title: Mental simulation and meaning in life.

abstract: Mental simulation, the process of self-projection into alternate temporal, spatial, social, or hypothetical realities is a distinctively human capacity. Numerous lines of research also suggest that the tendency for mental simulation is associated with enhanced meaning. The present research tests this association specifically examining the relationship between two forms of simulation (temporal and spatial) and meaning in life. Study 1 uses neuroimaging to demonstrate that enhanced connectivity in the medial temporal lobe network, a subnetwork of the brain�s default network implicated in prospection and retrospection, correlates with self-reported meaning in life. Study 2 demonstrates that experimentally inducing people to think about the past or future versus the present enhances self-reported meaning in life, through the generation of more meaningful events. Study 3 demonstrates that experimentally inducing people to think specifically versus generally about the past or future enhances self-reported meaning in life. Study 4 turns to spatial simulation to demonstrate that experimentally inducing people to think specifically about an alternate spatial location (from the present location) increases meaning derived from this simulation compared to thinking generally about another location or specifically about one�s present location. Study 5 demonstrates that experimentally inducing people to think about an alternate spatial location versus one�s present location enhances meaning in life, through meaning derived from this simulation. Study 6 demonstrates that simply asking people to imagine completing a measure of meaning in life in an alternate location compared with asking them to do so in their present location enhances reports of meaning. This research sheds light on an important determinant of meaning in life and suggests that undirected mental simulation benefits psychological well-being. (PsycINFO Database Record (c) 2015 APA, all rights reserved)


title: Using Word2Vec to process big text data

abstract: Big data is a broad data set that has been used in many fields. To process huge data set is a time consuming work, not only due to its big volume of data size, but also because data type and structure can be different and complex. Currently, many data mining and machine learning technique are being applied to deal with big data problem; some of them can construct a good learning algorithm in terms of lots of training example. However, considering the data dimension, it will be more efficient if learning algorithm is capable of selecting useful features or decreasing the feature dimension. Word2Vec, proposed and supported by Google, is not an individual algorithm, but it consists of two learning models, Continuous Bag of Words (CBOW) and Skip-gram. By feeding text data into one of learning models, Word2Vec outputs word vectors that can be represented as a large piece of text or even the entire article. In our work, we first training the data via Word2Vec model and evaluated the word similarity. In addition, we clustering the similar words together and use the generated clusters to fit into a new data dimension so that the data dimension is decreased.


title: Estimation of mean arterial pressure from the oscillometric cuff pressure: comparison of different techniques

abstract: Mean arterial pressure (MAP) is determined in most automated oscillometric blood pressure devices, but its derivation has been little studied. In this research, different techniques were studied and compared with the auscultatory technique. Auscultatory systolic and diastolic blood pressure (SBP and DBP) were obtained in 55 healthy subjects by two trained observers, and auscultatory MAP was estimated. Automated MAP was determined by six techniques from oscillometric cuff pressures recorded digitally and simultaneously during manual measurement. MAPs were derived from the peak and foot of the largest oscillometric pulse, and from time domain curves fitted to the sequence of oscillometric pulse amplitudes (4th order and three versions of the 6th order polynomial curve). The agreement between automated and auscultatory MAPs was assessed. Compared with the auscultatory MAP, the automated MAP from the baseline cuff pressure at the peak of the 6th order polynomial curve had the smallest mean paired difference (-1.0 mmHg), and smallest standard deviation of paired differences (3.7 mmHg). These values from the peak of the largest oscillometric pulse were -1.3 and 6.2 mmHg, respectively. Determining MAP from a model of the oscillometric pulse waveform had the smallest differences from the manual auscultatory technique.


title: Synthetic to Real Adaptation with Deep Generative Correlation Alignment Networks

abstract: Synthetic images rendered from 3D CAD models have been used in the past to augment training data for object recognition algorithms. However, the generated images are non-photorealistic and do not match real image statistics. This leads to a large domain discrepancy, causing models trained on synthetic data to perform poorly on real domains. Recent work has shown the great potential of deep convolutional neural networks to generate realistic images, but has not addressed synthetic-to-real domain adaptation. Inspired by these ideas, we propose the Deep Generative Correlation Alignment Network (DGCAN) to synthesize training images using a novel domain adaption algorithm. DGCAN leverages the L2 and the correlation alignment (CORAL) losses to minimize the domain discrepancy between generated and real images in deep feature space. The rendered results demonstrate that DGCAN can synthesize the object shape from 3D CAD models together with structured texture from a small amount of real background images. Experimentally, we show that training classifiers on the generated data can significantly boost performance when testing on the real image domain, improving upon several existing methods.


title: Distributed computing in practice: the Condor experience

abstract: SUMMARY Since 1984, the Condor project has enabled ordinary users to do extraordinary computing. Today, the project continues to explore the social and technical problems of cooperative computing on scales ranging from the desktop to the world-wide computational Grid. In this paper, we provide the history and philosophy of the Condor project and describe how it has interacted with other projects and evolved along with the field of distributed computing. We outline the core components of the Condor system and describe how the technology of computing must correspond to social structures. Throughout, we reflect on the lessons of experience and chart the course travelled by research ideas as they grow into production systems. Copyright c � 2005 John Wiley & Sons, Ltd.


title: An interactive 360° light field display

abstract: While a great deal of computer generated imagery is modeled and rendered in 3D, the vast majority of this 3D imagery is shown on 2D displays. Various forms of 3D displays have been contemplated and constructed for at least one hundred years [Lippman 1908], but only recent evolutions in digital capture, computation, and display have made functional and practical 3D displays possible.


title: Black-Scholes versus artificial neural networks in pricing FTSE 100 options

abstract: This paper compares the performance of Black-Scholes with an artificial neural network (ANN) in pricing European style call options on the FTSE 100 index. It is the first extensive study of the performance of ANNs in pricing UK options, and the first to allow for dividends in the closed-form model. For out-of-the-money options, the ANN is clearly superior to Black-Scholes. For in-the-money options, if the sample space is restricted by excluding deep in-the-money and long maturity options (3.4% of total volume), the performance of the ANN is comparable with that of Black-Scholes. The superiority of the ANN is a surprising result, given that European style equity options are the home ground of Black-Scholes, and suggests that ANNs may have an important role to play in pricing other options for which there is either no closed-form model, or the closed-form model is less successful than Black-Scholes for equity options.


title: Data-confined HTML5 Applications

abstract: Rich client-side applications written in HTML5 proliferate on diverse platforms, access sensitive data, and need to maintain data-confinement invariants. Applications currently enforce these invariants using implicit, ad-hoc mechanisms. We propose a new primitive called a data-confined sandbox or DCS. A DCS enables complete mediation of communication channels with a small TCB. Our primitive extends currently standardized primitives and has negligible performance overhead and a modest compatibility cost. We retrofit our design on four real-world HTML5 applications and demonstrate that a small amount of effort enables strong data-confinement guarantees.


title: TraceLink: A model of consolidation and amnesia

abstract: A connectionist model is presented, the TraceLink model, that implements an autonomous “off-line” consolidation process. The model consists of three subsystems: (1) a trace system (neocortex), (2) a link system (hippocampus and adjacent regions), and (3) a modulatory system (basal forebrain and other areas). The model is able to account for many of the characteristics of anterograde and retrograde amnesia, including Ribot gradients, transient global amnesia, patterns of shrinkage of retrograde amnesia, and correlations between anterograde and retrograde amnesia or the absence thereof (e.g., in isolated retrograde amnesia). In addition, it produces normal forgetting curves and can exhibit permastore. It also offers an explanation for the advantages of learning under high arousal for long-term retention.


title: Umbilical Pilonidal Sinus, an Underestimated and Little-Known Clinical Entity: Report of Two Cases

abstract: BACKGROUND Umbilical pilonidal sinus (UPS) is a rare disease of young, hirsute, dark men with deep navels and poor personal hygiene. UPS could easily be misdiagnosed and mistreated due to its rarity and lack of awareness of the condition by physicians. However, the diagnosis is easy to establish with physical examination and a detailed history. Although it is being diagnosed and reported more frequently, there is still no consensus regarding best treatment options. CASE REPORT In this report, we present two cases of UPS, one in a man and one in a woman, who had typical symptoms of pain, swelling, and intermittent malodorous discharge from the umbilicus. They had small sinus openings with hair protruding deep in the navel. Because these two patients had previous histories of failed conservative treatments, an umbilicus preserving surgery was performed for both cases. Wounds were healed in 2-3 weeks with acceptable cosmetic results. During a more than 2 year follow-up period, there were no signs of recurrence. CONCLUSIONS In a patient presenting with a history of intermittent discharge, itching, pain, or bleeding from the umbilicus and the presence of granulation tissue with or without protruding hair and periumbilical dermatitis, the diagnosis should consider UPS, even in female patients. Treatment generally depends on the severity of the disease, ranging from good personal hygiene to surgical excision of umbilical complex. The treatment of choice for chronic intermittent cases is surgical removal of the affected portion; paying special attention to cosmetic appearance.


