title: Extracting 3D urban models from oblique aerial images

abstract: Oblique airborne cameras are increasingly used for area covering image collection of building facades in urban environments. Until recently, these images were mainly used to improve the visual appearance of relatively simple 3D building models by providing suitable facade texture. Meanwhile, oblique airborne images are also used to generate dense 3D point clouds using state-of-the-art pixel-based multi-stereo image matching. Subsequently, these point clouds can then enhance the amount of geometric and semantic detail of 3D urban models especially for the depicted building facades. However, occlusions and large view-point changes are especially demanding during dense image matching can be very challenging for oblique imagery in complex urban environment. As described in the paper, our matching pipeline tackles these problems by a coarse-to-fine modification of the SGM method. Furthermore the raw 3D point clouds are efficiently analyzed and filtered in subsequent steps, thus 3D data capture from oblique airborne imagery while aiming at the extraction of 3D urban models becomes feasible.


title: Learning Audio Sequence Representations for Acoustic Event Classification

abstract: Acoustic Event Classification (AEC) has become a significant task for machines to perceive the surrounding auditory scene. However, extracting effective representations that capture the underlying characteristics of the acoustic events is still challenging. Previous methods mainly focused on designing the audio features in a 'hand-crafted' manner. Interestingly, data-learnt features have been recently reported to show better performance. Up to now, these were only considered on the frame-level. In this paper, we propose an unsupervised learning framework to learn a vector representation of an audio sequence for AEC. This framework consists of a Recurrent Neural Network (RNN) encoder and a RNN decoder, which respectively transforms the variable-length audio sequence into a fixed-length vector and reconstructs the input sequence on the generated vector. After training the encoder-decoder, we feed the audio sequences to the encoder and then take the learnt vectors as the audio sequence representations. Compared with previous methods, the proposed method can not only deal with the problem of arbitrary-lengths of audio streams, but also learn the salient information of the sequence. Extensive evaluation on a large-size acoustic event database is performed, and the empirical results demonstrate that the learnt audio sequence representation yields a significant performance improvement by a large margin compared with other state-of-the-art hand-crafted sequence features for AEC.


title: Evaluating the Performance of ANN Prediction System at Shanghai Stock Market in the Period 21-Sep-2016 to 11-Oct-2016

abstract: This research evaluates the performance of an Artificial Neural Network based prediction system that was employed on the Shanghai Stock Exchange for the period 21-Sep-2016 to 11-Oct-2016. It is a follow-up to a previous paper in which the prices were predicted and published before September 21. Stock market price prediction remains an important quest for investors and researchers. This research used an Artificial Intelligence system, being an Artificial Neural Network that is feedforward multi-layer perceptron with error backpropagation for prediction, unlike other methods such as technical, fundamental or time series analysis. While these alternative methods tend to guide on trends and not the exact likely prices, neural networks on the other hand have the ability to predict the real value prices, as was done on this research. Nonetheless, determination of suitable network parameters remains a challenge in neural network design, with this research settling on a configuration of 5:21:21:1 with 80% training data or 4-year of training data as a good enough model for stock prediction, as already determined in a previous research by the author. The comparative results indicate that neural network can predict typical stock market prices with mean absolute percentage errors that are as low as 1.95% over the ten prediction instances that was studied in this research.


title: Specifications of a high-level conflict-free firewall policy language for multi-domain networks

abstract: Multiple firewalls typically cooperate to provide security properties for a network, despite the fact that these firewalls are often spatially distributed and configured in isolation. Without a global view of the network configuration, such a system is ripe for misconfiguration, causing conflicts and major security vulnerabilities. We propose FLIP, a high-level firewall configuration policy language for traffic access control, to enforce security and ensure seamless configuration management. In FLIP, firewall security policies are defined as high-level service-oriented goals, which can be translated automatically into access control rules to be distributed to appropriate enforcement devices. FLIP guarantees that the rules generated will be conflict-free, both on individual firewall and between firewalls. We prove that the translation algorithm is both sound and complete. FLIP supports policy inheritance and customization features that enable defining a global firewall policy for large-scale enterprise network quickly and accurately. Through a case study, we argue that firewall policy management for large-scale networks is efficient and accurate using FLIP.


title: Fault detection for circulating water pump using time series forecasting and outlier detection

abstract: In any power plants, it is crucial to perform a preventive maintenance to avoid unexpected breakdown of machinery, e.g., circulating water pump, using data collected from various sensors. There have been prior attempts using just traditional prediction techniques. In this paper, we propose a two-stage model that employs a technique from time series analysis to predict when the machine tends to be failed for one day in advance. The first stage focuses on forecasting trends of each sensor using “Auto-Regression Integrated Moving Average (ARIMA).” Then, the second stage aims to classify failure mode using the predicted sensor values. The experiment was conducted on data collected from eight sensors within one year. The result is shown that our proposed algorithm significantly outperforms an existing technique, Regression Artificial Neural Network.


Development of a New Geneva Mechanism With Improved Kinematic Characteristics


title: Improving Deep Pancreas Segmentation in CT and MRI Images via Recurrent Neural Contextual Learning and Direct Loss Function

abstract: Deep neural networks have demonstrated very promising performance on accurate segmentation of challenging organs (e.g., pancreas) in abdominal CT and MRI scans. The current deep learning approaches conduct pancreas segmentation by processing sequences of 2D image slices independently through deep, dense per-pixel masking for each image, without explicitly enforcing spatial consistency constraint on segmentation of successive slices. We propose a new convolutional/recurrent neural network architecture to address the contextual learning and segmentation consistency problem. A deep convolutional sub-network is first designed and pre-trained from scratch. The output layer of this network module is then connected to recurrent layers and can be fine-tuned for contextual learning, in an end-to-end manner. Our recurrent sub-network is a type of Long short-term memory (LSTM) network that performs segmentation on an image by integrating its neighboring slice segmentation predictions, in the form of a dependent sequence processing. Additionally, a novel segmentation-direct loss function (named Jaccard Loss) is proposed and deep networks are trained to optimize Jaccard Index (JI) directly. Extensive experiments are conducted to validate our proposed deep models, on quantitative pancreas segmentation using both CT and MRI scans. Our method outperforms the state-of-the-art work on CT [11] and MRI pancreas segmentation [1], respectively.


title: Deep Multitask Architecture for Integrated 2D and 3D Human Sensing

abstract: We propose a deep multitask architecture for fully automatic 2d and 3d human sensing (DMHS), including recognition and reconstruction, in monocular images. The system computes the figure-ground segmentation, semantically identifies the human body parts at pixel level, and estimates the 2d and 3d pose of the person. The model supports the joint training of all components by means of multi-task losses where early processing stages recursively feed into advanced ones for increasingly complex calculations, accuracy and robustness. The design allows us to tie a complete training protocol, by taking advantage of multiple datasets that would otherwise restrictively cover only some of the model components: complex 2d image data with no body part labeling and without associated 3d ground truth, or complex 3d data with limited 2d background variability. In detailed experiments based on several challenging 2d and 3d datasets (LSP, HumanEva, Human3.6M), we evaluate the sub-structures of the model, the effect of various types of training data in the multitask loss, and demonstrate that state-of-the-art results can be achieved at all processing levels. We also show that in the wild our monocular RGB architecture is perceptually competitive to a state-of-the art (commercial) Kinect system based on RGB-D data.


title: A Multi-Objective Exploration Strategy for Mobile Robots Under Operational Constraints

abstract: Multi-objective robot exploration constitutes one of the most challenging tasks for autonomous robots performing in various operations and different environments. However, the optimal exploration path depends heavily on the objectives and constraints that both these operations and environments introduce. Typical environment constraints include partially known or completely unknown workspaces, limited-bandwidth communications, and sparse or dense clattered spaces. In such environments, the exploration robots must satisfy additional operational constraints, including time-critical goals, kinematic modeling, and resource limitations. Finding the optimal exploration path under these multiple constraints and objectives constitutes a challenging non-convex optimization problem. In our approach, we model the environment constraints in cost functions and utilize the cognitive-based adaptive optimization algorithm to meet time-critical objectives. The exploration path produced is optimal in the sense of globally minimizing the required time as well as maximizing the explored area of a partially unknown workspace. Since obstacles are sensed during operation, initial paths are possible to be blocked leading to a robot entrapment. A supervisor is triggered to signal a blocked passage and subsequently escape from the basin of cost function local minimum. Extensive simulations and comparisons in typical scenarios are presented to show the efficiency of the proposed approach.


title: Development of easily wearable assistive device with elastic exoskeleton for paralyzed hand

abstract: Numerous robotic devices have been developed to assist hand rehabilitation; however, a majority of these are difficult for stroke survivors to wear. The purpose of this study was to develop an assistive device for treating flexion contracture, which supports the extension of each finger and may easily be worn on a paralyzed hand. To facilitate ease of use, we suggested a new wearing method for this wire-driven device with an elastic skeleton, allowing users to extend the device from the back of the hand onto the fingertip. The functional capacity of this device was measured through fingertip contact force and estimations of supporting torque. Results showed the device provides sufficient torque for finger extension with controlled wire tension. Moreover, experimental results confirmed that the novel design significantly decreased the time it took users to don the device compared to other designs.


