosis of schizophrenia, bipolar disorder, major depressive disorder and a non-psychiatric control group. Factors in the parents, the infant and the social environment will be evaluated at 1, 4, 16 and 52 weeks in terms of evolution of very early indicators of developmental risk and resilience focusing on three possible environmental transmission mechanisms: stress, maternal caregiver representation, and caregiver-infant interaction. The study will provide data on very early risk developmental status and associated psychosocial risk factors, which will be important for developing targeted preventive interventions for infants of parents with severe mental disorder. NCT02306551, date of registration November 12, 2014.


Functional outcomes in ICU – what should we be using? - an observational study


title: The Cognitive Neuroscience of Human Memory Since H.M.

abstract: Work with patient H.M., beginning in the 1950s, established key principles about the organization of memory that inspired decades of experimental work. Since H.M., the study of human memory and its disorders has continued to yield new insights and to improve understanding of the structure and organization of memory. Here we review this work with emphasis on the neuroanatomy of medial temporal lobe and diencephalic structures important for memory, multiple memory systems, visual perception, immediate memory, memory consolidation, the locus of long-term memory storage, the concepts of recollection and familiarity, and the question of how different medial temporal lobe structures may contribute differently to memory functions.


title: Tapping the power of text mining

abstract: Sifting through vast collections of unstructured or semistructured data beyond the reach of data mining tools, text mining tracks information sources, links isolated concepts in distant documents, maps relationships between activities, and helps answer questions.


title: Role of Vitamin E on Antispermatogenic Effects of Indomethacin on Number of Sperm Containing Seminiferous Tubules of Testes in Long Evans Rats

abstract: Context: Indomethacin is the most commonly and widely used nonsteroidal antiinflammatory analgesic and antipyretic drug. Despite its effectiveness as an antiinflammatory use, indomethacin causes inhibition of spermatogenesis leading to infertility. On the other hand, vitamin E enhances spermatogenesis. Therefore, the present study was designed to observe the protective role of vitamin E on indomethacin induced testicular damage. Objective: To observe the effects of vitamin E on indomethacin induced testicular damage in Long Evans rats. Study design: An experimental study. Place and period of study: The study was carried out in the Department of Anatomy, Sir Salimullah Medical College, Dhaka. Materials and methods: Eightyfour mature Long Evans male rats were divided into four groups (I, II, III and IV). The rats of group I, II and III were treated with indomethacin at different doses and duration. Group IV rats were treated with indomethacin and vitamin E at different doses for 49 days. Histologically the number of sperm containing and nonsperm containing seminiferous tubules were counted. Results: There was significant reduction (P<0.001) in number of sperm containing seminiferous tubules when the rats were treated with indomethacin at low (2 mg/kg body wt/day) and high dose (10 mg/kg body wt/day) for 7, 14 and 42 days, respectively. On the other hand, rats treated with indomethacin and vitamin E for 49 days showed increase in number of sperm containing seminiferous tubules compared to the other groups (P<0.001). Conclusion: It can be concluded from this study that vitamin E has potential role in the prevention of the antispermatogenic effects of indomethacin. Key words: Seminiferous tubules, Indomethacin, Vitamin E doi: 10.3329/bja.v7i1.3009 Bangladesh Journal of Anatomy January 2009, Vol. 7 No. 1 pp. 5-9


title: Bilingual Chronological Classification of Hafez's Poems

abstract: We present a novel task: the chronological classification of Hafez’s poems (ghazals). We compiled a bilingual corpus in digital form, with consistent idiosyncratic properties. We have used Hooman’s labeled ghazals in order to train automatic classifiers to classify the remaining ghazals. Our classification framework uses a Support Vector Machine (SVM) classifier with similarity features based on Latent Dirichlet Allocation (LDA). In our analysis of the results we use the LDA topics’ main terms that are passed on to a Principal Component Analysis (PCA) module.


title: Heartbeat Classification Using Morphological and Dynamic Features of ECG Signals

abstract: In this paper, we propose a new approach for heartbeat classification based on a combination of morphological and dynamic features. Wavelet transform and independent component analysis (ICA) are applied separately to each heartbeat to extract morphological features. In addition, RR interval information is computed to provide dynamic features. These two different types of features are concatenated and a support vector machine classifier is utilized for the classification of heartbeats into one of 16 classes. The procedure is independently applied to the data from two ECG leads and the two decisions are fused for the final classification decision. The proposed method is validated on the baseline MIT-BIH arrhythmia database and it yields an overall accuracy (i.e., the percentage of heartbeats correctly classified) of 99.3% (99.7% with 2.4% rejection) in the “class-oriented” evaluation and an accuracy of 86.4% in the “subject-oriented” evaluation, comparable to the state-of-the-art results for automatic heartbeat classification.


