title: Intelligent Security Model of Smart Phone Based on Human Behavior in Mobile Cloud Computing

abstract: Diverse researches following the advancement of IT technology and emergence of smart devices are common in current research. In particular, the most commercialized smart device, the smart phone, provides convenient communication among users due to easy portability and real-time information sharing within a diverse system. Moreover, spatial and temporal use of smart devices greatly increased because these devices are capable of performing the same functions as a desktop personal computer, making smart phones extremely popular. However, research on smart phones has mainly focused on the convenience of the services offered on these devices, while the issue of security has been neglected. Smart phones are vulnerable to diverse, malicious attacks, which have increased as new functions are developed and commonly used. For this reason, a number of applications were developed to detect a variety of malicious codes in smart phones and prevent private information leakage. However, due to the difficulties of recognizing and responding to attacks, such as advanced persistent threats (APTs), an intelligent security model is urgently required. This paper suggests intelligent security model of smart phone based on human behavior in mobile cloud computing to detect diverse types of malicious code in smart phones and respond to advanced attacks, such as APTs. The suggested intelligent security model of smart phone securely protects users from private information leaks and illegal billing action. Moreover, intelligent response to malicious, difficult-to-detect code is provided by a user behavior-based intelligent analysis, which makes it possible to shut down network, registry, drive, and system accessibility and to notify users to respond to malicious attacks.


5 G-XHaul : A Converged Optical and Wireless Solution for 5 G Transport Networks


title: Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit?

abstract: Binary neural networks (BNN) have been studied extensively since they run dramatically faster at lower memory and power consumption than floating-point networks, thanks to the efficiency of bit operations. However, contemporary BNNs whose weights and activations are both single bits suffer from severe accuracy degradation. To understand why, we investigate the representation ability, speed and bias/variance of BNNs through extensive experiments. We conclude that the error of BNNs is predominantly caused by intrinsic instability (training time) and non-robustness (train & test time). Inspired by this investigation, we propose the Binary Ensemble Neural Network (BENN) which leverages ensemble methods to improve the performance of BNNs with limited efficiency cost. While ensemble techniques have been broadly believed to be only marginally helpful for strong classifiers such as deep neural networks, our analysis and experiments show that they are naturally a perfect fit to boost BNNs. We find that our BENN, which is faster and more robust than state-of-the-art binary networks, can even surpass the accuracy of the full-precision floating number network with the same architecture.


title: The valve movement response of mussels: a tool in biological monitoring

abstract: Biological sensors are becoming more important to monitor the quality of the aquatic environment. In this paper the valve movement response of freshwater (Dreissena polymorpha) and marine (Mytilus edulis) mussels is presented as a tool in monitoring studies. Examples of various methods for data storage and data treatment are presented, elucidating easier operation and lower detection limits. Several applications are mentioned, including an early warning system based on this valve movement response of mussels.


title: Overview of the TECSAR Satellite Hardware and Mosaic Mode

abstract: TECSAR satellite is part of a spaceborne synthetic-aperture-radar (SAR) satellite technology demonstration program. The purpose of this program is to develop and evaluate the technologies required to achieve high-resolution images combined with large-area coverage. These requirements can be fulfilled by designing a satellite with multimode operation. The TECSAR satellite is developed by the MBT Space Division, Israel Aerospace Industries, acting as a prime contractor, which develops the satellite bus, and by ELTA Systems Ltd., which develops the SAR payload. This paper reviews the TECSAR radar system design, which enables to perform a variety of operational modes. It also describes the unique hardware components: deployable parabolic mesh antenna, multitube transmitter, and data-link transmission unit. The unique mosaic mode is presented. It is shown that this mode is the spot version of the scan mode.


title: Impact of Frequent Interruption on Nurses' Patient-Controlled Analgesia Programming Performance

abstract: Objective:The purpose was to add to the body of knowledge regarding the impact of interruption on acute care nurses’ cognitive workload, total task completion times, nurse frustration, and medication administration error while programming a patient-controlled analgesia (PCA) pump.Background:Data support that the severity of medication administration error increases with the number of interruptions, which is especially critical during the administration of high-risk medications. Bar code technology, interruption-free zones, and medication safety vests have been shown to decrease administration-related errors. However, there are few published data regarding the impact of number of interruptions on nurses’ clinical performance during PCA programming.Method:Nine acute care nurses completed three PCA pump programming tasks in a simulation laboratory. Programming tasks were completed under three conditions where the number of interruptions varied between two, four, and six. Outcome measures included cognitive w...


title: Constellation Design via Capacity Maximization

abstract: Traditional constellations are uniformally spaced. By giving up uniform spacing, constellations can be designed to have larger joint (i.e. overall) capacity or parallel decoding capacity. In this paper non-uniformally spaced (i.e. 'geometrically' shaped) constellations are designed to maximize either of these quantities. By way of numerical capacity computations we show that except in special cases, there are no universally optimal geometrically shaped constellations across all code rates, and that the optimization of a constellation has to target a specific code rate. Unlike joint capacity, optimizing for parallel decoding capacity is label dependent. For PAM and PSK constellations, we found the maximum parallel decoding capacity to be achieved using gray (not necessarily binary reflective gray) labels. However, for PAM constellations, not all gray labels can yield the highest parallel decoding capacity. Besides the conventional use of a (log2 (M) -1) /log2 (M) code rate with an M-point constellation for bandwidth efficient communications, the optimized constellations could offer further non-trivial gains at lower code rates (unlike traditional constellations). An optimized constellation is used with a state-of-the-art LDPC code and simulation results are presented. This paper also draws a distinction between probabilistic shaping and geometric shaping and in fact proves under broad conditions, that any gain in capacity which can be found via probabilistic shaping can also be achieved or exceeded solely through geometric shaping.


title: In-car gps navigation: engagement with and disengagement from the environment

abstract: Although in-car GPS navigation technology is proliferating, it is not well understood how its use alters the ways people interpret their environment and navigate through it. We argue that GPS-based car navigation might disengage people from their surrounding environment, but also has the potential to open up novel ways to engage with it. We present an ethnographically-informed study with GPS users, showing evidence for practices of disengagement as well as new opportunities for engagement, illustrating our findings using rich descriptions from the field. Grounded in our observations we propose design principles for GPS systems that support richer experiences of driving. We argue that for a fuller understanding of issues of disengagement and engagement with the environment we need to move beyond a focus on the (re)design of GPS devices, and point to future directions of work that embrace a broader perspective.


title: Long distance Automatic Number Plate Recognition under perspective distortion using zonal density and Support Vector Machine

abstract: Automatic Number Plate Recognition (ANPR) is one of computer vision applications to extract information in vehicles plate number. Nevertheless, perspective distortion is unavoidable when taking pictures of the plate number. Another factor that causes inaccuracy is the distance of the camera from the plate number. To solve these problems, we propose a new method to automatically detect and recognize vehicle plate number with regards to perspective distortion and distance of capturing plate number. We used zonal density with Support Vector Machine (SVM) as a classifier. We tested our algorithm on 21 vehicles plate number with 1, 3, and 5 meter of capturing distance. Our method yields an accuracy of 89.77%, 82.86%, and 65.22% for 1, 3, and 5 meters capturing distance, respectively. Compared with previous work, our method is able to preserve high accuracy when segmenting characters of plate number taken from 5 meter distance.


Antidepressant Ef fi cacy of Ketamine in Treatment-Resistant Major Depression : A Two-Site Randomized Controlled Trial


title: Use of variance estimation in the multi-armed bandit problem

abstract: An important aspect of most decision making problems concerns the appropriate balance between exploitation (acting optimally according to the partial knowledge acquired so far) and exploration of the environment (acting sub-optimally in order to refine the current knowledge and improve future decisions). A typical example of this so-called exploration versus exploitation dilemma is the multi-armed bandit problem, for which many strategies have been developed. Here we investigate policies based the choice of the arm having the highest upper-confidence bound, where the bound takes into account the empirical variance of the different arms. Such an algorithm was found earlier to outperform its peers in a series of numerical experiments. The main contribution of this paper is the theoretical investigation of this algorithm. Our contribution here is twofold. First, we prove that with probability at least $1-\beta$, the regret after $n$ plays of a variant of the UCB algorithm (called $\beta$-UCB) is upper-bounded by a constant, that scales linearly with $\log(1/\beta)$, but which is independent from $n$. We also analyse a variant which is closer to the algorithm suggested earlier. We prove a logarithmic bound on the expected regret of this algorithm and argue that the bound scales favourably with the variance of the suboptimal arms.


title: Learning term embeddings for hypernymy identification

abstract: Hypernymy identification aims at detecting if is A relationship holds between two words or phrases. Most previous methods are based on lexical patterns or the Distributional Inclusion Hypothesis, and the accuracy of such methods is not ideal. In this paper, we propose a simple yet effective supervision framework to identify hypernymy relations using distributed term representations (a.k.a term embeddings). First, we design a distance-margin neural network to learn term embeddings based on some pre-extracted hypernymy data. Then, we apply such embeddings as term features to identify positive hypernymy pairs through a supervision method. Experimental results demonstrate that our approach outperforms other supervised methods on two popular datasets and the learned term embeddings has better quality than existing term distributed representations with respect to hypernymy identification.


title: Social Networking Sites in Romantic Relationships: Attachment, Uncertainty, and Partner Surveillance on Facebook

abstract: Abstract Social networking sites serve as both a source of information and a source of tension between romantic partners. Previous studies have investigated the use of Facebook for monitoring former and current romantic partners, but why certain individuals engage in this behavior has not been fully explained. College students (N=328) participated in an online survey that examined two potential explanatory variables for interpersonal electronic surveillance (IES) of romantic partners: attachment style and relational uncertainty. Attachment style predicted both uncertainty and IES, with preoccupieds and fearfuls reporting the highest levels. Uncertainty did not predict IES, however. Future directions for research on romantic relationships and online surveillance are explored.