title: EnsembleMatrix: interactive visualization to support machine learning with multiple classifiers

abstract: Machine learning is an increasingly used computational tool within human-computer interaction research. While most researchers currently utilize an iterative approach to refining classifier models and performance, we propose that ensemble classification techniques may be a viable and even preferable alternative. In ensemble learning, algorithms combine multiple classifiers to build one that is superior to its components. In this paper, we present EnsembleMatrix, an interactive visualization system that presents a graphical view of confusion matrices to help users understand relative merits of various classifiers. EnsembleMatrix allows users to directly interact with the visualizations in order to explore and build combination models. We evaluate the efficacy of the system and the approach in a user study. Results show that users are able to quickly combine multiple classifiers operating on multiple feature sets to produce an ensemble classifier with accuracy that approaches best-reported performance classifying images in the CalTech-101 dataset.


title: Design, fabrication, and implementation of self-sealing suction cup arrays for grasping

abstract: Suction cups have long been used as a means to grasp and manipulate objects. They enable active control of grasp, enhance grasp stability, and handle some objects such as large flat plates more easily than standard graspers. However, the application of suction cups to object manipulation has been confined to a relatively small, well-defined problem set. Their potential for grasping a large range of unknown objects remains relatively unexplored. This seems in part due to the complexity involved with the design and fabrication of various materials comprising the grasper as well as actuators used to enable grasping. This paper introduces the design of a suction cup that is “self-selecting.” In other words, the suction cups comprising the grasper do not exert any suction force when the cup(s) are not in contact with the object, but instead exert a suction force only when they are in physical contact with the object. Since grasping is achieved purely by passive means, the cost and weight associated with individual sensors, valves, and/or actuators are essentially eliminated. Furthermore, the design permits the use of a central vacuum pump, thereby maximizing the suction force on an object and enabling some suction on surfaces that may prohibit tight seals. This paper presents the design, analysis, fabrication, and experimental results of such a “self-selecting” suction cup array.


The Totalitarian Ego -- Fabrication and Revision of Personal History


title: Terms over LOAD: Leveraging Named Entities for Cross-Document Extraction and Summarization of Events

abstract: Real world events, such as historic incidents, typically contain both spatial and temporal aspects and involve a specific group of persons. This is reflected in the descriptions of events in textual sources, which contain mentions of named entities and dates. Given a large collection of documents, however, such descriptions may be incomplete in a single document, or spread across multiple documents. In these cases, it is beneficial to leverage partial information about the entities that are involved in an event to extract missing information. In this paper, we introduce the LOAD model for cross-document event extraction in large-scale document collections. The graph-based model relies on co-occurrences of named entities belonging to the classes locations, organizations, actors, and dates and puts them in the context of surrounding terms. As such, the model allows for efficient queries and can be updated incrementally in negligible time to reflect changes to the underlying document collection. We discuss the versatility of this approach for event summarization, the completion of partial event information, and the extraction of descriptions for named entities and dates. We create and provide a LOAD graph for the documents in the English Wikipedia from named entities extracted by state-of-the-art NER tools. Based on an evaluation set of historic data that include summaries of diverse events, we evaluate the resulting graph. We find that the model not only allows for near real-time retrieval of information from the underlying document collection, but also provides a comprehensive framework for browsing and summarizing event data.


title: Air pollution monitoring and prediction using IoT

abstract: The internet of Things (IoT) is a course of action interrelated computing devices, mechanical and advanced machines, objects, or people that are given unique identifiers and the capability of exchange information over a system without anticipating that human to human or human to machine communication. In this work an IoT based air pollution monitoring and prediction system is proposed. This system can be utilized for monitoring air pollutants of a particular area and to air quality analysis as well as forecasting the air quality. The proposed system will focus on the monitoring of air pollutants focus with the combination of IoT with a machine learning algorithm called Recurrent Neural Network more specifically Long Short Term Memory (LSTM).


title: 3D-PSRNet: Part Segmented 3D Point Cloud Reconstruction From a Single Image

abstract: We propose a mechanism to reconstruct part annotated 3D point clouds of objects given just a single input image. We demonstrate that jointly training for both reconstruction and segmentation leads to improved performance in both the tasks, when compared to training for each task individually. The key idea is to propagate information from each task so as to aid the other during the training procedure. Towards this end, we introduce a location-aware segmentation loss in the training regime. We empirically show the effectiveness of the proposed loss in generating more faithful part reconstructions while also improving segmentation accuracy. We thoroughly evaluate the proposed approach on different object categories from the ShapeNet dataset to obtain improved results in reconstruction as well as segmentation. Codes are available at https://github.com/val-iisc/3d-psrnet.


title: Web-based Nominal Group Technique Decision Making Tool Using Blockchain

abstract: The overall interest of modern managers is to find effective group decision-making methods when members with diverse backgrounds and perspectives have to solve problems together. Nominal Group Technique is used as one of the methods for group decision making. However, the Nominal Group Technique is not easy to ensure anonymity in a face-to-face environment, and there is no clear evidence to support the judgment of results after voting. In addition, it is difficult to trust the data because the integrity of the data generated by the voting result is not ensured. In this paper, propose a web-based Nominal Group Technique using blockchain. Web-based Nominal Group Technique can construct an agent - based model and request the blockchain network when data management is needed to guarantee the integrity of the data. In addition, the anonymity of all members is ensured through the web chat function, and the voting results can be visualized using the Priority Graph Tool, which is implemented on the web during the group decision making.


title: Processing of Reference and the Structure of Language: An Analysis of Complex Noun Phrases

abstract: Five experiments used self-paced reading time to examine the ways in which complex noun phrases (both conjoined NPs and possessive NPs) influence the interpretation of referentially dependent expressions. The experimental conditions contrasted the reading of repeated names and pronouns referring to components of a complex NP and to the entire complex NP. The results indicate that the entity introduced by a major constituent of a sentence is more accessible as a referent than the entities introduced by component noun phrases. This pattern of accessibility departs from the advantage of first mention that has been demonstrated using probe-word recognition tasks. It supports the idea that reduced expressions are interpreted as referring directly to prominent entities in a mental model whereas reference by names to entities that are already represented in a mental model is mediated by additional processes. The same interpretive processes appear to operate on coreference within and between sentences.


title: Decisional enhancement and autonomy: public attitudes towards

abstract: Ubiquitous cognitive biases hinder optimal decision making. Recent calls to assist decision makers in mitigating these biases---via interventions commonly called ``nudges''---have been criticized as infringing upon individual autonomy. We tested the hypothesis that such ``decisional enhancement'' programs that target overt decision making---i.e., conscious, higher-order cognitive processes---would be more acceptable than similar programs that affect covert decision making---i.e., subconscious, lower-order processes. We presented respondents with vignettes in which they chose between an option that included a decisional enhancement program and a neutral option. In order to assess preferences for overt or covert decisional enhancement, we used the contrastive vignette technique in which different groups of respondents were presented with one of a pair of vignettes that targeted either conscious or subconscious processes. Other than the nature of the decisional enhancement, the vignettes were identical, allowing us to isolate the influence of the type of decisional enhancement on preferences. Overall, we found support for the hypothesis that people prefer conscious decisional enhancement. Further, respondents who perceived the influence of the program as more conscious than subconscious reported that their decisions under the program would be more ``authentic''. However, this relative favorability was somewhat contingent upon context. We discuss our results with respect to the implementation and ethics of decisional enhancement.


title: Comparison of Alternative Construction Project Transaction Method Costs

abstract: Considering a dearth of comparing construction project transaction method, from the point of transaction costs, the overall construction project transaction costs is divided into ex ante transaction costs, construction costs, ex post transaction costs. A framework for comparing construction project transaction method is presented, and applied to a detailed analysis of the department of Taihu drainage area's fist design-build pilot project. The results of the study provide a comprehensive understanding of how this new design-build transaction method affects project cost performance as compared to their traditional design-bid-build transaction method. Other owners can apply this framework to assess their own project transaction performance when employing alternative transaction methods.


Paxos made practical


title: Empirical evidence of bias. Dimensions of methodological quality associated with estimates of treatment effects in controlled trials.

abstract: OBJECTIVE ::: To determine if inadequate approaches to randomized controlled trial design and execution are associated with evidence of bias in estimating treatment effects. ::: ::: ::: DESIGN ::: An observational study in which we assessed the methodological quality of 250 controlled trials from 33 meta-analyses and then analyzed, using multiple logistic regression models, the associations between those assessments and estimated treatment effects. ::: ::: ::: DATA SOURCES ::: Meta-analyses from the Cochrane Pregnancy and Childbirth Database. ::: ::: ::: MAIN OUTCOME MEASURES ::: The associations between estimates of treatment effects and inadequate allocation concealment, exclusions after randomization, and lack of double-blinding. ::: ::: ::: RESULTS ::: Compared with trials in which authors reported adequately concealed treatment allocation, trials in which concealment was either inadequate or unclear (did not report or incompletely reported a concealment approach) yielded larger estimates of treatment effects (P < .001). Odds ratios were exaggerated by 41% for inadequately concealed trials and by 30% for unclearly concealed trials (adjusted for other aspects of quality). Trials in which participants had been excluded after randomization did not yield larger estimates of effects, but that lack of association may be due to incomplete reporting. Trials that were not double-blind also yielded larger estimates of effects (P = .01), with odds ratios being exaggerated by 17%. ::: ::: ::: CONCLUSIONS ::: This study provides empirical evidence that inadequate methodological approaches in controlled trials, particularly those representing poor allocation concealment, are associated with bias. Readers of trial reports should be wary of these pitfalls, and investigators must improve their design, execution, and reporting of trials.


