title: The Costs and Benefits of Sharing: Sequential Individual Rationality and Sequential Fairness

abstract: We introduce a cost sharing framework for ridesharing that explicitly takes into account the "inconvenience costs" of passengers due to detours. We introduce the notion of "sequential" individual rationality (SIR) that requires that the "disutility" of existing passengers is non-increasing as additional passengers are picked up, and show that these constraints induce a natural limit on the incremental detours permissible as the ride progresses. We provide an exact characterization of all routes for which there exists some cost sharing scheme that is SIR on that route, and under these constraints, for realistic scenarios, we also show a $\Theta(\sqrt{n})$ upper bound and a $\Theta(\log n)$ lower bound on the total detour experienced by the passengers as a fraction of the direct distance to their destination. Next, we observe that under any budget-balanced cost sharing scheme that is SIR on a route, the total amount by which the passengers' disutilities decrease (which can be viewed as the total incremental benefit due to ridesharing) is a constant. This observation inspires a "dual" notion of viewing cost sharing schemes as benefit sharing schemes, under which we introduce a natural definition of "sequential" fairness-the total incremental benefit due to the addition of a new passenger is (partly) shared among the existing passengers in proportion to the incremental inconvenience costs they suffer. We then provide an exact characterization of sequentially fair cost sharing schemes, which brings out several useful structural properties, including a strong requirement that passengers must compensate each other for the detour inconveniences that they cause. Finally, we conclude with an extended discussion of new algorithmic problems related to and motivated by SIR, and important future work.


title: Deep learning architectures for vector representations of patients and exploring predictors of 30-day hospital readmissions in patients with multiple chronic conditions

abstract: This empirical study of a complex group of patients with multiple chronic concurrent conditions (diabetes, cardiovascular and kidney diseases) explores the use of deep learning architectures to identify patient segments and contributing factors to 30-day hospital readmissions. We implemented Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) on sequential Electronic Health Records data at the Danderyd Hospital in Stockholm, Sweden. Three distinct sub-types of patient groups were identified: chronic obstructive pulmonary disease, kidney transplant, and paroxysmal ventricular tachycardia. The CNN learned about vector representations of patients, but the RNN was better able to identify and quantify key contributors to readmission such as myocardial infarction and echocardiography. We suggest that vector representations of patients with deep learning should precede predictive modeling of complex patients. The approach also has potential implications for supporting care delivery, care design and clinical decision-making.


title: Artificial Noise Aided Secure Cognitive Beamforming for Cooperative MISO-NOMA Using SWIPT

abstract: Cognitive radio (CR) and non-orthogonal multiple access (NOMA) have been deemed two promising technologies due to their potential to achieve high spectral efficiency and massive connectivity. This paper studies a multiple-input single-output NOMA CR network relying on simultaneous wireless information and power transfer conceived for supporting a massive population of power limited battery-driven devices. In contrast to most of the existing works, which use an ideally linear energy harvesting model, this study applies a more practical non-linear energy harvesting model. In order to improve the security of the primary network, an artificial-noise-aided cooperative jamming scheme is proposed. The artificial-noise-aided beamforming design problems are investigated subject to the practical secrecy rate and energy harvesting constraints. Specifically, the transmission power minimization problems are formulated under both perfect channel state information (CSI) and the bounded CSI error model. The problems formulated are non-convex, hence they are challenging to solve. A pair of algorithms either using semidefinite relaxation (SDR) or a cost function are proposed for solving these problems. Our simulation results show that the proposed cooperative jamming scheme succeeds in establishing secure communications and NOMA is capable of outperforming the conventional orthogonal multiple access in terms of its power efficiency. Finally, we demonstrate that the cost function algorithm outperforms the SDR-based algorithm.


title: Robot-assisted laparoscopic surgery versus conventional laparoscopic surgery in randomized controlled trials: A systematic review and meta-analysis

abstract: Importance ::: This review provides a comprehensive comparison of treatment outcomes between robot-assisted laparoscopic surgery (RLS) and conventional laparoscopic surgery (CLS) based on randomly-controlled trials (RCTs). ::: ::: ::: Objectives ::: We employed RCTs to provide a systematic review that will enable the relevant community to weigh the effectiveness and efficacy of surgical robotics in controversial fields on surgical procedures both overall and on each individual surgical procedure. ::: ::: ::: Evidence review ::: A search was conducted for RCTs in PubMed, EMBASE, and Cochrane databases from 1981 to 2016. Among a total of 1,517 articles, 27 clinical reports with a mean sample size of 65 patients per report (32.7 patients who underwent RLS and 32.5 who underwent CLS), met the inclusion criteria. ::: ::: ::: Findings ::: CLS shows significant advantages in total operative time, net operative time, total complication rate, and operative cost (p < 0.05 in all cases), whereas the estimated blood loss was less in RLS (p < 0.05). As subgroup analyses, conversion rate on colectomy and length of hospital stay on hysterectomy statistically favors RLS (p < 0.05). ::: ::: ::: Conclusions ::: Despite higher operative cost, RLS does not result in statistically better treatment outcomes, with the exception of lower estimated blood loss. Operative time and total complication rate are significantly more favorable with CLS.


title: Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection

abstract: Abstract Multispectral pedestrian detection has received extensive attention in recent years as a promising solution to facilitate robust human target detection for around-the-clock applications (e.g., security surveillance and autonomous driving). In this paper, we demonstrate illumination information encoded in multispectral images can be utilized to boost the performance of pedestrian detection significantly. A novel illumination-aware weighting mechanism is present to depict illumination condition of a scene accurately. Such illumination information is incorporated into two-stream deep convolutional neural networks to learn multispectral human-related features under different illumination conditions (daytime and nighttime). Moreover, we utilized illumination information together with multispectral data to generate more accurate semantic segmentation which is used to supervise the training of pedestrian detector. Putting all of the pieces together, we present an effective framework for multispectral pedestrian detection based on multi-task learning of illumination-aware pedestrian detection and semantic segmentation. Our proposed method is trained end-to-end using a well-designed multi-task loss function and outperforms state-of-the-art approaches on KAIST multispectral pedestrian dataset.


Space-Time Codes for High Data Rate Wireless Communications


title: Financial education level of high school students and its economic reflections

abstract: This research contributes to the understanding of the level of financial education of high school students from public schools, as well as verifying how their financial knowledge has been generated, providing a view of the gaps in financial education with which these students are able to attend undergraduate courses later. The objective of the research was to determine the level of financial education of high school students from public schools, according to individual, demographic and socializing aspects. The research methodology was characterized as descriptive regarding procedures such as survey and the approach of the quantitative nature of the problem. The research population included 4698 high school students from 14 public schools in the city of Blumenau. In the data processing, the Kruskal-Wallis and chi-square tests were used. The results indicate that there is an effective financial education among young high school students, which can be noticed in findings such as: some of the young are not obliged to explain to parents where they are spending their financial resources; students have acquired, largely, their financial knowledge with parents and relatives, and in day-to-day practices, but there is little dialog in the family on financial matters. The financial knowledge coming from the school is low, requiring an improvement in the quality of this knowledge at this stage or in the future, including undergraduate courses. Finally, potential workers may cause social problems through their inability to manage their resources and/or the expenses of their families.


title: Alpha/Theta Neurofeedback Increases Mentalization and Default Mode Network Connectivity in a Non-Clinical Sample

abstract: Several studies showed the effectiveness of alpha/theta (A/T) neurofeedback training in treating some psychiatric conditions. Despite the evidence of A/T effectiveness, the psychological and neurobiological bases of its effects is still unclear. The aim of the present study was to explore the usefulness of the A/T training in increasing mentalization in a non-clinical sample. The modifications of electroencephalographic (EEG) functional connectivity in Default Mode Network (DMN) associated with A/T training were also investigated. Forty-four subjects were enrolled in the study and randomly assigned to receive ten sessions of A/T training [neurofeedback group (NFG) = 22], or to act as controls [waiting list group (WLG) = 22]. All participants were administered the mentalization questionnaire (MZQ) and the Symptom Checklist-90-Revised (SCL-90-R). In the post training assessment, compared to WLG, NFG showed a significant increase of MZQ total scores (3.94 ± 0.73 vs. 3.53 ± 0.77; F1;43 = 8.19; p = 0.007; d = 0.863). Furthermore, A/T training was also associated with a significant increase of EEG functional connectivity in several DMN brain areas (e.g. Posterior Cingulate Cortex). Taken together our results support the usefulness of the A/T training in enhancing mentalization and DMN connectivity.