title: The Enriched UTAUT Model for the Acceptance of Software Engineering Tools in Academic Education

abstract: This article provides an enriched technology acceptance model explaining the impact of both classic and additional variables on software engineering tools acceptance within Information Systems Development courses. Two novel, domain-specific variables were identified i.e., Professional Training Diffusion and Model Interchange. Statistical verification of the proposed model and study regarding the influence of the variables on students’ intention to use specific tools were conducted—providing users with enhanced means of selecting optimal software for real-life projects.


title: RBox-CNN: rotated bounding box based CNN for ship detection in remote sensing image

abstract: In this paper, we propose a rotated bounding box based convolutional neural network (RBox-CNN) for arbitrary-oriented ship detection. RBox-CNN is an end-to-end model based on Faster R-CNN. The region proposal network generates proposals as the rotated bounding box, and then the rotation region-of-interest (RRoI) pooling layer is applied to extract region features corresponding the proposals. In addition, the diagonal region-of-interest (DRoI) pooling layer is applied simultaneously to extract context features and alleviate the problem of misalignment in RRoI pooling layer. To stably predict locations with the angle, we apply the regression of distance's projection in width/height. Experiments on HRSC2016 show that our model achieves state-of-the-art detection accuracy on ship detection. Furthermore, RBox-CNN achieves a significant improvement on DOTA for oriented general object detection in remote sensing images.


title: Rapid building of an ASR system for Under-Resourced Languages based on Multilingual Unsupervised Training

abstract: This paper presents our work on rapid language adaptation of acoustic models based on multilingual cross-language bootstrapping and unsupervised training. We used Automatic Speech Recognition (ASR) systems in the six source languages English, French, German, Spanish, Bulgarian and Polish to build from scratch an ASR system for Vietnamese, an underresourced language. System building was performed without using any transcribed audio data by applying three consecutive steps, i.e. cross-language transfer, unsupervised training based on the “multilingual A-stabil” confidence score [1], and bootstrapping. We investigated the correlation between performance of “multilingual A-stabil” and the number of source languages and improved the performance of “multilingual A-stabil” by applying it at the syllable level. Furthermore, we showed that increasing the amount of source language ASR systems for the multilingual framework results in better performance of the final ASR system in the target language Vietnamese. The final Vietnamese recognition system has a Syllable Error Rate (SyllER) of 16.8% on the development set and 16.1% on the evaluation set. Index Terms: rapid language adaptation of ASR, unsupervised training, multilingual A-Stabil


title: A stochastic all-digital weak physically unclonable function for analog/mixed-signal applications

abstract: Physically Unclonable Functions (PUFs) are a promising security technique which utilize the random process variation in silicon fabrication in order to create unique identifiers and other security features that are impossible to recreate exactly. This paper builds upon and evaluates a weak PUF which employs dynamic latched comparators and their random input offset voltages to create a chip-specific identifier. The proposed PUF can be used in analog/mixed-signal (AMS) chips due to the analog characteristics of the comparators employed. Because comparators are a fundamental block in AMS applications, the proposed PUF can reuse comparators in an AMS chip to generate unique identifiers with minimal hardware overhead. Additionally, the comparators tested in this work can be created with digital components, making this PUF suitable for use in digital chips as well. The proposed PUF is fabricated using a 0.13 μm CMOS process. Measurements show that the PUF achieves a normalized intra-Hamming Distance (HD) of less than 0.15% and 0.96% across 0°C–80°C and 0.8V–1.4 V, respectively. The normalized inter-HD is 48.5% for a 64-bit PUF output key. The power consumption of the PUF is 1.5 nJ/bit with a throughput of 4Mb/s.


title: Design of an UHF UWB doubled annular ring antenna using characteristic mode analysis

abstract: In this paper, characteristic modes of a doubled annular ring are analyzed. Based on properties of significant radiating modes, energy radiated by dipoles close to the ring is coupled to excite the modes. Finally, resonances of multiple radiating modes join together and an ultra-high frequency (UHF) ultra-wide band (UWB) antenna is achieved.


title: Shilling recommender systems for fun and profit

abstract: Recommender systems have emerged in the past several years as an effective way to help people cope with the problem of information overload. One application in which they have become particularly common is in e-commerce, where recommendation of items can often help a customer find what she is interested in and, therefore can help drive sales. Unscrupulous producers in the never-ending quest for market penetration may find it profitable to shill recommender systems by lying to the systems in order to have their products recommended more often than those of their competitors. This paper explores four open questions that may affect the effectiveness of such shilling attacks: which recommender algorithm is being used, whether the application is producing recommendations or predictions, how detectable the attacks are by the operator of the system, and what the properties are of the items being attacked. The questions are explored experimentally on a large data set of movie ratings. Taken together, the results of the paper suggest that new ways must be used to evaluate and detect shilling attacks on recommender systems.


