title: The eficiency of mechanical processing of sorghum and miscanthus biomass in the production of 2nd generation bioethanol

abstract: Introduction Bioethanol produced from organic raw materials is a renewable and clean energy source, which is used both as fuel for car engines and in chemical, cosmetic and pharmaceutical industries. The main source for the production of 1st generation of bioethanol is grains of cereals and maize, potatoes, sugar beet and others. However the use of these crops for bioenergy is controversial as it causes reductions on the cultivation area for food and feed production. Taking into account domestic climatic conditions, lignocellulosic plants such as sorghum and miscanthus can become an alternative for bioenergy production (Photo 1). Sorghum, similarly to maize, sugar cane and proso millet is classiied in the Panicoideae family, Poaceae, in genus Sorghum [1]. Sorghum is an annual plant reaching the height of 4 m, tolerant to drought, and providing high yields of dry mass at the so called milk-wax phase of seed. Sorghum biomass contains high amounts of monosaccharides, what indicates its high usability for obtaining bioethanol [2]. Miscanthus, of Miscanthus sp. genus is a perennial tuft grass with low water-mineral needs, resistant to diseases and pests, which can be grown on poor grade soils. Again, it is characterized with high growth of biomass, what leads to possible improvement of the cost-eficiency of the production of 2nd generation biofuels [3]. Moreover, the actual species of Miscanthus giganteus is a hybrid bred from two other species i.e. diploid Miscanthus sinensis and tetraploid Miscanthus saccharilorus and this hybrid can grow to the height of 3.5 m in one vegetation season [4].


title: Supercooling and Cold Hardiness in Sour Cherry Germplasm: Flower Buds

abstract: Flower buds of two sweet cherry (Prunus avium L.), 12 sour cherry (Prunus cerasus L.) and one ground cherry (P. fruticosa Pall.) were collected monthly from Aug. 1990 to Mar. 1991, and subjected to freeze tests to deter- mine the level of cold hardiness. LT50 values (temperatures at which 50% of the fl ower buds were killed) summed over all months were signifi cantly correlated (r = 0.6844, P ≤ 0.01) to the fl ower bud low temperature exotherms (LTEs). Correlation of LTEs to LT50 values was highest, r = 0.85, P ≤ 0.01 for the acclimation and midwinter period, November to February collections. During this period the average LT50 occurred before and within 2.5 °C of the LTE, indicating tissue injury before the LTE occurrence. During deacclimation, represented by the March collection, the LT50 began within 2.0 °C, on average, of the LTE, and in 11 of 12 cultivars and seedlings preceded the LTE. In March, the correla- tion of LTEs to LT50 values was less, r = 0.69, P ≤ 0.05, indicating possible changes fl ower bud deep supercooling. LTE values were selected as a measure of fl ower bud hardiness in sour cherry. Exotherms were not detected in the fl ower buds of all germplasm tested on all evaluation dates, but were the best means of separating selections. While LTE analyses expressed signifi cant differences in November, December, and March at P ≤ 0.01, the LT50 analyses expressed differences only in December and February at P ≤ 0.05. The relationship between ambient temperatures and fl oral tissue hardiness indicated that November and March are two critical times for fl ower bud injury. November injury would occur in years when sudden low temperatures occur without suffi cient pre-exposure to freezing temperatures. March injury would occur in years when sudden freezing temperatures follow warm days. This type of injury would be most pronounced in southern genotypes. Spring freeze injury could be signifi cantly reduced by the selection of cultivars and seedlings that have delayed deacclimation. Exotherm occurrence and bud volume were correlated (r = 0.95, P ≤ 0.05). In January, when exotherms were least prevalent, they were generally present only in the fi ve cultivars and seedlings with large bud volumes. The LTEs in midwinter, occurred within 3 °C of the reported average annual minimum temperature for the northern range of Prunus commercial production (Zone 6). The results of the principal component analysis of fl ower bud LTEs indicated that other selection criteria as fl owering time might have played a more signifi cant role in the hardiness range of sour cherry than simply geographic origin. The fi rst principal component (PC1), which accounted for 77% of the total variance was used to separate among cultivars and seedlings. Selections at the positive end of PC1 had fl ower buds that were more cold susceptible than selections at the negative end of PC. This concurs with other research showing that fl ower bud hardiness is related more to commercial range (i.e., the range of commercial production) than to geographic distribution. Sour cherry yields in Michigan are reduced signifi cantly one out of three years by spring freeze injury to fl oral tissues. The lack of consistent crop availability, from year to year, is a major factor limiting market development (Flore and Howell, 1987). The temperature at which fl ower buds are injured depends on the stage of development (Iezzoni and Hamilton, 1985). One ana- lytical procedure frequently used in cold hardiness and freezing injury studies is exotherm or differential thermal analysis (DTA). In DTA, heat of fusion is detected by recording the temperature difference between a dry reference sample and a wet sample during freezing. The type of freezing observed by DTAs occurs in plants that resist freezing by supercooling. The DTA profi le in species which deep supercool indicates two exotherms. The fi rst is associated with extracellular freezing that results from heterogeneous nucleation and is called the high temperature exotherm (HTE). The second exotherm is associated with the


title: A Desktop Virtual Reality Earth Motion System in Astronomy Education

abstract: In this study, a desktop virtual reality earth motion system (DVREMS) is designed and developed to be applied in the classroom. The system is implemented to assist elementary school students to clarify earth motion concepts using virtual reality principles. A study was conducted to observe the influences of the proposed system in learning. Twenty-one sixth-grade students participated in the study. Statistical results show that the scores in the pre-test and post-test significantly differ and using virtual reality can assist students in understanding the concepts. Besides, four design recommendations – information, spatial behavior, manipulation and concept representation – for improving the desktop VR system in education are also presented.


title: A frequency domain equalization algorithm for fast time-varying fading channels

abstract: Conventional frequency domain equalization (FDE) schemes were originally devised for quasi-static channels. Thus, such equalization schemes could suffer from significant performance degradation in fast-fading channels. This paper proposes a frequency domain equalization algorithm to mitigate the effect of fast time-varying fading. First, a mathematical expression is derived to quantify the total interference resulting from the time variation of the channel. Then, the proposed approach attempts to eliminate the effect of time-variations of the channel. This cancellation allows efficient use of the classical FDE structures in fast time-varying fading environments, although they are built upon the quasi-static channel model. Simulation results of bit-error-rate performance are provided to demonstrate the effectiveness of the proposed algorithm.


title: Reduction Techniques for Model Checking Markov Decision Processes

abstract: The quantitative analysis of a randomized system, modeled by a Markov decision process, against an LTL formula can be performed by a combination of graph algorithms, automata-theoretic concepts and numerical methods to compute maximal or minimal reachability probabilities. In this paper, we present various reduction techniques that serve to improve the performance of the quantitative analysis, and report on their implementation on the top of the probabilistic model checker \LiQuor. Although our techniques are purely heuristic and cannot improve the worst-case time complexity of standard algorithms for the quantitative analysis, a series of examples illustrates that the proposed methods can yield a major speed-up.


title: Pharmacotherapy for erectile dysfunction.

abstract: Erectile dysfunction (ED) is defined as the consistent inability to obtain or maintain an erection for satisfactory sexual relations. An estimated 20-30 million men suffer from some degree of sexual dysfunction. The past 20 years of research on erectile physiology have increased our understanding of the biochemical factors and intracellular mechanisms responsible for corpus cavernosal smooth muscle contraction and relaxation, and revealed that ED is predominantly a disease of vascular origin. Since the advent of sildenafil (Viagra), there has been a resurgence of interest in ED, and an increase in patients presenting with this disease. A thorough knowledge of the physiology of erection is essential for future pharmacological innovations in the field of male ED.


title: Wideband Microstrip Patch Antenna With U-Shaped Parasitic Elements

abstract: A wideband U-shaped parasitic patch antenna is proposed. Two parasitic elements are incorporated into the radiating edges of a rectangular patch whose length and width are lambdag/2 and lambdag/4, respectively, in order to achieve wide bandwidth with relatively small size. Coupling between the main patch and U-shaped parasitic patches is realized by either horizontal or vertical gaps. These gaps are found to be the main factors of the wideband impedance matching. The proposed antenna is designed and fabricated on a small size ground plane (25 mmtimes30 mm) for application of compact transceivers. The fabricated antenna on a FR4 substrate shows an impedance bandwidth of 27.3% (1.5 GHz) at 5.5 GHz center frequency. The measured radiation patterns are similar to those of a conventional patch antenna with slightly higher gains of 6.4 dB and 5.2 dB at each resonant frequency


title: Presentation Attack Detection for Face Recognition Using Light Field Camera

abstract: The vulnerability of face recognition systems is a growing concern that has drawn the interest from both academic and research communities. Despite the availability of a broad range of face presentation attack detection (PAD) (or countermeasure or antispoofing) schemes, there exists no superior PAD technique due to evolution of sophisticated presentation attacks (or spoof attacks). In this paper, we present a new perspective for face presentation attack detection by introducing light field camera (LFC). Since the use of a LFC can record the direction of each incoming ray in addition to the intensity, it exhibits an unique characteristic of rendering multiple depth (or focus) images in a single capture. Thus, we present a novel approach that involves exploring the variation of the focus between multiple depth (or focus) images rendered by the LFC that in turn can be used to reveal the presentation attacks. To this extent, we first collect a new face artefact database using LFC that comprises of 80 subjects. Face artefacts are generated by simulating two widely used attacks, such as photo print and electronic screen attack. Extensive experiments carried out on the light field face artefact database have revealed the outstanding performance of the proposed PAD scheme when benchmarked with various well established state-of-the-art schemes.


title: Torque and workspace analysis for flexible tendon driven mechanisms

abstract: Tendon driven mechanisms have been considered in robotic design for several decades. They provide lightweight end effectors with high dynamics. Using remote actuators it is possible to free more space for mechanics or electronics. Nevertheless, lightweight mechanism are fragile and unfortunately their control software can not protect them during the very first instant of an impact. Compliant mechanisms address this issue, providing a mechanical low pass filter, increasing the time available before the controller reacts. Using adjustable stiffness elements and an antagonistic architecture, the joint stiffness can be adjusted by variation of the tendon pre-tension. In this paper, the fundamental equations of m antagonistic tendon driven mechanisms are reviewed. Due to limited tendon forces the maximum torque and the maximum acheivable stiffness are dependent. This implies, that not only the torque workspace, or the stiffness workspace must be considered but also their interactions. Since the results are of high dimensionality, quality measures are necessary to provide a synthetic view. Two quality measures, similar to those used in grasp planning, are presented. They both provide the designer with a more precise insight into the mechanism.


