title: A system analysis of the basel capital accord's regulatory control over the behaviour of modern banking systems

abstract: The banking system plays a critical role in the economy controlling both the supply of money and a significant percentage of total debt. Originating in medieval book keeping practices, it has developed into a distributed recursive feedback system, where money in the form of bank deposits is created by banks when loans are made, and removed as they are repaid. The total quantity of loan capital that can be created by the entire banking system, and by extension the total amount of money in the economy is locally controlled at each bank by several factors including the bank's individual capital reserve, its required central bank reserves, and its liquidity with respect to the daily difference between its income, expenses and liquidity holdings. With the stability and successful operation of the pricing mechanism for the entire economy directly linked to the money supply, understanding the mechanical operation and behaviour over time of banking systems under different regulatory frameworks can be regarded as a critical problem for modern systems analysis. In this paper we present a computer simulation of a simplified banking system using the internationally agreed Basel Accords in use since the 1980's. We show that the Basel Framework does not appear to provide the stability guarantees that might be expected for such a critical system, especially with respect to its regulation of the supply of money and credit over time, and the multiplier relationships embedded in the system. We show that loans to customers at other banks, interbank lending relationships, and failures in loan demand, continue to be a source of instability for the system. We also show that in the current regulatory framework, there appear to be no absolute constraints on the bank deposit creation/lending process and that the main factor controlling the expansion rates of the system appears to be the individual bank's liquidity constraints, which originate from the difference between their profits and expenses. Since banking profits are directly proportional to interest rates minus their loan defaults, this implies that the the relationship between interest rates and expansionary economic policies may be significantly more complex than currently presented by economic theory. It also suggests that interest rate raises may be required in order to stimulate lending in many of today's economies.


title: Canopy: An End-to-End Performance Tracing And Analysis System

abstract: This paper presents Canopy, Facebook's end-to-end performance tracing infrastructure. Canopy records causally related performance data across the end-to-end execution path of requests, including from browsers, mobile applications, and backend services. Canopy processes traces in near real-time, derives user-specified features, and outputs to performance datasets that aggregate across billions of requests. Using Canopy, Facebook engineers can query and analyze performance data in real-time. Canopy addresses three challenges we have encountered in scaling performance analysis: supporting the range of execution and performance models used by different components of the Facebook stack; supporting interactive ad-hoc analysis of performance data; and enabling deep customization by users, from sampling traces to extracting and visualizing features. Canopy currently records and processes over 1 billion traces per day. We discuss how Canopy has evolved to apply to a wide range of scenarios, and present case studies of its use in solving various performance challenges.


title: A Hybrid ACO+CP for Balancing Bicycle Sharing Systems

abstract: Balancing bike sharing systems is an increasingly important problem, because of the rising popularity of this mean of transportation. Bike sharing systems need to be balanced so that bikes (and empty slots for returning bikes) are available to the customers, thus ensuring an adequate level of service.


title: Orange: From Experimental Machine Learning to Interactive Data Mining

abstract: Orange (www.ailab.si/orange) is a suite for machine learning and data mining. For researchers in machine learning, Orange offers scripting to easily prototype new algorithms and experimental procedures. For explorative data analysis, it provides a visual programming framework with emphasis on interactions and creative combinations of visual components.


title: The cloud is not enough: saving iot from the cloud

abstract: The Internet of Things (IoT) represents a new class of applications that can benefit from cloud infrastructure. However, the current approach of directly connecting smart devices to the cloud has a number of disadvantages and is unlikely to keep up with either the growing speed of the IoT or the diverse needs of IoT applications. ::: ::: In this paper we explore these disadvantages and argue that fundamental properties of the IoT prevent the current approach from scaling. What is missing is a well-architected system that extends the functionality of the cloud and provides seamless interplay among the heterogeneous components in the IoT space. We argue that raising the level of abstraction to a data-centric design-- focused around the distribution, preservation and protection of information--provides a much better match to the IoT. We present early work on such a distributed platform, called the Global Data Plane (GDP), and discuss how it addresses the problems with the cloud-centric architecture.


title: Incremental learning abstract discrete planning domains and mappings to continuous perceptions

abstract: Most of the works on planning and learning, e.g., planning by (model based) reinforcement learning, are based on two main assumptions: (i) the set of states of the planning domain is fixed; (ii) the mapping between the observations from the real word and the states is implicitly assumed or learned offline, and it is not part of the planning domain. Consequently, the focus is on learning the transitions between states. In this paper, we drop such assumptions. We provide a formal framework in which (i) the agent can learn dynamically new states of the planning domain; (ii) the mapping between abstract states and the perception from the real world, represented by continuous variables, is part of the planning domain; (iii) such mapping is learned and updated along the "life" of the agent. We define an algorithm that interleaves planning, acting, and learning, and allows the agent to update the planning domain depending on how much it trusts the model w.r.t. the new experiences learned by executing actions. We define a measure of coherence between the planning domain and the real world as perceived by the agent. We test our approach showing that the agent learns increasingly coherent models, and that the system can scale to deal with models with an order of $10^6$ states.


title: Minimality Attack in Privacy Preserving Data Publishing

abstract: Data publishing generates much concern over the protection of individual privacy. Recent studies consider cases where the adversary may possess different kinds of knowledge about the data. In this paper, we show that knowledge of the mechanism or algorithm of anonymization for data publication can also lead to extra information that assists the adversary and jeopardizes individual privacy. In particular, all known mechanisms try to minimize information loss and such an attempt provides a loophole for attacks. We call such an attack a minimality attack. In this paper, we introduce a model called m-confidentiality which deals with minimality attacks, and propose a feasible solution. Our experiments show that minimality attacks are practical concerns on real datasets and that our algorithm can prevent such attacks with very little overhead and information loss.


title: BotGraph: Large Scale Spamming Botnet Detection

abstract: Network security applications often require analyzing huge volumes of data to identify abnormal patterns or activities. The emergence of cloud-computing models opens up new opportunities to address this challenge by leveraging the power of parallel computing. ::: ::: In this paper, we design and implement a novel system called BotGraph to detect a new type of botnet spamming attacks targeting major Web email providers. Bot-Graph uncovers the correlations among botnet activities by constructing large user-user graphs and looking for tightly connected subgraph components. This enables us to identify stealthy botnet users that are hard to detect when viewed in isolation. To deal with the huge data volume, we implement BotGraph as a distributed application on a computer cluster, and explore a number of performance optimization techniques. Applying it to two months of Hotmail log containing over 500 million users, BotGraph successfully identified over 26 million botnet-created user accounts with a low false positive rate. The running time of constructing and analyzing a 220GB Hot-mail log is around 1.5 hours with 240 machines. We believe both our graph-based approach and our implementations are generally applicable to a wide class of security applications for analyzing large datasets.


title: A variable buoyancy system and a recovery system developed for a deep-sea AUV Qianlong I

abstract: As a new generation of deep-sea Autonomous Underwater Vehicle (AUV), Qianlong Ⅰ is a 6000m rated glass deep-sea manganese nodules detection AUV which based on the CR01 and the CR02 deep-sea AUVs and developed by Shenyang Institute of Automation, the Chinese Academy of Sciences from 2010. The Qianlong Ⅰ was tested in the thousand-isles lake in Zhejiang Province of China during November 2012 to March 2013 and the sea trials were conducted in the South China Sea during April 20-May 2, 2013 after the lake tests and the ocean application completed in October 2013. This paper describes two key problems encountered in the process of developing Qianlong Ⅰ, including the launch and recovery systems development and variable buoyancy system development. Results from the recent lake and sea trails are presented, and future missions and development plans are discussed.


title: Residual Useful Life Prediction for Rolling Element Bearings Based on Multi-feature Fusion Regression

abstract: Residual useful life (RUL) prediction is critical in efficient implementation of condition-based maintenances for rolling element bearings (REBs). A multi-feature fusion regression method is reported for predicting the RUL of REBs in this work. In the proposed approach, locally linear embedding technique is employed to fuse original features for erecting a condition indicator of the REBs. The fused condition indicator is then modeled by an adaptive network-based fuzzy inference system for predicting the RUL. The present approach is applied to experimental data collected from an REB. Experimental results show that the proposed approach exhibits better performance than peer approaches. It is capable of accurately predicting the RUL of the REB.


title: Linking Servant Leadership with Organizational Citizenship Behavior through Trust: An Embryonic Structural Modelling Approach

abstract: The objective of the present study is to examine the relationship between servant leadership and organizational citizenship behaviors (OCBs) along with mediating effect of trust on servant leadership and OCB relationship. The Data was collected through selfadministered questionnaire from 345 respondents of four public sector universities in Pakistan. The embryonic structural equation modelling method was used for data analysis.


title: A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems

abstract: This paper presents a comparative study of type-2 fuzzy logic systems with respect to interval type-2 and type-1 fuzzy logic systems to show the efficiency and performance of a generalized type-2 fuzzy logic controller (GT2FLC). We used different types of fuzzy logic systems for designing the fuzzy controllers of complex non-linear plants. The theory of alpha planes is used for approximating generalized type-2 fuzzy logic in fuzzy controllers. In the defuzzification process, the Karnik and Mendel Algorithm is used. Simulation results with a type-1 fuzzy logic controller (T1FLC), an interval type-2 fuzzy logic controller (IT2FLC) and with a generalized type-2 fuzzy logic controller (GT2FLC) for benchmark plants are presented. The advantage of using generalized type-2 fuzzy logic in fuzzy controllers is verified with four benchmark problems. We considered different levels of noise, number of alpha planes and four types of membership functions in the simulations for comparison and to analyze the approach of generalized type-2 fuzzy logic systems when applied in fuzzy control.


