title: Marker based attendance systems in education process

abstract: This paper describes a simply use case, to do students attendance using only classroom live video stream and a simply organized list of optical markers. Each student is wearing his own marker on visible part of his body. This marker exactly defines its own ID and cannot be interchanged with another one. As marker detection method / system we used Aruco markers. This selection is a result of the attached simply state of the art marker detection methods comparison. We have done a series of test to determine the best marker wearing place on human body for best detection rate. After evaluating all researched facts the attendance evaluation model was designed.


title: A saturating lumped-parameter model for an interior PM synchronous machine

abstract: This paper presents a lumped-parameter magnetic model for an interior permanent-magnet synchronous machine. The model accounts for the effects of saturation through a nonlinear reluctance-element network used to estimate the q-axis inductance. The magnetic model is used to calculate inductance and torque in the presence of saturation. Furthermore, these calculations are compared to those from finite-element analysis with good agreement.


title: Angiogenesis in Pancreatic Cancer: Pre-Clinical and Clinical Studies

abstract: Angiogenesis is a crucial event in tumor development and progression, occurring by different mechanisms and it is driven by pro- and anti-angiogenic molecules. Pancreatic cancer vascularization is characterized by a high microvascular density, impaired microvessel integrity and poor perfused vessels with heterogeneous distribution. In this review article, after a brief introduction on pancreatic cancer classification and on angiogenesis mechanisms involved in its progression, the pre-clinical and clinical trials conducted in pancreatic cancer treatment using anti-angiogenic inhibitors will be described. Finally, we will discuss the anti-angiogenic therapy paradox between the advantage to abolish vessel supply to block tumor growth and the disadvantage due to reduction of drug delivery at the same time. The purpose is to identify new anti-angiogenic molecules that may enhance treatment regimen.


E-Banking Security using Cryptography, Steganography and Data Mining


title: An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization

abstract: Bagging and boosting are methods that generate a diverse ensemble of classifiers by manipulating the training data given to a “base” learning algorithm. Breiman has pointed out that they rely for their effectiveness on the instability of the base learning algorithm. An alternative approach to generating an ensemble is to randomize the internal decisions made by the base algorithm. This general approach has been studied previously by Ali and Pazzani and by Dietterich and Kong. This paper compares the effectiveness of randomization, bagging, and boosting for improving the performance of the decision-tree algorithm C4.5. The experiments show that in situations with little or no classification noise, randomization is competitive with (and perhaps slightly superior to) bagging but not as accurate as boosting. In situations with substantial classification noise, bagging is much better than boosting, and sometimes better than randomization.


title: 60-GHz Thin Broadband High-Gain LTCC Metamaterial-Mushroom Antenna Array

abstract: A low-profile broadband metamaterial-mushroom antenna array is proposed for high-gain 60-GHz band applications. The antenna array consists of a single-layer mushroom radiating structure and a simplified single-layer substrate integrated waveguide (SIW) feeding network. A new transmission-line based model is presented to estimate the resonant frequencies of the operating TM10 and antiphase TM20 modes. With the metamaterial-mushroom structure, the closely-placed antenna elements realize less mutual couplings, higher aperture illumination efficiency compared to conventional patch antennas. An 8 × 8 mushroom antenna array is designed and prototyped using low-temperature cofired ceramic (LTCC), the measurement shows the antenna array with the feeding transition proposed an impedance bandwidth of 56.3-65.7 GHz with a boresight gain greater than 21.2 dBi and up to 24.2 dBi at 62.3 GHz. The proposed antenna array features the merits of high gain, broadband, compact size, and low cross-polarization levels.


title: A diverse range of gene products are effectors of the type I interferon antiviral response

abstract: There is growing interest in antiviral interferon-stimulated genes (ISGs), such as IFITM3 and BST2 (also known as tetherin), because of their potential as drug targets. An overexpression screen has been used to assess the impact of several hundred ISGs on the replication of a number of viruses, including HIV-1 and hepatitis C virus. Combinations of validated antiviral ISGs were found to have additive effects and to converge on translational inhibition. Surprisingly, some ISGs actually enhance the replication of certain viruses, underlining the complexity of the response to interferon. The type I interferon response protects cells against invading viral pathogens. The cellular factors that mediate this defence are the products of interferon-stimulated genes (ISGs). Although hundreds of ISGs have been identified since their discovery more than 25 years ago1,2,3, only a few have been characterized with respect to antiviral activity. For most ISG products, little is known about their antiviral potential, their target specificity and their mechanisms of action. Using an overexpression screening approach, here we show that different viruses are targeted by unique sets of ISGs. We find that each viral species is susceptible to multiple antiviral genes, which together encompass a range of inhibitory activities. To conduct the screen, more than 380 human ISGs were tested for their ability to inhibit the replication of several important human and animal viruses, including hepatitis C virus, yellow fever virus, West Nile virus, chikungunya virus, Venezuelan equine encephalitis virus and human immunodeficiency virus type-1. Broadly acting effectors included IRF1, C6orf150 (also known as MB21D1), HPSE, RIG-I (also known as DDX58), MDA5 (also known as IFIH1) and IFITM3, whereas more targeted antiviral specificity was observed with DDX60, IFI44L, IFI6, IFITM2, MAP3K14, MOV10, NAMPT (also known as PBEF1), OASL, RTP4, TREX1 and UNC84B (also known as SUN2). Combined expression of pairs of ISGs showed additive antiviral effects similar to those of moderate type I interferon doses. Mechanistic studies uncovered a common theme of translational inhibition for numerous effectors. Several ISGs, including ADAR, FAM46C, LY6E and MCOLN2, enhanced the replication of certain viruses, highlighting another layer of complexity in the highly pleiotropic type I interferon system.


title: Research and Challenges on Bitcoin Anonymity

abstract: Bitcoin has emerged as the most successful crypto currency since its appearance back in 2009. Besides its security robustness, two main properties have probably been its key to success: anonymity and decentralization. In this paper, we provide a comprehensive description on the details that make such cryptocurrency an interesting research topic in the privacy community. We perform an exhaustive review of the bitcoin anonymity research papers that have been published so far and we outline some research challenges on that topic.


title: User Modeling for Adaptive News Access

abstract: We present a framework for adaptive news access, based on machine learning techniques specifically designed for this task. First, we focus on the system's general functionality and system architecture. We then describe the interface and design of two deployed news agents that are part of the described architecture. While the first agent provides personalized news through a web-based interface, the second system is geared towards wireless information devices such as PDAs (personal digital assistants) and cell phones. Based on implicit and explicit user feedback, our agents use a machine learning algorithm to induce individual user models. Motivated by general shortcomings of other user modeling systems for Information Retrieval applications, as well as the specific requirements of news classification, we propose the induction of hybrid user models that consist of separate models for short-term and long-term interests. Furthermore, we illustrate how the described algorithm can be used to address an important issue that has thus far received little attention in the Information Retrieval community: a user's information need changes as a direct result of interaction with information. We empirically evaluate the system's performance based on data collected from regular system users. The goal of the evaluation is not only to understand the performance contributions of the algorithm's individual components, but also to assess the overall utility of the proposed user modeling techniques from a user perspective. Our results provide empirical evidence for the utility of the hybrid user model, and suggest that effective personalization can be achieved without requiring any extra effort from the user.


title: Personalized interactive faceted search

abstract: Faceted search is becoming a popular method to allow users to interactively search and navigate complex information spaces. A faceted search system presents users with key-value metadata that is used for query refinement. While popular in e-commerce and digital libraries, not much research has been conducted on which metadata to present to a user in order to improve the search experience. Nor are there repeatable benchmarks for evaluating a faceted search engine. This paper proposes the use of collaborative filtering and personalization to customize the search interface to each user's behavior. This paper also proposes a utility based framework to evaluate the faceted interface. In order to demonstrate these ideas and better understand personalized faceted search, several faceted search algorithms are proposed and evaluated using the novel evaluation methodology.


title: Scalable Dynamic Topic Modeling with Clustered Latent Dirichlet Allocation (CLDA)

abstract: Topic modeling is an increasingly important component of Big Data analytics, enabling the sense-making of highly dynamic and diverse streams of text data. Traditional methods such as Dynamic Topic Modeling (DTM), while mathematically elegant, do not lend themselves well to direct parallelization because of dependencies from one time step to another. Data decomposition approaches that partition data across time segments and then combine results in a global view of the dynamic change of topics enable execution of topic models on much larger datasets than is possibly without data decomposition. However, these methods are difficult to analyze mathematically and are relatively untested for quality of topics and performance on parallel systems. In this paper, we introduce and empirically analyze Clustered Latent Dirichlet Allocation (CLDA), a method for extracting dynamic latent topics from a collection of documents. CLDA uses a data decomposition strategy to partition data. CLDA takes advantage of parallelism, enabling fast execution for even very large datasets and a large number of topics. A large corpus is split into local segments to extract textual information from different time steps. Latent Dirichlet Allocation (LDA) is applied to infer topics at local segments. The results are merged, and clustering is used to combine topics from different segments into global topics. Results show that the perplexity is comparable and that topics generated by this algorithm are similar to those generated by DTM. In addition, CLDA is two orders of magnitude faster than existing approaches and allows for more freedom of experiment design. In this paper CLDA is applied successfully to seventeen years of NIPS conference papers, seventeen years of computer science journal abstracts, and to forty years of the PubMed corpus.