NEAL: A Neurally Enhanced Approach to Linking Citation and Reference


title: An Efficient Privacy-Preserving Ranked Keyword Search Method

abstract: Cloud data owners prefer to outsource documents in an encrypted form for the purpose of privacy preserving. Therefore it is essential to develop efficient and reliable ciphertext search techniques. One challenge is that the relationship between documents will be normally concealed in the process of encryption, which will lead to significant search accuracy performance degradation. Also the volume of data in data centers has experienced a dramatic growth. This will make it even more challenging to design ciphertext search schemes that can provide efficient and reliable online information retrieval on large volume of encrypted data. In this paper, a hierarchical clustering method is proposed to support more search semantics and also to meet the demand for fast ciphertext search within a big data environment. The proposed hierarchical approach clusters the documents based on the minimum relevance threshold, and then partitions the resulting clusters into sub-clusters until the constraint on the maximum size of cluster is reached. In the search phase, this approach can reach a linear computational complexity against an exponential size increase of document collection. In order to verify the authenticity of search results, a structure called minimum hash sub-tree is designed in this paper. Experiments have been conducted using the collection set built from the IEEE Xplore. The results show that with a sharp increase of documents in the dataset the search time of the proposed method increases linearly whereas the search time of the traditional method increases exponentially. Furthermore, the proposed method has an advantage over the traditional method in the rank privacy and relevance of retrieved documents.


title: On blind signatures and perfect crimes

abstract: David Chaum has introduced the idea of blind signatures, an extension of the concept of digital signatures, as a way to protect the identity and privacy of a user in electronic payment and service networks. Blind signatures also prevent so-called ''dossier creation'' about users by organizations. While the concept of blind signatures still allows authorities to distinguish between valid and false data, it prevents these authorities from connecting specific data or actions to specific users. With the growing emphasis on the protection of the privacy of user data and user actions in electronic systems, blind signatures seem to be a perfect solution. This paper however, discusses a problematic aspect of blind signatures, showing that this perfect solution can potentially lead to perfect crime. We use a real crime case as an example.


title: Forensic analysis of video file formats

abstract: Abstract Video file format standards define only a limited number of mandatory features and leave room for interpretation. Design decisions of device manufacturers and software vendors are thus a fruitful resource for forensic video authentication. This paper explores AVI and MP4-like video streams of mobile phones and digital cameras in detail. We use customized parsers to extract all file format structures of videos from overall 19 digital camera models, 14 mobile phone models, and 6 video editing toolboxes. We report considerable differences in the choice of container formats, audio and video compression algorithms, acquisition parameters, and internal file structure. In combination, such characteristics can help to authenticate digital video files in forensic settings by distinguishing between original and post-processed videos, verifying the purported source of a file, or identifying the true acquisition device model or the processing software used for video processing.


title: A Hybrid Approach for Facial Expression Recognition

abstract: Facial expression recognition is an emerging research area for improving human and computer interaction. This research plays a significant role in the field of social communication, commercial enterprise, law enforcement, and other computer interactions. In this paper, we propose a hybrid design for facial expression recognition combining image pre-processing steps and different Convolutional Neural Network (CNN) structures providing better accuracy and greatly improved training time. We are predicting seven basic emotions of human faces: sadness, happiness, disgust, anger, fear, surprise and neutral. The model performs well regarding challenging facial expression recognition where the emotion expressed could be one of several due to their quite similar facial characteristics such as anger, disgust, and sadness. The experiment to test the model was conducted across multiple databases and different facial orientations, and to best of our knowledge, the model provided an accuracy of about 89.58% for KDEF dataset, 100% accuracy for JAFFE dataset and 71.975% accuracy for combined (KDEF + JAFFE + SFEW) dataset across these different scenarios. Performance evaluation was done by cross-validation techniques to avoid bias towards a specific set of images from a database.


title: What is a robot companion - friend, assistant or butler?

abstract: The study presented in this paper explored people's perceptions and attitudes towards the idea of a future robot companion for the home. A human-centred approach was adopted using questionnaires and human-robot interaction trials to derive data from 28 adults. Results indicated that a large proportion of participants were in favour of a robot companion and saw the potential role as being an assistant, machine or servant. Few wanted a robot companion to be a friend. Household tasks were preferred to child/animal care tasks. Humanlike communication was desirable for a robot companion, whereas humanlike behaviour and appearance were less essential. Results are discussed in relation to future research directions for the development of robot companions.


title: BD180 - a new 0.18 μm BCD (Bipolar-CMOS-DMOS) Technology from 7V to 60V

abstract: We present a new BCD technology in a 0.18 μm technology platforms with a capability of 7 to 60V high-voltage devices such as DEMOS and LDMOS. The developed 0.18 mum BCD process provides various kinds of high voltage LDMOS such as 7, 12, 20, 50, 60 V LDMOS transistors for variety of applications. The power LDMOS transistors in the process have very competitive specific on-resistance compared to previous results.


Learning biological interactions from multiple data sources


title: HMS-Net: Hierarchical Multi-Scale Sparsity-Invariant Network for Sparse Depth Completion

abstract: Dense depth cues are important and have wide applications in various computer vision tasks. In autonomous driving, LIDAR sensors are adopted to acquire depth measurements around the vehicle to perceive the surrounding environments. However, depth maps obtained by LIDAR are generally sparse because of its hardware limitation. The task of depth completion attracts increasing attention, which aims at generating a dense depth map from an input sparse depth map. To effectively utilize multi-scale features, we propose three novel sparsity-invariant operations, based on which, a sparsity-invariant multi-scale encoder-decoder network (HMS-Net) for handling sparse inputs and sparse feature maps is also proposed. Additional RGB features could be incorporated to further improve the depth completion performance. Our extensive experiments and component analysis on two public benchmarks, KITTI depth completion benchmark and NYU-depth-v2 dataset, demonstrate the effectiveness of the proposed approach. As of Aug. 12th, 2018, on KITTI depth completion leaderboard, our proposed model without RGB guidance ranks 1st among all peer-reviewed methods without using RGB information, and our model with RGB guidance ranks 2nd among all RGB-guided methods.


title: Poverty of the Stimulus Revisited

abstract: A central goal of modern generative grammar has been to discover invariant properties of human languages that reflect ‘‘the innate schematism of mind that is applied to the data of experience’’ and that ‘‘might reasonably be attributed to the organism itself as its contribution to the task of the acquisition of knowledge’’ (Chomsky, 1971). Candidates for such invariances include the structure dependence of grammatical rules, and in particular, certain constraints on question formation. Various ‘‘poverty of stimulus’’ (POS) arguments suggest that these invariances reflect an innate human endowment, as opposed to common experience: Such experience warrants selection of the grammars acquired only if humans assume, a priori, that selectable grammars respect substantive constraints. Recently, several researchers have tried to rebut these POS arguments. In response, we illustrate why POS arguments remain an important source of support for appeal to a priori structure-dependent constraints on the grammars that humans naturally acquire.


title: Automatic tuning of PID controllers based on transfer function estimation

abstract: Abstract A method for the automatic tuning of PID controllers in a closed loop, based on the estimation of a parametric ‘black-box’ transfer function model, is proposed. The system is excited by generating limit cycle oscillations at two different frequencies, which are approximately the crossover frequency and the critical frequency for the feedback loop. A discrete parametric transfer function model is estimated from the experimental data. Important parameters concerning the estimation, such as the prefilter cut-off frequency and the sampling interval, are determined automatically from the experimental data. The PID parameters are determined from a constrained optimization in the frequency domain. The constraints are classical control system properties, such as the maximum amplitudes of the sensitivity and the complementary sensitivity functions. Given these constraints, the PID parameters are determined such that the low frequency amplitude characteristic for the controller is maximized. Simulation experiments show that the tuning procedure has low sensitivity to disturbances and noise during the tuning experiment.


title: A Latent-class Model for Estimating Product-choice Probabilities from Clickstream Data

abstract: This paper analyzes customer product-choice behavior based on the recency and frequency of each customers page views on e-commerce sites. Recently, we devised an optimization model for estimating product-choice probabilities that satisfy monotonicity, convexity, and concavity constraints with respect to recency and frequency. This shape-restricted model delivered high predictive performance even when there were few training samples. However, typical e-commerce sites deal in many different varieties of product, so the predictive performance of the model can be further improved by integrating such product heterogeneity. For this purpose, we develop a novel latent-class shape-restricted model for estimating product-choice probabilities for each latent class of products. We also give a tailored expectation-maximization algorithm for parameter estimation. Computational results demonstrate that higher predictive performance is achieved with our latent-class model than with the previous shape-restricted model or latent-class logistic regression.


