title: Designing a smartphone app to teach English (L2) vocabulary

abstract: The researcher developed a Basic4Android smartphone app (named as Word Learning-CET6) and investigated its effectiveness as a tool in helping English as a Foreign Language college students learn English vocabulary. The app, containing 1274 English words, was designed to be installed into smartphones with Android operating system. To test the program's effectiveness, two groups of students were set up as a test group (those with Word Learning-CET6) and a control group (those without Word Learning-CET6). Knowledge of the vocabulary was tested before and after the study to assess the impact of the program. The study showed that the students using the program significantly outperformed those in the control group in acquiring new vocabulary. At the conclusion of this study, the researcher designed an app and established a pedagogical paradigm which can be followed as a way of mobile learning. The author designed a smartphone app to teach English (L2) vocabulary.Experiment was conducted to evaluate the effectiveness of the app.It was a pioneer research of developing a smartphone app to teach English (L2) vocabulary.


title: Unsupervised Learning of Multi-Frame Optical Flow with Occlusions

abstract: Learning optical flow with neural networks is hampered by the need for obtaining training data with associated ground truth. Unsupervised learning is a promising direction, yet the performance of current unsupervised methods is still limited. In particular, the lack of proper occlusion handling in commonly used data terms constitutes a major source of error. While most optical flow methods process pairs of consecutive frames, more advanced occlusion reasoning can be realized when considering multiple frames. In this paper, we propose a framework for unsupervised learning of optical flow and occlusions over multiple frames. More specifically, we exploit the minimal configuration of three frames to strengthen the photometric loss and explicitly reason about occlusions. We demonstrate that our multi-frame, occlusion-sensitive formulation outperforms existing unsupervised two-frame methods and even produces results on par with some fully supervised methods.


title: Social news, citizen journalism and democracy

abstract: This article aims to contribute to a critical research agenda for investigating the democratic implications of citizen journalism and social news. The article calls for a broad conception of ‘citizen journalism’ which is (1) not an exclusively online phenomenon, (2) not confined to explicitly ‘alternative’ news sources, and (3) includes ‘metajournalism’ as well as the practices of journalism itself. A case is made for seeing democratic implications not simply in the horizontal or ‘peer-to-peer’ public sphere of citizen journalism networks, but also in the possibility of a more ‘reflexive’ culture of news consumption through citizen participation. The article calls for a research agenda that investigates new forms of gatekeeping and agenda-setting power within social news and citizen journalism networks and, drawing on the example of three sites, highlights the importance of both formal and informal status differentials and of the software ‘code’ structuring these new modes of news production.


title: Effects and Moderators of a Short Theory of Mind Intervention for Children with Autism Spectrum Disorder: A Randomized Controlled Trial

abstract: Limited perspective taking or “Theory of Mind” (ToM) abilities are a core deficit of autism, and many interventions are aimed to improve ToM abilities. In this study, we investigated the effectiveness of a ToM treatment for children with autism spectrum disorders (ASD) and, for the first time, the moderating roles of social interaction style (SIS) and disruptive behavior (DB), to determine which children are most likely to respond to this intervention. The trial protocol is registered at www.trialregister.nl, trial number 2327 and published before the data collection was finished (www.trialsjournal.com). Children with autism aged 7–12 years (n = 97) were randomized over a waitlist control or a treatment condition. Outcome measures included ToM and emotion understanding, parent and teacher questionnaires on children's social skills, ToM-related social behavior, and autistic traits. Six-month follow-up parent reported data were collected for the treatment group. The treatment had a positive effect on ToM understanding, parent-reported ToM behavior, and autistic traits, but not on parent or teacher-reported social behavior. Passive SIS was associated with diminished treatment effects on autistic traits, but DB was unrelated to outcomes. The ToM intervention improved conceptual social understanding and ToM-related behavior of children with ASD. However, broader application of learned skills to other domains of functioning was limited. Individual differences with regard to treatment response are discussed. Autism Res 2015, 8: 738–748. © 2015 International Society for Autism Research, Wiley Periodicals, Inc.


title: Design of an under-actuated wrist based on adaptive synergies

abstract: An effective robotic wrist represents a key enabling element in robotic manipulation, especially in prosthetics. In this paper, we propose an under-actuated wrist system, which is also adaptable and allows to implement different under-actuation schemes. Our approach leverages upon the idea of soft synergies — in particular the design method of adaptive synergies — as it derives from the field of robot hand design. First we introduce the design principle and its implementation and function in a configurable test bench prototype, which can be used to demonstrate the feasibility of our idea. Furthermore, we report on results from preliminary experiments with humans, aiming to identify the most probable wrist pose during the pre-grasp phase in activities of daily living. Based on these outcomes, we calibrate our wrist prototype accordingly and demonstrate its effectiveness to accomplish grasping and manipulation tasks.


title: A Path-Tracking Criterion for an LHD Articulated Vehicle

abstract: A path-tracking criterion for the so-called LHD (load-haul-dump) truck used in underground mining is proposed in this paper. It exploits the particular configuration of this vehicle, composed of two units connected by an actuated articulation. The task is to follow the path represented by the middle of the tunnel, maintaining the whole vehicle at a reduced distance from the path itself, to decrease the risk of crashes against the walls of the tunnel. This is accomplished via feedback through the synthesis of an appropriate path-tracking criterion. The criterion is based on monitoring the distances of the midpoints of both axles of the vehicle from their orthogonal projections on the path, using two different moving frames simultaneously. Local asymptotic stability to paths of constant curvature is achieved by means of linear-state feedback.


title: A multiscale measure for mixing

abstract: Abstract We present a multiscale measure for mixing that is based on the concept of weak convergence and averages the “mixedness” of an advected scalar field at various scales. This new measure, referred to as the Mix-Norm, resolves the inability of the L 2 variance of the scalar density field to capture small-scale variations when advected by chaotic maps or flows. In addition, the Mix-Norm succeeds in capturing the efficiency of a mixing protocol in the context of a particular initial scalar field, wherein Lyapunov-exponent based measures fail to do so. We relate the Mix-Norm to the classical ergodic theoretic notion of mixing and present its formulation in terms of the power spectrum of the scalar field. We demonstrate the utility of the Mix-Norm by showing how it measures the efficiency of mixing due to various discrete dynamical systems and to diffusion. In particular, we show that the Mix-Norm can capture known exponential and algebraic mixing properties of certain maps. We also analyze numerically the behaviour of scalar fields evolved by the Standard Map using the Mix-Norm.


title: Using Curiosity and Group-buying Navigation to Explore the Influence of Perceived hedonic Value, Attitude, and Group-buying Behavioral Intention

abstract: This study investigates through perceived hedonic values the influence of two features of group-buying websites, novel curiosity and site navigation, on online consumer purchase intentions and behavior. Using GROUPON Taiwan as a case study, a web-based online questionnaire is used to collect survey data from 290 valid participants. By analyzing this information through the Partial Least Squares program, the results support the hypothesis that curiosity and navigation have significantly positive effects on perceived hedonic values and attitudes. Furthermore, these values likewise have a significant positive effect on online group-buying intentions.


title: Noticing Future Me Reducing Egocentrism Through Mental Imagery

abstract: People drastically overestimate how often others attend to them or notice their unusual features, a phenomenon termed the spotlight effect. Despite the prevalence of this egocentric bias, little is known about how to reduce the tendency to see oneself as the object of others’ attention. Here, we tested the hypothesis that a basic property of mental imagery—the visual perspective from which an event is viewed—may alleviate a future-oriented variant of the spotlight effect. The results of three experiments supported this prediction. Experiment 1 revealed a reduction in egocentric spotlighting when participants imagined an event in the far compared with near future. Experiments 2 and 3 demonstrated reduced spotlighting and feelings of embarrassment when participants viewed an impending event from a third-person (vs. first-person) vantage point. Simple changes in one’s visual perspective may be sufficient to diminish the illusion of personal salience.


title: Wideband Omnidirectional Circularly Polarized Antenna Based on Tilted Dipoles

abstract: A wideband omnidirectional circularly polarized (CP) antenna is developed. The omnidirectional CP antenna includes four tilted dipoles that are wrapped around a cylinder for omnidirectional CP pattern. Each tilted dipole is coupled with a parasitic element for bandwidth enhancement. It is shown that the wideband omnidirectional CP antenna achieves an axial ratio (AR) bandwidth of 44% (1.69–2.64 GHz) for $\text{AR} and an impedance bandwidth of 61% (1.35–2.54 GHz) for ${\text{return loss}} > 10\;\text{dB}$ . The antenna gain is around 1 dBi with an omnidirectionality of less than 1.5 dB.


title: Estimating Attributes: Analysis and Extensions of RELIEF

abstract: In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies among them. Kira and Rendell (1992a,b) developed an algorithm called RELIEF, which was shown to be very efficient in estimating attributes. Original RELIEF can deal with discrete and continuous attributes and is limited to only two-class problems. In this paper RELIEF is analysed and extended to deal with noisy, incomplete, and multi-class data sets. The extensions are verified on various artificial and one well known real-world problem.