title: Publish/subscribe-enabled software defined networking for efficient and scalable IoT communications

abstract: The Internet of Things (IoT) is the result of many different enabling technologies such as embedded systems, wireless sensor networks, cloud computing, big-data, etc., which are used to gather, process, infer, and transmit data. Combining all these technologies requires a research effort to address all the challenges of these technologies, especially for sensing and delivering information from the physical world to cloud-hosted services. In this article we outline the most important issues related to standardization efforts, mobility of objects, networking and gateway access, and QoS support. In particular, we describe a novel IoT network architecture that integrates software defined networking (SDN) and data distribution service (DDS) middleware. The proposed architecture will improve service delivery of IoT systems and will bring flexibility to the network.


title: LOD Laundromat: A Uniform Way of Publishing Other People's Dirty Data

abstract: It is widely accepted that proper data publishing is difficult. The majority of Linked Open Data (LOD) does not meet even a core set of data publishing guidelines. Moreover, datasets that are clean at creation, can get stains over time. As a result, the LOD cloud now contains a high level of dirty data that is difficult for humans to clean and for machines to process. ::: ::: Existing solutions for cleaning data (standards, guidelines, tools) are targeted towards human data creators, who can (and do) choose not to use them. This paper presents the LOD Laundromat which removes stains from data without any human intervention. This fully automated approach is able to make very large amounts of LOD more easily available for further processing right now. ::: ::: LOD Laundromat is not a new dataset, but rather a uniform point of entry to a collection of cleaned siblings of existing datasets. It provides researchers and application developers a wealth of data that is guaranteed to conform to a specified set of best practices, thereby greatly improving the chance of data actually being (re)used.


title: Transparent Queries: investigation users' mental models of search engines

abstract: Typically, commercial Web search engines provide very little feedback to the user concerning how a particular query is processed and interpreted. Specifically, they apply key query transformations without the users knowledge. Although these transformations have a pronounced effect on query results, users have very few resources for recognizing their existence and understanding their practical importance. We conducted a user study to gain a better understanding of users knowledge of and reactions to the operation of several query transformations that web search engines automatically employ. Additionally, we developed and evaluated Transparent Queries, a software system designed to provide users with lightweight feedback about opaque query transformations. The results of the study suggest that users do indeed have difficulties understanding the operation of query transformations without additional assistance. Finally, although transparency is helpful and valuable, interfaces that allow direct control of query transformations might ultimately be more helpful for end-users.


title: A morphologic two-stage approach for automated optic disk detection in color eye fundus images

abstract: A new adaptive morphological method for the automatic detection of the optic disk in digital color eye fundus images is presented in this paper. This method has been designed to detect the optic disk center and the optic disk rim. In our experiments with the DRIVE and DIARETDB1 databases, the proposed method was able to detect the optic disk center with 100% and 97.75% of accuracy, respectively. We considered correct all automatically detected optic disk location that is within the borders of the optic disk marked manually. Using our proposed method, the rim of the optic disk was detected in all images of the DRIVE database with average sensitivity and specificity of 83.54% and 99.81%, respectively, and on the DIARETDB1 database with average sensitivity and specificity of 92.51% and 99.76%, respectively.


title: The Significance of the Default Mode Network (DMN) in Neurological and Neuropsychiatric Disorders: A Review

abstract: The relationship of cortical structure and specific neuronal circuitry to global brain function, particularly its perturbations related to the development and progression of neuropathology, is an area of great interest in neurobehavioral science. Disruption of these neural networks can be associated with a wide range of neurological and neuropsychiatric disorders. Herein we review activity of the Default Mode Network (DMN) in neurological and neuropsychiatric disorders, including Alzheimer’s disease, Parkinson’s disease, Epilepsy (Temporal Lobe Epilepsy - TLE), attention deficit hyperactivity disorder (ADHD), and mood disorders. We discuss the implications of DMN disruptions and their relationship to the neurocognitive model of each disease entity, the utility of DMN assessment in clinical evaluation, and the changes of the DMN following treatment.


title: End-To-End Interpretable Neural Motion Planner

abstract: In this paper, we propose a neural motion planner for learning to drive autonomously in complex urban scenarios that include traffic-light handling, yielding, and interactions with multiple road-users. Towards this goal, we design a holistic model that takes as input raw LIDAR data and a HD map and produces interpretable intermediate representations in the form of 3D detections and their future trajectories, as well as a cost volume defining the goodness of each position that the self-driving car can take within the planning horizon. We then sample a set of diverse physically possible trajectories and choose the one with the minimum learned cost. Importantly, our cost volume is able to naturally capture multi-modality. We demonstrate the effectiveness of our approach in real-world driving data captured in several cities in North America. Our experiments show that the learned cost volume can generate safer planning than all the baselines.


title: An End-to-End Workflow for Engineering of Biological Networks from High-Level Specifications

abstract: We present a workflow for the design and production of biological networks from high-level program specifications. The workflow is based on a sequence of intermediate models that incrementally translate high-level specifications into DNA samples that implement them. We identify algorithms for translating between adjacent models and implement them as a set of software tools, organized into a four-stage toolchain: Specification, Compilation, Part Assignment, and Assembly. The specification stage begins with a Boolean logic computation specified in the Proto programming language. The compilation stage uses a library of network motifs and cellular platforms, also specified in Proto, to transform the program into an optimized Abstract Genetic Regulatory Network (AGRN) that implements the programmed behavior. The part assignment stage assigns DNA parts to the AGRN, drawing the parts from a database for the target cellular platform, to create a DNA sequence implementing the AGRN. Finally, the assembly stage comp...


title: Predicting short-term interests using activity-based search context

abstract: A query considered in isolation offers limited information about a searcher's intent. Query context that considers pre-query activity (e.g., previous queries and page visits), can provide richer information about search intentions. In this paper, we describe a study in which we developed and evaluated user interest models for the current query, its context (from pre-query session activity), and their combination, which we refer to as intent. Using large-scale logs, we evaluate how accurately each model predicts the user's short-term interests under various experimental conditions. In our study we: (i) determine the extent of opportunity for using context to model intent; (ii) compare the utility of different sources of behavioral evidence (queries, search result clicks, and Web page visits) for building predictive interest models, and; (iii) investigate optimally combining the query and its context by learning a model that predicts the context weight for each query. Our findings demonstrate significant opportunity in leveraging contextual information, show that context and source influence predictive accuracy, and show that we can learn a near-optimal combination of the query and context for each query. The findings can inform the design of search systems that leverage contextual information to better understand, model, and serve searchers' information needs.


title: The failure of CSMA in emerging wireless network scenarios

abstract: The current family of 802.11 protocols are based on the Carrier Sense Multiple Access (CSMA) mechanism which is a simple and robust means of sharing a channel. However, two current trends in wireless networks point towards a situation where CSMA fails to perform better than pure random access solutions such as ALOHA. The first trend is the ever increasing raw data rate in each generation of 802.11 which is set to continue with the current 802.11ax standardisation. The second is the move towards smaller frames as end users increasingly use mobile devices instead of desktop computers. We show that as the ratio of propagation delay to packet transmission time increases, the performance of CSMA degrades correspondingly, to the point where ALOHA outperforms CSMA.


title: Zero-Voltage- and Zero-Current-Switching Full-Bridge Converter With Secondary Resonance

abstract: A zero-voltage- and zero-current-switching full-bridge (FB) converter with secondary resonance is presented and analyzed. The primary side of the converter is composed of FB insulated-gate bipolar transistors, which are driven by phase-shift control. The secondary side is composed of a resonant tank and a half-wave rectifier. Without an auxiliary circuit, zero-voltage switching (for leading-leg switches) and zero-current switching (for lagging-leg switches) are achieved in the entire operating range. To implement the converter without an additional inductor, the leakage inductance of the transformer is utilized as the resonant inductor. Due to its many advantages, including high efficiency, minimum number of devices, and low cost, this converter is attractive for high-voltage and high-power applications. The analysis and design considerations of the converter are presented. A prototype was implemented for an application requiring a 5-kW output power, an input-voltage range varying from 250 to 350 V, and a 350-V output voltage. The experimental results obtained from a prototype verify the analysis. The prototype's efficiency at full load is over 95.5%.


title: Overview of Audio Forensics

abstract: Audio forensics applies the tools and techniques of audio engineering and digital signal processing to study audio data as part of a legal proceeding or an official investigation of some kind. This chapter summarizes the principal audio forensic tasks, including authentication, enhancement, and interpretation. The chapter explains the relevant procedural and historical background, presents several examples of audio forensic applications, and reviews several important areas for future research and development.