title: Domain-Specific Modeling: Enabling Full Code Generation

abstract: Foreword. Preface . PART 1: BACKGROUND AND MOTIVATION. 1. Introduction. 1.1 Seeking the better level of abstraction. 1.2 Code-driven and model-driven development. 1.3 An example: modeling with a general-purpose language and with a domain-specific language. 1.4 What is DSM? 1.5 When to use DSM? 1.6 Summary. 2. Business value. 2.1 Productivity. 2.2 Quality. 2.3 Leverage expertise. 2.4 The economics of DSM. 2.5 Summary. PART 2: FUNDAMENTALS. 3. DSM defined. 3.1 DSM characteristics. 3.2 Implications of DSM for users. 3.3 Difference to other modeling approaches. 3.4 Tooling for DSM. 3.5 Summary. 4. Architecture of DSM. 4.1 Introduction. 4.2 Language. 4.3 Models. 4.4 Code generator. 4.5 Domain framework and target environment. 4.6 DSM organization and process. 4.7 Summary. PART 3: DSM EXAMPLES. 5. IP telephony and call processing. 5.1 Introduction and objectives. 5.2 Development process. 5.3 Language for modeling call processing services. 5.4 Modeling IP telephony service. 5.5 Generator for XML. 5.6 Framework support. 5.7 Main results. 5.8 Summary. 6. Insurance products. 6.1 Introduction and objectives. 6.2 Development process. 6.3 Language for modeling insurances. 6.4 Modeling insurance products. 6.5 Generator for Java. 6.6 Framework support. 6.7 Main results. 6.8 Summary. 7. Home Automation. 7.1 Introduction and objectives. 7.2 Development process. 7.3 Home automation modeling language. 7.4 Home automation modeling language in use. 7.5 Generator. 7.6 Main results. 7.7 Summary. 8. Mobile phone applications using Python framework. 8.1 Introduction and objectives. 8.2 Development process. 8.3 Language for application modeling. 8.4 Modeling phone applications. 8.5 Generator for Python. 8.6 Framework support. 8.7 Main results. 8.8 Extending the solution to native S60 C++. 8.9 Summary. 9. Digital Wristwatch. 9.1 Introduction and Objectives. 9.2 Development Process. 9.3 Modeling Language. 9.4 Models. 9.5 Code Generation for Watch Models. 9.6 The Domain Framework. 9.7 Main Results. 9.8 Summary. PART 4: CREATING DSM SOLUTIONS. 10 DSM language definition. 10.1 Introduction and objectives. 10.2 Identifying and defining modeling concepts. 10.3 Formalizing languages with metamodeling. 10.4 Defining language rules. 10.5 Integrating multiple languages. 10.6 Notation for the language. 10.7 Testing the languages. 10.8 Maintaining the languages. 10.9 Summary. 11. Generator definition. 11.1 "Here's one I made earlier". 11.2 Types of generator facilities. 11.3 Generator output patterns. 11.4 Generator structure. 11.5 Process. 11.6 Summary. 12. Domain Framework. 12.1 Removing duplication from generated code. 12.2 Hiding platform details. 12.3 Providing an interface for the generator. 12.4 Summary. 13. DSM definition process. 13.1 Choosing among possible candidate domains. 13.2 Organizing for DSM. 13.3 Proof of concept. 13.4 Defining the DSM solution. 13.5 Pilot project. 13.6 DSM deployment. 13.7 DSM as a continuous process in the real world. 13.8 Summary. 14. Tools for DSM. 14.1 Different approaches to building tool support. 14.2 A Brief History of Tools. 14.3 What is needed in a DSM environment. 14.4 Current tools. 14.5 Summary. 15. DSM in use. 15.1 Model reuse. 15.2 Model sharing and splitting. 15.3 Model versioning. 15.4 Summary. 16. Conclusion. 16.1 No sweat shops--But no Fritz Lang's Metropolis either. 16.2 The onward march of DSM. Appendix A: Metamodeling Language. References. Index.


title: Head and eye gaze dynamics during visual attention shifts in complex environments

abstract: The dynamics of overt visual attention shifts evoke certain patterns of responses in eye and head movements. In this work, we detail novel findings regarding the interaction of eye gaze and head pose under various attention-switching conditions in complex environments and safety critical tasks such as driving. In particular, we find that sudden, bottom-up visual cues in the periphery evoke a different pattern of eye–head movement latencies as opposed to those during top-down, task-oriented attention shifts. In laboratory vehicle simulator experiments, a unique and significant (p G 0.05) pattern of preparatory head motions, prior to the gaze saccade, emerges in the top-down case. This finding is validated in qualitative analysis of naturalistic real-world driving data. These results demonstrate that measurements of eye–head dynamics are useful data for detecting driver distractions, as well as in classifying human attentive states in time and safety critical tasks.


title: Elliptic Curve Cryptography with Efficiently Computable Endomorphisms and Its Hardware Implementations for the Internet of Things

abstract: Verification of an ECDSA signature requires a double scalar multiplication on an elliptic curve. In this work, we study the computation of this operation on a twisted Edwards curve with an efficiently computable endomorphism, which allows reducing the number of point doublings by approximately 50 percent compared to a conventional implementation. In particular, we focus on a curve defined over the 207-bit prime field $\mathbb {F}_p$ with $p = 2^{207}-5{,}131$ . We develop several optimizations to the operation and we describe two hardware architectures for computing the operation. The first architecture is a small processor implemented in 0.13 $\mu$ m CMOS ASIC and is useful in resource-constrained devices for the Internet of Things (IoT) applications. The second architecture is designed for fast signature verifications by using FPGA acceleration and can be used in the server-side of these applications. Our designs offer various trade-offs and optimizations between performance and resource requirements and they are valuable for IoT applications.


title: A Systematic Literature Review of Emotion Regulation Measurement in Individuals With Autism Spectrum Disorder

abstract: Emotion regulation (ER) difficulties are a potential common factor underlying the presentation of multiple emotional and behavioral problems in individuals with Autism Spectrum Disorder (ASD). To provide an overview of how ER has been studied in individuals with ASD, we conducted a systematic review of the past 20 years of ER research in the ASD population, using established keywords from the most comprehensive ER literature review of the typically developing population to date. Out of an initial sampling of 305 studies, 32 were eligible for review. We examined the types of methods (self-report, informant report, naturalistic observation/ behavior coding, physiological, and open-ended) and the ER constructs based on Gross and Thompson's modal model (situation selection, situation modification, attention deployment, cognitive change, and response modulation). Studies most often assessed ER using one type of method and from a unidimensional perspective. Across the 32 studies, we documented the types of measures used and found that 38% of studies used self-report, 44% included an informant report measure, 31% included at least one naturalistic observation/behavior coding measure, 13% included at least one physiological measure, and 13% included at least one open-ended measure. Only 25% of studies used more than one method of measurement. The findings of the current review provide the field with an in-depth analysis of various ER measures and how each measure taps into an ER framework. Future research can use this model to examine ER in a multicomponent way and through multiple methods. Autism Res 2014, 7: 629–648. © 2014 The Authors. Autism Research published by Wiley Periodicals, Inc. on behalf of International Society for Autism Research


title: Social networking site use: Linked to adolescents' social self‐concept, self‐esteem, and depressed mood

abstract: Adolescents spend a substantial amount of time using social networking sites (SNSs); however, little is known regarding whether such use is associated with indicators of adjustment. The present study employed a multidimensional measure of SNS use to investigate the link between Australian adolescent SNS use and indicators of adjustment. Youth (N = 1,819, 55% female) from 34 diverse high schools across Western Australia were surveyed. The results showed that frequency of SNS use was linked to higher social self-concept while investment in SNSs was associated with lower self-esteem and higher depressed mood. Furthermore, having an SNS was linked to more negative indicators for female adolescents compared with male adolescents, although the link between frequency of use and investment in SNSs to indicators of adjustment was not moderated by gender. The present study highlights the complexity of the relationship between adolescent SNS use and indicators of adjustment, and offers insight into the diverse types of adolescent use of SNSs.


title: Development, implementation and evaluation of an evidence-based program for introduction of new health technologies and clinical practices in a local healthcare setting

abstract: This paper reports the process of establishing a transparent, accountable, evidence-based program for introduction of new technologies and clinical practices (TCPs) in a large Australian healthcare network. Many countries have robust evidence-based processes for assessment of new TCPs at national level. However many decisions are made by local health services where the resources and expertise to undertake health technology assessment (HTA) are limited and a lack of structure, process and transparency has been reported. An evidence-based model for process change was used to establish the program. Evidence from research and local data, experience of health service staff and consumer perspectives were incorporated at each of four steps: identifying the need for change, developing a proposal, implementation and evaluation. Checklists assessing characteristics of success, factors for sustainability and barriers and enablers were applied and implementation strategies were based on these findings. Quantitative and qualitative methods were used for process and outcome evaluation. An action research approach underpinned ongoing refinement to systems, processes and resources. A Best Practice Guide developed from the literature and stakeholder consultation identified seven program components: Governance, Decision-Making, Application Process, Monitoring and Reporting, Resources, Administration, and Evaluation and Quality Improvement. The aims of transparency and accountability were achieved. The processes are explicit, decisions published, outcomes recorded and activities reported. The aim of ascertaining rigorous evidence-based information for decision-making was not achieved in all cases. Applicants proposing new TCPs provided the evidence from research literature and local data however the information was often incorrect or inadequate, overestimating benefits and underestimating costs. Due to these limitations the initial application process was replaced by an Expression of Interest from applicants followed by a rigorous HTA by independent in-house experts. The program is generalisable to most health care organisations. With one exception, the components would be achievable with minimal additional resources; the lack of skills and resources required for HTA will limit effective application in many settings. A toolkit containing details of the processes and sample materials is provided to facilitate replication or local adaptation by those wishing to establish a similar program.


