title: The Effects of Specific Learning Difficulties on Processes of Multilingual Language Development

abstract: This paper reviews current research findings on how specific learning difficulties (SLDs) impact on the processes of multilingual language development. The review includes studies of young language learners in instructed classroom settings as well as of multilingual children in second language contexts. The paper starts with a definition of the concepts of disability and SLDs. Next, it discusses the interaction of cognitive factors with L1 and L2 literacy development and L2 learning. The paper also outlines possible ways in which SLDs can be identified in different L2 learning contexts. A detailed analysis of how cognitive factors influence the development of second language skills of young learners with learning difficulties is given in the last part of the paper.


title: Effects of MMF Harmonics on Rotor Eddy-Current Losses for Inner-Rotor Fractional Slot Axial Flux Permanent Magnet Synchronous Machines

abstract: This paper proposed a semi-analytical method for calculating eddy-currents losses in an inner-rotor type axial flux permanent magnet machine (AFPM), which have surface-mounted PM rotor structure and inserted PM rotor structure. The fractional slot concentrated winding has large amount of magneto-motive force (MMF) harmonics, which induce extra eddy-current losses in rotor. In the proposed method, each MMF harmonic is applied to a simplified finite element analysis model which is treated as time-harmonic magnetic problem, and the losses of each order are superposed. Various fractional slot winding designs are studied when equipped two different rotor structures.


title: Development and initial experiment of modular undulating fin for untethered biorobotic AUVs

abstract: An environment-friendly propulsion system mimicking undulating fins of cuttlefish or stingray has been built. A nonconventional method was considered to model the flexibility of the fins of cuttlefish or stingray. A two-degree-of-freedom mechanism comprises several linkages was designed and constructed to mimic the actual flexible fin. The driving linkages are used to form a mechanical fin consisting of several fin segments, which are able to produce undulations, similar to those produced by the actual fins. Owing to the modularity of the design of the mechanical fin, we were able to construct various biomimetic robots of fish swimming by fin(s) undulations. Two variants are shown in this paper: the first is a biomimetic robot of a South American electric fish swimming by undulations of a long anal fin, and the second is a biomimetic robot of a cuttlefish swimming by undulations of its lateral fins. Some qualitative observations, obtained by experiments, predicted several factors affecting the thrust produced by the mechanical fins. Future work may include development other variants of biomimetic undulating fin robot and further development of the cuttlefish robot for autonomous deployment


Learning agents for uncertain environments (extended abstract)


title: Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization

abstract: This work proposes a new algorithm for automated and simultaneous phenotyping of multiple co-occurring medical conditions, also referred as comorbidities, using clinical notes from the electronic health records (EHRs). A basic latent factor estimation technique of non-negative matrix factorization (NMF) is augmented with domain specific constraints to obtain sparse latent factors that are anchored to a fixed set of chronic conditions. The proposed anchoring mechanism ensures a one-to-one identifiable and interpretable mapping between the latent factors and the target comorbidities. Qualitative assessment of the empirical results by clinical experts suggests that the proposed model learns clinically interpretable phenotypes while being predictive of 30 day mortality. The proposed method can be readily adapted to any non-negative EHR data across various healthcare institutions.


title: Sequence-Aware Recommender Systems

abstract: Recommender systems are one of the most successful applications of data mining and machine learning technology in practice. Academic research in the field is historically often based on the matrix completion problem formulation, where for each user-item-pair only one interaction (e.g., a rating) is considered. In many application domains, however, multiple user-item interactions of different types can be recorded over time. And, a number of recent works have shown that this information can be used to build richer individual user models and to discover additional behavioral patterns that can be leveraged in the recommendation process. In this work we review existing works that consider information from such sequentially-ordered user- item interaction logs in the recommendation process. Based on this review, we propose a categorization of the corresponding recommendation tasks and goals, summarize existing algorithmic solutions, discuss methodological approaches when benchmarking what we call sequence-aware recommender systems, and outline open challenges in the area.


title: The Gesture Watch: A Wireless Contact-free Gesture based Wrist Interface

abstract: We introduce the Gesture Watch, a mobile wireless device worn on a user's wrist that allows hand gesture control of other devices. The Gesture Watch utilizes an array of infrared proximity sensors to sense hand gestures made over the device and interprets the gestures using hidden Markov models. The Gesture Watch maps intuitive gross hand gestures to control signals such as the play and pause commands commonly found on mobile media players. We present our evaluation of the Gesture Watch designed to determine its the accuracy and usability. In our study, 10 participants used the Gesture Watch in both mobile and stationary conditions as well as indoors and outdoors. Overall, we attained a recognition accuracy of 95.5% and found that the Gesture Watch worked well in both indoor and outdoor environments and while mobile.


title: ClusterNet: 3D Instance Segmentation in RGB-D Images

abstract: We propose a method for instance-level segmentation that uses RGB-D data as input and provides detailed information about the location, geometry and number of individual objects in the scene. This level of understanding is fundamental for autonomous robots. It enables safe and robust decision-making under the large uncertainty of the real-world. In our model, we propose to use the first and second order moments of the object occupancy function to represent an object instance. We train an hourglass Deep Neural Network (DNN) where each pixel in the output votes for the 3D position of the corresponding object center and for the object's size and pose. The final instance segmentation is achieved through clustering in the space of moments. The object-centric training loss is defined on the output of the clustering. Our method outperforms the state-of-the-art instance segmentation method on our synthesized dataset. We show that our method generalizes well on real-world data achieving visually better segmentation results.


title: Flooding through the lens of mobile phone activity

abstract: Natural disasters affect hundreds of millions of people worldwide every year. Emergency response efforts depend upon the availability of timely information, such as information concerning the movements of affected populations. The analysis of aggregated and anonymized Call Detail Records (CDR) captured from the mobile phone infrastructure provides new possibilities to characterize human behavior during critical events. In this work, we investigate the viability of using CDR data combined with other sources of information to characterize the floods that occurred in Tabasco, Mexico in 2009. An impact map has been reconstructed using Landsat-7 images to identify the floods. Within this frame, the underlying communication activity signals in the CDR data have been analyzed and compared against rainfall levels extracted from data of the NASA-TRMM project. The variations in the number of active phones connected to each cell tower reveal abnormal activity patterns in the most affected locations during and after the floods that could be used as signatures of the floods - both in terms of infrastructure impact assessment and population information awareness. The representativeness of the analysis has been assessed using census data and civil protection records. While a more extensive validation is required, these early results suggest high potential in using cell tower activity information to improve early warning and emergency management mechanisms.


title: ENN: Extended Nearest Neighbor Method for Pattern Recognition [Research Frontier]

abstract: This article introduces a new supervised classification method - the extended nearest neighbor (ENN) - that predicts input patterns according to the maximum gain of intra-class coherence. Unlike the classic k-nearest neighbor (KNN) method, in which only the nearest neighbors of a test sample are used to estimate a group membership, the ENN method makes a prediction in a "two-way communication" style: it considers not only who are the nearest neighbors of the test sample, but also who consider the test sample as their nearest neighbors. By exploiting the generalized class-wise statistics from all training data by iteratively assuming all the possible class memberships of a test sample, the ENN is able to learn from the global distribution, therefore improving pattern recognition performance and providing a powerful technique for a wide range of data analysis applications.


title: A localization and tracking scheme for target gangs based on big data of Wi-Fi locations

abstract: The modeling and analysis of target gangs’ usual haunts plays a very important role in law enforcement and supervision. Existing localization and tracking schemes usually need to deploy a large number of monitoring devices or continue to move with the target, which lead to high cost. In this paper, a localization and tracking scheme based on big data of Wi-Fi locations is proposed. Firstly, the characteristic of the smart mobile device that continuously broadcasts probe request frames is used to obtain its MAC address and Wi-Fi connection history. Secondly, the service set identifier (SSID) in the Wi-Fi connection history of smart mobile devices held by the target gangs are queried from the Wi-Fi location database, and the target gangs’ usual haunts are gained by statistical analysis. Lastly, monitoring devices are deployed in these places, and most of the target gangs’ activity pattern are known with only a small number of monitoring devices. The results of the related experimental tests demonstrate the feasibility of the proposed scheme.


SUBWORD LANGUAGE MODELING WITH NEURAL NETWORKS


title: The perseverative cognition hypothesis: A review of worry, prolonged stress-related physiological activation, and health

abstract: Perseverative cognition, as manifested in worry and rumination, is a common response to stress, but biopsychological models of stress and health have largely ignored it. These models have generally focused on physiological activation that occurs during stress and have insufficiently addressed effects that occur in anticipation of, or following, stressful events. We argue that perseverative cognition moderates the health consequences of stressors because it can prolong stress-related affective and physiological activation, both in advance of and following stressors. We review evidence that worry, rumination, and anticipatory stress are associated with enhanced cardiovascular, endocrinological, immunological, and neurovisceral activity. The findings yield preliminary support for our hypothesis, suggesting that perseverative cognition might act directly on somatic disease via enhance activation via the cardiovascular, immune, endocrine, and neurovisceral systems.


title: A Sliding Mode Control for a Planar 4-Cable Direct Driven Robot

abstract: Cable Direct Driven Robots (CDDRs) are structurally similar to parallel robots but these are formed by replacing all the supporting rigid legs with cables, where the motion of the end-effector is controlled through cables which are pulled from actuators placed off-board the robot. CDDRs control is a challenge due to the physical characteristics of cables because these can only apply tensile forces and no compression. The aim of this paper is to present a Sliding Mode Control (SMC) for a Planar 4-Cable Direct Driven Robot, SMC is designed in order to obtain fast system response and the robustness against the model uncertainty. For the purpose of validating the proposed controller the trajectory tracking test and model uncertainty test were performed. Simulation was carried out for the proposed controller and the results were compared with a PD Controller in terms of Integral Square Error (ISE) index.


