title: Detecting parametric objects in large scenes by Monte Carlo sampling

abstract: Point processes constitute a natural extension of Markov random fields (MRF), designed to handle parametric objects. They have shown efficiency and competitiveness for tackling object extraction problems in vision. Simulating these stochastic models is however a difficult task. The performances of the existing samplers are limited in terms of computation time and convergence stability, especially on large scenes. We propose a new sampling procedure based on a Monte Carlo formalism. Our algorithm exploits the Markovian property of point processes to perform the sampling in parallel. This procedure is embedded into a data-driven mechanism so that the points are distributed in the scene in function of spatial information extracted from the input data. The performances of the sampler are analyzed through a set of experiments on various object detection problems from large scenes, including comparisons to the existing algorithms. The sampler is also tested as optimization algorithm for MRF-based labeling problems.


title: Structure tensor regularization for texture analysis

abstract: Texture analysis has been a particularly dynamic field with different computer vision and image processing applications. Most of the existing texture analysis techniques yield to significant results in different applications but fail in difficult situations with high sensitivity to noise. Inspired by previous works on texture analysis by structure layer modeling, this paper deals with representing the texture's structure layer using the structure tensor field. Based on texture pattern size approximation, the proposed algorithm investigates the adaptability of the structure tensor to the local geometry of textures by automatically estimating the sub-optimal structure tensor size. An extension of the algorithm targeting non-structured textures is also proposed. Results show that using the proposed tensor size regularization method, relevant local information can be extracted by eliminating the need of repetitive tensor field computation with different tensor size to reach an acceptable performance.


title: Tunnel field-effect transistors as energy-efficient electronic switches

abstract: Power dissipation is a fundamental problem for nanoelectronic circuits. Scaling the supply voltage reduces the energy needed for switching, but the field-effect transistors (FETs) in today's integrated circuits require at least 60 mV of gate voltage to increase the current by one order of magnitude at room temperature. Tunnel FETs avoid this limit by using quantum-mechanical band-to-band tunnelling, rather than thermal injection, to inject charge carriers into the device channel. Tunnel FETs based on ultrathin semiconducting films or nanowires could achieve a 100-fold power reduction over complementary metal–oxide–semiconductor (CMOS) transistors, so integrating tunnel FETs with CMOS technology could improve low-power integrated circuits.


title: Measuring multitasking behavior with activity-based metrics

abstract: Multitasking is the result of time allocation decisions made by individuals faced with multiple tasks. Multitasking research is important in order to improve the design of systems and applications. Since people typically use computers to perform multiple tasks at the same time, insights into this type of behavior can help develop better systems and ideal types of computer environments for modern multitasking users. In this paper, we define multitasking based on the principles of task independence and performance concurrency and develop a set of metrics for computer-based multitasking. The theoretical foundation of this metric development effort stems from an application of key principles of Activity Theory and a systematic analysis of computer usage from the perspective of the user, the task and the technology. The proposed metrics, which range from a lean dichotomous variable to a richer measure based on switches, were validated with data from a sample of users who self-reported their activities during a computer usage session. This set of metrics can be used to establish a conceptual and methodological foundation for future multitasking studies.


title: The production of “new” and “similar” phones in a foreign language: evidence for the effect of equivalence classification

abstract: Acoustic measurements were made of the voice onset time (VaT) and vowel formants (F,-F)) in French and English words spoken by native French subjects who were highly experienced in English. and by three groups of native English subjects differing according to French-language experience. The speech of monolingual subjects was also examined to estimate the phonetic norms of French and English. It was hypothesized that equivalence classification limits the extent to which L2 learners approximate L2 phonetic norms for "similar"' L2 phones judged to be realizations of a category in L I (e.g. lul and It I of French and English). but not a "new" L2 phone which has no counterpart in L I (French Iyl for the native English subjects). Native English subjects who were experienced in French did not differ from French monolinguals in producing French /y/. However. the subjects in all four groups produced lul in their L2 with F! values which differed significantly from those of native speakers; and they all produced it! in their L2 with mean vaT values that either closely resembled the L I phonetic norm. or were intermediate to the phonetic norm for VaT in L I and L2. L2 learning was also shown to influence production of /tI in L I. The native French subjects who spoke English produced French /tI with longer (i.e .. English-like) vaT values than French monolinguals: and the most experienced native English speakers of French produced English It I with shorter (French-like) VaT values than English monolinguals. Taken together, these results suggest that the phonetic space of adults is restructured during L2 learning, and support the hypothesis that equivalence classification prevents experienced Lllearners from producing similar L2 phones. but not new L2 phones. authentically.


title: Design and prototyping of 3-phase BLDC motor

abstract: The development of electric vehicle is now growing rapidly. Demands to deliver a reliable and easy to drive in motor control causes Brushless Direct Current (BLDC) motor becomes a potential candidate. A BLDC motor drive is a potential option for an electric vehicle since it has a high reliability, simple design, and ability to work at high rotation per minute (RPM). This paper discussed the Permanent Magnet BLDC Motor design method. The structure of an interior rotor permanent magnet type is selected to be used in the design of Permanent Magnet BLDC motor so that it can be applied in a drive that requires a large torque and capable of acceleration and deceleration with good response. Selection of 12 slots and 8 poles configuration aims for improving the motor performance. The motor is designed and simulated using a software-based Motor Solve FEA (Finite Element Analysis). Based on this design and simulation results, a prototype of BLDC motor is built. Parameters testing as stator resistance, inductances (the d-axis and q-axis inductance), and the back emf constant (Ke) were used to evaluate the result of the design and prototype motor. Measuring the prototype motor's parameters was carried out by several different methods depending on the parameters tested. Stator resistance testing is performed with the measurement of current in the coil which is then obtained by calculating the magnitude of stator resistance as 0.14710296 Ω. Measurements of d-axis stator inductance, q-axis stator inductance, and back emf constant of prototype permanent magnet BLDC motor is obtained as results of 0.35304710 mH, 0.38246769 mH and 0.09690626 Vs/rad respectively. The test results between design and prototype testing were quite good. The difference between the test results and the design of the prototype test results was caused by incompatibility of material composition although using the same type of material. The evaluation shows the electromagnetic parameters is influenced by its constituent materials.


title: Semi-supervised Conditional GANs

abstract: We introduce a new model for building conditional generative models in a semi-supervised setting to conditionally generate data given attributes by adapting the GAN framework. The proposed semi-supervised GAN (SS-GAN) model uses a pair of stacked discriminators to learn the marginal distribution of the data, and the conditional distribution of the attributes given the data respectively. In the semi-supervised setting, the marginal distribution (which is often harder to learn) is learned from the labeled + unlabeled data, and the conditional distribution is learned purely from the labeled data. Our experimental results demonstrate that this model performs significantly better compared to existing semi-supervised conditional GAN models.


title: Fuzzy semantic distance measures between ontological concepts

abstract: An emphasis has been placed on the use of ontologies for representing application domain knowledge. Determining a degree or measure of semantic similarity, semantic distance, or semantic relatedness between concepts from different systems or domains, is becoming an increasingly important task. This paper presents a brief overview of such measures between concepts within ontological representations and provides several examples of such measures found in the research literature. These measures are then examined within the framework of fuzzy set similarity measures. The use of a semantic similarity measure between elements that are part of a domain for which an ontological structure exists is explored in order to extend standard fuzzy set compatibility measures.


title: A 1-pJ/bit, 10-Gb/s/ch Forwarded-Clock Transmitter Using a Resistive Feedback Inverter-Based Driver in 65-nm CMOS

abstract: An energy-efficient forwarded-clock transmitter that offers a scalable pre-emphasis equalization and output voltage swing is presented. A resistive-feedback inverter-based driver is used to overcome the drawbacks of the conventional drivers. Moreover, half-rate clocking structure is employed in order to minimize power consumption in 65-nm CMOS technology. The proposed transmitter consists of two data lanes, a shared clock lane, and a global impedance regulator. The prototype chip is fabricated in 65-nm CMOS technology and occupies an active area of 0.15 mm $^{2}$ . The proposed transmitter achieves 100–250 mV single-ended swing and exhibits the energy efficiency of 1 pJ/bit at the per-pin data rate of 10 Gb/s.


title: R.: Keeping mobile robot swarms connected

abstract: Designing robust algorithms for mobile agents with reliable communication is difficult due to the distributed nature of computation, in mobile ad hoc networks (MANETs) the matter is exacerbated by the need to ensure connectivity. Existing distributed algorithms provide coordination but typically assume connectivity is ensured by other means. We present a connectivity service that encapsulates an arbitrary motion planner and can refine any plan to preserve connectivity (the graph of agents remains connected) and ensure progress (the agents advance towards their goal). The service is realized by a distributed algorithm that is modular in that it makes no assumptions of the motion-planning mechanism except the ability for an agent to query its position and intended goal position, local in that it uses 1-hop broadcast to communicate with nearby agents but doesn't need any network routing infrastructure, and oblivious in that it does not depend on previous computations. ::: ::: We prove the progress of the algorithm in one round is at least Ω(min(d, r)), where d is the minimum distance between an agent and its target and r is the communication radius. We characterize the worst case configuration and show that when d ≥ r this bound is tight and the algorithm is optimal, since no algorithm can guarantee greater progress. Finally we show all agents get Ɛ-close to their targets within O(D0/r+n2/Ɛ) rounds where n is the number of agents and D0 is the sum of the initial distances to the targets.