title: Decentralized and Optimal Resource Cooperation in Geo-Distributed Mobile Cloud Computing

abstract: Mobile cloud computing is a key enabling technology in the era of the Internet of Things. Geo-distributed mobile cloud computing (GMCC) is a new scenario that adds geography consideration in mobile cloud computing. In GMCC, users are able to access cloud resources that are geographically close to their mobile devices. This is expected to reduce the communication delay and the service providers’ cost compared with the traditional centralized approach. In this paper, we focus on resource sharing through the cooperation among the service providers in geo-distributed mobile cloud computing. Then, we propose two different strategies for efficient resource cooperation in geographically distributed data centers. Furthermore, we present a coalition game theoretical approach to deal with the competition and the cooperation among the service providers. Utility functions have been specifically considered to incorporate the cost related to virtual machine migration and resource utilization. Illustrative results indicate that our proposed schemes are able to efficiently utilize limited resources with quality-of-service consideration.


title: Contextual Decision Processes with Low Bellman Rank are PAC-Learnable

abstract: This paper studies systematic exploration for reinforcement learning (RL) with rich observations and function approximation. We introduce contextual decision processes (CDPs), that unify most prior RL settings. Our first contribution is a complexity measure, the Bellman rank, that we show enables tractable learning of near-optimal behavior in CDPs and is naturally small for many well-studied RL models. Our second contribution is a new RL algorithm that does systematic exploration to learn near-optimal behavior in CDPs with low Bellman rank. The algorithm requires a number of samples that is polynomial in all relevant parameters but independent of the number of unique contexts. Our approach uses Bellman error minimization with optimistic exploration and provides new insights into efficient exploration for RL with function approximation.


title: GeTrust: A Guarantee-Based Trust Model in Chord-Based P2P Networks

abstract: More and more users are attracted by P2P networks characterized by decentralization, autonomy and anonymity. However, users’ unconstrained behavior makes it necessary to use a trust model when establishing trust relationships between peers. Most existing trust models are based on recommendations, which, however, suffer from the shortcomings of slow convergence and high complexity of trust computations, as well as huge overhead of network traffic. Inspired by the establishment of trust relationships in human society, a guarantee-based trust model, GeTrust, is proposed for Chord-based P2P networks. A service peer needs to choose its guarantee peer(s) for the service it is going to provide, and they are both required to pledge reputation mortgages for the service. The request peer makes evaluations on all the candidates of service peer by referring their service reputations and their guarantee peers’ reputations, and selects the one with highest evaluation to be its service provider. In order to enhance GeTrust's availability and prevent malicious behavior, we also present incentive mechanism and anonymous reputation management strategy. Simulation results show that GeTrust is effective and efficient in terms of improving successful transaction rate, resisting complex attacks, reducing network overhead and lowering computational complexity.


Information Science


title: Identifying the User in Healthcare Information Systems Resear

abstract: Applying IS research to the healthcare context is an important endeavor. However, IS researchers must be cautious about identifying individual roles, the context of the setting, and postulating generalizability. Much of IS theory is rooted within the organization, its business processes, and stakeholders. IS researchers need to understand the healthcare setting before they can appropriately apply IS theory. Hospitals, emergency rooms, and laboratories are very different from the normal "business" environment, and "healthcare users" vary considerably in the role they play. Obviously, if we are studying the wrong person, or group of people, we cannot expect to get relevant results. In order to alleviate confusion regarding who is the user in healthcare IS research, we provide examples of several healthcare scenarios, perform a simplified stakeholder analysis in each scenario, and identify the stakeholder and/or user in each scenario


title: Co-training for Semi-supervised Sentiment Classification Based on Dual-view Bags-of-words Representation

abstract: A review text is normally represented as a bag-of-words (BOW) in sentiment classification. Such a simplified BOW model has fundamental deficiencies in modeling some complex linguistic phenomena such as negation. In this work, we propose a dual-view co-training algorithm based on dual-view BOW representation for semisupervised sentiment classification. In dual-view BOW, we automatically construct antonymous reviews and model a review text by a pair of bags-of-words with opposite views. We make use of the original and antonymous views in pairs, in the training, bootstrapping and testing process, all based on a joint observation of two views. The experimental results demonstrate the advantages of our approach, in meeting the two co-training requirements, addressing the negation problem, and enhancing the semi-supervised sentiment classification efficiency.


title: Autonomy infused teleoperation with application to brain computer interface controlled manipulation

abstract: Robot teleoperation systems face a common set of challenges including latency, low-dimensional user commands, and asymmetric control inputs. User control with Brain---Computer Interfaces (BCIs) exacerbates these problems through especially noisy and erratic low-dimensional motion commands due to the difficulty in decoding neural activity. We introduce a general framework to address these challenges through a combination of computer vision, user intent inference, and arbitration between the human input and autonomous control schemes. Adjustable levels of assistance allow the system to balance the operators capabilities and their perception of control authority. Additionally, a custom servo controller design allow for safe interactions of the robotic arm with the environment. We present experimental results demonstrating significant performance improvement using our shared-control assistance framework on adapted rehabilitation benchmarks with two subjects at various timepoints relative to their implantation with intracortical BCIs. Our results indicate that shared assistance mitigates perceived user difficulty in using a seven-degree of freedom robotic arm as a prosthetic and enables successful performance on previously infeasible tasks. We showcase the extensibility of our architecture with applications to quality-of-life tasks such as opening a door, pouring liquids from containers, and manipulation with objects previously unknown to the system in densely cluttered environments.


title: An aperture antenna for bluetooth, WLAN, WiMax, ZigBee and LTE applications

abstract: Compact printed antennas are an essential need of modern wireless world. An UWB planar aperture antenna for various wireless applications is presented in the literature. This antenna covers a wide range of frequencies from 2.14 GHz to 6 GHz. In this whole range of frequencies S11 is under −10 dB. The designers designed a bowtie shaped aperture from the thin copper layer fed by CPW feed line aperture. The antenna structure is more compact as the CPW ground and the aperture radiator shares the same plane. The antenna has got a size of 40×52×1.57 mm3 which is compact. The antenna prototype is designed and simulated using HFSS. The antenna offers good impedance matching, good radiation characteristics, moderate gain and good efficiency.


title: Computer tomography as imaging technique for ground penetrating radar — A case study

abstract: Computer tomography (CT) is mainly known for biological applications where inner parts of the human body are visualized. In this work computer tomography will be investigated as imaging technique for remote sensing and visualization of subsurface objects. This contribution presents a case study where CT is used in a unconventional way to demonstrate its capabilities for mapping subsurface objects in a GPR like scenario. The CT experiments for identification of buried objects will be contrasted with measurements of a conventional microwave GPR setup. The measured results underline the capability of both imaging methods for portraying visually obscured objects. The reconstructed image quality dependents largely on the object properties that have influence on the physical principle of the underlying imaging technique such as permittivity or specific absorption rate of x-rays. The reconstructed image obtained by the CT scan unveils a variety of details of the inner parts of the subsurface object.


title: An empowerment framework for nursing leadership development: supporting evidence

abstract: AIM ::: This article is a report on a descriptive study of nurse leaders' perspectives of the outcomes of a formal leadership programme. ::: ::: ::: BACKGROUND ::: Effective nurse leaders are necessary to address complex issues associated with healthcare systems reforms. Little is known about the types of leadership development programmes that most effectively prepare nurse leaders for healthcare challenges. When nurse leaders use structural and psychological empowerment strategies, the results are safer work environments and better nurse outcomes. The leadership development programme associated with this study is based on a unifying theoretical empowerment framework to empower nurse leaders and enable them to empower others. ::: ::: ::: METHODS ::: Twenty seven front-line and mid-level nurse leaders with variable years of experience were interviewed for 1 year after participating in a formal leadership development programme. Data were gathered in 2008-2009 from four programme cohorts. Four researchers independently developed code categories and themes using qualitative content analysis. ::: ::: ::: RESULTS ::: Evidence of leadership development programme empowerment included nurse leader reports of increased self-confidence with respect to carrying out their roles and responsibilities; positive changes in their leadership styles; and perceptions of staff recognition of positive stylistic changes. Regardless of years of experience, mid-level leaders had a broader appreciation of practice environment issues than front-line leaders. Time for reflection was valuable to all participants, and front-line leaders, in particular, appreciated the time to discuss nurse-specific issues with their colleagues. ::: ::: ::: CONCLUSION ::: This study provides evidence that a theoretical empowerment framework and strategies can empower nurse leaders, potentially resulting in staff empowerment.


title: Vega-Lite: A Grammar of Interactive Graphics

abstract: We present Vega-Lite, a high-level grammar that enables rapid specification of interactive data visualizations. Vega-Lite combines a traditional grammar of graphics, providing visual encoding rules and a composition algebra for layered and multi-view displays, with a novel grammar of interaction. Users specify interactive semantics by composing selections. In Vega-Lite, a selection is an abstraction that defines input event processing, points of interest, and a predicate function for inclusion testing. Selections parameterize visual encodings by serving as input data, defining scale extents, or by driving conditional logic. The Vega-Lite compiler automatically synthesizes requisite data flow and event handling logic, which users can override for further customization. In contrast to existing reactive specifications, Vega-Lite selections decompose an interaction design into concise, enumerable semantic units. We evaluate Vega-Lite through a range of examples, demonstrating succinct specification of both customized interaction methods and common techniques such as panning, zooming, and linked selection.