Decety Dissecting the Neural Mechanisms Mediating Empathy


title: Tadalafil pharmacokinetics in healthy subjects

abstract: Aims ::: ::: To characterize tadalafil plasma pharmacokinetics in healthy subjects following single and multiple doses. ::: ::: ::: ::: Methods ::: ::: Noncompartmental parameters were calculated for healthy subjects receiving a single 2.5–20-mg tadalafil dose in 13 clinical pharmacology studies. An integrated statistical analysis of results in 237 subjects provided global averages and an assessment of effects of body mass index (BMI), age, gender and smoking status. Diurnal variation, food effects and proportionality of exposure to dose were analysed in three studies. Multiple-dose pharmacokinetics were evaluated in a separate study in which parallel groups of 15 subjects received 10 or 20 mg tadalafil once daily for 10 days. ::: ::: ::: ::: Results ::: ::: Tadalafil was absorbed rapidly with mean Cmax (378 µg l−1 for 20 mg) observed at 2 h; thereafter, concentrations declined nearly monoexponentially with a mean (5th, 95th percentiles) t1/2 of 17.5 (11.5, 29.6) hours. Mean oral clearance (CL/F) was 2.48 (1.35, 4.35) l h−1 and apparent volume of distribution (Vz/F) was 62.6 (39.5, 92.1) l. No clinically meaningful effect of BMI, age, gender or smoking was identified. Exposure was not substantially affected by time of dosing. Food had negligible effects on bioavailability as assessed by 90% confidence intervals for Cmax and AUC mean ratios. Parameters were proportional to dose, indicating that doubling the dose doubled exposure. Steady state was attained by day 5 following once-daily administration, and accumulation (1.6-fold) was consistent with the t1/2. ::: ::: ::: ::: Conclusions ::: ::: Tadalafil pharmacokinetics are linear with respect to dose and time, and are not affected by food. Systemic clearance is low relative to other phosphodiesterase 5 inhibitors.


title: Sentiment Classification On Customer Feedback Data: Noisy Data, Large Feature Vectors, And The Role Of Linguistic Analysis

abstract: We demonstrate that it is possible to perform automatic sentiment classification in the very noisy domain of customer feedback data. We show that by using large feature vectors in combination with feature reduction, we can train linear support vector machines that achieve high classification accuracy on data that present classification challenges even for a human annotator. We also show that, surprisingly, the addition of deep linguistic analysis features to a set of surface level word n-gram features contributes consistently to classification accuracy in this domain.


title: TASC:Topic-Adaptive Sentiment Classification on Dynamic Tweets

abstract: Sentiment classification is a topic-sensitive task, i.e., a classifier trained from one topic will perform worse on another. This is especially a problem for the tweets sentiment analysis. Since the topics in Twitter are very diverse, it is impossible to train a universal classifier for all topics. Moreover, compared to product review, Twitter lacks data labeling and a rating mechanism to acquire sentiment labels. The extremely sparse text of tweets also brings down the performance of a sentiment classifier. In this paper, we propose a semi-supervised topic-adaptive sentiment classification (TASC) model, which starts with a classifier built on common features and mixed labeled data from various topics. It minimizes the hinge loss to adapt to unlabeled data and features including topic-related sentiment words, authors’ sentiments and sentiment connections derived from “@” mentions of tweets, named as topic-adaptive features. Text and non-text features are extracted and naturally split into two views for co-training. The TASC learning algorithm updates topic-adaptive features based on the collaborative selection of unlabeled data, which in turn helps to select more reliable tweets to boost the performance. We also design the adapting model along a timeline (TASC-t) for dynamic tweets. An experiment on 6 topics from published tweet corpuses demonstrates that TASC outperforms other well-known supervised and ensemble classifiers. It also beats those semi-supervised learning methods without feature adaption. Meanwhile, TASC-t can also achieve impressive accuracy and F-score. Finally, with timeline visualization of “river” graph, people can intuitively grasp the ups and downs of sentiments’ evolvement, and the intensity by color gradation.


title: OTP-Based Two-Factor Authentication Using Mobile Phones

abstract: Two-factor authentication (2FA) provides improved protection, since users are prompted to provide something they know and something they have. This method delivers a higher-level of authentication assurance, which is essential for online banking security. Many banking systems have satisfied the2FA requirements by sending a One Time Password (OTP), something possessed, through an SMS to the user's phone device. Unfortunately, international roaming and SMS costs and delays put restrictions on this system reliability. This paper presents a novel two-factor authentication scheme whereby a user's device produces multiples OTPs from an initial seed using the proposed production scheme. The initial seed is produced by the communications partners' unique parameters. Applying the many from one function to a certain seed removes the requirement of sending SMS-based OTPs to users, and reduces the restrictions caused by the SMS system.


title: A spreading-activation theory of semantic processing.

abstract: This paper presents a spreading-acti vation theory of human semantic processing, which can be applied to a wide range of recent experimental results. The theory is based on Quillian's theory of semantic memory search and semantic preparation, or priming. In conjunction with this, several of the miscondeptions concerning Qullian's theory are discussed. A number of additional assumptions are proposed for his theory in order to apply it to recent experiments. The present paper shows how the extended theory can account for results of several production experiments by Loftus, Juola and Atkinson's multiple-category experiment, Conrad's sentence-verification experiments, and several categorization experiments on the effect of semantic relatedness and typicality by Holyoak and Glass, Rips, Shoben, and Smith, and Rosch. The paper also provides a critique of the Smith, Shoben, and Rips model for categorization judgments. Some years ago, Quillian1 (1962, 1967) proposed a spreading-acti vation theory of human semantic processing that he tried to implement in computer simulations of memory search (Quillian, 1966) and comprehension (Quillian, 1969). The theory viewed memory search as activation spreading from two or more concept nodes in a semantic network until an intersection was found. The effects of preparation (or priming) in semantic memory were also explained in terms of spreading activation from the node of the primed concept. Rather than a theory to explain data, it was a theory designed to show how to build human semantic structure and processing into a computer.


title: Doherty amplifier design for 3.5 GHz WiMAX considering load line and loop stability

abstract: Legacy Doherty amplifier is characterized by using a larger transistor for the peak amplifier such that this reaches saturation with a smaller excitation signal. However due to device availability and modelling considerations, this is not often feasible. In this paper, the design and measurement of a Doherty amplifier utilizing only single sized device is realized. Unlike previous research works, intrinsic load line is utilized to tune the offset lines, as well as to verify the actual dynamic load principle. Stability aspects are covered for this type of amplifier, which are not normally included in earlier works. Finally an assessment of its applicability and benefits for WiMAX at 3.5 GHz is realized using a class AB amplifier as a comparison basis.


title: Improved Determination of Sea Surface Heights Close to the Australian Coast from ERS-2 Satellite Radar Altimetry

abstract: The homogeneous and repeated data coverage over coastal regions from satellite radar altimetry is one important data source for oceanographic and geodetic applications. However, the sea surface heights (SSH) extracted from the altimeter data are often in error close to the coast, due in part to the complex nature of echoes returned from rapidly varying coastal topographic surfaces (both land and sea) and the generally rougher sea state. This paper presents improved SSH results derived from ERS-2 altimeter waveform data (two cycles of 35-day repeat orbit, March to May 1999) near the Australian coast using a coastal retracking system. This system was developed based upon a systematic and comprehensive analysis of return waveforms. Central to the system is the use of two retracking techniques: the iterative least squares fitting and the threshold retracking algorithms. Using the AUSGeoid98 geoid grid as a quasi-independent ground reference and comparing with a broad contaminated distance of ∼10 km obtained from a previous study before retracking, the use of the retracking system is able to reduce this contaminated distance to ∼5 km. However, improved SSH data cannot be recovered by waveform retracking less than ∼5 km from the coastline due to predominant land returns in the waveform range window.


title: An Efficient Four-Parameter Affine Motion Model for Video Coding

abstract: In this paper, we study a simplified affine motion model-based coding framework to overcome the limitation of a translational motion model and maintain low-computational complexity. The proposed framework mainly has three key contributions. First, we propose to reduce the number of affine motion parameters from 6 to 4. The proposed four-parameter affine motion model can not only handle most of the complex motions in natural videos, but also save the bits for two parameters. Second, to efficiently encode the affine motion parameters, we propose two motion prediction modes, i.e., an advanced affine motion vector prediction scheme combined with a gradient-based fast affine motion estimation algorithm and an affine model merge scheme, where the latter attempts to reuse the affine motion parameters (instead of the motion vectors) of neighboring blocks. Third, we propose two fast affine motion compensation algorithms. One is the one-step sub-pixel interpolation that reduces the computations of each interpolation. The other is the interpolation-precision-based adaptive block size motion compensation that performs motion compensation at the block level rather than the pixel level to reduce the number of interpolation. Our proposed techniques have been implemented based on the state-of-the-art high-efficiency video coding standard, and the experimental results show that the proposed techniques altogether achieve, on average, 11.1% and 19.3% bits saving for random access and low-delay configurations, respectively, on typical video sequences that have rich rotation or zooming motions. Meanwhile, the computational complexity increases of both the encoder and the decoder are within an acceptable range.