title: Simultaneous measurement of electronic and vibrational dynamics using a few-cycle pulse laser

abstract: Using 6.3fs pulse laser, both spectral change in the electronic absorption spectrum and instantaneous vibrational frequency could be observed by real-time vibrational spectroscopy. Ultrafast geometrical relaxation taking place in 60-100fs could be clarified.


title: A 120pW 50Hz notch filter for low-frequency physiological acquisition

abstract: A CMOS OTA-C notch filter for 50Hz interference was presented in this paper. The OTAs were working in weak inversion region in order to achieve ultra low transconductance and power consumptions. The circuits were designed using SMIC mixed-signal 0.18nm 1P6M process. The post-annotated simulation indicated that an attenuation of 47.2dB for power line interference and a 120pW consumption. The design achieved a dynamic range of 75.8dB and a THD of 0.1%, whilst the input signal was a 1 Hz 20mV pp sine wave.


title: An online mechanism for dynamic virtual cluster provisioning in geo-distributed clouds

abstract: It is common for cloud users to require clusters of inter-connected virtual machines (VMs) in a geo-distributed IaaS cloud, to run their services. Compared to isolated VMs, key challenges on dynamic virtual cluster (VC) provisioning (computation + communication resources) lie in two folds: (1) optimal placement of VCs and inter-VM traffic routing involve NP-hard problems, which are non-trivial to solve offline, not to mention if an online efficient algorithm is sought; (2) an efficient pricing mechanism is missing, which charges a market-driven price for each VC as a whole upon request, while maximizing system efficiency or provider revenue over the entire span. This paper proposes efficient online auction mechanisms to address the above challenges. We first design SWMOA, a novel online algorithm for dynamic VC provisioning and pricing, achieving truthfulness, individual rationality, computation efficiency, and (1 + 2 log μ)-competitiveness in social welfare, where μ is related to the problem size. Next, applying a randomized reduction technique, we convert the social welfare maximizing auction into a revenue maximizing online auction, PRMOA, achieving O(log μ)-competitiveness in provider revenue, as well as truthfulness, individual rationality and computation efficiency. We validate the efficacy of the mechanisms through solid theoretical analysis and trace-driven simulations.


title: Private and public financing--health care reform in eastern and central Europe.

abstract: There are conspicuous limits to what privatization and the free play of market forces can do for health care. In particular, they cannot ensure access, adequacy and affordability for users. Therefore, in moving from a centrally planned economy, governments need to take care to avoid some of the costly mistakes that have been made in western countries.


title: Systematic and effective design of nonlinear feedback controllers via the state-dependent Riccati equation (SDRE) method

abstract: Since the 1990s, state-dependent Riccati equation (SDRE) strategies have emerged as general design methods that provide a systematic and effective means of designing nonlinear controllers, observers and filters. These methods overcome many of the difficulties and shortcomings of existing methodologies, and deliver computationally simple algorithms that have been highly effective in a variety of practical and meaningful applications in very diverse fields of study. These include missiles, aircraft, unmanned aerial vehicles, satellites and spacecraft, ships, autonomous underwater vehicles, automotive systems, biomedical systems, process control, and robotics, along with various benchmark problems, as well as nonlinear systems exhibiting several interesting phenomena such as parasitic effects of friction and backlash, unstable nonminimum-phase dynamics, time-delay, vibration and chaotic behavior. SDRE controllers, in particular, have become very popular within the control community, providing attractive stability, optimality, robustness and computational properties, making real-time implementation in feedback form feasible. However, despite a documented history of SDRE control in the literature, there is a significant lack of theoretical justification for logical choices of the design matrices, which have depended on intuitive rules of thumb and extensive simulation for evaluation and performance. In this paper, the capabilities and design flexibility of SDRE control are emphasized, addressing the issues on systematic selection of the design matrices and going into detail concerning the art of systematically carrying out an effective SDRE design for systems that both do and do not conform to the basic structure and conditions required by the method. Several situations that prevent the direct application of the SDRE technique, such as the presence of control and state constraints, are addressed, demonstrating how these situations can be readily handled using the method. In order to provide a clear understanding of the proposed methods, systematic and effective design tools of SDRE control are illustrated on a single-inverted pendulum nonlinear benchmark problem and a practical application problem of optimally administering chemotherapy in cancer treatment. Lastly, real-time implementation aspects are discussed with relevance to practical applicability.


title: SPIRAL: Code Generation for DSP Transforms

abstract: Fast changing, increasingly complex, and diverse computing platforms pose central problems in scientific computing: How to achieve, with reasonable effort, portable optimal performance? We present SPIRAL, which considers this problem for the performance-critical domain of linear digital signal processing (DSP) transforms. For a specified transform, SPIRAL automatically generates high-performance code that is tuned to the given platform. SPIRAL formulates the tuning as an optimization problem and exploits the domain-specific mathematical structure of transform algorithms to implement a feedback-driven optimizer. Similar to a human expert, for a specified transform, SPIRAL "intelligently" generates and explores algorithmic and implementation choices to find the best match to the computer's microarchitecture. The "intelligence" is provided by search and learning techniques that exploit the structure of the algorithm and implementation space to guide the exploration and optimization. SPIRAL generates high-performance code for a broad set of DSP transforms, including the discrete Fourier transform, other trigonometric transforms, filter transforms, and discrete wavelet transforms. Experimental results show that the code generated by SPIRAL competes with, and sometimes outperforms, the best available human tuned transform library code.


title: Keeping Your API Keys in a Safe

abstract: Cloud API (Application Programming Interface) enables client applications to access services and manage resources hosted in the Cloud. To protect themselves and their customers, Cloud service providers (CSP) often require client authentication for each API call. The authentication usually depends on some kind of secret (or called API key), for example, secret access key, password, or access token. As such, the API key unlocks the door to the treasure inside the Cloud. Hence, protecting these keys is critical. It is a difficult task especially on the client side, such as users' computers or mobile devices. How do CSPs authenticate client applications? What are security risks of managing API keys in common practices? How can we mitigate these risks? This paper focuses on finding answers to these questions. By reviewing popular client authentication methods that CSPs use and using Cloud APIs as software developers, we identified various security risks associated with API keys. To mitigate these risks, we use hardware secure elements for secure key provisioning, storage, and usage. The solution replaces the manual key handling with end-to-end security between CSP and its customers' secure elements. This removes the root causes of the identified risks and enhances API security. It also enhances the usability by eliminating manual key operations and alleviating software developers' worries of working with cryptography.


Watching You Watch Movies: Using Eye Tracking to Inform Cognitive Film Theory


title: Deep Activity Recognition Models with Triaxial Accelerometers

abstract: Despite the widespread installation of accelerometers in almost all mobile phones and wearable devices, activity recognition using accelerometers is still immature due to the poor recognition accuracy of existing recognition methods and the scarcity of labeled training data. We consider the problem of human activity recognition using triaxial accelerometers and deep learning paradigms. This paper shows that deep activity recognition models (a) provide better recognition accuracy of human activities, (b) avoid the expensive design of handcrafted features in existing systems, and (c) utilize the massive unlabeled acceleration samples for unsupervised feature extraction. Moreover, a hybrid approach of deep learning and hidden Markov models (DL-HMM) is presented for sequential activity recognition. This hybrid approach integrates the hierarchical representations of deep activity recognition models with the stochastic modeling of temporal sequences in the hidden Markov models. We show substantial recognition improvement on real world datasets over state-of-the-art methods of human activity recognition using triaxial accelerometers.


title: Multi-source domain adaptation: a causal view

abstract: This paper is concerned with the problem of domain adaptation with multiple sources from a causal point of view. In particular, we use causal models to represent the relationship between the features X and class label Y, and consider possible situations where different modules of the causal model change with the domain. In each situation, we investigate what knowledge is appropriate to transfer and find the optimal target-domain hypothesis. This gives an intuitive interpretation of the assumptions underlying certain previous methods and motivates new ones. We finally focus on the case where Y is the cause for X with changing PY and PX|Y, that is, PY and PX|Y change independently across domains. Under appropriate assumptions, the availability of multiple source domains allows a natural way to reconstruct the conditional distribution on the target domain; we propose to model PX|Y (the process to generate effect X from cause Y) on the target domain as a linear mixture of those on source domains, and estimate all involved parameters by matching the target-domain feature distribution. Experimental results on both synthetic and real-world data verify our theoretical results.


title: A taxonomy of behavior change techniques used in interventions.

abstract: Objective: Without standardized definitions of the techniques included in behavior change interventions, it is difficult to faithfully replicate effective interventions and challenging to identify techniques contributing to effectiveness across interventions. This research aimed to develop and test a theory-linked taxonomy of generally applicable behavior change techniques (BCTs). Design: Twenty-six BCTs were defined. Two psychologists used a 5-page coding manual to independently judge the presence or absence of each technique in published intervention descriptions and in intervention manuals. Results: Three systematic reviews yielded 195 published descriptions. Across 78 reliability tests (i.e., 26 techniques applied to 3 reviews), the average kappa per technique was 0.79, with 93% of judgments being agreements. Interventions were found to vary widely in the range and type of techniques used, even when targeting the same behavior among similar participants. The average agreement for intervention manuals was 85%, and a comparison of BCTs identified in 13 manuals and 13 published articles describing the same interventions generated a technique correspondence rate of 74%, with most mismatches (73%) arising from identification of a technique in the manual but not in the article. Conclusions: These findings demonstrate the feasibility of developing standardized definitions of BCTs included in behavioral interventions and highlight problematic variability in the reporting of intervention content.


