title: Comparison of adaptive features with linear discriminant classifier for Brain computer Interfaces

abstract: Many Brain-computer Interfaces (BCI) use band-power estimates with more or less subject-specific optimization of the frequency bands. However, a number of alternative EEG features do not need to select the frequency bands; estimators for these features have been modified for an adaptive use. The popular band power estimates were compared with Adaptive AutoRegressive parameters, Hjorth, Barlow, Wackermann, Brain-Rate and a new feature type called Time Domain Parameter. The results from 21 subjects show that several features provide an equally good or even better performance, while no subject-specific optimization is needed, and they are also preferable to band power when the most discriminating frequency band of a subject is not known.


title: Level of automation effects on performance, situation awareness and workload in a dynamic control task

abstract: Various levels of automation (LOA) designating the degree of human operator and computer control were explored within the context of a dynamic control task as a means of improving overall human/machine performance. Automated systems have traditionally been explored as binary function allocations; either the human or the machine is assigned to a given task. More recently, intermediary levels of automation have been discussed as a means of maintaining operator involvement in system performance, leading to improvements in situation awareness and reductions in out-of-the-loop performance problems. A LOA taxonomy applicable to a wide range of psychomotor and cognitive tasks is presented here. The taxonomy comprises various schemes of generic control system function allocations. The functions allocated to a human operator and/or computer included monitoring displays, generating processing options, selecting an 'optimal' option and implementing that option. The impact of the LOA taxonomy was assessed within a dynamic and complex cognitive control task by measuring its effect on human/system performance, situation awareness and workload. Thirty subjects performed simulation trials involving various levels of automation. Several automation failures occurred and out-of-the-loop performance decrements were assessed. Results suggest that, in terms of performance, human operators benefit most from automation of the implementation portion of the task, but only under normal operating conditions; in contrast, removal of the operator from task implementation is detrimental to performance recovery if the automated system fails. Joint human/system option generation significantly degraded performance in comparison to human or automated option generation alone. Lower operator workload and higher situation awareness were observed under automation of the decision making portion of the task (i.e. selection of options), although human/system performance was only slightly improved. The implications of these findings for the design of automated systems are discussed.


title: Self-image resilience and dissonance: the role of affirmational resources.

abstract: It was predicted that high self-esteem Ss (HSEs) would rationalize an esteem-threatening decision less than low self-esteem Ss (LSEs), because HSEs presumably had more favorable self-concepts with which to affirm, and thus repair, their overall sense of self-integrity. This prediction was supported in 2 experiments within the "free-choice" dissonance paradigm--one that manipulated self-esteem through personality feedback and the other that varied it through selection of HSEs and LSEs, but only when Ss were made to focus on their self-concepts. A 3rd experiment countered an alternative explanation of the results in terms of mood effects that may have accompanied the experimental manipulations. The results were discussed in terms of the following: (a) their support for a resources theory of individual differences in resilience to self-image threats--an extension of self-affirmation theory, (b) their implications for self-esteem functioning, and (c) their implications for the continuing debate over self-enhancement versus self-consistency motivation.


A Cloud Computing Framework for Ethiopian Higher Education Institutions


title: Top-k Query Evaluation with Probabilistic Guarantees

abstract: Top-k queries based on ranking elements of multidimensional datasets are a fundamental building block for many kinds of information discovery. The best known general-purpose algorithm for evaluating top-k queries is Fagin's threshold algorithm (TA). Since the user's goal behind top-k queries is to identify one or a few relevant and novel data items, it is intriguing to use approximate variants of TA to reduce run-time costs. This paper introduces a family of approximate top-k algorithms based on probabilistic arguments. When scanning index lists of the underlying multidimensional data space in descending order of local scores, various forms of convolution and derived bounds are employed to predict when it is safe, with high probability, to drop candidate items and to prune the index scans. The precision and the efficiency of the developed methods are experimentally evaluated based on a large Web corpus and a structured data collection.


title: Combining Different Summarization Techniques for Legal Text

abstract: Summarization, like other natural language processing tasks, is tackled with a range of different techniques - particularly machine learning approaches, where human intuition goes into attribute selection and the choice and tuning of the learning algorithm. Such techniques tend to apply differently in different contexts, so in this paper we describe a hybrid approach in which a number of different summarization techniques are combined in a rule-based system using manual knowledge acquisition, where human intuition, supported by data, specifies not only attributes and algorithms, but the contexts where these are best used. We apply this approach to automatic summarization of legal case reports. We show how a preliminary knowledge base, composed of only 23 rules, already outperforms competitive baselines.


title: Secure data processing framework for mobile cloud computing

abstract: In mobile cloud computing, mobile devices can rely on cloud computing and information storage resource to perform computationally intensive operations such as searching, data mining, and multimedia processing. In addition to providing traditional computation services, mobile cloud also enhances the operation of traditional ad hoc network by treating mobile devices as service nodes, e.g., sensing services. The sensed information, such as location coordinates, health related information, should be processed and stored in a secure fashion to protect user's privacy in the cloud. To this end, we present a new mobile cloud data processing framework through trust management and private data isolation. Finally, an implementation pilot for improving teenagers' driving safety, which is called FocusDrive, is presented to demonstrate the solution.


title: How Often Do I Agree: an Experimental Test of Item Format Method Variance in Stress Measures

abstract: Three studies experimentally investigated response formats of agreement (A) and frequency (F) to determine their effect on correlations between often used measures of job stressors and strains. Study 1 used a within-subject design where respondents were given two sets of the same 6 stressor measures that varied in A versus F formats, as well as measures of 6 strains. Study 2 replicated Study 1 using a between-subject design where each respondent was randomly assigned to either the A or F stressor scale format. Study 3 utilized a 2 (A vs. F for stressors) by 2 (A vs. F for strains) design to test the assumption that matched response formats would result in higher correlations than unmatched due to common method variance. The first two studies provide little evidence for a systematic difference between A and F formats in predicting strains. Study 3 found that matching formats more often resulted in lower, not higher, correlations between stressor and strain pairs. Taken together, these experimental studies suggest that response format has little effect on correlations, means, standard deviations, and internal consistencies, and is not a method variance concern, at least with these commonly used measures of job stressors and strains.


title: Network organization of coopetitive genetic influences on cortical morphologies

abstract: Brain can be represented as a network, where regions are the nodes and relations between the regions are edges. Within a network, co-existence of cooperative and competitive relationships between different nodes is called coopetition. Inter-regional genetic influences on morphological phenotypes (cortical thickness, surface area) of cortex display such coopetitive relationships. Here, we have represented these genetic influences as a network and shown that cooperative and competitive genetic influences on cortical morphological phenotypes follow distinct organization principles. Utilizing the theory of structural balance, we have shown that the pattern of collective regulation of cortical morphological phenotypes by cooperative and competitive genetic influences are overall bilaterally symmetric and such patterns of collective genetic regulation are similar to the principal modes of population variation of cortical morphological phenotypes. Finally, we have observed that the maximally and minimally imbalanced regions corresponding to the collective genetic regulation partially overlap with the cortical structural network hubs.


title: The Geneva affective picture database (GAPED): a new 730-picture database focusing on valence and normative significance

abstract: In emotional research, efficient designs often rely on successful emotion induction. For visual stimulation, the only reliable database available so far is the International Affective Picture System (IAPS). However, extensive use of these stimuli lowers the impact of the images by increasing the knowledge that participants have of them. Moreover, the limited number of pictures for specific themes in the IAPS database is a concern for studies centered on a specific emotion thematic and for designs requiring a lot of trials from the same kind (e.g., EEG recordings). Thus, in the present article, we present a new database of 730 pictures, the Geneva Affective PicturE Database, which was created to increase the availability of visual emotion stimuli. Four specific negative contents were chosen: spiders, snakes, and scenes that induce emotions related to the violation of moral and legal norms (human rights violation or animal mistreatment). Positive and neutral pictures were also included: Positive pictures represent mainly human and animal babies as well as nature sceneries, whereas neutral pictures mainly depict inanimate objects. The pictures were rated according to valence, arousal, and the congruence of the represented scene with internal (moral) and external (legal) norms. The constitution of the database and the results of the picture ratings are presented.


title: On the Assessment of Expertise Profiles

abstract: Expertise retrieval has attracted significant interest in the field of information retrieval. Expert finding has been studied extensively, with less attention going to the complementary task of expert profiling, that is, automatically identifying topics about which a person is knowledgeable. We describe a test collection for expert profiling in which expert users have self-selected their knowledge areas. Motivated by the sparseness of this set of knowledge areas, we report on an assessment experiment in which academic experts judge a profile that has been automatically generated by state-of-the-art expert-profiling algorithms; optionally, experts can indicate a level of expertise for relevant areas. Experts may also give feedback on the quality of the systemgenerated knowledge areas. We report on a content analysis of these comments and gain insights into what aspects of profiles matter to experts. We provide an error analysis of the system-generated profiles, identifying factors that help explain why certain experts may be harder to profile than others. We also analyze the impact on evaluating expert-profiling systems of using selfselected versus judged system-generated knowledge areas as ground truth; they rank systems somewhat differently but detect about the same amount of pairwise significant differences despite the fact that the judged system-generated assessments are more sparse.