title: ShuffleNASNets: Efficient CNN models through modified Efficient Neural Architecture Search

abstract: Neural network architectures found by sophistic search algorithms achieve strikingly good test performance, surpassing most human-crafted network models by significant margins. Although computationally efficient, their design is often very complex, impairing execution speed. Additionally, finding models outside of the search space is not possible by design. While our space is still limited, we implement undiscoverable expert knowledge into the economic search algorithm Efficient Neural Architecture Search (ENAS), guided by the design principles and architecture of ShuffleNet V2. While maintaining baseline-like 2.85% test error on CIFAR-10, our ShuffleNASNets are significantly less complex, require fewer parameters, and are two times faster than the ENAS baseline in a classification task. These models also scale well to a low parameter space, achieving less than 5% test error with little regularization and only 236K parameters.


title: Graph Cut Segmentation Methods Revisited with a Quantum Algorithm

abstract: The design and performance of computer vision algorithms are greatly influenced by the hardware on which they are implemented. CPUs, multi-core CPUs, FPGAs and GPUs have inspired new algorithms and enabled existing ideas to be realized. This is notably the case with GPUs, which has significantly changed the landscape of computer vision research through deep learning. As the end of Moores law approaches, researchers and hardware manufacturers are exploring alternative hardware computing paradigms. Quantum computers are a very promising alternative and offer polynomial or even exponential speed-ups over conventional computing for some problems. This paper presents a novel approach to image segmentation that uses new quantum computing hardware. Segmentation is formulated as a graph cut problem that can be mapped to the quantum approximation optimization algorithm (QAOA). This algorithm can be implemented on current and near-term quantum computers. Encouraging results are presented on artificial and medical imaging data. This represents an important, practical step towards leveraging quantum computers for computer vision.


title: A Review on Prognosis of Rolling Element Bearings

abstract: Bearings are amongst the frequently encountered components to be found in rotating machinery. Though inexpensive, their failure can interrupt the production in a plant causing unscheduled downtime and production losses. So the bearing prognosis plays a significant role in reducing plant down time and enhanced operation safety, by estimating the Remaining Useful Life (RUL) of damaged bearing. Admitting the importance of bearing prognosis, this literature review attempts to summarize various techniques, methods and models used in the prognosis of bearing till date. The definition of bearing prognosis is discussed in the beginning, followed by classification of various prognostic methods, review of methods used by various investigators in this research domain and concluding the topic with the summary of future research directions.


title: Human Behavior-Based Particle Swarm Optimization

abstract: Particle swarm optimization (PSO) has attracted many researchers interested in dealing with various optimization problems, owing to its easy implementation, few tuned parameters, and acceptable performance. However, the algorithm is easy to trap in the local optima because of rapid losing of the population diversity. Therefore, improving the performance of PSO and decreasing the dependence on parameters are two important research hot points. In this paper, we present a human behavior-based PSO, which is called HPSO. There are two remarkable differences between PSO and HPSO. First, the global worst particle was introduced into the velocity equation of PSO, which is endowed with random weight which obeys the standard normal distribution; this strategy is conducive to trade off exploration and exploitation ability of PSO. Second, we eliminate the two acceleration coefficients c1 and c2 in the standard PSO (SPSO) to reduce the parameters sensitivity of solved problems. Experimental results on 28 benchmark functions, which consist of unimodal, multimodal, rotated, and shifted high-dimensional functions, demonstrate the high performance of the proposed algorithm in terms of convergence accuracy and speed with lower computation cost.


title: Single-Frame Vulnerable Road Users Classification with a 77 GHz FMCW Radar Sensor and a Convolutional Neural Network

abstract: Road traffic accidents accounted in 2013 for over a million deaths worldwide. Pedestrians and cyclists are especially vulnerable in road accidents and therefore it is essential to identify them in a timely manner to foresee dangerous situations. Radar sensors are excellent candidates for this task since they are able to simultaneously measure range, radial velocity and angle while remaining robust in adverse weather conditions. In this paper, a method to classify moving subjects as pedestrians, cyclists or cars using single radar measurement frames from a 77 GHz FMCW radar sensor is proposed. To perform the classification the range-Doppler-angle power spectrum is run through a convolutional neural network. A dataset of around 9.1k frames gathered in urban scenarios is used to train the convolutional neural network. A classification accuracy as high as 97.3(%) is achieved on a set consisting of tracks not seen during training but on known locations. The classification accuracy drops to 84.2(%) when tested on unseen tracks gathered in an unseen location.


title: First Place Solution for NLPCC 2017 Shared Task Social Media User Modeling

abstract: With the popularity of mobile Internet, many social networking applications provide users with the function to share their personal information. It is of high commercial value to leverage the users’ personal information such as tweets, preferences and locations for user profiling. There are two subtasks working in user profiling. Subtask one is to predict the Point-of-Interest (POI) a user will check in at. We adopted a combination of multiple approach results, including user-based collaborative filtering (CF) and social-based CF to predict the locations. Subtask two is to predict the users’ gender. We divided the users into two groups, depending on whether the user has posted or not. We treat this task subtask as a classification task. Our results achieved first place in both subtasks.


title: Classification of applications in HTTP tunnels

abstract: Accurate traffic classification is an essential element of emergent cloud and datacenter architectures. Increasingly, however, different types of application traffic from the cloud are tunnelled over HTTP, thereby making accurate classification a challenge. Applications tunnelled over HTTP are wide in scope and diverse in nature, and include mapping, email, video, image, audio and file. This paper presents a novel approach for the accurate and effective classification of the dominant types of HTTP tunnelled applications, namely video, audio and file-transfer. The classification is carried out using information that is only available from flow-based protocols such as NetFlow v5. The proposed scheme is tested on live data traffic in a small enterprise network with a realistic mixture of regular HTTP and non-HTTP traffic. Outsourcing enterprise networks to cloud is a major cloud application. For the scenarios tested, the proposed algorithm accurately classifies at least 70% of the HTTP tunnelled traffic, and in some cases, up to 90%. In comparison to the results from approaches based on NaiveBayes algorithm and Support-Vector-Machine, the proposed scheme outperforms them by at least 10% as per performance measures.


title: Effect of Vanadium Introduction on the Activity of NiMo/Al2O3 Catalysts in the Hydrotreating of Diesel Fractions

abstract: The influence of the introduction of V2O5 into NiMo/Al2O3 catalysts on their activity in hydrodesulfurization (HDS) and hydrogenation reactions of the components of petroleum fractions has been studied. The activity of the synthesized catalysts has been determined in the straight-run diesel and light coker gas oil hydrotreating processes in a flow-through unit under hydrogen pressure. The most active catalyst for HDS and hydrogenation of polycyclic aromatic hydrocarbons has been synthesized using VMo12 heteropoly compounds: the activity increases by 6–10 and 11–13 wt % in HDS and PAH hydrogenation, respectively, at different temperatures. It has been shown that the activity of the regenerated catalyst further impregnated with the vanadium compound in HDS and PAH hydrogenation increases by 2–5 rel. %, as compared to the regenerated catalyst.


title: Pattern Reconfigurable Antenna Based on Morphing Bistable Composite Laminates

abstract: In this paper, a novel pattern reconfigurable antenna based on morphing bistable composite laminates is presented. The bistable asymmetric glass-fiber reinforced polymer (GFRP) composite laminates have two stable configurations with curvatures of opposite signs. The antenna pattern is reconfigured by transforming the configuration of the bistable GFRP laminate which acts as the substrate of the antenna. The coplanar waveguide transmission lines feeding technique is used for the microstrip quasi-Yagi antenna. A prototype of the proposed antenna is fabricated using a semi-automatic screen printer and an autoclave. The transformation between the two stable states of the proposed antenna using Ni/Ti shape memory alloy springs is investigated experimentally. The out-of-plane displacements, reflection coefficients and radiation patterns for the two stable configurations of the antenna are measured, which agree well with the simulated results. The main beam direction is 89° and 59° for the two stable configurations, respectively. In addition, the influences of various bending radii on the radiation patterns are investigated to gain a thorough understanding of the reconfigurable mechanism of the proposed antenna. Finally, a two-element array of such an antenna is presented and measured. The proposed antenna provides a potential application in multifunctional, conformal, morphing, and integrated structures.


title: Original Loop-closure Detection Algorithm for Monocular vSLAM

abstract: Vision-based simultaneous localization and mapping (vSLAM) is a well-established problem in mobile robotics and monocular vSLAM is one of the most challenging variations of that problem nowadays. In this work we study one of the core post-processing optimization mechanisms in vSLAM, e.g. loop-closure detection. We analyze the existing methods and propose original algorithm for loop-closure detection, which is suitable for dense, semi-dense and feature-based vSLAM methods. We evaluate the algorithm experimentally and show that it contribute to more accurate mapping while speeding up the monocular vSLAM pipeline to the extent the latter can be used in real-time for controlling small multi-rotor vehicle (drone).


title: The Yin and Yang of Processing Data Warehousing Queries on GPU Devices

abstract: Database community has made significant research efforts to optimize query processing on GPUs in the past few years. However, we can hardly find that GPUs have been truly adopted in major warehousing production systems. Preparing to merge GPUs to the warehousing systems, we have identified and addressed several critical issues in a three-dimensional study of warehousing queries on GPUs by varying query characteristics, software techniques, and GPU hardware configurations. We also propose an analytical model to understand and predict the query performance on GPUs. Based on our study, we present our performance insights for warehousing query execution on GPUs. The objective of our work is to provide a comprehensive guidance for GPU architects, software system designers, and database practitioners to narrow the speed gap between the GPU kernel execution (the fast mode) and data transfer to prepare GPU execution (the slow mode) for high performance in processing data warehousing queries. The GPU query engine developed in this work is open source to the public.


