title: Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution

abstract: Convolutional neural networks have recently demonstrated high-quality reconstruction for single-image super-resolution. In this paper, we propose the Laplacian Pyramid Super-Resolution Network (LapSRN) to progressively reconstruct the sub-band residuals of high-resolution images. At each pyramid level, our model takes coarse-resolution feature maps as input, predicts the high-frequency residuals, and uses transposed convolutions for upsampling to the finer level. Our method does not require the bicubic interpolation as the pre-processing step and thus dramatically reduces the computational complexity. We train the proposed LapSRN with deep supervision using a robust Charbonnier loss function and achieve high-quality reconstruction. Furthermore, our network generates multi-scale predictions in one feed-forward pass through the progressive reconstruction, thereby facilitates resource-aware applications. Extensive quantitative and qualitative evaluations on benchmark datasets show that the proposed algorithm performs favorably against the state-of-the-art methods in terms of speed and accuracy.


title: A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification

abstract: Band selection for remotely sensed image data is an effective means to mitigate the curse of dimensionality. Many criteria have been suggested in the past for optimal band selection. In this paper, a joint band-prioritization and band-decorrelation approach to band selection is considered for hyperspectral image classification. The proposed band prioritization is a method based on the eigen (spectral) decomposition of a matrix from which a loading-factors matrix can be constructed for band prioritization via the corresponding eigenvalues and eigenvectors. Two approaches are presented, principal components analysis (PCA)-based criteria and classification-based criteria. The former includes the maximum-variance PCA and maximum SNR PCA, whereas the latter derives the minimum misclassification canonical analysis (MMCA) (i.e., Fisher's discriminant analysis) and subspace projection-based criteria. Since the band prioritization does not take spectral correlation into account, an information-theoretic criterion called divergence is used for band decorrelation. Finally, the band selection can then be done by an eigenanalysis based band prioritization in conjunction with a divergence-based band decorrelation. It is shown that the proposed band-selection method effectively eliminates a great number of insignificant bands. Surprisingly, the experiments show that with a proper band selection, less than 0.1 of the total number of bands can achieve comparable performance using the number of full bands. This further demonstrates that the band selection can significantly reduce data volume so as to achieve data compression.


title: Small circularly polarized patch antenna

abstract: This paper presents a novel size reduced circularly polarized (CP) patch antenna by introducing a substrate integrated metallic wall structure to provide capacitive and inductive loading to the patch. Pairs of metallic walls are formed by conducting vias and assigned at four corners underneath the patch. To generate circular polarization, a square patch with unbalanced tails is applied. The antenna works at 3.24GHz. With the presents of the proposed metallic wall structure, the antenna has a considerable size reduction by the capacitive and inductive loading effect. Comparing with a traditional half-wavelength square patch antenna, the proposed CP antenna has achieved 86% size reduction experimentally.


title: Design of Wideband, FSS-Based MultiBeam Antennas Using the Effective Medium Approach

abstract: We present the design, simulation, and measurement results of a broadband, low-profile, multibeam antenna. The antenna uses multiple feed elements placed on the focal plane of a planar microwave lens to achieve high-gain, multibeam operation with a wide field of view. The lens is based on a recently reported design employing the constituting unit cells of appropriately designed miniaturized-element frequency selective surfaces (MEFSSs) as its spatial time-delay units. A new technique for modeling such lenses is also presented that greatly simplifies the full-wave electromagnetic simulation of MEFSS-based lenses. This technique is based on treating the pixels of the lens as effective media with the same effective permittivity and permeability and significantly reduces the difficulty of modeling and optimizing the proposed multibeam antenna with its relatively large aperture size in a full-wave electromagnetic simulation tool. Using this procedure, a prototype multibeam antenna operating in the 8-10 GHz range is designed. The prototype is fabricated and characterized using a multiprobe, spherical near field system. The measurement results are in good agreement with the simulation results obtained using the proposed simplified modeling technique. Measurements demonstrate consistent radiation characteristics over the antenna's entire operational band with multiple beams in a field of view of ±45°.


title: Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies

abstract: Identifying causal mechanisms is a fundamental goal of social science. Researchers seek to study not only whether one variable affects another but also how such a causal relationship arises. Yet commonly used statistical methods for identifying causal mechanisms rely upon untestable assumptions and are often inappropriate even under those assumptions. Randomizing treatment and intermediate variables is also insufficient. Despite these difficulties, the study of causal mechanisms is too important to abandon. We make three contributions to improve research on causal mechanisms. First, we present a minimum set of assumptions required under standard designs of experimental and observational studies and develop a general algorithm for estimating causal mediation effects. Second, we provide a method for assessing the sensitivity of conclusions to potential violations of a key assumption. Third, we offer alternative research designs for identifying causal mechanisms under weaker assumptions. The proposed approach is illustrated using media framing experiments and incumbency advantage studies.


Implementation of LSB Steganography and its Evaluation for Various File Formats


This paper was presented at a colloquium entitled ‘ ‘ Memory : Recording Experience in Cells and Circuits , ’ ’ organized by


title: Active Balancing of Li-Ion Battery Cells Using Transformer as Energy Carrier

abstract: A circuit for balancing Li-ion battery cells is proposed. This circuit requires one small transformer and N + 3 bilateral switches to equalize the charging states of N serially connected battery cells. The transformer works as an energy carrier, and the switches select two unbalanced cells that require an energy transfer from one to the other cell. The circuit was tested for a 12-cell Li-ion battery under static, cyclic, and dynamic charging/discharging conditions. Under static condition, the power-transfer efficiency was measured as 80.4% at a balancing power of 0.78 W; two 4400-mA·h battery cells at a state of charge $(\text{SOC}) = 70$ and 80% were equalized after 78 min. The results of cyclic and dynamic charging/discharging conditions show that the circuit is appropriate for balancing the Li-ion battery cells for vehicles and energy storage systems.


title: Epithelial solute permeability, ion transport and tight junction morphology in the developing lung of the fetal lamb.

abstract: 1. Experiments were performed on exteriorized fetal lambs of between 69 days' gestation and term (147 days) in order to observe changes in lung volume and lung liquid secretion rate, and to delineate any alterations in solute permeability, ion transport and tight junction morphology in the maturing lung epithelium. Whilst it was technically possible to measure solute permeability as early as 69 days it was not feasible to apply the Ussing flux ratio technique before 84 days.2. Fetal lung liquid volume and secretion rate, when normalized for body weight, increase linearly with gestation, whereas tracheal volume expressed in the same manner remains constant.3. When expressed in terms of pore theory, epithelial permeability to small polar non-electrolytes does not change between 69 days and term (equivalent pore radius 0.66 nm and 0.64 nm respectively).4. In the immature fetus of 69-76 days, mean epithelial tight junction strand number is 8.3, whereas by the end of gestation it has fallen to 4.6.5. The transfer constants (min(-1)) for sodium and chloride movement in the direction lung liquid to plasma are, respectively, some 6 and 4 times greater at 84-87 days than at term.6. As in the mature fetus, the lung epithelium at 84-87 days actively transports chloride from plasma to lung lumen, albeit with a slightly reduced transport e.m.f. Sodium movement does not, at any gestational age, differ from the predictions for passive transfer.7. In lung liquid the concentrations of chloride and potassium increase and that of bicarbonate decreases during gestation, whilst that of sodium does not change. The rises in lung liquid chloride and potassium concentrations follow those in plasma, maintaining plasma/lung liquid ratios of 0.7 and 0.95 respectively. However, plasma bicarbonate remains constant and the plasma/lung liquid ratio for bicarbonate rises from 3 at 69-76 days to 20 near term as the lung liquid bicarbonate falls from 9.8 to under 2 m-mole kg(-1) H(2)O.8. Whereas lung liquid protein concentration remains constant and low at about 0.35 g l.(-1), plasma protein concentration rises from 23 g l.(-1) at 69-76 days to 43 g l.(-1) near term. During the same period arterial blood pressure doubles.


title: Wind Noise Reduction using Non-Negative Sparse Coding

abstract: We introduce a new speaker independent method for reducing wind noise in single-channel recordings of noisy speech. The method is based on non-negative sparse coding and relies on a wind noise dictionary which is estimated from an isolated noise recording. We estimate the parameters of the model and discuss their sensitivity. We then compare the algorithm with the classical spectral subtraction method and the Qualcomm-ICSI-OGI noise reduction method. We optimize the sound quality in terms of signal-to-noise ratio and provide results on a noisy speech recognition task.


title: A novel coaxial probe waveguide to microstrip transition

abstract: In this paper, a modified coaxial probe waveguide-to-microstrip transition structure at K-band is proposed. The transition uses two kinds of coaxial probes and has hermetic characteristics. It can also be used in other band, such as Ka-band. The structure is demonstrated for designing the transition. HFSS simulation and measurement results for the transition structure have been compared in terms of |S/sub 21/|. The comparison shows that the measurement agrees well with the simulation result. The measurement has shown 0.5 dB insertion loss.


title: Metastatic niche functions and therapeutic opportunities

abstract: Metastasis is an inefficient process, especially during colonization at a distant organ. This bottleneck underlies the importance of the metastatic niche for seeding and outgrowth of metastases. Here, we classify the common functions of different metastatic niches: anchorage, survival support, protection from external insults, licensing proliferation and outgrowth. We highlight the emerging role of the metastatic niche in maintaining cancer stemness and promoting immune evasion, and discuss therapeutic opportunities against the metastatic niche.