Colorization Using ConvNet and GAN


title: Texture feature extraction using co-occurrence matrices of sub-band image for batik image classification

abstract: In this study, we propose a new method to extract texture features of batik images. The proposed method is called co-occurrence matrices of sub-band images. This method is proposed to overcome the problem in classifying batik images that are acquired randomly from the internet. The problem of those images is the batik images contain various types of noise, such as unbalanced brightness, there are folds on fabrics images, the different size of basic motifs, low contrast, and there is watermark on the images. This method combines the advantages of gray-level co-occurrence matrices (GLCM) and discrete wavelet transform (DWT). First, the original image is decomposed using DWT to provide sub-band images. Second, GLCM is applied to sub-band images to extract the texture features. Those features will become the input for the probabilistic neural network (PNN). The results show that this method is robust enough to classify batik images. The maximum accuracy that can be achieved is 72%.


title: A Robust Statistical Color Edge Detection for Noisy Images

abstract: Edge detection plays a significant role in image processing and performance of high-level tasks such as image segmentation and object recognition depends on its efficiency. It is clear that accurate edge map generation is more difficult when images are corrupted with noise. Moreover, most of edge detection methods have parameters which must be set manually. Here we propose a new color edge detector based on a statistical test, which is robust to noise. Also, the parameters of this method will be set automatically based on image content. To show the effectiveness of the proposed method, four state-of-the-art edge detectors are implemented and the results are compared. Experimental results on five of the most well-known edge detection benchmarks show that the proposed method is robust to noise. The performance of our method for lower levels of noise is very comparable to the existing approaches, whose performances highly depend on their parameter tuning stage. However, for higher levels of noise, the observed results significantly highlight the superiority of the proposed method over the existing edge detection methods, both quantitatively and qualitatively.


title: Efficiency and harmonics generation in microwave to DC conversion circuits of half-wave and full-wave rectifier types

abstract: A Rectifying Antenna (Rectenna) is one of the most important components for a wireless power transmission. It has been developed for many applications such as Space Solar Power System (SSPS), and Radio Frequency Identification (RFID) etc. The Rectenna consisting of RF-DC conversion circuits and receiving antennas needs to be designed for high conversion efficiency to achieve efficient power transmission. we try to design the mw-Class RF-DC conversion circuit using half-and full-wave rectification. The measured conversion efficiencies is 59.3 % and 65.3 % athalf- and full-wave type, respectively. And the full-wave type has lower 2nd harmonic reflection coefficient.


title: A quick method for finding shortest pairs of disjoint paths

abstract: Let G be a directed graph containing n vertices, one of which is a distinguished source s, and m edges, each with a non-negative cost. We consider the problem of finding, for each possible sink vertex v, a pair of edge-disjoint paths from s to v of minimum total edge cost. Suurballe has given an O(n2 logn)-time algorithm for this problem. We give an implementation of Suurballe's algorithm that runs in O(m log(1+ m/n)n) time and O(m) space. Our algorithm builds an implicit representation of the n pairs of paths; given this representation, the time necessary to explicitly construct the pair of paths for any given sink is O(1) per edge on the paths.


title: Ayahuasca as Antidepressant? Psychedelics and Styles of Reasoning in Psychiatry

abstract: There is a growing interest among scientists and the lay public alike in using the South American psychedelic brew, ayahuasca, to treat psychiatric disorders like depression and anxiety. Such a practice is controversial due to a style of reasoning within conventional psychiatry that sees psychedelic-induced modified states of consciousness as pathological. This article analyzes the academic literature on ayahuasca's psychological effects to determine how this style of reasoning is shaping formal scientific discourse on ayahuasca's therapeutic potential as a treatment for depression and anxiety. Findings from these publications suggest that different kinds of experiments are differentially affected by this style of reasoning but can nonetheless indicate some potential therapeutic utility of the ayahuasca-induced modified state of consciousness. The article concludes by suggesting ways in which conventional psychiatry's dominant style of reasoning about psychedelic modified states of consciousness could be reconsidered.


title: Design Techniques for Continuous-Time ΔΣ Modulators With Embedded Active Filtering

abstract: Continuous-time ΔΣ modulators (CTDSM) used in wireless systems need to process signals in the presence of interferers. Peaking in the Signal Transfer Function of a conventional design necessitates a higher in-band dynamic range to accommodate interferers. A filter up front solves this problem at the expense of increased power dissipation and degraded linearity of the signal chain. Embedding the filter in the modulator achieves the same objective in a power efficient manner, while improving out-of-band linearity and reducing active area. However, having the filter inside a ΔΣ loop can be problematic with respect to stability. We show that such a system can be stabilized in a robust manner without extra hardware. Measurements of a CTDSM (signal BW = 2 MHz), with a built-in VGA (0 to 18 dB) and a second order Butterworth filter (4 MHz cutoff), show that the in-band/out-of-band IIP 3 improves by about 3/10 dB when compared to the filter-CTDSM cascade, and achieves a similar dynamic range and consumes 25% lower power.


title: Hybrid music recommender using content-based and social information

abstract: Internet resources available today, including songs, albums, playlists or podcasts, that a user cannot discover if there is not a tool to filter the items that the user might consider relevant. Several recommendation techniques have been developed since the Internet explosion to achieve this filtering task. In an attempt to recommend relevant songs to users, we propose an hybrid recommender that considers real-world users information and high-level representation for audio data. We use a deep learning technique, convolutional deep neural networks, to represent an audio segment in a n-dimensional vector, whose dimensions define the probability of the segment to belong to a specific music genre. To capture the listening behavior of a user, we investigate a state-of-the-art technique, estimation of distribution algorithms. The designed hybrid music recommender outperforms the predictions compared with a traditional content-based recommender.


title: Security in Wireless Sensor Networks

abstract: Wireless sensor networks have attracted a lot of interest over the last decade in wireless and mobile computing research community. Applications of these networks are numerous and growing, which range from indoor deployment scenarios in the home and office to outdoor deployment in adversary's territory in a tactical battleground. However, due to distributed nature and their deployment in remote areas, these networks are vulnerable to numerous security threats that can adversely affect their performance. This chapter provides a comprehensive discussion on the state of the art in security technologies for wireless sensor networks. It identifies various possible attacks at different layers of the communication protocol stack in a typical sensor network and their possible countermeasures. A brief discussion on the future direction of research in WSN security is also included.


title: Risk factors for neck pain: a longitudinal study in the general population

abstract: The objective of the study was to examine the 1-year cumulative incidence of episodic neck pain and to explore its associations with individual risk factors, including a history of previous neck injury. A baseline cross-sectional survey of an adult general population sample made up of all 7669 adults aged 18-75 years, registered with two family practices in South Manchester, United Kingdom, identified the study population of adults with no current neck pain. This study population was surveyed again 12 months later to identify all those who had experienced neck pain during the follow-up period. At follow-up, cumulative 1-year episode incidence of neck pain was estimated at 17.9% (95% confidence interval 16.0-19.7%). Incidence was independent of age, but was more common in women. A history of previous neck injury at baseline was a significant risk factor for subsequent neck pain in the follow-up year (risk ratio 1.7, 95% confidence interval 1.2-2.5), independent of gender and psychological status. Other independent baseline risk factors for subsequent neck pain included number of children, poor self-assessed health, poor psychological status and a past history of low back pain. We have carried out a prospective study in a general population sample and demonstrated that established risk factors for chronic pain predict future episodes of neck pain, and shown that in addition a history of neck injury is an independent and distinct risk factor. This finding may have major public health and medicolegal implications.


title: Automatic Algorithm Recognition of Source-Code Using Machine Learning

abstract: As codebases for software projects get larger, reaching ranges of millions of lines of code, the need for computeraided program comprehension grows. We define one of the tasks of program comprehension to be algorithm recognition: given a piece of source-code from a file, identify the algorithm this code is implementing, such as brute-force or dynamic programming. Most research in this area is making use of pattern matching, which involves much human effort and is of questionable accuracy when the structure and semantics of programs change. Thus, this paper proposes to let go of defined patterns, and make use of simpler features, such as counts of variables and counts of different constructs to recognize algorithms. We then feed these features to a classification algorithm to predict the class or type of algorithm used in this source code. We show through experimental results that our proposed method achieves a good improvement over baseline.


title: Authentication, access control, and audit

abstract: —Authentication establishes the identity of one party to another. Most commonly authentication establishes the identity of a user to some part of the system, typically by means of a password. More generally, authentication can be computer-to-computer or process-toprocess and mutual in both directions. —Access control determines what one party will allow another to do with respect to resources and objects mediated by the former. Access control usually requires authentication as a prerequisite. —The audit process gathers data about activity in the system and analyzes it to discover security violations or diagnose their cause. Analysis can occur offline after the fact or online in real time. In the latter case, the process is usually called intrusion detection.


title: A Coreless Permanent-Magnet Machine for a Magnetically Levitated Shaft-Less Flywheel

abstract: This paper proposes a framework for the design and analysis of a coreless permanent magnet (PM) machine for a 100 kWh shaft-less high strength steel flywheel energy storage system. The PM motor/generator is designed to meet the required specifications in terms of torque–speed and power–speed characteristics given by the application. The design challenges of a motor/generator for this architecture include: the poor flux paths due to a large-scale solid carbon steel rotor and zero-thermal convection of the airgap due to the operation of the machine in a vacuum. The magnetic flux in this architecture tends to be 3-D rather than constrained due to lack of a core in the stator. In order to tackle these challenges, several other parameters such as a proper number of magnets and slots combination, wire thickness, number of turns in each coil, magnets with high saturated flux density, and magnet size are carefully considered in the proposed design framework. Magnetic levitation allows the use of a coreless stator that is placed on a supporting structure. The proposed PM motor/generator comprehensive geometry, electromagnetic design, and mechanical dimensioning are followed by detailed 3-D finite-element analysis (FEA). The torque, power, and speed determined by the FEA electromagnetic analysis meet the application design requirements and constraints for both the charging and discharging modes of operation. Furthermore, the design and analysis of the coil holding structures are discussed. Finally, the motor/generator static thermal analysis is discussed in order to validate the proposed cooling system functionality.


title: Multi-atlas segmentation with augmented features for cardiac MR images

abstract: Abstract Multi-atlas segmentation infers the target image segmentation by combining prior anatomical knowledge encoded in multiple atlases. It has been quite successfully applied to medical image segmentation in the recent years, resulting in highly accurate and robust segmentation for many anatomical structures. However, to guide the label fusion process, most existing multi-atlas segmentation methods only utilise the intensity information within a small patch during the label fusion process and may neglect other useful information such as gradient and contextual information (the appearance of surrounding regions). This paper proposes to combine the intensity, gradient and contextual information into an augmented feature vector and incorporate it into multi-atlas segmentation. Also, it explores the alternative to the K nearest neighbour (KNN) classifier in performing multi-atlas label fusion, by using the support vector machine (SVM) for label fusion instead. Experimental results on a short-axis cardiac MR data set of 83 subjects have demonstrated that the accuracy of multi-atlas segmentation can be significantly improved by using the augmented feature vector. The mean Dice metric of the proposed segmentation framework is 0.81 for the left ventricular myocardium on this data set, compared to 0.79 given by the conventional multi-atlas patch-based segmentation (Coupe et al., 2011; Rousseau et al., 2011). A major contribution of this paper is that it demonstrates that the performance of non-local patch-based segmentation can be improved by using augmented features.