title: Color, context, and compositionality

abstract: Color adjectives have played a central role in work on language typology and variation, but there has been relatively little investigation of their meanings by researchers in formal semantics. This is surprising given the fact that color terms have been at the center of debates in the philosophy of language over foundational questions, in particular whether the idea of a compositional, truth-conditional theory of natural language semantics is even coherent. The challenge presented by color terms is articulated in detail in the work of Charles Travis. Travis argues that structurally isomorphic sentences containing color adjectives can shift truth value from context to context depending on how they are used and in the absence of effects of vagueness or ambiguity/polysemy, and concludes that a deterministic mapping from structures to truth conditions is impossible. The goal of this paper is to provide a linguistic perspective on this issue, which we believe defuses Travis’ challenge. We provide empirical arguments that color adjectives are in fact ambiguous between gradable and nongradable interpretations, and that this simple ambiguity, together with independently motivated options concerning scalar dimension within the gradable reading accounts for the Travis facts in a simpler, more constrained, and thus ultimately more successful fashion than recent contextualist analyses such as those in Szabo (Perspectives on semantics, pragmatics and discourse: A festschrift for Ferenc Kiefer, 2001) or Rothschild and Segal (Mind Lang, 2009).


title: Designing and implementing a cyberwar laboratory exercise for a computer security course

abstract: The development of a cyberwar laboratory exercise for a computer security course raises many pedagogical and management issues relating to the structure of the laboratory, its network and the exercise itself. We have designed, implemented and given such an exercise, and faced many of these issues. Evaluation of this exercise leads to multiple insights about the proper goals, structure and implementation of such an exercise.


Trend Analysis of News Topics on Twitter


title: Discriminative spoken language understanding using word confusion networks

abstract: Current commercial dialogue systems typically use hand-crafted grammars for Spoken Language Understanding (SLU) operating on the top one or two hypotheses output by the speech recogniser. These systems are expensive to develop and they suffer from significant degradation in performance when faced with recognition errors. This paper presents a robust method for SLU based on features extracted from the full posterior distribution of recognition hypotheses encoded in the form of word confusion networks. Following [1], the system uses SVM classifiers operating on n-gram features, trained on unaligned input/output pairs. Performance is evaluated on both an off-line corpus and on-line in a live user trial. It is shown that a statistical discriminative approach to SLU operating on the full posterior ASR output distribution can substantially improve performance both in terms of accuracy and overall dialogue reward. Furthermore, additional gains can be obtained by incorporating features from the previous system output.


Magellan: A Searchable Metadata Architecture for Large-Scale File Systems


title: Improving deep neural networks for LVCSR using rectified linear units and dropout

abstract: Recently, pre-trained deep neural networks (DNNs) have outperformed traditional acoustic models based on Gaussian mixture models (GMMs) on a variety of large vocabulary speech recognition benchmarks. Deep neural nets have also achieved excellent results on various computer vision tasks using a random “dropout” procedure that drastically improves generalization error by randomly omitting a fraction of the hidden units in all layers. Since dropout helps avoid over-fitting, it has also been successful on a small-scale phone recognition task using larger neural nets. However, training deep neural net acoustic models for large vocabulary speech recognition takes a very long time and dropout is likely to only increase training time. Neural networks with rectified linear unit (ReLU) non-linearities have been highly successful for computer vision tasks and proved faster to train than standard sigmoid units, sometimes also improving discriminative performance. In this work, we show on a 50-hour English Broadcast News task that modified deep neural networks using ReLUs trained with dropout during frame level training provide an 4.2% relative improvement over a DNN trained with sigmoid units, and a 14.4% relative improvement over a strong GMM/HMM system. We were able to obtain our results with minimal human hyper-parameter tuning using publicly available Bayesian optimization code.


title: Interactive painterly stylization of images, videos and 3D animations

abstract: We introduce a real-time system that converts images, video, or 3D animation sequences to artistic renderings in various painterly styles. The algorithm, which is entirely executed on the GPU, can efficiently process 512 resolution frames containing 60,000 individual strokes at over 30 fps. In order to exploit the parallel nature of GPUs, our algorithm determines the placement of strokes entirely from local pixel neighborhood information. The strokes are rendered as point sprites with textures. Temporal coherence is achieved by treating the brush strokes as particles and moving them based on optical flow. Our system renders high quality results while allowing the user interactive control over many stylistic parameters such as stroke size, texture and density.


title: Some Aspects of Stability in Microgrids

abstract: This paper investigates some aspects of stability in microgrids. There are different types of microgrid applications. The system structure and the control topology vary depending on the application and so does the aspect of stability in a microgrid. This paper briefly encompasses the stability aspects of remote, utility connected and facility microgrids depending on the modes of operation, control topology, types of micro sources and network parameters. The small signal, transient and the voltage stability aspects in each type of the microgrid are discussed along with scope of improvements. With a brief review of the existing microgrid control methods in the literature and different industry solutions, this paper sets up an initial platform for different types of microgrids stability assessment. Various generalized stability improvement methods are demonstrated for different types of microgrids. The conventional stability study of microgrids presented in this paper facilitates an organized way to plan the micro source operation, microgrid controller design, islanding procedure, frequency control and the load shedding criteria. The stability investigations are presented with different control methods, eigen value analysis and time domain simulations to justify different claims.


title: Modeling and real-time simulation architectures for virtual prototyping of off-road vehicles

abstract: Virtual Reality-based simulation technology has evolved as a useful design and analysis tool at an early stage in the design for evaluating performance of human-operated agricultural and construction machinery. Detecting anomalies in the design prior to building physical prototypes and expensive testing leads to significant cost savings. The efficacy of such simulation technology depends on how realistically the simulation mimics the real-life operation of the machinery. It is therefore necessary to achieve ‘real-time’ dynamic simulation of such machines with operator-in-the-loop functionality. Such simulation often leads to intensive computational burdens. A distributed architecture was developed for off-road vehicle dynamic models and 3D graphics visualization to distribute the overall computational load of the system across multiple computational platforms. Multi-rate model simulation was also used to simulate various system dynamics with different integration time steps, so that the computational power can be distributed more intelligently. This architecture consisted of three major components: a dynamic model simulator, a virtual reality simulator for 3D graphics, and an interface to the controller and input hardware devices. Several off-road vehicle dynamics models were developed with varying degrees of fidelity, as well as automatic guidance controller models and a controller area network interface to embedded controllers and user input devices. The simulation architecture reduced the computational load to an individual machine and increased the real-time simulation capability with complex off-road vehicle system models and controllers. This architecture provides an environment to test virtual prototypes of the vehicle systems in real-time and the opportunity to test the functionality of newly developed controller software and hardware.


title: Spatial-Temporal Algorithm for Moving Objects Detection in Infrared Video Sequences

abstract: Performance of moving objects detection algorithm based on spatiotemporal blocks on infrared videos is discussed. The algorithm decomposes spatiotemporal blocks using dimensionality reduction technique to obtain a compact vector representation of each block and to suppress the influence of noise. The proposed method is evaluated on monochrome and multispectral DR videos.


title: Reconciling Manual and Automated Testing: The AutoTest Experience

abstract: Software can be tested either manually or automatically. The two approaches are complementary: automated testing can perform a large number of tests in little time, whereas manual testing uses the knowledge of the testing engineer to target testing to the parts of the system that are assumed to be more error-prone. Despite this complementarity, tools for manual and automatic testing are usually different, leading to decreased productivity and reliability of the testing process. AutoTest is a testing tool that provides a "best of both worlds" strategy: it integrates developers' test cases into an automated process of systematic contract-driven testing. This allows it to combine the benefits of both approaches while keeping a simple interface, and to treat the two types of tests in a unified fashion: evaluation of results is the same, coverage measures are added up, and both types of tests can be saved in the same format


title: Real-time free space detection and navigation using omnidirectional vision and parametric and geometric active contours.

abstract: This paper contributes to adapt parametric and geometric active contour methods in a new framework to handle real time free space extraction while taking advantage of the properties of omnivision. Both methods were formally and algorithmically adapted and improved. Some comparative results, achieved on unknown indoor and outdoor images, are presented to validate the efficiency of our two snake based approaches. We also show that active contours can be applied to make a robot navigate autonomously, only using real omni-images, thanks to the extracted free space skeleton.


Application of Recurrent Neural Networks for User Verification based on Keystroke Dynamics


title: Chip-packaging interaction: a critical concern for Cu/low k packaging

abstract: Chip-packaging interaction is becoming a critical reliability issue for Cu/low k chips during package assembly. With the traditional TEOS interlevel dielectric being replaced by much weaker low k dielectrics, packaging induced interfacial delamination in low k interconnects has been widely observed, raising serious reliability concerns for Cu/low k chips. In a flip-chip package, the thermal deformation of the package can be directly coupled into the Cu/low k interconnect structure inducing large local deformation to drive interfacial crack formation. In this paper, we will first review the experimental techniques for package thermal deformation measurement and interfacial fracture energy measurement for low k interfaces. Then 3D finite element analysis (FEA) based on a multilevel sub-modeling approach in combina


title: Word Segmentation of Informal Arabic with Domain Adaptation

abstract: Segmentation of clitics has been shown to improve accuracy on a variety of Arabic NLP tasks. However, state-of-the-art Arabic word segmenters are either limited to formal Modern Standard Arabic, performing poorly on Arabic text featuring dialectal vocabulary and grammar, or rely on linguistic knowledge that is hand-tuned for each dialect. We extend an existing MSA segmenter with a simple domain adaptation technique and new features in order to segment informal and dialectal Arabic text. Experiments show that our system outperforms existing systems on newswire, broadcast news and Egyptian dialect, improvingsegmentationF1 scoreonarecently released Egyptian Arabic corpus to 95.1%, compared to 90.8% for another segmenter designed specifically for Egyptian Arabic.