title: Visualization in Meteorology—A Survey of Techniques and Tools for Data Analysis Tasks

abstract: This article surveys the history and current state of the art of visualization in meteorology, focusing on visualization techniques and tools used for meteorological data analysis. We examine characteristics of meteorological data and analysis tasks, describe the development of computer graphics methods for visualization in meteorology from the 1960s to today, and visit the state of the art of visualization techniques and tools in operational weather forecasting and atmospheric research. We approach the topic from both the visualization and the meteorological side, showing visualization techniques commonly used in meteorological practice, and surveying recent studies in visualization research aimed at meteorological applications. Our overview covers visualization techniques from the fields of display design, 3D visualization, flow dynamics, feature-based visualization, comparative visualization and data fusion, uncertainty and ensemble visualization, interactive visual analysis, efficient rendering, and scalability and reproducibility. We discuss demands and challenges for visualization research targeting meteorological data analysis, highlighting aspects in demonstration of benefit, interactive visual analysis, seamless visualization, ensemble visualization, 3D visualization, and technical issues.


title: A unified model of knowledge sharing behaviours: theoretical development and empirical test

abstract: Research and practice on knowledge management KM have shown that information technology alone cannot guarantee that employees will volunteer and share knowledge. While previous studies have linked motivational factors to knowledge sharing KS, we took a further step to thoroughly examine this theoretically and empirically. We developed a unified model that is comprehensive and yet parsimonious, based on the decomposed theory of planned behaviour DTPB with three sets of critical antecedents: psychological, organisational and technological that are theorised to influence KS behaviours. Results of a field survey of knowledge workers support the majority of hypothesised relationships, and explained 41.3% of the variance in the actual KS behaviours and 60.8% of the variance in the intention to share knowledge. These results far exceed the predictive powers achieved by previous studies. Among our significant findings include a strong positive influence of perceived enjoyment in helping others PEH and a strong negative influence of perceived loss of knowledge power PLK. Based on the findings, we discussed the study's implications for research and practice.


title: Magnetic and Structural Studies of the Quasi-Two-Dimensional Spin-Gap System (CuCl)LaNb2O7

abstract: We report magnetization, nuclear magnetic resonance (NMR), nuclear quadrupole resonance (NQR), and transmission electron microscopy (TEM) studies on the quasi-two-dimensional spin-gap system (CuCl)LaNb2O7, a possible candidate for the J1-J2 model on a square lattice. A sharp single NQR line is observed at the Cu and Cl sites, indicating that both Cu and Cl atoms occupy a unique site. However, the electric field gradient tensors at the Cu, Cl, and La sites do not have axial symmetry. This is incompatible with the reported crystal structure. Thus the J1-J2 model has to be modified. We propose alternative two-dimensional dimer models based on the NMR, NQR, and TEM results. The value of the hyperfine coupling constant at the Cu sites indicates that the spin density is mainly on the d(3z2-r2) orbital (z parallel c). At 1.5 K, Cu- and Nb-NMR signals disappear above the critical field Bc1 = 10.3 T determined from the onset of the magnetization, indicating a field-induced magnetic phase transition at Bc1.


title: Clusters, language models, and ad hoc information retrieval

abstract: The language-modeling approach to information retrieval provides an effective statistical framework for tackling various problems and often achieves impressive empirical performance. However, most previous work on language models for information retrieval focused on document-specific characteristics, and therefore did not take into account the structure of the surrounding corpus, a potentially rich source of additional information. We propose a novel algorithmic framework in which information provided by document-based language models is enhanced by the incorporation of information drawn from clusters of similar documents. Using this framework, we develop a suite of new algorithms. Even the simplest typically outperforms the standard language-modeling approach in terms of mean average precision (MAP) and recall, and our new interpolation algorithm posts statistically significant performance improvements for both metrics over all six corpora tested. An important aspect of our work is the way we model corpus structure. In contrast to most previous work on cluster-based retrieval that partitions the corpus, we demonstrate the effectiveness of a simple strategy based on a nearest-neighbors approach that produces overlapping clusters.


title: A data-driven approach to mapping cortical and subcortical intrinsic functional connectivity along the longitudinal hippocampal axis.

abstract: The hippocampus (HPC) is functionally heterogeneous along the longitudinal anterior-posterior axis. In rodent models, gene expression maps define at least three discrete longitudinal subregions, which also differ in function, and in anatomical connectivity with the rest of the brain. In humans, equivalent HPC subregions are less well defined, resulting in a lack of consensus in neuroimaging approaches that limits translational study. This study determined whether a data-driven analysis, namely independent component analysis (ICA), could reproducibly define human HPC subregions, and map their respective intrinsic functional connectivity (iFC) with the rest of the brain. Specifically, we performed ICA of resting-state fMRI activity spatially restricted within the HPC, to determine the configuration and reproducibility of functional HPC components. Using dual regression, we then performed multivariate analysis of iFC between resulting HPC components and the whole brain, including detailed connectivity with the hypothalamus, a functionally important connection not yet characterized in human. We found hippocampal ICA resulted in highly reproducible longitudinally discrete components, with greater functional heterogeneity in the anterior HPC, consistent with animal models. Anterior hippocampal components shared iFC with the amygdala, nucleus accumbens, medial prefrontal cortex, posterior cingulate cortex, midline thalamus, and periventricular hypothalamus, whereas posterior hippocampal components shared iFC with the anterior cingulate cortex, retrosplenial cortex, and mammillary bodies. We show that spatially masked hippocampal ICA with dual regression reproducibly identifies functional subregions in the human HPC, and maps their respective brain intrinsic connectivity. Hum Brain Mapp 37:462-476, 2016. © 2015 Wiley Periodicals, Inc.


Remote control : governance lessons for and from small, insular, and remote regions


title: “It wasn't a disaster or anything”: Parents’ experiences of their child's uncertain chromosomal microarray result

abstract: Chromosomal microarray is an increasingly utilized diagnostic test, particularly in the pediatric setting. However, the clinical significance of copy number variants detected by this technology is not always understood, creating uncertainties in interpreting and communicating results. The aim of this study was to explore parents' experiences of an uncertain microarray result for their child. This research utilized a qualitative approach with a phenomenological methodology. Semi-structured interviews were conducted with nine parents of eight children who received an uncertain microarray result for their child, either a 16p11.2 microdeletion or 15q13.3 microdeletion. Interviews were transcribed verbatim and thematic analysis was used to identify themes within the data. Participants were unprepared for the abnormal test result. They had a complex perception of the extent of their child's condition and a mixed understanding of the clinical relevance of the result, but were accepting of the limitations of medical knowledge, and appeared to have adapted to the result. The test result was empowering for parents in terms of access to medical and educational services; however, they articulated significant unmet support needs. Participants expressed hope for the future, in particular that more information would become available over time. This research has demonstrated that parents of children who have an uncertain microarray result appeared to adapt to uncertainty and limited availability of information and valued honesty and empathic ongoing support from health professionals. Genetic health professionals are well positioned to provide such support and aid patients' and families' adaptation to their situation as well as promote empowerment. © 2016 Wiley Periodicals, Inc.


title: Autonomous Leader-Follower Architecture of A.R. Drones in GPS Constrained Environments

abstract: In this paper, we present a low cost leader-follower formation control architecture of UAVs. The low cost architecture comprises of two AR. Drones and two Raspberry Pi. The computation of each drones has been done in cost effective Raspberry Pi. The relative localization among the drones has been done using Aruco Marker. A gradient descent based self-tuning PID controller is used by the follower drone to preserve the formation with respect to the leader drone. Experimental results as well as simulation results have shown in this paper.


title: Handheld Augmented Reality Indoor Navigation with Activity-Based Instructions

abstract: We present a novel design of an augmented reality interface to support indoor navigation. We combine activity-based instructions with sparse 3D localisation at selected info points in the building. Based on localisation accuracy and the users' activities, such as walking or standing still, the interface adapts the visualisation by changing the density and quality of information shown. We refine and validate our design through user involvement in pilot studies. We finally present the results of a comparative study conducted to validate the effectiveness of our design and to explore how the presence of info points affects users' performance on indoor navigation tasks. The results of this study validate our design and show an improvement in task performance when info points are present, which act as confirmation points and provide an overview of the task.


title: "I loan because...": understanding motivations for pro-social lending

abstract: As a new paradigm of online communities, microfinance sites such as Kiva.org have attracted much public attention. To understand lender motivations on Kiva, we classify the lenders' self-stated motivations into ten categories with human coders and machine learning based classifiers. We employ text classifiers using lexical features, along with social features based on lender activity information on Kiva, to predict the categories of lender motivation statements. Although the task appears to be much more challenging than traditional topic-based categorization, our classifiers can achieve high precision in most categories. Using the results of this classification along with Kiva teams information, we predict lending activity from lender motivation and team affiliations. Finally, we make design recommendations regarding Kiva practices which might increase pro-social lending.


title: Neurobiology of knowledge and misperception of lyrics

abstract: We conducted two functional magnetic resonance imaging (fMRI) experiments to investigate the neural underpinnings of knowledge and misperception of lyrics. In fMRI experiment 1, a linear relationship between familiarity with lyrics and activation was found in left-hemispheric speech-related as well as bilateral striatal areas which is in line with previous research on generation of lyrics. In fMRI experiment 2, we employed so called Mondegreens and Soramimi to induce misperceptions of lyrics revealing a bilateral network including middle temporal and inferior frontal areas as well as anterior cingulate cortex (ACC) and mediodorsal thalamus. ACC activation also correlated with the extent to which misperceptions were judged as amusing corroborating previous neuroimaging results on the role of this area in mediating the pleasant experience of chills during music perception. Finally, we examined the areas engaged during misperception of lyrics using diffusion-weighted imaging (DWI) to determine their structural connectivity. These combined fMRI/DWI results could serve as a neurobiological model for future studies on other types of misunderstanding which are events with potentially strong impact on our social life.