Do Institutions Cause Growth?


title: Adaptive deep learning model selection on embedded systems

abstract: The recent ground-breaking advances in deep learning networks (DNNs) make them attractive for embedded systems. However, it can take a long time for DNNs to make an inference on resource-limited embedded devices. Offloading the computation into the cloud is often infeasible due to privacy concerns, high latency, or the lack of connectivity. As such, there is a critical need to find a way to effectively execute the DNN models locally on the devices. This paper presents an adaptive scheme to determine which DNN model to use for a given input, by considering the desired accuracy and inference time. Our approach employs machine learning to develop a predictive model to quickly select a pre-trained DNN to use for a given input and the optimization constraint. We achieve this by first training off-line a predictive model, and then use the learnt model to select a DNN model to use for new, unseen inputs. We apply our approach to the image classification task and evaluate it on a Jetson TX2 embedded deep learning platform using the ImageNet ILSVRC 2012 validation dataset. We consider a range of influential DNN models. Experimental results show that our approach achieves a 7.52% improvement in inference accuracy, and a 1.8x reduction in inference time over the most-capable single DNN model.


title: Cell Detection with Star-convex Polygons

abstract: Automatic detection and segmentation of cells and nuclei in microscopy images is important for many biological applications. Recent successful learning-based approaches include per-pixel cell segmentation with subsequent pixel grouping, or localization of bounding boxes with subsequent shape refinement. In situations of crowded cells, these can be prone to segmentation errors, such as falsely merging bordering cells or suppressing valid cell instances due to the poor approximation with bounding boxes. To overcome these issues, we propose to localize cell nuclei via star-convex polygons, which are a much better shape representation as compared to bounding boxes and thus do not need shape refinement. To that end, we train a convolutional neural network that predicts for every pixel a polygon for the cell instance at that position. We demonstrate the merits of our approach on two synthetic datasets and one challenging dataset of diverse fluorescence microscopy images.


title: IC-Stripline design optimization using response surface methodology

abstract: This paper presents design optimization of closed version of an IC-Stripline. The geometrical parameters of the IC-Stripline are optimized in order to improve VSWR performance in the frequency range up to 6 GHz. Since the optimization performed directly in 3D EM simulator is computationally expensive, the response surface methodology (RSM) based on design of experiment (DOE) is used to develop surrogate models and to accelerate IC-Stripline design optimization. The design of experiment approach is used to systematically vary geometrical parameters of the IC-Stripline structure which are then simulated in a 3D electromagnetic simulator. The equivalent model circuit parameters are extracted according to the 3D EM simulation results. The response surface formed by the extracted parameter values is modelled using a higher order polynomial. The VSWR optimization of the IC-Stripline is performed by optimizing the equivalent circuit model having the parameters defined by the response surface models. The proposed design of the IC-Stripline shows the improvement in VSWR performance over the frequency range up to 6 GHz.


title: Honeywords: making password-cracking detectable

abstract: We propose a simple method for improving the security of hashed passwords: the maintenance of additional ``honeywords'' (false passwords) associated with each user's account. An adversary who steals a file of hashed passwords and inverts the hash function cannot tell if he has found the password or a honeyword. The attempted use of a honeyword for login sets off an alarm. An auxiliary server (the ``honeychecker'') can distinguish the user password from honeywords for the login routine, and will set off an alarm if a honeyword is submitted.


title: Portal imaging for evaluation of daily on-line setup errors and off-line organ motion during conformal irradiation of carcinoma of the prostate

abstract: Abstract Purpose: To use portal imaging to measure daily on-line setup error and off-line prostatic motion in patients treated with conformal radiotherapy to determine an optimum planning target volume (PTV) margin incorporating both setup error and organ motion. Results: A total of 2549 portal images from 33 patients were acquired over the course of the study. Of these patients, 23 were analyzed for setup errors while the remaining 10 were analyzed for prostatic motion. Setup errors were characterized by standard deviations of 1.8 mm in the anterior-posterior (AP) direction and 1.4 mm in the superior-inferior (SI) direction. Displacements due to prostatic motion, with standard deviations of 5.8 mm AP and 3.3 mm SI, were found to be more significant than setup errors. Conclusions: Taking into account both setup errors and target organ motion, optimum PTV margins to ensure 95% coverage are 10.0 mm AP and 5.9 mm SI. The portal imaging protocol established in this study allows radiation therapists to accept or adjust a treatment setup based upon daily on-line image matching results. The successful localization of radiopaque fiducial markers on a significant number of portal images acquired in the study gives hope that more accurate on-line targeting verification may soon be possible through the visualization of the prostate itself as opposed to the surrounding bony structures of the pelvis.


title: Domain-Adaptive Discriminative One-Shot Learning of Gestures

abstract: The objective of this paper is to recognize gestures in videos – both localizing the gesture and classifying it into one of multiple classes.


title: Relax, No Need to Round: Integrality of Clustering Formulations

abstract: We study exact recovery conditions for convex relaxations of point cloud clustering problems, focusing on two of the most common optimization problems for unsupervised clustering: k-means and k-median clustering. Motivations for focusing on convex relaxations are: (a) they come with a certificate of optimality, and (b) they are generic tools which are relatively parameter-free, not tailored to specific assumptions over the input. More precisely, we consider the distributional setting where there are k clusters in Rm and data from each cluster consists of n points sampled from a symmetric distribution within a ball of unit radius. We ask: what is the minimal separation distance between cluster centers needed for convex relaxations to exactly recover these k clusters as the optimal integral solution? For the k-median linear programming relaxation we show a tight bound: exact recovery is obtained given arbitrarily small pairwise separation ε > O between the balls. In other words, the pairwise center separation is δ > 2+ε. Under the same distributional model, the k-means LP relaxation fails to recover such clusters at separation as large as δ = 4. Yet, if we enforce PSD constraints on the k-means LP, we get exact cluster recovery at separation as low as δ > min{2 + √2k/m}, 2+√2 + 2/m} + ε. In contrast, common heuristics such as Lloyd's algorithm (a.k.a. the k means algorithm) can fail to recover clusters in this setting; even with arbitrarily large cluster separation, k-means++ with overseeding by any constant factor fails with high probability at exact cluster recovery. To complement the theoretical analysis, we provide an experimental study of the recovery guarantees for these various methods, and discuss several open problems which these experiments suggest.


title: Conformal double exponentially tapered slot antenna (DETSA) on LCP for UWB applications

abstract: We discuss the use of a double exponentially tapered slot antenna (DETSA) fabricated on flexible liquid crystal polymer (LCP) as a candidate for ultrawideband (UWB) communications systems. The features of the antenna and the effect of the antenna on a transmitted pulse are investigated. Return loss and E and H plane radiation pattern measurements are presented in several frequencies covering the whole ultra wide band. The return loss remains below -10 dB and the shape of the radiation pattern remains fairly constant in the whole UWB range (3.1 to 10.6 GHz). The main lobe characteristic of the radiation pattern remains stable even when the antenna is significantly conformed. The major effect of the conformation is an increase in the cross polarization component amplitude. The system: transmitter DETSA-channel receiver DETSA is measured in frequency domain and shows that the antenna adds very little distortion on a transmitted pulse. The distortion remains small even when both transmitter and receiver antennas are folded, although it increases slightly.


title: Path-Tracking Control of an Autonomous 4WS4WD Electric Vehicle Using Its Natural Feedback Loops

abstract: A four-wheel steering four-wheel drive electric vehicle has a steering motor and a driving motor for each wheel, for a total of eight motors. Almost all of the existing path-tracking controllers for this vehicle are designed by viewing the mathematical model of the vehicle as the system of equations ${\dot {x} = f(x,u), y = h(x,u)}$ , and are mathematically sophisticated. In contrast, this brief presents a block diagrammatic representation of the model, and exploits the natural feedback loops revealed through this representation to develop two novel and useful results: 1) a mathematically simpler path-tracking controller that promises to be easier to tune and 2) a constraint on the wheel accelerations that helps constrain the wheel slips to a desired value. Simulations illustrate this solution.


title: Work-Related Musculoskeletal Disorders of the Hand and Wrist: Epidemiology, Pathophysiology, and Sensorimotor Changes

abstract: The purpose of this commentary is to present recent epidemiological findings regarding work-related musculoskeletal disorders (WMSDs) of the hand and wrist, and to summarize experimental evidence of underlying tissue pathophysiology and sensorimotor changes in WMSDs. Sixty-five percent of the 333 800 newly reported cases of occupational illness in 2001 were attributed to repeated trauma. WMSDs of the hand and wrist are associated with the longest absences from work and are, therefore, associated with greater lost productivity and wages than those of other anatomical regions. Selected epidemiological studies of hand/wrist WMSDs published since 1998 are reviewed and summarized. Results from selected animal studies concerning underlying tissue pathophysiology in response to repetitive movement or tissue loading are reviewed and summarized. To the extent possible, corroborating evidence in human studies for various tissue pathomechanisms suggested in animal models is presented. Repetitive, hand-intensive movements, alone or in combination with other physical, nonphysical, and nonoccupational risk factors, contribute to the development of hand/wrist WMSDs. Possible pathophysiological mechanisms of tissue injury include inflammation followed by repair and/or fibrotic scarring, peripheral nerve injury, and central nervous system reorganization. Clinicians should consider all of these pathomechanisms when examining and treating patients with hand/wrist WMSDs.