title: CollAFL: Path Sensitive Fuzzing

abstract: Coverage-guided fuzzing is a widely used and effective solution to find software vulnerabilities. Tracking code coverage and utilizing it to guide fuzzing are crucial to coverage-guided fuzzers. However, tracking full and accurate path coverage is infeasible in practice due to the high instrumentation overhead. Popular fuzzers (e.g., AFL) often use coarse coverage information, e.g., edge hit counts stored in a compact bitmap, to achieve highly efficient greybox testing. Such inaccuracy and incompleteness in coverage introduce serious limitations to fuzzers. First, it causes path collisions, which prevent fuzzers from discovering potential paths that lead to new crashes. More importantly, it prevents fuzzers from making wise decisions on fuzzing strategies. In this paper, we propose a coverage sensitive fuzzing solution CollAFL. It mitigates path collisions by providing more accurate coverage information, while still preserving low instrumentation overhead. It also utilizes the coverage information to apply three new fuzzing strategies, promoting the speed of discovering new paths and vulnerabilities. We implemented a prototype of CollAFL based on the popular fuzzer AFL and evaluated it on 24 popular applications. The results showed that path collisions are common, i.e., up to 75% of edges could collide with others in some applications, and CollAFL could reduce the edge collision ratio to nearly zero. Moreover, armed with the three fuzzing strategies, CollAFL outperforms AFL in terms of both code coverage and vulnerability discovery. On average, CollAFL covered 20% more program paths, found 320% more unique crashes and 260% more bugs than AFL in 200 hours. In total, CollAFL found 157 new security bugs with 95 new CVEs assigned.


title: Software-Defined Networking Using OpenFlow: Protocols, Applications and Architectural Design Choices

abstract: We explain the notion of software-defined networking (SDN), whose southbound interface may be implemented by the OpenFlow protocol. We describe the operation of OpenFlow and summarize the features of specification versions 1.0–1.4. We give an overview of existing SDN-based applications grouped by topic areas. Finally, we point out architectural design choices for SDN using OpenFlow and discuss their performance implications.


title: A modified recursive x-y cut algorithm for solving block ordering problems

abstract: To achieve the best results from an OCR system, the pre-processing steps must be performed with a high degree of accuracy and reliability. There are two critically important steps in the OCR pre-processing phase. First, blocks must be extracted from each page of the scanned document. Secondly, all blocks resulting from the first step must be arranged in the correct order. One of the most notable techniques for block ordering in the second step is the recursive x-y cut (RXYC) algorithm. This technique works accurately only when applied to documents with a simple page layout but it causes incorrect block ordering when applied to documents with complex page layouts. This paper proposes a modified recursive x-y cut algorithm for solving block ordering problems for documents with complex page layouts. This proposed algorithm can solve problems such as (1) the overlapping block problem; (2) the blocks overlay problem, and (3) the L-Shaped block problem.


title: Critical Success Factors Framework for Implementing Effective IT Governance in Public Sector Organizations in a Developing Country

abstract: Today in many public sector organizations, the use of IT has become important in enabling public services delivery. This has caused a critical dependency on IT that call a specific focus on effecti ...


title: Memory Modification Induced by Pattern Completion and STDP in Hippocampal CA3 Model

abstract: One of the role of the hippocampus is viewed as modifying episodic memory so that it can contribute to form semantic memory. In this paper, we show that the pattern completion ability of the hippocampal CA3 and symmetric spike timing-dependent synaptic plasticity induce memory modification so that the hippocampal CA3 can memorize invariable parts of repetitive episode as essential elements and forget variable parts of those as unnecessary ones.


title: A Weighted Topic Model Enhanced Approach for Complementary Collaborator Recommendation

abstract: Collaborations among interdisciplinary scientists are playing an increasingly important role in science innovations. As it is very difficult for a researcher to master the full knowledge of his/her targeted research areas, how to find suitable collaborators of complementary expertise has turned to be a key factor for researchers to succeed. With the expansion of the Web, the availability of sheer volume of information has resulted in information overload issue and posed significant challenges on determining appropriate scientists to collaborate with effectively for research opportunities. However, current studies on collaborator recommendation ignored this phenomenon and particularly overlooked the complementarity of their expertise within a restrictive context, i.e. for a given funding proposal or a research manuscript draft. In this study we propose a complementary expertise analysis enhanced approach to retrieval experts for research collaboration. It produces recommendation list using a heuristic greedy algorithm based on probabilistic topic model, and generates experts who ought to be complemented in expertise as well as to have good ability. The proposed method has been implemented in ScholarMate research community (www.scholarmate.com). We have conducted a user study to verify the effectiveness of the proposed approach and the preliminary results show its good performance comparing to the benchmarks.


title: Explaining and predicting the adoption intention of mobile data services: A value-based approach

abstract: As mobile devices become more and more pervasive in our everyday life and their capabilities resemble more and more of those of desktop computers with the added advantage of mobility, examining intention for adoption seems relevant to consumers and mobile service providers alike. Existing research shows that despite this evolution on Mobile Data Services (MDS) development and use, the adoption of their full capabilities is yet to be realized. In this study we focus on the value consumers can potentially gain from using these services. We hypothesize that if we can examine the value that can be delivered to consumers through the use of MDS, then we can explain and predict consumers’ intentions to use MDS. We also postulate that perceptions of consumers regarding the value that can be captured when using MDS is directly affected by technological, social, and informational influences. However, in this research, perceived value is used as a multidimensional construct that encapsulates utilitarian, hedonic, uniqueness, epistemic, and economic value dimensions. Our results show that utilitarian value is, according to previous studies, an important adoption factor. Additionally, economic value is also important and significant. Nevertheless, it seems that in our context, hedonic, uniqueness, and epistemic value dimensions are not as important for the use of mobile data services as utilitarian and economic value dimensions. The results of this study can be used by mobile service providers to get insights about consumers’ needs and preferences in order to offer better and thus more popular services.


title: A formal verification framework for static analysis As well as its instantiation to the resource analyzer COSTA and formal verification tool KeY

abstract: Static analysis tools, such as resource analyzers, give useful information on software systems, especially in real-time and safety-critical applications. Therefore, the question of the reliability of the obtained results is highly important. State-of-the-art static analyzers typically combine a range of complex techniques, make use of external tools, and evolve quickly. To formally verify such systems is not a realistic option. In this work, we propose a different approach whereby, instead of the tools, we formally verify the results of the tools. The central idea of such a formal verification framework for static analysis is the method-wise translation of the information about a program gathered during its static analysis into specification contracts that contain enough information for them to be verified automatically. We instantiate this framework with costa, a state-of-the-art static analysis system for sequential Java programs, for producing resource guarantees and KeY, a state-of-the-art verification tool, for formally verifying the correctness of such resource guarantees. Resource guarantees allow to be certain that programs will run within the indicated amount of resources, which may refer to memory consumption, number of instructions executed, etc. Our results show that the proposed tool cooperation can be used for automatically producing verified resource guarantees.


title: Discovering Trepanation: The Contribution of Paul Broca

abstract: PAUL BROCA WAS an icon of neuroscience and neurosurgery who also happened to be intrigued by trepanned skulls. His anthropological work established that, thousands of years ago, individuals not only trepanned skulls but also successfully performed these operations on living persons. After first commenting on a pre-Columbian Peruvian skull in 1867 (the first case of trepanning on a living person widely recognized as such), he turned to even older trepanned skulls found on French soil. In the 1870s, he theorized that the procedure originated as a means to treat convulsions in infants. As he saw it, Neolithic man attributed such convulsions to evil spirits, for which trepanation provided a ready means of escape. Because simple infantile convulsions resolve on their own, the practice would have seemed successful, and therefore it would have been propagated and expanded by later generations. Broca’s theory skillfully integrated his anthropological and medical knowledge and helped to create the exciting environment in which scientists pondered what Neolithic and primitive people really knew regarding the brain and surgery. (Neurosurgery 49:1417–1425, 2001)


title: Realizing quality improvement through test driven development: results and experiences of four industrial teams

abstract: Test-driven development (TDD) is a software development practice that has been used sporadically for decades. With this practice, a software engineer cycles minute-by-minute between writing failing unit tests and writing implementation code to pass those tests. Test-driven development has recently re-emerged as a critical enabling practice of agile software development methodologies. However, little empirical evidence supports or refutes the utility of this practice in an industrial context. Case studies were conducted with three development teams at Microsoft and one at IBM that have adopted TDD. The results of the case studies indicate that the pre-release defect density of the four products decreased between 40% and 90% relative to similar projects that did not use the TDD practice. Subjectively, the teams experienced a 15---35% increase in initial development time after adopting TDD.


title: Capturing Temporal Structures for Video Captioning by Spatio-temporal Contexts and Channel Attention Mechanism

abstract: To generate a natural language description for videos, there has been tremendous interest in developing deep neural networks with the integration of temporal structures in different categories. Considering the spatial and temporal domains inherent in video frames, we contend that the video dynamics and the spatio-temporal contexts are both important for captioning, which correspond to two different temporal structures. However, while the video dynamics is well investigated, the spatio-temporal contexts have not been given sufficient attention. In this paper, we take both structures into account and propose a novel recurrent convolution model for captioning. Firstly, for a comprehensive and detailed representation, we propose to aggregate the local and global spatio-temporal contexts in the recurrent convolution networks. Secondly, to capture much subtler temporal dynamics, the channel attention mechanism is introduced and it helps to understand the involvement of the frame feature maps with the captioning process. Finally, a qualitative comparison with several variants of our model demonstrates the effectiveness of incorporating these two structures. Moreover, experiments on YouTube2Text dataset have shown that the proposed method achieves competitive performance to other state-of-the-art methods.