title: Narcissism and consumer behaviour: a review and preliminary findings

abstract: We review the literature on the relation between narcissism and consumer behaviour. Consumer behaviour is sometimes guided by self-related motives (e.g., self-enhancement) rather than by rational economic considerations. Narcissism is a case in point. This personality trait reflects a self-centred, self-aggrandizing, dominant, and manipulative orientation. Narcissists are characterised by exhibitionism and vanity, and they see themselves as superior and entitled. To validate their grandiose self-image, narcissists purchase high-prestige products (i.e., luxurious, exclusive, flashy), show greater interest in the symbolic than utilitarian value of products, and distinguish themselves positively from others via their materialistic possessions. Our review lays the foundation for a novel methodological approach in which we explore how narcissism influences eye movement behaviour during consumer decision-making. We conclude with a description of our experimental paradigm and report preliminary results. Our findings will provide insight into the mechanisms underlying narcissists’ conspicuous purchases. They will also likely have implications for theories of personality, consumer behaviour, marketing, advertising, and visual cognition.


title: Design of Ice-Storage Air Conditioning Control System Based on PLC and Touching Screen

abstract: Based on analysis of the structure and control requirements of ice-storage air conditioning system, a distributed control system design was introduced. The hardware environment was mainly based on Programmable Logic Controller ?PLC?, and a touching screen was also applied as the local platforms of SCADA ( Supervisory Control and Data Acquisition) ; The software were CX-Programmer 7.1 and EV5000 configuration soft ware respectively. Tests results show that the PLC based control system is not only capable of running stably and reliably, but also has higher control accuracy. The touching screen can communicate precisely with PLC, and monitor and control the statuses of ice-storage air conditioning system promptly via MPI(Multi-Point Interface) protocol.


title: Real-Time 3D Eye Performance Reconstruction for RGBD Cameras

abstract: This paper proposes a real-time method for 3D eye performance reconstruction using a single RGBD sensor. Combined with facial surface tracking, our method generates more pleasing facial performance with vivid eye motions. In our method, a novel scheme is proposed to estimate eyeball motions by minimizing the differences between a rendered eyeball and the recorded image. Our method considers and handles different appearances of human irises, lighting variations and highlights on images via the proposed eyeball model and the $L0$ -based optimization. Robustness and real-time optimization are achieved through the novel 3D Taylor expansion-based linearization. Furthermore, we propose an online bidirectional regression method to handle occlusions and other tracking failures on either of the two eyes from the information of the opposite eye. Experiments demonstrate that our technique achieves robust and accurate eye performance reconstruction for different iris appearances, with various head/face/eye motions, and under different lighting conditions.


title: A Systematic Survey of Self-Protecting Software Systems

abstract: Self-protecting software systems are a class of autonomic systems capable of detecting and mitigating security threats at runtime. They are growing in importance, as the stovepipe static methods of securing software systems have been shown to be inadequate for the challenges posed by modern software systems. Self-protection, like other self-* properties, allows the system to adapt to the changing environment through autonomic means without much human intervention, and can thereby be responsive, agile, and cost effective. While existing research has made significant progress towards autonomic and adaptive security, gaps and challenges remain. This article presents a significant extension of our preliminary study in this area. In particular, unlike our preliminary study, here we have followed a systematic literature review process, which has broadened the scope of our study and strengthened the validity of our conclusions. By proposing and applying a comprehensive taxonomy to classify and characterize the state-of-the-art research in this area, we have identified key patterns, trends and challenges in the existing approaches, which reveals a number of opportunities that will shape the focus of future research efforts.


title: Fine-grained classification of pedestrians in video: Benchmark and state of the art

abstract: A video dataset that is designed to study fine-grained categorisation of pedestrians is introduced. Pedestrians were recorded “in-the-wild” from a moving vehicle. Annotations include bounding boxes, tracks, 14 keypoints with occlusion information and the fine-grained categories of age (5 classes), sex (2 classes), weight (3 classes) and clothing style (4 classes). There are a total of 27,454 bounding box and pose labels across 4222 tracks. This dataset is designed to train and test algorithms for fine-grained categorisation of people; it is also useful for benchmarking tracking, detection and pose estimation of pedestrians. State-of-the-art algorithms for fine-grained classification and pose estimation were tested using the dataset and the results are reported as a useful performance baseline.


title: Adaptively Learning the Crowd Kernel

abstract: We introduce an algorithm that, given n objects, learns a similarity matrix over all n2 pairs, from crowdsourced data alone. The algorithm samples responses to adaptively chosen triplet-based relative-similarity queries. Each query has the form "is object a more similar to b or to c?" and is chosen to be maximally informative given the preceding responses. The output is an embedding of the objects into Euclidean space (like MDS); we refer to this as the "crowd kernel." SVMs reveal that the crowd kernel captures prominent and subtle features across a number of domains, such as "is striped" among neckties and "vowel vs. consonant" among letters.


title: Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomoly Detection

abstract: Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system. Research into this family of cells has revealed that they perform the role of co-ordinating T-cell based immune responses, both reactive and for generating tolerance. We have derived an algorithm based on the functionality of these cells, and have used the signals and differentiation pathways to build a control mechanism for an artificial immune system. We present our algorithmic details in addition to some preliminary results, where the algorithm was applied for the purpose of anomaly detection. We hope that this algorithm will eventually become the key component within a large, distributed immune system, based on sound immunological concepts.


title: A soft, wearable microfluidic device for the capture, storage, and colorimetric sensing of sweat

abstract: Capabilities in health monitoring enabled by capture and quantitative chemical analysis of sweat could complement, or potentially obviate the need for, approaches based on sporadic assessment of blood samples. Established sweat monitoring technologies use simple fabric swatches and are limited to basic analysis in controlled laboratory or hospital settings. We present a collection of materials and device designs for soft, flexible, and stretchable microfluidic systems, including embodiments that integrate wireless communication electronics, which can intimately and robustly bond to the surface of the skin without chemical and mechanical irritation. This integration defines access points for a small set of sweat glands such that perspiration spontaneously initiates routing of sweat through a microfluidic network and set of reservoirs. Embedded chemical analyses respond in colorimetric fashion to markers such as chloride and hydronium ions, glucose, and lactate. Wireless interfaces to digital image capture hardware serve as a means for quantitation. Human studies demonstrated the functionality of this microfluidic device during fitness cycling in a controlled environment and during long-distance bicycle racing in arid, outdoor conditions. The results include quantitative values for sweat rate, total sweat loss, pH, and concentration of chloride and lactate.


title: Spatially Nonuniform Scaling Functions for High Contrast Images

abstract: An algorithm is presented that scales the pixel intensities of a computer generated greyscale image so that they are all displayable on a standard CRT. This scaling is spatially nonuniform over the image in that different pixels with the same intensity in the original image may have different intensities in the resulting image. The goal of this scaling transformation is to produce an image on the CRT that perceptually mimics the calculated image, while staying within the physical limitations of the CRT.


title: The role of presence in virtual reality exposure therapy.

abstract: A growing body of literature suggests that virtual reality is a successful tool for exposure therapy in the treatment of anxiety disorders. Virtual reality (VR) researchers posit the construct of presence, defined as the interpretation of an artificial stimulus as if it were real, to be a presumed factor that enables anxiety to be felt during virtual reality exposure therapy (VRE). However, a handful of empirical studies on the relation between presence and anxiety in VRE have yielded mixed findings. The current study tested the following hypotheses about the relation between presence and anxiety in VRE with a clinical sample of fearful flyers: (1) presence is related to in-session anxiety; (2) presence mediates the extent that pre-existing (pre-treatment) anxiety is experienced during exposure with VR; (3) presence is positively related to the amount of phobic elements included within the virtual environment; (4) presence is related to treatment outcome. Results supported presence as a factor that contributes to the experience of anxiety in the virtual environment as well as a relation between presence and the phobic elements, but did not support a relation between presence and treatment outcome. The study suggests that presence may be a necessary but insufficient requirement for successful VRE.


title: The Effect of Service and Food Quality on Customer Satisfaction and Hence Customer Retention

abstract: The aim of this study was to investigate the relationships between service quality, food quality, customer satisfaction and customer retention in limited service restaurants in Jordan. A questionnaire-based survey was distributed to 400 students served at 10 limited service restaurants in the neighbourhood of universities in Amman, the capital city of Jordan. Service quality was measured in terms of SERVQUAL attributes. The key dimensions of food quality, customer satisfaction and customer retention were identified through literature. The data collected (283 valid questionnaires) were analysed using SPSS 20.0. The findings showed that service quality and food quality have a positive influence on customer satisfaction. In addition, service quality dimensions besides customer satisfaction have a positive influence on customer retention. Finally, the results confirmed that customer satisfaction mediates the relationship between service quality and customer retention. The small size of the sample is the main limitation of this study. The practical implications of this study are founded on the fact that limited service restaurants in the neighbourhood of universities should realize the critical role of service and food quality in satisfying their customers as an antecedent of their retention. This study is original as it examines the relationships between service and food quality and customer satisfaction and retention in a specific type of restaurants in Jordan.


title: Word Segmentation as Graph Partition

abstract: We propose a new approach to the Chinese word segmentation problem that considers the sentence as an undirected graph, whose nodes are the characters. One can use various techniques to compute the edge weights that measure the connection strength between characters. Spectral graph partition algorithms are used to group the characters and achieve word segmentation. We follow the graph partition approach and design several unsupervised algorithms, and we show their inspiring segmentation results on two corpora: (1) electronic health records in Chinese, and (2) benchmark data from the Second International Chinese Word Segmentation Bakeoff.


title: Body image: past, present, and future

abstract: Abstract This brief editorial article introduces the new scientific journal, Body Image : An International Journal of Research , and describes its rationale and mission in relation to the history and future of the study of body image and human appearance.