title: Cognitive radio network architecture: part II -- trusted network layer structure

abstract: Cognitive radios shall form cognitive radio network (CRN) to complete the packets delivery. As cognitive radio network is a general multihop wireless heterogeneous network, trust becomes a critical factor for network layer functions and network operation. We explore and develop the mathematical framework of trust in CRN, to further develop trusted routing with relevant explorations on network layer and above functions.


title: Phase-Error Measurement and Compensation in PLL Frequency Synthesizers for FMCW Sensors—I: Context and Application

abstract: The synthesis of linear frequency sweeps or chirps is required, among others, in frequency-modulated continuous-wave radar systems for object position estimation. Low phase and frequency errors in sweeps with high bandwidth are a prerequisite for good accuracy and resolution, but, in certain applications where high measurement rates are desired, the additional demand for short sweep cycles has to be met. Transient phenomena in dynamic synthesizers as well as nonlinear system behavior usually cause unknown phase errors in the system output. For the class of phase-locked-loop (PLL)-based frequency synthesizers, a novel output phase-measurement method and dedicated circuitry are proposed that allow significant reduction of phase errors by adaptive input predistortion. The measurement procedure is implemented within the PLL control circuitry and does not require external equipment. The application of this method to PLL system identification and linearization of extremely short frequency sweeps is shown


title: A selection model of ERP system in mobile ERP design science research: Case study: Mobile ERP usability

abstract: Nowadays, mobile apps penetrate in every aspect of our daily life, due to the proliferation of mobile devices, the constant advances in mobile computing, and the enormous leaps of mobile HCI. Thus, these factors have increased the demands to adopt the mobile ERP as a working model in enterprises. Regardless of these demands, mobile ERP has several challenges and issues that need to be addressed through a problem the design science research paradigm, since this paradigm is considered a problem-solving paradigm and it has its roots in engineering and the sciences of the artificial. Accordingly, this paper aims to propose a model for selecting an appropriate ERP system within the mobile ERP's design science research domain. The proposed model was applied to a set of ERP systems that are provided by the dominant ERP vendors. In addition, an integrated overview of the selected ERP system for the proposed use case will be presented in this paper.


title: MEMS pressure sensor array wearable for Traditional Chinese Medicine pulse-taking

abstract: Advances in wireless microelectronics and low-cost sensor manufacturing have led to a variety of wearable technologies, with many wearable devices today being used for monitoring health and wellness. Traditional Chinese Medicine (TCM) is a relatively unexplored area of interest as a type of ‘alternative medicine’. In this paper, we evaluated the suitability of a prototype system based on a 3-sensor array of 3 MEMS barometers for TCM pulse-taking applications: this included characterization of the sensitivity, thermal, and temporal response and its effectiveness in measuring pressure waveforms in a physiologic simulation with a graded-pressure fluid flowing through in an artery-mimicking phantom. Our results demonstrated that the prototype was adequate for such applications and confirmed the optimal specifications for the sensor casting rubber (5.7 mm thick) and design.


title: Rule-based visual speech synthesis

abstract: A system for rule based audiovisual text-to-speech synthesis has been created. The system is based on the KTH text-to-speech system which has been complemented with a three-dimensional parameterized model of a human face. The face can be animated in real time, synchronized with the auditory speech. The facial model is controlled by the same synthesis software as the auditory speech synthesizer. A set of rules that takes coarticulation into account has been developed. The audiovisual text-to-speech system has also been incorporated into a spoken man-machine dialogue system that is being developed at the department.


title: Probabilistic models for discovering e-communities

abstract: The increasing amount of communication between individuals in e-formats (e.g. email, Instant messaging and the Web) has motivated computational research in social network analysis (SNA). Previous work in SNA has emphasized the social network (SN) topology measured by communication frequencies while ignoring the semantic information in SNs. In this paper, we propose two generative Bayesian models for semantic community discovery in SNs, combining probabilistic modeling with community detection in SNs. To simulate the generative models, an EnF-Gibbs sampling algorithm is proposed to address the efficiency and performance problems of traditional methods. Experimental studies on Enron email corpus show that our approach successfully detects the communities of individuals and in addition provides semantic topic descriptions of these communities.


title: Randomized Comparison of Subcuticular Sutures Versus Staples for Skin Closure After Open Abdominal Surgery: a Multicenter Open-Label Randomized Controlled Trial

abstract: BACKGROUND ::: The incisional surgical site infection (SSI) is an extremely common complication following open abdominal surgery and imposes a considerable treatment and cost burden. ::: ::: ::: METHOD ::: We conducted a multicenter open-label randomized controlled trial at three Tokyo Metropolitan medical institutions. We enrolled adult patients who underwent either an elective or an emergency open laparotomy. Eligible patients were allocated preoperatively to undergo wound closure with either subcuticular sutures or staples. A central Web-based randomization tool was used to assign participants randomly by a permuted block sequence with a 1:1 allocation ratio and a block size of 4 before mass closure to each group. The primary endpoint was the occurrence of a superficial SSI within 30 days after surgery in accordance with the Centers for Disease Control and Prevention criteria. This trial was registered with UMIN-CTR as UMIN 000004836 ( http://www.umin.ac.jp/ctr ). ::: ::: ::: RESULTS ::: Between September 1, 2010 and August 31, 2015, 401 patients were enrolled and randomly assigned to either group. One hundred and ninety-nine patients were allocated to the subcuticular suture and 202 patients to the staple groups (hereafter the "suture" and "staple" group, respectively). Three hundred and ninety-nine were eligible for the primary endpoint. Superficial SSIs occurred in 25 of 198 suture patients and in 27 of 201 staple patients. Overall, the rate of superficial SSIs did not differ significantly between the suture and staple groups. ::: ::: ::: CONCLUSION ::: Subcuticular sutures did not increase the occurrence of superficial SSIs following open laparotomies mainly consisting of clean-contaminated surgical procedures. The applicability of the wound closure material and method is likely to depend on individual circumstances of the patient and surgical procedure.


title: A comparison of pose estimation techniques: hardware vs. video

abstract: Robotic navigation requires that the robotic platform have an idea of its location and orientation within the environment. This localization is known as pose estimation, and has been a much researched topic. There are currently two main categories of pose estimation techniques: pose from hardware, and pose from video (PfV). Hardware pose estimation utilizes specialized hardware such as Global Positioning Systems (GPS) and Inertial Navigation Systems (INS) to estimate the position and orientation of the platform at the specified times. PfV systems use video cameras to estimate the pose of the system by calculating the inter-frame motion of the camera from features present in the images. These pose estimation systems are readily integrated, and can be used to augment and/or supplant each other according to the needs of the application. Both pose from video and hardware pose estimation have their uses, but each also has its degenerate cases in which they fail to provide reliable data. Hardware solutions can provide extremely accurate data, but are usually quite pricey and can be restrictive in their environments of operation. Pose from video solutions can be implemented with low-cost off-the-shelf components, but the accuracy of the PfV results can be degraded by noisy imagery, ambiguity in the feature matching process, and moving objects. This paper attempts to evaluate the cost/benefit comparison between pose from video and hardware pose estimation experimentally, and to provide a guide as to which systems should be used under certain scenarios.


title: Speech recognition using deep neural network - recent trends

abstract: Deep neural networks (DNN) are special forms of learning-based structures composed of multiple hidden layers formed by artificial neurons. These are different to the conventional artificial neural networks (ANN) and are accepted as efficient tools for solving emerging real world problems. Recently, DNNs have become a mainstream speech recognition tool and are fast becoming part of evolving technologies emerging as a viable option to replace all other leading tools so far used. ANNs with deep learning which uses a generative, layer by-layer pre-training method for initialising the weights has provided best solution for acoustic modelling for speech recognition. This paper provides a brief description of the current technology related to speech recognition and its slow adoption of DNN-based approaches. Initially, a historical note on the technology development for speech recognition system is given. The later part explains the DNN-based acoustic modelling for speech recognition and recent technology developments reported and the ones available for actual use.


title: Survey of Cellular Mobile Radio Localization Methods: From 1G to 5G

abstract: Cellular systems evolved from a dedicated mobile communication system to an almost omnipresent system with unlimited coverage anywhere and anytime for any device. The growing ubiquity of the network stirred expectations to determine the location of the mobile devices themselves. Since the beginning of standardization, each cellular mobile radio generation has been designed for communication services, and satellite navigation systems, such as Global Positioning System (GPS), have provided precise localization as an add-on service to the mobile terminal. Self-contained localization services relying on the mobile network elements have offered only rough position estimates. Moreover, satellite-based technologies suffer a severe degradation of their localization performance in indoors and urban areas. Therefore, only in subsequent cellular standard releases, more accurate cellular-based location methods have been considered to accommodate more challenging localization services. This survey provides an overview of the evolution of the various localization methods that were standardized from the first to the fourth generation of cellular mobile radio, and looks over what can be expected with the new radio and network aspects for the upcoming generation of fifth generation.


title: On evaluation of outlier rankings and outlier scores

abstract: Outlier detection research is currently focusing on the development of new methods and on improving the computation time for these methods. Evaluation however is rather heuristic, often considering just precision in the top k results or using the area under the ROC curve. These evaluation procedures do not allow for assessment of similarity between methods. Judging the similarity of or correlation between two rankings of outlier scores is an important question in itself but it is also an essential step towards meaningfully building outlier detection ensembles, where this aspect has been completely ignored so far. In this study, our generalized view of evaluation methods allows both to evaluate the performance of existing methods as well as to compare different methods w.r.t. their detection performance. Our new evaluation framework takes into consideration the class imbalance problem and offers new insights on similarity and redundancy of existing outlier detection methods. As a result, the design of effective ensemble methods for outlier detection is considerably enhanced.