Nowcasting the Bitcoin Market with Twitter Signals 1


The issues of machine translation


title: Self-Adaptive Skin Segmentation in Color Images

abstract: In this paper, we present a new method for skin detection and segmentation, relying on spatial analysis of skin-tone pixels. Our contribution lies in introducing self-adaptive seeds, from which the skin probability is propagated using the distance transform. The seeds are determined from a local skin color model that is learned on-line from a presented image, without requiring any additional information. This is in contrast to the existing methods that need a skin sample for the adaptation, e.g., acquired using a face detector. In our experimental study, we obtained F-score of over 0.85 for the ECU benchmark, and this is highly competitive compared with several state-of-the-art methods.


title: A battery equalizer with zero-current switching and zero-voltage gap among cells based on three-resonant-state LC converters

abstract: For the conventional switched capacitor converter (SCC) based equalizers, it is difficult to achieve the full equalization among cells due to the inevitable voltage fall across MOSFET switches. Particularly, when the voltage gap among cells is larger, the balancing efficiency is lower, but the balancing speed gets slower as the voltage gap gets smaller. In order to soften these downsides, this paper proposes a battery equalization topology with zero-current switching (ZCS) and zero-voltage gap (ZVG) among cells based on three-resonant-state SCCs. An additional resonant path is built to release the charge of the capacitor into the inductor in each cycle, which lays the foundations to obtain ZVG among cells, improves the balancing efficiency at a large voltage gap, and increases the balancing speed at a small voltage gap. A four-lithium-ion-cell prototype is applied to validate the theoretical analysis. Experiment results show the proposed topology demonstrates good balancing performance with ZCS and ZVG among cells.


title: Seminal quality prediction using optimized artificial neural network with genetic algorithm

abstract: Infertility problem is an important issue in recent decades. Semen analysis is one of the principle tasks to evaluate male partner fertility potential. It has been seen in many researches that life habits and health status affect semen quality. Data mining as a decision support system can help to recognize this effect. The artificial neural network (ANN) is a powerful data mining tool that can be used for this goal. The performance of ANN depends heavily on network structure. It is a very difficult task to determine the appropriate structure and is a discussable matter. This paper utilizes a genetic algorithm to optimize the structure of artificial neural network to classify the semen samples. These samples usually suffer from unbalancing problem. Thus, this paper attempts to resolve it by using the bootstrap method. The performance of the proposed algorithm is significantly better than the previous works. We achieve accuracy equal to 93.86% in our experiments on a real fertility diagnosis dataset that is a good improvement compared with other classification methods.


title: Accelerated dynamic MRI using structured low rank matrix completion

abstract: We introduce a fast structured low-rank matrix completion algorithm with low memory & computational demand to recover the dynamic MRI data from undersampled measurements. The 3-D dataset is modeled as a piecewise smooth signal, whose discontinuities are localized to the zero sets of a bandlimited function. We show that a structured matrix corresponding to convolution with the Fourier coefficients of the signal derivatives is highly low-rank. This property enables us to recover the signal from undersampled measurements. The application of this scheme in dynamic MRI shows significant improvement over state of the art methods.


title: Improving Interpretability of Deep Neural Networks with Semantic Information

abstract: Interpretability of deep neural networks (DNNs) is essential since it enables users to understand the overall strengths and weaknesses of the models, conveys an understanding of how the models will behave in the future, and how to diagnose and correct potential problems. However, it is challenging to reason about what a DNN actually does due to its opaque or black-box nature. To address this issue, we propose a novel technique to improve the interpretability of DNNs by leveraging the rich semantic information embedded in human descriptions. By concentrating on the video captioning task, we first extract a set of semantically meaningful topics from the human descriptions that cover a wide range of visual concepts, and integrate them into the model with an interpretive loss. We then propose a prediction difference maximization algorithm to interpret the learned features of each neuron. Experimental results demonstrate its effectiveness in video captioning using the interpretable features, which can also be transferred to video action recognition. By clearly understanding the learned features, users can easily revise false predictions via a human-in-the-loop procedure.


title: Self-Disclosure in Social Media: Extending the Functional Approach to Disclosure Motivations and Characteristics on Social Network Sites

abstract: This article introduces the functional model of self-disclosure on social network sites by integrating a functional theory of self-disclosure and research on audience representations as situational cues for activating interpersonal goals. According to this model, people pursue strategic goals and disclose differently depending on social media affordances, and self-disclosure goals mediate between media affordances and disclosure intimacy. The results of the empirical study examining self-disclosure motivations and characteristics in Facebook status updates, wall posts, and private messaging lend support to this model and provide insights into the motivational drivers of self-disclosure on SNSs, helping to reconcile traditional views on self-disclosure and self-disclosing behaviors in new media contexts.


title: Mining data records in Web pages

abstract: A large amount of information on the Web is contained in regularly structured objects, which we call data records. Such data records are important because they often present the essential information of their host pages, e.g., lists of products or services. It is useful to mine such data records in order to extract information from them to provide value-added services. Existing automatic techniques are not satisfactory because of their poor accuracies. In this paper, we propose a more effective technique to perform the task. The technique is based on two observations about data records on the Web and a string matching algorithm. The proposed technique is able to mine both contiguous and non-contiguous data records. Our experimental results show that the proposed technique outperforms existing techniques substantially.


Bootstrap Prediction Intervals in Non-Parametric Regression with Applications to Anomaly Detection


title: Zapata and Gomez Comparative evaluation and combination of audio tempo estimation approaches COMPARATIVE EVALUATION AND COMBINATION OF AUDIO TEMPO ESTIMATION APPROACHES

abstract: The automatic analysis of musical tempo from audio is still an open research task in the Music Information Retrieval (MIR) community. The goal of this paper is to provide an updated comparative evaluation of different methods for audio tempo estimation. We overview, following the same block diagram, 23 documented methods. We then analyze their accuracy, error distribution and statistical differences, and we discuss which strategies can provide better performance for different input material. We then take advantage of their complementarity to improve the results by combining different methods, and we finally analyze the limitations of current approaches and give some ideas for future work on the task.


An Improved Harris-Affine Invariant Interest Point Detector


title: Emotion control in collaborative learning situations: Do students regulate emotions evoked by social challenges?

abstract: Background During recent decades, self-regulated learning (SRL) has become a major research field. SRL successfully integrates the cognitive and motivational components of learning. Self-regulation is usually seen as an individual process, with the social aspects of regulation conceptualized as one aspect of the context. However, recent research has begun to investigate whether self-regulation processes are complemented by socially shared regulation processes. ::: ::: Aims The presented study investigated what kind of socio-emotional challenges students experience during collaborative learning and whether the students regulate the emotions evoked during these situations. The interplay of the emotion regulation processes between the individual and the group was also studied. ::: ::: Sample The sample for this study was 63 teacher education students who studied in groups of three to five during three collaborative learning tasks. ::: ::: Method Students' interpretations of experienced social challenges and their attempts to regulate emotions evoked by these challenges were collected following each task using the Adaptive Instrument for the Regulation of Emotions. ::: ::: Results The results indicated that students experienced a variety of social challenges. Students also reported the use of shared regulation in addition to self-regulation. Finally, the results suggested that intrinsic group dynamics are derived from both individual and social elements of collaborative situations. ::: ::: Conclusion The findings of the study support the assumption that students can regulate emotions collaboratively as well as individually. The study contributes to our understanding of the social aspects of emotional regulation in collaborative learning contexts.


title: An Automatic Graph-Based Approach for Artery/Vein Classification in Retinal Images

abstract: The classification of retinal vessels into artery/vein (A/V) is an important phase for automating the detection of vascular changes, and for the calculation of characteristic signs associated with several systemic diseases such as diabetes, hypertension, and other cardiovascular conditions. This paper presents an automatic approach for A/V classification based on the analysis of a graph extracted from the retinal vasculature. The proposed method classifies the entire vascular tree deciding on the type of each intersection point (graph nodes) and assigning one of two labels to each vessel segment (graph links). Final classification of a vessel segment as A/V is performed through the combination of the graph-based labeling results with a set of intensity features. The results of this proposed method are compared with manual labeling for three public databases. Accuracy values of 88.3%, 87.4%, and 89.8% are obtained for the images of the INSPIRE-AVR, DRIVE, and VICAVR databases, respectively. These results demonstrate that our method outperforms recent approaches for A/V classification.


title: PanNet: A Deep Network Architecture for Pan-Sharpening

abstract: We propose a deep network architecture for the pan-sharpening problem called PanNet. We incorporate domain-specific knowledge to design our PanNet architecture by focusing on the two aims of the pan-sharpening problem: spectral and spatial preservation. For spectral preservation, we add up-sampled multispectral images to the network output, which directly propagates the spectral information to the reconstructed image. To preserve spatial structure, we train our network parameters in the high-pass filtering domain rather than the image domain. We show that the trained network generalizes well to images from different satellites without needing retraining. Experiments show significant improvement over state-of-the-art methods visually and in terms of standard quality metrics.


title: Location-leaking through Network Traffic in Mobile Augmented Reality Applications

abstract: Mobile Augmented Reality (AR) applications allow the user to interact with virtual objects positioned within the real world via a smart phone, tablet or smart glasses. As the popularity of these applications grows, recent researchers have identified several security and privacy issues pertaining to the collection and storage of sensitive data from device sensors. Location-based AR applications typically not only collect user location data, but transmit it to a remote server in order to download nearby virtual content. In this paper we show that the pattern of network traffic generated by this process alone can be used to infer the user's location. We demonstrate a side-channel attack against a widely available Mobile AR application inspired by Website Fingerprinting methods. Through the strategic placement of virtual content and prerecording of the network traffic produced by interacting with this content, we are able to identify the location of a user within the target area with an accuracy of 94%. This finding reveals a previously unexplored vulnerability in the implementation of Mobile AR applications and we offer several recommendations to mitigate this threat.