title: Single Image Super-Resolution With Non-Local Means and Steering Kernel Regression

abstract: Image super-resolution (SR) reconstruction is essentially an ill-posed problem, so it is important to design an effective prior. For this purpose, we propose a novel image SR method by learning both non-local and local regularization priors from a given low-resolution image. The non-local prior takes advantage of the redundancy of similar patches in natural images, while the local prior assumes that a target pixel can be estimated by a weighted average of its neighbors. Based on the above considerations, we utilize the non-local means filter to learn a non-local prior and the steering kernel regression to learn a local prior. By assembling the two complementary regularization terms, we propose a maximum a posteriori probability framework for SR recovery. Thorough experimental results suggest that the proposed SR method can reconstruct higher quality results both quantitatively and perceptually.


title: Reduction of Subcutaneous Adipose Tissue Using a Novel Vacuum-Cavitation Technology

abstract: Background and Objectives: The purpose of this study was to evaluate the efficacy and safety of a novel vacuum-cavitation device combining intermittent vacuum and cavitation induced by low-frequency ultrasound in a single applicator for reduction of unwanted local adipose tissue. The outcomes of the study were compared with the results of the previous study on cavitation treatment conducted by the same research group. Study Design/Materials and Methods: Eight (8) female patients aged 35 to 64 years were involved in the study. Each received a single vacuum-cavitation treatment followed by lymph drainage. The measurement of abdominal circumference and weight as well as the ultrasound evaluation of the fat layer thickness were performed before and after the treatment. Safety was determined by blood analysis (cholesterol, triglycerides, liver markers) before, 2 hours after the treatment, and 20 (30) days after the treatment. Results: All patients experienced a notable reduction in fat thickness within the treatment site. The mean reduction in circumference after a single treatment was 2,938 + 0,623 cm. The ultrasound measurement results showed the mean subcutaneous fat thickness reduction of 2,96 + 2,01 mm. Weight remained unchanged throughout the study, indicating that the reduction in circumference and fat thickness was not related to weight loss. No significant change in levels of cholesterol, triglycerides, and liver markers was observed. Side effects were limited to slight erythema that persisted up to 1 hour after the treatment. Conclusions: The study concludes that vacuum-cavitation treatment is safe and effective for reduction of local fat tissue. The results of vacuum-cavitation treatment have been found to be superior over the results of cavitation treatment both in terms of clinical efficacy and repeatability.


title: Understanding packet delivery performance in dense wireless sensor networks

abstract: Wireless sensor networks promise fine-grain monitoring in a wide variety of environments. Many of these environments (e.g., indoor environments or habitats) can be harsh for wireless communication. From a networking perspective, the most basic aspect of wireless communication is the packet delivery performance: the spatio-temporal characteristics of packet loss, and its environmental dependence. These factors will deeply impact the performance of data acquisition from these networks.In this paper, we report on a systematic medium-scale (up to sixty nodes) measurement of packet delivery in three different environments: an indoor office building, a habitat with moderate foliage, and an open parking lot. Our findings have interesting implications for the design and evaluation of routing and medium-access protocols for sensor networks.


title: AC resistance calculation of litz wire and its modeling with FEMM

abstract: This paper includes calculation of the AC resistance of litz wire in a wide frequency range for an optimal strand diameter by two methods and compared. Calculations of AC resistance for these methods are carried out by using mathematical software SCILAB. Analysis of skin effect is carried out with FEMM. Effect of pitch on AC resistance is analyzed. The results for AC resistance, skin effect and analysis of skin effect presented in this paper are compared with FEM simulations. A graphical comparison of the theoretical calculation and FEM simulation of ac resistance with frequency is done and found to be in good agreement.


title: Bipolar disorder in adults with Asperger׳s Syndrome: A systematic review

abstract: Abstract Background Asperger׳s Syndrome (AS) is a neurodevelopmental disorder included in the Autism Spectrum (ASD). The current literature shows growing evidence of a high rate of comorbidity between AS and other psychiatric disorders, particularly Bipolar Disorder (BD). We reviewed available epidemiological and clinical data on BD–AS comorbidity and its diagnostic and therapeutic implications Methods A systematic review of the literature was conducted through PubMed, Scopus and Psych-Info using combinations of the following search terms: Asperger׳s Syndrome, Bipolar Disorder, depression, mood disorder, psychiatric comorbidity, treatment, mood stabilizers, anticonvulsants, antipsychotics, and antidepressants. Results BD prevalence in adults with AS ranges from 6% to 21.4% of the cases. Relatives of patients with AS showed a doubled risk of being affected by BD and a BD prevalence near to 10%. When comorbid with AS, BD assumes peculiar features which might shape its under-recognition or misdiagnosis (especially schizophrenia when psychotic symptoms are prominent). Although controlled data on pharmacological treatments in BD–AS comorbidity are substantially lacking, information is derived by open observations, case series and chart reviews. Mood stabilizers should be considered the first choice, and antipsychotics, especially second generation drugs (SGA) with 5-HT2a antagonism, have been shown useful in controlling psychotic and behavioral symptoms and improving social withdrawal. Some evidence of efficacy for the treatment of anxiety, obsessive-compulsive symptoms and depression is reported for SSRI antidepressants. The use of these drugs should be carefully monitored, because activation with hypomanic or manic switches is reported up to 54% of the treated subjects. Conclusion BD in AS patients is frequent, usually it onsets during adolescence and is often characterized by atypical presentation, making its correct identification particularly difficult. A correct diagnosis of BD in AS individuals has relevant implications on the choice of adequate psychopharmacological, psycho-social and rehabilitative treatments.


title: Cogging-Torque Reduction of Transverse-Flux Motor by Skewing Stator Poles

abstract: Transverse-flux motors basically have coils wound in the rotational direction and armature cores surrounding them. This configuration allows the motors to be designed for multipole structures with the simple coil geometry independent of the pole number. Therefore, they have an advantage on high-torque generation over most motors having windings wound around teeth and put in slots. However, transverse-flux motors still have a production problem for their multipole rotor due to the assembly of the small and numerous permanent magnets. Thus, we have designed a consequent-pole transverse-flux motor, having a half amount of magnets on the rotor compared with the conventional surface-mounted magnet rotors, and capable of generating almost the same torque under the same size and excitation conditions. However, this motor also has large cogging torque due to the consequent poles, having deformed magnetomotive-force distribution. Thus, we propose a new skewed core structure for reducing the cogging torque, compatible to axially non-uniform structure of this motor. The Finite Element Method analysis result indicates the peak-to-peak value of the cogging torque that can reduce by 82% with this proposed skewed structure.


title: Natural Image Stitching with the Global Similarity Prior

abstract: This paper proposes a method for stitching multiple images together so that the stitched image looks as natural as possible. Our method adopts the local warp model and guides the warping of each image with a grid mesh. An objective function is designed for specifying the desired characteristics of the warps. In addition to good alignment and minimal local distortion, we add a global similarity prior in the objective function. This prior constrains the warp of each image so that it resembles a similarity transformation as a whole. The selection of the similarity transformation is crucial to the naturalness of the results. We propose methods for selecting the proper scale and rotation for each image. The warps of all images are solved together for minimizing the distortion globally. A comprehensive evaluation shows that the proposed method consistently outperforms several state-of-the-art methods, including AutoStitch, APAP, SPHP and ANNAP.


Modeling human development and disease in pluripotent stem cell-derived gastric organoids


title: Stator iron loss measurement method in permanent magnet synchronous motor to remove the mechanical loss effect

abstract: This paper presents an iron loss measurement method on the basis of locked rotor test to remove a mechanical loss effect. Iron loss can be estimated by reproducing the magnetic flux linkage which is made by locked rotor windings. Estimated no-load iron loss of stator is confirmed that it matches the original ones by finite element analysis.


title: Triangular Architecture for Rare Language Translation

abstract: Neural Machine Translation (NMT) performs poor on the low-resource language pair $(X,Z)$, especially when $Z$ is a rare language. By introducing another rich language $Y$, we propose a novel triangular training architecture (TA-NMT) to leverage bilingual data $(Y,Z)$ (may be small) and $(X,Y)$ (can be rich) to improve the translation performance of low-resource pairs. In this triangular architecture, $Z$ is taken as the intermediate latent variable, and translation models of $Z$ are jointly optimized with a unified bidirectional EM algorithm under the goal of maximizing the translation likelihood of $(X,Y)$. Empirical results demonstrate that our method significantly improves the translation quality of rare languages on MultiUN and IWSLT2012 datasets, and achieves even better performance combining back-translation methods.