title: A 1-V, 16.9 ppm/$^{\circ}$C, 250 nA Switched-Capacitor CMOS Voltage Reference

abstract: An ultra low-power, precise voltage reference using a switched-capacitor technique in 0.35-μm CMOS is presented in this paper. The temperature dependence of the carrier mobility and channel length modulation effect can be effectively minimized by using 3.3 and 5 V N-type transistors to operate in the saturation and subthreshold regions, respectively. In place of resistors, a precise reference voltage with flexible trimming capability is achieved by using capacitors. When the supply voltage is 1 V and the temperature is 80°C, the supply current is 250 nA. The line sensitivity is 0.76%/V; the PSRR is -41 dB at 100 Hz and -17 dB at 10 MHz. Moreover, the occupied die area is 0.049 mm2.


title: Stator design of a 2DOF traveling-wave rotary piezoelectric motor

abstract: A two degree-of-freedom motor system constructed with two sets of piezoelectric actuators mutually perpendicular around a spherical surface is designed. Each set is employed with a series of piezoelectric actuators driven by voltage with appropriate phase difference on the neighboring actuators so that traveling wave is generated. Such a system can be used for robot (eyes or joints), flight guide control system (seeker), and medical measurement for endoscope and magnetic resonance imaging (MRI), etc. Curved piezoelectric actuator and spherical stator design, etc. are the focus in this article. Via ANSYS and analytical computation, resolution and angular velocity are estimated for the present stator structure.


title: No Fuss Distance Metric Learning Using Proxies

abstract: We address the problem of distance metric learning (DML), defined as learning a distance consistent with a notion of semantic similarity. Traditionally, for this problem supervision is expressed in the form of sets of points that follow an ordinal relationship -- an anchor point $x$ is similar to a set of positive points $Y$, and dissimilar to a set of negative points $Z$, and a loss defined over these distances is minimized. While the specifics of the optimization differ, in this work we collectively call this type of supervision Triplets and all methods that follow this pattern Triplet-Based methods. These methods are challenging to optimize. A main issue is the need for finding informative triplets, which is usually achieved by a variety of tricks such as increasing the batch size, hard or semi-hard triplet mining, etc. Even with these tricks, the convergence rate of such methods is slow. In this paper we propose to optimize the triplet loss on a different space of triplets, consisting of an anchor data point and similar and dissimilar proxy points which are learned as well. These proxies approximate the original data points, so that a triplet loss over the proxies is a tight upper bound of the original loss. This proxy-based loss is empirically better behaved. As a result, the proxy-loss improves on state-of-art results for three standard zero-shot learning datasets, by up to 15% points, while converging three times as fast as other triplet-based losses.


title: A DC-11.5 GHz Low-Power, Wideband Amplifier Using Splitting-Load Inductive Peaking Technique

abstract: A DC-11.5 GHz low-power amplifier is developed in commercial 0.13 mum, CMOS technology. This amplifier design is based on a three-stage shunt-feedback inverter-configuration with splitting load inductive peaking technique. The peaking inductor is placed at the gate of the nMOS to compensate gain roll-off of the inverter stage and extend its operating bandwidth. This amplifier achieves a gain flatness of 13.21 dB from dc to 11.5 GHz with I/O return losses better than 17 dB at a power consumption of 9.1 mW. The measured noise figure is less than 5.6 dB between 1-11 GHz. The output P1 dB is 8 dBm and input third-order intercept point is 10 dBm. The total chip size is 0.34 mm2 including all testing pads, with a core area of only 0.08 mm2.


title: Current and Emerging Applications of Droplet Digital PCR in Oncology

abstract: The clinical management of cancer has evolved in recent years towards more personalized strategies that require a comprehensive knowledge of the complex molecular features involved in tumor growth and evolution, and the development of drug resistance mechanisms leading to disease progression. Droplet digital PCR (ddPCR) has become one of the most accurate and reliable tools for the examination of genetic alterations in a wide variety of cancers because of its high sensitivity and specificity. ddPCR is currently being applied for absolute allele quantification, rare mutation detection, analysis of copy number variations, DNA methylation, and gene rearrangements in different kinds of clinical samples. This methodology has proven useful for the evaluation of archival tumor tissues, where poor DNA quality and limited sample availability are major obstacles for standard methods, providing less subjective and more automated quantitative results. However, most applications of ddPCR in cancer are focused on liquid biopsies (including cell-free DNA as well as circulating tumor cells) because these represent non-invasive alternatives to tissue biopsies that can more accurately reflect intratumoral heterogeneity and track the dynamic changes in tumor burden that occur in response to treatment at different times during follow-up. A broad spectrum of molecular markers have been interrogated in blood using ddPCR for diagnostic, predictive, and monitoring purposes in various malignancies. Emerging alternative approaches using other body fluids such as cerebrospinal fluid and urine are also currently being developed. This article aims to give a complete overview of ddPCR applications for molecular screening in oncology.


title: Process of Ontology Design for Business Intelligence System

abstract: Business Intelligence systems tend more and more towards semantically rich functionalities. One of the main artefacts to create a semantic network is the ontology. There are many methods describing the procedure of creating ontology for information solutions. The article presents the approach to the conceptualisation of the financial knowledge for a Business Intelligence system. The content of the knowledge is focused on essential financial concepts and relationships related to the management of small and medium enterprises (SME). That includes the illustration of the process of conceptualization of the financial ontology, which can be implemented in the Business Intelligence system.


title: Time-Based Proxy Re-encryption Scheme for Secure Data Sharing in a Cloud Environment

abstract: A fundamental approach for secure data sharing in a cloud environment is to let the data owner encrypt data before outsouring. To simultaneously achieve fine-grained access control on encrypted data and scalable user revocation, existing work combines attribute-based encryption (ABE) and proxy re-encryption (PRE) to delegate the cloud service provider (CSP) to execute re-encryption. However, the data owner should be online in order to send the PRE keys to the CSP in a timely fashion, to prevent the revoked user from accessing the future data. The delay of issuing the PRE keys may cause potential security risks. In this paper, we propose a time-based proxy re-encryption (TimePRE) scheme to allow a user's access right to expire automatically after a predetermined period of time. In this case, the data owner can be offline in the process of user revocations. The basic idea is to incorporate the concept of time into the combination of ABE and PRE. Specifically, each data is associated with an attribute-based access structure and an access time, and each user is identified by a set of attributes and a set of eligible time periods which denote the period of validity of the user's access right. Then, the data owner and the CSP are required to share a root secret key in advance, with which CSP can automatically update the access time of the data with the time that it receives a data access request. Therefore, given the re-encrypted ciphertext, only the users whose attributes satisfy the access structure and whose access rights are effective in the access time can recover corresponding data.


title: Revascularization Outcomes: A Prospective Analysis of 16 Consecutive Cases (vol 40, pg 333, 2014)

abstract: INTRODUCTION ::: Recent reviews lament the lack of evidence on the efficacy of regenerative procedures to induce further root maturation despite claims of a paradigm shift in the way infected, necrotic immature teeth with arrested root development can be endodontically treated. The majority of reports are either case series or successful case reports where nonstandardized images may make interpretation uncertain. ::: ::: ::: METHODS ::: This prospective clinical study reports on preliminary outcomes of regenerative endodontic procedures carried out on 16 teeth, 3 mandibular premolars and 13 traumatized central incisors, after 18-month reviews. Qualitative analysis of resolution of periapical radiolucencies and apical closure was undertaken. Quantitative analysis compared preoperative and recall radiographs by using a geometrical imaging program that calculated percentage changes in root length and dentin wall thickness. ::: ::: ::: RESULTS ::: Qualitative assessment showed 90.3% resolution of the periapical radiolucency. Apical closure was assessed as incomplete in 47.2% and complete apical closure in 19.4% of cases. Quantitative assessment showed change in root length varying from -2.7% to 25.3% and change for root dentin thickness of -1.9% to 72.6%. ::: ::: ::: CONCLUSIONS ::: Patterns of continued root maturogenesis were variable at 18-month review. Reviews at 36 months showed continued root maturogenesis for 2 cases. Quantitative analysis can control for changes in angulation but may introduce other measurement errors. However, not all anterior teeth were suitable for TurboReg assessment because overlapping of the cementoenamel junctions and/or further eruption of teeth often precluded stable landmark positioning. Discoloration of the crown was a common consequence, with unaesthetic results in 10 of the 16 cases.


title: If all you have is a bit of the Bible: Learning POS taggers for truly low-resource languages

abstract: We present a simple method for learning part-of-speech taggers for languages like Akawaio, Aukan, or Cakchiquel – languages for which nothing but a translation of parts of the Bible exists. By aggregating over the tags from a few annotated languages and spreading them via wordalignment on the verses, we learn POS taggers for 100 languages, using the languages to bootstrap each other. We evaluate our cross-lingual models on the 25 languages where test sets exist, as well as on another 10 for which we have tag dictionaries. Our approach performs much better (20-30%) than state-of-the-art unsupervised POS taggers induced from Bible translations, and is often competitive with weakly supervised approaches that assume high-quality parallel corpora, representative monolingual corpora with perfect tokenization, and/or tag dictionaries. We make models for all 100 languages available.