title: Revisiting AdaBoost for Cost-Sensitive Classification. Part II: Empirical Analysis.

abstract: A lot of approaches, each following a different strategy, have been proposed in the literature to provide AdaBoost with cost-sensitive properties. In the first part of this series of two papers, we have presented these algorithms in a homogeneous notational framework, proposed a clustering scheme for them and performed a thorough theoretical analysis of those approaches with a fully theoretical foundation. The present paper, in order to complete our analysis, is focused on the empirical study of all the algorithms previously presented over a wide range of heterogeneous classification problems. The results of our experiments, confirming the theoretical conclusions, seem to reveal that the simplest approach, just based on cost-sensitive weight initialization, is the one showing the best and soundest results, despite having been recurrently overlooked in the literature.


title: Preliminary investigations of an inductive power transfer system for the rotor power supply of an electric traction drive

abstract: The paper is devoted to the substitution of slip rings through an inductive power transfer system (IPT) for field winding supply in wound rotor traction drives. By an inductively coupled rotational transformer, the rotor-side energizing of the excitation windings can be provided isolated and free of wear. For preliminary investigations, an analytical model of the inductive link and the compensation network has been set up. Based on this, possible types of compensation networks regarding an optimal integration into an electric traction drive were carried out and compared among themselves by parameter studies. Using an experimental prototype system and on the basis of accompanying FEM calculations, the analytical model has been validated and the system tested both stationary and transient.


Amplitude-selective filtering for remote-PPG


title: Motion Deblurring in the Wild

abstract: We propose a deep learning approach to remove motion blur from a single image captured in the wild, i.e., in an uncontrolled setting. Thus, we consider motion blur degradations that are due to both camera and object motion, and by occlusion and coming into view of objects. In this scenario, a model-based approach would require a very large set of parameters, whose fitting is a challenge on its own. Hence, we take a data-driven approach and design both a novel convolutional neural network architecture and a dataset for blurry images with ground truth. The network produces directly the sharp image as output and is built into three pyramid stages, which allow to remove blur gradually from a small amount, at the lowest scale, to the full amount, at the scale of the input image. To obtain corresponding blurry and sharp image pairs, we use videos from a high frame-rate video camera. For each small video clip we select the central frame as the sharp image and use the frame average as the corresponding blurred image. Finally, to ensure that the averaging process is a sufficient approximation to real blurry images we estimate optical flow and select frames with pixel displacements smaller than a pixel. We demonstrate state of the art performance on datasets with both synthetic and real images.


SOLVING UNIVERSITY COURSE TIMETABLING PROBLEM USING GENETIC ALGORITHM


title: An effective heuristic for the two-dimensional irregular bin packing problem

abstract: This paper proposes an adaptation, to the two-dimensional irregular bin packing problem of the Djang and Finch heuristic (DJD), originally designed for the one-dimensional bin packing problem. In the two-dimensional case, not only is it the case that the piece’s size is important but its shape also has a significant influence. Therefore, DJD as a selection heuristic has to be paired with a placement heuristic to completely construct a solution to the underlying packing problem. A successful adaptation of the DJD requires a routine to reduce computational costs, which is also proposed and successfully tested in this paper. Results, on a wide variety of instance types with convex polygons, are found to be significantly better than those produced by more conventional selection heuristics.


title: Odometry and motion planning for omni drive robots

abstract: Omni drive robots operate by combined motion of more than 2 wheels leading to a force vector in resultant direction and thus enabling the robot to travel in that direction. Omni drive systems are quite efficient from the travelling point of view as compared to non-holonomic (non omni drive) systems, which require stopping at a position and taking a turn. This paper describes a very sophisticated yet simple approach to motion planning of such omni drive robots that would not only decrease the time taken by these robots to perform several tasks, but also increase the efficiency to quite a greater extent. Moreover the system being less complex, it can be implemented on a low cost processing unit and there is no need for high end processors or computers to run such robots. These systems, if developed properly, can bring a quite breakthrough by involving robotics in our daily life commercial applications.


title: DANCin SEQ2SEQ: Fooling Text Classifiers with Adversarial Text Example Generation

abstract: Machine learning models are powerful but fallible. Generating adversarial examples - inputs deliberately crafted to cause model misclassification or other errors - can yield important insight into model assumptions and vulnerabilities. Despite significant recent work on adversarial example generation targeting image classifiers, relatively little work exists exploring adversarial example generation for text classifiers; additionally, many existing adversarial example generation algorithms require full access to target model parameters, rendering them impractical for many real-world attacks. In this work, we introduce DANCin SEQ2SEQ, a GAN-inspired algorithm for adversarial text example generation targeting largely black-box text classifiers. We recast adversarial text example generation as a reinforcement learning problem, and demonstrate that our algorithm offers preliminary but promising steps towards generating semantically meaningful adversarial text examples in a real-world attack scenario.


title: Network Slicing Based 5G and Future Mobile Networks: Mobility, Resource Management, and Challenges

abstract: 5G networks are expected to be able to satisfy users' different QoS requirements. Network slicing is a promising technology for 5G networks to provide services tailored for users' specific QoS demands. Driven by the increased massive wireless data traffic from different application scenarios, efficient resource allocation schemes should be exploited to improve the flexibility of network resource allocation and capacity of 5G networks based on network slicing. Due to the diversity of 5G application scenarios, new mobility management schemes are greatly needed to guarantee seamless handover in network-slicing-based 5G systems. In this article, we introduce a logical architecture for network-slicing-based 5G systems, and present a scheme for managing mobility between different access networks, as well as a joint power and subchannel allocation scheme in spectrum-sharing two-tier systems based on network slicing, where both the co-tier interference and cross-tier interference are taken into account. Simulation results demonstrate that the proposed resource allocation scheme can flexibly allocate network resources between different slices in 5G systems. Finally, several open issues and challenges in network-slicing-based 5G networks are discussed, including network reconstruction, network slicing management, and cooperation with other 5G technologies.


title: Active inference and learning

abstract: This paper offers an active inference account of choice behaviour and learning. It focuses on the distinction between goal-directed and habitual behaviour and how they contextualise each other. We show that habits emerge naturally (and autodidactically) from sequential policy optimisation when agents are equipped with state-action policies. In active inference, behaviour has explorative (epistemic) and exploitative (pragmatic) aspects that are sensitive to ambiguity and risk respectively, where epistemic (ambiguity-resolving) behaviour enables pragmatic (reward-seeking) behaviour and the subsequent emergence of habits. Although goal-directed and habitual policies are usually associated with model-based and model-free schemes, we find the more important distinction is between belief-free and belief-based schemes. The underlying (variational) belief updating provides a comprehensive (if metaphorical) process theory for several phenomena, including the transfer of dopamine responses, reversal learning, habit formation and devaluation. Finally, we show that active inference reduces to a classical (Bellman) scheme, in the absence of ambiguity.


title: Graphite Landau Levels in the Presence of Trigonal Warping

abstract: The Bohr-Sommerfeld quantization condition is applied to calculate the Landau levels in graphite in the presence of trigonal warping. The off-axis extrema arising from the trigonal warping result in unequally spaced leg and central electron and hole Landau levels which can be supported between two cut-off energies. Application to the de Haas-van Alphen effect and to interband Landau level transitions is discussed.


title: A vision system for traffic sign detection and recognition

abstract: The paper presents an automatic traffic sign recognition system using the videos recorded from an on-board dashcam. It is based on image processing, bilateral Chinese transform, and vertex and bisector transform techniques. The images captured from the dashcam are processed with the histogram of oriented gradients to form feature vectors, followed by support vector machines to detect the traffic signs. The bilateral Chinese transform and vertex and bisector transform are used to extract the area of traffic sign from images. Finally, a neural network is adopted to identify the traffic sign information. In this work, we test the algorithms using the images captured from the camera mounted behind the front windshield. The experiments are evaluated with real traffic scenes and the results have demonstrated the effectiveness of the proposed system.


title: Synthetic antibiotic peptides database.

abstract: A computerized database for synthetic antibiotic peptides, the SAPD, has been created and is available on the Internet at web address, http://oma.terkko.helsinki.fi:8080/~SAPD. The SAPD is modelled on two pre-existing computer databases for naturally occurring peptide antibiotics, the Antimicrobial Sequences Database and the Peptaibol Database, and it will contain both chemical and biological information on all published synthetic antibiotic peptides.