title: An Adaptive PID Neural Network for Complex Nonlinear System Control

abstract: Usually it is difficult to solve the control problem of a complex nonlinear system. In this paper, we present an effective control method based on adaptive PID neural network and particle swarm optimization (PSO) algorithm. PSO algorithm is introduced to initialize the neural network for improving the convergent speed and preventing weights trapping into local optima. To adapt the initially uncertain and varying parameters in the control system, we introduce an improved gradient descent method to adjust the network parameters. The stability of our controller is analyzed according to the Lyapunov method. The simulation of complex nonlinear multiple-input and multiple-output (MIMO) system is presented with strong coupling. Empirical results illustrate that the proposed controller can obtain good precision with shorter time compared with the other considered methods.


title: Fast Algorithms for Solving Path Problems

abstract: Let G = (V,E) be a directed graph with a distinguished source vertex s. The single-source path expression problem is to find, for each vertex v, a regular expression P(s,v) which represents the set of all paths in G from s to v. A solution to this problem can be used to solve shortest path problems, solve sparse systems of linear equations, and carry out global flow analysis. We describe a method to compute path expressions by dividing G into components, computing path expressions on the components by Gaussian elimination, and combining the solutions. This method requires O(m $\alpha$(m,n)) time on a reducible flow graph, where n is the number of vertices in G, m is the number of edges in G, and $\alpha$ is a functional inverse of Ackermann''s function. The method makes use of an algorithm for evaluating functions defined on paths in trees. A simplified version of the algorithm, which runs in O(m log n) time on reducible flow graphs, is quite easy to implement and efficient in practice.


title: Improving Robustness to Compressed Speech in Speaker Recognition

abstract: The goal of this paper is to analyze the impact of codecdegraded speech on a state-of-the-art speaker recognition system and propose mitigation techniques. Several acoustic features are analyzed, including the standard Mel filterbank cepstral coefficients (MFCC), as well as the noise-robust medium duration modulation cepstrum (MDMC) and power normalized cepstral coefficients (PNCC), to determine whether robustness to noise generalizes to audio compression. Using a speaker recognition system based on i-vectors and probabilistic linear discriminant analysis (PLDA), we compared four PLDA training scenarios. The first involves training PLDA on clean data, the second included additional noisy and reverberant speech, a third introduces transcoded data matched to the evaluation conditions and the fourth, using codec-degraded speech mismatched to the evaluation conditions. We found that robustness to compressed speech was marginally improved by exposing PLDA to noisy and reverberant speech, with little improvement using trancoded speech in PLDA based on codecs mismatched to the evaluation conditions. Noise-robust features offered a degree of robustness to compressed speech while more significant improvements occurred when PLDA had observed the codec matching the evaluation conditions. Finally, we tested i-vector fusion from the different features, which increased overall system performance but did not improve robustness to codec-degraded speech. Index Terms: speaker recognition, speech coding, codec degradation, speaker verification.


title: SoK: Uncentralisable Ledgers and their Impact on Voting Systems

abstract: As we observe a trend towards the recentralisation of the Internet, this paper raises the question of guaranteeing an everlasting decentralisation. We introduce the properties of strong and soft uncentralisability in order to describe systems in which all authorities can be untrusted at any time without affecting the system. We link the soft uncentralisability to another property called perfect forkability. Using that knowledge, we introduce a new cryptographic primitive called uncentralisable ledger and study its properties. We use those properties to analyse what an uncentralisable ledger may offer to classic electronic voting systems and how it opens up the realm of possibilities for completely new voting mechanisms. We review a list of selected projects that implement voting systems using blockchain technol- ogy. We then conclude that the true revolutionary feature enabled by uncentralisable ledgers is a self-sovereign and distributed identity provider.


title: Restorative Effects of Virtual Nature Settings

abstract: Abstract Previous research regarding the potential benefits of exposing individuals to surrogate nature (photographs and videos) has found that such immersion results in restorative effects such as increased positive affect, decreased negative affect, and decreased stress. In the current experiment, we examined whether immersion in a virtual computer-generated nature setting could produce restorative effects. Twenty-two participants were equally divided between two conditions, while controlling for gender. In each condition, participants performed a stress-induction task, and were then immersed in virtual reality (VR) for 10 minutes. The control condition featured a slide show in VR, and the nature experimental condition featured an active exploration of a virtual forest. Participants in the nature condition were found to exhibit increased positive affect and decreased stress after immersion in VR when compared to those in the control condition. The results suggest that immersion in virtual nature setting...


title: A framework of Urdu topic modeling using latent dirichlet allocation (LDA)

abstract: In this age, text mining research community has given an immense attention towards the development of text mining tools, techniques and models. Topic modeling is an area of Text Mining which is being used in various areas e.g. summarization, searching, semantics, and many other. Topic Modeling is used to uncover the hidden topics from large collection of documents or text. It is also equally important for many other interesting research areas like Natural Language Processing (NLP), Machine Learning (ML), statistics etc. In order to fulfill the goal of topic model, a lot of models have been proposed, in literature, for variety of languages such as English and Arabic etc. All of the models differ in their various nature, theories, and implementation strategies as all languages has their own morphological structure, semantics and syntax. The motivation behind this work is that there is no such work is available for Urdu language to extract topics from documents. Although some standard Topic Models has been proposed such as Latent Dirichlet Allocation (LDA), there is still a need of development of comprehensive model to cater Topic Model specific for Urdu text. In this research, we have proposed an effective topic model for Urdu language to cope with the challenges of Urdu morphological structure. The proposed Topic Model for Urdu is a framework that combine pre-processing techniques, LDA model and Gibbs sampling. This proposed Topic Model for Urdu used the standard LDA model therefore we named it Urdu Latent Dirichlet Allocation (ULDA). Experiments are conducted to show the efficacy of our proposed approach as compared to the competitors. The experimental results show the dominance of our proposed ULDA model as compared to existing systems. The work is being carried out for the first time in Urdu language.


title: Dismantling the Silos: Extracting New Value from

abstract: . ::: The drive for information technology-led organizational and operational change in public administration has lagged behind that in the private sector. For good reasons, central public administrations are conservative by nature. Most public administrations are bureaucracies and bureaucracies tend to resist change. Nevertheless the pressure to obtain better value from public administration information technology investments is growing and the debate as to how to achieve this is increasingly important. Part of this debate is concerned with how best to confront the formidable and specific challenges faced by the sector. These include cultural, structural, resource and technical problems as well as a legacy of isolated developments which do not interrelate. The difficulties are compounded by the problems of evaluation generally in public administration – problems which are reflected in the evaluation of public sector information systems. This drive for change is reviewed and discussed in the context of the Irish civil service, in which there is a growing awareness of the strategic importance of breaking down specialized vertical systems and providing an integrated service to the citizen. A new approach to the problem, based on adapting the concept of business objects, is suggested.


title: Continuous Positive Airway Pressure Reduces Night-Time Blood Pressure and Heart Rate in Patients With Obstructive Sleep Apnea and Resistant Hypertension: The RHOOSAS Randomized Controlled Trial

abstract: Objective. Most patients with resistant hypertension (RH) have obstructive sleep apnoea (OSA). We aimed to determine the impact of OSA and continuous positive airway pressure (CPAP) treatment on the leptin profile and blood pressure (BP) in patients with resistant hypertension (RH). Methods. After an initial case control study (RH with and without OSA), we performed a randomized, single blind study in OSA + RH patients receiving either sham CPAP (3-months) followed by active CPAP (6 months) or 6 months of active CPAP. The primary outcome was the comparison of leptin levels between groups of resistant hypertension patients with or without OSA. Secondary outcomes were the comparison of metabolic parameters, biomarkers of sympathetic activity and blood pressure indices between the two groups of resistant hypertension patients with or without OSA. The same outcomes were then evaluated and compared before and after sham and effective CPAP intervention. Results. Sixty-two patients (60 ± 10 years; 77% men) with RH (24-hour daytime SBP / DBP: 145 ± 13 / 85 ± 10 mmHg, 3.7 antihypertensive drugs) were included. The 37 RH patients exhibiting OSA (60%) were predominantly men (87 vs 64% for non-OSA patients), with a greater prevalence of metabolic syndrome and higher creatininemia. Their leptin concentrations were significantly lower than in non-OSA patients (9 [6 ; 15] vs 17 [6 ; 29] ng/mL) but increased after six months of CPAP. Three months of effective CPAP significantly decreased night-time SBP by 6.4 mmHg and heart rate by 6.0 bpm, compared to sham CPAP. Conclusions. The association between OSA and RH corresponds to a specific predominately male phenotype with a higher burden of metabolic syndrome and higher creatininemia but there was no significant difference between OSA and non-OSA patients regarding BP indices, and the number of anti-hypertensive drugs used. Active CPAP could be efficient at decreasing night-time BP and heart rate but there was no difference between CPAP and sham CPAP groups for all metabolic and SNS markers (NCT00746902 RHOOSAS).