A Survey on Outlier Detection Techniques for Credit Card Fraud Detection


title: MicroPlay: a networking framework for local multiplayer games

abstract: Smartphones are an ideal platform for local multiplayer games, thanks to their computational and networking capabilities as well as their popularity and portability. However, existing game engines do not exploit the locality of players to improve game latency. In this paper, we propose MicroPlay, a complete networking framework for local multiplayer mobile games. To the best of our knowledge, this is the first framework that exploits local connections between smartphones, and in particular, the broadcast nature of the wireless medium, to provide smooth, accurate rendering of all players with two desired properties. First, it performs direct-input rendering (i.e., without any inter- or extrapolation of game state) for all players; second, it provides very low game latency. We implement a MicroPlay prototype on Android phones, as well as an example multiplayer car racing game, called Racer, in order to demonstrate MicroPlay's capabilities. Our experiments show that cars can be rendered smoothly, without any prediction of state, and with only 20-30 ms game latency.


title: Remixed Reality: Manipulating Space and Time in Augmented Reality

abstract: We present Remixed Reality, a novel form of mixed reality. In contrast to classical mixed reality approaches where users see a direct view or video feed of their environment, with Remixed Reality they see a live 3D reconstruction, gathered from multiple external depth cameras. This approach enables changing the environment as easily as geometry can be changed in virtual reality, while allowing users to view and interact with the actual physical world as they would in augmented reality. We characterize a taxonomy of manipulations that are possible with Remixed Reality: spatial changes such as erasing objects; appearance changes such as changing textures; temporal changes such as pausing time; and viewpoint changes that allow users to see the world from different points without changing their physical location. We contribute a method that uses an underlying voxel grid holding information like visibility and transformations, which is applied to live geometry in real time.


title: Overview of Small Scale Electric Energy Storage Systems suitable for dedicated coupling with Renewable Micro Sources

abstract: Renewable Micro Sources (RENMSs) will strongly contribute to the accelerating electrification trend currently ongoing. Furthermore, the upcoming mass electrification of automotive, with the related pulverized, but numerically important, electricity storage potential, suggests to start considering how to guarantee a stable and sustainable grid power. In this frame, it is interesting to consider the possibility to couple to each RENMS a dedicated Small Scale Electrical Energy Storage System (SS-EESS), so to be able to dispatch out of the RENMS a grid-compliant RENMS-produced power. An overview of SS-EESSs is hence hereby given, under the points of view of their current main technical features and their prospected costs. It is found that mechanical-based systems like Small Scale Compressed Energy Storage and Flywheels are interesting options for RENMS/SS-EESS dedicated coupling, although fast technological progress in the field of SS-EESSs and the emergence of a clear trend towards joining more energy storage principles (like batteries-supercapacitors assemblies) will likely change the landscape of this field in the next years. In this view, further studies over dedicated coupling of SS-EESSs and RENMSs could help to avoid difficulties in dealing with exploding electricity storage problems in the next years.


Complements and Substitutes in Product Recommendations: The Differential Effects on Consumers' Willingness-to-pay


title: Single-Trial Analysis and Classification of ERP Components - a Tutorial

abstract: Abstract Analyzing brain states that correspond to event related potentials (ERPs) on a single trial basis is a hard problem due to the high trial-to-trial variability and the unfavorable ratio between signal (ERP) and noise (artifacts and neural background activity). In this tutorial, we provide a comprehensive framework for decoding ERPs, elaborating on linear concepts, namely spatio-temporal patterns and filters as well as linear ERP classification. However, the bottleneck of these techniques is that they require an accurate covariance matrix estimation in high dimensional sensor spaces which is a highly intricate problem. As a remedy, we propose to use shrinkage estimators and show that appropriate regularization of linear discriminant analysis (LDA) by shrinkage yields excellent results for single-trial ERP classification that are far superior to classical LDA classification. Furthermore, we give practical hints on the interpretation of what classifiers learned from the data and demonstrate in particular that the trade-off between goodness-of-fit and model complexity in regularized LDA relates to a morphing between a difference pattern of ERPs and a spatial filter which cancels non task-related brain activity.


title: Potential biomass energy from all-aged chir pine forest of Kumaun Himalaya

abstract: Abstract Energy stored by trees was assessed at four sites of natural chir pine forest. Total biomass and net production averaged 210·8 t ha−1 and 15·0 t ha−1 year−1, respectively. The total energy stored as biomass and fixed during one year were 3·738 TJ ha−1 and 0·26 TJ ha−1 year−1, respectively. Energy capture efficiency was 0·749 and 0·873% of photosynthetically active radiation respectively, for the above-ground and total tree vegetation. Energy stored in the biomass of the above-ground trees from 2935 ha or in the net annual above-ground production from 40 718 ha is sufficient to operate a 50 MW generating station for one year. Total biomass and net production from 1 ha of the natural chir pine forest are sufficient to meet energy needs (in terms of electrical energy) of an average household of Kumaun Himalaya for 60·0 and 4·3 years, respectively.


title: Attention-Aware Multi-Stroke Style Transfer

abstract: Neural style transfer has drawn considerable attention from both academic and industrial field. Although visual effect and efficiency have been significantly improved, existing methods are unable to coordinate spatial distribution of visual attention between the content image and stylized image, or render diverse level of detail via different brush strokes. In this paper, we tackle these limitations by developing an attention-aware multi-stroke style transfer model. We first propose to assemble self-attention mechanism into a style-agnostic reconstruction autoencoder framework, from which the attention map of a content image can be derived. By performing multi-scale style swap on content features and style features, we produce multiple feature maps reflecting different stroke patterns. A flexible fusion strategy is further presented to incorporate the salient characteristics from the attention map, which allows integrating multiple stroke patterns into different spatial regions of the output image harmoniously. We demonstrate the effectiveness of our method, as well as generate comparable stylized images with multiple stroke patterns against the state-of-the-art methods.


title: Evolutionary hierarchical dirichlet processes for multiple correlated time-varying corpora

abstract: Mining cluster evolution from multiple correlated time-varying text corpora is important in exploratory text analytics. In this paper, we propose an approach called evolutionary hierarchical Dirichlet processes (EvoHDP) to discover interesting cluster evolution patterns from such text data. We formulate the EvoHDP as a series of hierarchical Dirichlet processes~(HDP) by adding time dependencies to the adjacent epochs, and propose a cascaded Gibbs sampling scheme to infer the model. This approach can discover different evolving patterns of clusters, including emergence, disappearance, evolution within a corpus and across different corpora. Experiments over synthetic and real-world multiple correlated time-varying data sets illustrate the effectiveness of EvoHDP on discovering cluster evolution patterns.


title: Highly programmable wavelength selective switch based on liquid crystal on silicon switching elements

abstract: We present a novel wavelength selective switch (WSS) based on a liquid crystal on silicon (LCOS) switching element. The unit operates simultaneously at both 50 and 100 GHz channel spacing and is compatible with 40 G transmission requirements.


title: Palette-based photo recoloring

abstract: Image editing applications offer a wide array of tools for color manipulation. Some of these tools are easy to understand but offer a limited range of expressiveness. Other more powerful tools are time consuming for experts and inscrutable to novices. Researchers have described a variety of more sophisticated methods but these are typically not interactive, which is crucial for creative exploration. This paper introduces a simple, intuitive and interactive tool that allows non-experts to recolor an image by editing a color palette. This system is comprised of several components: a GUI that is easy to learn and understand, an efficient algorithm for creating a color palette from an image, and a novel color transfer algorithm that recolors the image based on a user-modified palette. We evaluate our approach via a user study, showing that it is faster and easier to use than two alternatives, and allows untrained users to achieve results comparable to those of experts using professional software.


title: Using the Hierarchical Pathfinding A* Algorithm in GIS to Find Paths through Rasters with Nonuniform Traversal Cost

abstract: A fair amount of research has been carried out on pathfinding problems in the context of transportation networks, whereas pathfinding in off-network space has received far less interest. In geographic information systems (GIS), the latter is usually associated with the cost surface method, which allows optimum paths to be calculated through rasters in which the value of each cell depicts the cost of traversal through that cell. One of the problems with this method is computational expense, which may be very high with large rasters. In this study, a pathfinding method called Hierarchical Pathfinding A* (HPA*), based on an abstraction strategy, is investigated as an alternative to the traditional approach. The aim of this study is to enhance the method to make it more suitable for calculating paths over cost rasters with nonuniform traversal cost. The method is implemented in GIS and tested with actual data. The results indicate that by taking into account the information embedded in the cost raster, paths of relatively good quality can be calculated while effecting significant savings in computational effort compared to the traditional, nonhierarchical approach.