title: SUN2 Modulates HIV-1 Infection and Latency through Association with Lamin A/C To Maintain the Repressive Chromatin

abstract: The postintegrational latency of HIV-1 is characterized by reversible silencing of long terminal repeat (LTR)-driven transcription of the HIV genome. It is known that the formation of repressive chromatin at the 5'-LTR of HIV-1 proviral DNA impedes viral transcription by blocking the recruitment of positive transcription factors. How the repressive chromatin is formed and modulated during HIV-1 infection remains elusive. Elucidation of which chromatin reassembly factor mediates the reorganization of chromatin is likely to facilitate the understanding of the host's modulation of HIV-1 transcription and latency. Here we revealed that "Sad1 and UNC84 domain containing 2" (SUN2), an inner nuclear membrane protein, maintained the repressive chromatin and inhibited HIV LTR-driven transcription of proviral DNA through an association with lamin A/C. Specifically, lamin A/C tethered SUN2 to the nucleosomes 1 and 2 of the HIV-1 5'-LTR to block the initiation and elongation of HIV-1 transcription. SUN2 knockdown converted chromatin to an active form and thus enhanced the phosphorylation of RNA polymerase II and its recruitment to the 5'-LTR HIV-1 proviral DNA, leading to reactivation of HIV-1 from latency. Conversely, the exogenous factors such as tumor necrosis factor alpha (TNF-α) induced reactivation, and the replication of HIV-1 led to the disassociation between SUN2 and lamin A/C, suggesting that disruption of the association between SUN2 and lamin A/C to convert the repressive chromatin to the active form might be a prerequisite for the initiation of HIV-1 transcription and replication. Together, our findings indicate that SUN2 is a novel chromatin reassembly factor that helps to maintain chromatin in a repressive state and consequently inhibits HIV-1 transcription.IMPORTANCE Despite the successful use of scores of antiretroviral drugs, HIV latency poses a major impediment to virus eradication. Elucidation of the mechanism of latency facilitates the discovery of new therapeutic strategies. It has been known that the formation of repressive chromatin at the 5'-LTR of HIV-1 proviral DNA impedes viral transcription and maintains viral latency, but how the repressive chromatin is formed and modulated during HIV-1 infection remains elusive. In this study, we performed in-depth virological and cell biological studies and discovered that an inner nuclear membrane protein, SUN2, is a novel chromatin reassembly factor that maintains repressive chromatin and thus modulates HIV-1 transcription and latency: therefore, targeting SUN2 may lead to new strategies for HIV cure.


title: Scalable game design and the development of a checklist for getting computational thinking into public schools

abstract: Game design appears to be a promising approach to interest K-12 students in Computer Science. Unfortunately, balancing motivational and educational concerns is truly challenging. Over a number of years, we have explored how to achieve a functional balance by creating a curriculum that combines increasingly complex game designs, computational thinking patterns and authoring tools. Scalable Game Design is a research project exploring new strategies of how to scale up from after school and summer programs into required curriculum of public schools through game design approaches. The project includes inner city schools, remote rural areas and Native American communities. A requirement checklist of computational thinking tools regarding curriculum, teacher training, standards and authoring tools has been developed and is being tested with thousands of students.


title: People's Perceptions of Personalized Ads

abstract: Advertising is key to the business model of many online services. Personalization aims to make ads more relevant for users and more effective for advertisers. However, relatively few studies into user attitudes towards personalized ads are available. We present a San Francisco Bay Area survey (N=296) and in-depth interviews (N=24) with teens and adults. People are divided and often either (strongly) agreed or disagreed about utility or invasiveness of personalized ads and associated data collection. Mobile ads were reported to be less relevant than those on desktop. Participants explained ad personalization based on their personal previous behaviors and guesses about demographic targeting. We describe both metrics improvements as well as opportunities for improving online advertising by focusing on positive ad interactions reported by our participants, such as personalization focused not just on product categories but specific brands and styles, awareness of life events, and situations in which ads were useful or even inspirational.