Hispanic Limited English-Proficient Students with Disabilities: A Case Study Example.


ELISA: a new tool for the large scale detection of Plum bark necrosis stem pitting-associated virus


title: Randomness evaluation of stream cipher for secure mobile communication

abstract: Information Security over mobile communication networks is important for secure communication. In GSM mobile standard, A5/1 stream cipher is used to provide security over-the-air voice transmissions. It is strong encryption algorithm among the cryptographic algorithms used in GSM. However, it is cryptanalized by a number of attacks. Recent studies show that it has serious security weaknesses in its architecture. In this paper, an enhanced version of A5/1 stream cipher to improve the security of A5/1 scheme is presented. Enhancements are made in clocking mechanism and combining function of the cipher. The proposed scheme is coded in VHDL. NIST Statistical test package is used to evaluate the randomness performance of output bit-streams of two ciphers. The randomness results show that the output bit-stream generated by the proposed stream cipher has better randomness performance.


Neural Network and Its Application in IR


title: The mystique of numbers: belief in quantitative approaches to segmentation and persona development

abstract: Quantitative market research and qualitative user-centered design research have long had an uneasy and complex relationship. A trend toward increasingly complex statistical segmentations and associated personas will once again increase the urgency of addressing paradigm differences to allow the two disciplines to collaborate effectively. We present an instructive case in which qualitative field research helped contribute to abandoning a "state of the art" quantitative user segmentation that was used in an attempt to unify both marketing and user experience planning around a shared model of users. This case exposes risks in quantitative segmentation research, common fallacies in the evolving practice of segmentation and use of personas, and the dangers of excessive deference to quantitative research generally.


title: Security and Usability: Analysis and Evaluation

abstract: The differences between the fields of Human-Computer Interaction and Security (HCISec) and Human-Computer Interaction (HCI) have not been investigated very closely. Many HCI methods and procedures have been adopted by HCISec researchers, however the extent to which these apply to the field of HCISec is arguable given the fine balance between improving the ease of use of a secure system and potentially weakening its security. That is to say that the techniques prevalent in HCI are aimed at improving users' effectiveness, efficiency or satisfaction, but they do not take into account the potential threats and vulnerabilities that they can introduce. To address this problem, we propose a security and usability threat model detailing the different factors that are pertinent to the security and usability of secure systems, together with a process for assessing these.


Architectural Acoustics and Signal Processing in Acoustics : Multiple-Microphone Measurements and Analysis in Room Acoustics I


title: Differential entropy feature for EEG-based vigilance estimation

abstract: This paper proposes a novel feature called differential entropy for EEG-based vigilance estimation. By mathematical derivation, we find an interesting relationship between the proposed differential entropy and the existing logarithm energy spectrum. We present a physical interpretation of the logarithm energy spectrum which is widely used in EEG signal analysis. To evaluate the performance of the proposed differential entropy feature for vigilance estimation, we compare it with four existing features on an EEG data set of twenty-three subjects. All of the features are projected to the same dimension by principal component analysis algorithm. Experiment results show that differential entropy is the most accurate and stable EEG feature to reflect the vigilance changes.


Determinants of Behavioral Intention to Mobile Banking Case From Yemen


title: A file is not a file: understanding the I/O behavior of Apple desktop applications

abstract: We analyze the I/O behavior of iBench, a new collection of productivity and multimedia application workloads. Our analysis reveals a number of differences between iBench and typical file-system workload studies, including the complex organization of modern files, the lack of pure sequential access, the influence of underlying frameworks on I/O patterns, the widespread use of file synchronization and atomic operations, and the prevalence of threads. Our results have strong ramifications for the design of next generation local and cloud-based storage systems.


title: Fast local image inpainting based on the Allen-Cahn model

abstract: In this paper, we propose a fast local image inpainting algorithm based on the Allen-Cahn model. The proposed algorithm is applied only on the inpainting domain and has two features. The first feature is that the pixel values in the inpainting domain are obtained by curvature-driven diffusions and utilizing the image information from the outside of the inpainting region. The second feature is that the pixel values outside of the inpainting region are the same as those in the original input image since we do not compute the outside of the inpainting region. Thus the proposed method is computationally efficient. We split the governing equation into one linear equation and one nonlinear equation by using an operator splitting technique. The linear equation is discretized by using a fully implicit scheme and the nonlinear equation is solved analytically. We prove the unconditional stability of the proposed scheme. To demonstrate the robustness and accuracy of the proposed method, various numerical results on real and synthetic images are presented. We present a fast and efficient algorithm for image inpainting.The proposed model is based on the local Allen-Cahn equation.The proposed scheme is unconditionally stable with respect to time step size.


title: ispc: A SPMD compiler for high-performance CPU programming

abstract: SIMD parallelism has become an increasingly important mechanism for delivering performance in modern CPUs, due its power efficiency and relatively low cost in die area compared to other forms of parallelism. Unfortunately, languages and compilers for CPUs have not kept up with the hardware's capabilities. Existing CPU parallel programming models focus primarily on multi-core parallelism, neglecting the substantial computational capabilities that are available in CPU SIMD vector units. GPU-oriented languages like OpenCL support SIMD but lack capabilities needed to achieve maximum efficiency on CPUs and suffer from GPU-driven constraints that impair ease of use on CPUs. We have developed a compiler, the Intel R® SPMD Program Compiler (ispc), that delivers very high performance on CPUs thanks to effective use of both multiple processor cores and SIMD vector units. ispc draws from GPU programming languages, which have shown that for many applications the easiest way to program SIMD units is to use a single-program, multiple-data (SPMD) model, with each instance of the program mapped to one SIMD lane. We discuss language features that make ispc easy to adopt and use productively with existing software systems and show that ispc delivers up to 35x speedups on a 4-core system and up to 240× speedups on a 40-core system for complex workloads (compared to serial C++ code).


title: Face recognition with learning-based descriptor

abstract: We present a novel approach to address the representation issue and the matching issue in face recognition (verification). Firstly, our approach encodes the micro-structures of the face by a new learning-based encoding method. Unlike many previous manually designed encoding methods (e.g., LBP or SIFT), we use unsupervised learning techniques to learn an encoder from the training examples, which can automatically achieve very good tradeoff between discriminative power and invariance. Then we apply PCA to get a compact face descriptor. We find that a simple normalization mechanism after PCA can further improve the discriminative ability of the descriptor. The resulting face representation, learning-based (LE) descriptor, is compact, highly discriminative, and easy-to-extract. To handle the large pose variation in real-life scenarios, we propose a pose-adaptive matching method that uses pose-specific classifiers to deal with different pose combinations (e.g., frontal v.s. frontal, frontal v.s. left) of the matching face pair. Our approach is comparable with the state-of-the-art methods on the Labeled Face in Wild (LFW) benchmark (we achieved 84.45% recognition rate), while maintaining excellent compactness, simplicity, and generalization ability across different datasets.


title: Design of Real-time Semantic Segmentation Decoder for Automated Driving

abstract: Semantic segmentation remains a computationally intensive algorithm for embedded deployment even with the rapid growth of computation power. Thus efficient network design is a critical aspect especially for applications like automated driving which requires real-time performance. Recently, there has been a lot of research on designing efficient encoders that are mostly task agnostic. Unlike image classification and bounding box object detection tasks, decoders are computationally expensive as well for semantic segmentation task. In this work, we focus on efficient design of the segmentation decoder and assume that an efficient encoder is already designed to provide shared features for a multi-task learning system. We design a novel efficient non-bottleneck layer and a family of decoders which fit into a small run-time budget using VGG10 as efficient encoder. We demonstrate in our dataset that experimentation with various design choices led to an improvement of 10\% from a baseline performance.


title: Photophobia and cortical visual impairment.

abstract: Photophobia, or intolerance of light, is not completely understood as a symptom. It has been divided into ocular and central types. This study shows that persistent, usually mild, photophobia occurs in about one-third of children with cortical visual impairment (CVI). When the CVI is congenital the photophobia is present from birth, and when it is acquired the sensitivity to light appears immediately after the brain insult. The intensity of photophobia tends to diminish with time and occasionally it may even disappear. The pathophysiology is unclear, as in all other neurological disorders associated with photophobia.


title: A smooth-walled spline-profile horn as an alternative to the corrugated horn for wide band millimeter-wave applications

abstract: At millimeter-wave frequencies, corrugated horns can be difficult and expensive to manufacture. As an alternative we present here the results of a theoretical and measurement study of a smooth-walled spline-profile horn for specific application in the 80-120 GHz band. While about 50% longer than its corrugated counterpart, the smooth-walled horn is shown to give improved performance across the band as well as being much easier to manufacture.


title: Connectivity maximization for narrowband IoT systems with NOMA

abstract: Narrowband Internet of Things (NB-IoT) is a recently standardized technology to support machine-type communications (MTC) in Long Term Evolution-Advanced (LTE-A) Pro networks. NB-IoT can enable energy-efficient communication with extended coverage on a narrow bandwidth of 180 kHz for low-cost MTC devices (MTCDs). The main challenge of supporting MTC in LTE-A Pro networks is to provide connectivity to a massive number of MTCDs. To overcome this challenge, in this paper, we propose a power-domain uplink non-orthogonal multiple access (NOMA) scheme for NB-IoT systems. By allowing multiple MTCDs to share the same sub-carrier, NOMA can provide connectivity to more MTCDs than orthogonal multiple access (OMA). We formulate a joint sub-carrier and transmission power allocation problem to maximize the number of MTCDs satisfying the quality of service (QoS) and transmission power requirements. We decompose the problem into two sub-problems and propose algorithms to solve them. Simulation results show that our proposed NOMA scheme can significantly increase the number of successfully connected MTCDs in NB-IoT systems compared to OMA.