title: The end is nigh: generic solving of text-based CAPTCHAs

abstract: Over the last decade, it has become well-established that a captcha's ability to withstand automated solving lies in the difficulty of segmenting the image into individual characters. The standard approach to solving captchas automatically has been a sequential process wherein a segmentation algorithm splits the image into segments that contain individual characters, followed by a character recognition step that uses machine learning. While this approach has been effective against particular captcha schemes, its generality is limited by the segmentation step, which is hand-crafted to defeat the distortion at hand. No general algorithm is known for the character collapsing anti-segmentation technique used by most prominent real world captcha schemes. ::: ::: This paper introduces a novel approach to solving captchas in a single step that uses machine learning to attack the segmentation and the recognition problems simultaneously. Performing both operations jointly allows our algorithm to exploit information and context that is not available when they are done sequentially. At the same time, it removes the need for any hand-crafted component, making our approach generalize to new captcha schemes where the previous approach can not. We were able to solve all the real world captcha schemes we evaluated accurately enough to consider the scheme insecure in practice, including Yahoo (5.33%) and ReCaptcha (33.34%), without any adjustments to the algorithm or its parameters. Our success against the Baidu (38.68%) and CNN (51.09%) schemes that use occluding lines as well as character collapsing leads us to believe that our approach is able to defeat occluding lines in an equally general manner. The effectiveness and universality of our results suggests that combining segmentation and recognition is the next evolution of catpcha solving, and that it supersedes the sequential approach used in earlier works. More generally, our approach raises questions about how to develop sufficiently secure captchas in the future.


title: Does the extended technology acceptance model apply to physicians

abstract: In previous studies, the technology acceptance model (TAM) (Davis, 1989) has been widely used by information technology researchers to gain a better understanding of information technology (IT) adoption and its use in organizations. While TAM has been applied and tested in academic and corporate settings, involving students, business managers, clerical and administrative types as subjects, few studies have evaluated TAM in the health care environment. This study examines the applicability of the extended technology acceptance model (TAM2) of Venkatesh and Davis (2000) in the context of physicians' intention to adopt Internet-based health applications. Data were collected in a survey of pediatricians to see how well the extended model, fits in the medical sector. Our results partially confirm the model, however significant theoretical aspects were not supported. One of the core perception variables, perceived ease of use, did not predict intention to use in this study. As theorized, the primary predictor variable perceived usefulness was a strong determinant of intention to use. This paper discusses the implications, limitations, and possible explanations for the inconsistencies within the model when applied to such professional users as physicians.


title: On-demand Learning for Deep Image Restoration

abstract: While machine learning approaches to image restoration offer great promise, current methods risk training models fixated on performing well only for image corruption of a particular level of difficulty—such as a certain level of noise or blur. First, we examine the weakness of conventional “fixated” models and demonstrate that training general models to handle arbitrary levels of corruption is indeed non-trivial. Then, we propose an on-demand learning algorithm for training image restoration models with deep convolutional neural networks. The main idea is to exploit a feedback mechanism to self-generate training instances where they are needed most, thereby learning models that can generalize across difficulty levels. On four restoration tasks—image inpainting, pixel interpolation, image deblurring, and image denoising—and three diverse datasets, our approach consistently outperforms both the status quo training procedure and curriculum learning alternatives.


title: Real time face recognition using adaboost improved fast PCA algorithm

abstract: This paper presents an automated system for human face recognition in a real time background world for a large homemade dataset of persons face. The task is very difficult as the real time background subtraction in an image is still a challenge. Addition to this there is a huge variation in human face image in terms of size, pose and expression. The system proposed collapses most of this variance. To detect real time human face AdaBoost with Haar cascade is used and a simple fast PCA and LDA is used to recognize the faces detected. The matched face is then used to mark attendance in the laboratory, in our case. This biometric system is a real time attendance system based on the human face recognition with a simple and fast algorithms and gaining a high accuracy rate..


title: Recursion aware modeling and discovery for hierarchical software event log analysis

abstract: This paper presents 1) a novel hierarchy and recursion extension to the process tree model; and 2) the first, recursion aware process model discovery technique that leverages hierarchical information in event logs, typically available for software systems. This technique allows us to analyze the operational processes of software systems under real-life conditions at multiple levels of granularity. The work can be positioned in-between reverse engineering and process mining. An implementation of the proposed approach is available as a ProM plugin. Experimental results based on real-life (software) event logs demonstrate the feasibility and usefulness of the approach and show the huge potential to speed up discovery by exploiting the available hierarchy.


title: Assessing Diabetes Self-Management with the Diabetes Self-Management Questionnaire (DSMQ) Can Help Analyse Behavioural Problems Related to Reduced Glycaemic Control

abstract: AIM ::: To appraise the Diabetes Self-Management Questionnaire (DSMQ)'s measurement of diabetes self-management as a statistical predictor of glycaemic control relative to the widely used SDSCA. ::: ::: ::: METHODS ::: 248 patients with type 1 diabetes and 182 patients with type 2 diabetes were cross-sectionally assessed using the two self-report measures of diabetes self-management DSMQ and SDSCA; the scales were used as competing predictors of HbA1c. We developed a structural equation model of self-management as measured by the DSMQ and analysed the amount of variation explained in HbA1c; an analogue model was developed for the SDSCA. ::: ::: ::: RESULTS ::: The structural equation models of self-management and glycaemic control showed very good fit to the data. The DSMQ's measurement of self-management showed associations with HbA1c of -0.53 for type 1 and -0.46 for type 2 diabetes (both P < 0.001), explaining 21% and 28% of variation in glycaemic control, respectively. The SDSCA's measurement showed associations with HbA1c of -0.14 (P = 0.030) for type 1 and -0.31 (P = 0.003) for type 2 diabetes, explaining 2% and 10% of glycaemic variation. Predictive power for glycaemic control was significantly higher for the DSMQ (P < 0.001). ::: ::: ::: CONCLUSIONS ::: This study supports the DSMQ as the preferred tool when analysing self-reported behavioural problems related to reduced glycaemic control. The scale may be useful for clinical assessments of patients with suboptimal diabetes outcomes or research on factors affecting associations between self-management behaviours and glycaemic control.


title: An Approach to QoS-based Task Distribution in Edge Computing Networks for IoT Applications

abstract: Abstract-Internet of Things (IoT) is emerging as part ofthe infrastructures for advancing a large variety of applicationsinvolving connection of many intelligent devices, leadingto smart communities. Due to the severe limitation on thecomputing resources of IoT devices, it is common to offloadtasks of various applications requiring substantial computingresources to computing systems with sufficient computingresources, such as servers, cloud systems, and/or data centersfor processing. However, the offloading method suffers fromthe difficulties of high latency and network congestion in theIoT infrastructures. Recently edge computing has emergedto reduce the negative impacts of these difficulties. Yet, edgecomputing has its drawbacks, such as the limited computingresources of some edge computing devices and the unbalancedload among these devices. In order to effectively explorethe potential of edge computing to support IoT applications,it is necessary to have efficient task management in edgecomputing networks. In this paper, an approach is presented toperiodically distributing incoming tasks in the edge computingnetwork so that the number of tasks, which can be processedin the edge computing network, is increased, and the qualityof-service (QoS) requirements of the tasks completed in theedge computing network are satisfied. Simulation results arepresented to show the improvement of using this approach onthe increase of the number of tasks to be completed in the edgecomputing network.


title: Stealing Hyperparameters in Machine Learning

abstract: Hyperparameters are critical in machine learning, as different hyperparameters often result in models with significantly different performance. Hyperparameters may be deemed confidential because of their commercial value and the confidentiality of the proprietary algorithms that the learner uses to learn them. In this work, we propose attacks on stealing the hyperparameters that are learned by a learner. We call our attacks hyperparameter stealing attacks. Our attacks are applicable to a variety of popular machine learning algorithms such as ridge regression, logistic regression, support vector machine, and neural network. We evaluate the effectiveness of our attacks both theoretically and empirically. For instance, we evaluate our attacks on Amazon Machine Learning. Our results demonstrate that our attacks can accurately steal hyperparameters. We also study countermeasures. Our results highlight the need for new defenses against our hyperparameter stealing attacks for certain machine learning algorithms.


title: Synthetic biology routes to bio-artificial intelligence

abstract: T.L. and O.K. acknowledge Russian Science Foundation (Project No. 14-12-00811). A.Z. acknowledges the Russian Science Foundation grant 16-12-00077.


title: Synthesis of 5-(2-,3- and 4-methoxyphenyl)-4H-1,2,4-triazole-3-thiol derivatives exhibiting anti-inflammatory activity.

abstract: Abstract New S -alkylated 5-(2-,3- and 4-methoxyphenyl)-4 H -1,2,4-triazole-3-thiols ( 5a–c, 6a–c ) and 5-(2-,3- and 4-methoxyphenyl)-4-phenyl-4 H -1,2,4-triazole-3-thiols ( 7a–c, 8a–c, 9a–c ) were synthesized by the alkylation of 3-(2-,3- and 4-methoxyphenyl)-4,5-dihydro-1 H -1,2,4-triazole-5-thiones ( 3a–c ) or 3-(2-,3- and 4-methoxyphenyl)-4-phenyl-4,5-dihydro-1 H -1,2,4-triazole-5-thiones ( 4a–c ) with 1-iodobutane or 1-(1,3-benzodioxol-5-yl)-2-bromo-1-ethanone, 2-bromo-1-(2,3-dihydro-1,4-benzodioxin-6-yl)-1-ethanone and 2-bromo-1-(3,4-dihydro-2 H -1,5-benzodioxepin-7-yl)-1-ethanone. Compounds 3a–c and 4a–c were synthesized by the acylation of thiosemicarbazide or 4-phenyl-3-thiosemicarbazide with 2-, 3- and 4-methoxybenzoyl chlorides and further cyclization of the obtained acylderivatives 1a–c and 2a–c . The synthesized compounds 4a–c, 5a, 6a–c, 7a–c, 8a–c, 9b,c exhibit anti-inflammatory activity.