title: Small-Size LTE/WWAN Two-Strip Monopole Exciter Antenna Integration With Metal Covers

abstract: A novel small-size LTE/WWAN two-strip monopole exciter antenna integration with metal covers for ultrabook computer applications is presented. The monopole exciter antenna is placed in the hinge slot between metal covers and hinges. By integrating the monopole exciter antenna with the metal covers and hinges, not only several modes of the monopole structure but those of the hinge slot are also excited to cover the desired eight-band LTE/WWAN operation (LTE700/GSM850/900 and GSM1800/ 1900/UMTS/LTE2300/2500 bands). The exciter antenna comprises a feeding strip and a folded strip to form a two-strip monopole structure. The proposed exciter antenna can be integrated with the surrounding metal planes (metal covers) to enhance the operating bandwidth to cover LTE/WWAN band with good radiation efficiency. The exciter antenna has a simple two-strip structure that is easy to be fabricated on a 0.8-mm thin FR4 substrate with a small area of only $25 \times 8$ mm2. Over the lower and upper bands, the antenna efficiencies are about 65%–88% and 55%–78%, respectively.


title: Real-time automation system using Arduino

abstract: The Internet of Things (IoT) is the interconnection of physical objects, devices, vehicles and other items embedded with electronics, software, sensors, and network connectivity that enables these objects to gather and exchange data. For example, in educational organizations, we require additional labor to do jobs such as turn on & off the lights, fans in the laboratories. In some cases, the students may forget to switch off the electrical components like fans, AC and lights in their classrooms. As we take the institution as our scenario, there comes the need of more labors to complete the tasks. And there occurs the wastage of power supply and additional labor cost. To overcome these problems, we are automating the whole system through IoT system. This system can save the energy by efficient power management system of a room which employs certain mechanisms managed by a Microcontroller. This system serves us to control the environment, power and the security management. To provide a better security, the user is provided with live stream from several IP cameras. This paper helps the user to control the system from anywhere at any time from a single terminal through the internet which can be done at a reasonable cost.


title: A backlight dimming algorithm for low power and high image quality LCD applications

abstract: A new backlight dimming algorithm is proposed to reduce power consumption of backlight and improve image quality in LCD applications. The proposed algorithm is consisted of two new techniques: the brightness modulation of the backlight and pixel compensation. The power saving ratio of four typical benchmarking images is 14-43% by using proposed brightness modulation algorithm. The proposed pixel compensation removes the clipping artifact and unnecessary luminance reduction. The proposed backlight dimming algorithm is demonstrated on 7-inch TFTLCD with LED backlight.


title: Lightweight Structure Design Under Force Location Uncertainty

abstract: We introduce a lightweight structure optimization approach for problems in which there is uncertainty in the force locations. Such uncertainty may arise due to force contact locations that change during use or are simply unknown a priori. Given an input 3D model, regions on its boundary where arbitrary normal forces may make contact, and a total force-magnitude budget, our algorithm generates a minimum weight 3D structure that withstands any force configuration capped by the budget. Our approach works by repeatedly finding the most critical force configuration and altering the internal structure accordingly. A key issue, however, is that the critical force configuration changes as the structure evolves, resulting in a significant computational challenge. To address this, we propose an efficient critical instant analysis approach. Combined with a reduced order formulation, our method provides a practical solution to the structural optimization problem. We demonstrate our method on a variety of models and validate it with mechanical tests.


title: View Synthesis by Appearance Flow

abstract: We address the problem of novel view synthesis: given an input image, synthesizing new images of the same object or scene observed from arbitrary viewpoints. We approach this as a learning task but, critically, instead of learning to synthesize pixels from scratch, we learn to copy them from the input image. Our approach exploits the observation that the visual appearance of different views of the same instance is highly correlated, and such correlation could be explicitly learned by training a convolutional neural network (CNN) to predict appearance flows – 2-D coordinate vectors specifying which pixels in the input view could be used to reconstruct the target view. Furthermore, the proposed framework easily generalizes to multiple input views by learning how to optimally combine single-view predictions. We show that for both objects and scenes, our approach is able to synthesize novel views of higher perceptual quality than previous CNN-based techniques.


title: Poster: online spam filtering in social networks

abstract: Online social networks (OSNs) are popular collaboration and communication tools for millions of users and their friends. Unfortunately, in the wrong hands, they are also effective tools for executing spam campaigns. In this paper, we present an online spam filtering system that can be deployed as a component of the OSN platform to inspect messages generated by users in real-time. We propose to reconstruct spam messages into campaigns for classification rather than examine them individually. Although campaign identification has been used for offline spam analysis, we apply this technique to aid the online spam detection problem with sufficiently low overhead. Accordingly, our system adopts a set of novel features that effectively distinguish spam campaigns. It is highly accurate and confidently drops messages classified as "spam" before they reach the intended recipients, thus protecting them from various kinds of fraud. In addition, the system achieves an average throughput of 1580 messages/sec and an average processing latency of 69.7ms for each message. The high throughput and low latency guarantees that it will not become the bottleneck of the whole OSN platform.


Resources for building applications with Dependency Minimal Recursion Semantics.


title: Gaze+Gesture: Expressive, Precise and Targeted Free-Space Interactions

abstract: Humans rely on eye gaze and hand manipulations extensively in their everyday activities. Most often, users gaze at an object to perceive it and then use their hands to manipulate it. We propose applying a multimodal, gaze plus free-space gesture approach to enable rapid, precise and expressive touch-free interactions. We show the input methods are highly complementary, mitigating issues of imprecision and limited expressivity in gaze-alone systems, and issues of targeting speed in gesture-alone systems. We extend an existing interaction taxonomy that naturally divides the gaze+gesture interaction space, which we then populate with a series of example interaction techniques to illustrate the character and utility of each method. We contextualize these interaction techniques in three example scenarios. In our user study, we pit our approach against five contemporary approaches; results show that gaze+gesture can outperform systems using gaze or gesture alone, and in general, approach the performance of "gold standard" input systems, such as the mouse and trackpad.


Algorithms for Sparse Non-negative Tucker decompositions


title: DTCTH: a discriminative local pattern descriptor for image classification

abstract: Despite lots of effort being exerted in designing feature descriptors, it is still challenging to find generalized feature descriptors, with acceptable discrimination ability, which are able to capture prominent features in various image processing applications. To address this issue, we propose a computationally feasible discriminative ternary census transform histogram (DTCTH) for image representation which uses dynamic thresholds to perceive the key properties of a feature descriptor. The code produced by DTCTH is more stable against intensity fluctuation, and it mainly captures the discriminative structural properties of an image by suppressing unnecessary background information. Thus, DTCTH becomes more generalized to be used in different applications with reasonable accuracies. To validate the generalizability of DTCTH, we have conducted rigorous experiments on five different applications considering nine benchmark datasets. The experimental results demonstrate that DTCTH performs as high as 28.08% better than the existing state-of-the-art feature descriptors such as GIST, SIFT, HOG, LBP, CLBP, OC-LBP, LGP, LTP, LAID, and CENTRIST.


title: Framing Contests: Strategy Making Under Uncertainty

abstract: I develop a model of framing contests to elucidate how cognitive frames influence organizational strategy making. By using ethnographic techniques to study the day-to-day practices of strategy making in one firm, I examine the ways actors attempted to transform their own cognitive frames of a situation into predominant frames through a series of interactions. Frames are the means by which managers make sense of ambiguous information from their environments. Actors each had cognitive frames about the direction the market was taking and about what kinds of solutions would be appropriate. Where frames about a strategic choice were not congruent, actors engaged in highly political framing practices to make their frames resonate and to mobilize action in their favor. Those actors who most skillfully engaged in these practices shaped the frame that prevailed in the organization. This framing perspective suggests that frames are not only instrumental tools for the ex post justification of actions taken through power, but rather are an ex ante part of the political process that produces decisions. Uncertainty opens up the possibility for new actors to gain power, and contesting frames is a way of changing the power structures in the organization. A principal contribution of the framing contests model is to locate a middle ground between cognitive and political models of strategy making, one in which frames are both constraints and resources and outcomes can be shaped by purposeful action and interaction to make meaning.


title: New interleaved current-fed resonant converter with significantly reduced high current output filter for EV and HEV application

abstract: This paper proposes a new interleaved current-fed resonant converter with significantly reduced high current output filter which is suitable for EV and HEV applications. The proposed interleaved converter has theoretically zero output filter capacitance, low input current ripple, ZCS turn on and off for all switches and diodes and zero di/dt at turn off of diodes when operated at load independent points. A two-stage power conversion technique is applied for wide input and output voltage range operation of EV and HEV. A 2kW prototype of the proposed converter has been built and tested to verify the validity of the proposed operation.