Deaf Mute Communication Interpreter-A Review


title: curlybot: designing a new class of computational toys

abstract: We introduce an educational toy, called curlybot, as the basis for a new class of toys aimed at children in their early stages of development — ages four and up. curlybot is an autonomous two-wheeled vehicle with embedded electronics that can record how it has been moved on any flat surface and then play back that motion accurately and repeatedly. Children can use curlybot to develop intuitions for advanced mathematical and computational concepts, like differential geometry, through play away from a traditional computer. In our preliminary studies, we found that children learn to use curlybot quickly. They readily establish an affective and body syntonic connection with curlybot, because of its ability to remember all of the intricacies of their original gesture; every pause, acceleration, and even the shaking in their hand is recorded. Programming by example in this context makes the educational ideas implicit in the design of curlybot accessible to young children.


title: Multi-level direction of autonomous creatures for real-time virtual environments

abstract: There have been several recent efforts to build behavior-based autonomous creatures. While competent autonomous action is highly desirable, there is an important need to integrate autonomy with “directability”. In this paper we discuss the problem of building autonomous animated creatures for interactive virtual environments which are also capable of being directed at multiple levels. We present an approach to control which allows an external entity to “direct” an autonomous creature at the motivational level, the task level, and the direct motor level. We also detail a layered architecture and a general behavioral model for perception and action-selection which incorporates explicit support for multi-level direction. These ideas have been implemented and used to develop several autonomous animated creatures.


title: Power optimization for FinFET-based circuits using genetic algorithms

abstract: Reducing power consumption is one of the important design goals for circuit designers. Power optimization techniques for bulk CMOS-based circuit designs have been extensively studied. As technology scales, FinFET has been proposed as an alternative for bulk CMOS when technology scales beyond 32 nm technology (E.J. Nowak et al., 2004). In this paper, we propose a power optimization framework for FinFET based circuit design, based on genetic algorithms. We exploit the unique feature of independent gate (IG) controls for FinFET devices to reduce the power consumption, and combine with other low power techniques such as multi-Vdd and gate sizing to achieve power optimization for FinFET-based circuits. We use 32 nm PTM FinFET device model (W. Zhao and Y. Cao, 2006) and conduct experiments on ISCAS benchmarks. The experimental results show that our methodology can achieve over 80% power reduction while satisfying the same performance constraints, comparing to the case that all the FinFET transistors are tuned to be the fastest ones.


Available Online at www.ijecse.org ISSN- 2277-1956 A Multimodal Biometric Recognition System based on Fusion of Palmprint, Fingerprint and Face


title: Atomic persistence for SCM with a non-intrusive backend controller

abstract: Non-volatile byte-addressable memory has the potential to revolutionize system architecture by providing instruction-grained direct access to vast amounts of persistent data. We describe a non-intrusive memory controller that uses backend operations for achieving lightweight failure atomicity. By moving synchronous persistent memory operations to the background, the performance overheads are minimized. Our solution avoids costly software intervention by decoupling isolation and concurrency-driven atomicity from failure atomicity and durability, and does not require changes to the front-end cache hierarchy. Two implementation alternatives — one using a hardware structure, and the other extending the memory controller with a firmware managed volatile space — are described. Our results show the performance is significantly better than traditional approaches.


Nonlinear Extensions of Reconstruction ICA


title: Spectral Analysis of Nonuniformly Sampled Data: A New Approach Versus the Periodogram

abstract: We begin by revisiting the periodogram to explain why arguably the plain least-squares periodogram (LSP) is preferable to the ldquoclassicalrdquo Fourier periodogram, from a data-fitting viewpoint, as well as to the frequently-used form of LSP due to Lomb and Scargle, from a computational standpoint. Then we go on to introduce a new enhanced method for spectral analysis of nonuniformly sampled data sequences. The new method can be interpreted as an iteratively weighted LSP that makes use of a data-dependent weighting matrix built from the most recent spectral estimate. Because this method is derived for the case of real-valued data (which is typically more complicated to deal with in spectral analysis than the complex-valued data case), it is iterative and it makes use of an adaptive (i.e., data-dependent) weighting, we refer to it as the real-valued iterative adaptive approach (RIAA). LSP and RIAA are nonparametric methods that can be used for the spectral analysis of general data sequences with both continuous and discrete spectra. However, they are most suitable for data sequences with discrete spectra (i.e., sinusoidal data), which is the case we emphasize in this paper.


title: Some Recent Research Aspects of Threshold Cryptography

abstract: In the traditional scenario in cryptography there is one sender, one receiver and an active or passive eavesdropper who is an opponent. Depending from the application the sender or the receiver (or both) need to use a secret key. Often we are not dealing with an individual sender/receiver, but the sender/receiver is an organization. The goal of threshold cryptography is to present practical schemes to solve such problems without the need to use the more general methods of mental games.


title: A Hidden Markov, Multivariate Autoregressive (HMM-mAR) Network Framework for Analysis of Surface EMG (sEMG) Data

abstract: As the primary noninvasive means to assess muscle activation, the surface electromyogram (sEMG) is of central importance for the study of motor behavior in both clinical and biomedical applications. However, multivariate sEMG analysis is complicated by the fact that data recorded during dynamic contractions are inherently nonstationary. To model this nonstationarity and to determine the dynamic muscle activity patterns during reaching movements, we propose combining hidden Markov models (HMMs) and multivariate autoregressive (mAR) models into a joint HMM-mAR framework. We further propose constructing muscle networks statistically by performing a second level, group analysis on the subject-specific models. Network structural features are subsequently investigated as input features for the purpose of classification. The proposed approach was applied to real sEMG recordings collected from healthy and stroke subjects during reaching movements. When examining group muscle networks, we note that specific muscle connection patterns were selectively recruited during reaching movements and were differentially recruited after stroke compared to healthy subjects. As the analysis was performed on the raw data, the amplitude and the underlying ldquocarrier datardquo of sEMG signals, we notice that the HMM-mAR model fits the amplitude data well, but not the raw or carrier data. The proposed sEMG analysis framework represents a fundamental departure from existing methods where only the amplitude is typically analyzed or the mAR coefficients are directly used for classification. As the method may provide additional insights into motor control, it appears a promising approach warranting further study.


title: Instance flow based online multiple object tracking

abstract: We present a method to perform online Multiple Object Tracking (MOT) of known object categories in monocular video data. Current Tracking-by-Detection MOT approaches build on top of 2D bounding box detections. In contrast, we exploit state-of-the-art instance aware semantic segmentation techniques to compute 2D shape representations of target objects in each frame. We predict position and shape of segmented instances in subsequent frames by exploiting optical flow cues. We define an affinity matrix between instances of subsequent frames which reflects locality and visual similarity. The instance association is solved by applying the Hungarian method. We evaluate different configurations of our algorithm using the MOT 2D 2015 train dataset. The evaluation shows that our tracking approach is able to track objects with high relative motions. In addition, we provide results of our approach on the MOT 2D 2015 test set for comparison with previous works. We achieve a MOTA score of 32.1.


title: 0.2 V 8T SRAM With PVT-Aware Bitline Sensing and Column-Based Data Randomization

abstract: In sub/near-threshold operation, SRAMs suffer from considerable bitline swing degradation when the data pattern of a column is skewed to ‘1’ or ‘0’. The worst scenarios regarding this problem occur when the currently read SRAM cell has different data compared to the rest of the cells in the same column. In this work, we overcome this challenge by using a column-based randomization engine (CBRE). This CBRE circuit randomizes data stored to SRAM. This makes distribution of “1” and “0” in each column close to 50%, significantly increasing bitline swing. To further improve the bitline swing, we employ bitline boost biasing and dynamic bitline keeper schemes. Based on the mentioned techniques, we fabricated a 256 rows × 128 columns (32 Kb) 8T SRAM array in 65 nm CMOS technology. In our silicon measurement, the SRAM array shows successful 200 mV operation at room temperature, where energy consumption and access time are 1 pJ and 2.5 s, respectively.


title: A Constraint-based Encoding for Domain-Independent Temporal Planning

abstract: We present a general constraint-based encoding for domain-independent task planning. Task planning is characterized by causal relationships expressed as conditions and effects of optional actions. Possible actions are typically represented by templates, where each template can be instantiated into a number of primitive actions. While most previous work for domain-independent task planning has focused on primitive actions in a state-oriented view, our encoding uses a fully lifted representation at the level of action templates. It follows a time-oriented view in the spirit of previous work in constraint-based scheduling. As a result, the proposed encoding is simple and compact as it grows with the number of actions in a solution plan rather than the number of possible primitive actions. When solved with an SMT solver, we show that the proposed encoding is slightly more efficient than state-of-the-art methods on temporally constrained planning benchmarks while clearly outperforming other fully constraint-based approaches.