title: Gestalt Theory in Visual Screen Design - A New Look at an Old Subject

abstract: Although often presented as a single basis for educational visual screen design, Gestalt theory is not a single small set of visual principles uniformly applied by all designers. In fact, it appears that instructional visual design literature often deals with only a small set of Gestalt laws. In this project Gestalt literature was consulted to distil the most relevant Gestalt laws for educational visual screen design. Eleven laws were identified. They deal with balance/symmetry, continuation, closure, figure-ground, focal point, isomorphic correspondence, pragnanz, proximity, similarity, simplicity, and unity/harmony.To test the usefulness of these laws in visual screen design they were applied to the redesign of an instructional multimedia application, 'WoundCare,' designed to teach nursing students wound management. The basic text-based screens in the original WoundCare application were replaced with graphical user interface screens, that were designed according to these principles. The new screen designs were then evaluated by asking students and others to compare the designs. The viewers were also asked to rate directly the value of using the eleven Gestalt design principles in the redesign, both for improving the product's appearance and improving its value for learning.The evaluation results were overwhelmingly positive. Both the new design and the value of applying the eleven Gestalt laws to improve learning were strongly supported by the students' opinions. However, some differences in the value of applying particular Gestalt laws to the interface design were identified and this forms a useful direction for future research.


title: Issues and Challenges in Situation Assessment (Level 2 Fusion)

abstract: Abstract : Situation assessment (SA) involves deriving relations among entities, e.g., the aggregation of object states (i.e., classification and location). While SA has been recognized in the information fusion and human factors literature, there still exist open questions regarding knowledge representation and reasoning methods to afford SA. For instance, while lots of data is collected over a region of interest, how does this information get presented to an attention constrained user? The information overload can deteriorate cognitive reasoning so a pragmatic solution to knowledge representation is needed for effective and efficient situation understanding. In this paper, we present issues associated with Level 2 Information Fusion (Situation Assessment) including: (1) user perception and perceptual reasoning representation, (2) knowledge discovery process models, (3) procedural versus logical reasoning about relationships, (4) userfusion interaction through performance metrics, and (5) syntactic and semantic representations. While a definitive conclusion is not the aim of the paper, many critical issues are proposed in order to characterize future successful strategies for knowledge representation, presentation, and reasoning for situation assessment.


title: IEEE 802.11ax: Joint Effects of Power Control and IQ Imbalance Mitigation Schemes on the Performance of OFDM Uplink Multi-User MIMO

abstract: The 802.11ax Specification Framework delivered by the IEEE High Efficiency WLANs (HEW) Task Group (TG) in early 2016 has specified the uplink multi-user multiple-input multiple-output (UL MU-MIMO) as a key medium access control (MAC) and physical layer (PHY) technique to improve the uplink network throughout in ultra-dense networks loaded with small form factor devices. In this paper, we first show that the phase noise (PN) hardware impairment does not degrade the 802.11ax UL MU-MIMO PHY performance even with differences on the received power among the clients. In the following, we show that the effects of in-phase and quadrature (IQ) imbalance on the UL MU-MIMO PHY performance can be catastrophic, especially when it is used high-cardinality modulation schemes without uplink power control. Finally, we show that a joint implementation of an uplink power control scheme and a time-domain IQ mitigation algorithm can mitigate the dramatic effects on the 802.11ax UL MU-MIMO PHY performance due to IQ imbalance and phase noise hardware impairments in channels where the average power received per client at the access point presents a significant standard deviation.


title: Interactive High-Relief Reconstruction for Organic and Double-Sided Objects from a Photo

abstract: We introduce an interactive user-driven method to reconstruct high-relief 3D geometry from a single photo. Particularly, we consider two novel but challenging reconstruction issues: i) common non-rigid objects whose shapes are organic rather than polyhedral/symmetric, and ii) double-sided structures, where front and back sides of some curvy object parts are revealed simultaneously on image. To address these issues, we develop a three-stage computational pipeline. First, we construct a 2.5D model from the input image by user-driven segmentation, automatic layering, and region completion, handling three common types of occlusion. Second, users can interactively mark-up slope and curvature cues on the image to guide our constrained optimization model to inflate and lift up the image layers. We provide real-time preview of the inflated geometry to allow interactive editing. Third, we stitch and optimize the inflated layers to produce a high-relief 3D model. Compared to previous work, we can generate high-relief geometry with large viewing angles, handle complex organic objects with multiple occluded regions and varying shape profiles, and reconstruct objects with double-sided structures. Lastly, we demonstrate the applicability of our method on a wide variety of input images with human, animals, flowers, etc.


title: Play and Learn: Using Video Games to Train Computer Vision Models

abstract: Video games are a compelling source of annotated data as they can readily provide fine-grained groundtruth for diverse tasks. However, it is not clear whether the synthetically generated data has enough resemblance to the real-world images to improve the performance of computer vision models in practice. We present experiments assessing the effectiveness on real-world data of systems trained on synthetic RGB images that are extracted from a video game. We collected over 60000 synthetic samples from a modern video game with similar conditions to the real-world CamVid and Cityscapes datasets. We provide several experiments to demonstrate that the synthetically generated RGB images can be used to improve the performance of deep neural networks on both image segmentation and depth estimation. These results show that a convolutional network trained on synthetic data achieves a similar test error to a network that is trained on real-world data for dense image classification. Furthermore, the synthetically generated RGB images can provide similar or better results compared to the real-world datasets if a simple domain adaptation technique is applied. Our results suggest that collaboration with game developers for an accessible interface to gather data is potentially a fruitful direction for future work in computer vision.


abstract: Jarodzka, H., Balslev, T., Holmqvist, K., Nystrom, M., Scheiter, K., Gerjets, P., & Eika, B. (2010). Learning perceptual aspects of diagnosis in medicine via eye movement modeling examples on patient video cases. In S. Ohlsson & R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 1703-1708). Austin, TX: Cognitive Science Society.


title: Spatial-Temporal Union of Subspaces for Multi-body Non-rigid Structure-from-Motion

abstract: Non-rigid structure-from-motion (NRSfM) has so far been mostly studied for recovering 3D structure of a single non-rigid/deforming object. To handle the real world challenging multiple deforming objects scenarios, existing methods either pre-segment different objects in the scene or treat multiple non-rigid objects as a whole to obtain the 3D non-rigid reconstruction. However, these methods fail to exploit the inherent structure in the problem as the solution of segmentation and the solution of reconstruction could not benefit each other. In this paper, we propose a unified framework to jointly segment and reconstruct multiple non-rigid objects. To compactly represent complex multi-body non-rigid scenes, we propose to exploit the structure of the scenes along both temporal direction and spatial direction, thus achieving a spatio-temporal representation. Specifically, we represent the 3D non-rigid deformations as lying in a union of subspaces along the temporal direction and represent the 3D trajectories as lying in the union of subspaces along the spatial direction. This spatio-temporal representation not only provides competitive 3D reconstruction but also outputs robust segmentation of multiple non-rigid objects. The resultant optimization problem is solved efficiently using the Alternating Direction Method of Multipliers (ADMM). Extensive experimental results on both synthetic and real multi-body NRSfM datasets demonstrate the superior performance of our proposed framework compared with the state-of-the-art methods.


title: HYDRA: Massively Compositional Model for Cross-Project Defect Prediction

abstract: Most software defect prediction approaches are trained and applied on data from the same project. However, often a new project does not have enough training data. Cross-project defect prediction, which uses data from other projects to predict defects in a particular project, provides a new perspective to defect prediction. In this work, we propose a HYbrid moDel Reconstruction Approach ( HYDRA ) for cross-project defect prediction, which includes two phases: genetic algorithm (GA) phase and ensemble learning (EL) phase. These two phases create a massive composition of classifiers. To examine the benefits of HYDRA , we perform experiments on 29 datasets from the PROMISE repository which contains a total of 11,196 instances (i.e., Java classes) labeled as defective or clean. We experiment with logistic regression as the underlying classification algorithm of HYDRA . We compare our approach with the most recently proposed cross-project defect prediction approaches: TCA+ by Nam et al., Peters filter by Peters et al., GP by Liu et al., MO by Canfora et al., and CODEP by Panichella et al. Our results show that HYDRA achieves an average F1-score of 0.544. On average, across the 29 datasets, these results correspond to an improvement in the F1-scores of 26.22 , 34.99, 47.43, 28.61, and 30.14 percent over TCA+, Peters filter, GP, MO, and CODEP, respectively. In addition, HYDRA on average can discover 33 percent of all bugs if developers inspect the top 20 percent lines of code, which improves the best baseline approach (TCA+) by 44.41 percent. We also find that HYDRA improves the F1-score of Zero-R which predict all the instances to be defective by 5.42 percent, but improves Zero-R by 58.65 percent when inspecting the top 20 percent lines of code. In practice, Zero-R can be hard to use since it simply predicts all of the instances to be defective, and thus developers have to inspect all of the instances to find the defective ones. Moreover, we notice the improvement of HYDRA over other baseline approaches in terms of F1-score and when inspecting the top 20 percent lines of code are substantial, and in most cases the improvements are significant and have large effect sizes across the 29 datasets.


title: On the Performance of Non-Orthogonal Multiple Access in 5G Systems with Randomly Deployed Users

abstract: In this letter, the performance of non-orthogonal multiple access (NOMA) is investigated in a cellular downlink scenario with randomly deployed users. The developed analytical results show that NOMA can achieve superior performance in terms of ergodic sum rates; however, the outage performance of NOMA depends critically on the choices of the users' targeted data rates and allocated power. In particular, a wrong choice of the targeted data rates and allocated power can lead to a situation in which the user's outage probability is always one, i.e. the user's targeted quality of service will never be met.