Road Surface Quality Detection Using Accelerometer


title: 2.5-Year-olds use cross-situational consistency to learn verbs under referential uncertainty

abstract: Recent evidence shows that children can use cross-situational statistics to learn new object labels under referential ambiguity (e.g., Smith & Yu, 2008). Such evidence has been interpreted as support for proposals that statistical information about word-referent co-occurrence plays a powerful role in word learning. But object labels represent only a fraction of the vocabulary children acquire, and arguably represent the simplest case of word learning based on observations of world scenes. Here we extended the study of cross-situational word learning to a new segment of the vocabulary, action verbs, to permit a stronger test of the role of statistical information in word learning. In two experiments, on each trial 2.5-year-olds encountered two novel intransitive (e.g., “She’s pimming!”; Experiment 1) or transitive verbs (e.g., “She’s pimming her toy!”; Experiment 2) while viewing two action events. The consistency with which each verb accompanied each action provided the only source of information about the intended referent of each verb. The 2.5-year-olds used cross-situational consistency in verb learning, but also showed significant limits on their ability to do so as the sentences and scenes became slightly more complex. These findings help to define the role of cross-situational observation in word learning.


title: Grammar Practice Activities: A Practical Guide for Teachers

abstract: For the teacher who wishes to combine grammar teaching with a broadly communicative methodology, this is an invaluable resource. It contains a collection of imaginative and interesting grammar practice activities suitable for a wide range of levels and ages. Part one provides: * a detailed introduction to grammar teaching * guidelines for the design of activities * practical hints on effective classroom presentation. Part two contains: * almost 200 game-like activities for practising important or problematical aspects of English grammar, with detailed descriptions of procedure * sample texts and visuals which may be photocopied for immediate use.


title: Understanding the diffusion of public bikesharing systems: evidence from Europe and North America

abstract: Since the mid-2000s, public bikesharing (also known as “bike hire”) has developed and spread into a new form of mobility in cities across the globe. This paper presents an analysis of the recent increase in the number of public bikesharing systems. Bikesharing is the shared use of a bicycle fleet, which is accessible to the public and serves as a form of public transportation. The initial system designs were pioneered in Europe and, after a series of technological innovations, appear to have matured into a system experiencing widespread adoption. There are also signs that the policy of public bikesharing systems is transferable and is being adopted in other contexts outside Europe. In public policy, the technologies that are transferred can be policies, technologies, ideals or systems. This paper seeks to describe the nature of these systems, how they have spread in time and space, how they have matured in different contexts, and why they have been adopted. ::: ::: Researchers provide an analysis from Europe and North America. The analysis draws on published data sources, a survey of 19 systems, and interviews with 12 decision-makers in Europe and 14 decision-makers in North America. The data are examined through the lens of diffusion theory, which allows for comparison of the adoption process in different contexts. A mixture of quantitative and qualitative analyses is used to explore the reasons for adoption decisions in different cities. The paper concludes that Europe is still in a major adoption process with new systems emerging and growth in some existing systems, although some geographic areas have adopted alternative solutions. Private sector operators have also been important entrepreneurs in a European context, which has accelerated the uptake of these systems. In North America, the adoption process is at an earlier stage and is gaining momentum, but signs also suggest the growing importance of entrepreneurs in North America with respect to technology and business models. There is evidence to suggest that the policy adoption processes have been inspired by successful systems in Paris, Lyon, Montreal, and Washington, DC, for instance, and that diffusion theory could be useful in understanding public bikesharing policy adoption in a global context.


title: Assessing eGovernment systems success: A validation of the DeLone and McLean model of information systems success

abstract: Abstract With the proliferation of the Internet and World Wide Web applications, people are increasingly interacting with government to citizen (G2C) eGovernment systems. It is therefore important to measure the success of G2C eGovernment systems from the citizen's perspective. While general information systems (IS) success models have received much attention from researchers, few studies have been conducted to assess the success of eGovernment systems. The extent to which traditional IS success models can be extended to investigating eGovernment systems success remains unclear. This study provides the first empirical test of an adaptation of DeLone and McLean's IS success model in the context of G2C eGovernment. The model consists of six dimensions: information quality, system quality, service quality, use, user satisfaction, and perceived net benefit. Structural equation modeling techniques are applied to data collected by questionnaire from 119 users of G2C eGovernment systems in Taiwan. Except for the link from system quality to use, the hypothesized relationships between the six success variables are significantly or marginally supported by the data. The findings provide several important implications for eGovernment research and practice. This paper concludes by discussing limitations of the study which should be addressed in future research.


title: Novel compact spider microstrip antenna with new Defected Ground Structure

abstract: Two novel Defected Ground Structures (DGS) were first proposed, which have better results than that of the dumbbell (published shape). Using the general model of DGS, its equivalent parameters were extracted. The two new proposed shapes of DGS were then used to design a novel compact spider microstrip antenna to minimize its area. The size of the developed antenna was reduced to about 90.5% of that of the conventional one. This antenna with two different novel shapes of DGS was designed and simulated by using the ready-made software package Zeland-IE3D. Finally, it was fabricated by using thin film and photolithographic technique and measured by using vector network analyzer. Good agreements were found between the simulated and measured results.


title: How does intergroup contact reduce prejudice? Meta-analytic tests of three mediators

abstract: Recent years have witnessed a renewal of interest in intergroup contact theory. A meta-analysis of more than 500 studies established the theory's basic contention that intergroup contact typically reduces prejudices of many types. This paper addresses the issue of process: just how does contact diminish prejudice? We test meta-analytically the three most studied mediators: contact reduces prejudice by (1) enhancing knowledge about the outgroup, (2) reducing anxiety about intergroup contact, and (3) increasing empathy and perspective taking. Our tests reveal mediational effects for all three of these mediators. However, the mediational value of increased knowledge appears less strong than anxiety reduction and empathy. Limitations of the study and implications of the results are discussed. Copyright © 2008 John Wiley & Sons, Ltd.


title: Feature Extraction for Terrain Classification with Crawling Robots

abstract: In this paper, we address the problem of terrain classification using a technically blind hexapod walking robot. The proposed approach is built on top of the ex- isting method based on analysis of the feedback from the robot's actuators and the desired trajectory. The formed method uses features for the Support Vector Machine clas- sification method that assumes a regular time-invariant gait to control the robot. However, such a gait does not allow the robot to traverse rough terrains, and therefore, it is nec- essary to consider adaptive motion gait to deal with small obstacles, which is, unfortunately, not a regular gait with some fixed predefined period. Therefore, we propose to al- ter the features extraction process to utilize the terrain clas- sification method also for an adaptive motion gait, which enables the robot to traverse rough terrains. The proposed method has been experimentally verified on several ter- rains that are not traversable by a default regular gait. The achieved results not only confirmed the high accuracy of the terrain classification as the existing approach, but also expanded the area of operation of a hexapod walking robot into more challenging terrains.


title: Switchable Quad-Band Antennas for Cognitive Radio Base Station Applications

abstract: A novel antenna configuration for quad-band operation is presented. The quad-band antenna has a directional radiation pattern in four frequency bands, i.e., B1 (800-900 MHz), B2 (1.7-2.5 GHz), B3 (3.3-3.6 GHz), and B4 (5.1-5.9 GHz), covering all spectrums for existing wireless applications, such as GSM, PCS, WCDMA, WiFi, and WiMax. The operating frequency of the quad-band antenna can be adjusted by the use of a MEMS switch, making it suitable for cognitive radio applications. First a switchable quad-band antenna element is introduced. Then a two-element antenna array is developed to increase the antenna gain for base station applications featuring a gain value of about 9-11 dBi over all four frequency bands.


title: Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media

abstract: History of mental illness is a major factor behind suicide risk and ideation. However research efforts toward characterizing and forecasting this risk is limited due to the paucity of information regarding suicide ideation, exacerbated by the stigma of mental illness. This paper fills gaps in the literature by developing a statistical methodology to infer which individuals could undergo transitions from mental health discourse to suicidal ideation. We utilize semi-anonymous support communities on Reddit as unobtrusive data sources to infer the likelihood of these shifts. We develop language and interactional measures for this purpose, as well as a propensity score matching based statistical approach. Our approach allows us to derive distinct markers of shifts to suicidal ideation. These markers can be modeled in a prediction framework to identify individuals likely to engage in suicidal ideation in the future. We discuss societal and ethical implications of this research.


title: Language switching and language representation in Spanish–English bilinguals: an fMRI study

abstract: The current experiment was designed to investigate the nature of cognitive control in within- and between-language switching in bilingual participants. To examine the neural substrate of language switching we used functional magnetic resonance imaging (fMRI) as subjects named pictures in one language only or switched between languages. Participants were also asked to name (only in English) a separate set of pictures as either the actions or the objects depicted or to switch between these two types of responses on each subsequent picture. Picture naming compared to rest revealed activation in the dorsolateral prefrontal cortex, which extended down into Broca's area in the left hemisphere. There were no differences in the activation pattern for each language. English and Spanish both activated overlapping areas of the brain. Similarly, there was no difference in activation for naming actions or objects in English. However, there was increased intensity of activation in the dorsolateral prefrontal cortex for switching between languages relative to no-switching, an effect which was not observed for naming of actions or objects in English. We suggest that the dorsolateral prefrontal cortex serves to attenuate interference that results from having to actively enhance and suppress two languages in alternation. These results are consistent with the view that switching between languages involves increased general executive processing. Finally, our results are consistent with the view that different languages are represented in overlapping areas of the brain in early bilinguals.