Neural Attention for Learning to Rank Questions in Community Question Answering


Two Useful Measures Of Word Order Complexity


title: Adaptive neuron apoptosis for accelerating deep learning on large scale systems

abstract: We present novel techniques to accelerate the convergence of Deep Learning algorithms by conducting low overhead removal of redundant neurons — apoptosis of neurons — which do not contribute to model learning, during the training phase itself. We provide in-depth theoretical underpinnings of our heuristics (bounding accuracy loss and handling apoptosis of several neuron types), and present the methods to conduct adaptive neuron apoptosis. Specifically, we are able to improve the training time for several datasets by 2–3x, while reducing the number of parameters by up to 30× (4–5× on average) on datasets such as ImageNet classification. For the Higgs Boson dataset, our implementation improves the accuracy (measured by Area Under Curve (AUC)) for classification from 0.88/1 to 0.94/1, while reducing the number of parameters by 3x in comparison to existing literature. The proposed methods achieve a 2.44x speedup in comparison to the default (no apoptosis) algorithm.


title: The gut microbiota in IBD

abstract: IBD-ulcerative colitis and Crohn's disease-is emerging as a worldwide epidemic. An association between the increased incidence of IBD and environmental factors linked to socioeconomic development has been persistently detected in different parts of the world. The lifestyle in developed countries might impair the natural patterns of microbial colonization of the human gut. The interaction of microbes with mucosal immune compartments in the gut seems to have a major role in priming and regulating immunity. In IBD, mucosal lesions are generated by an excessive or dysregulated immune response against commensal microbes in the gut. In individuals with a genetic susceptibility to IBD, abnormal microbial colonization of the gastrointestinal tract might be the origin of such dysregulation. Developments in gene-sequencing technologies, as well as increased availability of powerful bioinformatic tools, have enabled novel insights into the microbial composition of the human gut microbiota and the effect of microbial communities on human physiology and disease. Studies that used these technologies indicate that dysbiosis (that is, abnormal microbiota composition) and decreased complexity of the gut microbial ecosystem are common features in patients with Crohn's disease or ulcerative colitis. Whether such changes are a cause or a consequence of the disease remains to be elucidated.


title: Analysis of a multi-phase interleaved bidirectional DC/DC power converter with coupled inductor

abstract: This paper proposes a novel multi phase interleaved bi-directional dc/dc power converter to interface two dc sources of different voltage levels which applied for energy storage systems in high power level. The converter is proposed to reduce the input and output ripples, as well as, the size of passive components once the voltage and current ripple in the filters are four times higher than the switching frequency. A coupled inductor is employed to equal current splitting between the switch branch of the converter and obtain higher power density, and to reduce conduction loss, applying a multi phase interleaving technique. The operation and theoretical analysis of the converter are presented with different modes of power transfer. Furthermore, the key operational principle and simulation results are presented showing the converter operation in both directions of the power flow.


title: A Profust Reliability Based Approach to Prognostics and Health Management

abstract: Prognostics and health management (PHM) technology has been widely accepted, and employed to evaluate system performance. In practice, system performance often varies continually rather than just being functional or failed, especially for a complex system. Profust reliability theory extends the traditional binary state space {0, 1} into a fuzzy state space [0, 1], which is therefore suitable to characterize a gradual physical degradation. Moreover, in profust reliability theory, fuzzy state transitions can also help to describe the health evolution of a component or a system. Accordingly, this paper proposes a profust reliability based PHM approach, where the profust reliability is employed as a health indicator to evaluate the real-time system performance. On the basis of the health estimation, the system remaining useful life (RUL) is further defined, and the mean RUL estimate is predicted by using a degraded Markov model. Finally, an experimental case study of Li-ion batteries is presented to demonstrate the effectiveness of the proposed approach.


Infima of hyperspace topologies


title: Other Topics You May Also Agree or Disagree: Modeling Inter-Topic Preferences using Tweets and Matrix Factorization

abstract: We present in this paper our approach for modeling inter-topic preferences of Twitter users: for example, those who agree with the Trans-Pacific Partnership (TPP) also agree with free trade. This kind of knowledge is useful not only for stance detection across multiple topics but also for various real-world applications including public opinion surveys, electoral predictions, electoral campaigns, and online debates. In order to extract users' preferences on Twitter, we design linguistic patterns in which people agree and disagree about specific topics (e.g., "A is completely wrong"). By applying these linguistic patterns to a collection of tweets, we extract statements agreeing and disagreeing with various topics. Inspired by previous work on item recommendation, we formalize the task of modeling inter-topic preferences as matrix factorization: representing users' preferences as a user-topic matrix and mapping both users and topics onto a latent feature space that abstracts the preferences. Our experimental results demonstrate both that our proposed approach is useful in predicting missing preferences of users and that the latent vector representations of topics successfully encode inter-topic preferences.


SERS-based detection of haptoglobin in ovarian cyst fluid as a point-of-care diagnostic assay for epithelial ovarian cancer


title: Deep learning for financial sentiment analysis on finance news providers

abstract: Investors have always been interested in stock price forecasting. Since the development of electronic media, hundreds pieces of financial news are released on different media every day. Numerous studies have attempted to examine whether the stock price forecasting through text mining technology and machine learning could lead to abnormal returns. However, few of them involved the discussion on whether using different media could affect forecasting results. Financial sentiment analysis is an important research area of financial technology (FinTech). This research focuses on investigating the influence of using different financial resources to investment and how to improve the accuracy of forecasting through deep learning. The experimental result shows various financial resources have significantly different effects to investors and their investments, while the accuracy of news categorization could be improved through deep learning.


title: On the design, development and motion control of a HUmanoid Robotic Leg via pneumatic artificial muscles

abstract: In this article, the design and implementation of a HUmanoid Robotic Leg (HURL) is presented. The motion of the HURL is achieved via pneumatic muscle actuators, a pneumatic form of actuation possessing crucial attributes for the implementation of a biomimetic design that mimics the motion characteristics of a human ankle. The HURL's properties are described in detail, while its 2-DoF motion capabilities (dorsiflexion - plantar flexion, eversion - inversion) are experimentally evaluated via an advanced nonlinear PID-based control algorithm.


Survey on Multiclass Classification Methods


An SVD-free Pareto curve approach to rank minimization


title: Wideband and Dual-Band Design of a Printed Dipole Antenna

abstract: A wideband and dual-band dipole antenna with an integrated balun feed is given. First, the antenna structure is optimized and a 41.5% bandwidth is obtained where voltage standing wave ratio (VSWR) is less than 2. In order to further get a larger bandwidth and dual-band capabilities of this antenna, some rectangle apertures are etched onto the antenna surface, and a better dipole antenna is fabricated. Through investigating rectangle aperture with different sizes, a 47.8% bandwidth is obtained at L-band, and a 15.1% bandwidth comes true at S-band. Agreement between calculations and measurements is very good. The result indicates that the novel design not only may obtain better wideband characteristic but also can let the dipole antenna work at L- and S-bands.


title: Lumbar drainage for subarachnoid hemorrhage: technical considerations and safety analysis.

abstract: INTRODUCTION ::: Recent and on going clinical research trials may indicate that the incidence of vasospasm is less in patients treated with lumbar drains following SAH than with patients treated with external ventricular drainage (EVD) alone. These studies have sparked interest in the more aggressive use of LDs in patients with aneurysmal SAH but some clinicians have been concerned about safety. ::: ::: ::: METHODS ::: This paper is a review of 25 patients with aneurysmal SAH cared for over a 2.2 year period at the University of Mississippi Medical Center Neurosciences Critical Care Unit that were managed with a LD. In general the LD was used subsequent to an EVD, but in some cases alone, in order to improve evacuation of blood from the cisterns around the base of the brain. ::: ::: ::: RESULTS ::: This retrospective cohort review demonstrates that lumbar drainage is safe in the setting of aneurysmal SAH. Of the 25 patients treated with a lumbar drain only one suffered a reversible neurological event. No other complications occurred. ::: ::: ::: CONCLUSIONS ::: LDs are safe when used in the setting of aneurysmal SAH if appropriate patient selection and good technique are employed.


title: Robust Object Co-Segmentation Using Background Prior

abstract: Given a set of images that contain objects from a common category, object co-segmentation aims at automatically discovering and segmenting such common objects from each image. During the past few years, object co-segmentation has received great attention in the computer vision community. However, the existing approaches are usually designed with misleading assumptions, unscalable priors, or subjective computational models, which do not have sufficient robustness for dealing with complex and unconstrained real-world image contents. This paper proposes a novel two-stage co-segmentation framework, mainly for addressing the robustness issue. In the proposed framework, we first introduce the concept of union background and use it to improve the robustness for suppressing the image backgrounds contained by the given image groups. Then, we also weaken the requirement for the strong prior knowledge by using the background prior instead. This can improve the robustness when scaling up for the unconstrained image contents. Based on the weak background prior, we propose a novel MR-SGS model, i.e., manifold ranking with the self-learned graph structure, which can infer suitable graph structures in a data-driven manner rather than building the fixed graph structure relying on the subjective design. Such capacity is critical for further improving the robustness in inferring the foreground/background probability of each image pixel. Comprehensive experiments and comparisons with other state-of-the-art approaches can demonstrate the effectiveness of the proposed work.