Finding Maximal Clique In A Graph


title: Modeling user posting behavior on social media

abstract: User generated content is the basic element of social media websites. Relatively few studies have systematically analyzed the motivation to create and share content, especially from the perspective of a common user. In this paper, we perform a comprehensive analysis of user posting behavior on a popular social media website, Twitter. Specifically, we assume that user behavior is mainly influenced by three factors: breaking news, posts from social friends and user's intrinsic interest, and propose a mixture latent topic model to combine all these factors. We evaluated our model on a large-scale Twitter dataset from three different perspectives: the perplexity of held-out content, the performance of predicting retweets and the quality of generated latent topics. The results were encouraging, our model clearly outperformed its competitors.


title: Analyzing Energy-Time Tradeoff in Power Overprovisioned HPC Data Centers

abstract: Minimizing energy and power consumption of large scale data centers is one of the biggest challenges faced by the high performance computing community. In an over provisioned data center, nodes are power capped to run below their Thermal Design Power (TDP) value and therefore, an over provisioned data center has more nodes than a conventional data center with the same power budget. In this work, we study the energy versus time trade-off in a power over provisioned HPC data center. We show that over provisioning with the goal of maximizing performance can lead to excessive energy consumption. However, careful selection of configuration, that is number of nodes and power cap, can lead to significant savings in energy consumption with very small penalty on execution time of the application. We achieve up to 15% savings in energy consumption with only 2.8% increase in execution time as compared to a configuration that yields the best execution time.


title: Subsequence dynamic time warping as a method for robust step segmentation using gyroscope signals of daily life activities

abstract: The segmentation of gait signals into single steps is an important basis for objective gait analysis. Only a precise detection of step beginning and end enables the computation of step parameters like step height, variability and duration. A special challenge for the application is the accurateness of such an algorithm when based on signals from daily live activities. In this study, gyroscopes were attached laterally to sport shoes to collect gait data. For the automated step segmentation, subsequence Dynamic Time Warping was used. 35 healthy controls and ten patients with Parkinson's disease performed a four times ten meter walk. Furthermore 4 subjects were recorded during different daily life activities. The algorithm enabled counting steps, detecting precisely step beginning and end and rejecting other movements. Results showed a recognition rate of steps during ten meter walk exercises of 97.7% and in daily life activities of 86.7%. The segmentation procedure can be used for gait analysis from daily life activities and can constitute the basis for computation of precise step parameters. The algorithm is applicable for long-term gait monitoring as well as for analyzing gait abnormalities.


title: Do hospital fall prevention programs work? A systematic review.

abstract: OBJECTIVES ::: To analyze published hospital fall prevention programs to determine whether there is any effect on fall rates. To review the methodological quality of those programs and the range of interventions used. To provide directions for further research. ::: ::: ::: DESIGN ::: Systematic review of published hospital fall prevention programs. Meta-analysis. ::: ::: ::: METHODS ::: Keyword searches of Medline, CINAHL, monographs, and secondary references. All papers were included that described fall rates before and during intervention. Risk ratios and 95% Confidence Intervals (95% CI) were estimated and random effects meta-analysis employed. Begg's test was applied to detect possible publication bias. Separate meta-analysis regressions were performed to determine whether individual components of multifaceted interventions were effective. ::: ::: ::: RESULTS ::: A total of 21 papers met the criteria (18 from North America), although only 10 contained sufficient data to allow calculation of confidence intervals. A rate ratio of <1 indicates a reduction in the fall rate, resulting from an intervention. Three were randomized controlled trials (pooled rate ratio 1.0 (CI 0.60, 1.68)), seven prospective studies with historical control (0.76 (CI 0.65, 0.88)). Pooled effect rate ratio from these 10 studies was 0.79 (CI 0.69, 0.89). The remaining 11 studies were prospective studies with historical control describing fall rates only. Individual components of interventions showed no significant benefit. ::: ::: ::: DISCUSSION ::: The pooled effect of about 25% reduction in the fall rate may be a result of intervention but may also be biased by studies that used historical controls not allowing for historical trends in the fall rate before and during the intervention. The randomized controlled trials apparent lack of effect might be due to a change in practice when patients and controls were in the same unit at the same time during a study. Studies did not analyze compliance with the intervention or opportunity costs resulting from the intervention. Research and clinical programs in hospital fall prevention should pay more attention to study design and the nature of interventions.


title: Exploring Female Genital Cutting Among Survivors of Torture

abstract: Though the practice of female genital cutting (FGC) has been framed as a form of gender-based torture, few studies have examined the prevalence and impact of the practice among documented survivors of torture. This article presents a secondary analysis of data from 514 African-born women at an interdisciplinary clinic for survivors of torture. Results indicate few demographic differences between those who experienced FGC and those who had not, though a larger proportion of the FGC group were West African and identified as Muslim. Many with FGC were in the process of applying for asylum, reported sexual and psychological torture, and cited gender as a basis for their persecution. The FGC group evidenced unique correlates related to immigration status and psychological and sexual torture experiences that the non-FGC group did not. Findings indicate that female survivors of torture with FGC represent a distinct group with specific mental health needs.


title: Advertising value and credibility transfer: attitude towards web advertising and online information acquisition

abstract: China is undergoing a transition from offline to online advertising, but little is known about the causal relationship regarding credibility transfer from non-web advertising to web advertising. Meanwhile, web advertising usage behaviour is largely overlooked. The purpose of this study is to explore the formation of customers' attitude towards web advertising (ATT) which further impacts their online information acquisition behaviour, from the perspectives of advertising value and credibility transfer. This paper develops a research model drawing upon advertising value, credibility transfer, ATT and tie. Data collected from Chinese online shopping customers were used to test the model. The results indicate that perceived informativeness, perceived entertainment and credibility contribute to the formation of ATT, which further impacts web advertising usage for getting information. Meanwhile, tie positively moderates the credibility transfer from non-web advertising to web advertising. These findings and the...


A Proposed Theoretical Model for M-Learning Adoption in Developing Countries," in mLearn 2005 - 4th World conference on mLearning Cape Town


title: Towards automatic assessment of government web sites

abstract: This paper presents an approach for automatic assessment of web sites in large scale e-Government surveys. The approach aims at supplementing and to some extent replacing human evaluation which is typically the core part of these surveys. ::: The heart of the solution is a colony inspired algorithm, called the lost sheep, which automatically locates targeted governmental material online. The algorithm centers around classifying link texts to determine if a web page should be downloaded for further analysis. ::: The proposed algorithm is designed to work with minimum human interaction and utilize the available resources as best possible. Using the lost sheep, the people carrying out a survey will only provide sample data for a few web sites for each type of material sought after. The algorithm will automatically locate the same type of material in the other web sites part of the survey. This way it significantly reduces the need for manual work in large scale e-Government surveys.


Neural Machine Reading Comprehension: Methods and Trends


title: Understanding and selecting knowledge management systems for a health information provider

abstract: In the current market, there is a proliferation of knowledge management software (KMS) solutions. This paper discusses issues concerned with selecting such a system to support knowledge workers. Soft systems methodology (SSM) and SISTeM (Soft Information Systems and Technologies Methodology) Cycle 1 were used as problem-solving approaches within an action research framework to analyse KMS issues, human-computer interaction and the development of criteria for selecting a system. Selection criteria for KMSs are then outlined and applied to a case study in evidence-based health care.


title: Introduction to haptic rendering

abstract: For a long time, human beings have dreamed of a virtual world where it is possible to interact with synthetic entities as if they were real. To date, the advances in computer graphics allow us to see virtual objects and avatars, to hear them, to move them, and to touch them. It has been shown that the ability to touch virtual objects increases the sense of presence in virtual environments [Insko 2001].


title: Dimensionality reduction for large-scale neural recordings

abstract: Most sensory, cognitive and motor functions depend on the interactions of many neurons. In recent years, there has been rapid development and increasing use of technologies for recording from large numbers of neurons, either sequentially or simultaneously. A key question is what scientific insight can be gained by studying a population of recorded neurons beyond studying each neuron individually. Here, we examine three important motivations for population studies: single-trial hypotheses requiring statistical power, hypotheses of population response structure and exploratory analyses of large data sets. Many recent studies have adopted dimensionality reduction to analyze these populations and to find features that are not apparent at the level of individual neurons. We describe the dimensionality reduction methods commonly applied to population activity and offer practical advice about selecting methods and interpreting their outputs. This review is intended for experimental and computational researchers who seek to understand the role dimensionality reduction has had and can have in systems neuroscience, and who seek to apply these methods to their own data.


title: Adaptive Formative Paths in a Web-based Learning Environment

abstract: During the last decade, the interest for e-learning systems has grown thanks to availability of bandwidth and powerful computers. Thus the e-learning has moved from specific environments to larger communities. The main challenge of the last generation e-learning systems is to provide courses tailored to different students backgrounds; this pushes the research to create an adaptive environment able to just-in-time craft the best path for each student. This paper deals with this problem, introducing a proposal of a system for e-learning which provides students with all formative paths moving from an initial to a desired knowledge, and where paths are adapted according to student needs and capabilities, and dynamically modified when such needs change. We also present a prototype of a web-based e-learning environment, through which students can follow dynamically adapted formative paths, also exploiting collaborative learning tools.


title: Structured data and inference in DeepQA

abstract: Although the majority of evidence analysis in DeepQA is focused on unstructured information (e.g., natural-language documents), several components in the DeepQA system use structured data (e.g., databases, knowledge bases, and ontologies) to generate potential candidate answers or find additional evidence. Structured data analytics are a natural complement to unstructured methods in that they typically cover a narrower range of questions but are more precise within that range. Moreover, structured data that has formal semantics is amenable to logical reasoning techniques that can be used to provide implicit evidence. The DeepQA system does not contain a single monolithic structured data module; instead, it allows for different components to use and integrate structured and semistructured data, with varying degrees of expressivity and formal specificity. This paper is a survey of DeepQA components that use structured data. Areas in which evidence from structured sources has the most impact include typing of answers, application of geospatial and temporal constraints, and the use of formally encoded a priori knowledge of commonly appearing entity types such as countries and U.S. presidents. We present details of appropriate components and demonstrate their end-to-end impact on the IBM Watsoni system.