title: A Survey of User Modelling in Social Media Websites

abstract: With the widespread of social media websites in the internet, and the huge number of users participating and generating infinite number of contents in these websites, the need for personalisation increases dramatically to become a necessity. One of the major issues in personalisation is building users’ profiles, which depend on many elements; such as the used data, the application domain they aim to serve, the representation method and the construction methodology. Recently, this area of research has been a focus for many researchers, and hence, the proposed methods are increasing very quickly. This survey aims to discuss the available user modelling techniques for social media websites, and to highlight the weakness and strength of these methods and to provide a vision for future work in user modelling in social media websites.


title: UHF RFID Localization Based on Phase Evaluation of Passive Tag Arrays

abstract: This paper presents a 2-D localization system based on phase-of-arrival evaluation of passive ultrahigh frequency (UHF) radio frequency identification (RFID) transponders. To handle the ambiguity caused by phase evaluation, several tags are arranged to form a uniform linear array. A multiple input multiple output system, where sequentially each frontend is configured to work as transmitter, while the remaining frontends serve as receivers, is used to enable position estimation. For proof of concept, a local position measurement system demonstrator was built comprising conventional passive EPCglobal Class-1 Gen-2 UHF RFID tags, one commercial off-the-shelf RFID reader, several transceiver frontends, baseband hardware, and signal processing. Measurements were carried out in an indoor office environment, where the $3.5~{\rm m} \times 2.5$ m measurement zone was surrounded by drywalls and concrete floor and ceiling, and the experimental results showed robust and accurate localization with a root-mean-square deviation of 0.011 m and a maximum error of 0.032 m. These experimental results were confirmed by simulations, which were performed to determine the limits of the system accuracy.


title: AFP, AFP-L3, DCP, and GP73 as markers for monitoring treatment response and recurrence and as surrogate markers of clinicopathological variables of HCC

abstract: BACKGROUND ::: Alpha-fetoprotein (AFP), lens culinaris agglutinin-reactive fraction of AFP (AFP-L3), des-γ-carboxy prothrombin (DCP), and Golgi protein-73 (GP73) have been used or proposed as tumor markers for hepatocellular carcinoma (HCC). ::: ::: ::: METHODS ::: They were measured in 96 patients undergoing hepatectomy for HCC to investigate their treatment response and association with variables linked with tumor invasiveness and/or prognosis. Values at 1 month post-surgery in the 77 patients without recurrence within 6 postoperative months were adopted as those after surgery. ::: ::: ::: RESULTS ::: GP73 levels did not change after hepatectomy, but levels of other markers decreased and areas under receiver operating characteristic curves (95% CI) were: 0.64 (0.56-0.72), 0.63 (0.55-0.71), 0.79 (0.73-0.86), and 0.63 (0.55-0.71) for AFP, AFP-L3, DCP, and combination of AFP and AFP-L3, respectively. Cutoff points giving specificities of 96.1% (sensitivities at these points) were: 124 ng/mL (28.1%), 10% (21.9%), and 60 mAU/mL (52.1%), for AFP, AFP-L3, and DCP, respectively. The combination of AFP and AFP-L3 provided a sensitivity of 26.0% at a specificity of 96.1%. The increased DCP value was, or tended to be, associated with a larger tumor, vascular invasion, intrahepatic metastases, and a lower grade of tumor cell differentiation. Although similar associations were found between AFP and vascular invasion as well as a lower grade of tumor cell differentiation, no such relationship was found with AFP-L3. ::: ::: ::: CONCLUSIONS ::: DCP is a more effective tumor marker than AFP and AFP-L3. AFP-L3 showed comparable accuracy to AFP but no benefit was found in their combination. GP73 did not play a significant role in this context. Indices of tumor invasiveness were most closely associated with DCP.


title: HAREM: An Advanced NER Evaluation Contest for Portuguese

abstract: In this paper we provide an overview of the first evaluation contest for named entity recognition in Portuguese, HAREM, which features several original traits and provided the first state of the art for the field in Portuguese, as well as a public-domain evaluation architecture.


title: Current and Emerging Topics in Sports Video Processing

abstract: Sports video processing is an interesting topic for research, since the clearly defined game rules in sports provide the rich domain knowledge for analysis. Moreover, it is interesting because many specialized applications for sports video processing are emerging. This paper gives an overview of sports video research, where we describe both basic algorithmic techniques and applications.


title: Hyperprofile-Based Computation Offloading for Mobile Edge Networks

abstract: In recent studies, researchers have developed various computation offloading frameworks for bringing cloud services closer to the user via edge networks. Specifically, an edge device needs to offload computationally intensive tasks because of energy and processing constraints. These constraints present the challenge of identifying which edge nodes should receive tasks to reduce overall resource consumption. We propose a unique solution to this problem which incorporates elements from Knowledge-Defined Networking (KDN) to make intelligent predictions about offloading costs based on historical data. Each server instance can be represented in a multidimensional feature space where each dimension corresponds to a predicted metric. We compute features for a "hyperprofile" and position nodes based on the predicted costs of offloading a particular task. We then perform a k-Nearest Neighbor (kNN) query within the hyperprofile to select nodes for offloading computation. This paper formalizes our hyperprofile-based solution and explores the viability of using machine learning (ML) techniques to predict metrics useful for computation offloading. We also investigate the effects of using different distance metrics for the queries. Our results show various network metrics can be modeled accurately with regression, and there are circumstances where kNN queries using Euclidean distance as opposed to rectilinear distance is more favorable.


title: Rapid isolation method for lipopolysaccharide and lipid A from Gram-negative bacteria

abstract: A fast, convenient extraction method for lipopolysaccharide (LPS), using a commercial RNA isolating reagent, allows the isolation of LPS or lipid A from low milligram (dry weight) quantities of bacterial cells. The method avoids the use of specialized equipment and has been used for processing relatively large numbers of samples. The major components of the commercial RNA isolating reagent, Tri-Reagent, are phenol and guanidinium thiocyanate in aqueous solution. The bacterial cell membranes are disrupted with guanidinium thiocyanate, which eliminates the need for mechanical cell disruption (e.g. French press) or heating. LPS and its degradation products, with particular attention paid to its bioactive lipid A portion, were measured and compared with those from the most common conventional extraction method, hot phenol-water. Negative ion quadrupole ion trap and matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry, fatty acid composition analysis by capillary gas chromatography, total and free phosphate by UV spectrophotometry and sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) showed that LPS and lipid A isolated using the Tri-Reagent approach were cleaner and suffered less degradation through loss of phosphate and (or) fatty acyl side chains from lipid A. The Tri-Reagent extraction method generated low free phosphate contamination, 11% of the total phosphate concentration, whereas the hot phenol-water extraction method gave approximately 58% as free, inorganic phosphate. Similar results were observed for the degradation of fatty acyl side chains. The time required by the new method is considerably shorter (two or three days) than that required by conventional hot phenol-water extraction (about two weeks).


title: Transcriptomic characterization of MRI contrast with focus on the T1‐w/T2‐w ratio in the cerebral cortex

abstract: ABSTRACT Magnetic resonance (MR) images of the brain are of immense clinical and research utility. At the atomic and subatomic levels, the sources of MR signals are well understood. However, we lack a comprehensive understanding of the macromolecular correlates of MR signal contrast. To address this gap, we used genome‐wide measurements to correlate gene expression with MR signal intensity across the cerebral cortex in the Allen Human Brain Atlas (AHBA). We focused on the ratio of T1‐weighted and T2‐weighted intensities (T1‐w/T2‐w ratio image), which is considered to be a useful proxy for myelin content. As expected, we found enrichment of positive correlations between myelin‐associated genes and the ratio image, supporting its use as a myelin marker. Genome‐wide, there was an association with protein mass, with genes coding for heavier proteins expressed in regions with high T1‐w/T2‐w values. Oligodendrocyte gene markers were strongly correlated with the T1‐w/T2‐w ratio, but this was not driven by myelin‐associated genes. Mitochondrial genes exhibit the strongest relationship, showing higher expression in regions with low T1‐w/T2‐w ratio. This may be due to the pH gradient in mitochondria as genes up‐regulated by pH in the brain were also highly correlated with the ratio. While we corroborate associations with myelin and synaptic plasticity, differences in the T1‐w/T2‐w ratio across the cortex are more strongly linked to molecule size, oligodendrocyte markers, mitochondria, and pH. We evaluate correlations between AHBA transcriptomic measurements and a group averaged T1‐w/T2‐w ratio image, showing agreement with in‐sample results. Expanding our analysis to the whole brain results in strong positive T1‐w/T2‐w correlations for immune system, inflammatory disease, and microglia marker genes. Genes with negative correlations were enriched for neuron markers and synaptic plasticity genes. Lastly, our findings are similar when performed on T1‐w or inverted T2‐w intensities alone. These results provide a molecular characterization of MR contrast that will aid interpretation of future MR studies of the brain. HIGHLIGHTSSeveral hundred genes are correlated with T1‐w/T2‐w ratio in the cortex.Expression of myelin genes is correlated with T1‐w/T2‐w intensity in the cortex.T1‐w/T2‐w ratio is correlated with oligodendrocyte marker gene expression.Heavier proteins have higher expression in regions with high T1‐w/T2‐w values.Mitochondrial and pH‐related genes link H+ ion concentration to T1‐w/T2‐w intensity.