Specific Treatment to Neural Networks for Repeated Measures Analysis


title: Intravascular Ultrasound : Novel Pathophysiological Insights and Current Clinical Applications

abstract: Abstract —Intravascular ultrasound (IVUS) is a valuable adjunct to angiography, providing new insights in the diagnosis of and therapy for coronary disease. Angiography depicts only a 2D silhouette of the lumen, whereas IVUS allows tomographic assessment of lumen area, plaque size, distribution, and composition. The safety of IVUS is well documented, and the assessment of luminal dimensions represents an important application of this modality. Comparative studies show the greatest disparities between angiography and ultrasound after mechanical interventions. In young subjects, normal intimal thickness is typically ≈0.15 mm. With IVUS, lipid-laden lesions appear hypoechoic, fibromuscular lesions generate low-intensity echoes, and fibrous or calcified tissues are echogenic. Calcium obscures the underlying wall (acoustic shadowing). The extent and severity of disease by angiography and ultrasound are frequently discrepant. Arterial remodeling refers to changes in vascular dimensions during the development of atherosclerosis. At diseased sites, the external elastic membrane may actually shrink in size, contributing to luminal stenosis. The interpretation of IVUS relies on simple visual inspection of acoustic reflections to determine plaque composition. However, different tissue components may look quite similar, and artifacts may adversely affect ultrasound images. IVUS commonly detects occult disease in angiographically “normal” sites. In ambiguous lesions, ultrasound permits lesion quantification, particularly for left main coronary disease. IVUS has emerged as the optimal method for the detection of transplant vasculopathy. An important potential application of ultrasound is the identification of atheromas at risk of rupture. The mechanisms of action of interventional devices have been elucidated using IVUS, and ultrasound is used by some operators to select the most suitable interventional device. IVUS-derived residual plaque burden is the most useful predictor of clinical outcome. In restenosis after balloon angioplasty, negative remodeling is a major mechanism of late lumen loss. IVUS is not routinely used for stent optimization, and there is no consensus regarding optimal procedural end points. Ultrasound has proven useful in evaluating brachytherapy. New and emerging applications for IVUS are continuing to evolve, particularly in atherosclerosis regression-progression trials.


title: KI-Mon ARM: A Hardware-Assisted Event-triggered Monitoring Platform for Mutable Kernel Object

abstract: External hardware-based kernel integrity monitors have been proposed to mitigate kernel-level malwares. However, the existing external approaches have been limited to monitoring the static regions of kernel while the latest rootkits manipulate the dynamic kernel objects. To address the issue, we present KI-Mon, a hardware-based platform that introduces event-triggered monitoring techniques for kernel dynamic objects. KI-Mon advances the bus traffic snooping technique to not only detect memory write traffic on the host bus but also filter out all but meaningful traffic to generate events. We show how kernel invariant verification software can be developed around these events, and also provide a set of APIs for additional invariant verification development. We also report our findings and considerations on the unique challenges for external monitors – such as cache coherency, dynamic object tracing. We introduce host-side kernel changes that alleviate these issues that involve changes in kernel's object allocation and cache policy control. We have built a prototype of KI-Mon on the ARM architecture to demonstrate the efficacy of KI-Mon's event-triggered mechanism in terms of performance overhead for the monitored host system and the processor usage of the KI-Mon processor.


Modelling Crowd Dynamics During Evacuation Situations Using Simulation


title: Personhood and dementia: revisiting Tom Kitwood’s ideas

abstract: Person-centred care is often cited as an aim of gerontological nursing and promotion of personhood is said to be the basis for person-centred care. As such, it forms a cornerstone value for many gerontological nurses, particularly those working in dementia care. Tom Kitwood’s ideas and definition of personhood are widely referred to in the literature and used in the dementia care field. More recently, there is a move to critique and partially reject Kitwood’s ideas on personhood. This paper has three aims: (i) to explore some central ideas around key theories of personhood (ii) to critique Kitwood’s work on personhood. (iii) To summarize current critiques of Kitwood’s ideas and provide a response that outlines why Kitwoods’ ideas are still relevant. It is suggested many critiques ignore Kitwoods’ ultimate purpose; that of moral concern for ‘others’. However, the main criticism put forward in this paper is that, rather than completely rejecting personhood theories, Kitwood locates his work on what it means to be a person within a traditional Cartesian personhood framework, albeit from a revised or pragmatic viewpoint. Finally, it is suggested that definitions of persons and personhood need to take account of the body and time (corporeality and temporality) and gerontological nursing may want to reassess how much allegiance is given to basing nursing frameworks on the concept of personhood.


title: Operational experience with Dynamic Line Rating forecast-based solutions to increase usable network transfer capacity

abstract: SUMMARY This proposed paper focuses on the criteria that need to be met to allow network operators to effectively use Dynamic Line Ratings in real-life operations. Today DLR has reached a point where the focus of research & testing has moved on from how to determine the most reliable & accurate rating to how to effectively integrate DLR in the processes of the network operators and maximize the use of the network. Different pilot projects in Europe and around the world have proven the DLR technology works in the field and many papers within CIGRE and other organizations highlight those results, but now the focus is shifting to how this technology can be put to best use: where it makes sense and how the results should be used to maximize the benefits while reducing the operational risks. The paper will highlight the experience the Belgian TSO, Elia, has had regarding those questions. The specific aspects detailed here is the usability of day-ahead forecasting of DLR. Indeed, most decisions regarding network operation and Electricity Market are taken many hours/days in advance; therefore if DLR is to influence these decisions, a reliable forecast of the dynamic rating values is required. This is very similar to the need for the forecast of wind & sun production that allows the safe integration of those intermittent energy sources into the power system. Within the EU-funded FP7 Twenties project, the University of Liege, Belgium, together with Ampacimon has developed such a capability. Elia and Coreso have evaluated the usability of such a DLR forecast value to increase the flexibility of the network, to allow more exchange capacities for the market, and help solve pan-european congestion issues related to the increasing share of intermittent power from RES (Renewable Energy Sources) in the energy mix. Two-day ahead DLR forecast has shown an average capacity improvement of more than 10% over seasonal rating with 98% confidence. The confidence interval may be adjusted to operational needs, as a tradeoff between more gain and more confidence in the forecast has to be set, depending on the risk policy. Different cases may even feature different risk policies, making them flexible as well, e.g. what are the other available options to increase flexibility at that moment? These forecast capacities can then further be used for dayahead management of PSTs (Phase-Shifting Transformers) in a coordinated way, together 21,� rue� d’Artois,� F -75008 PARIS


title: Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing

abstract: This paper proposes a neural semantic parsing approach -- Sequence-to-Action, which models semantic parsing as an end-to-end semantic graph generation process. Our method simultaneously leverages the advantages from two recent promising directions of semantic parsing. Firstly, our model uses a semantic graph to represent the meaning of a sentence, which has a tight-coupling with knowledge bases. Secondly, by leveraging the powerful representation learning and prediction ability of neural network models, we propose a RNN model which can effectively map sentences to action sequences for semantic graph generation. Experiments show that our method achieves state-of-the-art performance on OVERNIGHT dataset and gets competitive performance on GEO and ATIS datasets.


title: Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds

abstract: Question Answering (QA), as a research field, has primarily focused on either knowledge bases (KBs) or free text as a source of knowledge. These two sources have historically shaped the kinds of questions that are asked over these sources, and the methods developed to answer them. In this work, we look towards a practical use-case of QA over user-instructed knowledge that uniquely combines elements of both structured QA over knowledge bases, and unstructured QA over narrative, introducing the task of multi-relational QA over personal narrative. As a first step towards this goal, we make three key contributions: (i) we generate and release TextWorldsQA, a set of five diverse datasets, where each dataset contains dynamic narrative that describes entities and relations in a simulated world, paired with variably compositional questions over that knowledge, (ii) we perform a thorough evaluation and analysis of several state-of-the-art QA models and their variants at this task, and (iii) we release a lightweight Python-based framework we call TextWorlds for easily generating arbitrary additional worlds and narrative, with the goal of allowing the community to create and share a growing collection of diverse worlds as a test-bed for this task.


title: Small Organ Segmentation in Whole-body MRI using a Two-stage FCN and Weighting Schemes

abstract: Accurate and robust segmentation of small organs in whole-body MRI is difficult due to anatomical variation and class imbalance. Recent deep network based approaches have demonstrated promising performance on abdominal multi-organ segmentations. However, the performance on small organs is still suboptimal as these occupy only small regions of the whole-body volumes with unclear boundaries and variable shapes. A coarse-to-fine, hierarchical strategy is a common approach to alleviate this problem, however, this might miss useful contextual information. We propose a two-stage approach with weighting schemes based on auto-context and spatial atlas priors. Our experiments show that the proposed approach can boost the segmentation accuracy of multiple small organs in whole-body MRI scans.


Chatbots and Conversational Interfaces: Three Domains of Use


title: Blockchain Based Credibility Verification Method for IoT Entities

abstract: With the fast development of mobile Internet, Internet of Things (IoT) has been found in many important applications recently. However, it still faces many challenges in security and privacy. Blockchain (BC) technology, which underpins the cryptocurrency Bitcoin, has played an important role in the development of decentralized and data intensive applications running on millions of devices. In this paper, to establish the relationship between IoT and BC for device credibility verification, we propose a framework with layers, intersect, and self-organization Blockchain Structures (BCS). In this new framework, each BCS is organized by Blockchain technology. We describe the credibility verification method and show how it provide the verification. The efficiency and security analysis are also given in this paper, including its response time, storage efficiency, and verification. The conducted experiments have been shown to demonstrate the validity of the proposed method in satisfying the credible requirement achieved by Blockchain technology and certain advantages in storage space and response time.