title: Towards a Global Elevation Product: Combination of Multi-Source Digital Elevation Models, Symposium on Geospatial Theory, Processing and Applications

abstract: Mapping and monitoring the Earth’s surface is subject to various application fields. This three-dimensional problem is usually split into a two-dimensional planar description of the Earth’s surface supplemented with the height information provided as separate digital elevation models. However, from the global point of view there is still a tremendous need for suitable height information at a resolution level of about 1 to 3 arc seconds. The Shuttle Radar Topography Mission (SRTM) will help to fill this gap by providing high quality digital elevation model (DEM). In order to provide terrain information for areas where either SRTM data will not be available or the corresponding resolution is not sufficient a combination with other sources will be required. The German Remote Sensing Data Center (DFD) of DLR intends to implement such a global DEM service. All SRTM/X-SAR data will be processed to elevation data and will serve as the backbone as it provides a global net of homogenous elevation information. This net can be used for the absolute orientation of other DEMs as geometric reference, but also for the improvement of the height quality by integrating elevation data from a variety of other sources by DEM fusion and mosaicking techniques. The paper describes the principles and corresponding accuracy of space borne missions for the derivation of DEMs. The main focus is on the DEM products of SRTM/X-SAR. Furthermore, the ERS-1/2 tandem configuration and the MOMS-2P mission are described. The technique to combine multi-source DEMs is outlined, which is based on the concept of height error maps. The method is illustrated by practical examples. Finally, an outlook is given on further investigations.


title: Deep Learning Approach for Car Detection in UAV Imagery

abstract: This paper presents an automatic solution to the problem of detecting and counting cars in unmanned aerial vehicle (UAV) images. This is a challenging task given the very high spatial resolution of UAV images (on the order of a few centimetres) and the extremely high level of detail, which require suitable automatic analysis methods. Our proposed method begins by segmenting the input image into small homogeneous regions, which can be used as candidate locations for car detection. Next, a window is extracted around each region, and deep learning is used to mine highly descriptive features from these windows. We use a deep convolutional neural network (CNN) system that is already pre-trained on huge auxiliary data as a feature extraction tool, combined with a linear support vector machine (SVM) classifier to classify regions into “car” and “no-car” classes. The final step is devoted to a fine-tuning procedure which performs morphological dilation to smooth the detected regions and fill any holes. In addition, small isolated regions are analysed further using a few sliding rectangular windows to locate cars more accurately and remove false positives. To evaluate our method, experiments were conducted on a challenging set of real UAV images acquired over an urban area. The experimental results have proven that the proposed method outperforms the state-of-the-art methods, both in terms of accuracy and computational time.


Subfigure and Multi-Label Classification using a Fine-Tuned Convolutional Neural Network


title: The Construction of Firm’s IT Capability and Its Impact on IT Assimilation: An Empirical Investigation in China

abstract: The paper’s research purpose is to discuss the key firm-specific IT capability and its impact on the business value of IT. In the context of IT application in China, the paper builds research model based on Resource-Based View, this model describes how the partnership between business and IT management partially mediates the effects of IT infrastructure capability and managerial IT skills on the organization-level of IT assimilation(as proxy for business value of IT ). This research releases 105 questionnaires to part-time MBA in the Renmin University of China and gets 70 valid questionnaires, then analyzed the measurement and structural research model by PLS method. The result of the structural model shows the investment in infrastructure capability and managerial IT skills should be transformed into the partnership between IT and business, and then influence the IT assimilation. The paper can give suggestions to the firms about how to identify and improve IT capability, which will help organization to get superior business value from IT investment.


title: The impact of hybrid and electric powertrains on vehicle dynamics, control systems and energy regeneration

abstract: The background to the development of so-called green or low-carbon vehicles continues to be relentlessly reviewed throughout the literature. Research and development (R&D) on novel powertrains - often based on electric or hybrid technology - has been dominating automotive engineering around the world for the first two decades of the twenty-first century. Inevitably, most of the R&D has focused on powertrain technology and energy management challenges. However, as new powertrains have started to become commercially available, their effects on other aspects of vehicle performance have become increasingly important. This article focuses on the review of the integration of new electrified powertrains with the vehicle dynamics and control systems. The integration effects can be discussed in terms of three generic aspects of vehicle motions, namely roll-plane, pitch-plane and yaw-plane, which however are strongly coupled. The topic on regenerative suspension is further discussed. It quickly becomes clear that this integration poses some interesting future engineering challenges to maintain currently accepted levels of ride, handling and stability performance.


title: Visible light communication using OFDM

abstract: In this paper wireless communication using white, high brightness LEDs (light emitting diodes) is considered. In particular, the use of OFDM (orthogonal frequency division multiplexing) for intensity modulation is investigated. The high peak-to-average ratio (PAR) in OFDM is usually considered a disadvantage in radio frequency transmission systems due to non-linearities of the power amplifier. It is demonstrated theoretically and by means of an experimental system that the high PAR in OFDM can be exploited constructively in visible light communication to intensity modulate LEDs. It is shown that the theoretical and the experimental results match very closely, and that it is possible to cover a distance of up to one meter using a single LED.


title: The methodology of focus groups: the importance of interaction between research participants

abstract: What are focus groups? How are they distinct from ordinary group discussions and what use are they anyway? This article introduces focus group methodology, explores ways of conducting such groups and examines what this technique of data collection can offer researchers in general and medical sociologists in particular. It concentrates on the one feature which inevitably distinguishes focus groups from one-to-one interviews or questionnaires – namely the interaction between research participants - and argues for the overt exploration and exploitation of such interaction in the research process.


title: Trends in Microgrid Control

abstract: The increasing interest in integrating intermittent renewable energy sources into microgrids presents major challenges from the viewpoints of reliable operation and control. In this paper, the major issues and challenges in microgrid control are discussed, and a review of state-of-the-art control strategies and trends is presented; a general overview of the main control principles (e.g., droop control, model predictive control, multi-agent systems) is also included. The paper classifies microgrid control strategies into three levels: primary, secondary, and tertiary, where primary and secondary levels are associated with the operation of the microgrid itself, and tertiary level pertains to the coordinated operation of the microgrid and the host grid. Each control level is discussed in detail in view of the relevant existing technical literature.


title: Applying case-based reasoning and multi-agent intelligent

abstract: Comparative shopping is a promising web service in the field of mobile commerce. This paper aims to propose a context-aware comparative shopping. Multi-agent intelligent architecture is adopted to implement the autonomous negotiation mechanism between buyers and sellers. To automatically estimate user preferences to determine the best purchase, case-based reasoning and negotiation mechanism are utilized. We developed a prototype system and experiment to show the possibility of the mechanism proposed in this paper. We found that our mechanism with multi-agents yields more pay-off, total sales, and wins than the system without those features.


title: A preliminary investigation of the efficacy of oral motor exercises for children with mild articulation disorders

abstract: Although oral motor therapy is sometimes used to treat articulation disorders in school-age children, several reports question its efficacy. In this case study, four first-grade students, two boys and two girls, received 15 half-hour sessions of oral motor treatment based on Easy Does it for Articul


title: Blockchain for government services — Use cases, security benefits and challenges

abstract: Public sector and governments have been actively exploring new technologies to enable the smart services transformation and to achieve strategic objectives such as citizens satisfaction and happiness, services efficiency and cost optimization. The Blockchain technology is a good example of an emerging technology that is attracting government attention. Many government entities such as United Kingdom, Estonia, Honduras, Denmark, Australia, Singapore and others have taken steps to unleash the potential of Blockchain technology. Dubai Government is aiming to become paperless by adopting the Blockchain technology for all transactions by 2021. The Blockchain is a disruptive technology that is playing a vital role in many sectors. It's a revolutionary technology transforming the way we think about trust as it enables transacting data in a decentralized structure without the need to have trusted central authorities. Blockchain technology promises to overcome security challenges in IoT enabled services such as enabling secure data sharing and data integrity. However, it also introduces new security challenges that should be investigated and tackled. In this paper, we review the literature to identify the potential use cases and application of Blockchain to enable government services. We also synthesized literature related to the security of Blockchain implementations to identify the security benefits, challenges and the proposed solutions. The analysis shows that is huge potential for Blockchain technology to be used in to enable smart government services. This paper also highlights future research in the areas of concerns that required further investigation.


title: High-accuracy optical computing based on interval arithmetic and the fixed-point theorem.

abstract: A method for high-accuracy analog optical computing based on interval arithmetic and the fixed-point theorem is considered. Two-variable simultaneous equations are studied to investigate the proposed method. An optical implementation is considered by the use of spatial coding of intervals, affine transformation, and image magnification. Computational simulation verifies the principle of the method.


title: The Emotional Brain, Fear, and the Amygdala

abstract: 1. Considerable progress has been made over the past 20 years in relating specific circuits of the brain to emotional functions. Much of this work has involved studies of Pavlovian or classical fear conditioning, a behavioral procedure that is used to couple meaningless environmental stimuli to emotional (defense) response networks.


title: Diode-based tuned ESD protection for 5.25-GHz CMOS LNAs

abstract: CMOS diodes are evaluated in terms of figure-of-merit (It2/C) and quality factor. Five 5.25-GHz LNAs with ESD protection are designed and tested; all have high ESD protection levels. The LNA protected with a one-diode variant of the cancellation circuit has RF performance almost identical to that of a sixth, unprotected LNA.


title: Enveloping Lie Algebras of Low Dimensional Leibniz Algebras

abstract: We calculate the enveloping Lie algebras of Leibniz algebras of dimensions two and three. We show how these Lie algebras could be used to distinguish non-isomorphic (nilpotent) Leibniz algebras of low dimension in some cases. These results could be used to associate geometric objects (loop spaces) to low dimensional Leibniz algebras.