title: Out of fear or desire? Toward a better understanding of employees' motivation to follow IS security policies

abstract: Given the significant role of people in the management of security, attention has recently been paid to the issue of how to motivate employees to improve security performance of organizations. However, past work has been dependent on deterrence theory rooted in an extrinsic motivation model to help understand why employees do or do not follow security rules in their organization. We postulated that we could better explain employees' security-related rule-following behavior with an approach rooted in an intrinsic motivation model. We therefore developed a model of employees' motivation to comply with IS security policies which incorporated both extrinsic and intrinsic models of human behavior. It was tested with data collected through a survey of 602 employees in the United States. We found that variables rooted in the intrinsic motivation model contributed significantly more to the explained variance of employees' compliance than did those rooted in the extrinsic motivation model.


title: Boosted MMI for model and feature-space discriminative training

abstract: We present a modified form of the maximum mutual information (MMI) objective function which gives improved results for discriminative training. The modification consists of boosting the likelihoods of paths in the denominator lattice that have a higher phone error relative to the correct transcript, by using the same phone accuracy function that is used in Minimum Phone Error (MPE) training. We combine this with another improvement to our implementation of the Extended Baum-Welch update equations for MMI, namely the canceling of any shared part of the numerator and denominator statistics on each frame (a procedure that is already done in MPE). This change affects the Gaussian-specific learning rate. We also investigate another modification whereby we replace I-smoothing to the ML estimate with I-smoothing to the previous iteration's value. Boosted MMI gives better results than MPE in both model and feature-space discriminative training, although not consistently.


title: Generative Data Models for Validation and Evaluation of Visualization Techniques

abstract: We argue that there is a need for substantially more research on the use of generative data models in the validation and evaluation of visualization techniques. For example, user studies will require the display of representative and uncon-founded visual stimuli, while algorithms will need functional coverage and assessable benchmarks. However, data is often collected in a semi-automatic fashion or entirely hand-picked, which obscures the view of generality, impairs availability, and potentially violates privacy. There are some sub-domains of visualization that use synthetic data in the sense of generative data models, whereas others work with real-world-based data sets and simulations. Depending on the visualization domain, many generative data models are "side projects" as part of an ad-hoc validation of a techniques paper and thus neither reusable nor general-purpose. We review existing work on popular data collections and generative data models in visualization to discuss the opportunities and consequences for technique validation, evaluation, and experiment design. We distill handling and future directions, and discuss how we can engineer generative data models and how visualization research could benefit from more and better use of generative data models.


title: Exploring semantic inter-class relationships (SIR) for zero-shot action recognition

abstract: Automatically recognizing a large number of action categories from videos is of significant importance for video understanding. Most existing works focused on the design of more discriminative feature representation, and have achieved promising results when the positive samples are enough. However, very limited efforts were spent on recognizing a novel action without any positive exemplars, which is often the case in the real settings due to the large amount of action classes and the users' queries dramatic variations. To address this issue, we propose to perform action recognition when no positive exemplars of that class are provided, which is often known as the zero-shot learning. Different from other zero-shot learning approaches, which exploit attributes as the intermediate layer for the knowledge transfer, our main contribution is SIR, which directly leverages the semantic inter-class relationships between the known and unknown actions followed by label transfer learning. The inter-class semantic relationships are automatically measured by continuous word vectors, which learned by the skip-gram model using the large-scale text corpus. Extensive experiments on the UCF101 dataset validate the superiority of our method over fully-supervised approaches using few positive exemplars.


title: VRCodes: Unobtrusive and active visual codes for interaction by exploiting rolling shutter

abstract: We show a new visible tagging solution for active displays which allows a rolling-shutter camera to detect active tags from a relatively large distance in a robust manner. Current planar markers are visually obtrusive for the human viewer. In order for them to be read from afar and embed more information, they must be shown larger thus occupying valuable physical space on the design. We present a new active visual tag which utilizes all dimensions of color, time and space while remaining unobtrusive to the human eye and decodable using a 15fps rolling-shutter camera. The design exploits the flicker fusion-frequency threshold of the human visual system, which due to the effect of metamerism, can not resolve metamer pairs alternating beyond 120Hz. Yet, concurrently, it is decodable using a 15fps rolling-shutter camera due to the effective line-scan speed of 15×400 lines per second. We show an off-the-shelf rolling-shutter camera can resolve the metamers flickering on a television from a distance over 4 meters. We use intelligent binary coding to encode digital positioning and show potential applications such as large screen interaction. We analyze the use of codes for locking and tracking encoded targets. We also analyze the constraints and performance of the sampling system, and discuss several plausible application scenarios.


title: Network-based statistic: Identifying differences in brain networks

abstract: Large-scale functional or structural brain connectivity can be modeled as a network, or graph. This paper presents a statistical approach to identify connections in such a graph that may be associated with a diagnostic status in case-control studies, changing psychological contexts in task-based studies, or correlations with various cognitive and behavioral measures. The new approach, called the network-based statistic (NBS), is a method to control the family-wise error rate (in the weak sense) when mass-univariate testing is performed at every connection comprising the graph. To potentially offer a substantial gain in power, the NBS exploits the extent to which the connections comprising the contrast or effect of interest are interconnected. The NBS is based on the principles underpinning traditional cluster-based thresholding of statistical parametric maps. The purpose of this paper is to: (i) introduce the NBS for the first time; (ii) evaluate its power with the use of receiver operating characteristic (ROC) curves; and, (iii) demonstrate its utility with application to a real case-control study involving a group of people with schizophrenia for which resting-state functional MRI data were acquired. The NBS identified a expansive dysconnected subnetwork in the group with schizophrenia, primarily comprising fronto-temporal and occipito-temporal dysconnections, whereas a mass-univariate analysis controlled with the false discovery rate failed to identify a subnetwork.


title: MOBILE ROBOT TRAJECTORY TRACKING USING MODEL PREDICTIVE CONTROL

abstract: This work focus on the application of model-based predictive control (MPC) to the trajectory tracking problem of nonholonomic wheeled mobile robots (WMR). The main motivation of the use of MPC in this case relies on its ability in considering, in a straightforward way, control a nd state constraints that naturally arise in practical proble ms. Furthermore, MPC techniques consider an explicit performance criterion to be minimized during the computation of the control law. The trajectory tracking problem is solved using two approaches: (1) nonlinear MPC and (2) linear MPC. Simulation results are provided in order to show the effectiveness of both schemes. Considerations regarding the computational effort of the MPC are developed with the purpose of analyzing the real-time implementation viability of the proposed techniques.


Final Year Project Final Report SimNav : Simulation Training Framework for Collision Avoidance in Robot Navigation


title: Peer Assessment for Massive Open Online Courses (MOOCs)

abstract: The teach-learn-assess cycle in education is broken in a typical massive open online course (MOOC). Without formative assessment and feedback, MOOCs amount to information dump or broadcasting shows, not educational experiences. A number of remedies have been attempted to bring formative assessment back into MOOCs, each with its own limits and problems. The most widely applicable approach for all MOOCs to date is to use peer assessment to provide the necessary feedback. However, unmoderated peer assessment results suffer from a lack of credibility. Several methods are available today to improve on the accuracy of peer assessment results. Some combination of these methods may be necessary to make peer assessment results sufficiently accurate to be useful for formative assessment. Such results can also help to facilitate peer learning, online discussion forums, and may possibly augment summative evaluation for credentialing.


title: Characterizing the life cycle of online news stories using social media reactions

abstract: This paper presents a study of the life cycle of news articles posted online. We describe the interplay between website visitation patterns and social media reactions to news content. We show that we can use this hybrid observation method to characterize distinct classes of articles. We also find that social media reactions can help predict future visitation patterns early and accurately. We validate our methods using qualitative analysis as well as quantitative analysis on data from a large international news network, for a set of articles generating more than 3,000,000 visits and 200,000 social media reactions. We show that it is possible to model accurately the overall traffic articles will ultimately receive by observing the first ten to twenty minutes of social media reactions. Achieving the same prediction accuracy with visits alone would require to wait for three hours of data. We also describe significant improvements on the accuracy of the early prediction of shelf-life for news stories.


title: A Capacitor-Isolated LED Driver With Inherent Current Balance Capability

abstract: This paper presents a capacitor-isolated light-emitting diode (LED) driver with inherent current-balancing capability. Based on a series resonant converter, the resonant capacitor can be used both for safety isolation and the current balancing with the proposed two-output rectifier structure. Compared to the conventional current-sharing technique, the proposed LED driver circuit is simple and has low cost and high performance. Also, the capacitor-isolated structure is more efficient and compact compared to the conventional transformer isolation. The detailed theoretical analysis and design considerations of the proposed circuit are presented. The performance of the proposed circuit is validated by the experimental results from a 60-W prototype with two balanced outputs.


title: Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks

abstract: Convolutional Neural Networks have achieved state-of-the-art performance on a wide range of tasks. Most benchmarks are led by ensembles of these powerful learners, but ensembling is typically treated as a post-hoc procedure implemented by averaging independently trained models with model variation induced by bagging or random initialization. In this paper, we rigorously treat ensembling as a first-class problem to explicitly address the question: what are the best strategies to create an ensemble? We first compare a large number of ensembling strategies, and then propose and evaluate novel strategies, such as parameter sharing (through a new family of models we call TreeNets) as well as training under ensemble-aware and diversity-encouraging losses. We demonstrate that TreeNets can improve ensemble performance and that diverse ensembles can be trained end-to-end under a unified loss, achieving significantly higher "oracle" accuracies than classical ensembles.