Code-Switched Named Entity Recognition with Embedding Attention


title: Approximate Matching of Curves to Point Sets

abstract: Let P be a polygonal curve in R of length n, and S be a point set of size k. The Curve/Point Set Matching problem consists of finding a polygonal curve Q on S such that the Frechet distance from P is less than a given e. We consider eight variations of the problem based on the distance metric used and the omittability and repeatability of the points. We provide closure to a recent series of complexity results for the case where S consists of precise points. We also formulate a more realistic version of the problem that takes into account measurement errors and attempts to match a given curve to a set of imprecise points. We show that all three variations of the problem that are in P when S consists of precise points become NP-complete when S consists of imprecise points. Finally, we present a 3-factor approximation algorithm for a version of the problem.


title: A space-time smooth artificial viscosity method with wavelet noise indicator and shock collision scheme, Part 2: the 2-D case

abstract: Abstract In this first part of two papers, we extend the C-method developed in [40] for adding localized, space-time smooth artificial viscosity to nonlinear systems of conservation laws that propagate shock waves, rarefaction waves, and contact discontinuities in one space dimension. For gas dynamics, the C-method couples the Euler equations to a scalar reaction-diffusion equation, whose solution C serves as a space-time smooth artificial viscosity indicator. The purpose of this paper is the development of a high-order numerical algorithm for shock-wall collision and bounce-back. Specifically, we generalize the original C-method by adding a new collision indicator, which naturally activates during shock-wall collision. Additionally, we implement a new high-frequency wavelet-based noise detector together with an efficient and localized noise removal algorithm. To test the methodology, we use a highly simplified WENO-based discretization scheme. We show that our scheme improves the order of accuracy of our WENO algorithm, handles extremely strong discontinuities (ranging up to nine orders of magnitude), allows for shock collision and bounce back, and removes high frequency noise. The causes of the well-known “wall heating” phenomenon are discussed, and we demonstrate that this particular pathology can be effectively treated in the framework of the C-method. This method is generalized to two space dimensions in the second part of this work [41] .


title: Design of an low power miller compensated two stage OP-AMP using 45 nm technology for high data rate communication

abstract: This paper reports a new design of a low power two stage operational amplifier with Miller compensated topology for enhancing stability. The circuit is designed and simulated in Tanner EDA tool using 45 nm. CMOS technology. A significant reduction in power and enhancement of unity gain BW is achieved with a satisfactory phase margin. Several parameters are computed and compared with a few contemporary works to establish the efficacy of the design.


title: An analysis of dag-consistent distributed shared-memory algorithms

abstract: In this paper, we analyze the performance of parallel multithreaded algorithms that use dag-consistent distributed shared memory. Specifically, we analyze execution time, page faults, and space requirements for multithreaded algorithms executed by a workstealing thread scheduler and the BACKER algorithm for maintaining dag consistency. We prove that if the accesses to the backing store are random and independent (the BACKER algorithm actually uses hashing), the expected execution time TP(C) of a “fully strict” multithreaded computation on P processors, each with a LRU cache of C pages, is O(T1(C)=P+mCT∞), where T1(C) is the total work of the computation including page faults, T∞ is its critical-path length excluding page faults, and m is the minimum page transfer time. As a corollary to this theorem, we show that the expected number FP(C) of page faults incurred by a computation executed on P processors can be related to the number F1(C) of serial page faults by the formula FP(C) F1(C)+O(CPT∞). Finally, we give simple bounds on the number of page faults and the space requirements for “regular” divide-and-conquer algorithms. We use these bounds to analyze parallel multithreaded algorithms for matrix multiplication and LU-decomposition.


title: Revisiting Karnik–Mendel Algorithms in the framework of Linear Fractional Programming

abstract: Abstract For Interval Type-2 (IT2) fuzzy sets and systems, calculating the centroid and performing type reduction are operations that must be taken into consideration. The Karnik–Mendel Algorithm (KMA) and its enhancements have been usually employed to perform these operations. In KMAs, these IT2 fuzzy operations are defined as nonlinear optimization problems which are solved iteratively by finding the optimal Switching Points (SPs). In this study, we will examine these optimization problems and will reveal the nature of KMAs in the framework of Linear Fractional Programming (LFP). In this context, we will firstly transform IT2 fuzzy operations into 0-1 LFP problems and then we will show that there exists a direct relationship between the SPs of the KMAs and the solution vectors of the defined LFP problems. Hence, the meaning of the SPs will be revealed in the framework of LFP theory. Thus, it will be concluded that the KMAs can be seen as LFP methods. Then, by taking account of the LFP representations, we will show that it is possible to perform the IT2 fuzzy operations via well-known LFP methods, namely the Charnes Cooper Transformation and the Dinkelbach's Algorithm (DA). It is believed that the presented LFP approaches will be very helpful in employing IT2 fuzzy sets and systems in different programming languages since they only use and employ built-in Linear Programming (LP) routines. However, since LP solvers are used, it will be concluded that the LFP based approaches are computationally intensive and thus might not be feasible for real-time applications. Therefore, we will present a computationally efficient implementation of presented the DA based method, namely the KMK Algorithm (KMKA). Then, through analytical investigations on the solution vectors and optimality conditions of the KMKA, it will be revealed that the KMA is algorithmic equivalent to the presented implementation of the DA based KMKA. Thus, it will be revealed that the commonly employed KMA and its enhancements can be seen as efficient implementations of the DA in the framework of LFP. Based on this connection, since the DA is an adaptation of the Newton method to a non-differentiable context, it will be also concluded that the discrete version of KMA can be also seen equivalent to a root-finding problem. We will present comparative numerical results to validate the analytical derivations and observations.


title: Incremental Natural Actor-Critic Algorithms

abstract: We present four new reinforcement learning algorithms based on actor-critic and natural-gradient ideas, and provide their convergence proofs. Actor-critic reinforcement learning methods are online approximations to policy iteration in which the value-function parameters are estimated using temporal difference learning and the policy parameters are updated by stochastic gradient descent. Methods based on policy gradients in this way are of special interest because of their compatibility with function approximation methods, which are needed to handle large or infinite state spaces. The use of temporal difference learning in this way is of interest because in many applications it dramatically reduces the variance of the gradient estimates. The use of the natural gradient is of interest because it can produce better conditioned parameterizations and has been shown to further reduce variance in some cases. Our results extend prior two-timescale convergence results for actor-critic methods by Konda and Tsitsiklis by using temporal difference learning in the actor and by incorporating natural gradients, and they extend prior empirical studies of natural actor-critic methods by Peters, Vijayakumar and Schaal by providing the first convergence proofs and the first fully incremental algorithms.


title: Cloud-fog-dew architecture for refined driving assistance: The complete service computing ecosystem

abstract: Mapping services like Google Maps, Bing Maps, and Mapbox etc. have been assisting the vehicle drivers for real-time traffic, shortest routes, locations and road topologies since several years, and these services are evolving unceasingly. Road accidents is a prominent cause of deaths in India and so does the road conditions. State of art driving assistance systems guide the drivers for sharp turns and road intersections but they lack in providing the fine grained assistance like potholes, speed breakers, sudden inclinations, and declinations on the road surface. The foremost hurdles for such highly sensitive systems are latency requirements, affordability, and high availability. With cloud computing, we can have high availability and affordability but it may not offer quick response due to its considerable round trip time. Fog computing gives huge advantage on latency side, geo-distribution, mobility, and affordability but high availability (fault-tolerance) cannot be always guaranteed due to its substantial geo-distribution. Moreover, end nodes, in both the cases acts purely as clients. With dew computing, we propose to allocate computing tasks on end devices and make them service providers instead of service consumers. The projected approach is heavily based on peer-to-peer communication which would be complementary in nature with cloud and fog. In this article, we explain how a driver can experience a fine-grained driving assistance if we intertwine all the three computing paradigms together. As per the knowledge of authors, this is the first ever proposal till date which entangles all three computing paradigms (Cloud, Fog and Dew) together.


title: An introduction to multibiometrics

abstract: Information fusion refers to the reconciliation of evidence presented by multiple sources of information in order to generate a decision. In the context of biometrics, evidence reconciliation plays a pivotal role in enhancing the recognition accuracy of human authentication systems and is referred to as multibiometrics. Multibiometric systems combine the information presented by multiple biometric sensors, algorithms, samples, units, or traits. Besides enhancing matching performance, these systems are expected to improve population coverage, deter spoofing and impart fault-tolerance to biometric applications. This introductory paper enumerates the various sources of biometric information that can be consolidated as well as the different levels of fusion in a biometric system. The role of using ancillary information such as biometric data quality and soft biometric traits (e.g., height) to enhance the performance of these systems is also discussed. It is becoming increasingly apparent that multibiometric systems will play a pivotal role in establishing identity in the 21st century.


title: Fast and Accurate Time-Series Clustering

abstract: The proliferation and ubiquity of temporal data across many disciplines has generated substantial interest in the analysis and mining of time series. Clustering is one of the most popular data-mining methods, not only due to its exploratory power but also because it is often a preprocessing step or subroutine for other techniques. In this article, we present k-Shape and k-MultiShapes (k-MS), two novel algorithms for time-series clustering. k-Shape and k-MS rely on a scalable iterative refinement procedure. As their distance measure, k-Shape and k-MS use shape-based distance (SBD), a normalized version of the cross-correlation measure, to consider the shapes of time series while comparing them. Based on the properties of SBD, we develop two new methods, namely ShapeExtraction (SE) and MultiShapesExtraction (MSE), to compute cluster centroids that are used in every iteration to update the assignment of time series to clusters. k-Shape relies on SE to compute a single centroid per cluster based on all time series in each cluster. In contrast, k-MS relies on MSE to compute multiple centroids per cluster to account for the proximity and spatial distribution of time series in each cluster. To demonstrate the robustness of SBD, k-Shape, and k-MS, we perform an extensive experimental evaluation on 85 datasets against state-of-the-art distance measures and clustering methods for time series using rigorous statistical analysis. SBD, our efficient and parameter-free distance measure, achieves similar accuracy to Dynamic Time Warping (DTW), a highly accurate but computationally expensive distance measure that requires parameter tuning. For clustering, we compare k-Shape and k-MS against scalable and non-scalable partitional, hierarchical, spectral, density-based, and shapelet-based methods, with combinations of the most competitive distance measures. k-Shape outperforms all scalable methods in terms of accuracy. Furthermore, k-Shape also outperforms all non-scalable approaches, with one exception, namely k-medoids with DTW, which achieves similar accuracy. However, unlike k-Shape, this approach requires tuning of its distance measure and is significantly slower than k-Shape. k-MS performs similarly to k-Shape in comparison to rival methods, but k-MS is significantly more accurate than k-Shape. Beyond clustering, we demonstrate the effectiveness of k-Shape to reduce the search space of one-nearest-neighbor classifiers for time series. Overall, SBD, k-Shape, and k-MS emerge as domain-independent, highly accurate, and efficient methods for time-series comparison and clustering with broad applications.