title: Design of three phase interleaved DC/DC boost converter with all SiC semiconductors for electric vehicle applications

abstract: The design of a SiC semiconductor based three phase interleaved boost converter has been presented. The proposed SiC based converter has 27 kW output power and 201.6 V–650 V input-output voltage ratings to be used in Electric Vehicle (EV) applications. The proposed SiC based converter has been compared with the Si IGBT based converter used in Toyota Prius (3rd Generation) Hybrid Electric Vehicle (HEV) in terms of the efficiency, converter size and power density. The validity of the proposed converter has been verified with the computer simulation results. The analytical loss calculations have been made for both converters with the commercial Si and SiC power semiconductors. The passive component sizes has been reduced by %40, and the total converter efficiency of %98.75 has been achieved by using the interleaved operation and SiC devices. The proposed converter thus found to be quite convenient to use in EV applications.


title: Viewpoint-aware object detection and pose estimation

abstract: We describe an approach to category-level detection and viewpoint estimation for rigid 3D objects from single 2D images. In contrast to many existing methods, we directly integrate 3D reasoning with an appearance-based voting architecture. Our method relies on a nonparametric representation of a joint distribution of shape and appearance of the object class. Our voting method employs a novel parametrization of joint detection and viewpoint hypothesis space, allowing efficient accumulation of evidence. We combine this with a re-scoring and refinement mechanism, using an ensemble of view-specific Support Vector Machines. We evaluate the performance of our approach in detection and pose estimation of cars on a number of benchmark datasets.


title: The 3 D Model Acquisition Pipeline

abstract: Three-dimensional (3D) image acquisition systems are rapidly becoming more affordable, especially systems based on commodity electronic cameras. At the same time, personal computers with graphics hardware capable of displaying complex 3D models are also becoming inexpensive enough to be available to a large population. As a result, there is potentially an opportunity to consider new virtual reality applications as diverse as cultural heritage and retail sales that will allow people to view realistic 3D objects on home computers. Although there are many physical techniques for acquiring 3D data—including laser scanners, structured light and time-of-flight—there is a basic pipeline of operations for taking the acquired data and producing a usable numerical model. We look at the fundamental problems of range image registration, line-of-sight errors, mesh integration, surface detail and color, and texture mapping. In the area of registration we consider both the problems of finding an initial global alignment using manual and automatic means, and refining this alignment with variations of the Iterative Closest Point methods. To account for scanner line-of-sight errors we compare several averaging approaches. In the area of mesh integration, that is finding a single mesh joining the data from all scans, we compare various methods for computing interpolating and approximating surfaces. We then look at various ways in which surface properties such as color (more properly, spectral reflectance) can be extracted from acquired imagery. Finally, we examine techniques for producing a final model representation that can be efficiently rendered using graphics hardware.


title: Deliberate Practice and the Acquisition and Maintenance of Expert Performance in Medicine and Related Domains

abstract: The factors that cause large individual differences in professional achievement are only partially understood. Nobody becomes an outstanding professional without experience, but extensive experience does not invariably lead people to become experts. When individuals are first introduced to a professional domain after completing their education, they are often overwhelmed and rely on help from others to accomplish their responsibilities. After months or years of experience, they attain an acceptable level of proficiency and are able to work independently. Although everyone in a given domain tends to improve with experience initially, some develop faster than others and continue to improve during ensuing years. These individuals are eventually recognized as experts and masters. In contrast, most professionals reach a stable, average level of performance within a relatively short time frame and maintain this mediocre status for the rest of their careers. The nature of the individual differences that cause the large variability in attained performance is still debated. The most common explanation is that achievement in a given domain is limited by innate factors that cannot be changed through experience and training; hence, limits of attainable performance are determined by one’s basic endowments, such as abilities, mental capacities, and innate talents. Educators with this widely held view of professional development have focused on identifying and selecting students who possess the necessary innate talents that would allow them to reach expert levels with adequate experience. Therefore, the best schools and professional organizations nearly always rely on extensive testing and interviews to find the most talented applicants. This general view also explains age-related declines in professional achievement in terms of the inevitable reductions in general abilities and capacities believed to result from aging. In this article, I propose an alternative framework to account for individual differences in attained professional development, as well as many aspects of age-related decline. This framework is based on the assumption that acquisition of expert performance requires engagement in deliberate practice and that continued deliberate practice is necessary for maintenance of many types of professional performance. In order to contrast this alternative framework with the traditional view, I first describe the account based on innate talent. I then provide a brief review of the evidence on deliberate practice in the acquisition of expert performance in several performance domains, including music, chess, and sports. Finally, I review evidence from the acquisition and maintenance of expert performance in medicine and examine the role of deliberate practice in this domain.