title: A Comparative Study of Rule Extraction for Recurrent Neural Networks

abstract: Understanding recurrent networks through rule extraction has a long history. This has taken on new interests due to the need for interpreting or verifying neural networks. One basic form for representing stateful rules is deterministic finite automata (DFA). Previous research shows that extracting DFAs from trained second-order recurrent networks is not only possible but also relatively stable. Recently, several new types of recurrent networks with more complicated architectures have been introduced. These handle challenging learning tasks usually involving sequential data. However, it remains an open problem whether DFAs can be adequately extracted from these models. Specifically, it is not clear how DFA extraction will be affected when applied to different recurrent networks trained on data sets with different levels of complexity. Here, we investigate DFA extraction on several widely adopted recurrent networks that are trained to learn a set of seven regular Tomita grammars. We first formally analyze the complexity of Tomita grammars and categorize these grammars according to that complexity. Then we empirically evaluate different recurrent networks for their performance of DFA extraction on all Tomita grammars. Our experiments show that for most recurrent networks, their extraction performance decreases as the complexity of the underlying grammar increases. On grammars of lower complexity, most recurrent networks obtain desirable extraction performance. As for grammars with the highest level of complexity, while several complicated models fail with only certain recurrent networks having satisfactory extraction performance.


title: DeepFoodCam: A DCNN-based Real-time Mobile Food Recognition System

abstract: Due to the recent progress of the studies on deep learning, deep convolutional neural network (DCNN) based methods have outperformed conventional methods with a large margin. Therefore, DCNN-based recognition should be introduced into mobile object recognition. However, since DCNN computation is usually performed on GPU-equipped PCs, it is not easy for mobile devices where memory and computational power is limited.


title: On multi-copy forwarding protocols for large data chunk dissemination in vehicular sensor networks

abstract: Moving vehicles have been sensing all kinds of data on the road in which multimedia data possesses a large portion. These data is often forwarded to vehicles in a region of interest or the monitoring center in an opportunistic manner. With respect to the large volume content, the storage space of relay vehicles is becoming the bottleneck of achieving higher performance, e.g., a data chunk may be rejected or dropped due to insufficient storage of intermediate vehicles. Thus, previous work that only focuses on the delivery metric without considering the data size is not likely to work efficiently in the proposed scenario. As deploying stationary infrastructures is of very large cost and not feasible everywhere, in this paper, we focus on the inter-vehicle data forwarding problem with storage and communication capacity constraints. First, we considered the situation when the vehicles are distributed sparsely. The multi-copy routing challenge is modeled as a multiple knapsack problem. Then, it is extended to a dense scenario. An optimization to the broadcast data forwarding is investigated. With the real data trace, the experiments show that our scheme achieves better performance than the competitors in terms of delay and delivery ratio. A better balance between duplication and performance is also achieved by the multi-copy algorithm.


title: Aggressive vehicle control using polynomial spiral curves

abstract: Autonomous driving at the limits of friction is not only a concept for racing autonomous cars but also a reliable method for emergency evasive manoeuvres in bad weather conditions. In this paper we introduce a method of incorporating smooth trajectory planner with an aggressive driving controller to implement double lane change manoeuvre while driving at the limits of tire friction.


title: Combinatorial Optimization Approach for Feasible Low-Power and Real-Time Flexible OS Tasks

abstract: The chapter presents a combinatorial optimization method for the low-power adaptive scheduling problem on variable speed processors and reconfigurable architectures. It deals with synchronous and flexible real-time OS tasks. A reconfiguration scenario is assumed to be a run-time software intervention which act on the system state to allow the addition-removal-update of OS tasks and consequently adapt the system to its environment under functional and extra-functional requirements. A reconfiguration can change the system behavior where temporal properties are violated or the energy consumption overcomes its limit and pushes the system to a non feasible state. A configuration scenario can be a result of the addition-removal-update of tasks in the system. The difficulty is how to recover the violated temporal parameters of the system’s tasks after any reconfiguration. We use a DVS processor which is with a variable speed to support run-time solutions to re-obtain the system’s feasibility. The challenge is to choose the suitable scaling factors for the processor speed to ensure the best compromise between the execution time and the energy consumption where all constraints are respected. We reformulate the problem and propose a combinatorial optimization method based on integer programming and heuristics to solve the problem. We compensate each approach by a mechanism which is based on the deadline adjustment of the tasks to satisfy the feasibility conditions when the available speeds of the processor do not filfull the needs. This mechanism make the system more reliable and flexible to respond appropriately to its environment.


title: User Interfaces and HCI for Ambient Intelligence and Smart Environments

abstract: As this book clearly demonstrates, there are many ways to create smart environments and to realize the vision of ambient intelligence. But whatever constitutes this smartness or intelligence, has to manifest itself to the human user through the human senses. Interaction with the environment can only take place through phenomena which can be perceived through these senses and through physical actions xecuted by the human. Therefore, the devices which create these phenomena (e.g., light, sound, force, …) or sense these actions are the user’s contact point with the underlying smartness or intelligence.


Signal recovery from partial information via Orthogonal Matching Pursuit


title: Digital lecture recording: A cautionary tale

abstract: Abstract Increasing application of information technology including web-based lectures and live-lecture recording appears to have many advantages for undergraduate nursing education. These include greater flexibility, opportunity for students to review content on demand and the improved academic management of increasing class sizes without significant increase in physical infrastructure. This study performed a quasi-experimental comparison between two groups of nursing students undertaking their first anatomy and physiology course, where one group was also provided access to streaming of recorded copies of the live lectures and the other did not. For the course in which recorded lectures were available student feedback indicated overwhelming support for such provision with 96% of students having accessed recorded lectures. There was only a weak relationship between access of recorded lectures and overall performance in the course. Interestingly, the nursing students who had access to the recorded lectures demonstrated significantly poorer overall academic performance ( P


Modelling and solving the airport slot scheduling problem with efficiency, fairness, and accessibility considerations


title: Visual Classification of Furniture Styles

abstract: Furniture style describes the discriminative appearance characteristics of furniture. It plays an important role in real-world indoor decoration. In this article, we explore the furniture style features and study the problem of furniture style classification. Differing from traditional object classification, furniture style classification aims at classifying different furniture in terms of the “style” that describes its appearance (e.g., American style, Gothic style, Rococo style, etc.) rather than the “kind” that is more related to its functional structure (e.g., bed, desk, etc.). To pursue efficient furniture style features, we construct a novel dataset of furniture styles that contains 16 common style categories and implement three strategies with respect to two categories of classification, that is, handcrafted classification and learning-based classification. First, we follow the typical image classification pipeline to extract the handcrafted features and train the classifier by support vector machine. Then we use the convolutional neural network to extract learning-based features from training images. To obtain comprehensive furniture style features, we finally combine the handcrafted image classification pipeline and the learning-based network. We experimentally evaluate the performances of handcrafted features and learning-based features of each strategy, and the results show the superiority of learning-based features and also the comprehensiveness of handcrafted features.


title: Spam Filtering using SVM with different Kernel Functions

abstract: The growing volume of unwanted bulk e-mail (also known as junk-mail or spam) has generated a need for trustworthy antispam filters. Now a day, many Machine learning techniques have been used which are robotically filter the junk e-mail in much unbeaten rate. In this paper, we used one of the most popular machine learning Algorithm support vector machine (SVM) with different parameters using different kernelfunctions (linear, polynomial, RBF, sigmoid) are implemented on spambase-dataset. Comparison of SVM performance for all kernels (linear, polynomial, RBF, sigmoid) using different parameters (C-SVC, NU-SVC) evaluated on spambase-dataset to get best accuracy.


title: MAV urban localization from Google street view data

abstract: We tackle the problem of globally localizing a camera-equipped micro aerial vehicle flying within urban environments for which a Google Street View image database exists. To avoid the caveats of current image-search algorithms in case of severe viewpoint changes between the query and the database images, we propose to generate virtual views of the scene, which exploit the air-ground geometry of the system. To limit the computational complexity of the algorithm, we rely on a histogram-voting scheme to select the best putative image correspondences. The proposed approach is tested on a 2 km image dataset captured with a small quadroctopter flying in the streets of Zurich. The success of our approach shows that our new air-ground matching algorithm can robustly handle extreme changes in viewpoint, illumination, perceptual aliasing, and over-season variations, thus, outperforming conventional visual place-recognition approaches.


title: Serious games for health

abstract: Abstract Maintaining and restoring health is a basic aspect of well being. On the other hand, serious games is an emerging technology growing in importance for specialized training, taking advantage of 3D games and game engines in order to improve the realistic experience of users. Thus, according to the advancement of technology and the desire to achieve good health using an interesting and enjoyable way, different serious games for health have been proposed during the last few years. In this paper, we present the core process of serious games and explain their functionalities. Then, we survey more than one hundred serious games for health and propose new classifications in four different aspects. Finally, we use fifteen relevant characteristics to classify all the surveyed games and present them with plenty of graphs and charts with corresponding discussion.


title: The Nature of Organizational Leadership Understanding the Performance Imperatives Confronting Today ' s Leaders

abstract: The nature of organizational leadership. The performance imperatives of organizational leadership: conceptual requirements of organizational leadership leadership, sense-making, and mental models social and behavioural complexity social capital and organizational leadership the acquisition of power and the emergence of organizational executives personal values, organizational vision, and strategic management the political dynamics of organizational leadership leadership, organizational evolution and technological change the financial press of organizational leadership. The staffing imperatives for organizational leadership: leader training and development leader assessment and selection assessment of leadership outcomes. Conclusion and commentary: the nature of organizational leadership - conclusions and implications leadership research methods.


title: Capsular antigens of bacteria. Capsular antigens as the basis of vaccines against pathogenic bacteria

abstract: The role of bacterial capsular antigens represented in capsular polysaccharides and exoglycans in pathogenicity and virulence of bacteria is discussed in this review. Using capsular antigens for vaccines against severe diseases caused by capsular microorganisms is considered in detail. The use of conjugates of capsular polysaccharides and their fragments with proteins and peptides for vaccine as well as using liposomes as adjuvants for the capsular antigens are described. Data concerning structural elucidation of bacterial capsular antigens are given in the first part of this review.


title: Understanding online purchase intentions: contributions from technology and trust perspectives

abstract: This paper explores factors that influence consumer's intentions to purchase online at an electronic commerce website. Specifically, we investigate online purchase intention using two different perspectives: a technology-oriented perspective and a trust-oriented perspective. We summarise and review the antecedents of online purchase intention that have been developed within these two perspectives. An empirical study in which the contributions of both perspectives are investigated is reported. We study the perceptions of 228 potential online shoppers regarding trust and technology and their attitudes and intentions to shop online at particular websites. In terms of relative contributions, we found that the trust-antecedent 'perceived risk' and the technology-antecedent 'perceived ease-of-use' directly influenced the attitude towards purchasing online.