HD-CNN: Hierarchical Deep Convolutional Neural Network for Image Classification


title: Individual Differences in the Benefits of Feedback for Learning

abstract: Objective: Research on learning from feedback has produced ambiguous guidelines for feedback design—some have advocated minimal feedback, whereas others have recommended more extensive feedback that highly supported performance. The objective of the current study was to investigate how individual differences in cognitive resources may predict feedback requirements and resolve previous conflicted findings.Method: Cognitive resources were controlled for by comparing samples from populations with known differences, older and younger adults. To control for task demands, a simple rule-based learning task was created in which participants learned to identify fake Windows pop-ups. Pop-ups were divided into two categories—those that required fluid ability to identify and those that could be identified using crystallized intelligence.Results: In general, results showed participants given higher feedback learned more. However, when analyzed by type of task demand, younger adults performed comparably with both level...


title: Optimizing bitmap indices with efficient compression

abstract: Bitmap indices are efficient for answering queries on low-cardinality attributes. In this article, we present a new compression scheme called Word-Aligned Hybrid (WAH) code that makes compressed bitmap indices efficient even for high-cardinality attributes. We further prove that the new compressed bitmap index, like the best variants of the B-tree index, is optimal for one-dimensional range queries. More specifically, the time required to answer a one-dimensional range query is a linear function of the number of hits. This strongly supports the well-known observation that compressed bitmap indices are efficient for multidimensional range queries because results of one-dimensional range queries computed with bitmap indices can be easily combined to answer multidimensional range queries. Our timing measurements on range queries not only confirm the linear relationship between the query response time and the number of hits, but also demonstrate that WAH compressed indices answer queries faster than the commonly used indices including projection indices, B-tree indices, and other compressed bitmap indices.


title: Churn prediction in telecom using Random Forest and PSO based data balancing in combination with various feature selection strategies

abstract: The telecommunication industry faces fierce competition to retain customers, and therefore requires an efficient churn prediction model to monitor the customer's churn. Enormous size, high dimensionality and imbalanced nature of telecommunication datasets are main hurdles in attaining the desired performance for churn prediction. In this study, we investigate the significance of a Particle Swarm Optimization (PSO) based undersampling method to handle the imbalance data distribution in collaboration with different feature reduction techniques such as Principle Component Analysis (PCA), Fisher's ratio, F-score and Minimum Redundancy and Maximum Relevance (mRMR). Whereas Random Forest (RF) and K Nearest Neighbour (KNN) classifiers are employed to evaluate the performance on optimally sampled and reduced features dataset. Prediction performance is evaluated using sensitivity, specificity and Area under the curve (AUC) based measures. Finally, it is observed through simulations that our proposed approach based on PSO, mRMR, and RF termed as Chr-PmRF, performs quite well for predicting churners and therefore can be beneficial for highly competitive telecommunication industry.


title: Oculovagal reflex in paediatric orbital floor fractures mimicking head injury

abstract: Background: The oculovagal reflex is well described in ophthalmic surgery, but may be caused by any manner of pressure on the globe. Children with orbital blowout fractures present in a different manner from adults. The classic presentation in children is a white eye injury (ie, no subconjunctival haemorrhage) with upgaze diplopia and general malaise. Methods: A retrospective audit is presented of paediatric and young adult patients referred to the Orbital Service at St George’s Hospital who required surgical intervention. Results: One-third of children with orbital blowout fractures are admitted for head injury observations, while the true cause for the symptoms goes unrecognised and uninvestigated. Conclusions: Orbital blowout fractures in children require more swift intervention than in adults if muscle ischaemia and permanent impairment of the vision is to be avoided. The delay for head injury observation may therefore compromise the surgical outcome.


title: Backpropagation of Hebbian plasticity for lifelong learning.

abstract: Hebbian plasticity allows biological agents to learn from their lifetime experience, extending the fixed information provided by evolutionary search. Conversely, backpropagation methods can build high-performance fixed-weights networks, but are not currently equipped to design networks with Hebbian connections. Here we use backpropagation to train fully-differentiable plastic networks, such that backpropagation determines not only the baseline weights, but also the plasticity of each connection. To perform this backpropagation of Hebbian plasticity (BOHP), we derive error gradients for neural networks with Hebbian plastic connections. The equations for these gradients turn out to follow a simple, recursive form. We apply this method to train small networks for simple learning tasks inspired from classical conditioning. We show that, through Hebbian plasticity, the networks perform fast learning of unpredictable environmental features during their lifetime, successfully solving a task that fixed-weight feedforward networks cannot possibly solve. We conclude that backpropagation of Hebbian plasticity offers a powerful model for lifelong learning.


title: The Feet in Human--Computer Interaction: A Survey of Foot-Based Interaction

abstract: Foot-operated computer interfaces have been studied since the inception of human--computer interaction. Thanks to the miniaturisation and decreasing cost of sensing technology, there is an increasing interest exploring this alternative input modality, but no comprehensive overview of its research landscape. In this survey, we review the literature on interfaces operated by the lower limbs. We investigate the characteristics of users and how they affect the design of such interfaces. Next, we describe and analyse foot-based research prototypes and commercial systems in how they capture input and provide feedback. We then analyse the interactions between users and systems from the perspective of the actions performed in these interactions. Finally, we discuss our findings and use them to identify open questions and directions for future research.


title: Synthesis of Intermediary Phases in Ti-Al-Si System by Reactive Sintering

abstract: Reactive sintering as a simple and cost-effective method of preparation of bulk intermediary phases in the Ti-Al-Si system is presented. In this process, a pressed mixture of metal powders is transformed into the phases by thermally-activated chemical reactions. The alloys containing 10?20 wt.% of Si and 10?30 wt.% of Al were prepared by this technology. Pre-alloyed Al-Si powders are preferably used instead of Al and Si powders to improve the reactive sintering, reducing the product porosity. By optimization of the process parameters (temperature, heating rate, powder particle size), a porosity of less than 2 vol.% can be obtained. The material structure is formed by Ti5Si3 particles in the TiAl or Ti3Al matrix. The kinetics of the process is controlled by the formation of an intermediary phase. The materials exhibit very high hardness (up to 800 HV 10) due to hard Ti5Si3 particles. The oxidation stability of the materials at high temperatures is comparable with that of commercial Ni-based alloys, but is much better than that of Ti-Al alloys.


title: Recommending citations with translation model

abstract: Citation Recommendation is useful for an author to find out the papers or books that can support the materials she is writing about. It is a challengeable problem since the vocabulary used in the content of papers and in the citation contexts are usually quite different. To address this problem, we propose to use translation model, which can bridge the gap between two heterogeneous languages. We conduct an experiment and find the translation model can provide much better candidates of citations than the state-of-the-art methods.


title: An Experimental Study of Text Representation Methods for Cross-Site Purchase Preference Prediction Using the Social Text Data

abstract: Nowadays, many e-commerce websites allow users to login with their existing social networking accounts. When a new user comes to an e-commerce website, it is interesting to study whether the information from external social media platforms can be utilized to alleviate the cold-start problem. In this paper, we focus on a specific task on cross-site information sharing, i.e., leveraging the text posted by a user on the social media platform (termed as social text) to infer his/her purchase preference of product categories on an e-commerce platform. To solve the task, a key problem is how to effectively represent the social text in a way that its information can be utilized on the e-commerce platform. We study two major kinds of text representation methods for predicting cross-site purchase preference, including shallow textual features and deep textual features learned by deep neural network models. We conduct extensive experiments on a large linked dataset, and our experimental results indicate that it is promising to utilize the social text for predicting purchase preference. Specially, the deep neural network approach has shown a more powerful predictive ability when the number of categories becomes large.


title: ChangeScribe: A Tool for Automatically Generating Commit Messages

abstract: During software maintenances tasks, commit messages are an important source of information, knowledge, and documentation that developers rely upon. However, the number and nature of daily activities and interruptions can influence the quality of resulting commit messages. This formal demonstration paper presents ChangeScribe, a tool for automatically generating commit messages. ChangeScribe is available at http://www.cs.wm.edu/semeru/changescribe (Eclipse plugin, instructions, demos and the source code).


title: Knowing Where You Stand: Physical Isolation, Perceived Respect, and Organizational Identification Among Virtual Employees

abstract: This research investigates the relationship between virtual employees' degree of physical isolation and their perceived respect in the organization. Respect is an identity-based status perception that reflects the extent to which one is included and valued as a member of the organization. We hypothesize that the degree of physical isolation is negatively associated with virtual employees' perceived respect and that this relationship explains the lower organizational identification among more physically isolated virtual employees. In two field studies using survey methods, we find that perceived respect is negatively associated with the degree of physical isolation, and respect mediates the relationship between physical isolation and organizational identification. These effects hold for shorter-and longer-tenured employees alike. Our research contributes to the virtual work literature by drawing attention to physical isolation and the important but neglected role of status perceptions in shaping virtual employees' organizational identification. We also contribute to the literature on perceived respect by demonstrating how respect is affected by the physical context of work.


title: Pixelated image abstraction with integrated user constraints

abstract: We present an automatic method that can be used to abstract high resolution images into very low resolution outputs with reduced color palettes in the style of pixel art. Our method simultaneously solves for a mapping of features and a reduced palette needed to construct the output image. The results are an approximation to the results generated by pixel artists. We compare our method against the results of two naive methods common to image manipulation programs, as well as the hand-crafted work of pixel artists. Through a formal user study and interviews with expert pixel artists we show that our results offer an improvement over the naive methods. By integrating a set of manual controls into our algorithm, we give users the ability to add constraints and incorporate their own choices into the iterative process. Graphical abstractDisplay Omitted Highlights? We propose an automated image processing method that approximates pixel art. ? We compare our method to naive methods and the work of pixel artists. ? Formal user study and interviews with experts demonstrate advantages of our method. ? Additional user controls allow flexible use of feedback to guide algorithm.


title: Robust LQR Control for PWM Converters: An LMI Approach

abstract: A consistent framework for robust linear quadratic regulators (LQRs) control of power converters is presented. Systems with conventional LQR controllers present good stability properties and are optimal with respect to a certain performance index. However, LQR control does not assure robust stability when the system is highly uncertain. In this paper, a convex model of converter dynamics is obtained taking into account uncertainty of parameters. In addition, the LQR control for switching converters is reviewed. In order to apply the LQR control in the uncertain converter case, we propose to optimize the performance index by using linear matrix inequalities (LMIs). As a consequence, a new robust control method for dc-dc converters is derived. This LMI-LQR control is compared with classical LQR control when designing a boost regulator. Performance of both cases is discussed for load and line perturbations, working at nominal and non nominal conditions. Finally, the correctness of the proposed approach is verified with experimental prototypes.