Character and Subword-Based Word Representation for Neural Language Modeling Prediction


title: Nonconvex Splitting for Regularized Low-Rank + Sparse Decomposition

abstract: We develop new nonconvex approaches for matrix optimization problems involving sparsity. The heart of the methods is a new, nonconvex penalty function that is designed for efficient minimization by means of a generalized shrinkage operation. We apply this approach to the decomposition of video into low rank and sparse components, which is able to separate moving objects from the stationary background better than in the convex case. In the case of noisy data, we add a nonconvex regularization, and apply a splitting approach to decompose the optimization problem into simple, parallelizable components. The nonconvex regularization ameliorates contrast loss, thereby allowing stronger denoising without losing more signal to the residual.


title: Comparative Survey of Indoor Positioning Technologies, Techniques, and Algorithms

abstract: The user location information represents a core dimension as understanding user context is a prerequisite for providing human-centered services that generally improve quality of life. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task due in part to the expected various objects (such as walls and people) that reflect and disperse signals. In this paper, we survey the related work in the field of indoor positioning by providing a comparative analysis of the state-of-the-art technologies, techniques, and algorithms. Unlike previous studies and surveys, our survey present new taxonomies, review some major recent advances, and argue on the area open problems and future potential. We believe this paper would spur further exploration by the research community of this challenging problem space.


title: Molecular evidence of kobuviruses in free-ranging red foxes (Vulpes vulpes)

abstract: Abstract ::: Red foxes (Vulpes vulpes) are susceptible to viral diseases of domestic carnivores. In this study, by screening rectal swabs collected from 34 red foxes in Italy, we identified kobuvirus RNA in five samples. Based on analysis of partial RdRp and full-length VP1 genes, all of the strains shared the highest identity with canine kobuviruses (CaKVs) recently detected in the US, the UK and Italy. These findings provide the first evidence of the circulation of these novel viruses in foxes.


title: Two-stream Attentive CNNs for Image Retrieval

abstract: In content-based image retrieval, the most challenging (and ambiguous) part is to define the similarity between images. For the human-being, such similarity can be defined with respect to where they pay attention to and what semantic attributes they understand. Inspired by this fact, this paper presents two-stream attentive CNNs for image retrieval. As the human-being does, the proposed network has two streams that simultaneously handle two tasks. The Main stream focuses on extracting discriminative visual features that are tightly correlated with semantic attributes. Meanwhile, the Auxiliary stream aims to facilitate the main stream by redirecting the feature extraction operation mainly to the image content that human may pay attention to. By fusing these two streams into the Main and Auxiliary CNNs (MAC), image similarity can be computed as the human-being does by reserving the conspicuous content and suppressing the irrelevant regions. Extensive experiments show that the proposed model achieves impressive performance in image retrieval on four public datasets.


title: Review TRENDS in Cognitive Sciences Vol.10 No.7 July 2006 Special Issue: Probabilistic models of cognition Bayesian decision theory in sensorimotor control

abstract: Action selection is a fundamental decision process for us, and depends on the state of both our body and the environment. Because signals in our sensory and motor systems are corrupted by variability or noise, the nervous system needs to estimate these states. To select an optimal action these state estimates need to be combined with knowledge of the potential costs or rewards of different action outcomes. We review recent studies that have investigated the mechanisms used by the nervous system to solve such estimation and decision problems, which show that human behaviour is close to that predicted by Bayesian Decision Theory. This theory defines optimal behaviour in a world characterized by uncertainty, and provides a coherent way of describing sensorimotor processes.


title: Modeling and Sliding Mode Control of Dc-Dc Buck-Boost Converter

abstract: In this paper a dc-dc buck-boost converter is modeled and controlled using sliding mode technique. First the buck-boost converter is modeled and dynamic equations describing the converter are derived and sliding mode controller is designed. The robustness of the converter system is tested against step load changes and input voltage variations. Matlab/Simulink is used for the simulations. The simulation results are presented..


title: A Closer Look at Memorization in Deep Networks

abstract: We examine the role of memorization in deep learning, drawing connections to capacity, generalization, and adversarial robustness. While deep networks are capable of memorizing noise data, our results suggest that they tend to prioritize learning simple patterns first. In our experiments, we expose qualitative differences in gradient-based optimization of deep neural networks (DNNs) on noise vs. real data. We also demonstrate that for appropriately tuned explicit regularization (e.g., dropout) we can degrade DNN training performance on noise datasets without compromising generalization on real data. Our analysis suggests that the notions of effective capacity which are dataset independent are unlikely to explain the generalization performance of deep networks when trained with gradient based methods because training data itself plays an important role in determining the degree of memorization.


On the Cost of Factoring RSA-1024


title: A Dynamic Virtual Machine Placement and Migration Scheme For Data Centers

abstract: We study the problem of virtual machine (VM) placement and migration in a data center. In the current approaches, VMs are assigned to physical servers using on-demand provisioning. Such an approach is simple but it often results in a poor performance due to resource fragmentation. Additionally, sub-optimal VM placement usually generates unneeded VM migration and unnecessary cross network traffic. The efficiency of a datacenter therefore significantly depends on how VMs are provisioned and where they are placed. A good placement scheme will not only improve the quality of service but also reduce the operation cost of the data center. In this paper, we study the problem of optimal VM placement and migration to minimize resource usage and power consumption in a data center. We formulate the optimization problem as a joint multiple objective function and solve it by leveraging the framework of convex optimization. Due to the intractable nature of the combinatorial optimization, we then propose Multi-level Join VM Placement and Migration (MJPM) algorithms based on the relaxed convex optimization framework to approximate the optimal solution. The theoretical analysis demonstrates the effectiveness of our proposed algorithms that substantially increases data center efficiency. In addition, our extensive simulation results on different practical topologies show significant performance improvement over the existing approaches.


title: A case for exploiting subarray-level parallelism (SALP) in DRAM

abstract: Modern DRAMs have multiple banks to serve multiple memory requests in parallel. However, when two requests go to the same bank, they have to be served serially, exacerbating the high latency of off-chip memory. Adding more banks to the system to mitigate this problem incurs high system cost. Our goal in this work is to achieve the benefits of increasing the number of banks with a low cost approach. To this end, we propose three new mechanisms that overlap the latencies of different requests that go to the same bank. The key observation exploited by our mechanisms is that a modern DRAM bank is implemented as a collection of subarrays that operate largely independently while sharing few global peripheral structures. Our proposed mechanisms (SALP-1, SALP-2, and MASA) mitigate the negative impact of bank serialization by overlapping different components of the bank access latencies of multiple requests that go to different subarrays within the same bank. SALP-1 requires no changes to the existing DRAM structure and only needs reinterpretation of some DRAM timing parameters. SALP-2 and MASA require only modest changes (


title: Analyzing Facebook Activities for Personality Recognition

abstract: Facebook is the largest and the most popular online social network application that records large amount of users’ behavior expressed in various activities such as Facebook likes, status updates, posts, comments, photos, tags and shares. One of the major attractions of such a dataset relates to the predictability of the individuals’ psychological traits from their digital footprints. Such predictions help researchers and service providers to improve personalized offering of products and services. The goal of this work is to investigate the predictability of Facebook users’ personality traits, measured by BIG5 test as a function of their digital records of behavior such as Facebook likes. This research is based on a dataset of 92,255 users who provided their Facebook likes and the results of their BIG5 personality test. As the Facebook likes data includes 600 attributes, the proposed model uses LASSO algorithm to select the best features and to predict Facebook users’ BIG5 personality traits. The best accuracy level of these predictions is achieved for Openness and Extraversion, the lowest accuracy level is obtained for Agreeableness while the accuracy levels of Conscientiousness and Neuroticism are in the middle.


title: Antenna-on-Chip and Antenna-in-Package Solutions to Highly Integrated Millimeter-Wave Devices for Wireless Communications

abstract: Antenna-on-chip (AoC) and antenna-in-package (AiP) solutions are studied for highly integrated millimeter-wave (mmWave) devices in wireless communications. First, the background, regulations, standard, and applications of 60-GHz wireless communications are briefly introduced. Then, highly integrated 60-GHz radios are overviewed as a basis for the link budget analysis to derive the antenna gain requirement. Next, in order to have deep physical insight into the AoC solution, the silicon substrate's high permittivity and low resistivity effects on the AoC efficiency are examined. It is shown that the AoC solution has low efficiency, less than 12% due to large ohmic losses and surface waves, which requires the development of techniques to improve the AoC efficiency. After that, the AiP solution and associated challenges such as how to realize low-loss interconnection between the chip and antenna are addressed. It is shown that wire-bonding interconnects, although inferior to the flip-chip, are still feasible in the 60-GHz band if proper compensation schemes are utilized. An example of the AiP solution in a low-temperature cofired ceramic (LTCC) process is presented in the 60-GHz band showing an efficiency better than 90%. A major concern with both AoC and AiP solutions is electromagnetic interference (EMI), which is also discussed. Finally, the systems level pros and cons of both AoC and AiP solutions are highlighted from the electrical and economic perspectives for system designers.