title: From high-level deep neural models to FPGAs

abstract: Deep Neural Networks (DNNs) are compute-intensive learning models with growing applicability in a wide range of domains. FPGAs are an attractive choice for DNNs since they offer a programmable substrate for acceleration and are becoming available across different market segments. However, obtaining both performance and energy efficiency with FPGAs is a laborious task even for expert hardware designers. Furthermore, the large memory footprint of DNNs, coupled with the FPGAs' limited on-chip storage makes DNN acceleration using FPGAs more challenging. This work tackles these challenges by devising D nn W eaver , a framework that automatically generates a synthesizable accelerator for a given (DNN, FPGA) pair from a high-level specification in Caffe [1]. To achieve large benefits while preserving automation, D nn W eaver generates accelerators using hand-optimized design templates. First, D nn W eaver translates a given high-level DNN specification to its novel ISA that represents a macro dataflow graph of the DNN. The D nn W eaver compiler is equipped with our optimization algorithm that tiles, schedules, and batches DNN operations to maximize data reuse and best utilize target FPGA's memory and other resources. The final result is a custom synthesizable accelerator that best matches the needs of the DNN while providing high performance and efficiency gains for the target FPGA. We use D nn W eaver to generate accelerators for a set of eight different DNN models and three different FPGAs, Xilinx Zynq, Altera Stratix V, and Altera Arria 10. We use hardware measurements to compare the generated accelerators to both multicore CPUs (ARM Cortex A15 and Xeon E3) and many-core GPUs (Tegra K1, GTX 650Ti, and Tesla K40). In comparison, the generated accelerators deliver superior performance and efficiency without requiring the programmers to participate in the arduous task of hardware design.


Design Methods and Electrical Performances of Small Normal-Mode Helical Antennas


title: Teens' concern for privacy when using social networking sites: An analysis of socialization agents and relationships with privacy-protecting behaviors

abstract: U.S. teens are spending substantial time on social networking sites (SNSs). Yet, only a few studies have documented teens' privacy-protecting behaviors on SNSs. Using data of Facebook teen users and their parents in the U.S. from the Pew Internet's Teens & Privacy Management Survey (N=622), this study investigated the socialization agents of teens' level of online privacy concern, and the relationship between teens' level of online privacy concern and their privacy-protecting behaviors on SNSs. Based on path analysis results, this study identified parents and SNS use as the two significant socialization agents. In particular, this study revealed the role of parents' privacy concern and the role of SNS use in motivating teens to increase online privacy concern, which, in turn, drives teens to adopt various privacy-setting strategies on SNSs and to set their Facebook profiles to private. Implications for policymakers and educators were discussed.


title: Real-time high-resolution sparse voxelization with application to image-based modeling

abstract: We present a system for real-time, high-resolution, sparse voxelization of an image-based surface model. Our approach consists of a coarse-to-fine voxel representation and a collection of parallel processing steps. Voxels are stored as a list of unsigned integer triples. An oracle kernel decides, for each voxel in parallel, whether to keep or cull its voxel from the list based on an image consistency criterion of its projection across cameras. After a prefix sum scan, kept voxels are subdivided and the process repeats until projected voxels are pixel size. These voxels are drawn to a render target and shaded as a weighted combination of their projections into a set of calibrated RGB images. We apply this technique to the problem of smooth visual hull reconstruction of human subjects based on a set of live image streams. We demonstrate that human upper body shapes can be reconstructed to giga voxel resolution at greater than 30 fps on modern graphics hardware.


title: Analyzing gene expression data: Fuzzy decision tree algorithm applied to the classification of cancer data

abstract: In data mining, decision tree algorithms are very popular methodologies since the algorithms have a simple inference mechanism and provide a comprehensible way to represent the model in the form of a decision tree. Over the past years, fuzzy decision tree algorithms have been proposed in order to provide a way to handle uncertainty in the data collected. Fuzzy decision tree algorithms have shown to outperform classical decision tree algorithms. This paper investigates a fuzzy decision tree algorithm applied to the classification of gene expression data. The fuzzy decision tree algorithm is compared to a classical decision tree algorithm as well as other well-known data mining algorithms commonly applied to classification tasks. Based on the five data sets analyzed, the fuzzy decision tree algorithm outperforms the classical decision tree algorithm. However, compared to other commonly used classification algorithms, both decision tree algorithms are competitive, although both do not reach the accuracy values of the best performing classifier.


UvA-DARE ( Digital Academic Repository ) Advanced information access to parliamentary debates


Context-Aware Service Integration for Elderly Care in A Smart Environment


title: Cost-sensitive linguistic fuzzy rule based classification systems under the MapReduce framework for imbalanced big data

abstract: Classification with big data has become one of the latest trends when talking about learning from the available information. The data growth in the last years has rocketed the interest in effectively acquiring knowledge to analyze and predict trends. The variety and veracity that are related to big data introduce a degree of uncertainty that has to be handled in addition to the volume and velocity requirements. This data usually also presents what is known as the problem of classification with imbalanced datasets, a class distribution where the most important concepts to be learned are presented by a negligible number of examples in relation to the number of examples from the other classes. In order to adequately deal with imbalanced big data we propose the Chi-FRBCS-BigDataCS algorithm, a fuzzy rule based classification system that is able to deal with the uncertainly that is introduced in large volumes of data without disregarding the learning in the underrepresented class. The method uses the MapReduce framework to distribute the computational operations of the fuzzy model while it includes cost-sensitive learning techniques in its design to address the imbalance that is present in the data. The good performance of this approach is supported by the experimental analysis that is carried out over twenty-four imbalanced big data cases of study. The results obtained show that the proposal is able to handle these problems obtaining competitive results both in the classification performance of the model and the time needed for the computation.


Dense WiFi : Challenges and Performance Measurements


title: Human Action Recognition Based on Selected Spatio-Temporal Features via Bidirectional LSTM

abstract: Recently, many deep convolutional networks approaches have been proposed for human action recognition. The challenge is to capture complementary information from still and motion frames. In contrast to previous models, which use holistic clips to model spatial information and traditional temporal information for sequential processing, in this paper, we propose a novel framework that can select the discriminative part in the spatial dimension and enrich the modeling action of motion in the temporal dimension. We utilize part selection within clips and consider the bidirectional temporal information when modeling the temporal pattern using multiple layers of a long short-term memory framework, which can learn compositional representations in space and time. Our results are evaluated on the standard benchmarks UCF101 and HMDB51 and show that the proposed architecture achieves state-of-the-art results.


title: A Study of Cybersickness and Sensory Conflict Theory Using a Motion-Coupled Virtual Reality System

abstract: Sensory conflict theory attempts to provide the framework of cyber-sickness in virtual reality (VR) systems by the mismatch between visual and vestibular senses. This study examined whether coupling motion sensations to the visual stimulus in a VR setting could reduce the discomfort. A motion-coupled VR system was used. Motion platform provides motion that supplements visual stimulus from the head-mounted display. Participants experience programmed visual and motion yaw rotations while viewing a virtual apartment. Three conditions were tested on how motion and visual stimuli synchronise which each other: purely visual, motion synchronised with visual, and visually-levelled frame of reference. Results showed that providing matching visual-motion stimuli decreased the miserable score (MISC) of cybersickness and increased the joyfulness score (JOSC) of their subjective feeling.


title: ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

abstract: The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied with unpleasant artifacts. To further enhance the visual quality, we thoroughly study three key components of SRGAN - network architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN). In particular, we introduce the Residual-in-Residual Dense Block (RRDB) without batch normalization as the basic network building unit. Moreover, we borrow the idea from relativistic GAN to let the discriminator predict relative realness instead of the absolute value. Finally, we improve the perceptual loss by using the features before activation, which could provide stronger supervision for brightness consistency and texture recovery. Benefiting from these improvements, the proposed ESRGAN achieves consistently better visual quality with more realistic and natural textures than SRGAN and won the first place in the PIRM2018-SR Challenge. The code is available at this https URL .


title: DEVELOPMENT OF A WIDEBAND HIGHLY EFFICIENT GAN VMCD VHF/UHF POWER AMPLIFIER

abstract: A 50 to 550MHz wideband gallium nitride (GaN) HEMT power amplifler with over 43dBm output power and 63% drain e-ciency has been successfully developed. The demonstrated wideband power amplifler utilizes two GaN HEMTs and operates in a push-pull voltage mode Class D (VMCD). The design is based on a large signal simulation to optimize the power amplifler's output power and e-ciency. To assure a wideband operation, a coaxial line impedance transformer has been used as part of the input matching network; meanwhile, a wideband a 1:1 ferrite loaded balun and low pass fllters are utilized on the amplifler's output side instead of the conventional serial harmonic termination.