Genitives, Relational Nouns, and Argument-Modifier Ambiguity


title: Implementation of a GNU radio and python FMCW radar toolkit

abstract: The use of GNU Radio in order to explore FMCW radar is growing rapidly, where it is used for radar signal processing. In this paper we implement FMCW surveillance radar for drone detection on SDR using GNU Radio and the USRP B210. The requirement was to design and implement an FMCW surveillance radar to detect a drone with a radar cross-section of 0.1 m2 and a maximum range of 150 m for the purpose of point detection. The signal processing takes the beat frequency which has pulse compression gain and performs coherent integration. It windows and displays the results on a range Doppler map using two-dimensional fast Fourier transform. The pulse repetition frequency of the waveform is selected as 2200 Hz with a chirp bandwidth of 28 MHz, allowing for resolving a maximum velocity of 30 m/s unambiguously, which is the typical maximum speed of a small drone. Experiments were conducted using a human being and a car target as a drone was not available. The B210 induced a phase drift in the results which causes a Doppler shift. The phase drift was resolved by creating a phase equalization matrix, which was used to correct the phase drift in real-time. Verification of the designed radar system yielded a 63% probability of detection and probability of false alarm of 1.7 × 10−6.


title: RF-MEMS switches for reconfigurable integrated circuits

abstract: This paper deals with a relatively new area of radio-frequency (RF) technology based on microelectro-mechanical systems (MEMS). RF MEMS provides a class of new devices and components which display superior high-frequency performance relative to conventional (usually semiconductor) devices, and which enable new system capabilities. In addition, MEMS devices are designed and fabricated by techniques similar to those of very large-scale integration, and can be manufactured by traditional batch-processing methods. In this paper, the only device addressed is the electrostatic microswitch - perhaps the paradigm RF-MEMS device. Through its superior performance characteristics, the microswitch is being developed in a number of existing circuits and systems, including radio front-ends, capacitor banks, and time-delay networks. The superior performance combined with ultra-low-power dissipation and large-scale integration should enable new system functionality as well. Two possibilities addressed here are quasi-optical beam steering and electrically reconfigurable antennas.


title: Automatic Game Progression Design through Analysis of Solution Features

abstract: A long-term goal of game design research is to achieve end-to-end automation of much of the design process, one aspect of which is creating effective level progressions. A key difficulty is getting the player to practice with interesting combinations of learned skills while maintaining their engagement. Although recent work in task generation and sequencing has reduced this effort, we still lack end-to-end automation of the entire content design process. We approach this goal by incorporating ideas from intelligent tutoring systems and proposing progression strategies that seek to achieve mastery of not only base concepts but arbitrary combinations of these concepts. The input to our system is a model of what the player needs to do to complete each level, expressed as either an imperative procedure for producing solutions or a representation of features common to all solutions. The output is a progression of levels that can be adjusted by changing high-level parameters. We apply our framework to a popular math puzzle game and present results from 2,377 players showing that our automatic level progression is comparable to expert-crafted progression after a few design iterations based on a key engagement metric.


ATTACHMENT ORIENTATIONS, STRESS AND JOB PERFORMANCE: THE MEDIATING ROLE OF SOCIAL RANK STRATEGIES


title: Ultra-Fast SOT-MRAM Cell with STT Current for Deterministic Switching

abstract: This paper presents a spin-orbit torque magnetic random access memory (SOT-MRAM) using perpendicular-anisotropy magnetic tunnel junction (p-MTJ). In spite of conventional p-MTJ based SOT-MRAMs which need an external magnetic field to achieve a deterministic switching, the proposed cell uses a spin-torque transfer (STT) current where we show that the cell needs only two access transistors. This can solve the thermal instability, complexity and process variation sensitivity issues of the conventional SOT-MRAMs. In addition, it maintains the advantage of conventional p-MTJ based SOT-MRAMs such as ultra-fast switching and enhanced reliability of the MTJ. Our simulation results show that the proposed SOT-MRAM can achieve 3.3X and 2.8X faster and energy efficient write operation in comparison with the conventional SOT-MRAMs.


title: Mycosis Fungoides Associated with Kaposi's Sarcoma, T-cell Rich B-cell Lymphoma, and T cell Lymphoma with Angioimmunoblastic Features

abstract: A patient with mycosis fungoides (MF), Kaposi's sarcoma, T-cell B-cell rich lymphoma and T-cell lymphoma with angioimmunoblastic features is described. The appearance of multiple malignancies in this patient may have been caused by previous exposure to radiation in the Chernobyl accident and/or systemic chemotherapy for the initial T-cell B-cell rich lymphoma which he underwent.


title: Lactate removal during active recovery related to the individual anaerobic and ventilatory thresholds in soccer players

abstract: The aim of this study was to compare the lactate (La) removal during active recovery at three different work rates below the individual anaerobic threshold (IAT). Recently, it has been recommended that exercise intensity should be determined in relation to the IAT instead of the percentage of maximal oxygen uptake ( V(.)O(2max)), especially for training and research purposes. Therefore, we defined the recovery work rates by calculating 50% of the threshold difference (Delta T) between the IAT and the individual ventilatory threshold (IVT) work rates, then choosing the IVT(+50%DeltaT), the IVT and the IVT(-50%DeltaT). All these work rates fell within the range (30-70% V(.)O(2max)) previously reported for optimal La removal. After a 6-min treadmill run at 90% V(.)O(2max), soccer players [ n=12 male, age 22 (1) years] performed, in a random order, four 30-min recovery treatments: (1) run at IVT(+50%DeltaT), (2) at IVT, (3) at IVT(-50%DeltaT), (4) passive recovery. La was obtained at 1, 3, 6, 9, 12, 15, 20, 25 and 30 min of recovery. The La removal curve was significantly affected by treatments ( P<0.01) and recovery timing ( P<0.01), with a significant interaction between them ( P<0.01). Although they were more efficient than passive recovery, the studied work rates [between 39 (7) and 60 (4)% V(.)O(2max)) produced different lactate removal curves. IVT and IVT(-50%DeltaT) were significantly more efficient than IVT(+50%DeltaT), while no difference was found between IVT and IVT(-50%DeltaT) for any time point. In conclusion, both IVT(-50%DeltaT) and IVT were efficient individual work rates for La removal, and no further La decrease occurred after 20 min.


title: Scalable real-time volumetric surface reconstruction

abstract: We address the fundamental challenge of scalability for real-time volumetric surface reconstruction methods. We design a memory efficient, hierarchical data structure for commodity graphics hardware, which supports live reconstruction of large-scale scenes with fine geometric details. Our sparse data structure fuses overlapping depth maps from a moving depth camera into a single volumetric representation, from which detailed surface models are extracted. Our hierarchy losslessly streams data bidirectionally between GPU and host, allowing for unbounded reconstructions. Our pipeline, comprised of depth map post-processing, camera pose estimation, volumetric fusion, surface extraction, and streaming, runs entirely in real-time. We experimentally demonstrate that a shallow hierarchy with relatively large branching factors yields the best memory/speed tradeoff, consuming an order of magnitude less memory than a regular grid. We compare an implementation of our data structure to existing methods and demonstrate higher-quality reconstructions on a variety of large-scale scenes, all captured in real-time.


title: One VM to rule them all

abstract: Building high-performance virtual machines is a complex and expensive undertaking; many popular languages still have low-performance implementations. We describe a new approach to virtual machine (VM) construction that amortizes much of the effort in initial construction by allowing new languages to be implemented with modest additional effort. The approach relies on abstract syntax tree (AST) interpretation where a node can rewrite itself to a more specialized or more general node, together with an optimizing compiler that exploits the structure of the interpreter. The compiler uses speculative assumptions and deoptimization in order to produce efficient machine code. Our initial experience suggests that high performance is attainable while preserving a modular and layered architecture, and that new high-performance language implementations can be obtained by writing little more than a stylized interpreter.


title: An Event Structure Account of English Resultatives

abstract: Current syntactic accounts of English resultatives are based on the assumption that result XPs are predicated of underlying direct objects. This assumption has helped to explain the presence of reflexive pronouns with some intransitive verbs but not others and the apparent lack of result XPs predicated of subjects of transitive verbs. We present problems for and counterexamples to some of the basic assumptions of the syntactic approach, which undermine its explanatory power. We develop an alternative account that appeals to principles governing the well-formedness of event structure and the event structure-to-syntax mapping. This account covers the data on intransitive verbs and predicts the distribution of subject-predicated result XPs with transitive verbs.


title: Uppaal Stratego

abstract: Uppaal Stratego is a novel tool which facilitates generation, optimization, comparison as well as consequence and performance exploration of strategies for stochastic priced timed games in a user-friendly manner. The tool allows for efficient and flexible "strategy-space" exploration before adaptation in a final implementation by maintaining strategies as first class objects in the model-checking query language. The paper describes the strategies and their properties, construction and transformation algorithms and a typical tool usage scenario.