Are Probiotics Ready for Prime Time


Variation in Working Memory Capacity as Variation in Executive Attention and Control


Review of Heterogeneous / Homogeneous Wireless Sensor Networks and Intrusion Detection System Techniques


title: When CSI Meets Public WiFi: Inferring Your Mobile Phone Password via WiFi Signals

abstract: In this study, we present WindTalker, a novel and practical keystroke inference framework that allows an attacker to infer the sensitive keystrokes on a mobile device through WiFi-based side-channel information. WindTalker is motivated from the observation that keystrokes on mobile devices will lead to different hand coverage and the finger motions, which will introduce a unique interference to the multi-path signals and can be reflected by the channel state information (CSI). The adversary can exploit the strong correlation between the CSI fluctuation and the keystrokes to infer the user's number input. WindTalker presents a novel approach to collect the target's CSI data by deploying a public WiFi hotspot. Compared with the previous keystroke inference approach, WindTalker neither deploys external devices close to the target device nor compromises the target device. Instead, it utilizes the public WiFi to collect user's CSI data, which is easy-to-deploy and difficult-to-detect. In addition, it jointly analyzes the traffic and the CSI to launch the keystroke inference only for the sensitive period where password entering occurs. WindTalker can be launched without the requirement of visually seeing the smart phone user's input process, backside motion, or installing any malware on the tablet. We implemented Windtalker on several mobile phones and performed a detailed case study to evaluate the practicality of the password inference towards Alipay, the largest mobile payment platform in the world. The evaluation results show that the attacker can recover the key with a high successful rate.


title: MATLAB implementation of image segmentation algorithms

abstract: Image segmentation is a relevant research area in Computer Vision, and several methods of segmentation have been proposed in the last 40 years. This paper presents the implementation using the GUI feature of the MATLAB and one best result can be selected for any algorithm using the subjective evaluation. This process can help to find out the best suitable value of parameters for the segmentation of different types of imagery. In this paper, one best algorithm has considered for each method of image segmentation. The interactive based method provides the facility to select the desired area as an object and produces better result. The proposed process also displays the duration of segmentation of each algorithm.


title: The "what" and "why" of goal pursuits: Human needs and the self-determination of behavior.

abstract: Self-determination theory (SDT) maintains that an understanding of human motivation requires a consideration of innate psychological needs for competence, autonomy, and relatedness. We discuss the SDT concept of needs as it relates to previous need theories, emphasizing that needs specify the necessary conditions for psychological growth, integrity, and well-being. This concept of needs leads to the hypotheses that different regulatory processes underlying goal pursuits are differentially associated with effective functioning and well-being and also that different goal contents have different relations to the quality of behavior and mental health, specifically because different regulatory processes and different goal contents are associated with differing degrees of need satisfaction. Social contexts and individual differences that support satisfaction of the basic needs facilitate natural growth processes including intrinsically motivated behavior and integration of extrinsic motivations, whereas those that forestall autonomy, competence, or relatedness are associated with poorer motivation, performance, and well-being. We also discuss the relation of the psychological needs to cultural values, evolutionary processes, and other contemporary motivation theories.


title: The Speech and Language FOXP2 Gene Modulates the Phenotype of Frontotemporal Lobar Degeneration

abstract: The FOXP2 gene is mutated in a severe monogenic form of speech and language deficits, but no study on the influence of genetic variations within FOXP2 in neurological disorders characterized by language impairment is available yet. In the present study, we investigated the impact of common FOXP2 polymorphisms with regard to frontotemporal lobar degeneration (FTLD). Two-hundred ten FTLD patients underwent clinical and a wide standardized neuropsychological examination as well as brain imaging. In all patients, and in 200 age-matched healthy controls, four FOXP2 polymorphisms were evaluated, namely rs2396753, rs1456031, rs17137124 and rs1852469. SPECT images were analyzed by Statistical Parametric Mapping (SPM5). No significant differences of the four FOXP2 polymorphisms in genotype distribution and allele frequency between FTLD and controls were observed. A significant and specific association between rs1456031 TT and rs17137124 TT genotypes and verbal fluency scores was reported. The two polymorphisms showed an addictive effect. When the analysis was computed on the number of observations over time, and 391 assessments considered, comparable results were obtained. FTLD patients carrying at-risk polymorphisms showed greater hypoperfusion in the frontal areas, namely the left inferior frontal gyrus, and putamen, compared to the non-carriers (p < 0.005). Genetic variations within FOXP2 do not represent a genetic risk to FTLD per se, but modulate FTLD presentation when disease is overt, affecting language performances and leading to hypoperfusion in language-associated brain areas.


title: Network Function Virtualization based on FPGAs:A Framework for all-Programmable network devices

abstract: Network Function Virtualization (NFV) refers to the use of commodity hardware resources as the basic platform to perform specialized network functions as opposed to specialized hardware devices. Currently, NFV is mainly implemented based on general purpose processors, or general purpose network processors. In this paper we propose the use of FPGAs as an ideal platform for NFV that can be used to provide both the flexibility of virtualizations and the high performance of the specialized hardware. We present the early attempts of using FPGAs dynamic reconfiguration in network processing applications to provide flexible network functions and we present the opportunities for an FPGA-based NFV platform.


title: High performance question/answering

abstract: In this paper we present the features of a Question/Answering (Q/A) system that had unparalleled performance in the TREC-9 evaluations. We explain the accuracy of our system through the unique characteristics of its architecture: (1) usage of a wide-coverage answer type taxonomy; (2) repeated passage retrieval; (3) lexico-semantic feedback loops; (4) extraction of the answers based on machine learning techniques; and (5) answer caching. Experimental results show the effects of each feature on the overall performance of the Q/A system and lead to general conclusions about Q/A from large text collections.


title: Solving sub-pixel image registration problems using phase correlation and Lucas-Kanade optical flow method

abstract: Image registration plays a major role in many areas such as remote sensing, astronomy, biomedical imaging, and so on. Our main contribution in this paper is to present a new subpixel image registration that aligns translated of pair images. This algorithm combines well-known phase correlation technique with the differential methods of the optical flow field, especially the Locus-Kanade technique to register images. The experiment results show that our method not only enhances the precision of registration but also reduces time complexity.


title: A simple and efficient method to align very long speech signals to acoustically imperfect transcriptions.

abstract: In the framework of a contract with the Basque Parliament for subtitling the videos of bilingual plenary sessions, which basically consisted of aligning very long (around 3 hours long) audio tracks with syntactically correct but acoustically inaccurate text transcriptions (since all the disfluencies, mistakes, etc. were edited), a very simple and efficient procedure (avoiding the need for language nor lexical models, which was key because of the mix of languages) was developed as a first approach, before trying more complex schemes found in the literature. Since it worked pretty well and the output was quite satisfactory for the intended application, that simple approach was finally chosen. In this paper, we describe the approach in detail and apply it to a widely known annotated dataset (specifically, to the 1997 Hub4 task), to allow the comparison to a reference approach. Results demonstrate that our approach provides only slightly worse segmentations at a much lower computational cost and requiring much fewer resources. Moreover, if the resource to be segmented includes speech in two or more languages and speakers conmute between them at any time, applying a speech recognizer becomes unfeasible in practice, whereas our approach can be still applied with no additional cost.


title: The influence of self-compassion on emotional well-being among early and older adolescent males and females

abstract: Self-compassion has been associated with well-being in adult samples, but has rarely been assessed in adolescents. In this study, 90 students ages 11–18 completed an online survey assessing self-compassion, life satisfaction, perceived stress and positive and negative affect. Findings indicated that older female adolescents had lower self-compassion than either older male adolescents or early adolescents of either gender, and self-compassion was associated significantly with all dimensions of emotional well-being with the exception of positive affect. Additionally, phase of adolescence, but not gender, was found to moderate the relationship between self-compassion and dimensions of well-being; for older adolescents, the inverse relationship between self-compassion and negative affect was stronger. Lastly, the influence of the various components of self-compassion was investigated and discussed.


title: Gooseneck barnacles (Lepas spp.) ingest microplastic debris in the North Pacific Subtropical Gyre

abstract: Substantial quantities of small plastic particles, termed “microplastic,” have been found in many areas of the world ocean, and have accumulated in particularly high densities on the surface of the subtropical gyres. While plastic debris has been documented on the surface of the North Pacific Subtropical Gyre (NPSG) since the early 1970s, the ecological implications remain poorly understood. Organisms associated with floating objects, termed the “rafting assemblage,” are an important component of the NPSG ecosystem. These objects are often dominated by abundant and fast-growing gooseneck barnacles (Lepas spp.), which predate on plankton and larval fishes at the sea surface. To assess the potential effects of microplastic on the rafting community, we examined the gastrointestinal tracts of 385 barnacles collected from the NPSG for evidence of plastic ingestion. We found that 33.5% of the barnacles had plastic particles present in their gastrointestinal tract, ranging from one plastic particle to a maximum of 30 particles. Particle ingestion was positively correlated to capitulum length, and no blockage of the stomach or intestines was observed. The majority of ingested plastic was polyethylene, with polypropylene and polystyrene also present. Our results suggest that barnacle ingestion of microplastic is relatively common, with unknown trophic impacts on the rafting community and the NPSG ecosystem.


title: Dissecting components of reward: 'liking', 'wanting', and learning

abstract: In recent years significant progress has been made delineating the psychological components of reward and their underlying neural mechanisms. Here we briefly highlight findings on three dissociable psychological components of reward: ‘liking’ (hedonic impact), ‘wanting’ (incentive salience), and learning (predictive associations and cognitions). A better understanding of the components of reward, and their neurobiological substrates, may help in devising improved treatments for disorders of mood and motivation, ranging from depression to eating disorders, drug addiction, and related compulsive pursuits of rewards.