On Holo-Hilbert spectral analysis: a full informational spectral representation for nonlinear and non-stationary data


title: A Distributed Hybrid Recommendation Framework to Address the New-User Cold-Start Problem

abstract: With the development of electronic commerce, recommender system is becoming an important research topic. Existing methods of recommender systems such as collaborative filtering and content analysis are suitable for different cases. Not every recommender system is capable of handling all kinds of situations. In this paper, we bring forward a distributed hybrid recommendation framework to address the new-user cold-start problem based on user classification. First, current user characteristics, user context and operating records are used to classify the user type. Then, suitable recommendation algorithms are dynamically selected based on the current user type, and executed in parallel. Finally, the recommendation results are merged into a consolidated list. In the experiment on movie ratings dataset Movie Lens, the proposed framework can enhance the accuracy of recommendation system. Also, a 100% coverage can be achieved in the case of new users. This shows the potential of the proposed framework in addressing new-user cold-start problem.


Serum Polyunsaturated Fatty Acids in Coronary Heart Disease


title: The Consumer Loan?s Payment Default Predictive Model: An Application in a Tunisian Commercial Bank

abstract: For the alarming growth in consumer credit in recent years, consumer credit scoring is the term used to describe methods of classifying credits' applicants asgood' andbad' risk classes.. In the current paper, we use the logistic regression as well as the discriminant analysis in order to develop predictive models allowing to distinguish between "good" and "bad" borrowers. The data have been collected from a commercial Tunisian bank over a 3-year period, from 2010 to 2012. These data consist of four selected and ordered variables. By comparing the respective performances of the Logistic Regression (LR) and the Discriminant Analysis (DA), we notice that the LR model yields a 89% good classification rate in predicting customer types and then, a significantly low error rate (11%), as compared with the DA approach (where the good classification rate is only equal to 68.49%, leading to a significantly high error rate, i.e. 31.51%).


title: VCloud: adding interactiveness to word clouds for knowledge exploration in large unstructured texts

abstract: The identification of relevant information in large text databases is a challenging task. One of the reasons is human beings' limitations in handling large volumes of data. A common solution for scavenging data from texts are word clouds. A word cloud illustrates word usage in a document by resizing individual words in documents proportionally to how frequently they appear. Even though word clouds are easy to understand, they are not particularly efficient, because they are static. In addition, the presented information lacks context, i.e., words are not explained and they may lead to radically erroneous interpretations. To tackle these problems we developed VCloud, a tool that allows the user to interact with word clouds, therefore allowing informative and interactive data exploration. Furthermore, our tool also allows one to compare two data sets presented as word clouds. We evaluated VCloud using real data about the evolution of gastritis research through the years. The papers indexed by Pubmed related to this medical context were selected for visualization and data analysis using VCloud. A domain expert explored these visualizations, being able to extract useful information from it. This illustrates how can VCloud be a valuable tool for visual text analytics.


title: Fast and Accurate Pedestrian Detection using Dual-Stage Group Cost-Sensitive RealBoost with Vector Form Filters

abstract: Despite significant research efforts in pedestrian detection over the past decade, there is still a ten-fold performance gap between the state-of-the-art methods and human perception. Deep learning methods can provide good performance but suffers from high computational complexity which prohibits their deployment on affordable systems with limited computational resources. In this paper, we propose a pedestrian detection framework that provides a major fillip to the robustness and run-time efficiency of the recent top performing non-deep learning Filtered Channel Feature (FCF) approach. The proposed framework overcomes the computational bottleneck of existing FCF methods by exploiting vector form filters to efficiently extract more discriminative channel features for pedestrian detection. A novel dual-stage group cost-sensitive RealBoost algorithm is used to explore different costs among different types of misclassification in the boosting process in order to improve detection performance. In addition, we propose two strategies, selective classification and selective scale processing, to further accelerate the detection process at the channel feature level and image pyramid level respectively. Experiments on the Caltech and INRIA datasets show that the proposed method achieves the highest detection performance among all the state-of-the-art non-CNN methods and is about 148X faster than the existing best performing FCF method on the Caltech dataset.


title: Does Computerized Working Memory Training with Game Elements Enhance Motivation and Training Efficacy in Children with ADHD?

abstract: Abstract This study examined the benefits of adding game elements to standard computerized working memory (WM) training. Specifically, it examined whether game elements would enhance motivation and training performance of children with ADHD, and whether it would improve training efficacy. A total of 51 children with ADHD aged between 7 and 12 years were randomly assigned to WM training in a gaming format or to regular WM training that was not in a gaming format. Both groups completed three weekly sessions of WM training. Children using the game version of the WM training showed greater motivation (i.e., more time training), better training performance (i.e., more sequences reproduced and fewer errors), and better WM (i.e., higher scores on a WM task) at post-training than children using the regular WM training. Results are discussed in terms of executive functions and reinforcement models of ADHD. It is concluded that WM training with game elements significantly improves the motivation, training performan...


title: Analytical Survey for Assuring Quality Standards in GPS Based Tracking System

abstract: In today's fast moving life GPS based tracking system has very much importance in everyone's life where loved ones, vehicles, mobile phones and other devices can be located with a touch of a button. Different quality parameters that are followed in GPS tracking systems are reported in this literature that helps in improving the quality of the tracking systems. This research paper is the survey based analysis of how GPS based tracking systems assures their quality in different applications. After analysis of the quality parameters, it has been explored that most of the GPS based tracking system are user friendly, cost effective, reliable and can be easily validated through different experiments and simulations. Most of the techniques are used for security and safety purpose and lack reusability factor. In future, we can work on the reusability of different techniques.


title: Diffusion, Seeding, and the Value of Network Information

abstract: Identifying the optimal set of individuals to first receive information (`seeds') in a social network is a widely-studied question in many settings, such as the diffusion of information, microfinance programs, and new technologies. Numerous studies have proposed various network-centrality based heuristics to choose seeds in a way that is likely to boost diffusion. Here we show that, for some frequently studied diffusion processes, randomly seeding S + x individuals can prompt a larger cascade than optimally targeting the best S individuals, for a small x. We prove our results for large classes of random networks, but also show that they hold in simulations over several real-world networks. This suggests that the returns to collecting and analyzing network information to identify the optimal seeds may not be economically significant. Given these findings, practitioners interested in communicating a message to a large number of people may wish to compare the cost of network-based targeting to that of slightly expanding initial outreach.


Generative Adversarial Network based Heterogeneous Bibliographic Network Representation for Personalized Citation Recommendation


title: Saudi Arabian Nurses. are they prone to burnout syndrome?

abstract: OBJECTIVE ::: To find out the prevalence of Burnout syndrome (BS) in Saudi nurses. ::: ::: ::: METHODS ::: This is a cross-sectional study involving 60 female Saudi nurses in the workforce of King Fahd University Hospital, Al-Khobar, which is a tertiary care center for the eastern province. Between May and August 2009, Maslach Burnout Inventory (MBI) individual-based questionnaire was distributed after modification to include age, marital status, unit working and number of years in service. We used the 3 MBI factors: emotional exhaustion (EE), depersonalization (DP), and personal accomplishment (PA) for analysis of BS. ::: ::: ::: RESULTS ::: Thirty-seven nurses (61.6%) completed the survey. The average age was 28.102.07 years and the average duration of work was 27.367.2 months. Seventeen (45.9%) had high EE and 35.1% (13) had moderate frequency of EE. Depersonalization was high in 18 (48.6%) and moderate in 15 (40.5%). Emotional exhaustion was significantly common in the married group with a frequency of 31.912.1 versus 22.559.67 (p=0.01). The nurses working in high activity areas were more emotionally exhausted and depersonalized when compared with the nurses taking care of patients in the wards and out patients clinics (p=0.003). ::: ::: ::: CONCLUSION ::: The findings show that Saudi nurses had a higher frequency of EE and DP, and most of them had low PA.


title: Mindfulness: A proposed operational definition

abstract: There has been substantial interest in mindfulness as an approach to reduce cognitive vulnerability to stress and emotional distress in recent years. However, thus far mindfulness has not been defined operationally. This paper describes the results of recent meetings held to establish a consensus on mindfulness and to develop conjointly a testable operational definition. We propose a two-component model of mindfulness and specify each component in terms of specific behaviors, experiential manifestations, and implicated psychological processes. We then address issues regarding temporal stability and situational specificity and speculate on the conceptual and operational distinctiveness of mindfulness. We conclude this paper by discussing implications for instrument development and briefly describing our own approach to measurement.