title: Toward the use of an upper ontology for U.S. government and U.S. military domains: An evaluation

abstract: Abstract : Momentum is gaining to develop a Semantic Web to allow people and machines to share the meaning (semantics) of data and ultimately of applications. Key to the vision of a Semantic Web is the ability to capture data and application semantics in ontologies and map these ontologies together via related concepts. One approach for mapping disparate ontologies is to use a standard upper ontology. In determining how Semantic Web technologies might be applied to United States (U.S.) Government domains, the authors consider whether the use of standard upper ontologies makes sense in these environments. This paper attempts to examine current candidate standard upper ontologies and assess their applicability for a U.S. Government or U.S. Military domain. They evaluate the state of the art and applicability of upper ontologies through the lens of potential application in these domains. The evaluation includes consideration of the ontology purpose, ontological content decisions, licensing restrictions, structural differences, and maturity. The report concludes with some recommendations and predictions.


title: LOOP: Logic-Oriented Opaque Predicate Detection in Obfuscated Binary Code

abstract: Opaque predicates have been widely used to insert superfluous branches for control flow obfuscation. Opaque predicates can be seamlessly applied together with other obfuscation methods such as junk code to turn reverse engineering attempts into arduous work. Previous efforts in detecting opaque predicates are far from mature. They are either ad hoc, designed for a specific problem, or have a considerably high error rate. This paper introduces LOOP, a Logic Oriented Opaque Predicate detection tool for obfuscated binary code. Being different from previous work, we do not rely on any heuristics; instead we construct general logical formulas, which represent the intrinsic characteristics of opaque predicates, by symbolic execution along a trace. We then solve these formulas with a constraint solver. The result accurately answers whether the predicate under examination is opaque or not. In addition, LOOP is obfuscation resilient and able to detect previously unknown opaque predicates. We have developed a prototype of LOOP and evaluated it with a range of common utilities and obfuscated malicious programs. Our experimental results demonstrate the efficacy and generality of LOOP. By integrating LOOP with code normalization for matching metamorphic malware variants, we show that LOOP is an appealing complement to existing malware defenses.


title: DRIP: A framework for purifying trojaned kernel drivers

abstract: Kernel drivers are usually provided in the form of loadable kernel extensions, which can be loaded/unloaded dynamically at runtime and execute with the same privilege as the core operating system kernel. The unrestricted security access from the drivers to the kernel is nevertheless a double-edged sword that makes them susceptible targets of trojan attacks. Given a benign driver, it is now easy to implant malicious logic with existing hacking tools. Once implanted, such malicious logic is difficult to detect. In this paper we propose DRIP, a framework for detecting and eliminating malicious logic embedded in a kernel driver through iteratively eliminating unnecessary kernel API invocations from the driver. When provided with the binary of a trojaned driver, DRIP generates a purified driver with benign functionalities preserved and malicious ones eliminated. Our evaluation shows that DRIP successfully eliminates malicious effects of trojaned drivers in the system, with the purified drivers maintaining or even improving their performance over the trojaned drivers.


title: How Diagnostic Tests Affect Prevention: a Cost-Benefit Analysis

abstract: The purpose of this paper is to offers insight for evaluating research and development in diagnostic tests. We show that a rational policy maker perfectly informed about health risks may choose to reduce investment in prevention when efficient diagnostic tests become available. We show that prevention and diagnostic tests are substitutes rather than complements. As a result the regular improvements in diagnostic technology that are observed can justify a lower investment on prevention at any given unitary price for this activity. The analysis is a useful tool for the allocation of funding between diagnostic and preventive medicine.


title: A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

abstract: This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot’s joint angles.


title: Zinc in wound healing: Theoretical, experimental, and clinical aspects

abstract: Zinc is an essential trace element in the human body and its importance in health and disease is appreciated. It serves as a cofactor in numerous transcription factors and enzyme systems including zinc-dependent matrix metalloproteinases that augment autodebridement and keratinocyte migration during wound repair. Zinc confers resistance to epithelial apoptosis through cytoprotection against reactive oxygen species and bacterial toxins possibly through antioxidant activity of the cysteine-rich metallothioneins. Zinc deficiency of hereditary or dietary cause can lead to pathological changes and delayed wound healing. Oral zinc supplementation may be beneficial in treating zinc-deficient leg ulcer patients, but its therapeutic place in surgical patients needs further clarification. Topical administration of zinc appears to be superior to oral therapy due to its action in reducing superinfections and necrotic material via enhanced local defense systems and collagenolytic activity, and the sustained release of zinc ions that stimulates epithelialization of wounds in normozincemic individuals. Zinc oxide in paste bandages (Unna boot) protects and soothes inflamed peri-ulcer skin. Zinc is transported through the skin from these formulations, although the systemic effects seem insignificant. We present here the first comprehensive account of zinc in wound management in relation to current concepts of wound bed preparation and the wound-healing cascade. This review article suggests that topical zinc therapy is underappreciated even though clinical evidence emphasizes its importance in autodebridement, anti-infective action, and promotion of epithelialization.


title: Visual saliency does not account for eye movements during visual search in real-world scenes

abstract: Publisher Summary This chapter presents testing of the hypothesis that fixation locations during scene viewing are primarily determined by visual salience. Eye movements were collected from participants who viewed photographs of real-world scenes during an active search task. Visual salience as determined by a popular computational model did not predict region-to-region saccades or saccade sequences any better than did a random model. Consistent with other reports in the literature, intensity, contrast, and edge density differed at fixated scene regions compared to regions that were not fixated, but these fixated regions also differ in rated semantic informativeness. Therefore, any observed correlations between fixation locations and image statistics cannot be unambiguously attributed to these image statistics. The chapter concludes that visual saliency does not account for eye movements during active search. The existing evidence is consistent with the hypothesis that cognitive factors play the dominant role in active gaze control.


title: The nature of human altruism

abstract: Some of the most fundamental questions concerning our evolutionary origins, our social relations, and the organization of society are centred around issues of altruism and selfishness. Experimental evidence indicates that human altruism is a powerful force and is unique in the animal world. However, there is much individual heterogeneity and the interaction between altruists and selfish individuals is vital to human cooperation. Depending on the environment, a minority of altruists can force a majority of selfish individuals to cooperate or, conversely, a few egoists can induce a large number of altruists to defect. Current gene-based evolutionary theories cannot explain important patterns of human altruism, pointing towards the importance of both theories of cultural evolution as well as gene–culture co-evolution.


title: The Efficacy of Treatment for Children With Developmental Speech and Language Delay/Disorder

abstract: A meta-analysis was carried out of interventions for children with primary developmental speech and language delays/disorders. The data were categorized depending on the control group used in the s...


title: A systematic review of the mental health outcomes associated with Facebook use

abstract: The effect of social networking site (SNS) use on mental health has attracted growing scholarly attention, yet the nature of this relationship remains contentious. A systematic review was conducted to examine mental health outcomes associated specifically with the SNS, Facebook. A total of 65 articles met the inclusion criteria and were included in the review. Facebook use was associated with six key mental health domains: Facebook addiction, anxiety, depression, body image and disordered eating, drinking cognitions and alcohol use, and other mental health problems, albeit the strength and validity of these relationships varied. The review highlighted the multidimensional nature of Facebook use, and the subsequent importance of assessing specific SNS platforms or similar functions over platforms. The application of meta-analytic techniques is required to quantify the nature and direction of the relationships between Facebook use and mental health outcomes, as well as to identify pertinent moderators and individual difference factors. Facebook use was associated with six outcomes: addiction, anxiety, depression, body image, alcohol use, and other problems.The strength and validity of these relationships varied.Facebook use is a multidimensional construct; important to assess specific SNS platforms or similar functions over platforms.Meta-analytic techniques will help to quantify the nature and direction of Facebook use and mental health outcomes.


title: An architecture for elastic resource allocation in Fog Computing

abstract: Cloud computing is an amazing technology that uses an internet and central remote server to maintain data in different applications .This technology allows delivery of computing resources over the internet. Cloud offers scalability, availability, elasticity and multitenancy.The most fundamental issue in cloud computing is virtualization .Virtualization is a technique which allows to run multiple virtual machines on a single hardware. In cloud computing, virtualization is used to create a virtual version of a device or resources such as server, storage devices, network or an operating system where the resources are divided into one or more execution environment. Its main motive is to optimize the usage of resources, maximizing throughput, minimizing response time. Beyond this cloud computing has number of limitations. To overcome the limitations of cloud computing there is a new approach as fog computing. In this paper we are submitting here a survey on virtualization methods and its techniques; identifying various issues, possible threats and attacks with virtualization and


title: Monoclonal antibodies: versatile platforms for cancer immunotherapy

abstract: Antibodies are important therapeutic agents for cancer. Recently, it has become clear that antibodies possess several clinically relevant mechanisms of action. Many clinically useful antibodies can manipulate tumour-related signalling. In addition, antibodies exhibit various immunomodulatory properties and, by directly activating or inhibiting molecules of the immune system, antibodies can promote the induction of antitumour immune responses. These immunomodulatory properties can form the basis for new cancer treatment strategies.