title: Mapping the Spatial Distribution of Winter Crops at Sub-Pixel Level Using AVHRR NDVI Time Series and Neural Nets

abstract: For large areas, it is difficult to assess the spatial distribution and inter-annual variation of crop acreages through field surveys. Such information, however, is of great value for governments, land managers, planning authorities, commodity traders and environmental scientists. Time series of coarse resolution imagery offer the advantage of global coverage at low costs, and are therefore suitable for large-scale crop type mapping. Due to their coarse spatial resolution, however, the problem of mixed pixels has to be addressed. Traditional hard classification approaches cannot be applied because of sub-pixel heterogeneity. We evaluate neural networks as a modeling tool for sub-pixel crop acreage estimation. The proposed methodology is based on the assumption that different cover type proportions within coarse pixels prompt changes in time profiles of remotely sensed vegetation indices like the Normalized Difference Vegetation Index (NDVI). Neural networks can learn the relation between temporal NDVI signatures and the sought crop acreage information. This learning step permits a non-linear unmixing of the temporal information provided by coarse resolution satellite sensors. For assessing the feasibility and accuracy of the approach, a study region in central Italy (Tuscany) was selected. The task consisted of mapping the spatial distribution of winter crops abundances within 1 km AVHRR pixels between 1988 and 2001. Reference crop acreage information for network training and validation was derived from high resolution Thematic Mapper/Enhanced Thematic Mapper (TM/ETM+) images and official agricultural statistics. Encouraging


title: Least-squares orthogonal distances fitting of circle, sphere, ellipse, hyperbola, and parabola

abstract: The least-squares fitting minimizes the squares sum of error-of-fit in predefined measures. By the geometric fitting, the error distances are defined with the orthogonal, or shortest, distances from the given points to the geometric feature to be fitted. For the geometric fitting of circle/sphere/ellipse/hyperbola/parabola, simple and robust nonparametric algorithms are proposed. These are based on the coordinate description of the corresponding point on the geometric feature for the given point, where the connecting line of the two points is the shortest path from the given point to the geometric feature to be fitted.


title: An Agent-Based Solution to the Resource Allocation Problem in Emergency Situations

abstract: Mass casualty incidents (MCI) are highly dynamic situations. In order to minimize potential losses associated primarily with human lives, it is particularly important to quickly and efficiently allocate the available limited resources. Thus, a flexible solution for rational resource allocation is needed. In this paper, we show a combination of technologies from different fields of research that have focused on the support for such an optimal solution. We describe a mathematical model and then demonstrate a prototype implementation with evaluation results of the suggested approach.


title: Towards a new era in medicine: therapeutic genome editing

abstract: Genome editing is the process of precisely modifying the nucleotide sequence of the genome. It has provided a powerful approach to research questions but, with the development of a new set of tools, it is now possible to achieve frequencies of genome editing that are high enough to be useful therapeutically. Genome editing is being developed to treat not only monogenic diseases but also infectious diseases and diseases that have both a genetic and an environmental component.


title: Citizen Motivation on the Go: The Role of Psychological Empowerment

abstract: Although advances in technology now enable people to communicate ‘anytime, anyplace’, it is not clear how citizens can be motivated to actually do so. This paper evaluates the impact of three principles of psychological empowerment, namely perceived self-efficacy, sense of community and causal importance, on public transport passengers’ motivation to report issues and complaints while on the move. A week-long study with 65 participants revealed that self-efficacy and causal importance increased participation in short bursts and increased perceptions of service quality over longer periods. Finally, we discuss the implications of these findings for citizen participation projects and reflect on design opportunities for mobile technologies that motivate citizen participation. RESEARCH HIGHLIGHTS • We evaluate three different factors of psychological empowerment in engaging users. • Perceived self-efficacy (PSE), causal importance (CI) and sense of community (SoC) were used. • PSE and CI lead to increased participation in short amounts of time. • PSE, CI and SoC lead to increased perceived public transportation service quality.


title: Windows on the world: 2D windows for 3D augmented reality

abstract: INTRODUCTION We describe the design and implementation of a prototype When we think of the use of head-mounted displays and 3D heads-up window system intended for use in a 3D environinteraction devices to present virtual worlds, it is often in ment. Our system includes a see-through head-mounted terms of environments populated solely by 3D objects. display that runs a full X server whose image is overlaid on There are many situations, however, in which 2D text and the user’s view of the physical world. The user’s head is graphics of the sort supported by current window systems tracked so that the display indexes into a large X bitmap, can be useful components of these environments. This is effectively placing the user inside a display space that is especially true in the case of the many applications that run mapped onto part of a surrounding virtual sphere. By under an industry standard window system such as X [13]. tracking the user’s body, and interpreting head motion relaWhile we might imagine porting or enhancing a significant tive to it, we create a portable information surround that X application to take advantage of the 3D capabilities of a envelopes the user as they move about. virtual world, the effort and cost may not be worth the return, especially if the application is inherently 2D. We support three kinds of windows implemented on top of Therefore, we have been exploring how we can incorporate the X server: windows fixed to the head-mounted display, an existing 2D window system within a 3D virtual world. windows fixed to the information surround, and windows fixed to locations and objects in the 3D world. Objects can We are building an experimental system that supports a full also be tracked, allowing windows to move with them. To X11 server on a see-through head-mounted display. Our demonstrate the utility of this model, we describe a small display overlays a selected portion of the X bitmap on the hypermedia system that allows links to be made between user’s view of the world, creating an X-based augmented windows and windows to be attached to objects. Thus, our reality. Depending on the situation and application, the hypermedia system can forge links between any combinauser may wish to treat a window as a stand-alone entity or tion of physical objects and virtual windows. to take advantage of the potential relationships that can be made between it and the visible physical world. To make this possible, we have developed facilities that allow X


title: AUDACIOUS: User-Driven Access Control with Unmodified Operating Systems

abstract: User-driven access control improves the coarse-grained access control of current operating systems (particularly in the mobile space) that provide only all-or-nothing access to a resource such as the camera or the current location. By granting appropriate permissions only in response to explicit user actions (for example, pressing a camera button), user-driven access control better aligns application actions with user expectations. Prior work on user-driven access control has relied in essential ways on operating system (OS) modifications to provide applications with uncompromisable access control gadgets, distinguished user interface (UI) elements that can grant access permissions. This work presents a design, implementation, and evaluation of user-driven access control that works with no OS modifications, thus making deployability and incremental adoption of the model more feasible. We develop (1) a user-level trusted library for access control gadgets, (2) static analyses to prevent malicious creation of UI events, illegal flows of sensitive information, and circumvention of our library, and (3) dynamic analyses to ensure users are not tricked into granting permissions. In addition to providing the original user-driven access control guarantees, we use static information flow to limit where results derived from sensitive sources may flow in an application. Our implementation targets Android applications. We port open-source applications that need interesting resource permissions to use our system. We determine in what ways user-driven access control in general and our implementation in particular are good matches for real applications. We demonstrate that our system is secure against a variety of attacks that malware on Android could otherwise mount.


title: Overview of coded light projection techniques for automatic 3D profiling

abstract: Obtaining automatic 3D profile of objects is one of the most important issues in computer vision. With this information, a large number of applications become feasible: from visual inspection of industrial parts to 3D reconstruction of the environment for mobile robots. In order to achieve 3D data, range finders can be used. Coded structured light approach is one of the most widely used techniques to retrieve 3D information of an unknown surface. An overview of the existing techniques as well as a new classification of patterns for structured light sensors is presented. This kind of systems belong to the group of active triangulation method, which are based on projecting a light pattern and imaging the illuminated scene from one or more points of view. Since the patterns are coded, correspondences between points of the image(s) and points of the projected pattern can be easily found. Once correspondences are found, a classical triangulation strategy between camera(s) and projector device leads to the reconstruction of the surface. Advantages and constraints of the different patterns are discussed.


title: Recurrent Psychotic Depression Is Treatable by Psychoanalytic Therapy without Medication

abstract: Psychotic depression is best treated by psychotherapy without medication. If medication is used it should be withdrawn as the patient can tolerate it. Every affect, including depression, has meaning. Depressed patients may feel sad, frightened, angry, undifferentiated negative affect, or no feelings at all. How to deal with sleep disturbances and with suicidal danger is discussed. Several clinical cases are discussed, including a man who was "cured" of depression with insulin coma treatment in his 20s and was rehospitalized nearly every year thereafter for depression. He also suffered from "spontaneous" panic attacks. Medications only partially helped. He began psychoanalytic therapy without medication in his 60s. When the meanings of his panic attacks and his depressions were discovered, he permanently recovered. Keywords: depression; psychotherapy; psychoanalysis medication In most of my papers (e.g., Karon, 2003) and in my book (Karon &. VandenBos, 1981) I have repeatedly made the point that schizophrenic patients are treatable by psychoanalytic therapy, that psychoanalytic therapy without medication is the treatment that is most effective, and that medication may be used as a temporary adjunct but it should be withdrawn as rapidly as the patient can tolerate. As we found in my Detroit project, with randomized assignment and blind evaluations, even 70 sessions of psychoanalytic therapy, as compared to medication properly used, led to a much greater improvement in the thought disorder and a more human life in a variety of ways, and consequently a much lower rate of rehospitalization. But the effectiveness of psychoanalytic therapy for deeply disturbed human beings is not confined to schizophrenics. As Michael Teixeira has written in his classic paper on the treatment of manic-depressive disorders by psychoanalytic therapy (Teixeira, 1992), similar approaches to psychoanalytic therapy are similarly effective with these patients as well. And again, patients are more likely to make optimal progress without the use of medication, or with temporary medication which is withdrawn as rapidly as the patient can tolerate. Every diagnostic category consists of a highly varied group of people. Among others, Harry Stack Sullivan (1953) and Karl Menninger (Menninger, Mayman, & Pruyser, 1963) emphasized how poorly diagnostic categories characterize patients. Luckily, psychoanalysts and other serious therapists treat people, not symptoms. For depressed patients, as with anyone, the conscious and unconscious fantasies underlying any symptom always result from the interaction of their actual life experiences with pre-existing fantasies so that the patient's whole life history from early infancy onward is relevant. The defense mechanism of isolation is an almost invariable defense of severely depressed patients. If you ask a depressed patient what was going on in their life just before the depression started, they usually will say, "Nothing." If you persist, they will typically say, "Nothing important." If you persist, that you want to know the unimportant things that were going on, they will tell you things which would depress anyone, but which they do not connect with their feelings. Unfortunately, it is not fashionable these days to get an accurate case history. After I presented a Grand Rounds at a major department of psychiatry 2 years ago, one of a group of advanced psychiatric residents said with a puzzled look, as if it were a new idea, "I guess what you are talking about might be called historicity. If we knew the patient's history, what they're talking about would make sense." Just because a patient calls him or herself depressed, or professionals have diagnosed the patient as depressed, does not mean you know what the affective experience of the patient really is. As Henry Krystal has pointed out (personal communication, 1980), one should always try to find out what the actual affect is. …