title: A Cognitive Profile of Individuals Who Tend to Worship Celebrities

abstract: Abstract Celebrity worship is a form of parasocial interaction in which individuals become obsessed with 1 or more celebrities, similar to an erotomanic type of delusional disorder. Drawing on the cognitive factors implicated in erotomania, the authors hypothesized that celebrity worshippers might be expected to exhibit verbal, visuospatial, and cognitive deficits related to flexibility and associative learning. This general hypothesis was tested in a sample of 102 participants who completed the Celebrity Attitude Scale (L. E. McCutcheon, R. Lange, & J. Houran, 2002), the Entertainment-Social, Intense-Personal, and Borderline Pathological subscales, and 6 cognitive measures that included creativity (verbal), crystallized intelligence, critical thinking, spatial ability, and need for cognition. The results were consistent with predictions and suggest that cognitive deficits only help facilitate an individual's susceptibility to engage in celebrity worship. The results are discussed in terms of the multivar...


title: Deadline-guaranteed scheduling algorithm with improved resource utilization for cloud computing

abstract: Cloud computing is computing in which large groups of remote servers are networked to allow centralized data storage and on-line access to computer services. Cloud computing providers install and operate application software in the cloud through Software as a Service (SaaS) model. Cloud users access the software and utilizes the services by cloning tasks onto multiple virtual machines (VMs). In recent years, offering high-performance computing capacity reminds cloud providers to utilize resources fully due to limitation of resource. Enhancing resource utilization is also essential for achieving cost effectiveness. In addition, SaaS applications support various services such as email, FTP, and multimedia services with different delay requirement. Among these application multimedia service has relatively low delay requirements. Therefore, in this paper, we propose a scheduling algorithm to enhance both deadline guarantee and resource utilization. We modified the conservative backfilling algorithm by utilizing the earliest deadline first (EDF) algorithm and the largest weight first (LWF) algorithm. The proposed algorithm first score all the jobs arrived at the data center(DC) and sort the jobs in ascending order to serve high priority job first. The proposed algorithm then select the largest possible backfill job as guaranteeing deadline. Simulation results show that the proposed algorithm significantly improve the performance in terms of resource utilization and deadline guarantee.


title: An efficient pattern mining approach for event detection in multivariate temporal data

abstract: This work proposes a pattern mining approach to learn event detection models from complex multivariate temporal data, such as electronic health records. We present recent temporal pattern mining, a novel approach for efficiently finding predictive patterns for event detection problems. This approach first converts the time series data into time-interval sequences of temporal abstractions. It then constructs more complex time-interval patterns backward in time using temporal operators. We also present the minimal predictive recent temporal patterns framework for selecting a small set of predictive and non-spurious patterns. We apply our methods for predicting adverse medical events in real-world clinical data. The results demonstrate the benefits of our methods in learning accurate event detection models, which is a key step for developing intelligent patient monitoring and decision support systems.


title: A Novel Coarse-to-Fine Scheme for Automatic Image Registration Based on SIFT and Mutual Information

abstract: Automatic image registration is a vital yet challenging task, particularly for remote sensing images. A fully automatic registration approach which is accurate, robust, and fast is required. For this purpose, a novel coarse-to-fine scheme for automatic image registration is proposed in this paper. This scheme consists of a preregistration process (coarse registration) and a fine-tuning process (fine registration). To begin with, the preregistration process is implemented by the scale-invariant feature transform approach equipped with a reliable outlier removal procedure. The coarse results provide a near-optimal initial solution for the optimizer in the fine-tuning process. Next, the fine-tuning process is implemented by the maximization of mutual information using a modified Marquardt-Levenberg search strategy in a multiresolution framework. The proposed algorithm is tested on various remote sensing optical and synthetic aperture radar images taken at different situations (multispectral, multisensor, and multitemporal) with the affine transformation model. The experimental results demonstrate the accuracy, robustness, and efficiency of the proposed algorithm.


title: Increasing Student Self-Efficacy in Computational Thinking via STEM Outreach Programs

abstract: This paper describes our experiences developing and teaching two different interventions focused on computational thinking and computer science at a yearly STEM outreach program hosted by a local school district. We describe the creation of our lesson plans, how we worked with experienced and pre-service teachers alike to deliver the lessons, and how we assessed the effectiveness of each intervention. We will discuss our successes and failures, and provide information on our future plans to incorporate more formalized education theory, pedagogy, and research methodology in future years to further this project. Based on our assessment results, we observed statistically significant gains in student self-efficacy with creating computer programs that perform a variety of operations. In addition, students reported a significantly higher understanding of how computer programming can be used in daily life. Our survey also highlighted differences in student self-efficacy between the two interventions, and we discuss possible sources for that result. We discuss observed results based on student groups with various backgrounds, previous STEM experiences, and socioeconomic status.


title: Evolutionary Algorithms for Reinforcement Learning

abstract: There are two distinct approaches to solving reinforcement learning problems, namely, searching in value function space and searching in policy space. Temporal difference methods and evolutionary algorithms are well-known examples of these approaches. Kaelbling, Littman and Moore recently provided an informative survey of temporal difference methods. This article focuses on the application of evolutionary algorithms to the reinforcement learning problem, emphasizing alternative policy representations, credit assignment methods, and problem-specific genetic operators. Strengths and weaknesses of the evolutionary approach to reinforcement learning are presented, along with a survey of representative applications.


title: Acceptance of e-commerce services: the case of electronic brokerages

abstract: This paper examines human motivations underlying individual acceptance of business-to-consumer (B2C) electronic commerce services. Such acceptance is the key to the survival of firms in this intensely competitive industry. A modified theory of planned behavior (TPB) is used to hypothesize a model of e-commerce service acceptance, which is then tested using a field survey of 172 e-brokerage users. We found TPB was useful in explaining e-commerce service acceptance, however, acceptance motivations were significantly different from that of typical IS products. Based on a broader conceptualization of TPB's subjective norm to include both external (mass-media) and interpersonal influences, we report that subjective norm is an important predictor of e-commerce acceptance, behavioral control has minimal impact on e-commerce acceptance, and external influence is a significant determinant of subjective norm. Implications of these findings in light of e-commerce research and practice are discussed.


title: Constructing consumer profiles from social media data

abstract: Social media is playing a growing role in providing consumer feedback to companies about their products and services. To maximize the benefit of this feedback, companies want to know how different consumer-segments they are interested in, such as parents, frequent travelers, and comic book fans react to their products and campaigns. In this paper, we describe how constructing consumer profiles is valuable to obtain such insights. We present the challenges in analyzing noisy social media data and the techniques we employ for building the profiles. We also present detailed experimental results from the analysis of over seven billion messages to construct profiles of over 100 million consumers. We demonstrate how consumer profiles can help in understanding consumer feedback by different key segments using a TV show analysis scenario.


title: Mitigating Slow Dynamics of Low-Cost Chemical Sensors for Mobile Air Quality Monitoring Sensor Networks

abstract: The last decade has seen a growing interest in air quality monitoring using networks of wireless low-cost sensor platforms. One of the unifying characteristics of chemical sensors typically used in real-world deployments is their slow response time. While the impact of sensor dynamics can largely be neglected when considering static scenarios, in mobile applications chemical sensor measurements should not be considered as point measurements (i.e. instantaneous in space and time). In this paper, we study the impact of sensor dynamics on measurement accuracy and locality through systematic experiments in the controlled environment of a wind tunnel. We then propose two methods for dealing with this problem: (i) reducing the effect of the sensor's slow dynamics by using an open active sampler, and (ii) estimating the underlying true signal using a sensor model and a deconvolution technique. We consider two performance metrics for evaluation: localization accuracy of specific field features and root mean squared error in field estimation. Finally, we show that the deconvolution technique results in consistent performance improvement for all the considered scenarios, and for both metrics, while the active sniffer design considered provides an advantage only for feature localization, particularly for the highest sensor movement speed.


title: Manifestation of Depression and Loneliness on Social Networks: A Case Study of Young Adults on Facebook

abstract: As people around the world are spending increasing amounts of time online, the question of how online experiences are linked to health and well-being is essential. This paper presents how activities on Facebook are associated with the depressive states of users. Based on online logs of 212 young adults, we show not only the sheer size of the network but also the frequency and diversity of interactions on social networks have close associations with depression. Depressed individuals reported smaller involved networks regarding comments and likes, the two popular forms of interactions. In contrast to the decreased level of interactions, depressed individuals showed an increase in the wall post rates and were active online during midday, which can be interpreted as an endemic behavior linked to the perceived degree of loneliness among young adults who are avid users of social media. We discuss these findings from theoretical, empirical, and subjective perspectives.


title: Dual-Frequency Circularly-Polarized Patch Antenna With Compact Size and Small Frequency Ratio

abstract: A novel probe-fed single-layer annular-ring patch antenna for dual-frequency circular polarization is evaluated numerically and experimentally verified. The proposed antenna consists of a small circular patch surrounded by two concentric annular-rings, which is loaded by an unequal lateral cross-slot ground plane. The circularly-polarized frequency ratio of the two resonant modes is tunable to a small value, suitable for wireless communications systems. The patch size of the proposed antenna is reduced by about 53% compared to the conventional circular patch at a given frequency. The radiation characteristics of the novel antenna are also presented.