title: Transformation of Python Applications into Function-as-a-Service Deployments

abstract: New cloud programming and deployment models pose challenges to software application engineers who are looking, often in vain, for tools to automate any necessary code adaptation and transformation. Function-as-a-Service interfaces are particular non-trivial targets when considering that most cloud applications are implemented in non-functional languages. Among the most widely used of these languages is Python. This starting position calls for an automated approach to transform monolithic Python code into modular FaaS units by partially automated decomposition. Hence, this paper introduces and evaluates Lambada, a Python module to dynamically decompose, convert and deploy unmodified Python code into AWS Lambda functions. Beyond the tooling in the form of a measured open source prototype implementation, the paper contributes a description of the algorithms and code rewriting rules as blueprints for transformations of other scripting languages.


title: TinyVM, an efficient virtual machine infrastructure for sensor networks

abstract: We present TinyVM, a Virtual Machine (VM) for nesC and C applications on sensor motes. TinyVM executes compressed bytecode on-the-fly to conserve memory. To facilitate creation of application-specific VMs, partitioning of applications into bytecode, VM instruction set extensions and machine-code is supported. We provide experimental evidence for the efficiency of TinyVM on Atmega128-based motes and on the Intel iMote2. TinyVM also runs on Windows and Linux, and we are currently porting TinyVM to Telos-based motes.


title: Design and optimization of an inset fed circular microstrip patch antenna using DGS structure for applications in the millimeter wave band

abstract: In this paper we present a performance comparison of an inset fed circular microstrip patch antenna designed to operate at milimeterwave frequencies, with and without a DGS (defected ground plane structure). The parameters such as gain, directivity, return loss and bandwidth are notably improved in our proposed prototype. Also a comparison is made with other previously published works [1]. The proposed antenna operates in the EHF band at frequency of 30 GHz and HFSSv15 software based on Finite elements is used for result simulations.


title: Mindfulness based cognitive therapy for psychiatric disorders: A systematic review and meta-analysis

abstract: Mindfulness- based Cognitive Therapy (MBCT) is a meditation program based on an integration of Cognitive behavioural therapy and Mindfulness-based stress reduction. The aim of the present work is to review and conduct a meta-analysis of the current findings about the efficacy of MBCT for psychiatric patients. A literature search was undertaken using five electronic databases and references of retrieved articles. Main findings included the following: 1) MBCT in adjunct to usual care was significantly better than usual care alone for reducing major depression (MD) relapses in patients with three or more prior depressive episodes (4 studies), 2) MBCT plus gradual discontinuation of maintenance ADs was associated to similar relapse rates at 1 year as compared with continuation of maintenance antidepressants (1 study), 3) the augmentation of MBCT could be useful for reducing residual depressive symptoms in patients with MD (2 studies) and for reducing anxiety symptoms in patients with bipolar disorder in remission (1 study) and in patients with some anxiety disorders (2 studies). However, several methodological shortcomings including small sample sizes, non-randomized design of some studies and the absence of studies comparing MBCT to control groups designed to distinguish specific from non-specific effects of such practice underscore the necessity for further research.


title: Indications for Penetrating Keratoplasty: 1980-1988

abstract: A retrospective analysis was undertaken of the clinical diagnoses of 1594 eyes that underwent penetrating keratoplasty performed in a private-referral corneal practice over a 9-year period, 1980-1988. The seven most common indications for surgery were keratoconus (24.0%), pseudophakic or aphakic bul


title: Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications

abstract: We present an overview and evaluation of a new, systematic approach for generation of highly realistic, annotated synthetic data for training of deep neural networks in computer vision tasks. The main contribution is a procedural world modeling approach enabling high variability coupled with physically accurate image synthesis, and is a departure from the hand-modeled virtual worlds and approximate image synthesis methods used in real-time applications. The benefits of our approach include flexible, physically accurate and scalable image synthesis, implicit wide coverage of classes and features, and complete data introspection for annotations, which all contribute to quality and cost efficiency. To evaluate our approach and the efficacy of the resulting data, we use semantic segmentation for autonomous vehicles and robotic navigation as the main application, and we train multiple deep learning architectures using synthetic data with and without fine tuning on organic (i.e. real-world) data. The evaluation shows that our approach improves the neural network's performance and that even modest implementation efforts produce state-of-the-art results.


title: From PID to Active Disturbance Rejection Control

abstract: Active disturbance rejection control (ADRC) can be summarized as follows: it inherits from proportional-integral-derivative (PID) the quality that makes it such a success: the error driven, rather than model-based, control law; it takes from modern control theory its best offering: the state observer; it embraces the power of nonlinear feedback and puts it to full use; it is a useful digital control technology developed out of an experimental platform rooted in computer simulations. ADRC is made possible only when control is taken as an experimental science, instead of a mathematical one. It is motivated by the ever increasing demands from industry that requires the control technology to move beyond PID, which has dominated the practice for over 80 years. Specifically, there are four areas of weakness in PID that we strive to address: 1) the error computation; 2) noise degradation in the derivative control; 3) oversimplification and the loss of performance in the control law in the form of a linear weighted sum; and 4) complications brought by the integral control. Correspondingly, we propose four distinct measures: 1) a simple differential equation as a transient trajectory generator; 2) a noise-tolerant tracking differentiator; 3) the nonlinear control laws; and 4) the concept and method of total disturbance estimation and rejection. Together, they form a new set of tools and a new way of control design. Times and again in experiments and on factory floors, ADRC proves to be a capable replacement of PID with unmistakable advantage in performance and practicality, providing solutions to pressing engineering problems of today. With the new outlook and possibilities that ADRC represents, we further believe that control engineering may very well break the hold of classical PID and enter a new era, an era that brings back the spirit of innovations.


title: Somatic variants in autosomal dominant genes are a rare cause of sporadic Alzheimer's disease

abstract: Abstract Introduction A minority of patients with sporadic early-onset Alzheimer's disease (AD) exhibit de novo germ line mutations in the autosomal dominant genes such as APP , PSEN1 , or PSEN2 . We hypothesized that negatively screened patients may harbor somatic variants in these genes. Methods We applied an ultrasensitive approach based on single-molecule molecular inversion probes followed by deep next generation sequencing of 11 genes to 100 brain and 355 blood samples from 445 sporadic patients with AD (>80% exhibited an early onset, Results We identified and confirmed nine somatic variants (allele fractions: 0.2%–10.8%): two APP , five SORL1 , one NCSTN , and one MARK4 variant by independent amplicon-based deep sequencing. Discussion Two of the SORL1 variant might have contributed to the disease, the two APP variants were interpreted as likely benign and the other variants remained of unknown significance. Somatic variants in the autosomal dominant AD genes may not be a common cause of sporadic AD, including early onset cases.


title: Respiratory Distress in the Newborn

abstract: The most common etiology of neonatal respiratory distress is transient tachypnea of the newborn; this is triggered by excessive lung fluid, and symptoms usually resolve spontane- ously. Respiratory distress syndrome can occur in premature infants as a result of surfactant deficiency and underdeveloped lung anatomy. Intervention with oxygenation, ventilation, and surfactant replacement is often necessary. Prenatal administration of corticosteroids between 24 and 34 weeks' gestation reduces the risk of respiratory distress syndrome of the newborn when the risk of preterm delivery is high. Meconium aspiration syndrome is thought to occur in utero as a result of fetal distress by hypoxia. The incidence is not reduced by use of amnio- infusion before delivery nor by suctioning of the infant during delivery. Treatment options are resuscitation, oxygenation, surfactant replacement, and ventilation. Other etiologies of respira- tory distress include pneumonia, sepsis, pneumothorax, persistent pulmonary hypertension, and congenital malformations; treatment is disease specific. Initial evaluation for persistent or severe respiratory distress may include complete blood count with differential, chest radiog- raphy, and pulse oximetry. (Am Fam Physician 2007;76:987-94. Copyright © 2007 American Academy of Family Physicians.)


title: Wisdom: A metaheuristic (pragmatic) to orchestrate mind and virtue toward excellence.

abstract: The primary focus of this article is on the presentation of research on wisdom conducted under the heading of the Berlin wisdom paradigm. Informed by a cultural-historical analysis of the wisdom concept, wisdom in this paradigm is defined as an expert knowledge system concerning the fundamental pragmatics of life. The fundamental pragmatics of life include knowledge and judgment about the meaning and conduct of life and the orchestration of human development towards excellence while attending conjointly to personal and collective well-being. Empirical measurement operations involve think-aloud protocols concerning various problems of life associated with life planning, life management, and life review. Responses are evaluated with reference to a family of five criteria: rich factual and procedural knowledge, lifespan contextualism, relativism of values and life priorities, and recognition and management of uncertainty. A series of studies is reported that aim at the description, explanation, and optimisation of wisdom. We conclude with a new theoretical perspective of characterizing wisdom as a cognitive and motivational meta-heuristic (pragmatic that organizes orchestrates knowledge toward human excellence in mind virtue, individually collectively.


title: Performance evaluation of color image segmentation using K means clustering and watershed technique

abstract: Image segmentation is a key technology in image processing which partition an image into its constituent regions. Watershed and k means segmentation techniques are practical approaches for color image segmentation. This paper discusses quantitative evaluation measures for color image segmentation based on these techniques. Color image segmentation can be viewed as an extension of gray level image segmentation. Quantitative measures like discrete entropy, root mean square error, visible color difference are proposed for color images.