title: Knowledge sharing in organisational contexts: a motivation‐based perspective

abstract: Purpose – Facilitating knowledge sharing within organisations is a difficult task: the willingness of individuals to share and integrate their knowledge is one of the central barriers. This paper aims to develop a motivation‐based perspective to explore how organisations resolve the social dilemma of knowledge sharing.Design/methodology/approach – The analysis builds on a three‐category taxonomy of motivation, adding “hedonic” motivation to the traditional dichotomy of “extrinsic” and “intrinsic” motivation. It uses case studies gleaned from the literature to explore the interactive effects between the different motivators in two different types of knowledge‐intensive organisations: professional bureaucracy and operating adhocracy.Findings – Within a professional bureaucracy, the social dilemma of knowledge sharing may be overcome through normative motivation, with provision of hedonic motivation through extrinsic incentives such as training and career progression. In an operating adhocracy where interdep...


title: Advances in Quantum Teleportation

abstract: Quantum teleportation is one of the most important protocols in quantum information. By exploiting the physical resource of entanglement, quantum teleportation serves as a key primitive in a variety of quantum information tasks and represents an important building block for quantum technologies, with a pivotal role in the continuing progress of quantum communication, quantum computing and quantum networks. Here we review the basic theoretical ideas behind quantum teleportation and its variant protocols. We focus on the main experiments, together with the technical advantages and disadvantages associated with the use of the various technologies, from photonic qubits and optical modes to atomic ensembles, trapped atoms, and solid-state systems. Analysing the current state-of-the-art, we finish by discussing open issues, challenges and potential future implementations.


title: An automatic driver drowsiness alert system by using GSM

abstract: Drowsy is the reason for most of the road accidents. Manually tracing the drowsy driver is not an easy task, because every day thousands of vehicles are running on the roads. So we need a system that must come with every car and if it detects the sleepy driver it must stop the vehicle immediately. In addition to this if the driver is slept the vehicle will be stopped, and it monitors the heart-beat, Respiration rate and temperature of the driver and displays it in the LCD. These three parameters are very important because it shows the body status of the driver. These parameters are monitored manually and in case of emergency the in-charge of the ward calls the doctor.


title: Neurotensin Phosphorylates GSK-3α/β through the Activation of PKC in Human Colon Cancer Cells

abstract: Neurotensin (NT), a gastrointestinal hormone, binds its receptor [neurotensin receptor (NTR)] to regulate the growth of normal and neoplastic intestinal cells; molecular mechanisms remain largely undefined. Glycogen synthase kinase-3 (GSK-3) regulates diverse cellular processes, including cell growth and apoptosis. Here, we show that NT induces the phosphorylation of GSK-3α/β in the human colon cancer cell line HT29, HCT116, or SW480, which possesses highaffinity NTR. The effect of NT was blocked by inhibitors of protein kinase C (PKC), but not by inhibitors of mitogen-activated protein kinase/extracellular signalregulated kinase (MEK1) or phosphatidylinositol-3 kinase, suggesting a predominant role for PKC in GSK-3β phosphorylation by NT. Pretreatment with Go6976 (which inhibits PKCα and PKCβ1) or downregulation of endogenous PKCα or PKCβ1 blocked NT-mediated GSK-3β (but not GSK-3α) phosphorylation. Moreover, a selective PKCβ inhibitor, LY379196, reduced NT-mediated GSK-3β (but not GSK-3α) phosphorylation, suggesting a role for PKCβ1 in the NT-mediated phosphorylation of GSK-3β and an undefined kinase in the NT-mediated phosphorylation of GSK-3α. Treatment with NT or the GSK-3 inhibitor SB216763 increased the expression of cyclin D1, a downstream effector protein of GSK-3 and a critical protein for the proliferation of various cells. Our results indicate that NT uses PKC-dependent pathways to modulate GSK-3, which may play a role in the NT regulation of intestinal cell growth.


title: Artificial intelligence: contemporary applications and future compass.

abstract: The clinical use of information technology in the dental profession has increased substantially in the past 10 to 20 years. In most developing countries an insufficiency of medical and dental specialists has increased the mortality of patients suffering from various diseases. Employing technology, especially artificial intelligence technology, in medical and dental application could reduce cost, time, human expertise and medical error. This approach has the potential to revolutionise the dental public health scenario in developing countries. Clinical decision support systems (CDSS) are computer programs that are designed to provide expert support for health professionals. The applications in dental sciences vary from dental emergencies to differential diagnosis of orofacial pain, radiographic interpretations, analysis of facial growth in orthodontia to prosthetic dentistry. However, despite the recognised need for CDSS, the implementation of these systems has been limited and slow. This can be attributed to lack of formal evaluation of the systems, challenges in developing standard representations, cost and practitioner scepticism about the value and feasibility of CDSS. Increasing public awareness of safety and quality has accelerated the adoption of generic knowledge based CDSS. Information technology applications for dental practice continue to develop rapidly and will hopefully contribute to reduce the morbidity and mortality of oral and maxillofacial diseases and in turn impact patient care.


title: Pentagon shaped microstrip patch antenna with metamaterial for UWB application

abstract: The trend in today's wireless application is to design antennas that are compact, robust and ease to integrate with RF circuit components. Microstrip patch antenna is one such type that satisfies the above requirements. But the two main factors of an antenna namely the gain and the bandwidth are low for patch antennas. Usually the gain range of patch antenna is 1–2dB. The most straightforward way of increasing these factors involves the use of low dielectric substrate with increased thickness, but this inevitably leads to surface wave generation. As a result, sensible substrate thickness has to be employed. In this paper pentagon shaped patch antennas with metamaterial on the substrate for UWB application is proposed. Three different dimensions with line feed are analyzed in this paper. The analysis is done using Ansoft HFSS software version 15.0. The performance parameters such as, bandwidth, gain and return loss of the proposed antennas are compared.


title: A qualitative analysis of young drivers' perceptions of driver distraction social marketing interventions

abstract: This study gives insight into why current driver distraction social marketing interventions are not motivating the high-risk target audience of young drivers to cease using their mobile phones when driving. Three focus groups (n=30) were conducted with drivers aged 18-25 years old to explore current attitudes and behaviours in regard to mobile phone use when driving. Additionally four emergent themes were identified from the target audience’s reactions to six social marketing interventions specifically targeting mobile phone cessation. These themes are analysed through the lens of the Extended Parallel Process Model (EPPM) comprising perceived severity, perceived susceptibility, response efficacy and self-efficacy.


title: A Modular Topology of Marx Generator Using Buck–Boost Converter

abstract: In this paper, a high-voltage unipolar pulse power generator is introduced for electroporation applications. Operating principle of the proposed structure is based on the buck-boost (BB) converter. The generator is comprised of parallel modules connected to the converter through an SCR and is supplied by a low-voltage dc source. Each module consists of an insulated gate bipolar transistor (IGBT), two diodes, and a capacitor. In fact, the proposed topology is a combination of the Marx generator and BB (MG-BB) converter. In comparison with other modular MG-BB structures, the proposed topology achieves interesting advantages including simpler structure, reduction of IGBTs voltage rating, and reducing number of components (inductors and switches). In order to clarify the features of the proposed structure, it has been compared with other unipolar MGs considering different aspects including the number of circuit components, number of required input supplies, amplitude of the output pulse, and the maximum voltage across switches and diodes. To validate the effectiveness of the structure, simulation and experimental results are presented. The results confirm that the proposed topology is capable of generating unipolar pulses with desirable amplitude and repetition frequency using a low-voltage dc supply.