title: Quantum Circuit Simplification and Level Compaction

abstract: Quantum circuits are time-dependent diagrams describing the process of quantum computation. Usually, a quantum algorithm must be mapped into a quantum circuit. Optimal synthesis of quantum circuits is intractable, and heuristic methods must be employed. With the use of heuristics, the optimality of circuits is no longer guaranteed. In this paper, we consider a local optimization technique based on templates to simplify and reduce the depth of nonoptimal quantum circuits. We present and analyze templates in the general case and provide particular details for the circuits composed of NOT, CNOT, and controlled-sqrt-of-NOT gates. We apply templates to optimize various common circuits implementing multiple control Toffoli gates and quantum Boolean arithmetic circuits. We also show how templates can be used to compact the number of levels of a quantum circuit. The runtime of our implementation is small, whereas the reduction in the number of quantum gates and number of levels is significant.


title: Causal Interpretations of Black-Box Models

abstract: AbstractThe fields of machine learning and causal inference have developed many concepts, tools, and theory that are potentially useful for each other. Through exploring the possibility of extracti...


title: Accurate and fast 3D head pose estimation with noisy RGBD images

abstract: Head pose estimation plays an essential role in many high-level face analysis tasks. However, accurate and robust pose estimation with existing approaches remains challenging. In this paper, we propose a novel method for accurate three-dimensional (3D) head pose estimation with noisy depth maps and high-resolution color images that are typically produced by popular RGBD cameras such as the Microsoft Kinect. Our method combines the advantages of the high-resolution RGB image with the 3D information of the depth image. For better accuracy and robustness, features are first detected using only the color image, and then the 3D feature points used for matching are obtained by combining depth information. The outliers are then filtered with depth information using rules proposed for depth consistency, normal consistency, and re-projection consistency, which effectively eliminate the influence of depth noise. The pose parameters are then iteratively optimized using the Extended LM (Levenberg-Marquardt) method. Finally, a Kalman filter is used to smooth the parameters. To evaluate our method, we built a database of more than 10K RGBD images with ground-truth poses recorded using motion capture. Both qualitative and quantitative evaluations show that our method produces notably smaller errors than previous methods.


title: Small-Size Octaband Monopole Antenna in an LTE/WWAN Mobile Phone

abstract: This letter proposes a small-size printed antenna with multiband WWAN/LTE operation in a mobile phone by introducing a novel loop parasitic shorted strip and a C-shaped ground plane. The obtained impedance bandwidths across LTE and WWAN operating bands approach 277 and 1176 MHz, respectively. The proposed printed monopole antenna reduces the antenna size by at least 22% since the overall antenna size is only 35 × 10 × 0.8 mm3. The measured peak gains and antenna efficiencies are approximately 2.2/3.1 dBi and 76%/82% for the LTE/WWAN bands, respectively.


title: Estimation of Forest Canopy Height and Aboveground Biomass from Spaceborne LiDAR and Landsat Imageries in Maryland

abstract: Mapping the regional distribution of forest canopy height and aboveground biomass is worthwhile and necessary for estimating the carbon stocks on Earth and assessing the terrestrial carbon flux. In this study, we produced maps of forest canopy height and the aboveground biomass at a 30 m spatial resolution in Maryland by combining Geoscience Laser Altimeter System (GLAS) data and Landsat spectral imageries. The processes for calculating the forest biomass included the following: (i) processing the GLAS waveform and calculating spatially discrete forest canopy heights; (ii) developing canopy height models from Landsat imagery and extrapolating them to spatially contiguous canopy heights in Maryland; and, (iii) estimating forest aboveground biomass according to the relationship between canopy height and biomass. In our study, we explore the ability to use the GLAS waveform to calculate canopy height without ground-measured forest metrics (R2 = 0.669, RMSE = 4.82 m, MRE = 15.4%). The machine learning models performed better than the principal component model when mapping the regional forest canopy height and aboveground biomass. The total forest aboveground biomass in Maryland reached approximately 160 Tg. When compared with the existing Biomass_CMS map, our biomass estimates presented a similar distribution where higher values were in the Western Shore Uplands region and Folded Application Mountain section, while lower values were located in the Delmarva Peninsula and Allegheny Mountain regions.


title: The analyse of the various methods for location of Data Matrix codes in images

abstract: Data Matrix codes belong to the group of 2-D bar codes, which are widely used in marking the products in storing, production, distribution and sales processes. Their compact matrix structure enables to store big amount of information in a very small area compared to conventional 1-D bar codes. In following paper we compare several methods for detection of Data Matrix codes in images. When locating the position of the code we start from typical bordering of Data Matrix code — which forms “L” so called Finder Pattern and to it the parallel dotting so called Timing Pattern. On the first stage we try to locate Finder Pattern using the edge detection or adaptive thresholding, then continuing with connecting the points into the continuous regions, we are searching for the regions, which could represent the Finder Pattern. On the second stage we verify Timing Pattern where the number of crossing between the background and foreground must be even.


title: Tactile interfaces for small touch screens

abstract: We present the design, implementation, and informal evaluation of tactile interfaces for small touch screens used in mobile devices. We embedded a tactile apparatus in a Sony PDA touch screen and enhanced its basic GUI elements with tactile feedback. Instead of observing the response of interface controls, users can feel it with their fingers as they press the screen. In informal evaluations, tactile feedback was greeted with enthusiasm. We believe that tactile feedback will become the next step in touch screen interface design and a standard feature of future mobile devices.


2,4-D safening of nicosulfuron and terbufos interaction in corn (Zea mays)


title: Ontology evolution without tears

abstract: The evolution of ontologies is an undisputed necessity in ontology-based data integration. Yet, few research efforts have focused on addressing the need to reflect the evolution of ontologies used as global schemata onto the underlying data integration systems. In most of these approaches, when ontologies change their relations with the data sources, i.e., the mappings, are recreated manually, a process which is known to be error-prone and time-consuming. In this paper, we provide a solution that allows query answering in data integration systems under evolving ontologies without mapping redefinition. This is achieved by rewriting queries among ontology versions and then forwarding them to the underlying data integration systems to be answered. To this purpose, initially, we automatically detect and describe the changes among ontology versions using a high level language of changes. Those changes are interpreted as sound global-as-view (GAV) mappings, and they are used in order to produce equivalent rewritings among ontology versions. Whenever equivalent rewritings cannot be produced we a) guide query redefinition or b) provide the best ''over-approximations'', i.e., the minimally-containing and minimally-generalized rewritings. We prove that our approach imposes only a small overhead over traditional query rewriting algorithms and it is modular and scalable. Finally, we show that it can greatly reduce human effort spent since continuous mapping redefinition is no longer necessary.


title: Randomized algorithms for scheduling VMs in the cloud

abstract: We consider the problem of scheduling VMs (Virtual Machines) in a multi-server system motivated by cloud computing applications. VMs arrive dynamically over time and require various amounts of resources (e.g., CPU, Memory, Storage, etc.) for the duration of their service. When a VM arrives, it is queued and later served by one of the servers that has sufficient remaining capacity to serve it. The scheduling of VMs is subject to: (i) packing constraints, i.e., multiple VMs can be be served simultaneously by a single server if their cumulative resource requirement does not violate the capacity of the server, and (ii) non-preemption, i.e., once a VM is scheduled in a server, it cannot be interrupted or migrated to another server. To achieve maximum throughput, prior results hinge on solving a hard combinatorial problem (Knapsack) at the instances that all the servers become empty (the so-called global refresh times which require synchronization among the servers). The main contribution of this paper is that it resolves these issues. Specifically, we present a class of randomized algorithms for placing VMs in the servers that can achieve maximum throughput without preemptions. The algorithms are naturally distributed, have low complexity, and each queue needs to perform limited operations. Further, our algorithms display good delay performance in simulations, comparable to delay of heuristics that may not be throughput-optimal, and much better than the delay of the prior known throughput-optimal algorithms.


Intelligent Street Lighting System Using Gsm


Automated Generation of Questions from Factual , Natural Language Sentences


title: Integratedmultilevel image fusion andmatch score fusion of visible and infrared face images for robust face recognition

abstract: This paper presents an integrated image fusion and match score fusion of multispectral face images. The fusion of visible and long wave infrared face images is performed using [email protected] SVM which uses multiple SVMs to learn both the local and global properties of the multispectral face images at different granularity levels and resolution. The [email protected] performs accurate classification which is subsequently used to dynamically compute the weights of visible and infrared images for generating a fused face image. 2D log polar Gabor transform and local binary pattern feature extraction algorithms are applied to the fused face image to extract global and local facial features, respectively. The corresponding match scores are fused using Dezert Smarandache theory of fusion which is based on plausible and paradoxical reasoning. The efficacy of the proposed algorithm is validated using the Notre Dame and Equinox databases and is compared with existing statistical, learning, and evidence theory based fusion algorithms.


title: Emotion analysis: A survey

abstract: Emotions form a very important and basic aspect of our lives. Whatever we do, whatever we say, somehow does reflect some of our emotions, though may not be directly. To understand the very fundamental behavior of a human, we need to analyze these emotions through some emotional data, also called, the affect data. This data can be text, voice, facial expressions etc. Using this emotional data for analyzing the emotions also forms an interdisciplinary field, called Affective Computing. Computation of emotions is a very challenging task, much work has been done but many more increments are also possible. With the advent of social networking sites, many people tend to get attracted towards analyzing this very text available on these various sites. Analyzing this data over the Internet means we are spanning across the whole continent, going through all the cultures and communities across. This paper summarizes the previous works done in the field of textual emotion analysis based on various emotional models and computational approaches used.