Unsupervised Approaches to Detecting Anomalous Behavior in the Bitcoin Transaction Network


The Efficacy of Technology Acceptance Model : A Review of Applicable Theoretical Models in Information Technology Researches


title: Visual Sedimentation

abstract: We introduce Visual Sedimentation, a novel design metaphor for visualizing data streams directly inspired by the physical process of sedimentation. Visualizing data streams (e. g., Tweets, RSS, Emails) is challenging as incoming data arrive at unpredictable rates and have to remain readable. For data streams, clearly expressing chronological order while avoiding clutter, and keeping aging data visible, are important. The metaphor is drawn from the real-world sedimentation processes: objects fall due to gravity, and aggregate into strata over time. Inspired by this metaphor, data is visually depicted as falling objects using a force model to land on a surface, aggregating into strata over time. In this paper, we discuss how this metaphor addresses the specific challenge of smoothing the transition between incoming and aging data. We describe the metaphor's design space, a toolkit developed to facilitate its implementation, and example applications to a range of case studies. We then explore the generative capabilities of the design space through our toolkit. We finally illustrate creative extensions of the metaphor when applied to real streams of data.


title: Gaussian Process Regression with Heteroscedastic or Non-Gaussian Residuals

abstract: Gaussian Process (GP) regression models typically assume that residuals are Gaussian and have the same variance for all observations. However, applications with input-dependent noise (heteroscedastic residuals) frequently arise in practice, as do applications in which the residuals do not have a Gaussian distribution. In this paper, we propose a GP Regression model with a latent variable that serves as an additional unobserved covariate for the regression. This model (which we call GPLC) allows for heteroscedasticity since it allows the function to have a changing partial derivative with respect to this unobserved covariate. With a suitable covariance function, our GPLC model can handle (a) Gaussian residuals with input-dependent variance, or (b) non- Gaussian residuals with input-dependent variance, or (c) Gaussian residuals with constant variance. We compare our model, using synthetic datasets, with a model proposed by Goldberg, Williams and Bishop (1998), which we refer to as GPLV, which only deals with case (a), as well as a standard GP model which can handle only case (c). Markov Chain Monte Carlo methods are developed for both modelsl. Experiments show that when the data is heteroscedastic, both GPLC and GPLV give better results (smaller mean squared error and negative log-probability density) than standard GP regression. In addition, when the residual are Gaussian, our GPLC model is generally nearly as good as GPLV, while when the residuals are non-Gaussian, our GPLC model is better than GPLV.


title: Crowdsourced live streaming over the cloud

abstract: Empowered by today's rich tools for media generation and distribution, and the convenient Internet access, crowdsourced streaming generalizes the single-source streaming paradigm by including massive contributors for a video channel. It calls a joint optimization along the path from crowdsourcers, through streaming servers, to the end-users to minimize the overall latency. The dynamics of the video sources, together with the globalized request demands and the high computation demand from each sourcer, make crowdsourced live streaming challenging even with powerful support from modern cloud computing. In this paper, we present a generic framework that facilitates a cost-effective cloud service for crowdsourced live streaming. Through adaptively leasing, the cloud servers can be provisioned in a fine granularity to accommodate geo-distributed video crowdsourcers. We present an optimal solution to deal with service migration among cloud instances of diverse lease prices. It also addresses the location impact to the streaming quality. To understand the performance of the proposed strategies in the realworld, we have built a prototype system running over the planetlab and the Amazon/Microsoft Cloud. Our extensive experiments demonstrate that the effectiveness of our solution in terms of deployment cost and streaming quality.


title: HyperFlex: An SDN virtualization architecture with flexible hypervisor function allocation

abstract: Network Virtualization (NV) and Software-Defined Networking (SDN) are both expected to increase the flexibility and programmability of today's communication networks. Combining both approaches may even be a further step towards increasing the efficiency of network resource utilization. Multiple solutions for virtualizing SDN networks have already been proposed, however, they are either implemented in software or they require special network hardware. We propose HyperFlex, an SDN hypervisor architecture that relies on the decomposition of the hypervisor into functions that are essential for virtualizing SDN networks. The hypervisor functions can be flexibly executed in software or hosted on SDN network elements. Furthermore, existing hypervisor solutions focus on data-plane virtualization mechanisms and neglect the virtualization of the control-plane of SDN networks. HyperFlex provides control-plane virtualization by adding a control-plane isolation function, either in software or on network elements. The isolation function ensures that the resources of the control-plane are shared correctly between each virtual SDN network while it also protects the hypervisor resources from resource exhaustion.


title: Game-based Learning and 21st century skills: A review of recent research

abstract: Game-based learning and 21st century skills have been gaining an enormous amount of attention from researchers and practitioners. Given numerous studies support the positive effects of games on learning, a growing number of researchers are committed to developing educational games to promote students' 21st century skill development in schools. However, little is known regarding how games may influence student acquisition of 21st century skills. This paper examines the most recent literature in regard to game-based learning and identified 29 studies which targeted 21st century skills as outcomes. The range of game genres and game design elements as well as learning theories used in these studies are discussed, together with the range of indicators, measures and outcomes for impacts on 21st century skills. The findings suggest that a game-based learning approach might be effective in facilitating students' 21st century skill development. The paper also provides valuable insights for researchers, game designers, and educators in issues related to educational game design and implementation in general. One hundred and thirty seven papers were included in the review.Twenty nine papers reported the impacts of games on 21st century skills development.Effect sizes were reported for 37% of the empirical findings.A number of learning theories were referenced in the 29 papers.Effective game design elements were identified.


title: Enterprise Architecture as Information Technology Strategy

abstract: Many organizations adopt cyclical processes to articulate and engineer technological responses to their business needs. Their objective is to increase competitive advantage and add value to the organization's processes, services and deliverables, in line with the organization's vision and strategy. The major challenges in achieving these objectives include the rapid changes in the business and technology environments themselves, such as changes to business processes, organizational structure, architectural requirements, technology infrastructure and information needs. No activity or process is permanent in the organization. To achieve their objectives, some organizations have adopted an Enterprise Architecture (EA) approach, others an Information Technology (IT) strategy approach, and yet others have adopted both EA and IT strategy for the same primary objectives. The deployment of EA and IT strategy for the same aims and objectives raises question whether there is conflict in adopting both approaches. The paper and case study presented here, aimed at both academics and practitioners, examines how EA could be employed as IT strategy to address both business and IT needs and challenges.


title: UPnP: An Optimal O(n) Solution to the Absolute Pose Problem with Universal Applicability

abstract: A large number of absolute pose algorithms have been presented in the literature. Common performance criteria are computational complexity, geometric optimality, global optimality, structural degeneracies, and the number of solutions. The ability to handle minimal sets of correspondences, resulting solution multiplicity, and generalized cameras are further desirable properties. This paper presents the first PnP solution that unifies all the above desirable properties within a single algorithm. We compare our result to state-of-the-art minimal, non-minimal, central, and non-central PnP algorithms, and demonstrate universal applicability, competitive noise resilience, and superior computational efficiency. Our algorithm is called Unified PnP (UPnP).


title: Post-processing NPR effects for video games

abstract: This paper describes different interactive, non photorealistic techniques which can be easily applied to video games. Based on a study of art and games, an emphasis is put on considering NPR tools as basic elements to provide different styles and moods, to convey different emotional and experiential representations of a game. We restrict ourselves to screen based effects, which permits any existing game to use our framework with practically no integration cost. This allows us not only to comply with user preferences in rendering style, but also the creation of multiple gaming experiences out of the same game. We show the resulting effects in an in-house videogame and in standard Unity demos, and show how the users can change the style of the videogame by means of a menu.


title: Social Influence and Defaults in Peer-to-Peer Lending Networks

abstract: We assess social influence on borrowers’ default decisions in a peer-to-peer lending market. Our analysis suggests that online borrowers are significantly influenced by defaults in their social networks. A friend’s default decision more than doubles a user’s default rate. We also find that not all friends have equal influences. The social influence is highly significant among online friends made through the peer-to-peer lending site. Social influence is much weaker in magnitude among offline friendships that were carried over to the peer-to-peer lending site.


title: Gossip Algorithms for Distributed Signal Processing

abstract: Gossip algorithms are attractive for in-network processing in sensor networks because they do not require any specialized routing, there is no bottleneck or single point of failure, and they are robust to unreliable wireless network conditions. Recently, there has been a surge of activity in the computer science, control, signal processing, and information theory communities, developing faster and more robust gossip algorithms and deriving theoretical performance guarantees. This paper presents an overview of recent work in the area. We describe convergence rate results, which are related to the number of transmitted messages and thus the amount of energy consumed in the network for gossiping. We discuss issues related to gossiping over wireless links, including the effects of quantization and noise, and we illustrate the use of gossip algorithms for canonical signal processing tasks including distributed estimation, source localization, and compression.


title: MODIFIED APPROACH OF HOUGH TRANSFORM FOR SKEW DETECTION AND CORRECTION IN DOCUMENTED IMAGES

abstract: In optical character recognition and document image analysis skew is introduced in coming documented image. Which degrade the performance of OCR and image analysis system so to detection and correction of skew angle is important step of preprocessing of document analysis. Many methods have been proposed by researchers for the detection of skew in binary image documents. The majority of them are based on Projection profile, Fourier transform, and cross-correlation, Hough transform, Nearest Neighbor connectivity, linear regression analysis and mathematical morphology. Main advantage of Hough transform is its accuracy and simplicity. But due to slow speed many researchers work on its speed complexity without compromising the accuracy. So, for improving computational efficiency of Hough transform there are various variations have been proposed by researchers to reduce the computational time for skew angle. In this Paper we introduced new method which reduces the time complexity without compromising the accuracy of Hough transform..


title: Interaction-Rich Transfer Learning for Collaborative Filtering with Heterogeneous User Feedback

abstract: A real recommender system can usually make use of more than one type of user feedback--for example, numerical ratings and binary ratings-to learn a user's true preferences. Recent work has proposed a transfer learning algorithm called transfer by collective factorization (TCF) to exploit such heterogeneous user feedback. TCF performs via sharing data-independent knowledge and modeling data-dependent effects simultaneously. However, TCF is a batch algorithm and updates the model parameters only once after scanning all data, which might not be efficient enough for real systems. This article proposes a novel and efficient transfer learning algorithm called interaction-rich transfer by collective factorization (iTCF), which extends the efficient collective matrix factorization (CMF) algorithm by providing more interactions between the user-specific latent features. The assumption under iTCF is that the predictability with regards to the same user's rating behaviors in two related data is likely to be similar. Considering the shared predictability, the authors derive novel update rules for iTCF in a stochastic algorithmic framework. The advantages of iTCF include its efficiency compared with TCF, and its higher prediction accuracy compared with CMF. Experimental results on three real-world datasets show the effectiveness of iTCF over the state-of-the-art methods.