LexPageRank: Prestige In Multi-Document Text Summarization


title: Automatic Detection of Helmet Uses for Construction Safety

abstract: The U.S. construction industry suffers from the highest number of fatalities among all industries, i.e., one in five worker deaths in private industry were in construction. Tremendous loss has occurred to the workers' families, the industry, and the nation. Considering the large and increasing number of construction projects that are being conducted in the U.S., there is a growing necessity of developing innovative methods to automatically monitor the safety for the workers at construction sites. Since the head is the most critical area of a human body and is the most vulnerable to an impact that could cause serious injury or death, the use of a protective helmet in construction work is needed. In this paper, we aim to automatically detect the uses of construction helmets (e.g., whether the construction worker wears the helmet or not) by analyzing the construction surveillance images. Based on the collected images, we first detect the object of interest (i.e., construction worker) and further analyze whether the worker wears the helmet or not, by using computer vision and machine learning techniques. In the first step, we incorporate frequency domain information of the image with a popular human detection algorithm Histogram of Oriented Gradient (HOG) for construction worker detection; in the second step, the combination of color-based and Circle Hough Transform (CHT) feature extraction techniques is applied to detect helmet uses for the construction worker.


title: A performance comparison between a horizontal source and a vertical source heat pump systems for a greenhouse heating in the mild climate Elaziğ, Turkey

abstract: Abstract The investigation presented in this article is aimed at demonstrating the technical and design feasibility of using ground-source heat pump systems in the mild climate applications for greenhouse heating, where heating requirements are dominant. An experimental comparison between a horizontal ground-source heat pump system and a vertical ground-source heat pump system was shown by focusing on the heating performance. For this purpose, an experimental set-up was constructed. The heating system mainly consists of two different ground heat exchangers, a heat pump, measuring units and a heating space of a model-sized glass greenhouse with 30 m 2 located in the greenhouse location. The heating coefficient of performances of the two ground-source heat pumps (COP HP,H–V ) and the overall system (COP sys,H–V ) were obtained to be in the range of 3.1–3.6 for HGSHP and 3.2–3.8 for VGSHP and 2.7–3.3 for HGSHP and 2.9–3.5 for VGSHP, respectively. Although significant savings are possible with these heating systems, a substantial investment in equipment and facilities may be required. The experimental results were obtained from November to April in heating seasons of 2007–2008. The results showed that the utilization of the ground-source heat pump is suitable for greenhouse heating in this district.


title: Printed circuit board deconstruction techniques

abstract: The primary purpose of printed circuit board (PCB) reverse engineering is to determine electronic system or subsystem functionality by analyzing how components are interconnected. We performed a series of experiments using both inexpensive home-based solutions and state-of-the-art technologies with a goal of removing exterior coatings and accessing individual PCB layers. This paper presents our results from the most effective techniques.


title: Reinforcement learning: The Good, The Bad and The Ugly

abstract: Reinforcement learning provides both qualitative and quantitative frameworks for understanding and modeling adaptive decision-making in the face of rewards and punishments. Here we review the latest dispatches from the forefront of this field, and map out some of the territories where lie monsters.


title: A tutorial survey of architectures, algorithms, and applications for deep learning

abstract: In this invited paper, my overview material on the same topic as presented in the plenary overview session of APSIPA-2011 and the tutorial material presented in the same conference [1] are expanded and updated to include more recent developments in deep learning. The previous and the updated materials cover both theory and applications, and analyze its future directions. The goal of this tutorial survey is to introduce the emerging area of deep learning or hierarchical learning to the APSIPA community. Deep learning refers to a class of machine learning techniques, developed largely since 2006, where many stages of non-linear information processing in hierarchical architectures are exploited for pattern classification and for feature learning. In the more recent literature, it is also connected to representation learning, which involves a hierarchy of features or concepts where higher-level concepts are defined from lower-level ones and where the same lower-level concepts help to define higher-level ones. In this tutorial survey, a brief history of deep learning research is discussed first. Then, a classificatory scheme is developed to analyze and summarize major work reported in the recent deep learning literature. Using this scheme, I provide a taxonomy-oriented survey on the existing deep architectures and algorithms in the literature, and categorize them into three classes: generative, discriminative, and hybrid. Three representative deep architectures – deep autoencoders, deep stacking networks with their generalization to the temporal domain (recurrent networks), and deep neural networks (pretrained with deep belief networks) – one in each of the three classes, are presented in more detail. Next, selected applications of deep learning are reviewed in broad areas of signal and information processing including audio/speech, image/vision, multimodality, language modeling, natural language processing, and information retrieval. Finally, future directions of deep learning are discussed and analyzed.


title: Hybrid Bellman-Ford-Dijkstra Algorithm

abstract: We consider the single-source shortest paths problem in a digraph with negative edge costs allowed. A hybrid of the BellmanFord and Dijkstra algorithms is suggested, improving the running time bound of BellmanFord for graphs with a sparse distribution of negative cost edges. The algorithm iterates Dijkstra several times without re-initializing the tentative value d(v) at vertices. At most k+2 iterations solve the problem, if for any vertex reachable from the source, there exists a shortest path to it with at most k negative cost edges.In addition, a new, straightforward proof is suggested that the BellmanFord algorithm produces a shortest paths tree.


title: Human-centered design can make application programming interfaces easier for developers to use . BY

abstract: Human-centered design can make application programming interfaces easier for developers to use.


title: A Structured Model of Video Reproduces Primary Visual Cortical Organisation

abstract: The visual system must learn to infer the presence of objects and features in the world from the images it encounters, and as such it must, either implicitly or explicitly, model the way these elements interact to create the image. Do the response properties of cells in the mammalian visual system reflect this constraint? To address this question, we constructed a probabilistic model in which the identity and attributes of simple visual elements were represented explicitly and learnt the parameters of this model from unparsed, natural video sequences. After learning, the behaviour and grouping of variables in the probabilistic model corresponded closely to functional and anatomical properties of simple and complex cells in the primary visual cortex (V1). In particular, feature identity variables were activated in a way that resembled the activity of complex cells, while feature attribute variables responded much like simple cells. Furthermore, the grouping of the attributes within the model closely parallelled the reported anatomical grouping of simple cells in cat V1. Thus, this generative model makes explicit an interpretation of complex and simple cells as elements in the segmentation of a visual scene into basic independent features, along with a parametrisation of their moment-by-moment appearances. We speculate that such a segmentation may form the initial stage of a hierarchical system that progressively separates the identity and appearance of more articulated visual elements, culminating in view-invariant object recognition.


The “Semiotic Animal” in Roland Barthes: A Reflection on Calculating the Self as “Difference in Man”


title: Multi-Sensor Sequential Change Detection with Unknown Change Propagation Dynamics

abstract: Detecting changes with multiple sensors has received significant attention in the literature. In many practical applications such as critical infrastructure monitoring and modeling of disease spread, a useful change propagation model is one where change eventually happens at all sensors, but where not all sensors witness change at the same time-instant. While the known change propagation dynamics was studied before, this paper studies a more general setting of unknown change propagation pattern (trajectory) in both centralized and decentralized settings with the goal of detecting the first time-instant any sensor witnesses a change. Using the dynamic programming framework, the optimal solution structures are derived and in the special rare-change regime, several more practical solutions are proposed. Under certain conditions, the first-order asymptotic optimality of a proposed algorithm called multichart test is shown as the false alarm probability vanishes. Moreover, low-complexity solutions are proposed based on online estimation of the change propagation pattern. Numerical studies illustrate that the proposed detectors offer near-optimal performance. Furthermore, in the decentralized setting, the level-crossing sampling is proposed for sampling and transmission of local statistics that significantly improves the detection performance using the same amount of communication resources compared to the conventional uniform-in-time sampling.


title: Why are some problems hard? Evidence from Tower of Hanoi

abstract: Abstract This paper analyzes the causes for large differences in difficulty of various isomorphic versions of the Tower of Hanoi problem. Some forms of the problem take 16 times as long to solve, on average, as other versions. Since isomorphism rules out size of task domain as a determinant of relative difficulty, these experiments seek and find causes for the differences in features of the problem representation. Analysis of verbal protocols and the temporal patterning of moves allows the problem-solving behavior to be divided into exploratory and final-path phases. Entry into the final-path phase depends on acquisition of the ability to plan pairs of moves, an achievement made difficult by the working memory load it entails. This memory load can be reduced by automating the rules governing moves, either through problem exploration or training. Once automation has occurred, the solution is obtained very rapidly. Memory load is also proposed as the locus of other differences in difficulty found between various problem representations.