title: Books and the Nation: The Making of Thailand's National Library

abstract: The transformation of the Thai "Buddhist kingdom" into a "modern nation-state" in the late nineteenth and early twentieth centuries is a well-trodden field in Thai studies. [1] Scholars have singled out for particular attention the creation of a number of key institutions which have become central to the modern Thai state. Among them, Tej Bunnag has described the setting up of a centralized system of territorial administration, the so-called thetsaphiban system, the basic structure of which remains largely in place today. [2] Noel Battye has studied the establishment of a modern standing army organized according to Western military models. [3] The foundation of a national, mass education system based on Western forms of knowledge has been outlined by David Wyatt. [4] Yoneo Ishii and others have described the development of a nationalized system of religious administration and the implementation of a standardized religious curriculum for monastic education. [5] Thongchai Winichakul has argued that the origin o f a shared sense of the territorial identity of the Thai nation, the "geobody", which is central to contemporary conceptions of the Thai nation, also dates from this period. [6] In regard to the origins of Thai nationalist discourses, Walter Vella's study of King Vajiravudh (r. 1910-25) highlights the latter's role as the first popularizer of official notions of Thai national identity (of which the monarchy was the central element),[7] while Eiji Murashima has contended that Vajiravudh's nationalism can in fact be traced back to the previous reign (that of Chulalongkorn, r. 1868-1910) as a reaction to colonial threats to Siam's territorial integrity and as a result of the increased influence of a Western-educated elite. [8] The importance of the Thai language to national integration has been highlighted by Nidhi Aeusrivongse and Anthony Diller, who describe the elevation of Central Thai by successive governments starting from Chulalongkorn's reign as the standard national language for use in schools, the medi a, and government offices, along with the simultaneous discouragement of the many other dialects and languages in use throughout the kingdom. [9] Outside of Thai studies Benedict Anderson's popular study of nationalism, Imagined Communities, has shown how the decline of sacred languages and the growing importance of print technology, coupled with a common print language, created a "unified field of exchange and communication" and with it a group of fellow readers who became the "embryo" of the new nationally imagined community. [10] Anderson also points out certain colonial "technologies" which have contributed to the "imagining" of the nation. Among these were (following Thongchai's work) the map; the census, which enabled colonial administrations to conceive of their dominions in ethnic or racial terms and to design policies accordingly; and the museum, which expressed the modem colonial state's link with ancient civilizations." [11] One institution which has received comparatively less attention but whose role in the construction of the concept of the Thai nation was no less significant is the national library. Founded in the early 1880s as a kind of "club" for the Thai aristocracy, and institutionalised as an official organ of the Thai state in 1905, the "State Library" (Hor Phrasamut samrap Phranakhorn) [12] -- renamed the "National Library" after 1932 -- played a fundamental role in the formation of a knowledge -- hitherto non-existent as such -- about the cultural identity of the Thai nation. The raw material from which this knowledge was obtained and which the Library played the major role in collecting, classifying, safeguarding, and later reproducing in standardized form, was the kingdom's literary heritage. This included palm leaf (bai lan) [13] or samut Khoi [14] manuscripts, stone inscriptions, and from the late nineteenth century onward, bound printed volumes. It was a heritage which comprised a great diversity of knowledges coexisting throughout the kingdom, including various kinds of Buddhist discourse, Brahmanism, chronicles, tales and legends, traditional law and custom, astrology and divination, magic, medicine, and the many treatises or manuals (tamra) written for the preservation, adaptation and application of practical knowledges. …


title: UPCaD: A Methodology of Integration Between Ontology-Based Context-Awareness Modeling and Relational Domain Data

abstract: Context-awareness is a key feature for ubiquitous computing scenarios applications. Currently, technologies and methodologies have been proposed for the integration of context-awareness concepts in intelligent information systems to adapt them to the execution of services, user interfaces and data retrieval. Recent research proposed conceptual modeling alternatives to the integration of the domain modeling in RDBMS and context-awareness modeling. The research described using highly expressiveness ontologies. The present work describes the UPCaD (Unified Process for Integration between Context-Awareness and Domain) methodology, which is composed of formalisms and processes to guide the data integration considering RDBMS and context modeling. The methodology was evaluated in a virtual learning environment application. The evaluation shows the possibility to use a highly expressive context ontology to filter the relational data query and discusses the main contributions of the methodology compared with recent approaches.


title: A Deep Convolutional Neural Network Based Classification Of Multi-Class Motor Imagery With Improved Generalization

abstract: Motor imagery (MI) based brain-computer interface (BCI) plays a crucial role in various scenarios ranging from post-traumatic rehabilitation to control prosthetics. Computer-aided interpretation of MI has augmented prior mentioned scenarios since decades but failed to address interpersonal variability. Such variability further escalates in case of multi-class MI, which is currently a common practice. The failures due to interpersonal variability can be attributed to handcrafted features as they failed to extract more generalized features. The proposed approach employs convolution neural network (CNN) based model with both filtering (through axis shuffling) and feature extraction to avail end-to-end training. Axis shuffling is performed adopted in initial blocks of the model for 1D preprocessing and reduce the parameters required. Such practice has avoided the overfitting which resulted in an improved generalized model. Publicly available BCI Competition-IV 2a dataset is considered to evaluate the proposed model. The proposed model has demonstrated the capability to identify subject-specific frequency band with an average and highest accuracy of 70.5% and S3.6% respectively. Proposed CNN model can classify in real time without relying on accelerated computing device like GPU.


title: Yet Another Text Captcha Solver: A Generative Adversarial Network Based Approach

abstract: Despite several attacks have been proposed, text-based CAPTCHAs are still being widely used as a security mechanism. One of the reasons for the pervasive use of text captchas is that many of the prior attacks are scheme-specific and require a labor-intensive and time-consuming process to construct. This means that a change in the captcha security features like a noisier background can simply invalid an earlier attack. This paper presents a generic, yet effective text captcha solver based on the generative adversarial network. Unlike prior machine-learning-based approaches that need a large volume of manually-labeled real captchas to learn an effective solver, our approach requires significantly fewer real captchas but yields much better performance. This is achieved by first learning a captcha synthesizer to automatically generate synthetic captchas to learn a base solver, and then fine-tuning the base solver on a small set of real captchas using transfer learning. We evaluate our approach by applying it to 33 captcha schemes, including 11 schemes that are currently being used by 32 of the top-50 popular websites including Microsoft, Wikipedia, eBay and Google. Our approach is the most capable attack on text captchas seen to date. It outperforms four state-of-the-art text-captcha solvers by not only delivering a significant higher accuracy on all testing schemes, but also successfully attacking schemes where others have zero chance. We show that our approach is highly efficient as it can solve a captcha within 0.05 second using a desktop GPU. We demonstrate that our attack is generally applicable because it can bypass the advanced security features employed by most modern text captcha schemes. We hope the results of our work can encourage the community to revisit the design and practical use of text captchas.


title: Tagging Your Tweets: A Probabilistic Modeling of Hashtag Annotation in Twitter

abstract: The adoption of hashtags in major social networks including Twitter, Facebook, and Google+ is a strong evidence of its importance in facilitating information diffusion and social chatting. To understand the factors (e.g., user interest, posting time and tweet content) that may affect hashtag annotation in Twitter and to capture the implicit relations between latent topics in tweets and their corresponding hashtags, we propose two PLSA-style topic models to model the hashtag annotation behavior in Twitter. Content-Pivoted Model (CPM) assumes that tweet content guides the generation of hashtags while Hashtag-Pivoted Model (HPM) assumes that hashtags guide the generation of tweet content. Both models jointly incorporate user, time, hashtag and tweet content in a probabilistic framework. The PLSA-style models also enable us to verify the impact of social factor on hashtag annotation by introducing social network regularization in the two models. We evaluate the proposed models using perplexity and demonstrate their effectiveness in two applications: retrospective hashtag annotation and related hashtag discovery. Our results show that HPM outperforms CPM by perplexity and both user and time are important factors that affect model performance. In addition, incorporating social network regularization does not improve model performance. Our experimental results also demonstrate the effectiveness of our models in both applications compared with baseline methods.


title: Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Network

abstract: Adaptive traffic signal control, which adjusts traffic signal timing according to real-time traffic, has been shown to be an effective method to reduce traffic congestion. Available works on adaptive traffic signal control make responsive traffic signal control decisions based on human-crafted features (e.g. vehicle queue length). However, human-crafted features are abstractions of raw traffic data (e.g., position and speed of vehicles), which ignore some useful traffic information and lead to suboptimal traffic signal controls. In this paper, we propose a deep reinforcement learning algorithm that automatically extracts all useful features (machine-crafted features) from raw real-time traffic data and learns the optimal policy for adaptive traffic signal control. To improve algorithm stability, we adopt experience replay and target network mechanisms. Simulation results show that our algorithm reduces vehicle delay by up to 47% and 86% when compared to another two popular traffic signal control algorithms, longest queue first algorithm and fixed time control algorithm, respectively.


title: Reading visually embodied meaning from the brain: Visually grounded computational models decode visual-object mental imagery induced by written text

abstract: Embodiment theory predicts that mental imagery of object words recruits neural circuits involved in object perception. The degree of visual imagery present in routine thought and how it is encoded in the brain is largely unknown. We test whether fMRI activity patterns elicited by participants reading objects' names include embodied visual-object representations, and whether we can decode the representations using novel computational image-based semantic models. We first apply the image models in conjunction with text-based semantic models to test predictions of visual-specificity of semantic representations in different brain regions. Representational similarity analysis confirms that fMRI structure within ventral-temporal and lateral-occipital regions correlates most strongly with the image models and conversely text models correlate better with posterior-parietal/lateral-temporal/inferior-frontal regions. We use an unsupervised decoding algorithm that exploits commonalities in representational similarity structure found within both image model and brain data sets to classify embodied visual representations with high accuracy (8/10) and then extend it to exploit model combinations to robustly decode different brain regions in parallel. By capturing latent visual-semantic structure our models provide a route into analyzing neural representations derived from past perceptual experience rather than stimulus-driven brain activity. Our results also verify the benefit of combining multimodal data to model human-like semantic representations.


title: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm

abstract: The probability of error in decoding an optimal convolutional code transmitted over a memoryless channel is bounded from above and below as a function of the constraint length of the code. For all but pathological channels the bounds are asymptotically (exponentially) tight for rates above R_{0} , the computational cutoff rate of sequential decoding. As a function of constraint length the performance of optimal convolutional codes is shown to be superior to that of block codes of the same length, the relative improvement increasing with rate. The upper bound is obtained for a specific probabilistic nonsequential decoding algorithm which is shown to be asymptotically optimum for rates above R_{0} and whose performance bears certain similarities to that of sequential decoding algorithms.