title: FACT: A Framework for Authentication in Cloud-Based IP Traceback

abstract: IP traceback plays an important role in cyber investigation processes, where the sources and the traversed paths of packets need to be identified. It has a wide range of applications, including network forensics, security auditing, network fault diagnosis, and performance testing. Despite a plethora of research on IP traceback, the Internet is yet to see a large-scale practical deployment of traceback. Some of the major challenges that still impede an Internet-scale traceback solution are, concern of disclosing Internet Service Provider (ISP’s) internal network topologies (in other words, concern of privacy leak), poor incremental deployment, and lack of incentives for ISPs to provide traceback services. In this paper, we argue that cloud services offer better options for the practical deployment of an IP traceback system. We first present a novel cloud-based traceback architecture, which possesses several favorable properties encouraging ISPs to deploy traceback services on their networks. While this makes the traceback service more accessible, regulating access to traceback service in a cloud-based architecture becomes an important issue. Consequently, we address the access control problem in cloud-based traceback. Our design objective is to prevent illegitimate users from requesting traceback information for malicious intentions (such as ISPs topology discovery). To this end, we propose a temporal token-based authentication framework, called FACT, for authenticating traceback service queries. FACT embeds temporal access tokens in traffic flows, and then delivers them to end-hosts in an efficient manner. The proposed solution ensures that the entity requesting for traceback service is an actual recipient of the packets to be traced. Finally, we analyze and validate the proposed design using real-world Internet data sets.


title: An Improved RANSAC homography Algorithm for Feature Based Image Mosaic

abstract: In this paper, we proposed a new feature based image mosaic algorithm. The improved RANSAC homography algorithm based on the modified media flow filter, to detect wrong matches for improving the stability of the normal RANSAC homography algorithm. The method improved the local registration between neighboring images. Experiments and Statistical Analysis show that our mosaic method is robust.


title: Deep Reinforcement Learning of Marked Temporal Point Processes

abstract: In a wide variety of applications, humans interact with a complex environment by means of asynchronous stochastic discrete events in continuous time. Can we design online interventions that will help humans achieve certain goals in such asynchronous setting? In this paper, we address the above problem from the perspective of deep reinforcement learning of marked temporal point processes, where both the actions taken by an agent and the feedback it receives from the environment are asynchronous stochastic discrete events characterized using marked temporal point processes. In doing so, we define the agent's policy using the intensity and mark distribution of the corresponding process and then derive a flexible policy gradient method, which embeds the agent's actions and the feedback it receives into real-valued vectors using deep recurrent neural networks. Our method does not make any assumptions on the functional form of the intensity and mark distribution of the feedback and it allows for arbitrarily complex reward functions. We apply our methodology to two different applications in personalized teaching and viral marketing and, using data gathered from Duolingo and Twitter, we show that it may be able to find interventions to help learners and marketers achieve their goals more effectively than alternatives.


title: Evaluation of Business Processes for Business Process Standardization

abstract: Companies often implement multiple process variants in their organizations at the same time. Often these variants differ widely in terms of efficiency, quality and cycle time. In times of highly volatile global economic markets, companies cannot afford unnecessary redundant business processes. Business Process Standardization has therefore become a major topic in the field of Business Process Management, both in research and practice. Management decisions concerning standardization are difficult and complex, due to limited project budgets, organizational and technological changes as well as the increasing challenges of globalization. Choosing suitable processes for standardization is the essential precondition for streamlining business processes in the first place and therefore for reaping the full benefits of Business Process Standardization. However, there is hardly any tool available that supports the evaluation of business processes for standardization. To close this research gap, we develop an instrument for the evaluation of business processes in the context of Business Process Standardization with the aim to help practitioners in their day-to-day operations. Our research aims at providing the necessary preconditions for companies to profit from the advantages of business process standardization and to support decision-making.


title: A fast transient and under/overshoot suppression DC-DC Buck converter with ACP control

abstract: In this paper, a novel state ACP (adaptive current position) is presented, which allows Buck converter to achieve fast load transient response. The PWM (pulse width modulation) and PFM (pulse frequency modulation) modes increase light-load efficiency and maintain good regulation over a wide load range. When the load rapidly changes from light to heavy, ACP can enhance the transient response, to minimize the settling time and suppresses under/overshoot voltage. Unlike other system, the proposed controller can operate without current-sensor. ACP can force the inductor current to catch up with load current quickly. Switch on high-side power MOS a period of time then change to PWM state. By using the information from pseudo current roof, the exact on-time and off-time at different V in can be calculated. Also, we can speculate the time to execute ACP without current-sensor. Simulation and experimental results demonstrate the superior dynamic response over that of a conventional digital Buck converter.


title: Study of Wind Turbine Fault Diagnosis Based on Unscented Kalman Filter and SCADA Data

abstract: Effective wind turbine fault diagnostic algorithms are crucial for wind turbine intelligent condition monitoring. An unscented Kalman filter approach is proposed to successfully detect and isolate two types of gearbox failures of a wind turbine in this paper. The state space models are defined for the unscented Kalman filter model by a detailed wind turbine nonlinear systematic principle analysis. The three failure modes being studied are gearbox damage, lubrication oil leakage and pitch failure. The results show that unscented Kalman filter model has special response to online input parameters under different fault conditions. Such property makes it effective on fault identification. It also shows that properly defining unscented Kalman filter state space vectors and control vectors are crucial for improving its sensitivity to different failures. Online fault detection capability of this approach is then proved on SCADA data. The developed unsented Kalman filter model provides an effective way for wind turbine fault detection using supervisory control and data acquisition data. This is essential for further intelligent WT condition monitoring.


DYNAMIC PREDICTION OF LGD MODELING METHODOLOGY


Reverse Proxy Patterns


title: A new keypoint-based copy-move forgery detection for small smooth regions

abstract: Copy-move forgery is one of the most common types of image forgeries, where a region from one part of an image is copied and pasted onto another part, thereby concealing the image content in the latter region. Keypoint based copy-move forgery detection approaches extract image feature points and use local visual features, rather than image blocks, to identify duplicated regions. Keypoint based approaches exhibit remarkable performance with respect to computational cost, memory requirement, and robustness. But unfortunately, they usually do not work well if smooth background areas are used to hide small objects, as image keypoints cannot be extracted effectively from those areas. It is a challenging work to design a keypoint-based method for detecting forgeries involving small smooth regions. In this paper, we propose a new keypoint-based copy-move forgery detection for small smooth regions. Firstly, the original tampered image is segmented into nonoverlapping and irregular superpixels, and the superpixels are classified into smooth, texture and strong texture based on local information entropy. Secondly, the stable image keypoints are extracted from each superpixel, including smooth, texture and strong texture ones, by utilizing the superpixel content based adaptive feature points detector. Thirdly, the local visual features, namely exponent moments magnitudes, are constructed for each image keypoint, and the best bin first and reversed generalized 2 nearest-neighbor algorithm are utilized to find rapidly the matching image keypoints. Finally, the falsely matched image keypoints are removed by customizing the random sample consensus, and the duplicated regions are localized by using zero mean normalized cross-correlation measure. Extensive experimental results show that the newly proposed scheme can achieve much better detection results for copy-move forgery images under various challenging conditions, such as geometric transforms, JPEG compression, and additive white Gaussian noise, compared with the existing state-of-the-art copy-move forgery detection methods.


title: A systematic review including meta-analysis of work environment and burnout symptoms

abstract: BACKGROUND ::: Practitioners and decision makers in the medical and insurance systems need knowledge on the relationship between work exposures and burnout. Many burnout studies - original as well as reviews - restricted their analyses to emotional exhaustion or did not report results on cynicism, personal accomplishment or global burnout. To meet this need we carried out this review and meta-analyses with the aim to provide systematically graded evidence for associations between working conditions and near-future development of burnout symptoms. ::: ::: ::: METHODS ::: A wide range of work exposure factors was screened. Inclusion criteria were: 1) Study performed in Europe, North America, Australia and New Zealand 1990-2013. 2) Prospective or comparable case control design. 3) Assessments of exposure (work) and outcome at baseline and at least once again during follow up 1-5 years later. Twenty-five articles met the predefined relevance and quality criteria. The GRADE-system with its 4-grade evidence scale was used. ::: ::: ::: RESULTS ::: Most of the 25 studies focused emotional exhaustion, fewer cynicism and still fewer personal accomplishment. Moderately strong evidence (grade 3) was concluded for the association between job control and reduced emotional exhaustion and between low workplace support and increased emotional exhaustion. Limited evidence (grade 2) was found for the associations between workplace justice, demands, high work load, low reward, low supervisor support, low co-worker support, job insecurity and change in emotional exhaustion. Cynicism was associated with most of these work factors. Reduced personal accomplishment was only associated with low reward. There were few prospective studies with sufficient quality on adverse chemical, biological and physical factors and burnout. ::: ::: ::: CONCLUSION ::: While high levels of job support and workplace justice were protective for emotional exhaustion, high demands, low job control, high work load, low reward and job insecurity increased the risk for developing exhaustion. Our approach with a wide range of work exposure factors analysed in relation to the separate dimensions of burnout expanded the knowledge of associations, evidence as well as research needs. The potential of organizational interventions is illustrated by the findings that burnout symptoms are strongly influenced by structural factors such as job demands, support and the possibility to exert control.