title: Psychometric properties of the Portuguese version of the Acceptance and Action Questionnaire–Trauma Specific (AAQ-TS): A study with Portuguese Colonial War Veterans

abstract: Abstract Background The Acceptance and Action Questionnaire-Trauma Specific (AAQ-TS) is a self-report measure designed to assess-trauma-related psychological (in)flexibility, as conceptualized in Acceptance and Commitment Therapy. However, there are no studies to date regarding its psychometric properties. This study explores such properties in the Portuguese version of the AAQ-TS, in Portuguese Colonial War Veterans. Method A Principal Components Analysis (PCA) was conducted in a sample from the general population of war Veterans ( N =371). Confirmatory Factor Analysis (CFA) as well as reliability and convergent validity studies were performed in a different sample from the same population ( N =312). For the discriminant validity a clinical sample with a war-related PTSD ( N =42) and a non-clinical sample without PTSD ( N =44) were used. Results The CFA suggested a re-specified 15-item model with good global adjustment and factorial validity. The AAQ-TS showed internal consistency, a good temporal reliability, convergent validity with psychopathological symptoms (related to PTSD, anxiety, depression and stress) and peritraumatic dissociation (altered awareness and depersonalization/derealization). The questionnaire also discriminates between war Veterans with and without a PTSD diagnosis. Limitations The major limitation relates to the samples' characteristics and sampling methods, which can limit the generalization of results. Conclusion The Portuguese version of the AAQ-TS is a reliable and valid measure to assess experiential avoidance related to trauma in Portuguese Colonial War Veterans.


title: Competency Requirements for Transformational E-Government

abstract: One key aspect of e-government is its potential for an ICT enabled transformation of the public sector. Through ICT, new forms of collaboration and inter-organizational public service networks become feasible, making it possible to carry out the public sector's tasks more efficiently and effectively. However, a rather significant gap exists between this transformational potential and the tangible results that have been achieved so far. One reason for this slow and cumbersome implementation seems to be that public managers lack the necessary competencies to bring the promises of e-government to fruition. This article analyzes the changing competency requirements for public managers that accompany e-government and describes the first steps in the development of an e-government competency framework for public managers. The article sums up the results of a literature review on e-government competencies, a survey carried out for the article, and data gathered in focus group workshops. Based on these results, a first set of e-government competencies is then outlined that goes beyond pure ICT skills. The article concludes with a discussion of the framework and its implications for human resource management in the public sector.


title: Trust as a facilitator in cloud computing: a survey

abstract: Cloud computing offers massively scalable, elastic resources (e.g., data, computing power, and services) over the internet from remote data centres to the consumers. The growing market penetration, with an evermore diverse provider and service landscape, turns Cloud computing marketplaces a highly competitive one. In this highly competitive and distributed service environment, the assurances are insufficient for the consumers to identify the dependable and trustworthy Cloud providers. This paper provides a landscape and discusses incentives and hindrances to adopt Cloud computing from Cloud consumers’ perspective. Due to these hindrances, potential consumers are not sure whether they can trust the Cloud providers in offering dependable services. Trust-aided unified evaluation framework by leveraging trust and reputation systems can be used to assess trustworthiness (or dependability) of Cloud providers. Hence, cloud-related specific parameters (QoS + ) are required for the trust and reputation systems in Cloud environments. We identify the essential properties and corresponding research challenges to integrate the QoS + parameters into trust and reputation systems. Finally, we survey and analyse the existing trust and reputation systems in various application domains, characterizing their individual strengths and weaknesses. Our work contributes to understanding 1) why trust establishment is important in the Cloud computing landscape, 2) how trust can act as a facilitator in this context and 3) what are the exact requirements for trust and reputation models (or systems) to support the consumers in establishing trust on Cloud providers.


title: Design of High-Speed Iterative Dividers in GF(2(m))

abstract: Fast algorithms for high-speed divider design in finite fields GF(2m) are very crucial in applications like cryptosystems. In this paper, we reformulated the conventional iterative division algorithm by changing the pre-defined variable and then updating its initial value accordingly. The reformulated division algorithm allows a restructuring of the divider architecture to further improve its operating speed without increasing latency or area cost. Using the proposed fast algorithm, we developed two high-speed iterative dividers based on the semi-systolic and bit-serial systolic architectures. Analytical results show that the cost of the initial value update and variable transformation in the reformulated algorithm is almost negligible in the hardware implementation. Our divider designs improve the critical path delay. Compared with related divider designs, the proposed designs have time and area advantages.


title: Car plate recognition based on CNN using embedded system with GPU

abstract: a built-in system was implemented with a GPU in order to recognize the license plate number without detection line. The deep-learning network to recognize the license plate number of the vehicle uses relatively simple AlexNet. Tests confirm that the vehicle license plate can be effectively recognized on the Jetson TX1 board system.


title: Sequential changes in activities of superoxide dismutase and catalase in brain regions and liver during (-)deprenyl infusion in male rats.

abstract: A continuous s.c. infusion of (-)deprenyl in young male rats at a dose of 2.0 mg/kg/day for 1 week significantly increased total superoxide dismutase (SOD) activities due to increases in both Cu Zn-SOD and Mn-SOD activities in certain brain regions such as the substantia nitra and striatum, but not in the hippocampus or cerebellum, or in the liver. With continuing infusion, enzyme activities of SOD were further increased in the following weeks, reaching a plateau at 3 weeks. In some cerebral cortices the increase became significant at 3 weeks. In contrast to SOD activities, an increase in catalase (CAT) activity became significant only after 2 weeks of infusion, and only in the brain regions where SOD activities were increased earlier. The delay in the increase in CAT activity following deprenyl infusion suggests that this increased CAT activity is an adaptive response to the earlier increase in deprenyl-induced SOD activities rather than a direct effect of deprenyl on CAT activity, although the latter possibility cannot be excluded.


Australian Charities and Not-for-Profit Commission: Enforcement Tools and Regulatory Approaches


title: Effective power delivery filtering of mixed-signal systems with negligible radiated emission

abstract: In this paper, an effective noise isolation filter, implemented between the power delivery planes of a mixedsignal system is designed and tested. The filter can be placed between the analog and digital circuits to minimize the wideband noise leakage from digital circuits to narrow-band and sensitive analog devices. The filter is established by an array of semi-lumped element resonators designed to electromagnetically connect the ground and power planes at the operating frequency of analog circuits. The filter is designed at 3.6 GHz and fabricated on a four layer FR4 board. To further reduce the noise coupling and electromagnetic radiations from the PCB, the application of microwave absorbers on the edges of the board are investigated. To compare the results, a full-wave finite element solver is employed.


title: Hooked on smartphones: an exploratory study on smartphone overuse among college students

abstract: The negative aspects of smartphone overuse on young adults, such as sleep deprivation and attention deficits, are being increasingly recognized recently. This emerging issue motivated us to analyze the usage patterns related to smartphone overuse. We investigate smartphone usage for 95 college students using surveys, logged data, and interviews. We first divide the participants into risk and non-risk groups based on self-reported rating scale for smartphone overuse. We then analyze the usage data to identify between-group usage differences, which ranged from the overall usage patterns to app-specific usage patterns. Compared with the non-risk group, our results show that the risk group has longer usage time per day and different diurnal usage patterns. Also, the risk group users are more susceptible to push notifications, and tend to consume more online content. We characterize the overall relationship between usage features and smartphone overuse using analytic modeling and provide detailed illustrations of problematic usage behaviors based on interview data.


Development of parsing abilities interacts with grammar learning : Evidence from Tagalog and Kannada


title: Identifying the Scan and Attack Infrastructures Behind Amplification DDoS Attacks

abstract: Amplification DDoS attacks have gained popularity and become a serious threat to Internet participants. However, little is known about where these attacks originate, and revealing the attack sources is a non-trivial problem due to the spoofed nature of the traffic. In this paper, we present novel techniques to uncover the infrastructures behind amplification DDoS attacks. We follow a two-step approach to tackle this challenge: First, we develop a methodology to impose a fingerprint on scanners that perform the reconnaissance for amplification attacks that allows us to link subsequent attacks back to the scanner. Our methodology attributes over 58% of attacks to a scanner with a confidence of over 99.9%. Second, we use Time-to-Live-based trilateration techniques to map scanners to the actual infrastructures launching the attacks. Using this technique, we identify 34 networks as being the source for amplification attacks at 98\% certainty.


title: Compositional Design of Dielectric, Ferroelectric and Piezoelectric Properties of (K, Na)NbO3 and (Ba, Na)(Ti, Nb)O3 Based Ceramics Prepared by Different Sintering Routes

abstract: Lead free piezoelectric materials are being intensively investigated in order to substitute lead based ones, commonly used in many different applications. Among the most promising lead-free materials are those with modified NaNbO3, such as (K, Na)NbO3 (KNN) and (Ba, Na)(Ti, Nb)O3 (BTNN) families. From a ceramic processing point of view, high density single phase KNN and BTNN ceramics are very difficult to sinter due to the volatility of the alkaline elements, the narrow sintering temperature range and the anomalous grain growth. In this work, Spark Plasma Sintering (SPS) and high-energy ball milling (HEBM), following heat treatments (calcining and sintering), in oxidative (O2) atmosphere have been used to prepare single phase highly densified KNN (“pure” and Cu2+ or Li1+ doped), with theoretical densities ρth > 97% and BTNN ceramics (ρth - 90%), respectively. Using BTTN ceramics with a P4mm perovskite-like structure, we showed that by increasing the NaNbO3 content, the ferroelectric properties change from having a relaxor effect to an almost “normal” ferroelectric character, while the tetragonality and grain size increase and the shear piezoelectric coefficients (k15, g15 and d15) improve. For KNN ceramics, the results reveal that the values for remanent polarization as well as for most of the coercive field are quite similar among all compositions. These facts evidenced that Cu2+ may be incorporated into the A and/or B sites of the perovskite structure, having both hardening and softening effects.