title: Speech recognition of Malayalam numbers

abstract: Digit speech recognition is important in many applications such as automatic data entry, PIN entry, voice dialing telephone, automated banking system, etc. This paper presents speaker independent speech recognition system for Malayalam digits. The system employs Mel frequency cepstrum coefficient (MFCC) as feature for signal processing and Hidden Markov model (HMM) for recognition. The system is trained with 21 male and female voices in the age group of 20 to 40 years and there was 98.5% word recognition accuracy (94.8% sentence recognition accuracy) on a test set of continuous digit recognition task.


title: PERCH: Perception via search for multi-object recognition and localization

abstract: In many robotic domains such as flexible automated manufacturing or personal assistance, a fundamental perception task is that of identifying and localizing objects whose 3D models are known. Canonical approaches to this problem include discriminative methods that find correspondences between feature descriptors computed over the model and observed data. While these methods have been employed successfully, they can be unreliable when the feature descriptors fail to capture variations in observed data; a classic cause being occlusion. As a step towards deliberative reasoning, we present PERCH: PErception via SeaRCH, an algorithm that seeks to find the best explanation of the observed sensor data by hypothesizing possible scenes in a generative fashion. Our contributions are: i) formulating the multi-object recognition and localization task as an optimization problem over the space of hypothesized scenes, ii) exploiting structure in the optimization to cast it as a combinatorial search problem on what we call the Monotone Scene Generation Tree, and iii) leveraging parallelization and recent advances in multi-heuristic search in making combinatorial search tractable. We prove that our system can guaranteedly produce the best explanation of the scene under the chosen cost function, and validate our claims on real world RGB-D test data. Our experimental results show that we can identify and localize objects under heavy occlusion--cases where state-of-the-art methods struggle.


title: Wideband Circularly Polarized 2 × 2 Antenna Array With Multibeam Steerable Capability

abstract: A circularly polarized (CP) switched-beam antenna array with the capability of generating nine steerable beams in the hemispherical space is presented in this letter. A single radiator is first constructed with two triangular-shaped dipoles crossing through printed rings, and four parasitic elements are introduced to enhance the CP operation bandwidth. For the beam steering operation, its array antennas are implemented in a $2 \times 2$ arrangement and fed by a four-input port feeding network composed of four double-box branch-line couplers. With a suitable feed excitation mechanism, the proposed antenna can generate CP axial beams in nine directions. The antenna is fabricated with an overall size of $2{{\rm{\lambda }}_{\rm{o}}} \times 2{{\rm{\lambda }}_{\rm{o}}} \times 0.2{{\rm{\lambda }}_{\rm{o}}}$ (at 6 GHz), and the measured results demonstrate that the proposed design provided very wide −10-dB impedance and 3-dB axial-ratio bandwidths of 48.6% and 46.2%, respectively. Additionally, stable radiation patterns with a gain of approximately 11.5 dBic and radiation efficiency of greater than 90% were observed for every beam direction.


title: Value-function reinforcement learning in Markov games

abstract: Markov games are a model of multiagent environments that are convenient for studying multiagent reinforcement learning. This paper describes a set of reinforcement-learning algorithms based on estimating value functions and presents convergence theorems for these algorithms. The main contribution of this paper is that it presents the convergence theorems in a way that makes it easy to reason about the behavior of simultaneous learners in a shared environment.


title: The Angry Brain: Neural Correlates of Anger, Angry Rumination, and Aggressive Personality

abstract: Very little is known about the neural circuitry guiding anger, angry rumination, and aggressive personality. In the present fMRI experiment, participants were insulted and induced to ruminate. Activity in the dorsal anterior cingulate cortex was positively related to self-reported feelings of anger and individual differences in general aggression. Activity in the medial prefrontal cortex was related to self-reported rumination and individual differences in displaced aggression. Increased activation in the hippocampus, insula, and cingulate cortex following the provocation predicted subsequent self-reported rumination. These findings increase our understanding of the neural processes associated with the risk for aggressive behavior by specifying neural regions that mediate the subjective experience of anger and angry rumination as well as the neural pathways linked to different types of aggressive behavior.


title: Mental Health Smartphone Apps: Review and Evidence-Based Recommendations for Future Developments

abstract: BACKGROUND ::: The number of mental health apps (MHapps) developed and now available to smartphone users has increased in recent years. MHapps and other technology-based solutions have the potential to play an important part in the future of mental health care; however, there is no single guide for the development of evidence-based MHapps. Many currently available MHapps lack features that would greatly improve their functionality, or include features that are not optimized. Furthermore, MHapp developers rarely conduct or publish trial-based experimental validation of their apps. Indeed, a previous systematic review revealed a complete lack of trial-based evidence for many of the hundreds of MHapps available. ::: ::: ::: OBJECTIVE ::: To guide future MHapp development, a set of clear, practical, evidence-based recommendations is presented for MHapp developers to create better, more rigorous apps. ::: ::: ::: METHODS ::: A literature review was conducted, scrutinizing research across diverse fields, including mental health interventions, preventative health, mobile health, and mobile app design. ::: ::: ::: RESULTS ::: Sixteen recommendations were formulated. Evidence for each recommendation is discussed, and guidance on how these recommendations might be integrated into the overall design of an MHapp is offered. Each recommendation is rated on the basis of the strength of associated evidence. It is important to design an MHapp using a behavioral plan and interactive framework that encourages the user to engage with the app; thus, it may not be possible to incorporate all 16 recommendations into a single MHapp. ::: ::: ::: CONCLUSIONS ::: Randomized controlled trials are required to validate future MHapps and the principles upon which they are designed, and to further investigate the recommendations presented in this review. Effective MHapps are required to help prevent mental health problems and to ease the burden on health systems.


title: A DWT-entropy-ANN based architecture for epilepsy diagnosis using EEG signals

abstract: Electroencephalogram (EEG) is one the most common tools for epilepsy diagnosis and analysis. Currently, epilepsy diagnosis is still mainly performed by a neurologist through manual or visual inspection of EEG signals. In this article, we develop a computer aided diagnosis (CAD) for epilepsy based on discrete wavelet transform (DWT), Shannon entropy and feed-forward neural network (FFNN). DWT decompose EEG signals into several frequency sub-bands such as delta, theta, alpha, beta and gamma. Shannon entropy extract the EEG features from each these frequency sub-bands. Finally, FFNN classifies the corresponding EEG signals into “normal” or “epileptic” class based on the extracted features. Our experimental results using publicly available University of Bonn EEG dataset show perfect accuracy (100%).


title: Changes in brain activity related to eating chocolate: From pleasure to aversion

abstract: We performed successive H215O-PET scans on volunteers as they ate chocolate to beyond satiety. Thus, the sensory stimulus and act (eating) were held constant while the reward value of the chocolate and motivation of the subject to eat were manipulated by feeding. Non-specific effects of satiety (such as feelings of fullness and autonomic changes) were also present and probably contributed to the modulation of brain activity. After eating each piece of chocolate, subjects gave ratings of how pleasant/unpleasant the chocolate was and of how much they did or did not want another piece of chocolate. Regional cerebral blood flow was then regressed against subjects' ratings. Different groups of structures were recruited selectively depending on whether subjects were eating chocolate when they were highly motivated to eat and rated the chocolate as very pleasant [subcallosal region, caudomedial orbitofrontal cortex (OFC), insula/operculum, striatum and midbrain] or whether they ate chocolate despite being satiated (parahippocampal gyrus, caudolateral OFC and prefrontal regions). As predicted, modulation was observed in cortical chemosensory areas, including the insula and caudomedial and caudolateral OFC, suggesting that the reward value of food is represented here. Of particular interest, the medial and lateral caudal OFC showed opposite patterns of activity. This pattern of activity indicates that there may be a functional segregation of the neural representation of reward and punishment within this region. The only brain region that was active during both positive and negative compared with neutral conditions was the posterior cingulate cortex. Therefore, these results support the hypothesis that there are two separate motivational systems: one orchestrating approach and another avoidance behaviours.


title: Neural Network Quine

abstract: Self-replication is a key aspect of biological life that has been largely overlooked in Artificial Intelligence systems. Here we describe how to build and train self-replicating neural networks. Th...


title: Evolutionary Robotics: What, Why, and Where to

abstract: Evolutionary robotics applies the selection, variation, and heredity principles of natural evolution to the design of robots with embodied intelligence. It can be considered as a subfield of robotics that aims to create more robust and adaptive robots. A pivotal feature of the evolutionary approach is that it considers the whole robot at once, and enables the exploitation of robot features in a holistic manner. Evolutionary robotics can also be seen as an innovative approach to the study of evolution based on a new kind of experimentalism. The use of robots as a substrate can help address questions that are difficult, if not impossible, to investigate through computer simulations or biological studies. In this paper we consider the main achievements of evolutionary robotics, focusing particularly on its contributions to both engineering and biology. We briefly elaborate on methodological issues, review some of the most interesting findings, and discuss important open issues and promising avenues for future work.