title: Social network integration and user content generation: evidence from natural experiments

abstract: This study examines how social network integration (i.e., integration of online platforms with other social media services, for example, with Facebook or Twitter) can affect the characteristics of user-generated content (volume and linguistic features) in the context of online reviews. Building on the social presence theory, we propose a number of hypotheses on how social network integration affects review volume and linguistic features of review text. We consider two natural experiments at leading online review platforms (Yelp.com and TripAdvisor.com), wherein each implemented a social network integration with Facebook. Constructing a unique panel dataset of online reviews for a matched set of restaurants across the two review sites, we estimate a difference-in-differences (DID) model to assess the impact of social network integration. We find that integration with Facebook increased the production of user-generated content and positive emotion in review text, while simultaneously decreasing cognitive language, negative emotion, and expressions of disagreement (negations) in review text. Our findings demonstrate that social network integration works as a double-edged sword. On the one hand, integration provides benefits in terms of increased review quantity. On the other hand, these benefits appear to come at the cost of reduced review quality, given past research which has found that positive, emotional reviews are perceived by users to be less helpful. We discuss the implications of these results as they relate to the creation of sustainable online social platforms for user content generation.


title: HaptiMoto: turn-by-turn haptic route guidance interface for motorcyclists

abstract: A national study by the Australian Transport Safety Bureau revealed that motorcyclist deaths were nearly thirty times more prevalent than that of drivers of other vehicles. These fatalities represent approximately 5% of all highway deaths each year, yet motorcycles account for only 2% of all registered vehicles in the USA. Motorcyclists are highly exposed on the road, so maintaining situational awareness at all times is crucial. Route guidance systems enable users to efficiently navigate between locations using dynamic visual maps and audio directions, and have been well tested with motorists, but remain unsafe for use by motorcyclists. Audio/visual routing systems decrease motorcyclists' situational awareness and vehicle control, and thus elevate chances of an accident. To enable motorcyclists to take advantage of route guidance while maintaining situational awareness, we created HaptiMoto, a wearable haptic route guidance system. HaptiMoto uses tactile signals to encode the distance and direction of approaching turns, thus avoiding interference with audio/visual awareness. Our evaluations demonstrate that HaptiMoto is both intuitive and a safer alternative for motorcyclists compared to existing solutions.


title: A geometric snake model for segmentation of medical imagery

abstract: We employ the new geometric active contour models, previously formulated, for edge detection and segmentation of magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound medical imagery. Our method is based on defining feature-based metrics on a given image which in turn leads to a novel snake paradigm in which the feature of interest may be considered to lie at the bottom of a potential well. Thus, the snake is attracted very quickly and efficiently to the desired feature.


title: Analysis of Telegram, An Instant Messaging Service

abstract: Telegram has become one of the most successful instant messaging services in recent years. In this paper, we developed a crawler to gather its public data. To the best of our knowledge, this paper is the first attempt to analyze the structural and topical aspects of messages published in Telegram instant messaging service using crawled data. We also extracted the mention graph and page rank of our data collection which indicates important differences between linking patterns of Telegram nodes and other usual networks. We also classified messages to detect advertisement and spam messages.


title: Optimal Parameters for XMSS MT

abstract: We introduce Multi Tree XMSS (XMSS MT ), a hash-based signature scheme that can be used to sign a virtually unlimited number of messages. It is provably forward and hence EU-CMA secure in the standard model and improves key and signature generation times compared to previous schemes. XMSS MT has — like all practical hash-based signature schemes — a lot of parameters that control different trade-offs between security, runtimes and sizes. Using linear optimization, we show how to select provably optimal parameter sets for different use cases.


title: Auto-Scaling of Containers: The Impact of Relative and Absolute Metrics

abstract: Today, The cloud industry is adopting the container technology both for internal usage and as commercial offering. The use of containers as base technology for large-scale systems opens many challenges in the area of resource management at run-time. This paper addresses the problem of selecting the more appropriate performance metrics to activate auto-scaling actions. Specifically, we investigate the use of relative and absolute metrics. Results demonstrate that, for CPU intense workload, the use of absolute metrics enables more accurate scaling decisions. We propose and evaluate the performance of a new autoscaling algorithm that could reduce the response time of a factor between 0.66 and 0.5 compared to the actual Kubernetes' horizontal auto-scaling algorithm.


title: Meeting of minds: the medial frontal cortex and social cognition

abstract: Social interaction is a cornerstone of human life, yet the neural mechanisms underlying social cognition are poorly understood. Recently, research that integrates approaches from neuroscience and social psychology has begun to shed light on these processes, and converging evidence from neuroimaging studies suggests a unique role for the medial frontal cortex. We review the emerging literature that relates social cognition to the medial frontal cortex and, on the basis of anatomical and functional characteristics of this brain region, propose a theoretical model of medial frontal cortical function relevant to different aspects of social cognitive processing.


title: Design of Active Circulators Using High-Speed Operational Amplifiers

abstract: Design formulas and prototype realizations of an operational amplifier based active circulators operating from dc to 500 MHz are presented. The operational unit of the circulator consists of a chip-resistor based voltage divider and an Op Amp differential amplifier which can be easily fabricated at low cost for general low power applications. An existing widely used circuit is shown to be a special case of the more generalized theory presented in this letter. Our design shows a return loss of greater than 10 dB, an insertion loss flatness within ±0.8 dB, and an isolation greater than 20 dB at frequencies below 500 MHz.


title: A Randomized Controlled Trial Determining Variances in Ostomy Skin Conditions and the Economic Impact (ADVOCATE Trial)

abstract: PURPOSE ::: To compare ostomy-related costs and incidence of peristomal skin complications (PSCs) for ceramide-infused ostomy skin barriers and control skin barriers. ::: ::: ::: DESIGN ::: The ADVOCATE trial is a multi-centered randomized controlled trial, and double-blinded international study with an adaptive design. ::: ::: ::: SUBJECTS AND SETTING ::: The sample comprised 153 adults from 25 sites from the United States, Canada, and Europe. Participants were seen in hospital and outpatient care settings. ::: ::: ::: METHODS ::: Data were collected by investigators at each site during face-to-face visits and during telephone check-in calls between visits. Cost of care data were collected using a questionnaire developed specifically for the study. The peristomal skin was assessed using the Ostomy Skin Tool. Health-related quality of life was measured using the SF-12v2. Patient-reported outcomes were collected using a patient-centered study-specific questionnaire. Cost of care was analyzed via analysis of covariance comparing total cost of care for 12 weeks between the 2 groups. The incidence of PSC was analyzed via Barnard's exact test comparing the incidence of PSCs between the control and treatment groups. Tertiary outcomes were exploratory in nature and not statistically powered. ::: ::: ::: RESULTS ::: Use of the ceramide-infused barrier significantly reduced stoma-related cost of care over a 12-week period, resulting in a $36.46 decrease in cost (14% relative decrease). The adjusted average costs were $223.73 in the treatment group and $260.19 in the control group (P = .017). The overall incidence of PSCs in the study was 47.7%; PSC incidence was 40.5% for the treatment group versus 55.4% for controls (P = .069, 95% confidence interval of the difference: -1.2 to 30.4). Significantly more participants using the ceramide-infused skin barrier were "very satisfied" with barrier performance (75% vs 55%; P = .033), prevention of leakage (63% vs 38%; P < .01), and prevention of itching (53% vs 31%; P = .016). General postoperative improvement in health-related quality of life was noted in both groups. ::: ::: ::: CONCLUSIONS ::: The use of a ceramide-infused barrier significantly decreased cost and increased satisfaction with patient-reported outcomes.


title: Deep learning through evolution: A hybrid approach to scheduling in a dynamic environment

abstract: Genetic Algorithms (GAs) have been shown to be a very effective optimisation tool on a wide variety of problems. However, they are not without their drawbacks. GAs require time to run, and evolve a bespoke solution to the desired problem in real time. This requirement can prove to be prohibitive in a high-frequency dynamic environment where on-line training time is limited. Neural Networks (NNs) on the other hand can be trained at length off-line, before being deployed on-line, allowing for fast generation of solutions on demand. This study presents a hybrid approach to time-frame scheduling in a high frequency domain. A GA approach is used to generate a dataset of optimised human-competitive solutions. Deep Learning is then deployed to extract the underlying model within the GA, enabling fast optimisation on unseen data. This hybrid approach allows for NNs to generate GA-quality schedules on-line, almost 100 times faster than running the GA.


title: Targeting the human microbiome with antibiotics, probiotics, and prebiotics: gastroenterology enters the metagenomics era.

abstract: Studies of metagenomics and the human microbiome will tremendously expand our knowledge of the composition of microbial communities in the human body. As our understanding of microbial variation and corresponding genetic parameters is refined, this information can be applied to rational remodeling or "tailoring" of human-associated microbial communities and their associated functions. Physiologic features such as the development of innate and adaptive immunity, relative susceptibilities to infections, immune tolerance, bioavailability of nutrients, and intestinal barrier function may be modified by changing the composition and functions of the microbial communities. The specialty of gastroenterology will be affected profoundly by the ability to modify the gastrointestinal microbiota through the rational deployment of antibiotics, probiotics, and prebiotics. Antibiotics might be used to remove or suppress undesirable components of the human microbiome. Probiotics can introduce missing microbial components with known beneficial functions for the human host. Prebiotics can enhance the proliferation of beneficial microbes or probiotics, to maximize sustainable changes in the human microbiome. Combinations of these approaches might provide synergistic and effective therapies for specific disorders. The human microbiome could be manipulated by such "smart" strategies to prevent and treat acute gastroenteritis, antibiotic-associated diarrhea and colitis, inflammatory bowel disease, irritable bowel syndrome, necrotizing enterocolitis, and a variety of other disorders.