title: Simultaneous State Initialization and Gyroscope Bias Calibration in Visual Inertial Aided Navigation

abstract: State of the art approaches for visual-inertial sensor fusion use filter-based or optimization-based algorithms. Due to the nonlinearity of the system, a poor initialization can have a dramatic impact on the performance of these estimation methods. Recently, a closed-form solution providing such an initialization was derived in [1] . That solution determines the velocity (angular and linear) of a monocular camera in metric units by only using inertial measurements and image features acquired in a short time interval. In this letter, we study the impact of noisy sensors on the performance of this closed-form solution. We show that the gyroscope bias, not accounted for in [1] , significantly affects the performance of the method. Therefore, we introduce a new method to automatically estimate this bias. Compared to the original method, the new approach now models the gyroscope bias and is robust to it. The performance of the proposed approach is successfully demonstrated on real data from a quadrotor MAV.


title: Locating Facial Features with an Extended Active Shape Model

abstract: We make some simple extensions to the Active Shape Model of Cootes et al. [4], and use it to locate features in frontal views of upright faces. We show on independent test data that with the extensions the Active Shape Model compares favorably with more sophisticated methods. The extensions are (i) fitting more landmarks than are actually needed (ii) selectively using two- instead of one-dimensional landmark templates (iii) adding noise to the training set (iv) relaxing the shape model where advantageous (v) trimming covariance matrices by setting most entries to zero, and (vi) stacking two Active Shape Models in series.


title: Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization

abstract: In this work, we focus on the task of generating natural language descriptions from a structured table of facts containing fields (such as nationality, occupation, etc) and values (such as Indian, actor, director, etc). One simple choice is to treat the table as a sequence of fields and values and then use a standard seq2seq model for this task. However, such a model is too generic and does not exploit task-specific characteristics. For example, while generating descriptions from a table, a human would attend to information at two levels: (i) the fields (macro level) and (ii) the values within the field (micro level). Further, a human would continue attending to a field for a few timesteps till all the information from that field has been rendered and then never return back to this field (because there is nothing left to say about it). To capture this behavior we use (i) a fused bifocal attention mechanism which exploits and combines this micro and macro level information and (ii) a gated orthogonalization mechanism which tries to ensure that a field is remembered for a few time steps and then forgotten. We experiment with a recently released dataset which contains fact tables about people and their corresponding one line biographical descriptions in English. In addition, we also introduce two similar datasets for French and German. Our experiments show that the proposed model gives 21% relative improvement over a recently proposed state of the art method and 10% relative improvement over basic seq2seq models. The code and the datasets developed as a part of this work are publicly available.


Chitosan and Its Derivatives for Application in Mucoadhesive Drug Delivery Systems


title: Smooth UCT search in computer poker

abstract: Self-play Monte Carlo Tree Search (MCTS) has been successful in many perfect-information two-player games. Although these methods have been extended to imperfect-information games, so far they have not achieved the same level of practical success or theoretical convergence guarantees as competing methods. In this paper we introduce Smooth UCT, a variant of the established Upper Confidence Bounds Applied to Trees (UCT) algorithm. Smooth UCT agents mix in their average policy during self-play and the resulting planning process resembles game-theoretic fictitious play. When applied to Kuhn and Leduc poker, Smooth UCT approached a Nash equilibrium, whereas UCT diverged. In addition, Smooth UCT outperformed UCT in Limit Texas Hold'em and won 3 silver medals in the 2014 Annual Computer Poker Competition.


title: Optimizing Recurrent Neural Networks Architectures under Time Constraints

abstract: Recurrent neural network (RNN)'s architecture is a key factor influencing its performance. We propose algorithms to optimize hidden sizes under running time constraint. We convert the discrete optimization into a subset selection problem. By novel transformations, the objective function becomes submodular and constraint becomes supermodular. A greedy algorithm with bounds is suggested to solve the transformed problem. And we show how transformations influence the bounds. To speed up optimization, surrogate functions are proposed which balance exploration and exploitation. Experiments show that our algorithms can find more accurate models or faster models than manually tuned state-of-the-art and random search. We also compare popular RNN architectures using our algorithms.


title: A signal transformational framework for breaking the noise floor and its applications in MRI

abstract: A long-standing problem in magnetic resonance imaging (MRI) is the noise-induced bias in the magnitude signals. This problem is particularly pressing in diffusion MRI at high diffusion-weighting. In this paper, we present a three-stage scheme to solve this problem by transforming noisy nonCentral Chi signals to noisy Gaussian signals. A special case of nonCentral Chi distribution is the Rician distribution. In general, the Gaussian-distributed signals are of interest rather than the Gaussian-derived (e.g., Rayleigh, Rician, and nonCentral Chi) signals because the Gaussian-distributed signals are generally more amenable to statistical treatment through the principle of least squares. Monte Carlo simulations were used to validate the statistical properties of the proposed framework. This scheme opens up the possibility of investigating the low signal regime (or high diffusion-weighting regime in the case of diffusion MRI) that contains potentially important information about biophysical processes and structures of the brain.


title: Multiprocessor fixed priority scheduling with limited preemptions

abstract: Challenges associated with allowing preemptions and migrations are compounded in multicore systems, particularly under global scheduling policies, because of the potentially high overheads. For example, multiple levels of cache greatly increase preemption and migration related overheads as well as the difficulty involved in accurately accounting for them, leading to substantially inflated worst-case execution times (WCETs). Preemption and migration related overheads can be significantly reduced, both in number and in size, by using fixed preemption points in the tasks' code; thus dividing each task into a series of non-preemptive regions (NPRs). This leads to an additional consideration in the scheduling policy. When a high priority task is released and all of the processors are executing non-preemptive regions of lower priority tasks, then there is a choice to be made in terms of how to manage the next preemption. With an eager approach the first lower priority task to reach a preemption point is preempted even if it is not the lowest priority running task. Alternatively, with a lazy approach, preemption is delayed until the lowest priority currently running task reaches its next preemption point. In this paper, we show that under global fixed priority scheduling with eager preemptions each task suffers from at most a single priority inversion each time it resumes execution. Building on this observation, we derive a new response time based schedulability test for tasks with fixed preemption points. Experimental evaluations show that global fixed priority scheduling with eager preemptions is significantly more effective than with lazy preemption using link based scheduling in terms of task set schedulability.


title: Decision Mining in ProM

abstract: Process-aware Information Systems typically log events (e.g., in transaction logs or audit trails) related to the actual business process executions. Proper analysis of these execution logs can yield important knowledge that can help organizations to improve the quality of their services. Starting from a process model, which can be discovered by conventional process mining algorithms, we analyze how data attributes influence the choices made in the process based on past process executions. Decision mining, also referred to as decision point analysis, aims at the detection of data dependencies that affect the routing of a case. In this paper we describe how machine learning techniques can be leveraged for this purpose, and we present a Decision Miner implemented within the ProM framework.


title: Sparsity Is Better with Stability: Combining Accuracy and Stability for Model Selection in Brain Decoding

abstract: Structured sparse methods have received significant attention in neuroimaging. These methods allow the incorporation of domain knowledge through additional spatial and temporal constraints in the predictive model and carry the promise of being more interpretable than non-structured sparse methods, such as LASSO or Elastic Net methods. However, although sparsity has often been advocated as leading to more interpretable models it can also lead to unstable models under subsampling or slight changes of the experimental conditions. In the present work we investigated the impact of using stability/reproducibility as an additional model selection criterion on several different sparse (and structured sparse) methods that have been recently applied for fMRI brain decoding. We compared three different model selection criteria: (i) classification accuracy alone; (ii) classification accuracy and overlap between the solutions; (iii) classification accuracy and correlation between the solutions. The methods we considered include LASSO, Elastic Net, Total Variation, sparse Total Variation, Laplacian and Graph Laplacian Elastic Net (GraphNET). Our results show that explicitly accounting for stability/reproducibility during the model optimisation can mitigate some of the instability inherent in sparse methods. In particular, using accuracy and overlap between the solutions as a joint optimisation criterion can lead to solutions that are more similar in terms of accuracy, sparsity levels and coefficient maps even when different sparsity methods are considered.


title: Texture-based liberation models for comminution

abstract: The relation between breakage mechanisms and liberation is critical in mineral processing. Recent studies underline the importance of texture in liberation. This study reviews relevant liberation m ...


title: Choosing the right business process maturity model

abstract: We have built and tested a decision tool which will help organisations properly select one business process maturity model (BPMM) over another. This prototype consists of a novel questionnaire with decision criteria for BPMM selection, linked to a unique data set of 69 BPMMs. Fourteen criteria (questions) were elicited from an international Delphi study, and weighed by the analytical hierarchy process. Case studies have shown (non-)profit and academic applications. Our purpose was to describe criteria that enable an informed BPMM choice (conform to decision-making theories, rather than ad hoc). Moreover, we propose a design process for building BPMM decision tools.


title: IDPS: An Integrated Intrusion Handling Model for Cloud

abstract: Today, many organizations are moving their computing services towards the Cloud. This makes their computer processing available much more conveniently to users. However, it also brings new security threats and challenges about safety and reliability. In fact, Cloud Computing is an attractive and cost-saving service for buyers as it provides accessibility and reliability options for users and scalable sales for providers. In spite of being attractive, Cloud feature poses various new security threats and challenges when it comes to deploying Intrusion Detection System (IDS) in Cloud environments. Most Intrusion Detection Systems (IDSs) are designed to handle specific types of attacks. It is evident that no single technique can guarantee protection against future attacks. Hence, there is a need for an integrated scheme which can provide robust protection against a complete spectrum of threats. On the other hand, there is great need for technology that enables the network and its hosts to defend themselves with some level of intelligence in order to accurately identify and block malicious traffic and activities. In this case, it is called Intrusion prevention system (IPS). Therefore, in this paper, we emphasize on recent implementations of IDS on Cloud Computing environments in terms of security and privacy. We propose an effective and efficient model termed as the Integrated Intrusion Detection and Prevention System (IDPS) which combines both IDS and IPS in a single mechanism. Our mechanism also integrates two techniques namely, Anomaly Detection (AD) and Signature Detection (SD) that can work in cooperation to detect various numbers of attacks and stop them through the capability of IPS.