title: Designing Noncircular Pulleys to Realize Target Motion Between Two Joints

abstract: Many mechanisms reduce the number of inputs for their target motions by restricting the motions of pairs of joints. This study designs a novel constraint mechanism using a pair of noncircular pulleys and a wire. The wire restricts the motion of the joints to the target motion while enabling a compact structure. We analytically derive the shape of the noncircular pulleys using the desired relation between the joints and the lengths of their moment arms. The usability of the proposed mechanism is evaluated on a robotic leg, which maintains the height and posture of its upper body under the constraints.


title: Finding Pictures of Objects in Large Collections of Images

abstract: Retrieving images from very large collections, using image content as a key, is becoming an important problem. Users prefer to ask for pictures using notions of content that are strongly oriented to the presence of abstractly defined objects. Computer programs that implement these queries automatically are desirable, but are hard to build because conventional object recognition techniques from computer vision cannot recognize very general objects in very general contexts. This paper describes our approach to object recognition, which is structured around a sequence of increasingly specialized grouping activities that assemble coherent regions of image that can be shown to satisfy increasingly stringent constraints. The constraints that are satisfied provide a form of object classification in quite general contexts. This view of recognition is distinguished by: far richer involvement of early visual primitives, including color and texture; hierarchical grouping and learning strategies in the classification process; the ability to deal with rather general objects in uncontrolled configurations and contexts. We illustrate these properties with four case-studies: one demonstrating the use of color and texture descriptors; one showing how trees can be described by fusing texture and geometric properties; one learning scenery concepts using grouped features; and one showing how this view of recognition yields a program that can tell, quite accurately, whether a picture contains naked people or not.


title: "I check my emails on the toilet": Email Practices and Work-Home Boundary Management

abstract: Email is widely used as a means of communication, a task ::: management system and an archive and it often seems impossible ::: to live without it. Our always-online society expects us to be ::: available 24/7 at the cost of potentially blurring the boundaries ::: between work and personal life. Furthermore, mobile and handheld ::: devices have made it even easier to be connected and ::: therefore increased the sense of needing to be available to respond ::: at any time. Whilst research to date has focused on identifying ::: email practices on the desktop, little has been done to understand ::: whether and how the introduction of mobile devices has changed ::: our way of handling emails. In this paper, we describe preliminary ::: results from an interview study that explored in particular the role ::: of mobile devices in email management and work-home boundary ::: management. We found that mobile technology impacts even on ::: the most private of non-work moments. We provide examples of ::: the ways in which technology supports frequent switching ::: between work and non-work contexts, and demonstrate the ::: strategies that people develop in order to manage these ::: boundaries, by using what we call micro-boundaries (e.g. having ::: two email apps on a phone).


title: Exploring the sheep rumen microbiome for carbohydrate-active enzymes

abstract: The rumen is a complex ecosystem enriched for microorganisms able to degrade biomass during the animal's digestion process. The recovery of new enzymes from naturally evolved biomass-degrading microbial communities is a promising strategy to overcome the inefficient enzymatic plant destruction in industrial production of biofuels. In this context, this study aimed to describe the bacterial composition and functions in the sheep rumen microbiome, focusing on carbohydrate-active enzymes (CAE). Here, we used phylogenetic profiling analysis (inventory of 16S rRNA genes) combined with metagenomics to access the rumen microbiome of four sheep and explore its potential to identify fibrolytic enzymes. The bacterial community was dominated by Bacteroidetes and Firmicutes, followed by Proteobacteria. As observed for other ruminants, Prevotella was the dominant genus in the microbiome, comprising more than 30 % of the total bacterial community. Multivariate analysis of the phylogenetic profiling data and chemical parameters showed a positive correlation between the abundance of Prevotellaceae (Bacteroidetes phylum) and organic matter degradability. A negative correlation was observed between Succinivibrionaceae (Proteobacteria phylum) and methane production. An average of 2 % of the shotgun metagenomic reads was assigned to putative CAE when considering nine protein databases. In addition, assembled contigs allowed recognition of 67 putative partial CAE (NCBI-Refseq) representing 12 glycosyl hydrolase families (Pfam database). Overall, we identified a total of 28 lignocellulases, 22 amylases and 9 other putative CAE, showing the sheep rumen microbiome as a promising source of new fibrolytic enzymes.


title: Kinect depth restoration via energy minimization with TV21 regularization

abstract: Depth maps generated by Kinect cameras often contain a significant amount of missing pixels and strong noise, limiting their usability in many computer vision applications. We present a new energy minimization method to fill the missing regions and remove noise in a depth map, by exploiting the strong correlation between color and depth values in local image neighborhoods. To preserve sharp edges and remove noise from the depth map, we propose to add a TV21 regularization term into the energy function. Finally, we show how to effectively minimize the total energy using an alternating optimization approach. Experimental results show that the proposed method outperforms commonly-used depth inpainting approaches.


title: Human Gut Microbiota Changes Reveal the Progression of Glucose Intolerance

abstract: To explore the relationship of gut microbiota with the development of type 2 diabetes (T2DM), we analyzed 121 subjects who were divided into 3 groups based on their glucose intolerance status: normal glucose tolerance (NGT; n = 44), prediabetes (Pre-DM; n = 64), or newly diagnosed T2DM (n = 13). Gut microbiota characterizations were determined with 16S rDNA-based high-throughput sequencing. T2DM-related dysbiosis was observed, including the separation of microbial communities and a change of alpha diversity between the different glucose intolerance statuses. To assess the correlation between metabolic parameters and microbiota diversity, clinical characteristics were also measured and a significant association between metabolic parameters (FPG, CRP) and gut microbiota was found. In addition, a total of 28 operational taxonomic units (OTUs) were found to be related to T2DM status by the Kruskal-Wallis H test, most of which were enriched in the T2DM group. Butyrate-producing bacteria (e.g. Akkermansia muciniphila ATCCBAA-835, and Faecalibacterium prausnitzii L2-6) had a higher abundance in the NGT group than in the pre-DM group. At genus level, the abundance of Bacteroides in the T2DM group was only half that of the NGT and Pre-DM groups. Previously reported T2DM-related markers were also compared with the data in this study, and some inconsistencies were noted. We found that Verrucomicrobiae may be a potential marker of T2DM as it had a significantly lower abundance in both the pre-DM and T2DM groups. In conclusion, this research provides further evidence of the structural modulation of gut microbiota in the pathogenesis of diabetes.


title: Man-Computer Interaction: A Challenge for Human Factors Research

abstract: This paper claims that the increasing heterogeneity of the community of conmputer users poses a challenge to psychologists and human factors researchers. There follows a brief discussion of why this challenge apparently has not yet evoked a strong response. Three problems, or problem areas, are identified as being particularly in need of human factors research. These are (1) the development and evaluation of conversational languages, (2) the determination of how the use patterns adopted by users depend on system characteristics, and (3) the description, or modelling, of man-computer interaction.


title: An optimized staining technique for the detection of Gram positive and Gram negative bacteria within tissue

abstract: BACKGROUND ::: Bacterial infections are a common clinical problem in both acute and chronic wounds. With growing concerns over antibiotic resistance, treatment of bacterial infections should only occur after positive diagnosis. Currently, diagnosis is delayed due to lengthy culturing methods which may also fail to identify the presence of bacteria. While newer costly bacterial identification methods are being explored, a simple and inexpensive diagnostic tool would aid in immediate and accurate treatments for bacterial infections. Histologically, hematoxylin and eosin (H&E) and Gram stains have been employed, but are far from optimal when analyzing tissue samples due to non-specific staining. The goal of the current study was to develop a modification of the Gram stain that enhances the contrast between bacteria and host tissue. ::: ::: ::: FINDINGS ::: A modified Gram stain was developed and tested as an alternative to Gram stain that improves the contrast between Gram positive bacteria, Gram negative bacteria and host tissue. Initially, clinically relevant strains of Pseudomonas aeruginosa and Staphylococcus aureus were visualized in vitro and in biopsies of infected, porcine burns using routine Gram stain, and immunohistochemistry techniques involving bacterial strain-specific fluorescent antibodies as validation tools. H&E and Gram stain of serial biopsy sections were then compared to a modification of the Gram stain incorporating a counterstain that highlights collagen found in tissue. The modified Gram stain clearly identified both Gram positive and Gram negative bacteria, and when compared to H&E or Gram stain alone provided excellent contrast between bacteria and non-viable burn eschar. Moreover, when applied to surgical biopsies from patients that underwent burn debridement this technique was able to clearly detect bacterial morphology within host tissue. ::: ::: ::: CONCLUSIONS ::: We describe a modification of the Gram stain that provides improved contrast of Gram positive and Gram negative microorganisms within host tissue. The samples used in this study demonstrate that this staining technique has laboratory and clinical applicability. This modification only adds minutes to traditional Gram stain with reusable reagents, and results in a cost- and time-efficient technique for identifying bacteria in any clinical biopsy containing connective tissue.


title: On the universality of cognitive tests

abstract: The analysis of the adaptive behaviour of many different kinds of systems such as humans, animals and machines, requires more general ways of assessing their cognitive abilities. This need is strengthened by increasingly more tasks being analysed for and completed by a wider diversity of systems, including swarms and hybrids. The notion of universal test has recently emerged in the context of machine intelligence evaluation as a way to define and use the same cognitive test for a variety of systems, using some principled tasks and adapting the interface to each particular subject. However, how far can universal tests be taken? This paper analyses this question in terms of subjects, environments, space-time resolution, rewards and interfaces. This leads to a number of findings, insights and caveats, according to several levels where universal tests may be progressively more difficult to conceive, implement and administer. One of the most significant contributions is given by the realisation that more universal tests are defined as maximisations of less universal tests for a variety of configurations. This means that universal tests must be necessarily adaptive.