A Tutorial on Bayesian Networks for System Health Management


title: Toward sensitive document release with privacy guarantees

abstract: Privacy has become a serious concern for modern Information Societies. The sensitive nature of much of the data that are daily exchanged or released to untrusted parties requires that responsible organizations undertake appropriate privacy protection measures. Nowadays, much of these data are texts (e.g., emails, messages posted in social media, healthcare outcomes, etc.) that, because of their unstructured and semantic nature, constitute a challenge for automatic data protection methods. In fact, textual documents are usually protected manually, in a process known as document redaction or sanitization. To do so, human experts identify sensitive terms (i.e., terms that may reveal identities and/or confidential information) and protect them accordingly (e.g., via removal or, preferably, generalization). To relieve experts from this burdensome task, in a previous work we introduced the theoretical basis of C-sanitization, an inherently semantic privacy model that provides the basis to the development of automatic document redaction/sanitization algorithms and offers clear and a priori privacy guarantees on data protection; even though its potential benefits C-sanitization still presents some limitations when applied to practice (mainly regarding flexibility, efficiency and accuracy). In this paper, we propose a new more flexible model, named (C, g(C))-sanitization, which enables an intuitive configuration of the trade-off between the desired level of protection (i.e., controlled information disclosure) and the preservation of the utility of the protected data (i.e., amount of semantics to be preserved). Moreover, we also present a set of technical solutions and algorithms that provide an efficient and scalable implementation of the model and improve its practical accuracy, as we also illustrate through empirical experiments. Theory and implementation of (C,g(C))-sanitization, a semantic document sanitization privacy model.An intuitive mechanism to configure the trade-off between privacy and utility.An heuristic and scalable algorithm implementing (C,g(C))-sanitization.An accurate assessment of disclosure risks based on the Web's information distribution.


title: Document image analysis with OCRopus

abstract: Document image analysis is the field of converting paper documents into an editable electronic representation by performing optical character recognition (OCR). In recent years, there has been a tremendous amount of progress in the development of open source OCR systems. OCRopus is one of the leading open source document analysis system with a modular and pluggable architecture. This paper presents an overview of different steps involved in a document image analysis system and illustrates them with examples from OCRopus.


title: A hybrid algorithm for synchronous generator parameter estimation

abstract: This paper presents a hybrid algorithm for parameter estimation of synchronous generator. For large-residual problems (i.e., f(x) is large or f(x) is severely nonlinear), the performance of the Gauss-Newton method and Levenberg-Marquardt method is usually poor, and the slow convergence even causes iteration emergence divergence. The Quasi-Newton method can superlinearly converge, but it is not robust in the global stage of the iteration. Hybrid algorithm combining the two methods above is proved globally convergent with a high convergence speed through the example of synchronous generator parameter identification.


title: Supervised Anomaly Detection in Uncertain Pseudoperiodic Data Streams

abstract: Uncertain data streams have been widely generated in many Web applications. The uncertainty in data streams makes anomaly detection from sensor data streams far more challenging. In this paper, we present a novel framework that supports anomaly detection in uncertain data streams. The proposed framework adopts an efficient uncertainty pre-processing procedure to identify and eliminate uncertainties in data streams. Based on the corrected data streams, we develop effective period pattern recognition and feature extraction techniques to improve the computational efficiency. We use classification methods for anomaly detection in the corrected data stream. We also empirically show that the proposed approach shows a high accuracy of anomaly detection on a number of real datasets.


title: Relation Between Language Experiences in Preschool Classrooms and Children’s Kindergarten and Fourth-Grade Language and Reading Abilities

abstract: Indirect effects of preschool classroom indexes of teacher talk were tested on fourth-grade outcomes for 57 students from low-income families in a longitudinal study of classroom and home influences on reading. Detailed observations and audiotaped teacher and child language data were coded to measure content and quantity of verbal interactions in preschool classrooms. Preschool teachers’ use of sophisticated vocabulary during free play predicted fourth-grade reading comprehension and word recognition (mean age = 9; 7), with effects mediated by kindergarten child language measures (mean age = 5; 6). In large group preschool settings, teachers’ attention-getting utterances were directly related to later comprehension. Preschool teachers’ correcting utterances and analytic talk about books, and early support in the home for literacy predicted fourth-grade vocabulary, as mediated by kindergarten receptive vocabulary.


The Metropolis-Hastings algorithm by example


title: A brain in a vat cannot break out: why the singularity must be extended, embedded and embodied

abstract: The present paper criticizes Chalmers's discussion of the Singularity, viewed as the emergence of a superhuman intelligence via the self-amplifying development of artificial intelligence. The situated and embodied view of cognition rejects the notion that intelligence could arise in a closed "brain-in-a-vat" system, because intelligence is rooted in a high-bandwidth, sensory-motor interaction with the outside world. Instead, it is proposed that superhuman intelligence can emerge only in a distributed fashion, in the form of a self-organizing network of humans, computers, and other technologies: the "Global Brain".


title: Robust photometric alignment for asymmetric camera system

abstract: Multiple image sensors have now become the defacto offering from all major mobile phone manufacturers. Their applications vary from improving the image quality to providing new features to the end-users. One of these multi-sensor system is a tele-wide asymmetric camera configuration which is aimed at providing better zoom experience with more crisper images. The system is basically a combination of tele-photo lens which captures images at a higher optical zoom with narrow field of view and a wide-view lens which captures a wider field of view. Most of the leading OEMs provide only a smooth transition between the two lens systems while zooming into a scene since fusing the information from the two lens at all zoom ratios has some significant challenges. One such major challenge is accurate photometric alignment which is required to compensate the difference in auto exposure(AE), auto white balance(AWB), and the inherent differences in terms of both intrinsic and extrinsic parameters of the two camera sub-systems. Conventional methods in literature for photometric alignment make several assumptions and are also not very adaptable to different lighting conditions. To the best of our knowledge, accurate photometric alignment intended towards fusion of images from asymmetric tele-wide cameras that works in unconstrained scenarios and at all zoom ratios, is not very well studied. In this paper, we propose a computationally efficient algorithm for brightness and color correction that is suitable for fusion of images from multiple asymmetric cameras. The proposed method is evaluated quantitatively using the RMSE metric and qualitatively as well. Further the evaluation is carried out for several scenes captured under different lighting conditions, undergoing camera and object motion and with different focus and exposure values without calibration of inputs. Experimental results show that the proposed method is superior to the existing methods for photometric alignment. It is well suited as a robust image pre-conditioning step towards image-fusion and superresolution applications.


title: End-to-end deep learning for autonomous navigation of mobile robot

abstract: This paper proposes an end-to-end method for training convolutional neural networks for autonomous navigation of a mobile robot. Traditional approach for robot navigation consists of three steps. The first step is extracting visual features from the scene using the camera input. The second step is to figure out the current position by using a classifier on the extracted visual features. The last step is making a rule for moving the direction manually or training a model to handle the direction. In contrast to the traditional multi-step method, the proposed visuo-motor navigation system can directly output the linear and angular velocities of the robot from an input image in a single step. The trained model gives wheel velocities for navigation as outputs in real-time making it possible to be implanted on mobile robots such as robotic vacuum cleaner. The experimental results show an average linear velocity error of 2.2 cm/s and average angular velocity error of 3.03 degree/s. The robot deployed with the proposed model can navigate in a real-world environment by only using the camera without relying on any other sensors such as LiDAR, Radar, IR, GPS, IMU.


title: The Dependent Variable in Social Media Use

abstract: What is the dependent variable in social media use? From a research perspective, this is a pertinent question to help explain and understand the behaviors that underpin the widespread adoption and use of social media throughout society. From a practical perspective, the question is relevant for social media technology providers, for businesses that use social media, and community organizations that turn towards social media to reach out to their constituents. We propose the construct ‘sense of community’ as the dependent variable, which is reflected in four sub-constructs related to the behaviors of social media users. These behaviors are information seeking, hedonic activities, sustaining of strong ties and extending weak ties. Empirical evidence for these constructs comes from a survey of social media use by 18-25 year-olds in Indonesia, a country with exceptionally high utilization of social media. We outline practical implications of the findings and areas for further theoretical development.