title: A tag in the hand: supporting semantic, social, and spatial navigation in museums

abstract: Designers of mobile, social systems must carefully think about how to help their users manage spatial, semantic, and social modes of navigation. Here, we describe our deployment of MobiTags, a system to help museum visitors interact with a collection of "open storage" exhibits, those where the museum provides little curatorial information. MobiTags integrates social tagging, art information, and a map to support navigation and collaborative curation of these open storage collections. We studied 23 people's use of MobiTags in a local museum, combining interview data with device use logs and tracking of people's movements to understand how MobiTags affected their navigation and experience in the museum. Despite a lack of social cues, people feel a strong sense of social presence--and social pressure--through seeing others' tags. The tight coupling of tags, item information, and map features also supported a rich set of practices around these modes of navigation.


title: THE PSYCHOLOGY AND NEUROSCIENCE OF FORGETTING

abstract: Traditional theories of forgetting are wedded to the notion that cue-overload interference procedures (often involving the A-B, A-C list-learning paradigm) capture the most important elements of forgetting in everyday life. However, findings from a century of work in psychology, psychopharmacology, and neuroscience converge on the notion that such procedures may pertain mainly to forgetting in the laboratory and that everyday forgetting is attributable to an altogether different form of interference. According to this idea, recently formed memories that have not yet had a chance to consolidate are vulnerable to the interfering force of mental activity and memory formation (even if the interfering activity is not similar to the previously learned material). This account helps to explain why sleep, alcohol, and benzodiazepines all improve memory for a recently learned list, and it is consistent with recent work on the variables that affect the induction and maintenance of long-term potentiation in the hippo...


Analysis and Classification of Stress Categories from Drivers' Speech


abstract: Summary: The HMMTOP transmembrane topology prediction server predicts both the localization of helical transmembrane segments and the topology of transmembrane proteins. Recently, several improvements have been introduced to the original method. Now, the user is allowed to submit additional information about segment localization to enhance the prediction power. This option improves the prediction accuracy as well as helps the interpretation of experimental results, i.e. in epitope insertion experiments. Availability: HMMTOP 2.0 is freely available to noncommercial users at http://www.enzim.hu/hmmtop. Source code is also available upon request to academic users.


title: Self-Gated Recurrent Neural Networks for Human Activity Recognition on Wearable Devices

abstract: This paper develops a self-gated recurrent neural network (SGRNN), and applies it to human activity recognition (HAR), using time-series signals collected from embedded sensors of wearable devices. Recurrent neural networks (RNNs) are very powerful for time-series signal analysis. Especially, by integrating gates into recurrent units, gated RNNs such as LSTM and GRU are more complexity, and do not suffer from the vanishing gradient problem, so can learn very long-term dependencies. However, for use on wearable devices, RNNs must be simplified to reduce resource consumption, including memory usage and computational cost. The proposed model is approximately the same size and burdensome computation as that of a standard RNN, but exhibits explicit properties of the gating mechanism, so it is unaffected by the problem of vanishing gradients. Experimental results on the HAR problem not only demonstrate that the accuracy of our model is superior to that of the standard RNN, and is comparable with that of LSTM and GRU, but the model is low in resource consumption.


title: DICE: A dual integrity convergent encryption protocol for client side secure data deduplication

abstract: Message Locked Encryption (MLE) provides a way to achieve deduplication of data over the cloud in a secured and efficient manner. Various cryptographic protocols that are variants of the MLE scheme have been introduced, but they are either vulnerable to poison attack or consume a large amount of bandwidth. We introduce a new client-side deduplication protocol, called DICE (Dual Integrity Convergent Encryption), in which we perform tag checking and send the hash instead of the entire message over the network, resulting in a reduction of communication and computation cost without any loss of security. Our strategy is secure against both the erasure and the duplicate-faking attacks (known as a poison attack when performed together). Comparative analysis with other existing strategies validate the efficacy of the proposed protocol.


title: Spatial ray features for real-time ego-lane extraction

abstract: In order to support driver assistance systems in unconstrained environments, we propose to extend local appearance-based road classification with a spatial feature generation and classification. Therefore, a hierarchical approach consisting of multiple low level base classifiers, the novel spatial feature generation, as well as a final road terrain classification, is used. The system perceives a variety of local properties of the environment by means of base classifiers operating on patches extracted from monocular camera images, each represented in a metric confidence map. The core of the proposed approach is the computation of spatial ray features (SPRAY) from these confidence maps. With this, the road-terrain classifier can decide based on local visual properties and their spatial layout in the scene. In order to show the feasibility of the approach, the extraction and evaluation of the metric ego-lane driving corridor on an inner city stream is demonstrated. This is a challenging task because on a local appearance level, ego-lane is not distinguishable from other asphalt parts on the road. However, by incorporating the proposed SPRAY features the distinction is possible without requiring an explicit lane model. Due to the parallel structure of this bottom-up approach, the implemented system operates in real-time with approximately 25 Hz on a GPU.


title: Diet-mediated social networks in shoaling fish

abstract: Very little is known about interaction patterns within animal groups, such as flocks of birds, swarms of insects, or shoals of fish, or the mechanisms which mediate interactions between group members. Fish, such as three-spined sticklebacks (Gasterosteus aculeatus), are known to use a variety of visual and olfactory cues to inform shoal choice and to maintain shoal cohesion. However, whether these cues may also mediate interactions within a shoal is not known. This study aimed to investigate individual level interactions between shoaling sticklebacks under different diet treatments. Fish fed on either bloodworm- or Daphnia-based diets were used to generate replicated social networks (n = 65), in which the constituent fish differed in diet treatment. The results showed that the structure of stickleback networks is nonrandom and mediated by an association preference for fish from the same diet treatment, most likely through diet-derived chemical cues. The findings of this study provide insights into the causes of intragroup interactions in shoaling fish and highlight the importance of chemical cues in stickleback communication.


title: Commonalities in Level II Fieldwork Failure

abstract: Despite the efforts of academic educators and fieldwork supervisors, not all students successfully complete their Level II field-work experience (AOTA, 1997). This study used quantitative and qualitative data to identify common characteristics surrounding Level II fieldwork failure. Eleven participants responded to a questionnaire regarding their experience in supervising a student who failed a Level II fieldwork experience; six also participated in a telephone interview to provide additional information regarding the experience. Students in this study who failed a Level II fieldwork experience exhibited poor problem solving skills, poor clinical reasoning skills, had difficulty getting the “big picture,” and had difficulty responding to constructive feedback. Additionally, supervisors reportedly believe students were academically unprepared for the fieldwork experience. In this study, there were common characteristics, including student attributes and supervisors' strategies for dealing with problematic ...


title: Spatiotemporal imaging with partially separable functions: A matrix recovery approach

abstract: There has been significant recent interest in fast imaging with sparse sampling. Conventional imaging methods are based on Shannon-Nyquist sampling theory. As such, the number of required samples often increases exponentially with the dimensionality of the image, which limits achievable resolution in high-dimensional scenarios. The partially-separable function (PSF) model has previously been proposed to enable sparse data sampling in this context. Existing methods to leverage PSF structure utilize tailored data sampling strategies, which enable a specialized two-step reconstruction procedure. This work formulates the PSF reconstruction problem using the matrix-recovery framework. The explicit matrix formulation provides new opportunities for data acquisition and image reconstruction with rank constraints. Theoretical results from the emerging field of low-rank matrix recovery (which generalizes theory from sparse-vector recovery) and our empirical results illustrate the potential of this new approach.


title: Developing and Evaluating Conversational Agents

abstract: Publisher Summary This chapter focuses on the development and evaluation of conversational agents. Computer users can benefit from interaction with conversational agents and the access to the many sources of information that they can provide. A completely animated synthetic talking head with which one can control and study the informative aspects and psychological processes in face-to face dialogues has been developed. The goal of this chapter is to advance the development of the talking head, its design, and its accompanying technology and to create a human–computer interface centered on a virtual, conversational agent. Such agents interact with human users in the most natural manner possible, including the ability to listen and understand as well as speak fluently. Agents will facilitate and enrich interaction between humans and machines. Moreover, communication among humans can also be enhanced when mediated by virtual agents. The conversational agent can also be the interface for a series of public, interactive, art installations. This chapter expands the use of the agent in educational and therapeutic environments, as in the learning of non-native languages and in learning to read.


title: Content-based multimedia information retrieval: State of the art and challenges

abstract: Extending beyond the boundaries of science, art, and culture, content-based multimedia information retrieval provides new paradigms and methods for searching through the myriad variety of media all over the world. This survey reviews 100p recent articles on content-based multimedia information retrieval and discusses their role in current research directions which include browsing and search paradigms, user studies, affective computing, learning, semantic queries, new features and media types, high performance indexing, and evaluation techniques. Based on the current state of the art, we discuss the major challenges for the future.