A Fluctuation Smoothing Approach for Unsupervised Automatic Short Answer Grading.


Credibility and computing technology


title: Demonstration of advanced reconnaissance techniques with the airborne SAR/GMTI sensor PAMIR

abstract: PAMIR (Phased Array Multifunctional Imaging Radar) is an experimental airborne radar system that has been designed and built by the Research Institute for High Frequency Physics and Radar Techniques (FHR) of Forschungsgesellschaft fur Angewandte Naturwissenschaften (FGAN). The goal is to meet the growing demands for future reconnaissance systems with respect to flexibility and multi-mode operation by the use of an electronically steerable phased array antenna. The X-band system with a bandwidth of 1.8 GHz serves as a platform for different tasks. One of the main objectives is to demonstrate synthetic aperture radar (SAR) imaging at a very high resolution and for a long range. The fine resolution will also be applied for inverse SAR (ISAR) imaging of ground moving targets. Moreover, five parallel receiving channels allow array processing techniques like ground moving target indication (GMTI) via space–time adaptive processing, electronic counter-counter-measures and interferometric SAR with a very high 3D-resolution. A multi-channel scan-MTI mode with a range resolution adapted to the target size allows for a wide area GMTI operation that can be complemented by target tracking. Together with the predecessor system AER-II, operating at a frequency band contained in that of PAMIR, the possibility of experimental investigation of bistatic SAR is given. SAR images of large urban areas and ISAR images of moving objects, both with finest resolution down to the sub-decimetre scale, are presented. Results of GMTI in a wide area scanning mode and broadband bistatic experiments including true bistatic SAR processing are shown as well.


title: Modelling mobility in disaster area scenarios

abstract: This paper provides a model that realistically represents the movements in a disaster area scenario. The model is based on an analysis of tactical issues of civil protection. This analysis provides characteristics influencing network performance in public safety communication networks like heterogeneous area-based movement, obstacles, and joining/leaving of nodes. As these characteristics cannot be modelled with existing mobility models, we introduce a new disaster area mobility model. To examine the impact of our more realistic modelling, we compare it to existing ones (modelling the same scenario) using different pure movement and link based metrics. The new model shows specific characteristics like heterogeneous node density. Finally, the impact of the new model is evaluated in an exemplary simulative network performance analysis. The simulations show that the new model discloses new information and has a significant impact on performance analysis.


title: Evaluating Information Extraction

abstract: The issue of how to experimentally evaluate information extraction (IE) systems has received hardly any satisfactory solution in the literature. In this paper we propose a novel evaluation model for IE and argue that, among others, it allows (i) a correct appreciation of the degree of overlap between predicted and true segments, and (ii) a fair evaluation of the ability of a system to correctly identify segment boundaries. We describe the properties of this models, also by presenting the result of a re-evaluation of the results of the CoNLL'03 and CoNLL'02 Shared Tasks on Named Entity Extraction.


title: DC electronic load based on an interleaved chopper converter to determine photovoltaic panel and fuel cell biasing curves

abstract: This paper presents the description of a DC electronic load based on an interleaved chopper converter to determine the biasing and dynamics characteristics of either fuel cells (FCs) and photovoltaic panels (PVs). Variable loads are used for experimental data acquisition of I–V power supply characteristics in a way that several operating points can be easily obtained. Laboratory load current tests using ordinary power resistors are not an easy solution due to their size, cost and overall weight. To resolve that it is proposed in this paper that the current through electronically varying loads can give better resolution to the I–V characteristics of FC and PV sources. The interleaved operation of the chopper converter ensures an input high frequency current ripple, reducing so the input LC filters size and converter volume. The converter transfer function is analyzed and controllers are designed to obtain a fast dynamic response and insure good stability in steady state operation. It is presented the converter operating principles, operating states and design. The simulated and results are compared to each other in order to prove the electronic load effectiveness in acquiring the I–V biasing curves of fuel cells and photovoltaic panels.


title: Integrative omics for health and disease

abstract: This article discusses how integrating different omics data types — such as DNA sequencing, transcriptomics and metabolomics — can provide a rich view of healthy and disease states, including novel clinical diagnoses. The authors discuss the value of the different data types, as well as strategies, considerations and challenges for multi-omic integration in various disease contexts.


title: The influence of perceived family support on post surgery recovery

abstract: AbstractThe objective of this work was to investigate the possible relationship between perceived family support, levels of cortisol and post surgery recovery. The study sample comprised 42 patients that were due to undergo open cholecystectomy surgery in a Regional Health Authority Reference Centre of the Autonomous Community of Extremadura in Spain. The FACES-II questionnaire was used for the evaluation of perceived family support and to measure the three fundamental dimensions of perceived family behaviour: cohesion, adaptability and family type. The day before surgery, a sample of saliva was taken from each subject in order to determine the level of cortisol. Results showed a clear relationship between family support and recovery. Patients with higher scores on the Cohesion Scale demonstrated better post surgery recovery (F = 8.8; gl = 40; p = .005). A relationship between levels of cortisol, perceived family support and recovery was also revealed. Patients with lower scores on the Cohesion scale and ...


title: Deep Learning-Aided SCMA

abstract: Sparse code multiple access (SCMA) is a promising code-based non-orthogonal multiple-access technique that can provide improved spectral efficiency and massive connectivity meeting the requirements of 5G wireless communication systems. We propose a deep learning-aided SCMA (D-SCMA) in which the codebook that minimizes the bit error rate (BER) is adaptively constructed, and a decoding strategy is learned using a deep neural network-based encoder and decoder. One benefit of D-SCMA is that the construction of an efficient codebook can be achieved in an automated manner, which is generally difficult due to the non-orthogonality and multi-dimensional traits of SCMA. We use simulations to show that our proposed scheme provides a lower BER with a smaller computation time than conventional schemes.


title: Executing SQL over encrypted data in the database-service-provider model

abstract: Rapid advances in networking and Internet technologies have fueled the emergence of the "software as a service" model for enterprise computing. Successful examples of commercially viable software services include rent-a-spreadsheet, electronic mail services, general storage services, disaster protection services. "Database as a Service" model provides users power to create, store, modify, and retrieve data from anywhere in the world, as long as they have access to the Internet. It introduces several challenges, an important issue being data privacy. It is in this context that we specifically address the issue of data privacy.There are two main privacy issues. First, the owner of the data needs to be assured that the data stored on the service-provider site is protected against data thefts from outsiders. Second, data needs to be protected even from the service providers, if the providers themselves cannot be trusted. In this paper, we focus on the second challenge. Specifically, we explore techniques to execute SQL queries over encrypted data. Our strategy is to process as much of the query as possible at the service providers' site, without having to decrypt the data. Decryption and the remainder of the query processing are performed at the client site. The paper explores an algebraic framework to split the query to minimize the computation at the client site. Results of experiments validating our approach are also presented.


title: libtissue - implementing innate immunity

abstract: In a previous paper the authors argued the case for incorporating ideas from innate immunity into artificial immune systems (AISs) and presented an outline for a conceptual framework for such systems. A number of key general properties observed in the biological innate and adaptive immune systems were highlighted, and how such properties might be instantiated in artificial systems was discussed in detail. The next logical step is to take these ideas and build a software system with which AISs with these properties can be implemented and experimentally evaluated. This paper reports on the results of that step - the libtissue system.


title: Internet of Things Based Systems for Food Safety Management

abstract: The paper is highlighting the advantages of integrating technologies and digital standards in public politics for protecting consumer rights. Todayâ€™s globalization of food production chains, their extensive complexity and limitations arising from manual insertion and data processing of products information make it impossible for consumers the task of being informed in real-time. This obstacle can be overcome, and the current level of technology and low implementation costs allow full automation of this process. We propose a sensors network architecture based on Internet of Things (IoT) components, which is using autonomous embedded modules and radio identification tags (RFID) that will automatically collect data, covering the entire life cycle of the food product and all the factors that influence its chemical composition. This architecture offers consumers complete data about their food products and how their components were obtained.


title: Observing the unexpected enhances infants’ learning and exploration

abstract: Given the overwhelming quantity of information available from the environment, how do young learners know what to learn about and what to ignore? We found that 11-month-old infants ( N = 110) used violations of prior expectations as special opportunities for learning. The infants were shown events that violated expectations about object behavior or events that were nearly identical but did not violate expectations. The sight of an object that violated expectations enhanced learning and promoted information-seeking behaviors; specifically, infants learned more effectively about objects that committed violations, explored those objects more, and engaged in hypothesis-testing behaviors that reflected the particular kind of violation seen. Thus, early in life, expectancy violations offer a wedge into the problem of what to learn.