title: Biomimetic inspiration for PKM torso design in humanoid robots

abstract: A new torso design of humanoid robot is proposed as a 3-DOF PKM (Parallel Kinematic Manipulator) structure by using results of a biomimetic analysis of torso in humans and animals. Main characteristics of torso in nature are presented from which design requirements are outlined for a solution in humanoid robot. The PKM torso is consist of two platforms and four UPU mechanisms. The usefulness of such a biomimetic inspiration is proved by a numerical simulation of the proposed PKM torso in its basic movements.


title: Outerjoin simplification and reordering for query optimization

abstract: Conventional database optimizers take full advantage of associativity and commutativity properties of join to implement e cient and powerful optimizations on select/project/join queries. However, only limited optimization is performed on other binary operators. In this paper, we present the theory and algorithms needed to generate alternative evaluation orders for the optimization of queries containing outerjoins. Our results include both a complete set of transformation rules, suitable for new-generation, transformation-based optimizers, and a bottom-up join enumeration algorithm compatible with those used by traditional optimizers.


title: A MAC Protocol for Opportunistic Spectrum Access in Cognitive Radio Networks

abstract: We present a MAC protocol for opportunistic spectrum access (OSA-MAC) in cognitive wireless networks. The proposed MAC protocol works in a multi-channel environment which is capable of performing channel sensing to discover spectrum opportunities. For this MAC protocol, two channel selection methods are considered which trade the implementation complexity with throughput improvement. We then analyze the saturation throughput performance of the proposed MAC protocol under scenarios where the probabilities for each channel to be available to different secondary flows are the same or different. We then derive analytically the probability of collision of secondary users with primary users due to sensing errors. This analysis can be used, for example, to determine the requirement of sensing accuracy for secondary users, and to design an admission control method. We present numerical results to demonstrate the throughput performance of the OSA-MAC protocol and applications of the proposed analytical model.


title: Large-scale deep unsupervised learning using graphics processors

abstract: The promise of unsupervised learning methods lies in their potential to use vast amounts of unlabeled data to learn complex, highly nonlinear models with millions of free parameters. We consider two well-known unsupervised learning models, deep belief networks (DBNs) and sparse coding, that have recently been applied to a flurry of machine learning applications (Hinton & Salakhutdinov, 2006; Raina et al., 2007). Unfortunately, current learning algorithms for both models are too slow for large-scale applications, forcing researchers to focus on smaller-scale models, or to use fewer training examples. In this paper, we suggest massively parallel methods to help resolve these problems. We argue that modern graphics processors far surpass the computational capabilities of multicore CPUs, and have the potential to revolutionize the applicability of deep unsupervised learning methods. We develop general principles for massively parallelizing unsupervised learning tasks using graphics processors. We show that these principles can be applied to successfully scaling up learning algorithms for both DBNs and sparse coding. Our implementation of DBN learning is up to 70 times faster than a dual-core CPU implementation for large models. For example, we are able to reduce the time required to learn a four-layer DBN with 100 million free parameters from several weeks to around a single day. For sparse coding, we develop a simple, inherently parallel algorithm, that leads to a 5 to 15-fold speedup over previous methods.


title: Multi-dimensional role of trust in Internet banking adoption

abstract: Trust has been identified as the key to e-commerce because it is crucial wherever uncertainty and interdependence exist. The strong association between a high level of trust and the banking sector has not yet been fully translated in the electronic world. The aim of this article is to develop and validate a multi-dimensional model of trust for Internet banking. The data are collected through 441 Internet banking users of Halifax Bank of Scotland. Findings suggest that trust and perceived risk are direct antecedents of intention, and trust is a multi-dimensional construct with three antecedents: perceived trustworthiness, perceived security, and perceived privacy.


title: The CMU METAL Farsi NLP Approach

abstract: While many high-quality tools are available for analyzing major languages such as English, equivalent freely-available tools for important but lower-resourced languages such as Farsi are more difficult to acquire and integrate into a useful NLP front end. We report here on an accurate and efficient Farsi analysis front end that we have assembled, which may be useful to others who wish to work with written Farsi. The pre-existing components and resources that we incorporated include the Carnegie Mellon TurboParser and TurboTagger (Martins et al., 2010) trained on the Dadegan Treebank (Rasooli et al., 2013), the Uppsala Farsi text normalizer PrePer (Seraji, 2013), the Uppsala Farsi tokenizer (Seraji et al., 2012a), and Jon Dehdari’s PerStem (Jadidinejad et al., 2010). This set of tools (combined with additional normalization and tokenization modules that we have developed and made available) achieves a dependency parsing labeled attachment score of 89.49%, unlabeled attachment score of 92.19%, and label accuracy score of 91.38% on a held-out parsing test data set. All of the components and resources used are freely available. In addition to describing the components and resources, we also explain the rationale for our choices.


title: Is Faster R-CNN Doing Well for Pedestrian Detection?

abstract: Detecting pedestrian has been arguably addressed as a special topic beyond general object detection. Although recent deep learning object detectors such as Fast/Faster R-CNN have shown excellent performance for general object detection, they have limited success for detecting pedestrian, and previous leading pedestrian detectors were in general hybrid methods combining hand-crafted and deep convolutional features. In this paper, we investigate issues involving Faster R-CNN for pedestrian detection. We discover that the Region Proposal Network (RPN) in Faster R-CNN indeed performs well as a stand-alone pedestrian detector, but surprisingly, the downstream classifier degrades the results. We argue that two reasons account for the unsatisfactory accuracy: (i) insufficient resolution of feature maps for handling small instances, and (ii) lack of any bootstrapping strategy for mining hard negative examples. Driven by these observations, we propose a very simple but effective baseline for pedestrian detection, using an RPN followed by boosted forests on shared, high-resolution convolutional feature maps. We comprehensively evaluate this method on several benchmarks (Caltech, INRIA, ETH, and KITTI), presenting competitive accuracy and good speed. Code will be made publicly available.


title: Technical section A new algorithm for Boolean operations on general polygons

abstract: A new algorithm for Boolean operations on general planar polygons is presented. It is available for general planar polygons (manifold or non-manifold, with or without holes). Edges of the two general polygons are subdivided at the intersection points and touching points. Thus, the boundary of the Boolean operation resultant polygon is made of some whole edges of the polygons after the subdivision process. We use the simplex theory to build the basic mathematical model of the new algorithm. The subordination problem between an edge and a polygon is reduced to a problem of determining whether a point is on some edges of some simplices or inside the simplices, and the associated simplicial chain of the resultant polygon is just an assembly of some simplices and their coefficients of the two polygons after the subdivision process. Examples show that the running time required by the new algorithm is less than one-third of that by the Rivero and Feito algorithm.


title: On-chip cooling by superlattice-based thin-film thermoelectrics

abstract: There is a significant need for site-specific and on-demand cooling in electronic, optoelectronic and bioanalytical devices, where cooling is currently achieved by the use of bulky and/or over-designed system-level solutions. Thermoelectric devices can address these limitations while also enabling energy-efficient solutions, and significant progress has been made in the development of nanostructured thermoelectric materials with enhanced figures-of-merit. However, fully functional practical thermoelectric coolers have not been made from these nanomaterials due to the enormous difficulties in integrating nanoscale materials into microscale devices and packaged macroscale systems. Here, we show the integration of thermoelectric coolers fabricated from nanostructured Bi2Te3-based thin-film superlattices into state-of-the-art electronic packages. We report cooling of as much as 15 degrees C at the targeted region on a silicon chip with a high ( approximately 1,300 W cm-2) heat flux. This is the first demonstration of viable chip-scale refrigeration technology and has the potential to enable a wide range of currently thermally limited applications.


Analytics-Aware Graph Database Modeling


title: Logician: A Unified End-to-End Neural Approach for Open-Domain Information Extraction

abstract: In this paper, we consider the problem of open information extraction (OIE) for extracting entity and relation level intermediate structures from sentences in open-domain. We focus on four types of valuable intermediate structures (Relation, Attribute, Description, and Concept), and propose a unified knowledge expression form, SAOKE, to express them. We publicly release a data set which contains more than forty thousand sentences and the corresponding facts in the SAOKE format labeled by crowd-sourcing. To our knowledge, this is the largest publicly available human labeled data set for open information extraction tasks. Using this labeled SAOKE data set, we train an end-to-end neural model using the sequenceto-sequence paradigm, called Logician, to transform sentences into facts. For each sentence, different to existing algorithms which generally focus on extracting each single fact without concerning other possible facts, Logician performs a global optimization over all possible involved facts, in which facts not only compete with each other to attract the attention of words, but also cooperate to share words. An experimental study on various types of open domain relation extraction tasks reveals the consistent superiority of Logician to other states-of-the-art algorithms. The experiments verify the reasonableness of SAOKE format, the valuableness of SAOKE data set, the effectiveness of the proposed Logician model, and the feasibility of the methodology to apply end-to-end learning paradigm on supervised data sets for the challenging tasks of open information extraction.


title: A runtime estimation framework for ALICE

abstract: Abstract The European Organization for Nuclear Research (CERN) is the largest research organization for particle physics. ALICE, short for ALarge Ion Collider Experiment, serves as one of the main detectors at CERN and produces approximately 15 petabytes of data each year. The computing associated with an ALICE experiment consists of both online and offline processing. An online cluster retrieves data while an offline cluster farm performs a broad range of data analysis. Online processing occurs as collision events are streamed from the detector to the online cluster. This process compresses and calibrates the data before storing it in a data storage system for subsequent offline processing, e.g., event reconstruction. Due to the large volume of stored data to process, offline processing seeks to minimize execution time and data-staging time of the applications via a two-tier offline cluster — the Event Processing Node (EPN) as the first tier and the World LHC Grid Computing (WLGC) as the second tier. This two-tier cluster requires a smart job scheduler to efficiently manage the running of the application. Thus, we propose a runtime estimation method for this offline processing in the ALICE environment. Our approach exploits application profiles to predict the runtime of a high-performance computing (HPC) application without the need for any additional metadata. To evaluate our proposed framework, we performed our experiment on the actual ALICE applications. In addition, we also test the efficacy of our runtime estimation method to predict the run times of the HPC applications on the Amazon EC2 cloud. The results show that our approach generally delivers accurate predictions, i.e., low error percentages.