title: Gender difference and employees' cybersecurity behaviors

abstract: Security breaches are prevalent in organizations and many of the breaches are attributed to human errors. As a result, the organizations need to increase their employees' security awareness and their capabilities to engage in safe cybersecurity behaviors. Many different psychological and social factors affect employees' cybersecurity behaviors. An important research question to explore is to what extent gender plays a role in mediating the factors that affect cybersecurity beliefs and behaviors of employees. In this vein, we conducted a cross-sectional survey study among employees of diverse organizations. We used structural equation modelling to assess the effect of gender as a moderator variable in the relations between psychosocial factors and self-reported cybersecurity behaviors. Our results show that gender has some effect in security self-efficacy (r=-0.435, p<0.001), prior experience (r=-0.235, p<0.001) and computer skills (r=-0.198, p<0.001) and little effect in cues-to-action (r=-0.152, p<0.001) and self-reported cybersecurity behaviors (r=-0.152, p<0.001). The role of gender in employees' self-reported cybersecurity behaviors is explored.Results show gender-wise differences for cybersecurity self-efficacy and behavior.Training is needed to close the gender gap in cybersecurity self-efficacy.


title: Hierarchical recurrent neural network for skeleton based action recognition

abstract: Human actions can be represented by the trajectories of skeleton joints. Traditional methods generally model the spatial structure and temporal dynamics of human skeleton with hand-crafted features and recognize human actions by well-designed classifiers. In this paper, considering that recurrent neural network (RNN) can model the long-term contextual information of temporal sequences well, we propose an end-to-end hierarchical RNN for skeleton based action recognition. Instead of taking the whole skeleton as the input, we divide the human skeleton into five parts according to human physical structure, and then separately feed them to five subnets. As the number of layers increases, the representations extracted by the subnets are hierarchically fused to be the inputs of higher layers. The final representations of the skeleton sequences are fed into a single-layer perceptron, and the temporally accumulated output of the perceptron is the final decision. We compare with five other deep RNN architectures derived from our model to verify the effectiveness of the proposed network, and also compare with several other methods on three publicly available datasets. Experimental results demonstrate that our model achieves the state-of-the-art performance with high computational efficiency.


title: Secure and Privacy Enhanced Gait Authentication on Smart Phone

abstract: Smart environments established by the development of mobile technology have brought vast benefits to human being. However, authentication mechanisms on portable smart devices, particularly conventional biometric based approaches, still remain security and privacy concerns. These traditional systems are mostly based on pattern recognition and machine learning algorithms, wherein original biometric templates or extracted features are stored under unconcealed form for performing matching with a new biometric sample in the authentication phase. In this paper, we propose a novel gait based authentication using biometric cryptosystem to enhance the system security and user privacy on the smart phone. Extracted gait features are merely used to biometrically encrypt a cryptographic key which is acted as the authentication factor. Gait signals are acquired by using an inertial sensor named accelerometer in the mobile device and error correcting codes are adopted to deal with the natural variation of gait measurements. We evaluate our proposed system on a dataset consisting of gait samples of 34 volunteers. We achieved the lowest false acceptance rate (FAR) and false rejection rate (FRR) of 3.92% and 11.76%, respectively, in terms of key length of 50 bits.


title: Phytostimulatory effect of Azospirillum brasilense wild type and mutant strains altered in IAA production on wheat

abstract: Auxin production by Azospirillum is believed to play a major role in the observed plant growth promoting effect. By using different genetically modified strains, the contribution of auxin biosynthesis by A. brasilense in altering root morphology was evaluated in a plate assay. Inoculation with the wild type strains A. brasilense Sp245 and Sp7 resulted in a strong decrease in root length and increase in root hair formation. This effect was abolished when inoculating with an ipdC mutant of A. brasilense. The ipdC gene encodes a key enzyme in the IPyA pathway of IAA synthesis by A. brasilense. On the other hand, the observed auxin effect was further enhanced by adding tryptophan, a precursor of IAA, to the plates and could be mimicked by replacing the Azospirillum cells by a particular concentration of IAA. Furthermore, particular mutants (rpoN, scrp) and transconjugants (extra copy of ipdC) of A. brasilense were tested in the plate assay. Together, these results confirm the important role of IAA produced by Azospirillum in altering root morphology and illustrate the power of combining genetic tools and bioassays to elucidate the mechanism of a beneficial Azospirillum-plant interaction.


title: Walking away from the desktop computer: distributed collaboration and mobility in a product design team

abstract: A study of a spatially distributed product design team shows that most members are rarely at their individual desks. Mobility is essential for the use of shared resources and for communication. It facilitates informal interactions and awareness unavailable to colleagues at remote sites. Implications for technology design include portable and distributed computing resources, in particular, moving beyond individual workstation-centric CSCW applications.


title: Building a large-scale corpus for evaluating event detection on twitter

abstract: Despite the popularity of Twitter for research, there are very few publicly available corpora, and those which are available are either too small or unsuitable for tasks such as event detection. This is partially due to a number of issues associated with the creation of Twitter corpora, including restrictions on the distribution of the tweets and the difficultly of creating relevance judgements at such a large scale. The difficulty of creating relevance judgements for the task of event detection is further hampered by ambiguity in the definition of event. In this paper, we propose a methodology for the creation of an event detection corpus. Specifically, we first create a new corpus that covers a period of 4 weeks and contains over 120 million tweets, which we make available for research. We then propose a definition of event which fits the characteristics of Twitter, and using this definition, we generate a set of relevance judgements aimed specifically at the task of event detection. To do so, we make use of existing state-of-the-art event detection approaches and Wikipedia to generate a set of candidate events with associated tweets. We then use crowdsourcing to gather relevance judgements, and discuss the quality of results, including how we ensured integrity and prevented spam. As a result of this process, along with our Twitter corpus, we release relevance judgements containing over 150,000 tweets, covering more than 500 events, which can be used for the evaluation of event detection approaches.


title: Post-rendering 3D warping

abstract: A pair of rendered images and their Z-buffers contain almost all of the information necessary to re-render from nearby viewpoints. For the small changes in viewpoint that occur in a fraction of a second, this information is sufficient for high quality re-rendering with cost independent of scene complexity. Re-rendering from previously computed views allows an order-of-magnitude increase in apparent frame rate over that provided by conventional rendering alone. It can also compensate for system latency in local or remote display. We use McMillan and Bishop’s image warping algorithm to re-render, allowing us to compensate for viewpoint translation as well as rotation. We avoid occlusion-related artifacts by warping two different reference images and compositing the results. This paper explains the basic design of our system and provides details of our reconstruction and multi-image compositing algorithms. We present our method for selecting reference image locations and the heuristic we use for any portions of the scene which happen to be occluded in both reference images. We also discuss properties of our technique which make it suitable for real-time implementation, and briefly describe our simpler real-time remote display system. CR


title: A Model-driven Approach for Integration of Interactive Applications and Web Services: A Case Study in Interactive Digital TV Platform

abstract: This work proposed a model-driven development approach related to interactive multimedia applications and Web services integration. It is based on extension of an existing modeling language, which integrates modeling concepts for interactive applications and adds support for Web Services. Three Interactive Digital TV applications were modeled and developed. As we show, the evaluation of the approach brought benefits not supported by related works, like requirements structuring and reducing amount of work needed to finalize the code generated.


title: A Comparative Study of Machine Learning Algorithms and Their Ensembles for Botnet Detection

abstract: A Botnet is a network of compromised devices that are controlled by malicious “botmaster” in order to perform various tasks, such as executing DoS attack, sending SPAM and obtaining personal data etc. As botmasters generate network traffic while communicating with their bots, analyzing network traffic to detect Botnet traffic can be a promising feature of Intrusion Detection System. Although such system has been applying various machine learning techniques, comparison of machine algorithms including their ensembles on botnet detection has not been figured out. In this study, not only the three most popular classification machine learning algorithms—Naive Bayes, Decision tree, and Neural network are evaluated, but also the ensemble methods known to strengthen classifier are tested to see if they indeed provide enhanced predictions on Botnet detection. This evaluation is conducted with the CTU-13 public dataset, measuring the training time of each classifier and its F measure and MCC score.


title: Bayesian Optimization of Combinatorial Structures

abstract: The optimization of expensive-to-evaluate black-box functions over combinatorial structures is an ubiquitous task in machine learning, engineering and the natural sciences. The combinatorial explosion of the search space and costly evaluations pose challenges for current techniques in discrete optimization and machine learning, and critically require new algorithmic ideas. This article proposes, to the best of our knowledge, the first algorithm to overcome these challenges, based on an adaptive, scalable model that identifies useful combinatorial structure even when data is scarce. Our acquisition function pioneers the use of semidefinite programming to achieve efficiency and scalability. Experimental evaluations demonstrate that this algorithm consistently outperforms other methods from combinatorial and Bayesian optimization.


title: Metabolic fate of combretastatin A-1: LC-DAD-MS/MS investigation and biological evaluation of its reactive metabolites.

abstract: Combretastatin A-1, an antineoplastic agent characterized by a remarkable cytotoxicity and a strong antitubulinic activity, is currently under investigation in phase I clinical trials. Yet a comprehensive metabolic study of CA-1 in human and animal models has so far not been reported in the literature. We have therefore investigated, through LC-UV and LC-MS analysis the in vitro/in vivo metabolism of CA-1, have synthesized its reactive quinone metabolite Q1, and have evaluated its cytotoxic and antitubulinic activities. In vitro CA-1 metabolism was studied in rat and human liver microsomes in the presence of the nucleophilic trapping agent GSH were performed while urine samples of the CA-1-treated rats were analyzed to establish the in vivo metabolic pathways. The metabolite Q1, that was synthesized in good yield using a polymer supported oxidant, displayed a significant cytotoxicity but was devoid of significant antitubulinic activity. Finally the chemical interaction of Q1 and the other combretastatin quinone metabolites with sulphydryl groups of tubulin was measured by Ellman's method: the results suggested the haptenization of the tubulin through the formation of a covalent bond.