title: SMS-based one-time passwords: attacks and defense

abstract: SMS-based One-Time Passwords (SMS OTP) were introduced to counter phishing and other attacks against Internet services such as online banking. Today, SMS OTPs are commonly used for authentication and authorization for many different applications. Recently, SMS OTPs have come under heavy attack, especially by smartphone Trojans. In this paper, we analyze the security architecture of SMS OTP systems and study attacks that pose a threat to Internet-based authentication and authorization services. We determined that the two foundations SMS OTP is built on, cellular networks and mobile handsets, were completely different at the time when SMS OTP was designed and introduced. Throughout this work, we show why SMS OTP systems cannot be considered secure anymore. Based on our findings, we propose mechanisms to secure SMS OTPs against common attacks and specifically against smartphone Trojans.


title: Flipped Classrooms: a Review of Key Ideas and Recommendations for Practice

abstract: Flipped classrooms refer to the practice of assigning lectures outside of class and devoting class time to a variety of learning activities. In this review, we discuss the range of approaches to the flipped classroom and focus on activities frequently used in these settings. Amongst these, we examine both out-of-class activities (e.g., video lectures) and in-class activities (e.g., quizzes, student discussions). We argue that the value of these activities reflects the particular cognitive processes engaged by the activity regardless of whether the setting is the traditional (lecture-based) classroom or the flipped classroom. Future work should continue to examine the influence of individual activities on student learning and behaviors, particularly when objective measures of learning, such as quizzes and exams, are held constant.


title: Self-compassion, body image, and disordered eating: A review of the literature.

abstract: Self-compassion, treating oneself as a loved friend might, demonstrates beneficial associations with body image and eating behaviors. In this systematic review, 28 studies supporting the role of self-compassion as a protective factor against poor body image and eating pathology are reviewed. Findings across various study designs consistently linked self-compassion to lower levels of eating pathology, and self-compassion was implicated as a protective factor against poor body image and eating pathology, with a few exceptions. These findings offer preliminary support that self-compassion may protect against eating pathology by: (a) decreasing eating disorder-related outcomes directly; (b) preventing initial occurrence of a risk factor of a maladaptive outcome; (c) interacting with risk factors to interrupt their deleterious effects; and (d) disrupting the mediational chain through which risk factors operate. We conclude with suggestions for future research that may inform intervention development, including the utilization of research designs that better afford causal inference.


title: Offset compensation in comparators with minimum input-referred supply noise

abstract: The resolution of a comparator is determined by the dc input offset and the ac noise. For mixed-mode applications with significant digital switching, input-referred supply noise can be a significant source of error. This paper proposes an offset compensation technique that can simultaneously minimize input-referred supply noise. Demonstrated with digital offset compensation, this scheme reduces input-referred supply noise to a small fraction (13%) of one least significant bit (LSB) digital offset. In addition, the same analysis can be applied to analog offset compensation.


title: Verifying Continuous Time Markov Chains

abstract: We present a logical formalism for expressing properties of continuous time Markov chains. The semantics for such properties arise as a natural extension of previous work on discrete time Markov chains to continuous time. The major result is that the verification problem is decidable; this is shown using results in algebraic and transcendental number theory.


title: Order-Optimal Rate of Caching and Coded Multicasting With Random Demands

abstract: We consider the canonical {\em shared link network} formed by a source node, hosting a library of $m$ information messages (files), connected via a noiseless common link to $n$ destination nodes (users), each with a cache of size M files. Users request files at random and independently, according to a given a-priori demand distribution $\qv$. A coding scheme for this network consists of a caching placement (i.e., a mapping of the library files into the user caches) and delivery scheme (i.e., a mapping for the library files and user demands into a common multicast codeword) such that, after the codeword transmission, all users can retrieve their requested file. The rate of the scheme is defined as the {\em average} codeword length normalized with respect to the length of one file, where expectation is taken over the random user demands. For the same shared link network, in the case of deterministic demands, the optimal min-max rate has been characterized within a uniform bound, independent of the network parameters. In particular, fractional caching (i.e., storing file segments) and using linear network coding has been shown to provide a min-max rate reduction proportional to 1/M with respect to standard schemes such as unicasting or "naive" uncoded multicasting. The case of random demands was previously considered by applying the same order-optimal min-max scheme separately within groups of files requested with similar probability. However, no order-optimal guarantee was provided for random demands under the average rate performance criterion. In this paper, we consider the random demand setting and provide general achievability and converse results. In particular, we consider a family of schemes that combine random fractional caching according to a probability distribution $\pv$ that depends on the demand distribution $\qv$, with a linear coded delivery scheme based on ...


title: Deep learning with domain adaptation for accelerated projection‐reconstruction MR

abstract: Purpose: The radial k-space trajectory is a well-established sampling trajectory used in conjunction with magnetic resonance imaging. However, the radial k-space trajectory requires a large number of radial lines for high-resolution reconstruction. Increasing the number of radial lines causes longer acquisition time, making it more difficult for routine clinical use. On the other hand, if we reduce the number of radial lines, streaking artifact patterns are unavoidable. To solve this problem, we propose a novel deep learning approach with domain adaptation to restore high-resolution MR images from under-sampled k-space data. ::: Methods: The proposed deep network removes the streaking artifacts from the artifact corrupted images. To address the situation given the limited available data, we propose a domain adaptation scheme that employs a pre-trained network using a large number of x-ray computed tomography (CT) or synthesized radial MR datasets, which is then fine-tuned with only a few radial MR datasets. ::: Results: The proposed method outperforms existing compressed sensing algorithms, such as the total variation and PR-FOCUSS methods. In addition, the calculation time is several orders of magnitude faster than the total variation and PR-FOCUSS methods.Moreover, we found that pre-training using CT or MR data from similar organ data is more important than pre-training using data from the same modality for different organ. ::: Conclusion: We demonstrate the possibility of a domain-adaptation when only a limited amount of MR data is available. The proposed method surpasses the existing compressed sensing algorithms in terms of the image quality and computation time.


title: Eye Tracking Research and Technology: Towards Objective Measurement of Data Quality

abstract: Two methods for objectively measuring eye tracking data quality are explored. The first method works by tricking the eye tracker to detect an abrupt change in the gaze position of an artificial eye that in actuality does not move. Such a device, referred to as an artificial saccade generator, is shown to be extremely useful for measuring the temporal accuracy and precision of eye tracking systems and for validating the latency to display change in gaze contingent display paradigms. The second method involves an artificial pupil that is mounted on a computer controlled moving platform. This device is designed to be able to provide the eye tracker with motion sequences that closely resemble biological eye movements. The main advantage of using artificial motion for testing eye tracking data quality is the fact that the spatiotemporal signal is fully specified in a manner independent of the eye tracker that is being evaluated and that nearly identical motion sequence can be reproduced multiple times with great precision. The results of the present study demonstrate that the equipment described has the potential to become an important tool in the comprehensive evaluation of data quality.


title: A modular software architecture for UAVs

abstract: There have been several attempts to create scalable and hardware independent software architectures for Unmanned Aerial Vehicles (UAV). In this work, we propose an onboard architecture for UAVs where hardware abstraction, data storage and communication between modules are efficiently maintained. All processing and software development is done on the UAV while state and mission status of the UAV is monitored from a ground station. The architecture also allows rapid development of mission-specific third party applications on the vehicle with the help of the core module.


title: Large-scale brain networks in affective and social neuroscience: towards an integrative functional architecture of the brain

abstract: Understanding how a human brain creates a human mind ultimately depends on mapping psychological categories and concepts to physical measurements of neural response. Although it has long been assumed that emotional, social, and cognitive phenomena are realized in the operations of separate brain regions or brain networks, we demonstrate that it is possible to understand the body of neuroimaging evidence using a framework that relies on domain general, distributed structure–function mappings. We review current research in affective and social neuroscience and argue that the emerging science of large-scale intrinsic brain networks provides a coherent framework for a domain-general functional architecture of the human brain.


title: An informatics perspective on computational thinking

abstract: In this paper, we examine computational thinking and its connections to critical thinking from the perspective of in- formatics. We developed an introductory course for students in our College of Informatics, which includes majors rang- ing from journalism to computer science. The course cov- ered a set of principles of informatics, using both lectures and active learning sessions designed to develop informat- ics and computational thinking skills. The set of principles was drawn from a wide set of sources, and included broad principles like those of Denning and Loidl, as well as more limited principles related to topics like universal computa- tion and undecidability. We evaluated the change in both computational and critical thinking skills over the course of the semester, using a well-known validated critical thinking test and a computational thinking test of our own devising.


title: Optimal Power Control for Energy Efficient D2D Communication and Its Distributed Implementation

abstract: In this letter, we propose an energy efficient power control scheme for resource sharing between cellular and device-to-device (D2D) users in cellular network assisted D2D communication. We take into account the circuit power consumption of the device-to-device user (DU) and aim at maximizing the DU's energy efficiency while guaranteeing the required throughputs of both the DU and the cellular user. Specifically, we define three different regions for the circuit power consumption of the DU and derive the optimal power control scheme for each region. Moreover, a distributed algorithm is proposed for implementation of the optimal power control scheme.


title: Sensorless position estimation in solenoid actuators with load compensation

abstract: This paper investigates the effects of varying external load on sensorless plunger position estimation regarding solenoid actuators. We propose an improved technique capable of compensating these forces applied to the solenoid. Major advantage of the method is that both plunger position and external load can be simultaneously estimated, thus further improving robustness and extending the possible fields of application of such devices. Experimental results are also presented in support of the usability of the method.