title: Coplanar waveguide to microstrip transition in thin polymer film for characterization and packaging of microwave photonic components

abstract: We report in this paper two types of broadband transitions between microstrip and coplanar lines on thin benzocyclobutene (BCB) polymer substrate. They are both via-free, using electromagnetic coupling between the bottom and top ground planes, which simplifies the manufacturing of components driven by microstrip electrodes. In the first ones, the bottom ground is not patterned, which makes them particularly suitable to on-wafer measurement of components under development with coplanar probes. An ultra-broad bandwidth of 68 GHz (from 1 GHz to 69 GHz) was achieved with 20-pm BCB. In the second ones, intended for connectorizing components on thin substrate with coplanar connectors, the bottom ground is patterned to match the narrow center conductor (54 μm) on thin substrate to the wide center conductor (127 μm) of the connector with a tapered section, achieving to a experimental bandwidth 13 GHz for the moment.


title: Cold Boot Attacks are Still Hot: Security Analysis of Memory Scramblers in Modern Processors

abstract: Previous work has demonstrated that systems with unencrypted DRAM interfaces are susceptible to cold boot attacks – where the DRAM in a system is frozen to give it sufficient retention time and is then re-read after reboot, or is transferred to an attacker's machine for extracting sensitive data. This method has been shown to be an effective attack vector for extracting disk encryption keys out of locked devices. However, most modern systems incorporate some form of data scrambling into their DRAM interfaces making cold boot attacks challenging. While first added as a measure to improve signal integrity and reduce power supply noise, these scram-blers today serve the added purpose of obscuring the DRAM contents. It has previously been shown that scrambled DDR3 systems do not provide meaningful protection against cold boot attacks. In this paper, we investigate the enhancements that have been introduced in DDR4 memory scramblers in the 6th generation Intel Core (Skylake) processors. We then present an attack that demonstrates these enhanced DDR4 scramblers still do not provide sufficient protection against cold boot attacks. We detail a proof-of-concept attack that extracts memory resident AES keys, including disk encryption keys. The limitations of memory scramblers we point out in this paper motivate the need for strong yet low-overhead full-memory encryption schemes. Existing schemes such as Intel's SGX can effectively prevent such attacks, but have overheads that may not be acceptable for performance-sensitive applications. However, it is possible to deploy a memory encryption scheme that has zero performance overhead by forgoing integrity checking and replay attack protections afforded by Intel SGX. To that end, we present analyses that confirm modern stream ciphers such as ChaCha8 are sufficiently fast that it is now possible to completely overlap keystream generation with DRAM row buffer access latency, thereby enabling the creation of strongly encrypted DRAMs with zero exposed latency. Adopting such low-overhead measures in future generation of products can effectively shut down cold boot attacks in systems where the overhead of existing memory encryption schemes is unacceptable. Furthermore, the emergence of non-volatile DIMMs that fit into DDR4 buses is going to exacerbate the risk of cold boot attacks. Hence, strong full memory encryption is going to be even more crucial on such systems.


title: Basic Principles of Homing Guidance

abstract: This article provides a conceptual foundation with respect to homing guidance upon which the next several articles are anchored. To this end, a basic geometric and notational framework is first established. Then, the well-known and often-used proportional navigation guidance concept is developed. The mechanization of proportional navigation in guided missiles depends on several factors, including the types of inertial and target sensors available on board the missile. Within this context, the line-of-sight reconstruction process (the collection and orchestration of the inertial and target sensor measurements necessary to support homing guidance) is discussed. Also, guided missiles typically have no direct control over longitudinal acceleration, and they maneuver in the direction specified by the guidance law by producing acceleration normal to the missile body. Therefore, we discuss a guidance command preservation technique that addresses this lack of control. The key challenges associated with designing effective homing guidance systems are discussed, followed by a cursory discussion of midcourse guidance for completeness' sake.


title: Private Information Retrieval With Side Information

abstract: We study the problem of Private Information Retrieval (PIR) in the presence of prior side information. The problem setup includes a database of <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> independent messages possibly replicated on several servers, and a user that needs to retrieve one of these messages. In addition, the user has some prior side information in the form of a subset of <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> messages, not containing the desired message and unknown to the servers. This problem is motivated by practical settings in which the user can obtain side information opportunistically from other users or has previously downloaded some messages using classical PIR schemes. The objective of the user is to retrieve the required message with downloading minimum amount of data from the servers while achieving information-theoretic privacy in one of the following two scenarios: (i) the user wants to protect jointly the identities of the demand and the side information; (ii) the user wants to protect only the identity of the demand, but not necessarily the side information. To highlight the role of side information, we focus first on the case of a single server (single database). In the first scenario, we prove that the minimum download cost is <inline-formula> <tex-math notation="LaTeX">$K-M$ </tex-math></inline-formula> messages, and in the second scenario it is <inline-formula> <tex-math notation="LaTeX">$\lceil K/(M+1)\rceil $ </tex-math></inline-formula> messages, which should be compared to <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> messages—the minimum download cost in the case of no side information. Then, we extend some of our results to the case of the database replicated on multiple servers. Our proof techniques relate PIR with side information to the index coding problem. We leverage this connection to prove converse results, as well as to design achievability schemes.


title: Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals

abstract: Understanding the consequences of regulatory variation in the human genome remains a major challenge, with important implications for understanding gene regulation and interpreting the many disease-risk variants that fall outside of protein-coding regions. Here, we provide a direct window into the regulatory consequences of genetic variation by sequencing RNA from 922 genotyped individuals. We present a comprehensive description of the distribution of regulatory variation--by the specific expression phenotypes altered, the properties of affected genes, and the genomic characteristics of regulatory variants. We detect variants influencing expression of over ten thousand genes, and through the enhanced resolution offered by RNA-sequencing, for the first time we identify thousands of variants associated with specific phenotypes including splicing and allelic expression. Evaluating the effects of both long-range intra-chromosomal and trans (cross-chromosomal) regulation, we observe modularity in the regulatory network, with three-dimensional chromosomal configuration playing a particular role in regulatory modules within each chromosome. We also observe a significant depletion of regulatory variants affecting central and critical genes, along with a trend of reduced effect sizes as variant frequency increases, providing evidence that purifying selection and buffering have limited the deleterious impact of regulatory variation on the cell. Further, generalizing beyond observed variants, we have analyzed the genomic properties of variants associated with expression and splicing and developed a Bayesian model to predict regulatory consequences of genetic variants, applicable to the interpretation of individual genomes and disease studies. Together, these results represent a critical step toward characterizing the complete landscape of human regulatory variation.


title: Optimal Control Based on Maximum Power Point Tracking (MPPT) of an Autonomous Hybrid Photovoltaic/Storage System in Micro Grid Applications

abstract: This paper investigates how to increase the efficiency of a photovoltaic/energy storage generation unit supplying dynamic loads by regulating and managing both the photovoltaic generator and the storage battery charge-discharge modes. The proposed photovoltaic/energy storage unit is proposed to supply an induction motor driven industrial pump with controlled speed and/or a DC motor driven water pump. An optimal proportional-integral-derivative control based on an Artificial Bee Colony Optimization algorithm is used to regulate the photovoltaic generator in case of normal operation or in case of maximum power point tracking (MPPT) and to also control the battery storage charge discharge modes. A vector control based on the proposed optimal control is used to regulate the induction motor rotor speed at its low reference values needed by the industrial pump. First, a total model of the entire system is obtained. The controller performance with the proposed system is studied with both a DC motor and/or induction motor loads. Simulation results show that the proposed photovoltaic/storage generator is able to supply the suggested dynamic loads under different conditions and with good performance. Also, it is noticed that operating the photovoltaic base on maximum power point tracking condition will give about 43% extra generation power than the normal operation case.


title: System Deadlocks

abstract: A problem of increasing importance in the design of large multiprogramming systems is the, so-called, deadlock or deadly-embrace problem. In this article we survey the work that has been done on the treatment of deadlocks from both the theoretical and practical points of view.


title: A randomized controlled trial comparing the AccuVein AV300 device to standard insertion technique for intravenous cannulation of anesthetized children.