title: A question/answer typology with surface text patterns

abstract: In this paper we announce the release of ISI's QA Typology, which is being made available on the web to support the rapid construction of new QA systems. The Typology has been augmented with surface-level patterns associated with answer types, allowing systems to locate answers of the desired type in text by simple string matching. These patterns are extracted from the web automatically. We describe the process of their extraction, compression, and accuracy determination.


abstract: Sentiment analysis is the procedure by which information is extracted from the opinions, appraisals and emotions of people in regards to entities, events and their attributes. In decision making, the opinions of others have a significant effect on customers ease in making choices regards to online shopping, choosing events, products, entities. In this paper, a rule based domain independent sentiment analysis method is proposed. The proposed method classifies subjective and objective sentences from reviews and blog comments. The semantic score of subjective sentences is extracted from SentiWordNet to calculate their polarity as positive, negative or neutral based on the contextual sentence structure. The results show the effectiveness of the proposed method and it outperforms the machine learning methods. The proposed method achieves an accuracy of 87% at the feedback level and 83% at the sentence level for comments. [Aurangzeb khan, Muhammad Zubair Asghar, Shakeel Ahmad, Fazal Masud Kundi, Maria Qasim, Furqan. Sentiment Classification through Semantic Orientation Using SentiWordNet. Life Sci J 2014; 11(10):309-315] (ISSN: 1097-8135). http://www.lifesciencesite.com. 44


title: The semantics of poetry: A distributional reading

abstract: The work of theoretical linguistics rarely focuses on poetry. This is far from surprising, as poetic language, especially in modern and contemporary literature, seems to defy the general rules of syntax and semantics, as observed in ordinary language. This article assumes, however, that linguistic theories should ideally be able to account for creative uses of language, down to their most difficult incarnations. It proposes that at the semantic level, what distinguishes poetry from other uses of language may be its ability to trace conceptual patterns which do not belong to everyday discourse but are latent in our shared language structure. Distributional semantics provides a theoretical and experimental basis for this exploration. First, the notion of a specific ‘semantics of poetry’ is discussed, with some help from literary criticism and philosophy. Then, distributionalism is introduced as a theory supporting the notion that the meaning of poetry comes from the meaning of ordinary language. In the second part of the article, experimental results are provided showing that (i) distributional representations can model the link between ordinary and poetic language, (ii) a distributional model can experimentally distinguish between poetic and randomized textual output, regardless of the complexity of the poetry involved, and (iii) there is a stable, but not immediately transparent, layer of meaning in poetry, which can be captured distributionally, across different levels of poetic complexity.


title: Automatic Book Spine Extraction and Recognition for Library Inventory Management

abstract: Manual inventory management in a library is by far arduous. Automation of book inspection can be achieved by using a simple camera based system that can recognize book spines in a book shelf. The book spines contain printed information such as title, author and publisher name, which can be extracted and verified with the library's database. Book spines can be segmented by detecting their rectangular boundaries which appear as straight lines. Line detection using hough transform and line segment detector may result in spurious boundaries due to the presence of long titles or graphics on the book spine. In this paper, we propose a technique to improve book spine border detection by devising set of constraints based on structural properties that can be used to filter the detected line segments so as to obtain book spine boundaries. The segmented book spines are binarized to extract the printed information such as title, author and publisher name. The text is recognized using Tesseract Optical Character Recognition Engine. The proposed algorithm was tested successfully on book shelf images with vertically oriented, uniformly inclined and multi-oriented book spines.


title: Diagnosis of diabetic retinopathy using morphological process and SVM classifier

abstract: Diabetic Retinopathy (DR), the most common eye disease of the diabetic patients, occurs when small blood vessels gets damaged in the retina, due to high glucose level. It affects 80% of all patients who have had diabetes for 10 years or more, which can also lead to vision loss. Detection of diabetic retinopathy in advance, protects patients from vision loss. The major symptom of diabetic retinopathy is the exudates. Exudate is a fluid that filters from the circulatory system into lesions or area of inflammation. Detecting retinal fundus diseases in an early stage, helps the ophthalmologists apply proper treatments that might eliminate the disease or decrease the severity of it. This paper focuses on automatic detection of diabetic retinopathy through detecting exudates in colour fundus retinal images and also classifies the rigorousness of the lesions. Decision making of the severity level of the disease was performed by SVM classifier.


title: A Fast Discrete Wavelet Transform Using Hybrid Parallelism on GPUs

abstract: Wavelet transform has been widely used in many signal and image processing applications. Due to its wide adoption for time-critical applications, such as streaming and real-time signal processing, many acceleration techniques were developed during the past decade. Recently, the graphics processing unit (GPU) has gained much attention for accelerating computationally-intensive problems and many solutions of GPU-based discrete wavelet transform (DWT) have been introduced, but most of them did not fully leverage the potential of the GPU. In this paper, we present various state-of-the-art GPU optimization strategies in DWT implementation, such as leveraging shared memory, registers, warp shuffling instructions, and thread- and instruction-level parallelism (TLP, ILP), and finally elaborate our hybrid approach to further boost up its performance. In addition, we introduce a novel mixed-band memory layout for Haar DWT, where multi-level transform can be carried out in a single fused kernel launch. As a result, unlike recent GPU DWT methods that focus mainly on maximizing ILP, we show that the optimal GPU DWT performance can be achieved by hybrid parallelism combining both TLP and ILP together in a mixed-band approach. We demonstrate the performance of our proposed method by comparison with other CPU and GPU DWT methods.


title: Current Approaches to Bone Tissue Engineering: The Interface between Biology and Engineering

abstract: The successful regeneration of bone tissue to replace areas of bone loss in large defects or at load-bearing sites remains a significant clinical challenge. Over the past few decades, major progress is achieved in the field of bone tissue engineering to provide alternative therapies, particularly through approaches that are at the interface of biology and engineering. To satisfy the diverse regenerative requirements of bone tissue, the field moves toward highly integrated approaches incorporating the knowledge and techniques from multiple disciplines, and typically involves the use of biomaterials as an essential element for supporting or inducing bone regeneration. This review summarizes the types of approaches currently used in bone tissue engineering, beginning with those primarily based on biology or engineering, and moving into integrated approaches in the areas of biomaterial developments, biomimetic design, and scalable methods for treating large or load-bearing bone defects, while highlighting potential areas for collaboration and providing an outlook on future developments.


title: Benchmarking Distributed Stream Data Processing Systems

abstract: The need for scalable and efficient stream analysis has led to the development of many open-source streaming data processing systems (SDPSs) with highly diverging capabilities and performance characteristics. While first initiatives try to compare the systems for simple workloads, there is a clear gap of detailed analyses of the systems' performance characteristics. In this paper, we propose a framework for benchmarking distributed stream processing engines. We use our suite to evaluate the performance of three widely used SDPSs in detail, namely Apache Storm, Apache Spark, and Apache Flink. Our evaluation focuses in particular on measuring the throughput and latency of windowed operations, which are the basic type of operations in stream analytics. For this benchmark, we design workloads based on real-life, industrial use-cases inspired by the online gaming industry. The contribution of our work is threefold. First, we give a definition of latency and throughput for stateful operators. Second, we carefully separate the system under test and driver, in order to correctly represent the open world model of typical stream processing deployments and can, therefore, measure system performance under realistic conditions. Third, we build the first benchmarking framework to define and test the sustainable performance of streaming systems. Our detailed evaluation highlights the individual characteristics and use-cases of each system.


title: Mobile cloud sensing, big data, and 5G networks make an intelligent and smart world

abstract: Through time, we have seen mobile phones transform into multifaceted devices, adapted to meet and exceed our everyday needs. These needs range from something as personal as a health care manager to something as purely analytical as an environment monitor. In effect, mobile phones have come into our lives, making life easier, smarter, and more efficient. In this article we discuss mobile sensing and cloud computing separately and in detail, then combine the two concepts to form the singular idea of mobile cloud sensing. We will also give an intuitive architectural description of mobile cloud sensing, along with discussions about each of its individual building blocks. There are limitations to mobile cloud sensing today, but with the emergence of 5G coupled with the analysis of big data, we can address the current issues at hand. We believe that with the advent of mobile cloud sensing, 5G, and big data analysis, our lives will continue to see an increase in overall quality.