title: Learning Brightness Transfer Functions for the Joint Recovery of Illumination Changes and Optical Flow

abstract: The increasing importance of outdoor applications such as driver assistance systems or video surveillance tasks has recently triggered the development of optical flow methods that aim at performing robustly under uncontrolled illumination. Most of these methods are based on patch-based features such as the normalized cross correlation, the census transform or the rank transform. They achieve their robustness by locally discarding both absolute brightness and contrast. In this paper, we follow an alternative strategy: Instead of discarding potentially important image information, we propose a novel variational model that jointly estimates both illumination changes and optical flow. The key idea is to parametrize the illumination changes in terms of basis functions that are learned from training data. While such basis functions allow for a meaningful representation of illumination effects, they also help to distinguish real illumination changes from motion-induced brightness variations if supplemented by additional smoothness constraints. Experiments on the KITTI benchmark show the clear benefits of our approach. They do not only demonstrate that it is possible to obtain meaningful basis functions, they also show state-of-the-art results for robust optical flow estimation.


title: Fully automatic facial feature point detection using Gabor feature based boosted classifiers

abstract: Locating facial feature points in images of faces is an important stage for numerous facial image interpretation tasks. In this paper we present a method for fully automatic detection of 20 facial feature points in images of expressionless faces using Gabor feature based boosted classifiers. The method adopts fast and robust face detection algorithm, which represents an adapted version of the original Viola-Jones face detector. The detected face region is then divided into 20 relevant regions of interest, each of which is examined further to predict the location of the facial feature points. The proposed facial feature point detection method uses individual feature patch templates to detect points in the relevant region of interest. These feature models are GentleBoost templates built from both gray level intensities and Gabor wavelet features. When tested on the Cohn-Kanade database, the method has achieved average recognition rates of 93%.


title: Diffractive and holographic optics as optical combiners in head mounted displays

abstract: We review in this paper the various architectures that have been developed in industry to implement see-through head-mounted display (HMD) optics, especially for the consumer electronics market. We will focus our investigations on holographic and diffractive optics.


Query Learning Strategies Using Boosting and Bagging


title: PSA: A novel optimization algorithm based on survival rules of porcellio scaber

abstract: Bio-inspired algorithms have received a significant amount of attention in both academic and engineering societies. In this paper, based on the observation of two major survival rules of a species of woodlice, i.e., porcellio scaber, we design and propose an algorithm called the porcellio scaber algorithm (PSA) for solving optimization problems, including differentiable and non-differential ones as well as the case with local optimums. Numerical results based on benchmark problems are presented to validate the efficacy of PSA.


title: Algorithm for Fire Detection using a Camera Surveillance System

abstract: In this paper, we propose a real-time fire detection algorithm for use with a surveillance camera system. Candidate regions of fire flames are recognized by temporal analysis of the flickering energy and color models of image pixels in frames. We also develop a robust fire flame classifier using a cascade model that joins many weak classifiers into one robust and precise classifier capable of distinguishing true fire and non-fire regions. Experimental results show that the proposed algorithm is absolutely suited for real-time fire detection with high precision.


title: Automated Chest X-Ray Screening: Can Lung Region Symmetry Help Detect Pulmonary Abnormalities?

abstract: Our primary motivator is the need for screening HIV+ populations in resource-constrained regions for exposure to Tuberculosis, using posteroanterior chest radiographs (CXRs). The proposed method is motivated by the observation that radiological examinations routinely conduct bilateral comparisons of the lung field. In addition, the abnormal CXRs tend to exhibit changes in the lung shape, size, and content (textures), and in overall, reflection symmetry between them. We analyze the lung region symmetry using multi-scale shape features, and edge plus texture features. Shape features exploit local and global representation of the lung regions, while edge and texture features take internal content, including spatial arrangements of the structures. For classification, we have performed voting-based combination of three different classifiers: Bayesian network, multilayer perception neural networks, and random forest. We have used three CXR benchmark collections made available by the U.S. National Library of Medicine and the National Institute of Tuberculosis and Respiratory Diseases, India, and have achieved a maximum abnormality detection accuracy (ACC) of 91.00% and area under the ROC curve (AUC) of 0.96. The proposed method outperforms the previously reported methods by more than 5% in ACC and 3% in AUC.


title: Caregiver Burden as People With Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder Transition into Adolescence and Adulthood in the United Kingdom

abstract: Objective There is increasing recognition that autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are associated with significant costs and burdens. However, research on their impact has focused mostly on the caregivers of young children; few studies have examined caregiver burden as children transition into adolescence and young adulthood, and no one has compared the impact of ASD to other neurodevelopmental disorders (e.g., ADHD). Method We conducted an observational study of 192 families caring for a young person (aged 14 to 24 years) with a childhood diagnosis of ASD or ADHD (n = 101 and n=91, respectively) in the United Kingdom. A modified stress-appraisal model was used to investigate the correlates of caregiver burden as a function of family background (parental education), primary stressors (symptoms), primary appraisal (need), and resources (use of services). Results Both disorders were associated with a high level of caregiver burden, but it was significantly greater in ASD. In both groups, caregiver burden was mainly explained by the affected young person's unmet need. Domains of unmet need most associated with caregiver burden in both groups included depression/anxiety and inappropriate behavior. Specific to ASD were significant associations between burden and unmet needs in domains such as social relationships and major mental health problems. Conclusions Adolescence and young adulthood are associated with high levels of caregiver burden in both disorders; in ASD, the level is comparable to that reported by persons caring for individuals with a brain injury. Interventions are required to reduce caregiver burden in this population.


title: Twitter Sentiment Analysis: Lexicon Method, Machine Learning Method and Their Combination

abstract: This paper covers the two approaches for sentiment analysis: i) lexicon based method; ii) machine learning method. We describe several techniques to implement these approaches and discuss how they can be adopted for sentiment classification of Twitter messages. We present a comparative study of different lexicon combinations and show that enhancing sentiment lexicons with emoticons, abbreviations and social-media slang expressions increases the accuracy of lexicon-based classification for Twitter. We discuss the importance of feature generation and feature selection processes for machine learning sentiment classification. To quantify the performance of the main sentiment analysis methods over Twitter we run these algorithms on a benchmark Twitter dataset from the SemEval-2013 competition, task 2-B. The results show that machine learning method based on SVM and Naive Bayes classifiers outperforms the lexicon method. We present a new ensemble method that uses a lexicon based sentiment score as input feature for the machine learning approach. The combined method proved to produce more precise classifications. We also show that employing a cost-sensitive classifier for highly unbalanced datasets yields an improvement of sentiment classification performance up to 7%.


title: Analyzing customer behavior from shopping path data using operation edit distance

abstract: Radio frequency identification (RFID) technology has been successfully applied to gather customers' shopping habits from their motion paths and other behavioral data. The customers' behavioral data can be used for marketing purposes, such as improving the store layout or optimizing targeted promotions to specific customers. Some data mining techniques, such as clustering algorithms can be used to discover customers' hidden behaviors from their shopping paths. However, shopping path data has peculiar challenges, including variable length, sequential data, and the need for a special distance measure. Due to these challenges, traditional clustering algorithms cannot be applied to shopping path data. In this paper, we analyze customer behavior from their shopping path data by using a clustering algorithm. We propose a new distance measure for shopping path data, called the Operation edit distance, to solve the aforementioned problems. The proposed distance method enables the RFID customer shopping path data to be processed effectively using clustering algorithms. We have collected a real-world shopping path data from a retail store and applied our method to the dataset. The proposed method effectively determined customers' shopping patterns from the data.


title: Numerical skills in children with Down syndrome. Can they be improved

abstract: Abstract The present study aims to explore the impact of a numerical skills training program on the basic mathematical skills and logical thinking in children with Down syndrome (DS). The training program was built specifically for children with DS, bearing the strengths and weaknesses of their particular cognitive profile in mind. Two groups of children with DS took part in the study. All children were tested before and after the training on measures of numerical skills and logical thinking. One group of 27 children was trained in numerical skills twice a week for 2 months, for about 30 min per session. A control group of 9 children was not involved in any training session. After training, children in the intervention group performed better in numerical tests, while those in the control group did not. These results suggest that our training program is both feasible and effective for children with Down syndrome.


title: Electrowetting --a versatile tool for controlling microdrop generation.

abstract: Integrating insulator-covered electrodes into a microfluidic flow focusing device (FFD) we demonstrate enhanced flexibility and control of the flow of two non-miscible liquids based on electrowetting (EW). In the parameters space, determined by liquid inlet pressures, we identify a specific region where drops can only be generated and addressed via EW. In this regime we show that the size distribution and the frequency of drop generation can be controlled by the applied voltage and the width of voltage pulses. Moreover it turns out that with EW the drop size and the frequency can be tuned independently. Finally we show that the same drop generation phenomena can also be observed in the presence of surfactants.


title: A Product Feature-Based User-Centric Ranking Model for E-Commerce Search

abstract: During the online shopping process, users search for interesting products in order to quickly access those that fit with their needs among a long tail of similar or closely related products. Our contribution addresses head queries that are frequently submitted on e-commerce Web sites. Head queries usually target featured products with several variations , accessories, and complementary products. We present in this paper a product feature-based user-centric model for product search involving, in addition to product characteristics, the user engagement toward the product. This model has been evaluated through the product search track of the LL4IR lab at CLEF 2015 in order to highlight the effectiveness of our model as well as the impact of the user engagement factor.