abstract: OBJECTIVES AND AIMS ::: To evaluate the efficacy of the AccuVein AV300 device in improving the first-time success rate of intravenous cannulation of anesthetized pediatric patients. ::: ::: ::: BACKGROUND ::: The AccuVein AV300 device was developed to assist venepuncture and intravenous cannulation by enhancing the visibility of superficial veins. It uses infrared light to highlight hemoglobin so that blood vessels are darkly delineated against a red background. ::: ::: ::: METHODS/MATERIALS ::: Patients were randomized to cannulation with the AccuVein AV300 device or standard insertion by experienced pediatric anesthesiologists. An observer recorded the number of skin punctures and cannulation attempts required, and the time between tourniquet application and successful cannulation or four skin punctures, whichever came first. ::: ::: ::: RESULTS AND CONCLUSIONS ::: There were 146 patients with a median age of 4.6 years (range, 0.18-17.1 years), 46.6% were male, 80.8% were light skin colored, and 15.7% were younger than 2 years. The first-attempt success rates were 75% (95% CI, 63.8-84.2%) using AV300 and 73% (95% CI, 61.9-81.9%) using the standard method (P = 0.85). Patients with dark or medium skin color were 0.38 times less likely to have a successful first attempt than patients with light skin color. The difference between the two treatment groups in number of skin punctures and the time to insertion was not significant. Although the AV300 was easy to use and improved visualization of the veins, we found no evidence that it was superior to the standard method of intravenous cannulation in unselected pediatric patients under anesthesia.


title: Database System Support for Personalized Recommendation Applications

abstract: Personalized recommendation has become popular in modern web services. For instance, Amazon recommends new items to shoppers. Also, Netflix recommends shows to viewers, and Facebook recommends friends to its users. Despite the ubiquity of recommendation applications, classic database management systems still do not provide in-house support for recommending data stored in the database. In this paper, we present the anatomy of RecDB an open source PostgreSQLbased system that provides a unified approach for declarative data recommendation inside the database engine. RecDB realizes the personalized recommendation functionality as query operators inside the database kernel. That facilitates applying the recommendation functionality and typical database operations (e.g., Selection, Join, Top-k) side-by-side. To further reduce the application latency, RecDB pre-computes and caches the generated recommendation in the database. In the paper, we present extensive experiments that study the performance of personalized recommendation applications based on an actual implementation inside PostgreSQL 9.2 using real Movie recommendation and location-aware recommendation scenarios. The results show that a recommendation-aware database engine, i.e., RecDB, outperforms the classic approach that implements the recommendation logic on-top of the database engine in various recommendation applications.


title: Shape Retrieval Based on Centroid Distance Ratio and Shape Context

abstract: Shape context is a classic shape retrieval method with translation invariance, but it has not scalar or rotational invariance, which limits its application. A new shape feature descriptor-centroid distance ratio (CdR) is proposed and an improved shape retrieval algorithm based on CdR and shape context is brought forth. First, the contour points are sampled in order to reduce computation and then, centroid distance values of the sample points are sorted in descending order. Ratio set of each pair of the list's head and tail constructs the CdR feature for pre-matching. Final retrieval is achieved according to the fused feature. Experiments show that the proposed algorithm not only supports good translation, scalar, rotational invariance but also enhances evidently the retrieval efficiency and accuracy.


Applications of Lexical Cohesion in the Topic Detection and Tracking Domain


title: A hybrid deep model with HOG features for Bangla handwritten numeral classification

abstract: Considering the practical significances, handwriting recognition is getting an intense interest to the research community. Through, several studies have been conducted for Bengali handwriting recognition, a robust model for Bengali numerals classification is still due. Therefore, a hybrid model is presented in this paper, which aims to classify the Bengali numerals more precisely. The proposed model bridges hand crafted feature extraction based approaches with the automatically learnt features of Convolutional Neural networks (CNN). It is observed that the proposed model outperforms existing models with lesser epochs. The proposed model is trained and tested with the ISI numeral dataset and also cross-validated with the CAMTERDB numeral dataset. For both scenarios, proposed model shows consistency and demonstrate the maximum accuracy of 99.02% and 99.17%, respectively. For the CMATERDB collection, the proposed model achieves the best accuracy rate reported till date.


title: A real-time model based Support Vector Machine for emotion recognition through EEG

abstract: Recently, there has been a significant amount of work on the recognition of human emotions. The results of the work can be applied in real applications, for example in market survey or neuro-marketing. This interesting problem requires to recognize naturally human emotions which come from our mind but ignore the external expressions fully controlled by a subject. A popular approach uses key information from electroencephalography (EEG) signals to identify human emotions. In this paper, we proposed an emotion recognition model based on the Russell's circumplex model, Higuchi Fractal Dimension (HFD) algorithm and Support Vector Machine (SVM) as a classifier. Moreover, we also proposed a method to determine an emotion label of a series of EEG signals. Our model includes two main approaches in machine learning step. In a first approach, machine learning was utilized for all EEG signals from numerous subjects while another used machine learning for each particular subject. We extensively implemented our model in several test data. The experimental results showed that the first approach is impossible to apply in practical applications because EEG signal of each subject has individual characteristic. In addition, in the second, our model can recognize five basic states of human emotion in real-time with average accuracy 70.5%.


title: Integrating Static and Dynamic Analysis for Detecting Vulnerabilities

abstract: A secure software demands effective techniques for vulnerability detection during its development cycle. The practice of detecting security flaws before the deployment phase eliminates the risks that vulnerabilities may impose for the company. Static analysis and dynamic analysis techniques offer two complimentary approaches for checking vulnerabilities. Static analysis involves the scanning of source code or binary eliminating the need of executing it. This approach is fast and has no run time overhead. However, static analysis are quite imprecise and generate huge false positives and false negatives. On the other hand, dynamic analysis involves the running of the software. The problem of false positives and negatives is less in case of dynamic analysis because they analyze by running the test cases. But this approach requires large number of test cases to ensure a certain confidence level in detecting security bugs. This paper describes a methodology which integrates the two approaches in a complimentary manner. It adopts the strengths of the two and eliminates their weaknesses. We are currently dealing with buffer overflow vulnerability with pointer aliasing. However the idea can be extended to other vulnerabilities also for e.g memory related errors, race conditions( Time of Check to Time to Use vulnerability), dangling pointer vulnerability, integer errors etc.


title: [POSTER] The Augmented Library: An Approach for Improving Users Awareness in a Campus Library

abstract: Most existing libraries use floor plans and call numbers in order to locate books rather than providing truly user's awareness of the extensive collections and services for students in the library context. With a collection of more than 48,568 volumes at the Engineering and Exact Sciences library of the Universidad Autonoma de Yucatan (UADY), the task of looking for a book can be really a time-consuming and frustrating task, specially for newcomers. This paper proposes an Augmented Reality (AR) based application which aims to solve users spatial unawareness at library by providing an AR shelf searching system, and to assist students and newcomers by providing a mobile guide with library information services into library. In this work, the results of a pilot study are presented demonstrating that user's experiences and performance at library were enhanced through the use of augmented reality technology.


title: Doing It Collaboratively! Addressing the Dilemmas of Designing Quantitative Effect Studies on Narrative Family Therapy in a Local Clinical Context

abstract: This article suggests an approach for addressing the dilemmas narrative therapists face, wanting to make narrative therapy accessible to people seeking help in contexts favoring evidence-based therapy. The approach is inspired by participatory action research and involves clinicians and clients in a local clinical context. The article offers a detailed case description of implementing psychometric effect measurements on narrative family therapy and of creating a shared collaborative stance for researchers using quantitative effect measurements and clinicians using narrative therapy. Our findings suggest that involving narrative clinicians and clients in the development of a research design in the local clinical context might be helpful in overcoming narrative skepticism and criticism towards quantitative effect research. It is our hope that this article will inspire more narrative therapists to participate in the creation of a quantitative evidence base for narrative therapy.


title: Big Problems for Artificial Intelligence

abstract: I compare the big problems studied in artificial intelligence and related fields in order to understand some major changes -- both internal and external -- recently suffered by AI. The comparison finds AI with few problems to call its own, and I identify some further major changes that may occur soon.