title: Understanding technology choices and values through social class

abstract: This ethnographic study of 22 diverse families in the San Francisco Bay Area provides a holistic account of parents' attitudes about their children's use of technology. We found that parents from different socioeconomic classes have different values and practices around technology use, and that those values and practices reflect structural differences in their everyday lives. Calling attention to class differences in technology use challenges the prevailing practice in human-computer interaction of designing for those similar to oneself, which often privileges middle-class values and practices. By discussing the differences between these two groups and the advantages of researching both, this research highlights the benefits of explicitly engaging with socioeconomic status as a category of analysis in design.


title: Integrating Configuration Management with Model-driven Cloud Management based on TOSCA

abstract: The paradigm of Cloud computing introduces new approaches to manage IT services going beyond concepts originating in traditional IT service management. The main goal is to automate the whole management of services to reduce costs and to make management tasks less error-prone. Two different service management paradigms are used in practice: configuration management and model-driven Cloud management. The latter one aims to be a holistic management approach for services in the Cloud. However, both management paradigms are originating in different backgrounds, thus model-driven Cloud management does not cover all aspects of configuration management that are key for Cloud services. This paper presents approaches for integrating configuration management with model-driven Cloud management and how they can be realized based on the OASIS Topology and Orchestration Specification for Cloud Applications and Chef, a popular configuration management tool. These approaches enable the creation of holistic and highly portable service models.


title: A Novel Weight-Shared Multi-Stage Network Architecture of CNNs for Scale Invariance

abstract: Convolutional neural networks (CNNs) have demonstrated remarkable results in image classification tasks for benchmark and practical uses. The CNNs with deeper architectures have achieved higher performances recently thanks to their robustness to parallel shift of objects in images aw well as their numerous parameters and resulting high expression ability. However, the CNNs have a limited robustness to other geometric transformations such as scaling and rotation. This problem is considered to limit performance improvement of the deep CNNs but there is no established solution. This study focuses on scale transformation and proposes a novel network architecture called weight-shared multi-stage network (WSMS-Net), consisting of multiple stages of CNNs. The WSMS-Net is easily combined with existing deep CNNs, such as ResNet and DenseNet, and enables them to acquire a robustness to scaling of objects. The experimental results demonstrate that existing deep CNNs combined with the proposed WSMS-Net achieve higher accuracy for image classification tasks only with a little increase in the number of parameters.


title: Recovery and mental health: a review of the British Literature

abstract: Recovery is not a new concept within mental health, although in recent times, it has come to the forefront of the policy agenda. However, there is no universal definition of recovery, and it is a contested concept. The aim of this study was to examine the British literature relating to recovery in mental health. Three contributing groups are identified: service users, health care providers and policy makers. A review of the literature was conducted by accessing all relevant published texts. A search was conducted using these terms: ‘recovery’, ‘schizophrenia’, ‘psychosis’, ‘mental illness’ and ‘mental health’. Over 170 papers were reviewed. A thematic analysis was conducted. Six main themes emerged, which were examined from the perspective of the stakeholder groups. The dominant themes were identity, the service provision agenda, the social domain, power and control, hope and optimism, risk and responsibility. Consensus was found around the belief that good quality care should be made available to service users to promote recovery both as inpatient or in the community. However, the manner in which recovery was defined and delivered differed between the groups.


title: Rotterdam Prostate Cancer Risk Calculator: Development and Usability Testing of the Mobile Phone App

abstract: BACKGROUND ::: The use of prostate cancer screening tools that take into account relevant prebiopsy information (ie, risk calculators) is recommended as a way of determining the risk of cancer and the subsequent need for a prostate biopsy. This has the potential to limit prostate cancer overdiagnosis and subsequent overtreatment. mHealth apps are gaining traction in urological practice and are used by both practitioners and patients for a variety of purposes. ::: ::: ::: OBJECTIVE ::: The impetus of the study was to design, develop, and assess a smartphone app for prostate cancer screening, based on the Rotterdam Prostate Cancer Risk Calculator (RPCRC). ::: ::: ::: METHODS ::: The results of the Rotterdam arm of the European Randomized Study of Screening for Prostate Cancer (ERSPC) study were used to elaborate several algorithms that allowed the risk of prostate cancer to be estimated. A step-by-step workflow was established to ensure that depending on the available clinical information the most complete risk model of the RPCRC was used. The user interface was designed and then the app was developed as a native app for iOS. The usability of the app was assessed using the Post-Study System Usability Questionnaire (PSSUQ) developed by IBM, in a group of 92 participants comprising urologists, general practitioners, and medical students. ::: ::: ::: RESULTS ::: A total of 11 questions were built into the app, and, depending on the answers, one of the different algorithms of the RPCRC could be used to predict the risk of prostate cancer and of clinically significant prostate cancer (Gleason score ≥7 and clinical stage >T2b). The system usefulness, information quality, and interface quality scores were high-92% (27.7/30), 87% (26.2/30), and 89% (13.4/15), respectively. No usability problems were identified. ::: ::: ::: CONCLUSIONS ::: The RPCRC app is helpful in predicting the risk of prostate cancer and, even more importantly, clinically significant prostate cancer. Its algorithms have been externally validated before and the usability score shows the app's interface is well designed. Further usability testing is required in different populations to verify these results and ensure that it is easy to use, to warrant a broad appeal, and to provide better patient care.


title: Microwave and Millimeter-Wave LTCC Filters Using Discriminating Coupling for Mode Suppression

abstract: In this paper, a new discriminating coupling mechanism, which is realized by choosing suitable coupling region between the feeding lines and resonators, is proposed to suppress the unwanted resonance modes without affecting the desirable operating modes. Utilizing such a coupling mechanism, two low-temperature cofired ceramic (LTCC) bandpass filters (BPFs) are designed operating at microwave and millimeter-wave bands. One BPF realizes a wide stopband, since the third and fifth harmonics are suppressed by the discriminating coupling. The other one is an oversized 60-GHz BPF with the fundamental mode suppressed by the discriminating coupling. Due to the multilayer characteristics of the LTCC techniques, the circuit layout can be controlled flexibly to realize desirable discriminating coupling. The two filters are theoretically analyzed and implemented. Good agreement between the simulation and the measurement validates the proposed method.


title: UAV degradation identification for pilot notification using machine learning techniques

abstract: Unmanned Aerial Vehicles are currently investigated as an important sub-domain of robotics, a fast growing and truly multidisciplinary research field. UAVs are increasingly deployed in real-world settings for missions in dangerous environments or in environments which are challenging to access. Combined with autonomous flying capabilities, many new possibilities, but also challenges, open up. To overcome the challenge of early identification of degradation, machine learning based on flight features is a promising direction. Existing approaches build classifiers that consider their features to be correlated. This prevents a fine-grained detection of degradation for the different hardware components. This work presents an approach where the data is considered uncorrelated and, using machine learning techniques, allows the precise identification of UAV's damages.


title: MHC class II proteins and disease: a structural perspective

abstract: MHC class II molecules on the surface of antigen-presenting cells display a range of peptides for recognition by the T-cell receptors of CD4+ T helper cells. Therefore, MHC class II molecules are central to effective adaptive immune responses, but conversely, genetic and epidemiological data have implicated these molecules in the pathogenesis of autoimmune diseases. Indeed, the strength of the associations between particular MHC class II alleles and disease render them the main genetic risk factors for autoimmune disorders such as type 1 diabetes. Here, we discuss the insights that the crystal structures of MHC class II molecules provide into the molecular mechanisms by which sequence polymorphisms might contribute to disease susceptibility.


title: Learning to Rewrite Queries

abstract: It is widely known that there exists a semantic gap between web documents and user queries and bridging this gap is crucial to advance information retrieval systems. The task of query rewriting, aiming to alter a given query to a rewrite query that can close the gap and improve information retrieval performance, has attracted increasing attention in recent years. However, the majority of existing query rewriters are not designed to boost search performance and consequently their rewrite queries could be sub-optimal. In this paper, we propose a learning to rewrite framework that consists of a candidate generating phase and a candidate ranking phase. The candidate generating phase provides us the flexibility to reuse most of existing query rewriters; while the candidate ranking phase allows us to explicitly optimize search relevance. Experimental results on a commercial search engine demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the important components of the proposed framework.


title: Synthesizing third normal form relations from functional dependencies

abstract: It has been proposed that the description of a relational database can be formulated as a set of functional relationships among database attributes. These functional relationships can then be used to synthesize algorithmically a relational scheme. It is the purpose of this paper to present an effective procedure for performing such a synthesis. The schema that results from this procedure is proved to be in Codd's third normal form and to contain the fewest possible number of relations. Problems with earlier attempts to construct such a procedure are also discussed.


title: Pose-conditioned joint angle limits for 3D human pose reconstruction

abstract: Estimating 3D human pose from 2D joint locations is central to the analysis of people in images and video. To address the fact that the problem is inherently ill posed, many methods impose a prior over human poses. Unfortunately these priors admit invalid poses because they do not model how joint-limits vary with pose. Here we make two key contributions. First, we collect a motion capture dataset that explores a wide range of human poses. From this we learn a pose-dependent model of joint limits that forms our prior. Both dataset and prior are available for research purposes. Second, we define a general parametrization of body pose and a new, multi-stage, method to estimate 3D pose from 2D joint locations using an over-complete dictionary of poses. Our method shows good generalization while avoiding impossible poses. We quantitatively compare our method with recent work and show state-of-the-art results on 2D to 3D pose estimation using the CMU mocap dataset. We also show superior results using manual annotations on real images and automatic detections on the Leeds sports pose dataset.