title: Image Compression based on Visual Saliency at Individual Scales

abstract: The goal of lossy image compression ought to be reducing entropy while preserving the perceptual quality of the image. Using gaze-tracked change detection experiments, we discover that human vision attends to one scale at a time. This evidence suggests that saliency should be treated on a per-scale basis, rather than aggregated into a single 2D map over all the scales. We develop a compression algorithm which adaptively reduces the entropy of the image according to its saliency map within each scale, using the Laplacian pyramid as both the multiscale decomposition and the saliency measure of the image. We finally return to psychophysics to evaluate our results. Surprisingly, images compressed using our method are sometimes judged to be better than the originals.


title: A Secure Erasure Code-Based Cloud Storage System with Secure Data Forwarding

abstract: A cloud storage system, consisting of a collection of storage servers, provides long-term storage services over the Internet. Storing data in a third party's cloud system causes serious concern over data confidentiality. General encryption schemes protect data confidentiality, but also limit the functionality of the storage system because a few operations are supported over encrypted data. Constructing a secure storage system that supports multiple functions is challenging when the storage system is distributed and has no central authority. We propose a threshold proxy re-encryption scheme and integrate it with a decentralized erasure code such that a secure distributed storage system is formulated. The distributed storage system not only supports secure and robust data storage and retrieval, but also lets a user forward his data in the storage servers to another user without retrieving the data back. The main technical contribution is that the proxy re-encryption scheme supports encoding operations over encrypted messages as well as forwarding operations over encoded and encrypted messages. Our method fully integrates encrypting, encoding, and forwarding. We analyze and suggest suitable parameters for the number of copies of a message dispatched to storage servers and the number of storage servers queried by a key server. These parameters allow more flexible adjustment between the number of storage servers and robustness.


title: Comparison of Cross-Gain Modulation Effect of Manchester-Duobinary, RZ-DPSK, NRZ-DPSK, RZ, and NRZ Modulation Formats in SOAs

abstract: Conventional on-off keyed signals, such as return-to-zero (RZ) and nonreturn-to-zero (NRZ) signals are susceptible to cross-gain modulation (XGM) in semiconductor optical amplifiers (SOAs) due to pattern effect. In this letter, XGM effect of Manchester-duobinary, RZ differential phase-shift keying (RZ-DPSK), NRZ-DPSK, RZ, and NRZ signals in SOAs were compared. The experimental results confirmed the reduction of crosstalk penalty in SOAs by using Manchester-duobinary signals


title: An origami paper-based bacteria-powered battery with an air-cathod

abstract: In this work, we created a stackable, 3-D, paper-based, bacteria-powered battery for potentially powering on-chip paper-based biosensors. The battery generated power from microbial respiration with one drop of bacteria-containing liquid. An air-cathode was also created on paper with activated carbon on the Nickel electrode that was sprayed on the paper. By applying paper-folding techniques, the four on-chip batteries were connected in series to obtain a high operating voltage (0.1V) under a 470kΩ external resistor. Shewanella oneidensis were loaded on to the folded battery stack, through which the bacterial cells were transported into each battery. For operation, the battery stack was unfolded to expose all air-cathodes to the air, thereby maximizing their cathodic reactions.


title: Evaluation of Biological Activity and Phenolic Compounds of Cardaria draba (L.) Extracts

abstract: "The antibacterial, antioxidant, anti-inflammatory and scolicidal activities, as well as phenolic compounds from various leaf and seed extracts of Cardaria draba (L.) Desv. (whitetop) were examined. The antibacterial activity against two Grampositive bacteria (Staphylococcus aureus and Bacillus subtilis) and two Gram-negative bacteria (Pseudomonas aeruginosa and Escherichia coli) was evaluated by the agar well diffusion method. Antioxidant activity was assayed by using the seed and leaf extracts on synthetic DPPH free radicals and assessing their capacity to inhibit the peroxidation of linoleic acid. Anti-inflammatory activity was investigated by protein denaturation. The viability of Echinococcus granulosus protoscolices was investigated by evaluating their motility under a light microscope. Phenolic compounds in leaf and seed extracts were analyzed by HPLC. Our result showed that the MICs of various C. draba extracts against bacterial strains ranged from 3 to 134 μg/mL. The maximum and minimum MIC values for S. aureus were 3 μg/mL for the ethanolic leaf extract (ELE) and 86 μg/mL for the aqueous seed extract (ASE), respectively. Results of antioxidant assays showed that the ethanolic seed extract (ESE) had a significantly higher antioxidant capacity than ELE, and that both extracts had significantly lower antioxidant capacity than BHA and ascorbic acid. Results of the anti-inflammatory assay showed that as the concentration of extracts and control increased, there was a concomitant increase in the percentage inhibition of protein denaturation. ELE (500 μg/mL) and ESE (100 μg/mL) showed maximum and minimum percentage inhibition of protein denaturation (90% and 48%, respectively). Scolicidal activity and percentage mortality increased with exposure time, but the level depended on the extract concentration. In total, 16 compounds were identified from both extracts. Isorhamnetin (13.85%), quercetin (12.9%), and kaempferol (11.5%) were most abundant compounds in ELE, while the most abundant compounds in ESE were caffeic acid (13.3%), p-coumaric acid (7.9%), sinapic acid (7.9%), and ellagic acid (7.9%). These results support the therapeutic potential of C. draba."


title: Mediterranean Diet and Other Dietary Patterns in Primary Prevention of Heart Failure and Changes in Cardiac Function Markers: A Systematic Review

abstract: Background: Heart failure (HF) is a complex syndrome and is recognized as the ultimate pathway of cardiovascular disease (CVD). Studies using nutritional strategies based on dietary patterns have proved to be effective for the prevention and treatment of CVD. Although there are studies that support the protective effect of these diets, their effects on the prevention of HF are not clear yet. Methods: We searched the Medline, Embase, and Cochrane databases for studies that examined dietary patterns, such as dietary approaches to stop hypertension (DASH diet), paleolithic, vegetarian, low-carb and low-fat diets and prevention of HF. No limitations were used during the search in the databases. Results: A total of 1119 studies were identified, 14 met the inclusion criteria. Studies regarding the Mediterranean, DASH, vegetarian, and Paleolithic diets were found. The Mediterranean and DASH diets showed a protective effect on the incidence of HF and/or worsening of cardiac function parameters, with a significant difference in relation to patients who did not adhere to these dietary patterns. Conclusions: It is observed that the adoption of Mediterranean or DASH-type dietary patterns may contribute to the prevention of HF, but these results need to be analyzed with caution due to the low quality of evidence.


title: Toward a Distributed Data Flow Platform for the Web of Things (Distributed Node-RED)

abstract: Several web-based platforms have emerged to ease the development of interactive or near real-time IoT applications by providing a way to connect things and services together and process the data they emit using a data flow paradigm. While these platforms have been found to be useful on their own, many IoT scenarios require the coordination of computing resources across the network: on servers, gateways and devices themselves. To address this, we explore how to extend existing IoT data flow platforms to create a system suitable for execution on a range of run time environments, toward supporting distributed IoT programs that can be partitioned between servers, gateways and devices. Eventually we aim to automate the distribution of data flows using appropriate distribution mechanism, and optimization heuristics based on participating resource capabilities and constraints imposed by the developer.


Linearity enhancement technique of ramp generator for ADC testing


title: Paper mechatronics: a design case study for a young medium

abstract: Paper Mechatronics is a novel interdisciplinary design medium for children, enabled by recent advances in craft technologies: the term refers to a reappraisal of traditional educational papercrafts in combination with accessible mechanical, electronic, and computational elements. We present a design case study--building computationally-enhanced paper flowers--and discuss the iterative design process involved in the creation. We also describe a workshop we conducted with teenagers to evaluate paper mechatronics as a creative learning activity for children. We conclude with a discussion of future directions.


title: RECOMMENDATION SYSTEM USING COLLABORA TIVE FILTERING

abstract: Recommendation System Using Collaborative Filtering by Yunkyoung Lee Collaborative filtering is one of the well known and most extensive techniques in recommendation system its basic idea is to predict which items a user would be interested in based on their preferences. Recommendation systems using collaborative filtering are able to provide an accurate prediction when enough data is provided, because this technique is based on the user’s preference. User-based collaborative filtering has been very successful in the past to predict the customer’s behavior as the most important part of the recommendation system. However, their widespread use has revealed some real challenges, such as data sparsity and data scalability, with gradually increasing the number of users and items. To improve the execution time and accuracy of the prediction problem, this paper proposed item-based collaborative filtering applying dimension reduction in a recommendation system. It demonstrates that the proposed approach can achieve better performance and execution time for the recommendation system in terms of existing challenges, according to evaluation metrics using Mean Absolute Error (MAE).


title: A reconfigurable microstrip antenna for switchable polarization

abstract: A novel reconfigurable microstrip antenna with switchable polarization sense is proposed. The proposed antenna has a simple structure, consisting of a corner-truncated square radiating patch, four small triangular conductors, and a microstrip line feed. Using independently biased PIN diodes on the patch, it can produce linear polarization, or left- or right-hand circular polarization according to bias voltages. From the measured results, low cross-polarization levels when operated in the linear state and good axial ratios in the circular state are observed.


SAS-2017 Stacked Ensemble Models for Improved Prediction Accuracy