title: A Maximum Entropy Approach to Chinese Word Segmentation

abstract: We participated in the Second International Chinese Word Segmentation Bakeoff. Specifically, we evaluated our Chinese word segmenter in the open track, on all four corpora, namely Academia Sinica (AS), City University of Hong Kong (CITYU), Microsoft Research (MSR), and Peking University (PKU). Based on a maximum entropy approach, our word segmenter achieved the highest F measure for AS, CITYU, and PKU, and the second highest for MSR. We found that the use of an external dictionary and additional training corpora of different segmentation standards helped to further improve segmentation accuracy. 1 Chinese Word Segmenter The Chinese word segmenter we built is similar to the maximum entropy word segmenter we employed in our previous work (Ng and Low, 2004). Our word segmenter uses a maximum entropy framework (Ratnaparkhi, 1998; Xue and Shen, 2003) and is trained on manually segmented sentences. It classifies each Chinese character given the features derived from its surrounding context. Each Chinese character can be assigned one of four possible boundary tags: s for a character that occurs as a single-character word, b for a character that begins a multi-character (i.e., two or more characters) word, e for a character that ends a multi-character word, and m for a character that is neither the first nor last in a multi-character word. Our implementation used the opennlp maximum entropy package v2.1.0 from sourceforge.1


title: DTMF technology applied to the identification and control of a small mobile robot

abstract: An element to emphasize in student learning is the use of a motivational strategy that it is able to generate the intention to learn. The incorporation of active and collaborative methodologies in engineering classes such as: Problem and Project based Learning (PBL) and Challenge Problem (CP) have allowed that the student should reach a much more significant learning, at the same time that it assumes the responsibility of his own learning. This fact allows to the students to acquire knowledge and to put in practical numerous theoretical bases of an entertaining and fun form. The use of these educational strategies helps in turn to the evaluation of skills, abilities, knowledge and competences acquired during the learning process, being confirmed easily of experimental form. To control electronic devices of a local or remote way always has supposed an example of additional motivation on the students. In this document there appears the use of this type of devices as educational resource. The tone dialing system has been and is widely used by the digital telephony as method of communication between terminals and computers. Thus there have been developed different applications based on a system of dialing DTMF (Dual-Tone Multi-Frequency) tones used in the standard telephone system. So there have been implemented several circuits constituted by the MT8870 as a tones decoder module and \iC PIC16F876 as manager of the control and signal processing. A mobile telephone recycling NOKIA 3310 has been used to transmit the different frequencies to the control circuit and a small autonomous robot is controlled by the DTMF tones generated by the system via a GSM network.


title: Objective Measures of Emotion Related to Brand Attitude: A New Way to Quantify Emotion-Related Aspects Relevant to Marketing

abstract: With this study we wanted to test the hypothesis that individual like and dislike as occurring in relation to brand attitude can be objectively assessed. First, individuals rated common brands with respect to subjective preference. Then, they volunteered in an experiment during which their most liked and disliked brand names were visually presented while three different objective measures were taken. Participant's eye blinks as responses to acoustic startle probes were registered with electromyography (EMG) (i) and their skin conductance (ii) and their heart rate (iii) were recorded. We found significantly reduced eye blink amplitudes related to liked brand names compared to disliked brand names. This finding suggests that visual perception of liked brand names elicits higher degrees of pleasantness, more positive emotion and approach-oriented motivation than visual perception of disliked brand names. Also, skin conductance and heart rate were both reduced in case of liked versus disliked brand names. We conclude that all our physiological measures highlight emotion-related differences depending on the like and dislike toward individual brands. We suggest that objective measures should be used more frequently to quantify emotion-related aspects of brand attitude. In particular, there might be potential interest to introduce startle reflex modulation to measure emotion-related impact during product development, product design and various further fields relevant to marketing. Our findings are discussed in relation to the idea that self reported measures are most often cognitively polluted.


title: Text2Shape: Generating Shapes from Natural Language by Learning Joint Embeddings

abstract: We present a method for generating colored 3D shapes from natural language. To this end, we first learn joint embeddings of freeform text descriptions and colored 3D shapes. Our model combines and extends learning by association and metric learning approaches to learn implicit cross-modal connections, and produces a joint representation that captures the many-to-many relations between language and physical properties of 3D shapes such as color and shape. To evaluate our approach, we collect a large dataset of natural language descriptions for physical 3D objects in the ShapeNet dataset. With this learned joint embedding we demonstrate text-to-shape retrieval that outperforms baseline approaches. Using our embeddings with a novel conditional Wasserstein GAN framework, we generate colored 3D shapes from text. Our method is the first to connect natural language text with realistic 3D objects exhibiting rich variations in color, texture, and shape detail. See video at this https URL