Technical logic, rhetorical logic, and narrative rationality


title: Relaxed Concurrency Control in Software Transactional Memory

abstract: Some of today's TM systems implement the two-phase-locking (2PL) algorithm which aborts transactions every time a conflict occurs. 2PL is a simple algorithm that provides fast transactional operations. However, it limits concurrency in benchmarks with high contention because it increases the rate of aborts. We propose the use of a more relaxed concurrency control algorithm to provide better concurrency. This algorithm is based on the conflict-serializability (CS) model. Unlike 2PL, it allows some transactions to commit successfully even when they make conflicting accesses. We implement this algorithm in a STM system and evaluate its performance on 16 cores using standard benchmarks. Our evaluation shows that the algorithm improves the performance of applications with long transactions and high abort rates. Throughput is improved by up to 2.99 times despite the overheads of testing for CS at runtime. These improvements come with little additional implementation complexity and require no changes to the transactional programming model. We also propose an adaptive approach that switches between 2PL and CS to mitigate the overhead in applications that have low abort rates.


title: R-Extractor: A Method for Data Extraction from Template-Based Entity-Pages

abstract: The challenges in Big Data start during the data acquisition, where it is necessary to transform non-structured data into a structured format. The Big Data Era brought a new challenge to data acquisition: the need for eliminating user intervention. In this context, the domain-centric data extraction (DCDE) methods arose through replacing user intervention with content redundancy. The DCDE methods extract the attribute values of entities of the web that are restricted to a specific application domain. A research gap was identified from various analyzed methods, i.e., the DCDE methods are not effective in extracting attribute values that are differently presented within the same website. We have responded to this research gap by proposing the R-Extractor method, which extends a state-of-the-art DCDE method by adding a reinforcement stage for the treatment of attribute values that are differently presented within the same website. The R-Extractor method re-analyzes the extraction rules to identify those that can be combined to extract the values of a given attribute from all the pages that describe entities on a website. This identification is based on a novel score function that takes into account different features of the extraction rules. We carried out experiments on a dataset with more than 50k web pages from different real-world websites of a wide range of application domains. The R-Extractor method reached 98% of precision. Our method was compared with two baselines (an XPath-based method and a tree-based method) and outperformed them with an increase in precision up to 14%.


title: A temporal sampling framework for developmental dyslexia

abstract: Neural coding by brain oscillations is a major focus in neuroscience, with important implications for dyslexia research. Here, I argue that an oscillatory ‘temporal sampling' framework enables diverse data from developmental dyslexia to be drawn into an integrated theoretical framework. The core deficit in dyslexia is phonological. Temporal sampling of speech by neuroelectric oscillations that encode incoming information at different frequencies could explain the perceptual and phonological difficulties with syllables, rhymes and phonemes found in individuals with dyslexia. A conceptual framework based on oscillations that entrain to sensory input also has implications for other sensory theories of dyslexia, offering opportunities for integrating a diverse and confusing experimental literature.


title: Color laser printer identification using photographed halftone images

abstract: Due to the spread of color laser printers to the general public, numerous forgeries are made by color laser printers. Printer identification is essential to preventing damage caused by color laser printed forgeries. This paper presents a new method to identify a color laser printer using photographed halftone images. First, we preprocess the photographed images to extract the halftone pattern regardless of the variation of the illumination conditions. Then, 15 halftone texture features are extracted from the preprocessed images. A support vector machine is used to be trained and classify the extracted features. Experiments are performed on seven color laser printers. The experimental results show that the proposed method is suitable for identifying the source color laser printer using photographed images.


title: Choosing the best wireless protocol for typical applications.

abstract: This paper gives an overview of the existing wireless network standards with their main usage, used frequency band, modulation, transfer speed as well as features and limitations. The main focus on this paper is an assistance to choose the best fitting protocol for an application. Therefore some typical scenarios for applications are described and after a preselection based on the main usage, the wireless network standards are compared to find the best protocol for a given scenario. The scenarios are constructed that there is for every application an fitting scenario.


title: RFBoost: An improved multi-label boosting algorithm and its application to text categorisation

abstract: Abstract The AdaBoost.MH boosting algorithm is considered to be one of the most accurate algorithms for multi-label classification. AdaBoost.MH works by iteratively building a committee of weak hypotheses of decision stumps. In each round of AdaBoost.MH learning, all features are examined, but only one feature is used to build a new weak hypothesis. This learning mechanism may entail a high degree of computational time complexity, particularly in the case of a large-scale dataset. This paper describes a way to manage the learning complexity and improve the classification performance of AdaBoost.MH. We propose an improved version of AdaBoost.MH, called RFBoost . The weak learning in RFBoost is based on filtering a small fixed number of ranked features in each boosting round rather than using all features, as AdaBoost.MH does. We propose two methods for ranking the features: One Boosting Round and Labeled Latent Dirichlet Allocation (LLDA), a supervised topic model based on Gibbs sampling. Additionally, we investigate the use of LLDA as a feature selection method for reducing the feature space based on the maximal conditional probabilities of words across labels. Our experimental results on eight well-known benchmarks for multi-label text categorisation show that RFBoost is significantly more efficient and effective than the baseline algorithms. Moreover, the LLDA-based feature ranking yields the best performance for RFBoost.


title: Parsing As Deduction

abstract: By exploring the relationship between parsing and deduction, a new and more general view of chart parsing is obtained, which encompasses parsing for grammar formalisms based on unification, and is the basis of the Earley Deduction proof procedure for definite clauses. The efficiency of this approach for an interesting class of grammars is discussed.


CME Auricular Reconstruction for Microtia : Part I . Anatomy , Embryology , and Clinical Evaluation


title: A 120GHz in-band full-duplex PMF transceiver with tunable electrical-balance duplexer in 40nm CMOS

abstract: A 120GHz in-band full-duplex PMF transceiver with tunable electrical-balance duplexer with on-chip antenna is implemented in a 40nm bulk CMOS tech-nology. The self-interference (SI) cancellation is performed with a fully-differential transformer-based electrical-balance duplexer, resulting in a SI suppression of 30dB over a bandwidth of more than 14GHz. The insertion loss of the duplexer is less than 11dB from TX to fiber and less than 12dB from fiber to RX at a frequency of 120GHz. The balancing network impedance can tolerate load impedances from 45Ω to 93Ω and 38fF to 266fF at 120GHz. For in-band full-duplex operation, the maximum measured data rate is 6.2Gbps. The transceiver with integrated antenna occupies an area of 1.8mm × 1.53mm and has a DC power consumption of 73mW in total.


title: The RADIANCE lighting simulation and rendering system

abstract: This paper describes a physically-based rendering system tailored to the demands of lighting design and architecture. The simulation uses a light-backwards ray-tracing method with extensions to efficiently solve the rendering equation under most conditions. This includes specular, diffuse and directional-diffuse reflection and transmission in any combination to any level in any environment, including complicated, curved geometries. The simulation blends deterministic and stochastic ray-tracing techniques to achieve the best balance between speed and accuracy in its local and global illumination methods. Some of the more interesting techniques are outlined, with references to more detailed descriptions elsewhere. Finally, examples are given of successful applications of this free software by others.


title: Stackelberg games for adversarial prediction problems

abstract: The standard assumption of identically distributed training and test data is violated when test data are generated in response to a predictive model. This becomes apparent, for example, in the context of email spam filtering, where an email service provider employs a spam filter and the spam sender can take this filter into account when generating new emails. We model the interaction between learner and data generator as a Stackelberg competition in which the learner plays the role of the leader and the data generator may react on the leader's move. We derive an optimization problem to determine the solution of this game and present several instances of the Stackelberg prediction game. We show that the Stackelberg prediction game generalizes existing prediction models. Finally, we explore properties of the discussed models empirically in the context of email spam filtering.


title: Deformable parts model for people detection in heavy machines applications

abstract: In this paper we focus on the evaluation of the deformable part model (DPM) proposed by Felzenszwalb et al. [IO] in the context of vision-based people detection in heavy machines applications. The proposed system uses a single fisheye camera to provide a wide field-of-view (FOV) at low cost. However, the fisheye optical distortions present several difficulties for image processing and object recognition. The DPM approach shows important flexibility when dealing with varying object's form. It gives good performances on people detection when images present strong fisheye distortions. Base on the analysis of DPM in the context of fisheye image, we proposed an adaptive detector which is more suitable.


You can ' t play 20 questions with nature and win : projective comments on the papers of


title: Methods for Designing Multiple Classifier Systems

abstract: In the field of pattern recognition, multiple classifier systems based on the combination of outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. In this paper, the problem of design of multiple classifier system is discussed. Six design methods based on the so-called "overproduce and choose" paradigm are described and compared by experiments. Although these design methods exhibited some interesting features, they do not guarantee to design the optimal multiple classifier system for the classification task at hand. Accordingly, the main conclusion of this paper is that the problem of the optimal MCS design still remains open.


title: The VoiceBot: a voice controlled robot arm

abstract: We present a system whereby the human voice may specify continuous control signals to manipulate a simulated 2D robotic arm and a real 3D robotic arm. Our goal is to move towards making accessible the manipulation of everyday objects to individuals with motor impairments. Using our system, we performed several studies using control style variants for both the 2D and 3D arms. Results show that it is indeed possible for a user to learn to effectively manipulate real-world objects with a robotic arm using only non-verbal voice as a control mechanism. Our results provide strong evidence that the further development of non-verbal voice controlled robotics and prosthetic limbs will be successful.


title: An Investigation of Factors Influencing the Use of Computer‐Related Audit Procedures

abstract: ABSTRACT: We provide data on the extent to which computer‐related audit procedures are used and whether two factors, control risk assessment and audit firm size, influence computer‐related audit pr...


title: INDIVIDUAL TECHNOLOGY ACCEPTANCE UNDER CONDITIONS OF CHANGE

abstract: Information technology is changing rapidly. However, existing technology acceptance models do not consider the impact of changes on individual acceptance of information technology. This paper introduces the construct of technology change and integrates current technology acceptance models with social psychology theories of attitude change to propose a theory of technology acceptance under conditions of change. Beginning with a review of current technology acceptance models, this paper considers how they implicitly deal with IT change. A conceptualization of technology change focused on rate, magnitude, and distribution of change is then presented. Finally, a three-stage theoretical model of how technology change affects acceptance through individual attitudes and beliefs is presented. The core of the model focuses on the processes of forming attitudes and beliefs about technology change, changing attitudes and beliefs about the technology, and technology acceptance decision making. These processes are expected to be moderated by the visibility, relevance, and compatibility of the changes. This model, by integrating the reality of technology change with extensive research on technology acceptance, extends our understanding of the complex interplay between technology and people in dynamic, evolving information systems.