title: Equality and Justice in Early Greek Cosmologies: The Paradigm of the “Line of the Horizon”

abstract: The founders of Greek scientific thought believed justice in terms of equality. Cosmic equality, in fact, was conceived as the guaranty of cosmic justice: the order of nature is maintained because it is an order of equals. That the main components of the universe are equal was an old tradition in popular cosmology. In Hesiod earth and sky are declared equal (Theog. 126); and the distance between sky and earth is equal to that between earth and Tartarus (ibid. 719-25). Anaximander’s own cosmology is designed with just such a sense of aesthetic symmetry, with equality as the main motif: the intervals between each of the infinite worlds are equal; the intervals between earth, fixed stars, moon, and sun are also equal; earth and sun are equal. This is exactly the sense in which equality figures in the whole development of early cosmological theory from Anaximander to Empedocles: powers are equal if they can hold another in check, in a way that no one of them is more powerful than any other. The objective of this paper is to propose a cosmological interpretation of the term dikē in ancient Greek according to Aristotle, who establishes synonymy between justice and equality through the use of the «dividing line» paradigm. Aristotle reveals, in effect, that the words diksastḗs «judge» and dikaion «just» come from the root dikē, «judgment or sentence», which in their turn are derived from the adverb dīksā, «division into two equal parts». Moreover, the adverb dīksā comes from the Greek root dis-, «divided into two parts, dichotomous», which in its turn is derived from the Sanskrit root *diś-(dik) whose meaning indicates the astronomical concept of the «horizon line», — i.e. the boundary line that divides apparently the cosmos in two equal parts, the Earth and the Sky. As said by Palmer and Gagarin, in effect, the meaning of the word Dikē is associated with making a «judgment or decision» between two contestants, that is, placing a «dividing line» (straight or crooked) between them. Furthermore, this original conception representing justice as a division of the cosmos into two equal parts, or cosmic dasmos, has its roots in ancient cosmogony not only Greek but also Indo-Iranian, Hindus, Old Persian, Egyptian, Babylonian and Chinese. To conclude, according to my research, also Plato could have used the paradigm of the «Line of the horizon» to explain his cosmological Doctrine of Ideas.


title: Aggregates Caching in Columnar In-Memory Databases

abstract: The mixed database workloads found in enterprise applications are comprised of short-running transactional as well as analytical queries with resource-intensive data aggregations. In this context, caching the query results of long-running queries is desirable as it increases the overall performance. However, traditional caching approaches are inefficient in a way that changes in the base data result in invalidation or recalculation of cached results.


title: An empirical study of machine learning techniques for affect recognition in human–robot interaction

abstract: Given the importance of implicit communication in human interactions, it would be valuable to have this capability in robotic systems wherein a robot can detect the motivations and emotions of the person it is working with. Recognizing affective states from physiological cues is an effective way of implementing implicit human-robot interaction. Several machine learning techniques have been successfully employed in affect-recognition to predict the affective state of an individual given a set of physiological features. However, a systematic comparison of the strengths and weaknesses of these methods has not yet been done. In this paper we present a comparative study of four machine learning methods - k-nearest neighbor, regression tree, Bayesian network and support vector machine as applied to the domain of affect recognition using physiological signals. The results showed that support vector machine gave the best classification accuracy even though all the methods performed competitively. Regression tree gave the next best classification accuracy and was the most space and time efficient.


title: Fusing Partial Camera Signals for Noncontact Pulse Rate Variability Measurement

abstract: Remote camera-based measurement of physiology has great potential for healthcare and affective computing. Recent advances in computer vision and signal processing have enabled photoplethysmography (PPG) measurement using commercially available cameras. However, there remain challenges in recovering accurate noncontact PPG measurements in the presence of rigid head motion. When a subject is moving, their face may be turned away from one camera, be obscured by an object, or move out of the frame resulting in missing observations. As the calculation of pulse rate variability (PRV) requires analysis over a time window of several minutes, the effect of missing observations on such features is deleterious. We present an approach for fusing partial color-channel signals from an array of cameras that enable physiology measurements to be made from moving subjects, even if they leave the frame of one or more cameras, which would not otherwise be possible with only a single camera. We systematically test our method on subjects ( N=25) using a set of six, 5-min tasks (each repeated twice) involving different levels of head motion. This results in validation across 25 h of measurement. We evaluate pulse rate and PRV parameter estimation including statistical, geometric, and frequency-based measures. The median absolute error in pulse rate measurements was 0.57 beats-per-minute (BPM). In all but two tasks with the greatest motion, the median error was within 0.4 BPM of that from a contact PPG device. PRV estimates were significantly improved using our proposed approach compared to an alternative not designed to handle missing values and multiple camera signals; the error was reduced by over 50%. Without our proposed method, errors in pulse rate would be very high, and estimation of PRV parameters would not be feasible due to significant data loss.


title: From High Intellectual Potential to Asperger Syndrome: Evidence for Differences and a Fundamental Overlap—A Systematic Review

abstract: Background: An increasing number of clinicians point to similar clinical features between some children with High Intellectual Potential (HIP or “Giftedness” = Total IQ > 2 SD), and children with Autism Spectrum Disorder (ASD) without intellectual or language delay, formerly diagnosed with Asperger Syndrome. Some of these common features are social interaction impairments, special interests, and in some cases high-verbal abilities. The aim of this article is to determine whether theses similarities exist at more fundamental levels, other than clinical, and to explore the literature in order to provide empirical support for an overlap between ASD and HIP. Method: First, comparative studies between ASD and HIP children were sought. Because of a lack of data, the respective characteristics of ASD and HIP subjects were explored by a cross-sectional review of different areas of research. Emphasis was placed on psychometric and cognitive evaluations, experimental and developmental assessments, and neurobiological research, following a “bottom-up” procedure. Results: This review highlights the existence of similarities in the neurocognitive, developmental and neurobiological domains between these profiles, which require further study. In addition, the conclusions of several studies show that there are differences between HIP children with a homogeneous Intellectual Quotient profile and children with a heterogeneous Intellectual Quotient profile. Conclusion: HIP seems to cover different developmental profiles, one of which might share features with ASD. A new line of investigation providing a possible starting-point for future research is proposed. Its implications, interesting from both clinical and research perspectives, are discussed.


title: Multiphase Bidirectional Flyback Converter Topology for Hybrid Electric Vehicles

abstract: For hybrid electric vehicles, the batteries and the drive dc link may be at different voltages. The batteries are at low voltage to obtain higher volumetric efficiencies, and the dc link is at higher voltage to have higher efficiency on the motor side. Therefore, a power interface between the batteries and the drive's dc link is essential. This power interface should handle power flow from battery to motor, motor to battery, external genset to battery, and grid to battery. This paper proposes a multi-power-port topology which is capable of handling multiple power sources and still maintains simplicity and features like obtaining high gain, wide load variations, lower output-current ripple, and capability of parallel-battery energy due to the modular structure. The scheme incorporates a transformer winding technique which drastically reduces the leakage inductance of the coupled inductor. The development and testing of a bidirectional flyback dc-dc converter for hybrid electric vehicle is described in this paper. Simple hysteresis voltage control is used for dc-link voltage regulation. The experimental results are presented to show the working of the proposed converter.


title: Conical Conformal Shaped-Beam Substrate-Integrated Waveguide Slot Array Antenna With Conical-to-Cylindrical Transition

abstract: The analysis and design of a millimeter-wave conical conformal shaped-beam substrate-integrated waveguide (SIW) array antenna is demonstrated in this paper. After investigating the influence of the conical surface on the propagation characteristics of a conformal SIW, a modification for the width of a conical conformal SIW is proposed to obtain the same propagation characteristic along the longitudinal direction. This feature is indispensable to employ the classic equivalent circuit of a planar slot array antenna in the design of a conical conformal antenna. In this case, the design process of the conformal antenna can be simplified. An efficient and accurate model method of the conical conformal SIW antenna is presented as well. Then, a design process of the conical conformal SIW slot array antenna is introduced. Furthermore, to implement the transition between a conical surface and a cylindrical surface, a flexible SIWtransition is designed with a good impedance matching. Finally, two low sidelobe level (SLL) SIW conical conformal antennas with and without the flexible transitions are designed. Both of them have −28 dB SLLs in H-plane at the center frequency of 35 GHz.


title: Understanding the Adoption of Multipurpose Information Appliances: The Case of Mobile Data Services

abstract: We have come to a stage when information technology (IT) innovations have permeated every walk of life. Many new technologies can be used for many different purposes and in different contexts other than the workplace. The current study attempts to understand individual adoption of IT innovations that are used beyond work settings. We define a new class of IT innovations called multipurpose information appliances, which are personal, universally accessible, and multipurpose. The ubiquitous nature of these appliances has led to a constant permeability between the separate contexts of social life. An adoption model that reflects the unique characteristics and usage contexts of multipurpose information appliances was developed. The model consists of five sets of adoption factors and was tested using data collected on mobile data services adoption. Our findings show that the determinants of multipurpose information appliance adoption decisions are not only different from those in the workplace, but are also dependent on the nature of the target technology and its usage context. Theoretical and practical implications of the findings are discussed.


Unsupervised learning of human action categories using spatial-temporal words


title: A CB Protective Firefighter Turnout Suit

abstract: This paper describes research that developed a prototype chemical and biological (CB) protective firefighter suit. It is presented as a case study demonstrating an integrated systems approach to designing, developing and evaluating a protective clothing ensemble based on end user requirements. It includes a discussion of the process that was used to gain an understanding of firefighter performance needs for a structural turnout suit that also incorporated chemical protection. It describes the design features of the turnout suit that were developed to meet these expectations as well as the program of testing and evaluation used to characterize garment performance. It discusses ensemble level performance evaluations in instrumented fire manikin tests and man-in-stimulant test procedures. It describes studies conducted to determine the impact of prototype garment design features on heat stress, wear comfort and ergonomic function in structural firefighting applications.