A Design and Analysis of Graphical Password


title: Sparse Phase Retrieval: Uniqueness Guarantees and Recovery Algorithms

abstract: The problem of signal recovery from its Fourier transform magnitude, or equivalently, autocorrelation, is of paramount importance in various fields of engineering and has been around for over 100 years. In order to achieve this, additional structure information about the signal is necessary. In this work, we first provide simple and general conditions, which when satisfied, allow unique recovery almost surely. In particular, we focus our attention on sparse signals and show that most O(n)-sparse signals, i.e., signals with O(n) non-zero components, have distinct Fourier transform magnitudes (up to time-shift, time-reversal and global sign). Our results are a significant improvement over the existing identifiability results, which provide such guarantees for only O(n^{1/4})-sparse signals. Then, we exploit the sparse nature of the signals and develop a Two-stage Sparse Phase Retrieval algorithm (TSPR), which involves: (i) identifying the support, i.e., the locations of the non-zero components, of the signal (ii) identifying the signal values using the support knowledge. We show that the proposed algorithm can provably recover O(n^{1/2-\eps})-sparse signals (up to time-shift, time-reversal and global sign) with arbitrarily high probability in quadratic-time. To the best of our knowledge, state of the art phase retrieval algorithms provide such recovery guarantees for only O(n^{1/4})-sparse signals. Numerical experiments complement our theoretical analysis and verify the effectiveness of the proposed algorithms.


Worm Classification and Detection using Data Mining Techniques


title: A new technique for characterization of digital-to-analog converters in high-speed systems

abstract: In this paper, a new technique for characterization of digital-to-analog converters (DAC) used in wideband applications is described. Unlike the standard narrowband approach, this technique employs Least Square Estimation to characterize the DAC from dc to any target frequency. Characterization is performed using a random sequence with certain temporal and probabilistic characteristics suitable for intended operating conditions. The technique provides a linear estimation of the system and decomposes nonlinearity into higher-order harmonics and deterministic periodic noise. The technique can also be used to derive the impulse response of the converter, predict its operating bandwidth, and provide far more insight into its sources of distortion.


title: An overview of the MOP runtime verification framework

abstract: This article gives an overview of the, monitoring oriented programming framework (MOP). In MOP, runtime monitoring is supported and encouraged as a fundamental principle for building reliable systems. Monitors are automatically synthesized from specified properties and are used in conjunction with the original system to check its dynamic behaviors. When a specification is violated or validated at runtime, user-defined actions will be triggered, which can be any code, such as information logging or runtime recovery. Two instances of MOP are presented: JavaMOP (for Java programs) and BusMOP (for monitoring PCI bus traffic). The architecture of MOP is discussed, and an explanation of parametric trace monitoring and its implementation is given. A comprehensive evaluation of JavaMOP attests to its efficiency, especially in comparison with similar systems. The implementation of BusMOP is discussed in detail. In general, BusMOP imposes no runtime overhead on the system it is monitoring.


title: A Character-Level Method for Text Classification

abstract: We propose a language model of mix CNN (Convolution Neural Network) with bi-RNN (Bi-directional Recurrent Neural Network) to classify the text at the character-level. Unlike word-level model is that avoiding the problem of unregistered words and improves the robustness of the text representation in character-level model. The language model mainly uses the data augment by different convolution filters of CNN and then the bi-RNN obtain the contextual information in both directions to classify the text. The results show that this model have a better performance than the common CNN and LSTM(long short-term memory) classification methods.


title: A Scalable Streaming Big Data Architecture for Real-Time Sentiment Analysis

abstract: The systems with a short window of opportunity for actions and decisions require developing solutions providing real-time streaming analytics. Real-time big data streaming analytics is a challenging task. In this paper, we propose a streaming big data architecture for real-time social network analysis. As a case study, we investigated the relation between the public opinions on social media about cryptocurrencies and the changes in their prices using lexicon-based sentiment analysis approaches with the goal of assessing the feasibility of predicting the prices of cryptocurrencies. Two different approaches with two lexicons were used for sentiment analysis score calculations to assess the consistency of correlation measures on the collected dataset. Our model indicates that the prediction of cryptocurrency price changes using lexicon-based sentiment analysis methods is not reliable.


title: 18.4 A 4.9mΩ-sensitivity mobile electrical impedance tomography IC for early breast-cancer detection system

abstract: A mobile electrical impedance tomography (EIT) IC is proposed for early breast cancer detection personally at home. To assemble the entire system into a simple brassiere shape, EIT IC is integrated via a multi-layered fabric circuit board which includes 90 EIT electrodes and two reference electrodes for current stimulation and voltage sensing. The IC supports three operating modes; gain scanning, contact impedance monitoring, and EIT modes for the clear EIT image. A differential sinusoidal current stimulator (DSCS) is proposed for injection of low-distortion programmable current which has harmonics less than $-$ 59 dBc at a load impedance of 2 kΩ. To get high sensitivity, a 6-channel voltage sensing amplifier can adaptively control the gain up to a maximum of 60 dB, and has low input referred noise, 36 nV/ $\surd$ Hz. The 2.5 × 5 mm chip is fabricated in a 0.18 µm 1P6M CMOS process and consumes 53.4 mW on average. As a result, a sensitivity of 4.9 mΩ is achieved which enables the detection of a 5 mm cancer mass within an agar test phantom.


title: M2M Service Platforms: Survey, Issues, and Enabling Technologies

abstract: Machine-to-Machine (M2M) refers to technologies with various applications. In order to provide the vision and goals of M2M, an M2M ecosystem with a service platform must be established by the key players in industrial domains so as to substantially reduce development costs and improve time to market of M2M devices and services. The service platform must be supported by M2M enabling technologies and standardization. In this paper, we present a survey of existing M2M service platforms and explore the various research issues and challenges involved in enabling an M2M service platform. We first classify M2M nodes according to their characteristics and required functions, and we then highlight the features of M2M traffic. With these in mind, we discuss the necessity of M2M platforms. By comparing and analyzing the existing approaches and solutions of M2M platforms, we identify the requirements and functionalities of the ideal M2M service platform. Based on these, we propose an M2M service platform (M2SP) architecture and its functionalities, and present the M2M ecosystem with this platform. Different application scenarios are given to illustrate the interaction between the components of the proposed platform. In addition, we discuss the issues and challenges of enabling technologies and standardization activities, and outline future research directions for the M2M network.


title: Finding the optimal cut-point for Gaussian and Gamma distributed biomarkers

abstract: Categorization is often needed for clinical decision making when dealing with diagnostic (prognostic) biomarkers and a binary outcome (true disease status). Four common methods used to dichotomize a continuous biomarker X are compared: the minimum P-value, the Youden index, the concordance probability and the point closest-to-(0, 1) corner in the ROC plane. These methods are compared from a theoretical point of view under Normal or Gamma biomarker distributions, showing whether or not they lead to the identification of the same true cut-point. The performance of the corresponding non-parametric estimators is then compared by simulation. Two motivating examples are presented. In all simulation scenarios, the point closest-to-(0, 1) corner in the ROC plane and concordance probability approaches outperformed the other methods. Both these methods showed good performance in the estimation of the optimal cut-point of a biomarker. However, when methods do not lead to the same optimal cut-point, scientists should focus on which one is truly what they want to estimate, and use it in practice. In addition, to improve communicability, the Youden index or the concordance probability associated to the estimated cut-point could be reported to summarize the associated classification accuracy. The use of the minimum P-value approach for cut-point finding is strongly not recommended because its objective function is computed under the null hypothesis of absence of association between the true disease status and X. This is in contrast with the presence of some discrimination potential of X that leads to the dichotomization issue.


title: Vector field design on surfaces

abstract: Vector field design on surfaces is necessary for many graphics applications: example-based texture synthesis, nonphotorealistic rendering, and fluid simulation. For these applications, singularities contained in the input vector field often cause visual artifacts. In this article, we present a vector field design system that allows the user to create a wide variety of vector fields with control over vector field topology, such as the number and location of singularities. Our system combines basis vector fields to make an initial vector field that meets user specifications.The initial vector field often contains unwanted singularities. Such singularities cannot always be eliminated due to the Poincaré-Hopf index theorem. To reduce the visual artifacts caused by these singularities, our system allows the user to move a singularity to a more favorable location or to cancel a pair of singularities. These operations offer topological guarantees for the vector field in that they only affect user-specified singularities. We develop efficient implementations of these operations based on Conley index theory. Our system also provides other editing operations so that the user may change the topological and geometric characteristics of the vector field.To create continuous vector fields on curved surfaces represented as meshes, we make use of the ideas of geodesic polar maps and parallel transport to interpolate vector values defined at the vertices of the mesh. We also use geodesic polar maps and parallel transport to create basis vector fields on surfaces that meet the user specifications. These techniques enable our vector field design system to work for both planar domains and curved surfaces.We demonstrate our vector field design system for several applications: example-based texture synthesis, painterly rendering of images, and pencil sketch illustrations of smooth surfaces.