title: Precision Evaluation of Search Engines

abstract: In this paper, we present a general approach for statistically evaluating precision of search engines on the Web. Search engines are evaluated in two steps based on a large number of sample queries: (a) computing relevance scores of hits from each search engine, and (b) ranking the search engines based on statistical comparison of the relevance scores. In computing relevance scores of hits, we study four relevance scoring algorithms. Three of them are variations of algorithms widely used in the traditional information retrieval field. They are cover density ranking, Okapi similarity measurement, and vector space model algorithms. In addition, we develop a new three-level scoring algorithm to mimic commonly used manual approaches. In ranking the search engines in terms of precision, we apply a statistical metric called probability of win. In our experiments, six popular search engines, AltaVista, Fast, Google, Go, iWon, and NorthernLight, were evaluated based on queries from two domains of interest: parallel and distributed processing, and knowledge and data engineering. The first query set contains 1726 queries collected from the index terms of papers published in the IEEE Transactions on Knowledge and Data Engineering. The second set contains 1383 queries collected from the index terms of papers published in the IEEE Transactions on Parallel and Distributed Systems. Search engines were queried and compared in two different search modes: the default search mode and the exact phrase search mode. Our experimental results show that these six search engines performed differently under different search modes and scoring methods. Overall, Google was the best. NorthernLight was mostly second in the default search mode, whereas iWon was mostly second in the exact phrase search mode.


title: Joint Shape and Texture Based X-Ray Cargo Image Classification

abstract: Security & Inspection X-Ray Systems is widely used by custom to accomplish some security missions by inspecting import-export cargo. Due to the specificity of cargo X-Ray image, such as overlap, viewpoint dependence, and variants of cargo categories, it couldn't be understood easily like natural ones by human. Even for experienced screeners, it's very difficult to judge cargo category and contraband. In this paper, cargo X-Ray image is described by joint shape and texture feature, which could reflect both cargo stacking mode and interior details. Classification performance is compared with the benchmark method by top hit 1, 3, 5 ratio, and it's demonstrated that good performance is achieved here. In addition, we also discuss X-Ray image property and explore some reasons why cargo classification under X-Ray is very difficult.


title: Assessing the Adoption of e-Government Services by Teachers in Greece

abstract: Technological developments and governments’ understanding of what citizens need usually determine the design of public online services. For successful implementation of e-Government services, governments have to place the user in the center of future developments, understand what citizens need and measure what increases citizens’ willingness to adopt e-government services. The paper uses the Technology Acceptance Model (TAM), the extended TAM, the Diffusion of Innovations (DOI) theory and the important determinants of user acceptance perceived risk and trust, in order to describe teachers’ behavioral intensions to adopt e-Government services. A model containing trust and risk, along with cognitive, social and intrinsic factors is used to study the intentions of e-Government use by Greek primary and secondary education teachers. Two hundred and thirty teachers responded to an online survey. Findings reveal that cognitive and intrinsic factors have significant effects on intentions to use e-Government websites.


title: NoSQL evaluation: A use case oriented survey

abstract: Motivated by requirements of Web 2.0 applications, a plethora of non-relational databases raised in recent years. Since it is very difficult to choose a suitable database for a specific use case, this paper evaluates the underlying techniques of NoSQL databases considering their applicability for certain requirements. These systems are compared by their data models, query possibilities, concurrency controls, partitioning and replication opportunities.


title: A fuzzy-logic controlled PV powered buck-boost DC-DC converter for Battery-Load system

abstract: In this paper, a fuzzy logic controlled buck-boost DC-DC converter powered by Photo Voltaic (PV) for Battery-Load system is presented. General design of the converter-load system is performed using Matlab/Simulink. In the FLC, different fuzzy membership functions are used and the results are compared with each other. The complete converter-load system has been developed, analyzed, and validated by the simulation. Performances have then been evaluated in detail for different operation conditions. The effects of the different fuzzy membership functions and the PV module parameters on the controller are also compared.


title: Project-Out Cascaded Regression with an application to face alignment

abstract: Cascaded regression approaches have been recently shown to achieve state-of-the-art performance for many computer vision tasks. Beyond its connection to boosting, cascaded regression has been interpreted as a learning-based approach to iterative optimization methods like the Newton's method. However, in prior work, the connection to optimization theory is limited only in learning a mapping from image features to problem parameters. In this paper, we consider the problem of facial deformable model fitting using cascaded regression and make the following contributions: (a) We propose regression to learn a sequence of averaged Jacobian and Hessian matrices from data, and from them descent directions in a fashion inspired by Gauss-Newton optimization. (b) We show that the optimization problem in hand has structure and devise a learning strategy for a cascaded regression approach that takes the problem structure into account. By doing so, the proposed method learns and employs a sequence of averaged Jacobians and descent directions in a subspace orthogonal to the facial appearance variation; hence, we call it Project-Out Cascaded Regression (PO-CR). (c) Based on the principles of PO-CR, we built a face alignment system that produces remarkably accurate results on the challenging iBUG data set outperforming previously proposed systems by a large margin. Code for our system is available from http://www.cs.nott.ac.uk/∼yzt/.


title: Return-oriented programming without returns

abstract: We show that on both the x86 and ARM architectures it is possible to mount return-oriented programming attacks without using return instructions. Our attacks instead make use of certain instruction sequences that behave like a return, which occur with sufficient frequency in large libraries on (x86) Linux and (ARM) Android to allow creation of Turing-complete gadget sets. Because they do not make use of return instructions, our new attacks have negative implications for several recently proposed classes of defense against return-oriented programming: those that detect the too-frequent use of returns in the instruction stream; those that detect violations of the last-in, first-out invariant normally maintained for the return-address stack; and those that modify compilers to produce code that avoids the return instruction.


title: Network traffic characteristics of data centers in the wild

abstract: Although there is tremendous interest in designing improved networks for data centers, very little is known about the network-level traffic characteristics of data centers today. In this paper, we conduct an empirical study of the network traffic in 10 data centers belonging to three different categories, including university, enterprise campus, and cloud data centers. Our definition of cloud data centers includes not only data centers employed by large online service providers offering Internet-facing applications but also data centers used to host data-intensive (MapReduce style) applications). We collect and analyze SNMP statistics, topology and packet-level traces. We examine the range of applications deployed in these data centers and their placement, the flow-level and packet-level transmission properties of these applications, and their impact on network and link utilizations, congestion and packet drops. We describe the implications of the observed traffic patterns for data center internal traffic engineering as well as for recently proposed architectures for data center networks.


title: PERSONALITY PROCESSES AND INDIVIDUAL DIFFERENCES -- On the Relative Independence of Thinking Biases and Cognitive Ability

abstract: In 7 different studies, the authors observed that a large number of thinking biases are uncorrelated with cognitive ability. These thinking biases include some of the most classic and well-studied biases in the heuristics and biases literature, including the conjunction effect, framing effects, anchoring effects, outcome bias, base-rate neglect, “less is more” effects, affect biases, omission bias, myside bias, sunk-cost effect, and certainty effects that violate the axioms of expected utility theory. In a further experiment, the authors nonetheless showed that cognitive ability does correlate with the tendency to avoid some rational thinking biases, specifically the tendency to display denominator neglect, probability matching rather than maximizing, belief bias, and matching bias on the 4-card selection task. The authors present a framework for predicting when cognitive ability will and will not correlate with a rational thinking tendency.


title: Otsu Method and K-means

abstract: Otsu method is one of the most successful methods for image thresholding. This paper proves that the objective function of Otsu method is equivalent to that of K-means method in multilevel thresholding . They are both based on a same criterion that minimizes the within-class variance. However, Otsu method is an exhaustive algorithm of searching the global optimal threshold, while K-means is a local optimal method. Moreover, K-means does not require computing a gray-level histogram before running, but Otsu method needs to compute a gray-level histogram firstly. Therefore, K-means can be more efficiently extended to multilevel thresholding method, two-dimensional thresholding method and three-dimensional method than Otsu method. This paper proved that the clustering results of K-means keep the order of the initial centroids with respect to one-dimensional data set. The experiments show that the k-means thresholding method performs well with less computing time than Otsu method does on three dimensional image thresholding.


title: ProbAct: A Probabilistic Activation Function for Deep Neural Networks

abstract: Activation functions play an important role in the training of artificial neural networks and the Rectified Linear Unit (ReLU) has been the mainstream in recent years. Most of the activation functions currently used are deterministic in nature, whose input-output relationship is fixed. In this work, we propose a probabilistic activation function, called ProbAct. The output value of ProbAct is sampled from a normal distribution, with the mean value same as the output of ReLU and with a fixed or trainable variance for each element. In the trainable ProbAct, the variance of the activation distribution is trained through back-propagation. We also show that the stochastic perturbation through ProbAct is a viable generalization technique that can prevent overfitting. In our experiments, we demonstrate that when using ProbAct, it is possible to boost the image classification performance on CIFAR-10, CIFAR-100, and STL-10 datasets.