Aerodynamics of wind turbine wakes Literature review


title: A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities

abstract: We present a weakly supervised deep learning model for classifying thoracic diseases and identifying abnormalities in chest radiography. In this work, instead of learning from medical imaging data with region-level annotations, our model was merely trained on imaging data with image-level labels to classify diseases, and is able to identify abnormal image regions simultaneously. Our model consists of a customized pooling structure and an adaptive DenseNet front-end, which can effectively recognize possible disease features for classification and localization tasks. Our method has been validated on the publicly available ChestX-ray14 dataset. Experimental results have demonstrated that our classification and localization prediction performance achieved significant improvement over the previous models on the ChestX-ray14 dataset. In summary, our network can produce accurate disease classification and localization, which can potentially support clinical decisions.


title: Community membership identification from small seed sets

abstract: In many applications we have a social network of people and would like to identify the members of an interesting but unlabeled group or community. We start with a small number of exemplar group members -- they may be followers of a political ideology or fans of a music genre -- and need to use those examples to discover the additional members. This problem gives rise to the seed expansion problem in community detection: given example community members, how can the social graph be used to predict the identities of remaining, hidden community members? In contrast with global community detection (graph partitioning or covering), seed expansion is best suited for identifying communities locally concentrated around nodes of interest. A growing body of work has used seed expansion as a scalable means of detecting overlapping communities. Yet despite growing interest in seed expansion, there are divergent approaches in the literature and there still isn't a systematic understanding of which approaches work best in different domains. Here we evaluate several variants and uncover subtle trade-offs between different approaches. We explore which properties of the seed set can improve performance, focusing on heuristics that one can control in practice. As a consequence of this systematic understanding we have found several opportunities for performance gains. We also consider an adaptive version in which requests are made for additional membership labels of particular nodes, such as one finds in field studies of social communities. This leads to interesting connections and contrasts with active learning and the trade-offs of exploration and exploitation. Finally, we explore topological properties of communities and seed sets that correlate with algorithm performance, and explain these empirical observations with theoretical ones. We evaluate our methods across multiple domains, using publicly available datasets with labeled, ground-truth communities.


title: Extended technology acceptance model of Internet utilization behavior

abstract: Based on the belief-attitude-performance chain, this study combines Davis's technology acceptance model (TAM) and the information behavior model to develop an extended TAM for Internet use. The theoretical model was tested via a questionnaire survey of 203 Taiwanese office workers. The empirical results not only confirmed TAM, but also showed that the relevance of information needs strongly determines perceived usefulness, perceived ease of use, and user attitudes toward Internet use for information seeking, as well as strongly influencing individual performance during the information use stage. More importantly, relevance has greater positive effect on perceived performance and perceived usefulness for enterprise Intranet users than for simple/interactive Intraweb users. Enterprise Intranet users have more positive attitudes toward the Internet and more positive perceptions of system effectiveness for supporting office tasks than simple/interactive Intraweb users. Furthermore, perceived ease of use is the strongest determinant of user attitude toward Internet use in both enterprise Intranet and simple/interactive Intraweb applications. Overall, the extended TAM explains the behavior of enterprise Intranet users better than that of simple/ interactive Intraweb users. Limitations of this study are discussed.


title: Substituent Effects on the Electrochemical Properties of Pyrroles and Small Oligopyrroles

abstract: The electrochemical oxidation of several oligopyrroles, either unprotected, partially blocked, or totally blocked on their α-terminal positions or N-substituted, has been investigated in acetonitrile by cyclic voltammetry. For all the studied oligopyrroles, reversible voltammograms have been obtained at moderate to high scan rates, whereas chemical evolutions of the cation radicals have been observed at lower scan rates. The one-electron redox potentials, E°, of the oligopyrrole/oligopyrrole cation-radical couples have been measured and the lifetimes of the cation radicals have been estimated. The stabilities of the cation radicals have been examined as a function of the chain length and the nature of the substituent.


title: Psychiatric and psychological aspects in the Ehlers–Danlos syndromes

abstract: There is increasing amount of evidence pointing toward a high prevalence of psychiatric conditions among individuals with hypermobile type of Ehlers-Danlos syndrome (JHS/hEDS). A literature review confirms a strong association between anxiety disorders and JHSh/hEDS, and there is also limited but growing evidence that JHSh/hEDS is also associated with depression, eating, and neuro-developmental disorders as well as alcohol and tobacco misuse. The underlying mechanisms behind this association include genetic risks, autonomic nervous system dysfunction, increased exteroceptive and interoceptive mechanisms and decreased proprioception. Recent neuroimaging studies have also shown an increase response in emotion processing brain areas which could explain the high affective reactivity seen in JHS/hEDS. Management of these patients should include psychiatric and psychological approaches, not only to relieve the clinical conditions but also to improve abilities to cope through proper drug treatment, psychotherapy, and psychological rehabilitation adequately coupled with modern physiotherapy. A multidimensional approach to this "neuroconnective phenotype" should be implemented to ensure proper assessment and to guide for more specific treatments. Future lines of research should further explore the full dimension of the psychopathology associated with JHS/hEDS to define the nature of the relationship. © 2017 Wiley Periodicals, Inc.


title: The molecular neurobiology of depression

abstract: Unravelling the pathophysiology of depression is a unique challenge. Not only are depressive syndromes heterogeneous and their aetiologies diverse, but symptoms such as guilt and suicidality are impossible to reproduce in animal models. Nevertheless, other symptoms have been accurately modelled, and these, together with clinical data, are providing insight into the neurobiology of depression. Recent studies combining behavioural, molecular and electrophysiological techniques reveal that certain aspects of depression result from maladaptive stress-induced neuroplastic changes in specific neural circuits. They also show that understanding the mechanisms of resilience to stress offers a crucial new dimension for the development of fundamentally novel antidepressant treatments.


title: Pelvic floor muscle electromyography during different running speeds: an exploratory and reliability study

abstract: Purpose ::: Stress urinary incontinence (SUI) affects women of all ages including young athletes, especially those involved in high-impact sports. To date, hardly any studies are available testing pelvic floor muscles (PFM) during sports activities. The aim of this study was the description and reliability test of six PFM electromyography (EMG) variables during three different running speeds. The secondary objective was to evaluate whether there was a speed-dependent difference between the PFM activity variables.


title: Hybrid optical switching for data center networks

abstract: Current data centers networks rely on electronic switching and point-to-point interconnects. When considering future data center requirements, these solutions will raise issues in terms of flexibility, scalability, performance, and energy consumption. For this reason several optical switched interconnects, which make use of optical switches and wavelength division multiplexing (WDM), have been recently proposed. However, the solutions proposed so far suffer from low flexibility and are not able to provide service differentiation. In this paper we introduce a novel data center network based on hybrid optical switching (HOS). HOS combines optical circuit, burst, and packet switching on the same network. In this way different data center applications can be mapped to the optical transport mechanism that best suits their traffic characteristics. Furthermore, the proposed HOS network achieves high transmission efficiency and reduced energy consumption by using two parallel optical switches. We consider the architectures of both a traditional data center network and the proposed HOS network and present a combined analytical and simulation approach for their performance and energy consumption evaluation. We demonstrate that the proposed HOS data center network achieves high performance and flexibility while considerably reducing the energy consumption of current solutions.


title: A broadband microstrip array antenna with rectangular waveguide mode transition at millimeter-wave band

abstract: In this paper, a broadband microstrip array antenna with rectangular waveguide mode transition operating at lower E-band is proposed. The microstrip array antenna is corporate-feed structure configured by microstrip line with a mode transition from rectangular waveguide. Broadband operation can be achieved by using a slit in the transition and employing a set of feed point in the center of microstrip-line feeder which also results in fixed-beam radiation. Validity is confirmed by the evaluation of a fabricated antenna. This antenna is suitable for E-band applications such as mobile back-haul.


title: PerfAugur: Robust diagnostics for performance anomalies in cloud services

abstract: Cloud platforms involve multiple independently developed components, often executing on diverse hardware configurations and across multiple data centers. This complexity makes tracking various key performance indicators (KPIs) and manual diagnosing of anomalies in system behavior both difficult and expensive. In this paper, we describe PerfAugur, an automated system for mining service logs to identify anomalies and help formulate data-driven hypotheses. PerfAugur includes a suite of efficient mining algorithms for detecting significant anomalies in system behavior, along with potential explanations for such anomalies, without the need for an explicit supervision signal. We perform extensive experimental evaluation using both synthetic and real-life data sets, and present detailed case studies showing the impact of this technology on operations of the Windows Azure Service.


Software-Defined Network Testbed Using ZodiacFX a Hardware Switch for OpenFlow


THEODOROS REKATSINAS


title: Digital Image Processing: Principles and Applications

abstract: What Is Image Processing?. Fundamentals of Digital Image Processing. The Digital Image. PROCESSING CONCEPTS. Image Enhancement and Restoration. Image Analysis. Image Compression. Image Synthesis. PROCESSING SYSTEMS. Image Origination and Display. Image Data Handling. Image Data Processing. PROCESSING IN ACTION. Image Operation Studies. Appendices. Glossary. Index.


title: Online Parameter Estimation of Permanent-Magnet Assisted Synchronous Reluctance Motor

abstract: In comparison with the conventional synchronous reluctance machines, permanent-magnet assisted synchronous reluctance motor drives (PMa-SynRMs) have offered better torque capabilities and power factors. In order to achieve maximum efficiency, knowledge of the motor parameters is necessary to implement advanced control algorithms. Variation of motor parameters due to temperature and airgap flux has been reported in literature. Using offline models to estimate the motor parameters is known as a computationally intensive method, especially when the effect of cross saturation is included in the models. Therefore, in practical applications, online parameter estimation is favored to achieve a high-performance control system. In this paper, a simple practical method for parameter estimation of PMa-SynRM is introduced. This method is capable of identifying d- and q-axes inductances and the permanent-magnet back electromotive force


HISTORY OF THE FORWARD CONVERTER


title: Pursuits: spontaneous interaction with displays based on smooth pursuit eye movement and moving targets

abstract: Although gaze is an attractive modality for pervasive interactions, the real-world implementation of eye-based interfaces poses significant challenges, such as calibration. We present Pursuits, an innovative interaction technique that enables truly spontaneous interaction with eye-based interfaces. A user can simply walk up to the screen and readily interact with moving targets. Instead of being based on gaze location, Pursuits correlates eye pursuit movements with objects dynamically moving on the interface. We evaluate the influence of target speed, number and trajectory and develop guidelines for designing Pursuits-based interfaces. We then describe six realistic usage scenarios and implement three of them to evaluate the method in a usability study and a field study. Our results show that Pursuits is a versatile and robust technique and that users can interact with Pursuits-based interfaces without prior knowledge or preparation phase.