title: On Tensor Completion via Nuclear Norm Minimization

abstract: Many problems can be formulated as recovering a low-rank tensor. Although an increasingly common task, tensor recovery remains a challenging problem because of the delicacy associated with the decomposition of higher order tensors. To overcome these difficulties, existing approaches often proceed by unfolding tensors into matrices and then apply techniques for matrix completion. We show here that such matricization fails to exploit the tensor structure and may lead to suboptimal procedure. More specifically, we investigate a convex optimization approach to tensor completion by directly minimizing a tensor nuclear norm and prove that this leads to an improved sample size requirement. To establish our results, we develop a series of algebraic and probabilistic techniques such as characterization of subdifferetial for tensor nuclear norm and concentration inequalities for tensor martingales, which may be of independent interests and could be useful in other tensor related problems.


title: Joint Optimization of Constellation With Mapping Matrix for SCMA Codebook Design

abstract: Sparse code multiple access (SCMA) is being considered as a promising multiple access solution for 5G systems. A distinguishing feature of SCMA is that it combines the procedures of bit to constellation symbol mapping and subsequent spreading using multidimensional codebooks differentiated by users. Such codebooks dominate the system implementation as a main source of not only performance gain but also design complexity. This letter presents a joint constellation with mapping matrix design for SCMA codebooks, which formulates the constellations optimization as a nonconvex quadratically constrained quadratic programming problem based on a set of well-constructed mapping matrices. We elaborately solve the problem to achieve outperformance over existing SCMA design in terms of bit error rate (BER). For improving practicality, an approximate approach is further proposed to reduce the complexity significantly with a limited BER loss.


title: Exploration of efficient symmetric AES algorithm

abstract: Security threats have been a major concern as a result of emergence of technology in every aspect including internet market, computational and communication technologies. To solve this issue effective mechanism of “cryptography” is used to ensure integrity, privacy, availability, authentication, computability, identification and accuracy. Cryptology methods like PKC and SKC are used of data recovery. In this work, we describe exploration of efficient approach of private key architecture that is efficient symmetric AES Algorithm on the basis of attributes: encipherment and Decipherment and degree of security issues essential for safe wired and wireless communication. The work explores efficient private key algorithm based on security of individual system and to improve encipherment and Decipherment time with encipherment/Decipherment performance. The work opens a new direction over cloud security and internet of things.


title: The new frontier of communications research: smart grid and smart metering

abstract: This paper discusses some of the challenges and opportunities of communications research in the area of smart grids and smart metering. It is clear that the communications research community has been actively seeking the 'next big thing' after interests in recent hot topics such as cognitive radio, cooperative communications, and MIMO have more or less peaked. We argue that the new initiative on smart grid worldwide provides an ideal opportunity for communication and networking researchers to apply various existing technologies as well as inventing new ones in this exciting area.


title: Learning Discrete Bayesian Networks from Continuous Data

abstract: Real data often contains a mixture of discrete and continuous variables, but many Bayesian network structure learning and inference algorithms assume all random variables are discrete. Continuous variables are often discretized, but the choice of discretization policy has significant impact on the accuracy, speed, and interpretability of the resulting models. This paper introduces a principled Bayesian discretization method for continuous variables in Bayesian networks with quadratic complexity instead of the cubic complexity of other standard techniques. Empirical demonstrations show that the proposed method is superior to the state of the art. In addition, this paper shows how to incorporate existing methods into the structure learning process to discretize all continuous variables and simultaneously learn Bayesian network structures.


title: Is there a fingerprint pattern in the image?

abstract: A fingerprint orientation field has distinct characteristics which can differentiate fingerprints from any other flow patterns: it has a specific number of singular points (cores and deltas), the configuration of singular points follows a certain spatial distribution, and its global shape is like an arch. In this paper, we propose a global fingerprint orientation field model, represented in terms of ordinary differential equations, which does not require any prior information such as singular points or orientation of a fingerprint. Further, the model requires only a small number of polynomial terms to represent the global fingerprint orientation field. The coefficients of the model are found subject to the constraints on the total number of singular points (i.e., 0, 2, or 4) in a fingerprint. The proposed model is used to distinguish fingerprint images from non-fingerprint images and altered fingerprints by measuring the abnormality in the orientation field of the image.


title: Transient Stability Enhancement by Fuzzy Logic-Controlled SMES Considering Coordination With Optimal Reclosing of Circuit Breakers

abstract: This paper aims at investigating the effect of the coordination of fuzzy logic-controlled superconducting magnetic energy storage (SMES) and optimal reclosing on the transient stability in a simulated multi-machine power system during unsuccessful reclosing of circuit breakers. The performance of the coordinated operation of fuzzy logic-controlled SMES and optimal reclosing is compared to that of the coordinated operation of fuzzy logic-controlled SMES and conventional auto-reclosing. Also, the performance of the fuzzy logic-controlled SMES is compared to that of an alternative static nonlinear controlled SMES. The control scheme of SMES is based on a pulse width modulation (PWM) voltage source converter (VSC) and a two-quadrant dc-dc chopper using gate-turn-off (GTO) thyristor. The parameters of the proposed fuzzy logic controller are optimally tuned by the genetic algorithm (GA) method. Simulation results of both balanced and unbalanced faults at different points in a multi-machine power system show that the coordinated operation of fuzzy controlled SMES and optimal reclosing is able to stabilize the system well in case of an unsuccessful reclosing. Moreover, the transient stability performance of the coordinated operation of fuzzy controlled SMES and optimal reclosing is better than that of the coordinated operation of fuzzy controlled SMES and conventional auto-reclosing. Also, the performance of the fuzzy logic-controlled SMES is better than that of the static nonlinear controlled SMES.


Acceptance of BYOD among Employees at Small to Medium-sized Organizations


title: Caffeine and exercise: metabolism, endurance and performance.

abstract: Caffeine is a common substance in the diets of most athletes and it is now appearing in many new products, including energy drinks, sport gels, alcoholic beverages and diet aids. It can be a powerful ergogenic aid at levels that are considerably lower than the acceptable limit of the International Olympic Committee and could be beneficial in training and in competition. Caffeine does not improve maximal oxygen capacity directly, but could permit the athlete to train at a greater power output and/or to train longer. It has also been shown to increase speed and/or power output in simulated race conditions. These effects have been found in activities that last as little as 60 seconds or as long as 2 hours. There is less information about the effects of caffeine on strength; however, recent work suggests no effect on maximal ability, but enhanced endurance or resistance to fatigue. There is no evidence that caffeine ingestion before exercise leads to dehydration, ion imbalance, or any other adverse effects. The ingestion of caffeine as coffee appears to be ineffective compared to doping with pure caffeine. Related compounds such as theophylline are also potent ergogenic aids. Caffeine may act synergistically with other drugs including ephedrine and anti-inflammatory agents. It appears that male and female athletes have similar caffeine pharmacokinetics, i.e., for a given dose of caffeine, the time course and absolute plasma concentrations of caffeine and its metabolites are the same. In addition, exercise or dehydration does not affect caffeine pharmacokinetics. The limited information available suggests that caffeine non-users and users respond similarly and that withdrawal from caffeine may not be important. The mechanism(s) by which caffeine elicits its ergogenic effects are unknown, but the popular theory that it enhances fat oxidation and spares muscle glycogen has very little support and is an incomplete explanation at best. Caffeine may work, in part, by creating a more favourable intracellular ionic environment in active muscle. This could facilitate force production by each motor unit.


title: Breast Pain: Engorgement, Nipple Pain, and Mastitis

abstract: Abstract This review explores current concepts surrounding breastfeeding complications including nipple pain and trauma, breast engorgement, mastitis, and breast abscess. The review discusses possible etiologies, risk factors, incidence, differential diagnosis, and suggested treatment strategies. The evidence that supports these management options is discussed.


title: A neuro-fuzzy approach for prediction of longitudinal wave velocity

abstract: Adaptive neuro-fuzzy inference system (ANFIS) is rapidly gaining popularity in the area of geophysics and geomechanics. This paper discusses the importance of ANFIS to prediction of p-wave velocity and its advantages over other conventional methods of computing. This paper deals with the application of a ANFIS to predict longitudinal wave velocity. P-wave measurement, which is also an indicator of peak particle velocity during blasting in a mine, is an important parameter to be determined to minimize the damage caused by ground vibrations. A number of previous researchers have tried to use different empirical methods to predict p-wave. But these empirical methods have their limitations due to its less versatile application. The fracture propagation is not only influenced by the physico-mechanical parameters of rock but also on the dynamic wave velocity of rock (e.g., compressional wave velocity). It has wide application in the different field of geophysics. An ANFIS model is designed to predict the compressional wave velocity of different rocks. The fracture roughness coefficient and physico-mechanical properties are taken as input parameters and compressional wave velocity as output parameters. The error for the predicted values is found to be negligible (0.5%) and generalization capability of the neuro-fuzzy model is found to be very useful for such type of geophysical problems.