title: Brain volume and shape in infants with deformational plagiocephaly

abstract: PURPOSE ::: Infants with deformational plagiocephaly (DP) have been shown to exhibit developmental delays relative to unaffected infants. Although the mechanisms accounting for these delays are unknown, one hypothesis focuses on underlying differences in brain development. In this study, we used MRI to examine brain volume and shape in infants with and without DP. ::: ::: ::: METHODS ::: Participants included 20 infants with DP (mean age = 7.9 months, SD = 1.2; n = 12 male) and 21 controls (mean age = 7.9 months, SD = 1.3; n = 11 male). Measures included volumes of the total brain and cerebellum; midsagittal areas of the corpus callosum and cerebellar vermis; and linear distance measures used to quantify the shape of selected brain structures. We also evaluated the association between shape measures and developmental scores on the Bayley Scales of Infant and Toddler Development-III (BSID-III). ::: ::: ::: RESULTS ::: Brain volume did not distinguish cases and controls (p = .214-.976). However, cases exhibited greater asymmetry and flattening of the posterior brain (p < .001-.002) and cerebellar vermis (p = .035), shortening of the corpus callosum (p = .012), and differences in the orientation of the corpus callosum (p = .005). Asymmetry and flattening of brain structures were associated with worse developmental outcomes on the BSID-III. ::: ::: ::: CONCLUSIONS ::: Infants with DP show differences in brain shape, consistent with the skull deformity characteristic of this condition, and shape measures were associated with infant development. Longitudinal studies, beginning in the neonatal period, are needed to clarify whether developmental effects precede or follow brain deformation.


A German-Language E-Mail Corpus


title: Optical Character Recognition (OCR) System

abstract: In the running world, there is growing demand for the software systems to recognize characters in computer system when information is scanned through paper documents as we know that we have number of newspapers and books which are in printed format related to different subjects. These days there is a huge demand in "storing the information available in these paper documents in to a computer storage disk and then later reusing this information by searching process". One simple way to store information in these paper documents in to computer system is to first scan the documents and then store them as IMAGES. But to reuse this information it is very difficult to read the individual contents and searching the contents form these documents line-by-line and word-by-word. The reason for this difficulty is the font characteristics of the characters in paper documents are different to font of the characters in computer system. As a result, computer is unable to recognize the characters while reading them. This concept of storing the contents of paper documents in computer storage place and then reading and searching the content is called DOCUMENT PROCESSING. Sometimes in this document processing we need to process the information that is related to languages other than the English in the world. For this document processing we need a software system called CHARCATER RECOGNITION SYSTEM. This process is also called DOCUMENT IMAGE ANALYSIS (DIA).


title: 3-D Floating-Gate Synapse Array With Spike-Time-Dependent Plasticity

abstract: This paper proposes a 3-D floating-gate (FG) synapse array for neuromorphic applications. The designed device has certain advantages over previous planar FG synapse devices: a smaller cell size due to the stacked structure and smaller operation voltage by the gate-all-around geometry. In addition, the operation method to implement spike time-dependent plasticity is proposed and demonstrated. The proposed array based on commercialized flash memory technology is expected be one of the most promising candidate architecture for neuromorphic applications.


title: $E$ -Band Substrate Integrated Waveguide Orthomode Transducer Integrated With Dual-Polarized Horn Antenna

abstract: This paper presents an E-band substrate integrated waveguide (SIW) dual-polarized antenna system, which consists of an orthomode transducer (OMT) and a dual-polarized horn antenna. On the basis of a two-layer substrate design, the function of OMT is realized by utilizing an SIW section to excite TE10 mode for the vertical polarization, a quasi-coaxial stripline to excite quasi-TEM mode for the horizontal polarization. And also, a slot line transition is placed at the common port of the two structures for impedance matching with the ridge SIW dual-polarized horn antenna. In this way, the dual-polarized horn antenna is designed with an exponential ridge structure simultaneously for better impedance matching and higher efficiency of the antenna. To verify the functionality of the proposed integrated OMT-antenna structure, an experimental prototype is fabricated and measured, and a good agreement is found between its simulation and measured results. Over the frequency range of 83–87 GHz, return loss better than 10 dB at all ports and cross-polarization less than −25 dB are obtained. The maximum antenna gain is about 14 dBi for the horizontal polarization and 15.2 dBi for the vertical polarization. It is believed that the proposed OMT-antenna design is suitable for ultrahigh capacity millimeter-wave backhaul applications in future wireless communication systems.


title: An Examination of Contextual Factors and Individual Characteristics Affecting Technology Implementation Decisions in Auditing

abstract: While computer-assisted audit techniques (CAATs) have the potential to increase efficiency and effectiveness of audit engagements, research in this area suggests that such techniques are under-utilized in public accounting. We propose that this condition is due to performance evaluation pressure and the use of budgets for multiple purposes, which result in the misalignment of firm and individual employee goals. We apply technology acceptance and budgeting theories to test this contention as well as potential organizational strategies for reducing the impediments to technology acceptance in the audit profession.


title: The Box-Cox Transformation Technique: A Review

abstract: Box & Cox (1964) proposed a parametric power transformation technique in order to reduce anomalies such as non-additivity, non-normality and heteroscedasticity. Although the transformation has been extensively studied, no bibliography of the published research exists at present. An attempt is made here to review the work relating to this transformation.


title: Vehicle Positioning Using 5G Millimeter-Wave Systems

abstract: Recent growth in traffic and the resulting congestion and accidents has increased the demand for vehicle positioning systems. Existing global navigation satellite systems were designed for line of sight environments and thus accurately determining the location of a vehicle in urban areas with tall buildings or regions with dense foliage is difficult. Fifth generation (5G) cellular networks provide device-to-device communication capabilities which can be exploited to determine the real-time location of vehicles. Millimeter-wave (mmWave) transmission is regarded as a key technology for 5G networks. This paper examines vehicle positioning using 5G mmWave signals. Both a correlation receiver and an energy detector are considered for timing estimation. Furthermore, fixed and dynamic thresholds for energy detection are examined. It is shown that a correlation receiver can provide excellent ranging accuracy but has high computational complexity, whereas an energy detector has low computational complexity and provides good ranging accuracy. Furthermore, the Gaussian raised-cosine pulse (RCP), Gaussian pulse, and Sinc-RCP impulse radio waveforms provide the best performance.


title: 3D Human Pose Estimation from Monocular Images with Deep Convolutional Neural Network

abstract: In this paper, we propose a deep convolutional neural network for 3D human pose estimation from monocular images. We train the network using two strategies: (1) a multi-task framework that jointly trains pose regression and body part detectors; (2) a pre-training strategy where the pose regressor is initialized using a network trained for body part detection. We compare our network on a large data set and achieve significant improvement over baseline methods. Human pose estimation is a structured prediction problem, i.e., the locations of each body part are highly correlated. Although we do not add constraints about the correlations between body parts to the network, we empirically show that the network has disentangled the dependencies among different body parts, and learned their correlations.


title: The MIR flickr retrieval evaluation

abstract: In most well known image retrieval test sets, the imagery typically cannot be freely distributed or is not representative of a large community of users. In this paper we present a collection for the MIR community comprising 25000 images from the Flickr website which are redistributable for research purposes and represent a real community of users both in the image content and image tags. We have extracted the tags and EXIF image metadata, and also make all of these publicly available. In addition we discuss several challenges for benchmarking retrieval and classification methods.


title: Context-Dependent Knowledge Graph Embedding

abstract: We consider the problem of embedding knowledge graphs (KGs) into continuous vector spaces. Existing methods can only deal with explicit relationships within each triple, i.e., local connectivity patterns, but cannot handle implicit relationships across different triples, i.e., contextual connectivity patterns. This paper proposes context-dependent KG embedding, a twostage scheme that takes into account both types of connectivity patterns and obtains more accurate embeddings. We evaluate our approach on the tasks of link prediction and triple classification, and achieve significant and consistent improvements over state-of-the-art methods.


title: Characterizing residential broadband networks

abstract: A large and rapidly growing proportion of users connect to the Internet via residential broadband networks such as Digital Subscriber Lines (DSL) and cable. Residential networks are often the bottleneck in the last mile of today's Internet. Their characteristics critically affect Internet applications, including voice-over-IP, online games, and peer-to-peer content sharing/delivery systems. However, to date, few studies have investigated commercial broadband deployments, and rigorous measurement data that characterize these networks at scale are lacking. In this paper, we present the first large-scale measurement study of major cable and DSL providers in North America and Europe. We describe and evaluate the measurement tools we developed for this purpose. Our study characterizes several properties of broadband networks, including link capacities, packet round-trip times and jitter, packet loss rates, queue lengths, and queue drop policies. Our analysis reveals important ways in which residential networks differ from how the Internet is conventionally thought to operate. We also discuss the implications of our findings for many emerging protocols and systems, including delay-based congestion control (e.g., PCP) and network coordinate systems (e.g., Vivaldi).


title: Optimization strategies for task specific path-following capabilities of a binary actuated snake-like robot using follow-the-leader control

abstract: Snake-like robots allow to perform examination tasks of difficultly accessible areas. In this context a hyper-redundant shaft concept based on unique binary, electromagnetic tilting actuators was proposed earlier. Because of the electromagnetic actuation principle, the system provides good path following capabilities in combination with high resistance against manipulation forces. An efficient and intuitive path planning algorithm for those snake-like, serial chain robots is a follow-the-leader approach, as it is less complex and promises high accuracy at the same time. However, classical follow-the-leader approaches are designed for continuously actuated joints and their applicability to binary actuated systems is limited. Therefore, an adaption to binary actuation with optimized switching sequences to achieve an appropriate path following performance in spatial settings despite of the restricted motion is presented in this paper. Optimization strategies are outlined and different cost functions for different use cases, including the adherence to constrains, are evaluated.