title: Tracking in cell and developmental biology

abstract: The past decade has seen an unprecedented data explosion in biology. It has become evident that in order to take full advantage of the potential wealth of information hidden in the data produced by even a single experiment, visual inspection and manual analysis are no longer adequate. To ensure efficiency, consistency, and completeness in data processing and analysis, computational tools are essential. Of particular importance to many modern live-cell imaging experiments is the ability to automatically track and analyze the motion of objects in time-lapse microscopy images. This article surveys the recent literature in this area. Covering all scales of microscopic observation, from cells, down to molecules, and up to entire organisms, it discusses the latest trends and successes in the development and application of computerized tracking methods in cell and developmental biology.


title: Efficacy of Mobile Apps to Support the Care of Patients With Diabetes Mellitus: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

abstract: Background: Diabetes Mellitus (DM) is a chronic disease that is considered a global public health problem. Education and self-monitoring by diabetic patients help to optimize and make possible a satisfactory metabolic control enabling improved management and reduced morbidity and mortality. The global growth in the use of mobile phones makes them a powerful platform to help provide tailored health, delivered conveniently to patients through health apps. Objective: The aim of our study was to evaluate the efficacy of mobile apps through a systematic review and meta-analysis to assist DM patients in treatment. Methods: We conducted searches in the electronic databases MEDLINE (Pubmed), Cochrane Register of Controlled Trials (CENTRAL), and LILACS (Latin American and Caribbean Health Sciences Literature), including manual search in references of publications that included systematic reviews, specialized journals, and gray literature. We considered eligible randomized controlled trials (RCTs) conducted after 2008 with participants of all ages, patients with DM, and users of apps to help manage the disease. The meta-analysis of glycated hemoglobin (HbA1c) was performed in Review Manager software version 5.3. Results: The literature search identified 1236 publications. Of these, 13 studies were included that evaluated 1263 patients. In 6 RCTs, there were a statistical significant reduction (P<.05) of HbA1c at the end of studies in the intervention group. The HbA1c data were evaluated by meta-analysis with the following results (mean difference, MD −0.44; CI: −0.59 to −0.29; P<.001; I²=32%).The evaluation favored the treatment in patients who used apps without significant heterogeneity. Conclusions: The use of apps by diabetic patients could help improve the control of HbA1c. In addition, the apps seem to strengthen the perception of self-care by contributing better information and health education to patients. Patients also become more self-confident to deal with their diabetes, mainly by reducing their fear of not knowing how to deal with potential hypoglycemic episodes that may occur. [JMIR Mhealth Uhealth 2017;5(3):e4]


title: Modeling and Simulation of Lithium-Ion Batteries from a Systems Engineering Perspective

abstract: The lithium-ion battery is an ideal candidate for a wide variety of applications due to its high energy/power density and operating voltage. Some limitations of existing lithium-ion battery technology include underutilization, stress-induced material damage, capacity fade, and the potential for thermal runaway. This paper reviews efforts in the modeling and simulation of lithium-ion batteries and their use in the design of better batteries. Likely future directions in battery modeling and design including promising research opportunities are outlined.


title: Quasicyclic low-density parity-check codes from circulant permutation matrices

abstract: While preparing [H. Hagiwara et al., 2006], we realized that the proof of [M. Fossorier, 2004, Theorem 2.3] was leading to confusion as written. More precisely, only e1 = o2 directly follows from o1 + e1 and o2 + e2 = e. The other equality o1 = e2 follows from e1 = e2 and the fact that the sum of the (distinct) Delta's between the two rows considered has to be zero. Actually, a much concise proof can be obtained by directly observing that for J = p = 2m, {Delta1,2 (I) mod p, 0 < I < L - 1} = {0,1,..., L - 1} from [M. Fossorier, 2004, Theorem 2.1], so that SigmaI=0L-1 Delta1,2 (I) = m mod p ne 0. Since Ruwei Chen recently pointed out this issue, we decided to clarify this point.


title: TESS: Temporal event sequence summarization

abstract: We suggest a novel method of clustering and exploratory analysis of temporal event sequences data (also known as categorical time series) based on three-dimensional data grid models. A data set of temporal event sequences can be represented as a data set of three-dimensional points, each point is defined by three variables: a sequence identifier, a time value and an event value. Instantiating data grid models to the 3D-points turns the problem into 3D-coclustering. The sequences are partitioned into clusters, the time variable is discretized into intervals and the events are partitioned into clusters. The cross-product of the univariate partitions forms a multivariate partition of the representation space, i.e., a grid of cells and it also represents a nonparametric estimator of the joint distribution of the sequences, time and events dimensions. Thus, the sequences are grouped together because they have similar joint distribution of time and events, i.e., similar distribution of events along the time dimension. The best data grid is computed using a parameter-free Bayesian model selection approach. We also suggest several criteria for exploiting the resulting grid through agglomerative hierarchies, for interpreting the clusters of sequences and characterizing their components through insightful visualizations. Extensive experiments on both synthetic and real-world data sets demonstrate that our approach is efficient, effective and discover meaningful underlying patterns in sets of temporal event sequences.


title: Interactive e-learning system using pattern recognition and augmented reality

abstract: This paper proposes an interactive e-learning system using pattern recognition and augmented reality. The goal of proposed system is to provide students with realistic audio-visual contents when they are leaning. The proposed e-learning system consists of image recognition, color and polka-dot pattern recognition, and augmented reality engine with audio-visual contents. When the Web camera on a PC captures the current page of textbook, the e-learning system first identifies the images on the page, and augments some audio-visual contents on the monitor. For interactive learning, the proposed e-learning system exploits the color-band or polka-dot markers which are stuck to the end of a finger. The color-band and polka-dot marker act like the mouse cursor to indicate the position in the textbook image. Appropriate interactive audio-visual contents are augmented as the marker is located on the predefined image objects in the textbook. The proposed e-learning system was applied to the educational courses in the elementary school, and we obtained satisfactory results for real applications. We expect that the proposed e-learning system is popular when the educational contents and scenarios are sufficiently provided.


Mood Classification of Hindi Songs based on Lyrics


title: Pixy: a static analysis tool for detecting Web application vulnerabilities

abstract: The number and the importance of Web applications have increased rapidly over the last years. At the same time, the quantity and impact of security vulnerabilities in such applications have grown as well. Since manual code reviews are time-consuming, error-prone and costly, the need for automated solutions has become evident. In this paper, we address the problem of vulnerable Web applications by means of static source code analysis. More precisely, we use flow-sensitive, interprocedural and context-sensitive dataflow analysis to discover vulnerable points in a program. In addition, alias and literal analysis are employed to improve the correctness and precision of the results. The presented concepts are targeted at the general class of taint-style vulnerabilities and can be applied to the detection of vulnerability types such as SQL injection, cross-site scripting, or command injection. Pixy, the open source prototype implementation of our concepts, is targeted at detecting cross-site scripting vulnerabilities in PHP scripts. Using our tool, we discovered and reported 15 previously unknown vulnerabilities in three Web applications, and reconstructed 36 known vulnerabilities in three other Web applications. The observed false positive rate is at around 50% (i.e., one false positive for each vulnerability) and therefore, low enough to permit effective security audits.


title: A Rule-Based Recommender System for Online Discussion Forums

abstract: In this paper we present a rule-based personalization framework for encapsulating and combining personalization algorithms known from adaptive hypermedia and recommender systems. We show how this personalization framework can be integrated into existing systems by example of the educational online board Comtella-D, which exploits the framework for recommending relevant discussions to the users. In our evaluations we compare different recommender strategies, investigate usage behavior over time, and show that a small amount of user data is sufficient to generate precise recommendations.


title: MonoFusion: Real-time 3D reconstruction of small scenes with a single web camera

abstract: MonoFusion allows a user to build dense 3D reconstructions of their environment in real-time, utilizing only a single, off-the-shelf web camera as the input sensor. The camera could be one already available in a tablet, phone, or a standalone device. No additional input hardware is required. This removes the need for power intensive active sensors that do not work robustly in natural outdoor lighting. Using the input stream of the camera we first estimate the 6DoF camera pose using a sparse tracking method. These poses are then used for efficient dense stereo matching between the input frame and a key frame (extracted previously). The resulting dense depth maps are directly fused into a voxel-based implicit model (using a computationally inexpensive method) and surfaces are extracted per frame. The system is able to recover from tracking failures as well as filter out geometrically inconsistent noise from the 3D reconstruction. Our method is both simple to implement and efficient, making such systems even more accessible. This paper details the algorithmic components that make up our system and a GPU implementation of our approach. Qualitative results demonstrate high quality reconstructions even visually comparable to active depth sensor-based systems such as KinectFusion.


Advanced Review Response surface methodology


title: Grammatical Voice and Person in Technical Writing: Results of a Survey

abstract: A survey-experiment at the University of Leeds, England, attempted to resolve two questions: (1) Are some grammatical structures more efficient than others in communicating scientific fact? and (2)...


title: Augmented Reality

abstract: Virtual Reality is becoming more than a part of our everyday life, helping us to identify quickly the elements of the environment or to better entertain us. The purpose of this paper is to present an application framework that we are developing, in order to help others implement their own applications. In the first section the focus is set on the Augmented Reality basic concepts and the necessity of developing such a framework. The prototype that we present in the second part of this paper comes to demonstrate how our framework can be used to achieve our targeted application for Augmented Reality. It also contains some future works to highlight the capabilities of the AR API.