title: Automatic Differentiation as a Tool in Engineering Design

abstract: Automatic Differentiation (AD) is a tool that systematically implements the chain rule of differentiation to obtain the derivatives of functions calculated by computer programs. In this paper, it is assessed as a tool for engineering design. The paper discusses the forward and reverse modes of AD, their computing requirements, as well as approaches to implementing AD. It continues with the application of two different tools to two medium-size structural analysis problems to generate sensitivity information typically necessary in an optimization or design situation. The paper concludes with the observation that AD is to be preferred to finite differencing in most cases, as long as sufficient computer storage is available; in some instances, AD may be the alternative to consider in lieu of analytical sensitivity analysis.


title: A Distributed Robot Garden System

abstract: Computational thinking is an important part of a modern education, and robotics provides a powerful tool for teaching programming logic in an interactive and engaging way. The robot garden presented in this paper is a distributed multi-robot system capable of running autonomously or under user control from a simple graphical interface. Over 100 origami flowers are actuated with LEDs and printed pouch motors, and are deployed in a modular array around additional swimming and crawling folded robots. The garden integrates state-of-the-art rapid design and fabrication technologies with distributed systems software techniques to create a scalable swarm in which robots can be controlled individually or as a group. The garden can be used to teach basic algorithmic concepts through its distributed algorithm demonstration capabilities and can teach programming concepts through its education-oriented user interface.


title: Microplate Agglutination Test for Canine Brucellosis Using Recombinant Antigen-Coated Beads

abstract: Brucella canis, a facultative intracellular pathogen, is the causative agent of canine brucellosis. The diagnosis of canine brucellosis is based on bacteriological examination and serological methods, including agglutination and gel diffusion tests. In this study, four recombinant antigens, heat shock protein 60, rhizopine-binding protein, Cu-Zn superoxide dismutase, and hypothetical protein (Ag 4), were constructed. These antigens were coated on latex beads and their usefulness in the serological diagnosis of canine brucellosis was examined. All recombinant antigens showed specific reaction with sera from B. canis-infected dogs in Western blotting. In a microplate agglutination test, mixing sera from B. canis-infected dogs, but not sera from B. canis-free dogs, with single or multiple antigens-coated latex beads produced clear agglutination. Moreover, the antigen-coated latex beads did not show nonspecific agglutination in hemolyzed serum samples. A survey of canine serum samples conducted by the microplate agglutination test using single antigen-coated latex beads showed that this method would be useful in the serological diagnosis of canine brucellosis. Further investigations using more serum samples are required to confirm the usefulness of our method.


title: IGFTT: towards an efficient alternative to SIFT and SURF

abstract: The invariant feature detectors are essential components in many computer vision applications, such as ::: tracking, simultaneous localization and mapping (SLAM), image search, machine vision, object recognition, 3D ::: reconstruction from multiple images, augmented reality, stereo vision, and others. However, it is very challenging ::: to detect high quality features while maintaining a low computational cost. Scale-Invariant Feature Transform ::: (SIFT) and Speeded-Up Robust Features (SURF) algorithms exhibit great performance under a variety of image ::: transformations, however these methods rely on costly keypoint’s detection. Recently, fast and efficient variants ::: such as Binary Robust Invariant Scalable Keypoints (BRISK) and Oriented Fast and Rotated BRIEF (ORB) were ::: developed to offset the computational burden of these traditional detectors. ::: In this paper, we propose to improve the Good Features to Track (GFTT) detector, coined IGFTT. It approximates ::: or even outperforms the state-of-art detectors with respect to repeatability, distinctiveness, and robustness, yet can ::: be computed much faster than Maximally Stable Extremal Regions (MSER), SIFT, BRISK, KAZE, Accelerated ::: KAZE (AKAZE) and SURF. This is achieved by using the search of maximal-minimum eigenvalue in the image ::: on scale-space and a new orientation extraction method based on eigenvectors. ::: A comprehensive evaluation on standard datasets shows that IGFTT achieves quite a high performance with a ::: computation time comparable to state-of-the-art real-time features. The proposed method shows exceptionally ::: good performance compared to SURF, ORB, GFTT, MSER, Star, SIFT, KAZE, AKAZE and BRISK.


title: Condensation methodologies for two-dimensional non-uniform constellations

abstract: Two-dimensional non-uniform constellations (2D-NUC) have been adopted for the most recent terrestrial broadcasting system called ATSC 3.0. They are known for being more efficient than one-dimensional non uniform constellations (1D-NUC) and uniform constellations (UC). They can be used in any communication system with the bit-interleaved coded modulation (BICM) structure. However, one of the main challenges of such constellations is their design for optimal behaviour in a wide range of cases and the design of a demapper that exploits their advantages. This paper presents two different condensation methodologies to design efficient 2D-NUCs simple in design and with limited demapping complexity. The proposal provides a complexity reduction of the design and demapping processes in the range from 13% to 94%. The demapping stage provides performance losses less than 0.1 dB if compared with standard 2D-NUC demapping.


title: Short-term stock price prediction based on echo state networks

abstract: Neural network has been popular in time series prediction in financial areas because of their advantages in handling nonlinear systems. This paper presents a study of using a novel recurrent neural network-echo state network (ESN) to predict the next closing price in stock markets. The Hurst exponent is applied to adaptively determine initial transient and choose sub-series with greatest predictability during training. The experiment results on nearly all stocks of S&P 500 demonstrate that ESN outperforms other conventional neural networks in most cases. Experiments also indicate that if we include principle component analysis (PCA) to filter noise in data pretreatment and choose appropriate parameters, we can effectively prevent coarse prediction performance. But in most cases PCA improves the prediction accuracy only a little.


title: Dynamic characterization of vegetation using remote sensing for hydrological modelling at basin scale

abstract: Spatio-temporal changes in vegetation at the basin scale are difficult to characterize, and remote sensing is a major ::: source of data for this purpose. These sensors may provide distributed series of spectral properties of the vegetation with ::: different spatial and temporal resolutions, but they do not always satisfy the requirements of some of the applications. ::: These limitations can be overcome with the use of image integration techniques, which allow for the combination of ::: sensors with different characteristics. This work presents the monitoring of the vegetation cover in the Guadalfeo River ::: Basin (Spain), with a view to its hydrological modeling, by using Landsat-TM and MODIS data, analyzing the ::: implications of the scale differences in an heterogeneous area. A preliminary study is carried out into the deviations of ::: NDVI and ground cover fraction (fv) between the concurrent data of both sensors. Thereafter, the STARFM integration ::: algorithm is applied and evaluated to obtain synthetic NDVI images at the spatial resolution of Landsat-TM data with ::: MODIS time steps. The comparison between Landsat-TM and MODIS parameters revealed deviations on average ::: between 2-5% for NDVI and 3-5% for fv. No direct relationship was found between these deviations and basin ::: topography. However, higher deviations corresponded with the vegetation types with higher ground cover fractions and ::: heterogeneous landuses (fv relative deviations of 10% and 6% for conifers and quercus-scrub, respectively) The ::: STARFM algorithm improved the NDVI estimations when compared to the previous Landsat-TM date, with reductions ::: in the average NDVI differences of around 0.02 on average for the six simulated dates, with the accuracy of the ::: predictions depending on data input for the model and vegetation cover types.


title: Conceptual approaches for defining data, information, and knowledge

abstract: The field of Information Science is constantly changing. Therefore, information scientists are required to regularly review-and if necessary-redefine its fundamental building blocks. This article is one of a group of four articles, which resulted from a Critical Delphi study conducted in 2003-2005. The study, "Knowledge Map of Information Science," was aimed at exploring the foundations of information science. The international panel was composed of 57 leading scholars from 16 countries, who represent (almost) all the major subfields and important aspects of the field. This particular article documents 130 definitions of data, information, and knowledge formulated by 45 scholars, and maps the major conceptual approaches for defining these three key concepts.


title: Prediction of election result by enhanced sentiment analysis on Twitter data using Word Sense Disambiguation

abstract: Sentiment analysis is the computational study of opinions, sentiments, evaluations, attitudes, views and emotions expressed in text. It refers to a classification problem where the main focus is to predict the polarity of words and then classify them into positive or negative sentiment. Sentiment analysis over Twitter offers people a fast and effective way to measure the public's feelings towards their party and politicians. The primary issues in previous sentiment analysis techniques are classification accuracy, as they incorrectly classify most of the tweets with the biasing towards the training data. In opinion texts, lexical content alone also can be misleading. Therefore, here we adopt a lexicon based sentiment analysis method, which will exploit the sense definitions, as semantic indicators of sentiment. Here we propose a novel approach for accurate sentiment classification of twitter messages using lexical resources SentiWordNet and WordNet along with Word Sense Disambiguation. Thus we applied the SentiWordNet lexical resource and Word Sense Disambiguation for finding political sentiment from real time tweets. Our method also uses a negation handling as a pre-processing step in order to achieve high accuracy.


title: Core consistency diagnostic in PARAFAC2

abstract: PARAFAC2 is applied in multiple research areas, for example, where data containing shifts are analysed, but it is a challenge to determine the appropriate number of components in the model. In this paper, it is hypothesized that the core consistency diagnostic, which is currently applied in, for example, PARAFAC1 can be used to determine model complexity in PARAFAC2. Theoretically, a PARAFAC1 model is fitted ‘inside’ the PARAFAC2 algorithm, and it should therefore be possible to apply the core consistency diagnostic from PARAFAC1 in PARAFAC2. To support this hypothesis, three different datasets, as well as simulated datasets, have been evaluated by means of PARAFAC2, and the core consistencies have been investigated. There is a general trend that if the core consistency is low, the model is overfitted as in PARAFAC1. Also, core consistency captures the true variation in the data, whereas small peaks are easily overlooked by visual inspection of noisy models. However, for determining the number of components in a PARAFAC2 model, we suggest usage of the core consistency in combination with other model parameters such as residuals, loadings, and split-half analysis. Copyright © 2013 John Wiley & Sons, Ltd.