title: Denoising Autoencoders for Laser-Based Scan Registration

abstract: In this letter, we build on recent advances in deep learning to improve SE(3) transformations, enabling more accurate motion estimation in mobile robots. We propose using denoising autoencoders (DAEs) to address the challenges presented by modern LIDARs. Our proposed approach is comprised of two stages: a novel pre-processing stage for robust feature identification and a scan matching stage for motion estimation. In the pre-processing stage, LIDAR data are projected into a two-dimensional (2-D) image format and a DAE is used to extract salient features. These features are used as a mask for the original data, which is then re-projected into full 3-D space. Scan matching is performed on the re-projected data to estimate motion in SE(3). We analyze the performance of our approach using the real-world data from the University of Michigan North Campus long-term vision and LIDAR dataset and test generalization on LIDAR data from the KITTI dataset. We show that our approach generalizes across domains, is capable of reducing the per-estimate error of standard iterative closest point (ICP) methods on average by 25.5% for the translational component and 57.53% for the rotational component, and is capable of reducing the computation time of state-of-the-art ICP methods by a factor of 7.94 on average while achieving competitive performance.


title: Survey on security threats and protection mechanisms in embedded automotive networks

abstract: Embedded electronic components, so-called ECU (Electronic Controls Units), are nowadays a prominent part of a car's architecture. These ECUs, monitoring and controlling the different subsystems of a car, are interconnected through several gateways and compose the global internal network of the car. Moreover, modern cars are now able to communicate with other devices through wired or wireless interfaces such as USB, Bluetooth, WiFi or even 3G. Such interfaces may expose the internal network to the outside world and can be seen as entry points for cyber attacks. In this paper, we present a survey on security threats and protection mechanisms in embedded automotive networks. After introducing the different protocols being used in the embedded networks of current vehicles, we then analyze the potential threats targeting these networks and describe how the attackers' opportunities can be enhanced by the new communication abilities of modern cars. Finally, we present the security solutions currently being devised to address these problems.


title: Self-determination theory: A macrotheory of human motivation, development, and health.

abstract: Self-determination theory (SDT) is an empirically based theory of human motivation, development, and wellness. The theory focuses on types, rather than just amount, of motivation, paying particular attention to autonomous motivation, controlled motivation, and amotivation as predictors of performance, relational, and well-being outcomes. It also addresses the social conditions that enhance versus diminish these types of motivation, proposing and finding that the degrees to which basic psychological needs for autonomy, competence, and relatedness are supported versus thwarted affect both the type and strength of motivation. SDT also examines people’s life goals or aspirations, showing differential relations of intrinsic versus extrinsic life goals to performance and psychological health. In this introduction we also briefly discuss recent developments within SDT concerning mindfulness and vitality, and highlight the applicability of SDT within applied domains, including work, relationships, parenting, education, virtual environments, sport, sustainability, health care, and psychotherapy.


Conversion of Wood To Carbohydrates


title: Positive Definite Matrices: Data Representation and Applications to Computer Vision

abstract: Numerous applications in computer vision and machine learning rely on representations of data that are compact, discriminative, and robust while satisfying several desirable invariances. One such recently successful representation is offered by symmetric positive definite (SPD) matrices. However, the modeling power of SPD matrices comes at a price: rather than a flat Euclidean view, SPD matrices are more naturally viewed through curved geometry (Riemannian or otherwise) which often complicates matters. We focus on models and algorithms that rely on the geometry of SPD matrices, and make our discussion concrete by casting it in terms of covariance descriptors for images. We summarize various commonly used distance metrics on SPD matrices, before highlighting formulations and algorithms for solving sparse coding and dictionary learning problems involving SPD data. Through empirical results, we showcase the benefits of mathematical models that exploit the curved geometry of SPD data across a diverse set of computer vision applications.


title: Improving Chinese Word Segmentation and POS Tagging with Semi-supervised Methods Using Large Auto-Analyzed Data

abstract: This paper presents a simple yet effective semi-supervised method to improve Chinese word segmentation and POS tagging. We introduce novel features derived from large auto-analyzed data to enhance a simple pipelined system. The auto-analyzed data are generated from unlabeled data by using a baseline system. We evaluate the usefulness of our approach in a series of experiments on Penn Chinese Treebanks and show that the new features provide substantial performance gains in all experiments. Furthermore, the results of our proposed method are superior to the best reported results in the literature.


title: Predictive Learning: Using Future Representation Learning Variantial Autoencoder for Human Action Prediction

abstract: The unsupervised Pretraining method has been widely used in aiding human action recognition. However, existing methods focus on reconstructing the already present frames rather than generating frames which happen in future.In this paper, We propose an improved Variantial Autoencoder model to extract the features with a high connection to the coming scenarios, also known as Predictive Learning. Our framework lists as following: two steam 3D-convolution neural networks are used to extract both spatial and temporal information as latent variables. Then a resample method is introduced to create new normal distribution probabilistic latent variables and finally, the deconvolution neural network will use these latent variables generate next frames. Through this possess, we train the model to focus more on how to generate the future and thus it will extract the future high connected features. In the experiment stage, A large number of experiments on UT and UCF101 datasets reveal that future generation aids Prediction does improve the performance. Moreover, the Future Representation Learning Network reach a higher score than other methods when in half observation. This means that Future Representation Learning is better than the traditional Representation Learning and other state- of-the-art methods in solving the human action prediction problems to some extends.


D-learning to estimate optimal individual treatment rules


title: Determinants of Customer Continuance Intention of Online Shopping

abstract: The purpose of this study is to clarify theory and identify factors that could explain the level of continuance intention of e-shopping. A revised technology acceptance model integrates expectation confirmation theory and investigates effects of age differences. An online survey of internet shoppers in Saudi Arabia. Structural equation modelling and invariance analysis confirm model fit. The findings confirm that perceived usefulness, enjoyment and social pressure are determinants of e-shopping continuance. The structural weights are mostly equivalent between young and old but the regression path from perceived usefulness to social pressure is stronger for younger respondents. This research moves beyond e-shopping intentions to factors affecting eshopping continuance, explaining 55% of intention to continue shopping online. Online strategies cannot ignore direct and indirect effects on continuance intentions. The findings contribute to literature on internet shopping and continuance intentions in the context of Saudi Arabia.


title: Behavioral Execution Comparison: Are Tests Representative of Field Behavior?

abstract: Software testing is the most widely used approach for assessing and improving software quality, but it is inherently incomplete and may not be representative of how the software is used in the field. This paper addresses the questions of to what extent tests represent how real users use software, and how to measure behavioral differences between test and field executions. We study four real-world systems, one used by endusers and three used by other (client) software, and compare test suites written by the systems' developers to field executions using four models of behavior: statement coverage, method coverage, mutation score, and a temporal-invariant-based model we developed. We find that developer-written test suites fail to accurately represent field executions: the tests, on average, miss 6.2% of the statements and 7.7% of the methods exercised in the field, the behavior exercised only in the field kills an extra 8.6% of the mutants, finally, the tests miss 52.6% of the behavioral invariants that occur in the field. In addition, augmenting the in-house test suites with automatically-generated tests by a tool targeting high code coverage only marginally improves the tests' behavioral representativeness. These differences between field and test executions—and in particular the finer-grained and more sophisticated ones that we measured using our invariantbased model—can provide insight for developers and suggest a better method for measuring test suite quality.


title: Finding interesting rules from large sets of discovered association rules

abstract: Association rules, introduced by Agrawal, Imielinski, and Swami, are rules of the form “for 90% of the rows of the relation, if the row has value 1 in the columns in set W , then it has 1 also in column B”. Efficient methods exist for discovering association rules from large collections of data. The number of discovered rules can, however, be so large that browsing the rule set and finding interesting rules from it can be quite difficult for the user. We show how a simple formalism of rule templates makes it possible to easily describe the structure of interesting rules. We also give examples of visualization of rules, and show how a visualization tool interfaces with rule templates.


title: Using an Interactive Avatar's Facial Expressiveness to Increase Persuasiveness and Socialness

abstract: Research indicates that the facial expressions of animated characters and agents can influence people's perceptions and interactions with these entities. We designed an experiment to examine how an interactive animated avatar's facial expressiveness influences dyadic conversations between adults and the avatar. We animated the avatar in realtime using the tracked facial motion of a confederate. To adjust facial expressiveness, we damped and exaggerated the avatar's facial motion. We found that ratings of the avatar's extroversion were positively related to its expressiveness. However, impressions of the avatar's realism and naturalness worsened with increased expressiveness. We also found that the confederate was more influential when she appeared as the damped or exaggerated avatar. Adjusting the expressiveness of interactive animated avatars may be a simple way to influence people's social judgments and willingness to collaborate with animated avatars. These results have implications for using avatar facial expressiveness to improve the effectiveness of avatars in various contexts. Adjusting the expressiveness of interactive animated avatars may be a simple way to influence people's social judgments and willingness to collaborate with animated avatars.