title: A comparison of usage evaluation and inspection methods for assessing groupware usability

abstract: Many researchers believe that groupware can only be evaluated by studying real collaborators in their real contexts, a process that tends to be expensive and time-consuming. Others believe that it is more practical to evaluate groupware through usability inspection methods. Deciding between these two approaches is difficult, because it is unclear how they compare in a real evaluation situation. To address this problem, we carried out a dual evaluation of a groupware system, with one evaluation applying user-based techniques, and the other using inspection methods. We compared the results from the two evaluations and concluded that, while the two methods have their own strengths, weaknesses, and trade-offs, they are complementary. Because the two methods found overlapping problems, we expect that they can be used in tandem to good effect, e.g., applying the discount method prior to a field study, with the expectation that the system deployed in the more expensive field study has a better chance of doing well because some pertinent usability problems will have already been addressed.


title: A “ Nutrition Label ” for Privacy

abstract: We used an iterative design process to develop a privacy label that presents to consumers the ways organizations collect, use, and share personal information. Many surveys have shown that consumers are concerned about online privacy, yet current mechanisms to present website privacy policies have not been successful. This research addresses the present gap in the communication and understanding of privacy policies, by creating an information design that improves the visual presentation and comprehensibility of privacy policies. Drawing from nutrition, warning, and energy labeling, as well as from the effort towards creating a standardized banking privacy notification, we present our process for constructing and refining a label tuned to privacy. This paper describes our design methodology; findings from two focus groups; and accuracy, timing, and likeability results from a laboratory study with 24 participants. Our study results demonstrate that compared to existing natural language privacy policies, the proposed privacy label allows participants to find information more quickly and accurately, and provides a more enjoyable information seeking experience.


title: A boosting approach to improving pseudo-relevance feedback

abstract: Pseudo-relevance feedback has proven effective for improving the average retrieval performance. Unfortunately, many experiments have shown that although pseudo-relevance feedback helps many queries, it also often hurts many other queries, limiting its usefulness in real retrieval applications. Thus an important, yet difficult challenge is to improve the overall effectiveness of pseudo-relevance feedback without sacrificing the performance of individual queries too much. In this paper, we propose a novel learning algorithm, FeedbackBoost, based on the boosting framework to improve pseudo-relevance feedback through optimizing the combination of a set of basis feedback algorithms using a loss function defined to directly measure both robustness and effectiveness. FeedbackBoost can potentially accommodate many basis feedback methods as features in the model, making the proposed method a general optimization framework for pseudo-relevance feedback. As an application, we apply FeedbackBoost to improve pseudo feedback based on language models through combining different document weighting strategies. The experiment results demonstrate that FeedbackBoost can achieve better average precision and meanwhile dramatically reduce the number and magnitude of feedback failures as compared to three representative pseudo feedback methods and a standard learning to rank approach for pseudo feedback.


title: SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial Networks

abstract: Autonomous driving is one of the most recent topics of interest which is aimed at replicating human driving behavior keeping in mind the safety issues. We approach the problem of learning synthetic driving using generative neural networks. The main idea is to make a controller trainer network using images plus key press data to mimic human learning. We used the architecture of a stable GAN to make predictions between driving scenes using key presses. We train our model on one video game (Road Rash) and tested the accuracy and compared it by running the model on other maps in Road Rash to determine the extent of learning.


title: IoT-based production logistics and supply chain system - Part 2: IoT-based cyber-physical system: a framework and evaluation

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this paper is threefold: to present internet of things (IoT)-based cyber-physical system (CPS) architecture framework to facilitate the integration of IoT and CPS; to implement an IoT-based CPS prototype based on the architecture framework for a PL application scenario of in a case study; and to devise evaluation methods and conduct experimental evaluations on an IoT-based CPS prototype. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The design research method, case study, emulation experiment method, and cost-benefit analysis are applied in this research. An IoT-based CPS architecture framework is proposed, and followed by the development of prototype system and testbed platform. Then, the emulation and experimental evaluation of IoT-based CPS are conducted on the testbed, and the experimental results are analyzed. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The emulation experiment results show that the proposed IoT-based CPS outperforms current barcode-based system regarding labor cost, efficiency, and operational adaptiveness. The evaluation of the IoT-based CPS prototype indicates significant improvements in PL tasks and reduced part inventory under a dynamic changing shop-floor environment. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: The case study shows that the proposed architecture framework and prototype system can be applied to many discrete manufacturing industries, such as automobile, airplane, bicycle, home appliance, and electronics. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: The proposed IoT-based CPS is among the first to address the need to integrate IoT and CPS for PL applications, and to conduct experimental evaluations and cost-benefit analysis of adopting IoT-based CPS for PL. This paper also contributes to the IoT research by using diverse research methods to offer broader insights into understanding IoT and CPS.


title: Automatic translation of Arabic text-to-Arabic sign language

abstract: Arabic sign language (ArSL) is a full natural language that is used by the deaf in Arab countries to communicate in their community. Unfamiliarity with this language increases the isolation of deaf people from society. This language has a different structure, word order, and lexicon than Arabic. The translation between ArSL and Arabic is a complete machine translation challenge, because the two languages have different structures and grammars. In this work, we propose a rule-based machine translation system to translate Arabic text into ArSL. The proposed system performs a morphological, syntactic, and semantic analysis on an Arabic sentence to translate it into a sentence with the grammar and structure of ArSL. To transcribe ArSL, we propose a gloss system that can be used to represent ArSL. In addition, we develop a parallel corpus in the health domain, which consists of 600 sentences, and will be freely available for researchers. We evaluate our translation system on this corpus and find that our translation system provides an accurate translation for more than 80% of the translated sentences.


title: Sentiment analysis techniques in recent works

abstract: Sentiment Analysis (SA) task is to label people's opinions as different categories such as positive and negative from a given piece of text. Another task is to decide whether a given text is subjective, expressing the writer's opinions, or objective, expressing. These tasks were performed at different levels of analysis ranging from the document level, to the sentence and phrase level. Another task is aspect extraction which originated from aspect-based sentiment analysis in phrase level. All these tasks are under the umbrella of SA. In recent years a large number of methods, techniques and enhancements have been proposed for the problem of SA in different tasks at different levels. This survey aims to categorize SA techniques in general, without focusing on specific level or task. And also to review the main research problems in recent articles presented in this field. We found that machine learning-based techniques including supervised learning, unsupervised learning and semi-supervised learning techniques, Lexicon-based techniques and hybrid techniques are the most frequent techniques used. The open problems are that recent techniques are still unable to work well in different domain; sentiment classification based on insufficient labeled data is still a challenging problem; there is lack of SA research in languages other than English; and existing techniques are still unable to deal with complex sentences that requires more than sentiment words and simple parsing.


title: End user cybercrime reporting: what we know and what we can do to improve it

abstract: After a physical crime occurs an important action typically takes place: the reporting of the crime to the police. However, this action becomes more complex for a victim to properly execute when a cybercrime is experienced, which can be partly explained for instance by a lack of knowledge about cybercrimes and computer security. Cybercrime reporting is crucial because it can provide a multitude of data such as the prevalence of cybercrimes, the types and nature of the cybercrimes present, and the various resulting types of loss or harm (e.g., financial, psychological, emotional). Moreover, cybercrime reporting data is also actionable for two reasons: (1) prevention tips can be produced to educate users of how they can mitigate commonly occurring cybercrimes they may be faced with, and (2) the information provided can be useful for the appropriate law enforcement agencies to potentially reach a proper resolution for the cybercrime victim (i.e., the cybercriminal being caught, the recovery of stolen property). However, comparatively few academic works have focused on better understanding computer users’ cybercrime reporting behaviors. In this paper, we first review the relevant literature, which predominantly focuses on the reasons that contribute to the underreporting of cybercrimes. Next, we highlight four particular challenges of cybercrime reporting. These challenges include the issue of computer users potentially having difficulty in properly identifying cybercrimes they may experience, fostering knowledge of how to report cybercrimes to the appropriate channels, providing incentives for cybercrime reporting, and the extent of feedback victims receive after filing a cybercrime report. Grounded in the surveyed literature and our previous work, we also provide a set of recommendations on how to approach these challenges in order to improve currently existing cybercrime reporting processes.


title: Reimagining Critical Race Theory in Education: Mental Health, Healing, and the Pathway to Liberatory Praxis

abstract: Long-standing theoretical education frameworks and methodologies have failed to provide space for the role mental health can play in mediating educational consequences. To illustrate the need for such space, Ebony McGee and David Stovall highlight the voices of black undergraduates they have served in the capacities of teacher, researcher, and mentor. Building from the theoretical contributions of intellectual giants like Frantz Fanon and W. E. B. Du Bois, the authors attempt to connect oppressive social systems to the psyche of the oppressed in a way that is relevant to black students. McGee and Stovall pose a challenge to the current research trend of attributing the survival of black students at traditionally white institutions primarily to grit, perseverance, and mental toughness, noting that research on the aforementioned qualities often fails to properly acknowledge multiple forms of suffering. Utilizing the lens of Critical Race Theory (CRT), the authors also challenge the construct of grit to consider the extent to which the mental health concerns of black students go undetected. Although critical race theorists have unmasked and attacked the racial trauma experienced at all levels of the educational system, the connection of CRT to mental health and wellness research is in its embryonic stages. For these reasons, McGee and Stovall argue that CRT scholars need to incorporate praxis to address mental health and wellness in order to address a fuller spectrum of black students' racialized worlds. Ultimately, they seek interdisciplinary perspectives that can help identify and foster strategies to support black students in the project and process of healing from multiple forms of racialized trauma they experience within and beyond their educational encounters.


The ALARA Concept in Pediatric CT: Myth or Reality?