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title: An Accurate Bandgap-Based Power-on-Detector in 14-nm CMOS Technology

abstract: A novel power-on-detector (POD) circuit is reported in 14-nm technology. This POD has a sigma accuracy of 12 mV, including process, voltage, and temperature variations with a power of 496 $\mu\mbox{W}$ and an area of 0.02 mm2. The POD uses an open-loop offset-cancelled bandgap-based circuit to achieve this high accuracy. It is also capable of detecting two operating voltages simultaneously and can measure the digital VCC power-up at supply levels lower than the diode voltage.


title: Simple and Effective Approach for Consistent Training of Hierarchical Phrase-based Translation Models

abstract: In this paper, we present a simple approach for consistent training of hierarchical phrase-based translation models. In order to consistently train a translation model, we perform hierarchical phrasebased decoding on training data to find derivations between the source and target sentences. This is done by synchronous parsing the given sentence pairs. After extracting k-best derivations, we reestimate the translation model probabilities based on collected rule counts. We show the effectiveness of our procedure on the IWSLT German!English and English!French translation tasks. Our results show improvements of up to 1.6 points BLEU.


title: RoboticPhysicalExercise andSystem (ROPES): A Cable-Driven Robotic Rehabilitation System for Lower-Extremity Motor Therapy

abstract: Assisted motor therapies play a critical role in enhancing the functional musculoskeletal recovery and neurological rehabilitation. Our focus here is to assist the performance of repetitive motor-therapy of the human lower limbs ‐ in both the sagittal and frontal planes. Hence, in this paper, we develop a lightweight, reconfigurable hybrid (articulated-multibody and cable) based robotic rehabilitative device as a surrogate for a human physiotherapists and analyze feasibility and performance. A hybrid cable-actuated articulated multibody system is formed when multiple cables are attached from a ground-frame to various locations on the lower limbs. The combined system now features multiple holonomic cable-loop-closure constraints acting on a tree-structured multibody system. Hence the paper initially focuses on developing the Newton-Euler dynamic equilibrium equations of the cable-driven lower limbs to develop a symbolic analysis framework. The desired motion for the proposed rehabilitative exercise are prescribed based upon normative subjects motion patterns. Trajectory-tracking within this system is realized by a position-based impedance controller in task-space and a feedback-linearized PD controllers in joint-space. Careful coordination of the multiple cable-motors are now necessary in order to achieve the co-robotic control of the overall system, avoiding development of internal stresses and ensuring continued satisfaction of the unilateral cable-tension constraints throughout the workspace. This is now evaluated via a simulation case-study and development of a physical testbed is underway.


title: Reliable Beamspace Channel Estimation for Millimeter-Wave Massive MIMO Systems with Lens Antenna Array

abstract: By employing the lens antenna array, beamspace MIMO can utilize beam selection to reduce the number of required RF chains in mmWave massive MIMO systems without obvious performance loss. However, to achieve the capacityapproaching performance, beam selection requires the accurate information of beamspace channel of large size, which is challenging, especially when the number of RF chains is limited. To solve this problem, in this paper we propose a reliable support detection (SD)-based channel estimation scheme. Specifically, we propose to decompose the total beamspace channel estimation problem into a series of sub-problems, each of which only considers one sparse channel component. For each channel component, we first reliably detect its support by utilizing the structural characteristics of mmWave beamspace channel. Then, the influence of this channel component is removed from the total beamspace channel estimation problem. After the supports of all channel components have been detected, the nonzero elements of the sparse beamspace channel can be estimated with low pilot overhead. Simulation results show that the proposed SD-based channel estimation outperforms conventional schemes and enjoys satisfying accuracy, even in the low SNR region.


title: Testing Programs with the Aid of a Compiler

abstract: if finite input-output specifications are added to the syntax of programs, these specifications can be verified at compile time. Programs which carry adequate tests with them in this way should be resistant to maintenance errors. If the specifications are independent of program details they are easy to give, and unlikely to contain errors in common with the program. Furthermore, certain finite specifications are maximal in that they exercise the control and expression structure of a program as well as any tests can.


Survey on Opinion Mining and Summarization of User Reviews on Web


title: The Effect of Whole Body Vibration on Torque of Evertor and Invertor Muscles of Ankle in Low Arched Feet

abstract: Background and purpose: Medial longitudinal arch (MLA) posture of the foot is usually associated with increased risk of lower extremity injury. This study investigated the effect of Whole Body Vibration (WBV) on torque of evertor and invertor muscles of ankle in low arched foot. Materials and methods: The participants were 40 young, untrained adults (male and female) with flat foot. The subjects were randomly divided into a vibration group (n=20m, mean age=23.85±3.15, mean weight=70.00±12.94, mean BMI=24.85±3.69 and a control group (n=20m, mean age=22.8±1.73, mean weight=72.14±12.53, mean BMI=24.59±3.99). Eversion and inversion isokinetic torque of ankle were measured at speeds of 30/s and 120/s before and after 6 weeks of WBV in both groups and 4 weeks after the end of last training session in intervention group. We also measured the maximum changes in concentric torque of invertor and evertor muscles of ankle. Results: T-test and repeated ANOVA revealed significant increase in concentric torque of evertor muscles of ankle in 30/s and 120/s in interventional group compared with that of the control group (P 0.05). Conclusion: This study suggests that WBV training could enhance ankle evertor muscles strength in flat foot.


title: Character Recognition in Natural Scenes Using Convolutional Co-occurrence HOG

abstract: Recognition of characters in natural images is a challenging task due to the complex background, variations of text size and perspective distortion, etc. Traditional optical character recognition (OCR) engine cannot perform well on those unconstrained text images. A novel technique is proposed in this paper that makes use of convolutional co occurrence histogram of oriented gradient (ConvCoHOG), which is more robust and discriminative than both the histogram of oriented gradient (HOG) and the co-occurrence histogram of oriented gradients (CoHOG). In the proposed technique, a more informative feature is constructed by exhaustively extracting features from every possible image patches within character images. Experiments on two public datasets including the ICDAr 2003 Robust Reading character dataset and the Street View Text (SVT) dataset, show that our proposed character recognition technique obtains superior performance compared with state-of-the-art techniques.


title: Multi-Agent Systems for Power Engineering Applications—Part II: Technologies, Standards, and Tools for Building Multi-agent Systems

abstract: This is the second part of a two-part paper that has arisen from the work of the IEEE Power Engineering Society's Multi-Agent Systems (MAS) Working Group. Part I of this paper examined the potential value of MAS technology to the power industry, described fundamental concepts and approaches within the field of multi-agent systems that are appropriate to power engineering applications, and presented a comprehensive review of the power engineering applications for which MAS are being investigated. It also defined the technical issues which must be addressed in order to accelerate and facilitate the uptake of the technology within the power and energy sector. Part II of this paper explores the decisions inherent in engineering multi-agent systems for applications in the power and energy sector and offers guidance and recommendations on how MAS can be designed and implemented. Given the significant and growing interest in this field, it is imperative that the power engineering community considers the standards, tools, supporting technologies, and design methodologies available to those wishing to implement a MAS solution for a power engineering problem. This paper describes the various options available and makes recommendations on best practice. It also describes the problem of interoperability between different multi-agent systems and proposes how this may be tackled.


title: Touch gestures in communicating emotional intention via vibrotactile stimulation

abstract: Remote communication between people typically relies on audio and vision although current mobile devices are increasingly based on detecting different touch gestures such as swiping. These gestures could be adapted to interpersonal communication by using tactile technology capable of producing touch stimulation to a user's hand. It has been suggested that such mediated social touch would allow for new forms of emotional communication. The aim was to study whether vibrotactile stimulation that imitates human touch can convey intended emotions from one person to another. For this purpose, devices were used that converted touch gestures of squeeze and finger touch to vibrotactile stimulation. When one user squeezed his device or touched it with finger(s), another user felt corresponding vibrotactile stimulation on her device via four vibrating actuators. In an experiment, participant dyads comprising a sender and receiver were to communicate variations in the affective dimensions of valence and arousal using the devices. The sender's task was to create stimulation that would convey unpleasant, pleasant, relaxed, or aroused emotional intention to the receiver. Both the sender and receiver rated the stimulation using scales for valence and arousal so that the match between sender's intended emotions and receiver's interpretations could be measured. The results showed that squeeze was better at communicating unpleasant and aroused emotional intention, while finger touch was better at communicating pleasant and relaxed emotional intention. The results can be used in developing technology that enables people to communicate via touch by choosing touch gesture that matches the desired emotion.


title: Reading checks with multilayer graph transformer networks

abstract: We propose a new machine learning paradigm called multilayer graph transformer network that extends the applicability of gradient-based learning algorithms to systems composed of modules that take graphs as input and produce graphs as output. A complete check reading system based on this concept is described. The system combines convolutional neural network character recognizers with graph-based stochastic models trained cooperatively at the document level. It is deployed commercially and reads million of business and personal checks per month with record accuracy.


title: Recurrent Additive Networks

abstract: We introduce recurrent additive networks (RANs), a new gated RNN which is distinguished by the use of purely additive latent state updates. At every time step, the new state is computed as a gated component-wise sum of the input and the previous state, without any of the non-linearities commonly used in RNN transition dynamics. We formally show that RAN states are weighted sums of the input vectors, and that the gates only contribute to computing the weights of these sums. Despite this relatively simple functional form, experiments demonstrate that RANs perform on par with LSTMs on benchmark language modeling problems. This result shows that many of the non-linear computations in LSTMs and related networks are not essential, at least for the problems we consider, and suggests that the gates are doing more of the computational work than previously understood.