abstract: Abstract Pakistan has suffered a devastating flood disaster in 2010. In the Kabul River basin (92 605 km2), large-scale riverine and flash floods caused destructive damage with more than 1100 casualties. This study analysed rainfall–runoff and inundation in the Kabul River basin with a newly developed model that simulates the processes of rainfall–runoff and inundation simultaneously based on two-dimensional diffusion wave equations. The simulation results showed a good agreement with an inundation map produced based on MODIS for large-scale riverine flooding. In addition, the simulation identified flash flood-affected areas, which were confirmed to be severely damaged based on a housing damage distribution map. Since the model is designed to be used even immediately after a disaster, it can be a useful tool for analysing large-scale flooding and to provide supplemental information to agencies for relief operations. Editor Z.W. Kundzewicz Citation Sayama, T., Ozawa, G., Kawakami, T., Nabesaka, S. and Fukami, K., 2012. Rainfall–runoff–inundation analysis of the 2010 Pakistan flood in the Kabul River basin. Hydrological Sciences Journal, 57 (2), 298–312.


title: Porting natural language interfaces between domains: an experimental user study with the ORAKEL system

abstract: We present a user-centered model for porting natural language interfaces (NLIs) between domains efficiently. The model assumes that domain experts without any background knowledge about computational linguistics will perform the customization of the NLI to a specific domain. In fact, it merely requires familiarity with the underlying knowledge base as well as with a few basic subcategorization types. Our model is iterative in the sense that the adaption of the NLI is performed in several cycles on the basis of the questions which the NLI failed to answer, thus iteratively increasing the coverage of the system. We provide experimental evidence in form of a user study as well as a case study involving a real-world application corroborating that our model is indeed a feasible way of customizing the interface to a certain domain.


title: Evaluating deep learning architectures for Speech Emotion Recognition

abstract: Abstract Speech Emotion Recognition (SER) can be regarded as a static or dynamic classification problem, which makes SER an excellent test bed for investigating and comparing various deep learning architectures. We describe a frame-based formulation to SER that relies on minimal speech processing and end-to-end deep learning to model intra-utterance dynamics. We use the proposed SER system to empirically explore feed-forward and recurrent neural network architectures and their variants. Experiments conducted illuminate the advantages and limitations of these architectures in paralinguistic speech recognition and emotion recognition in particular. As a result of our exploration, we report state-of-the-art results on the IEMOCAP database for speaker-independent SER and present quantitative and qualitative assessments of the models’ performances.


title: An Integral Model to Provide Reactive and Proactive Services in an Academic CSIRT Based on Business Intelligence

abstract: Cyber-attacks have increased in severity and complexity. That requires, that the CERT/CSIRT research and develops new security tools. Therefore, our study focuses on the design of an integral model based on Business Intelligence (BI), which provides reactive and proactive services in a CSIRT, in order to alert and reduce any suspicious or malicious activity on information systems and data networks. To achieve this purpose, a solution has been assembled, that generates information stores, being compiled from a continuous network transmission of several internal and external sources of an organization. However, it contemplates a data warehouse, which is focused like a correlator of logs, being formed by the information of feeds with diverse formats. Furthermore, it analyzed attack detection and port scanning, obtained from sensors such as Snort and Passive Vulnerability Scanner, which are stored in a database, where the logs have been generated by the systems. With such inputs, we designed and implemented BI systems using the phases of the Ralph Kimball methodology, ETL and OLAP processes. In addition, a software application has been implemented using the SCRUM methodology, which allowed to link the obtained logs to the BI system for visualization in dynamic dashboards, with the purpose of generating early alerts and constructing complex queries using the user interface through objects structures. The results demonstrate, that this solution has generated early warnings based on the level of criticality and level of sensitivity of malware and vulnerabilities as well as monitoring efficiency, increasing the level of security of member institutions.


title: RePro3D: full-parallax 3D display with haptic feedback using retro-reflective projection technology

abstract: We propose a novel full-parallax three-dimensional (3D) display system-RePro3D-that is suitable for interactive 3D applications with haptic feedback.Our approach is based on the retro-reflective projection technology in which several images projected from a projector array are displayed on a retro-reflective screen. When viewers view the screen through a half mirror, they see, without the aid of glasses, a 3D image superimposed in real space. RePro3D has a sensor function that recognizes user input; therefore, it can support some interactive features such as manipulation of 3D objects. In addition, a wearable haptic device, which is a part of our system, provides the user with a sensation of having touched the 3D image. In this paper, we describe the optical system of the high-density projector array used in RePro3D. Then, we describe the development of a prototype of RePro3D. The prototype is used to demonstrate that our system displays full-parallax images superimposed in real space from 42 different viewpoints. The proposed system enables a user to physically interact with the 3D image with haptic feedback.


title: M.: A Fabula Model for Emergent Narrative

abstract: In this paper we present our continuing efforts to generate narrative using a character-centric approach. In particular we discuss the advantages of explicitly representing the emergent event sequence in order to be able to exert influence on it and generate stories that ‘retell' the emergent narrative. Based on a narrative distinction between fabula, plot and presentation, we make a first step by presenting a model based on story comprehension that can capture the fabula, and show how it can be used for the automatic creation of stories.


title: Design, integration, and field evaluation of a robotic apple harvester

abstract: Every apple destined for the fresh market is picked by the human hand. Despite extensive research over the past four decades, there are no mechanical apple harvesters for the fresh market commercially available, which is a significant concern because of increasing uncertainty about the availability of manual labor and rising production costs. The highly unstructured orchard environment has been a major challenge to the development of commercially viable robotic harvesting systems. This paper reports the design and field evaluation of a robotic apple harvester. The approach adopted was to use a low-cost system to assess required sensing, planning, and manipulation functionality in a modern orchard system with a planar canopy. The system was tested in a commercial apple orchard in Washington State. Workspace modifications and performance criteria are thoroughly defined and reported to help evaluate the approach and guide future enhancements. The machine vision system was accurate and had an average localization time of 1.5 s per fruit. The seven degree of freedom harvesting system successfully picked 127 of the 150 fruit attempted for an overall success rate of 84% with an average picking time of 6.0 s per fruit. Future work will include integration of additional sensing and obstacle detection for improved system robustness.


title: Expanding a distributed deductive database with mobile computing

abstract: Mobile computing is a component that will be included in many future computing environments. This paper presents a scheme that will allow the addition of a mobile component to an existing distributed deductive database. This scheme allows for the following features: 1) data replication on the mobile unit, 2) transaction logging to log changes both in the mobile user's database and in the network's database, 3) concurrency control to prevent both copies from updating the same record, and 4) reconnection procedures to update both copies upon reconnection. To facilitate data replication and reconnection updates, we have established ‘Proxy Nodes’ in the network database to keep track of data of interest to the mobile user.


10. Vehicle routing: handling edge exchanges


Mutual Information Neural Estimator


title: An application of kappa‐type analyses to interobserver variation in classifying chest radiographs for pneumoconiosis

abstract: We investigated interobserver variation between three physician readers in the independent classification of chest radiographs from 1771 taconite workers for pneumoconiosis. We analysed variation with a general methodology for the analysis of categorical data, and quantified interobserver agreement in terms of kappatype statistics. The results revealed considerable variation in the profusion of opacities reported by each observer. This was especially so for the earliest radiographic changes, but variation also occurred with the small number of films in the series showing category 2 pneumoconiosis or over. Variation in the classification of profusion of small opacities was greater in films of poor quality, and also increased with the length of time between taking and reading the film (film age). We could not account for the effect of film age by controlling for film quality, as subjectively assessed by the readers.


title: Per-decision Multi-step Temporal Difference Learning with Control Variates

abstract: Multi-step temporal difference (TD) learning is an important approach in reinforcement learning, as it unifies one-step TD learning with Monte Carlo methods in a way where intermediate algorithms can outperform either extreme. They address a bias-variance trade off between reliance on current estimates, which could be poor, and incorporating longer sampled reward sequences into the updates. Especially in the off-policy setting, where the agent aims to learn about a policy different from the one generating its behaviour, the variance in the updates can cause learning to diverge as the number of sampled rewards used in the estimates increases. In this paper, we introduce per-decision control variates for multi-step TD algorithms, and compare them to existing methods. Our results show that including the control variates can greatly improve performance on both on and off-policy multi-step temporal difference learning tasks.


title: An aperture-coupled linear microstrip leaky-wave antenna array with two-dimensional dual-beam scanning capability

abstract: This paper describes an X-band 4/spl times/1 aperture-coupled series-fed electronically steerable microstrip leaky-wave antenna (LWA) array design, which has dual-beam radiation pattern and two-dimensional (2-D) beam-scanning capability. The LWA array is operated in the first higher order mode and excited by center-fed aperture coupled for dual-beam operation. The varactor-tuned phase shifters are placed between the antenna elements. The measured half-power beamwidth of the H-plane and quasi-E-plane radiation patterns are less than 30/spl deg/. By tuning the reverse dc bias of the varactor diodes, the main beam can be scanned in azimuth plane from -13/spl deg/ to +13/spl deg/ off broadside. In the elevation plane, the beam-scanning angle is close to 20/spl deg/ as the operating frequency tuned from 11.58-12.5 GHz. Taking into account each phase-shifter insertion loss and phase progression, the measured results compared closely with the theoretical prediction. The proposed antenna array is suitable for wireless communication and collision warning radar systems.