title: Relevance popularity: A term event model based feature selection scheme for text classification

abstract: Feature selection is a practical approach for improving the performance of text classification methods by optimizing the feature subsets input to classifiers. In traditional feature selection methods such as information gain and chi-square, the number of documents that contain a particular term (i.e. the document frequency) is often used. However, the frequency of a given term appearing in each document has not been fully investigated, even though it is a promising feature to produce accurate classifications. In this paper, we propose a new feature selection scheme based on a term event Multinomial naive Bayes probabilistic model. According to the model assumptions, the matching score function, which is based on the prediction probability ratio, can be factorized. Finally, we derive a feature selection measurement for each term after replacing inner parameters by their estimators. On a benchmark English text datasets (20 Newsgroups) and a Chinese text dataset (MPH-20), our numerical experiment results obtained from using two widely used text classifiers (naive Bayes and support vector machine) demonstrate that our method outperformed the representative feature selection methods.


title: Optical fiber sensor for monitoring the strain of concrete which subject to freezing and thawing test

abstract: In order to monitor the strain of concrete which caused by freeze-thaw (F-T) cycles, a novel type of white light interferometer (WLI) sensor is designed and tested. The single-mode optical fiber is embedded in the cement mortar ring as strain gauge, after that, poured the cement mortar ring into concrete cylinder. To study the feasibility of the proposed measurement, two concrete cylinders are exposed in a temperature chamber which uses to simulate the F-T environment. The strain is monitored at 15°C after every 10 cycles. Temperature compensation method is conducted to eliminate the optical fiber strain which caused by temperature. The results indicate that the monitoring method is stable and the residual strain increases with F-T cycles


title: A 50 MHz BW 73.5 dB SNDR two-stage continuous-time ΔΣ modulator with VCO quantizer nonlinearity cancellation

abstract: A two-stage continuous-time (CT) ΔΣ modulator with VCO quantizer (VCOQ) is presented. The presented modulator suppresses the VCOQ Voltage-to-Frequency nonlinearity through dual path cancellation to achieve high linearity. As an added advantage, this architecture exhibits strong immunity to the first stage quantization error leakage to the output due to gain mismatches between the two stages and temperature variations. Fabricated in a 65 nm CMOS technology, the prototype modulator operates at 1.5 GS/s and achieves 76.1 dB SNR, 73.5 dB SNDR, 88 dB SFDR and 76.1 dB dynamic range in 50 MHz bandwidth. This prototype demonstrates robust performance with less than 1.5 dB variation in SNDR for ±10% gain mismatch between the two stages. Also, the SNDR variation remains within 1 dB for a temperature variation of 0° C − 80° C. The modulator consumes 51.8 mW of power leading to a Walden FoM of 134 fJ/conv-step.


title: Electricity Smart Meters Interfacing the Households

abstract: The recent worldwide measures for energy savings call for a larger awareness of the household energy consumption, given the relevant contribution of domestic load to the national energy balance. On the other hand, electricity smart meters together with gas, heat, and water meters can be interconnected in a large network offering a potential value to implement energy savings and other energy-related services, as long as an efficient interface with the final user is implemented. Unfortunately, so far, the interface of such devices is mostly designed and addressed at the utilities supervising the system, giving them relevant advantages, while the communication with the household is often underestimated. This paper addresses this topic by proposing the definition of a local interface for smart meters, by looking at the actual European Union and international regulations, at the technological solutions available on the market, and at those implemented in different countries, and, finally, by proposing specific architectures for a proper consumer-oriented implementation of a smart meter network.


title: Counting of People in the Extremely Dense Crowd

abstract: Received Nov 13,2012 Revised Jan 05, 2013 Accepted Jan 12,2013 In this paper, we have proposed a framework to count the moving person in the video automatically in a very dense crowd situation. Median filter is used to segment the foreground from the background and blob analysis is done to count the people in the current frame. Optimization of different parameters is done by using genetic algorithm. This framework is used to count the people in the video recorded in the mattaf area where different crowd densities can be observed. An overall people counting accuracy of more than 96% is obtained. Keyword:


title: PMSM laboratory stand for investigations on advanced structures of electrical drive control

abstract: This paper presents design, structure and experimental tests of a laboratory stand for the investigation on advanced control algorithms for electrical drive. The main propulsion unit is a permanent magnet synchronous motor, which is connected with a brushless DC motor operating as a dynamically adjustable load. System is controlled by a DSP with the dedicated speed and position measurement extension boards. Availability of an external DSP bus allowed to incorporate FPGA system to the control layer. The paper describes also an innovative, object-oriented approach to the implementation of the control algorithms in DSP system.


title: Model-Free Imitation Learning with Policy Optimization

abstract: In imitation learning, an agent learns how to behave in an environment with an unknown cost function by mimicking expert demonstrations. Existing imitation learning algorithms typically involve solving a sequence of planning or reinforcement learning problems. Such algorithms are therefore not directly applicable to large, high-dimensional environments, and their performance can significantly degrade if the planning problems are not solved to optimality. Under the apprenticeship learning formalism, we develop alternative model-free algorithms for finding a parameterized stochastic policy that performs at least as well as an expert policy on an unknown cost function, based on sample trajectories from the expert. Our approach, based on policy gradients, scales to large continuous environments with guaranteed convergence to local minima.


title: Smartphone Applications Utilizing Biofeedback Can Aid Stress Reduction

abstract: Introduction Stress is one of the leading global causes of disease and premature mortality. Despite this, interventions aimed at reducing stress have low adherence rates. The proliferation of mobile phone devices along with gaming-style applications allows for a unique opportunity to broaden the reach and appeal of stress-reduction interventions in modern society. We assessed the effectiveness of two smartphone applications games combined with biofeedback in reducing stress. Methods We compared a control game to gaming-style smartphone applications combined with a skin conductance biofeedback device (the Pip). 50 participants aged between 18 and 35 completed the Trier Social Stress Test. They were then randomly assigned to the intervention (biofeedback game) or control group (a non-biofeedback game) for thirty minutes. Perceived stress, heart rate and mood were measured before and after participants had played the games. Results A mixed factorial ANOVA showed a significant interaction between time and game type in predicting perceived stress (F(1, 48) = 14.19, p< .001). Participants in the biofeedback intervention had significantly reduced stress compared to the control group. There was also a significant interaction between time and game in predicting heart rate (F(1, 48) = 6.41, p< .05). Participants in the biofeedback intervention showed significant reductions in heart rate compared to the control group. Discussion This illustrates the potential for gaming-style smartphone applications combined with biofeedback as stress reduction interventions.


title: A comparison of feature descriptors for visual SLAM

abstract: Feature detection and feature description plays an important part in Visual Simultaneous Localization and Mapping (VSLAM). Visual features are commonly used to efficiently estimate the motion of the camera (visual odometry) and link the current image to previously visited parts of the environment (place recognition, loop closure). Gradient histogram-based feature descriptors, like SIFT and SURF, are frequently used for this task. Recently introduced binary descriptors, as BRIEF or BRISK, claim to offer similar capabilities at lower computational cost. In this paper, we will compare the most popular feature descriptors in a typical graph-based VSLAM algorithm using two publicly available datasets to determine the impact of the choice for feature descriptor in terms of accuracy and speed in a realistic scenario.


title: Modelling floating offshore units moored in broken ice: model description

abstract: Abstract A simulation model that predicts the behaviour of a vessel moored in broken ice has been developed. The kinematics and dynamics of a broken ice field subject to external forcing are simulated using discrete element modelling. In the model each icefloe is represented by a circular disk while boundaries, land formations and structures may have any piecewise linear shape. Disk–disk or disk–structure collisions are modelled as linear viscous-elastic, or as a Coulomb friction contact in case of relative tangential sliding. The ice model was coupled to a model of a vessel moored on a catenary mooring, i.e., a model accounting for the wind and hydrodynamic forcing on a slender vessel, as well as restoring forces from mooring lines. The coupled numerical model is described in this paper.


title: Universal OWL Axiom Enrichment for Large Knowledge Bases

abstract: The Semantic Web has seen a rise in the availability and usage of knowledge bases over the past years, in particular in the Linked Open Data initiative. Despite this growth, there is still a lack of knowledge bases that consist of high quality schema information and instance data adhering to this schema. Several knowledge bases only consist of schema information, while others are, to a large extent, a mere collection of facts without a clear structure. The combination of rich schema and instance data would allow powerful reasoning, consistency checking, and improved querying possibilities as well as provide more generic ways to interact with the underlying data. In this article, we present a light-weight method to enrich knowledge bases accessible via SPARQL endpoints with almost all types of OWL 2 axioms. This allows to semi-automatically create schemata, which we evaluate and discuss using DBpedia.


title: Three-dimensional imaging techniques: A literature review

abstract: Imaging is one of the most important tools for orthodontists to evaluate and record size and form of craniofacial structures. Orthodontists routinely use 2-dimensional (2D) static imaging techniques, but deepness of structures cannot be obtained and localized with 2D imaging. Three-dimensional (3D) imaging has been developed in the early of 1990's and has gained a precious place in dentistry, especially in orthodontics. The aims of this literature review are to summarize the current state of the 3D imaging techniques and to evaluate the applications in orthodontics.