title: Survey on Security Challenges for Swarm Robotics

abstract: Swarm robotics is a relatively new technology that is being explored for its potential use in a variety of different applications and environments. Previous emerging technologies have often overlooked security until later developmental stages, when it has had to be undesirably (and sometimes expensively) retrofitted. We identify a number of security challenges for swarm robotics and argue that now is the right time to address these issues and seek solutions. We also identify several idiosyncrasies of swarm robotics that present some unique security challenges. In particular, swarms of robots potentially (i) employ different types of communication channels (ii) have special concepts of identity, and (iii) exhibit adaptive emergent behaviour which could be modified by an intruder. Addressing these issues now will prevent undesirable consequences for many applications of this type of technology.


title: A Noise-Shaped Buck DC–DC Converter With Improved Light-Load Efficiency and Fast Transient Response

abstract: A noise-shaped synchronous buck dc-dc converter based on a third-order active-passive continuous-time sigma-delta modulator is presented. Detailed system modeling and loop design methodology are discussed. A dual-mode compensator is proposed to achieve the loop stability over a wide load range. The light-load efficiency is improved by the proposed dynamic width control and sigma-delta pulse-skip mode techniques. In order to get a fast transient response, the linear-nonlinear control is adopted. Moreover, an on-chip soft-start circuit is proposed to save the system cost. The proposed converter has been fabricated using Chartered 0.35- μm 2P4M dual-gate mixed-signal CMOS process. Experimental results show that the converter effectively suppresses the switching harmonics at both continuous conduction mode and discontinuous conduction mode. Compared with the conventional pulsewidth modulation operation, the first harmonic peak has been reduced by -35 dB. The converter achieves a peak efficiency of 92% at 200 mA, and a light-load efficiency of 78% at 5 mA. With the load current skipping between 50 and 450 mA, the maximum output overshoot voltage is 87 mV, and the recovery time is below 12 μs. The start-up time for the output voltage is 160 μs. By configuring the soft-start circuit, this time can be prolonged to 680 μs. The area of the whole chip is 1.4 mm2.


title: Palm-line detection

abstract: Palm lines, which consist of principal lines and wrinkles, are stable and essential traits for palmprint-based individual identification and can be extracted in low-resolution images. However, the research on palm-line detection has done little. Due to special properties of palmprint, in addition to the structure feature, width of the palm-line, which generally reflects strength information, is important to identify palms especially when various palmprints have similar structures. In this paper, a palm-line detection approach is proposed to simultaneously extract structure and strength features of palm lines by minimizing a local image area which is of similar brightness to each individual pixel. The presented method has been tested on the PolyU palmprint database and compared with the canny edge detector and SUSAN edge finder. Experimental results illustrate the effectiveness of this approach.


title: Neural Networks and the Bias/Variance Dilemma

abstract: Feedforward neural networks trained by error backpropagation are examples of nonparametric regression estimators. We present a tutorial on nonparametric inference and its relation to neural networks, and we use the statistical viewpoint to highlight strengths and weaknesses of neural models. We illustrate the main points with some recognition experiments involving artificial data as well as handwritten numerals. In way of conclusion, we suggest that current-generation feedforward neural networks are largely inadequate for difficult problems in machine perception and machine learning, regardless of parallel-versus-serial hardware or other implementation issues. Furthermore, we suggest that the fundamental challenges in neural modeling are about representation rather than learning per se. This last point is supported by additional experiments with handwritten numerals.


title: Graphene Barristor, a Triode Device with a Gate-Controlled Schottky Barrier

abstract: Despite several years of research into graphene electronics, sufficient on/off current ratio I on / I off in graphene transistors with conventional device structures has been impossible to obtain. We report on a three-terminal active device, a graphene variable-barrier “barristor” (GB), in which the key is an atomically sharp interface between graphene and hydrogenated silicon. Large modulation on the device current (on/off ratio of 10 5 ) is achieved by adjusting the gate voltage to control the graphene-silicon Schottky barrier. The absence of Fermi-level pinning at the interface allows the barrier’s height to be tuned to 0.2 electron volt by adjusting graphene’s work function, which results in large shifts of diode threshold voltages. Fabricating GBs on respective 150-mm wafers and combining complementary p- and n-type GBs, we demonstrate inverter and half-adder logic circuits.


title: An integrated biomechanical analysis of normal stair ascent and descent.

abstract: Three normal males of similar height and weight ascended and descended a five step staircase with a riser height of 22 cm and a tread of 28 cm. EMG, force plate and cine data were collected for the stride over the second to fourth step during each mode. Kinematic and kinetic analyses were integrated with EMG to yield an interpretation of the mechanics of normal stair walking. Movement from one step to the next involved simultaneous lifting and horizontal translation of the body, and each stride showed specific phases for progression. The extensor muscles about the knee played a dominant role in progression from one step to the next in both modes coupled with the ankle plantar flexors. The total lower limb extensor pattern, called the support moment, was highly correlated between subjects and to level walking. Intra- and inter-subject variability of the motor patterns were also determined. The greatest variability was seen at the hip, while stereotypic kinetic patterns emerged at the ankle and knee for all subjects across the 24 trials of each mode.


title: Personalization technologies: a process-oriented perspective

abstract: By leveraging customer reactions to personalized products and services, companies continuously improve their personalization processes through an iterative feedback loop resulting in the `virtuous cycle' of personalization.


title: Digitalization in knowledge work: the dream of enhanced performance

abstract: The landscape and nature of knowledge work is changing due to digitalization. There are many expectations set for the digitalization and its impact on knowledge work performance. In this paper, the purpose is to critically examine the effects of digitalization on knowledge work performance through an empirical study consisted of multiple datasets representing knowledge workers in four different professions and three vocation sectors. The study is able to show that even though knowledge workers gain several benefits from digitalization, the effects of opposite, value destroying factors, are still stronger. This study contributes to the timely discussion on modern work by identifying the potential restraints and showing their relation to performance. Moreover, based on the results of the study, the critical areas that need managerial attention in digitalizing knowledge work are highlighted.


title: Characteristics of Adolescent Psychiatric Patients Who Engage in Problematic Behavior While Intoxicated

abstract: Abstract Although many adolescent psychiatric patients drink and use drugs, not all engage in problematic behaviors or experience social difficulties while intoxicated. The goal of this study was to identify characteristics of adolescent psychiatric patients for whom intoxication leads to behavioral, social, and medical problems. Alcoholinvolved adolescent psychiatric inpatients were divided into high and low problem severity groups and compared on a number of measures. The groups did not differ in alcohol or drug consumption or psychiatric diagnoses. High problem severity was primarily associated with drinking in response to unpleasant affect and the belief that alcohol enhances social behavior. Explanations for the results are discussed, along with clinical implications.


title: Recognizing composite sketches with digital face images via SSD dictionary

abstract: Sketch recognition has important law enforcement applications in detecting and apprehending suspects. Compared to hand drawn sketches, software generated composite sketches are faster to create and require lesser skill sets as well as bring consistency in sketch generation. While sketch generation is one side of the problem, recognizing composite sketches with digital images is another side. This paper presents an algorithm to address the second problem, i.e. matching composite sketches with digital images. The proposed algorithm utilizes a SSD based dictionary generated via 50,000 images from the CMU Multi-PIE database. The gallery-probe feature vectors created using SSD dictionary are matched using GentleBoostKO classifier. The results on extended PRIP composite sketch database show the effectiveness of the proposed algorithm.


title: Integrating Gaussian mixtures into deep neural networks: Softmax layer with hidden variables

abstract: In the hybrid approach, neural network output directly serves as hidden Markov model (HMM) state posterior probability estimates. In contrast to this, in the tandem approach neural network output is used as input features to improve classic Gaussian mixture model (GMM) based emission probability estimates. This paper shows that GMM can be easily integrated into the deep neural network framework. By exploiting its equivalence with the log-linear mixture model (LMM), GMM can be transformed to a large softmax layer followed by a summation pooling layer. Theoretical and experimental results indicate that the jointly trained and optimally chosen GMM and bottleneck tandem features cannot perform worse than a hybrid model. Thus, the question “hybrid vs. tandem” simplifies to optimizing the output layer of a neural network. Speech recognition experiments are carried out on a broadcast news and conversations task using up to 12 feed-forward hidden layers with sigmoid and rectified linear unit activation functions. The evaluation of the LMM layer shows recognition gains over the classic softmax output.


title: Mechanics of Flexible Needles Robotically Steered through Soft Tissue

abstract: The tip asymmetry of a bevel-tip needle results in the needle naturally bending when it is inserted into soft tissue. This enables robotic needle steering, which can be used in medical procedures to reach subsurface targets inaccessible by straight-line trajectories. However, accurate path planning and control of needle steering requires models of needle-tissue interaction. Previous kinematic models required empirical observations of each needle and tissue combination in order to fit model parameters. This study describes a mechanics-based model of robotic needle steering, which can be used to predict needle behavior and optimize system design based on fundamental mechanical and geometrical properties of the needle and tissue. We first present an analytical model for the loads developed at the tip, based on the geometry of the bevel edge and material properties of soft-tissue simulants (gels). We then present a mechanics-based model that calculates the deflection of a bevel-tipped needle inserted through a soft elastic medium. The model design is guided by microscopic observations of needle-gel interactions. The energy-based formulation incorporates tissue-specific parameters, and the geometry and material properties of the needle. Simulation results follow similar trends (deflection and radius of curvature) to those observed in experimental studies of robotic needle insertion.