title: Spectral Convolution Networks

abstract: Previous research has shown that computation of convolution in the frequency domain provides a significant speedup versus traditional convolution network implementations. However, this performance increase comes at the expense of repeatedly computing the transform and its inverse in order to apply other network operations such as activation, pooling, and dropout. We show, mathematically, how convolution and activation can both be implemented in the frequency domain using either the Fourier or Laplace transformation. The main contributions are a description of spectral activation under the Fourier transform and a further description of an efficient algorithm for computing both convolution and activation under the Laplace transform. By computing both the convolution and activation functions in the frequency domain, we can reduce the number of transforms required, as well as reducing overall complexity. Our description of a spectral activation function, together with existing spectral analogs of other network functions may then be used to compose a fully spectral implementation of a convolution network.


title: Periodicity in User Engagement with a Search Engine and Its Application to Online Controlled Experiments

abstract: Nowadays, billions of people use the Web in connection with their daily needs. A significant part of these needs are constituted by search tasks that are usually addressed by search engines. Thus, daily search needs result in regular user engagement with a search engine. User engagement with web services was studied in various aspects, but there appears to be little work devoted to its regularity and periodicity. In this article, we study periodicity of user engagement with a popular search engine through applying spectrum analysis to temporal sequences of different engagement metrics. First, we found periodicity patterns of user engagement and revealed classes of users whose periodicity patterns do not change over a long period of time. In addition, we give an exhaustive analysis of the stability and quality of identified clusters. Second, we used the spectrum series as key metrics to evaluate search quality. We found that the novel periodicity metrics outperform the state-of-the-art quality metrics both in terms of significance level (p-value) and sensitivity to a large set of larges-scale A/B experiments conducted on real search engine users.


title: Dark line detection with line width extraction

abstract: Automated line detection is a classical image processing topic with many applications such as road detection in remote images and vessel detection in medical images. Many traditional line detectors, such as Gabor filter, the second order derivative of Gaussian and Radon transform will response not only to lines but also to edges, e.g. they will give high responses to the edges of bright lines or blobs when only dark lines are required. To reduce false detections when extracting only dark (or bright) lines, in this paper we propose a line detector by using the first derivative of Gaussian. It can detect dark lines without much false detection on blobs or bright lines. Meanwhile, the proposed method can estimate line width simultaneously. Experiments on various images are performed to test the proposed algorithm.


Attention Networks for Weakly Supervised Object Localization Eu


title: Agricultural Robot for automatic ploughing and seeding

abstract: This paper strives to develop a robot capable of performing operations like automatic ploughing, seed dispensing, fruit picking and pesticide spraying. It also provides manual control when required and keeps tabs on the humidity with the help of humidity sensors. The main component here is the AVR At mega microcontroller that supervises the entire process. Initially the robot tills the entire field and proceeds to ploughing, simultaneously dispensing seeds side by side. The device used for navigation is an ultrasonic sensor which continuously sends data to the microcontroller. On the field the robot operates on automated mode, but outside the field is strictly operated in manual mode. For manual control the robot uses the Bluetooth pairing app as control device and helps in the navigation of the robot outside the field. The field is fitted with humidity sensors placed at various spots that continuously monitor the environment for humidity levels. It checks these levels with the set point for humidity and alerts the farmer. The alerting mechanism is GSM module that sends a text message to the farmer informing him about the breach in set point. The farmer then responds via SMS to either switch on the water sprinklers or ignore the alert. The water sprinklers, if on, bring down the humidity level thus providing an ideal growing environment to crop. The concept of fruit picking and pesticide spraying is described under the process domain. Farmers today spend a lot of money on machines that help them decrease labor and increase yield of crops but the profit and efficiency are very less. Hence automation is the ideal solution to overcome all the shortcomings by creating machines that perform one operations and automating it to increase yield on a large scale.


title: VISIR: Visual and Semantic Image Label Refinement

abstract: The social media explosion has populated the Internet with a wealth of images. There are two existing paradigms for image retrieval: 1)content-based image retrieval (BIR), which has traditionally used visual features for similarity search (e.g., SIFT features), and 2) tag-based image retrieval (TBIR), which has relied on user tagging (e.g., Flickr tags). CBIR now gains semantic expressiveness by advances in deep-learning-based detection of visual labels. TBIR benefits from query-and-click logs to automatically infer more informative labels. However, learning-based tagging still yields noisy labels and is restricted to concrete objects, missing out on generalizations and abstractions. Click-based tagging is limited to terms that appear in the textual context of an image or in queries that lead to a click. This paper addresses the above limitations by semantically refining and expanding the labels suggested by learning-based object detection. We consider the semantic coherence between the labels for different objects, leverage lexical and commonsense knowledge, and cast the label assignment into a constrained optimization problem solved by an integer linear program. Experiments show that our method, called VISIR, improves the quality of the state-of-the-art visual labeling tools like LSDA and YOLO.


title: The ecology of adolescent activity and experience.

abstract: Twenty-five adolescents reported their daily activities and the quality of their experiences for a total of 753 times during a normal week, in response to random beeps transmitted by an electronic paging device. In this sample adolescents were found to spend most of their time either in conversation with peers or in watching television. Negative affects were prevalent in most activities involving socialization into adult roles. Television viewing appears to be an affectless state associated with deviant behavior and antisocial personality traits. The research suggests the importance of a systemic approach which studies persons' activities and experiences in an ecological context. The experiential sampling method described in this paper provides a tool for collecting such systemic data.


title: On clustering graph streams

abstract: In this paper, we will examine the problem of clustering massive graph streams. Graph clustering poses significant challenges because of the complex structures which may be present in the underlying data. The massive size of the underlying graph makes explicit structural enumeration very difficult. Consequently, most techniques for clustering multi-dimensional data are difficult to generalize to the case of massive graphs. Recently, methods have been proposed for clustering graph data, though these methods are designed for static data, and are not applicable to the case of graph streams. Furthermore, these techniques are especially not effective for the case of massive graphs, since a huge number of distinct edges may need to be tracked simultaneously. This results in storage and computational challenges during the clustering process. In order to deal with the natural problems arising from the use of massive disk-resident graphs, we will propose a technique for creating hash-compressed micro-clusters from graph streams. The compressed micro-clusters are designed by using a hash-based compression of the edges onto a smaller domain space. We will provide theoretical results which show that the hash-based compression continues to maintain bounded accuracy in terms of distance computations. We will provide experimental results which illustrate the accuracy and efficiency of the underlying method.


title: Land selection criteria for lights out factory districts during the industry 4.0 process

abstract: Abstract The lights out factories that the Industry 4.0 revolution brings will become widespread due to the energy savings and the considerable reduction in labor costs, and will bring a transformation in the land selection for the industrial plants. This study is based on the assumption that existing manufacturing plant sites will be moved from urban centers in the industry 4.0 process and the urban settlement will be changed by transforming the land selection criteria with the direction of new requirements. The study aims to determine the land selection criteria of manufacturing facilities in the urban-rural areas, which will be transformed within the Industry 4.0 process. New land selection criteria for lights out factory districts are important in terms of using the spatial pattern effectively. At the end of the study, the effects of the new land selection criteria on entrepreneurs, urban planners and decision makers are discussed.


title: Ultrawideband Direction-of-Arrival Estimation Using Complex-Valued Spatiotemporal Neural Networks

abstract: We propose a direction of arrival (DoA) estimation method using a complex-valued neural network (CVNN) for ultrawideband (UWB) systems. We combine a complex-valued spatiotemporal neural network with power-inversion adaptive-array scheme for null-steering DoA estimation. Simulation and experiments demonstrate that the proposed method shows an estimation accuracy higher than that of conventional multiple signal classification method and a spectrum floor lower than that of real-valued neural network. These results suggest that the CVNN deals with signals more properly as wave information in the null synthesis in UWB systems.


title: Fusion of thermal imaging and CCD camera-based data for stereovision visual telepresence

abstract: The aim of this paper is to describe the fusion of data acquired from a CCD camera and a thermal imager. The fusion is realized by means of spatial data from a TOF camera to ensure “natural” representation of a robot's environment; thus, the thermal and color-related data are comprised in one stereo image presented to a binocular, head-mounted display. The data acquisition is performed using a sensor head, which is placed on an Orpheus-X3 robot; both the head and the robot were developed by our working group. The head contains five matrix sensors: a pair of CCD cameras, a pair of thermal imagers, and one TOF camera. After the geometrical calibration of each sensor, the positions of the sensors in 6DOFs are computed. The corresponding data from the CCD camera and the thermal imager are subsequently determined via homogeneous and perspective transformations. The result consists in an image containing aligned data from the CCD camera and the thermal imager for each eye.


title: Expert Stance Graphs for Computational Argumentation

abstract: We describe the construction of an Expert Stance Graph, a novel, large-scale knowledge resource that encodes the stance of more than 100,000 experts towards a variety of controversial topics. We suggest that this graph may be valuable for various fundamental tasks in computational argumentation. Experts and topics in our graph are Wikipedia entries. Both automatic and semi-automatic methods for building the graph are explored, and manual assessment validates the high accuracy of the resulting graph.