title: Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks

abstract: In today's financial markets, where most trades are performed in their entirety by electronic means and the largest fraction of them is completely automated, an opportunity has risen from analyzing this vast amount of transactions. Since all the transactions are recorded in great detail, investors can analyze all the generated data and detect repeated patterns of the price movements. Being able to detect them in advance, allows them to take profitable positions or avoid anomalous events in the financial markets. In this work we proposed a deep learning methodology, based on Convolutional Neural Networks (CNNs), that predicts the price movements of stocks, using as input large-scale, high-frequency time-series derived from the order book of financial exchanges. The dataset that we use contains more than 4 million limit order events and our comparison with other methods, like Multilayer Neural Networks and Support Vector Machines, shows that CNNs are better suited for this kind of task.


Grammatical Role Embeddings for Enhancements of Relation Density in the Princeton WordNet


title: Modelling of a Hall Effect-Based Current Sensor with an Open Core Magnetic Concentrator

abstract: The present paper deals with the modelling of a Hall effect current sensor with open core magnetic concentrator. 3D magnetic field modelling is carried out using the finite element method (FEM) and Comsol Multiphysics software. Two rectangular core constructions are considered. Different geometric parameters of the magnetic concentrator are varied and their influence on the sensor characteristic is studied, with the aim of reducing the dependence on the output signal on the distance to the conductor. Of the studied parameters, core window length leads to the most significant change in the sensor characteristic. Future work can include the optimization of the sensor construction.


Laplacian Damping for Projective Dynamics


title: Design and Analysis of a High-Speed Claw Pole Motor With Soft Magnetic Composite Core

abstract: Soft magnetic composite (SMC) material is formed by surface-insulated iron powder particles, generating unique properties like magnetic and thermal isotropy, and very low eddy currents. This paper presents the design and analysis of a high-speed claw pole motor with an SMC stator core for reducing core losses and cost. The analyses of magnetic and thermal fields are conducted based on a comprehensive understanding of the property of SMC materials. The 3-D finite-element analysis (FEA) is performed for accurate parameter calculation, design optimization, and thermal calculation. Because of the importance of core loss in high-speed motors, rotational core loss model is employed, and the core losses are coupled directly into thermal calculation by keeping the same hexahedral mesh structure between magnetic field analysis and thermal analysis. Since the rotor modal analysis is very important to high-speed motors, the natural frequencies and mode of the rotor are studied


title: Dual polarized Tightly Coupled Dipole Array (TCDA) for UHF to millimeter wave applications

abstract: We present a dual polarized Tightly Coupled Dipole Array (TCDA), representing the first practical realization of dual polarizations using a TCDA topology. This is accomplished through a dual-offset, split unit cell with minimized inter-feed coupling. The array achieves a minimum 6.1:1 bandwidth for VSWR<;2.25, 2.5, and 3.9 for 0°, 30°, and 60° scans, respectively. To our knowledge, this implies our design is simultaneously the widest band and widest scanning dual-polarized, conformal array in the literature.


title: Many-to-Many Geographically-Embedded Flow Visualisation: An Evaluation

abstract: Showing flows of people and resources between multiple geographic locations is a challenging visualisation problem. We conducted two quantitative user studies to evaluate different visual representations for such dense many-to-many flows. In our first study we compared a bundled node-link flow map representation and OD Maps [37] with a new visualisation we call MapTrix. Like OD Maps, MapTrix overcomes the clutter associated with a traditional flow map while providing geographic embedding that is missing in standard OD matrix representations. We found that OD Maps and MapTrix had similar performance while bundled node-link flow map representations did not scale at all well. Our second study compared participant performance with OD Maps and MapTrix on larger data sets. Again performance was remarkably similar.


title: BGP eye: a new visualization tool for real-time detection and analysis of BGP anomalies

abstract: Owing to the inter-domain aspects of BGP routing, it is difficult to correlate information across multiple domains in order to analyze the root cause of the routing outages. We present BGP Eye, a tool for visualization-aided root-cause analysis of BGP anomalies. In contrast to previous approaches, BGP Eye performs real-time analysis of BGP anomalies through hierarchical analysis. First, BGP updates are clustered to obtain BGP events that are more representative of an anomaly. These events are then correlated across all border routers to ascertain the extent of the anomaly. Furthermore, BGP Eye provides both the capability to analyze BGP anomalies from an Internet-Centric View through multiple vantage points as well as from a Home-Centric View of a particular Autonomous System. We present the capability for scalable and real-time root-cause analysis provided by BGP Eye through the analysis of two very different anomalies. First, we provide an Internet-Centric view from AS568 of the routing outages during the spread of the Slammer Worm on January 25th, 2003. Second, we provide a Home-Centric view from AS6458 of the routing outages caused by the inadvertent prefix hijacking by AS9121 on December 24th, 2004.


title: English Filler Gap Constructions

abstract: This article delineates and analyzes the syntactic and semantic parameters of variation exhibited by English filler -gap constructions . It demonstrates that a detailed, fully explicit account of the observed variation is available within a framework embracing the notion ‘grammatical construction’. This account, which explicates similarities and differences among topicalization, interrogatives, relatives, exclamatives, and comparative correlatives in terms of linguistic types and hierarchical constraint inheritance, is articulated in detail within the framework of sign -based construction grammar (SBCG), a version of head -driven phrase structure grammar (HPSG) integrating key insights from Berkeley construction grammar . The results presented here stand as a challenge to any analysis incorporating transformational operations, especially proposals couched within Chomsky’s ‘Minimalist program’.


title: Understanding emoji ambiguity in context: The role of text in emoji-related miscommunication

abstract: Recent studies have found that people interpret emoji characters inconsistently, creating significant potential for miscommunication. However, this research examined emoji in isolation, without consideration of any surrounding text. Prior work has hypothesized that examining emoji in their natural textual contexts would substantially reduce potential for miscommunication. To investigate this hypothesis, we carried out a controlled study with 2,482 participants who interpreted emoji both in isolation and in multiple textual contexts. After comparing the variability of emoji interpretation in each condition, we found that our results do not support the hypothesis in prior work: when emoji are interpreted in textual contexts, the potential for miscommunication appears to be roughly the same. We also identify directions for future research to better understand the interplay between emoji and textual context.


title: Graph Based Constrained Semi-Supervised Learning Framework via Label Propagation over Adaptive Neighborhood

abstract: A new graph based constrained semi-supervised learning (G-CSSL) framework is proposed. Pairwise constraints (PC) are used to specify the types (intra- or inter-class) of points with labels. Since the number of labeled data is typically small in SSL setting, the core idea of this framework is to create and enrich the PC sets using the propagated soft labels from both labeled and unlabeled data by special label propagation (SLP), and hence obtaining more supervised information for delivering enhanced performance. We also propose a Two-stage Sparse Coding, termed TSC, for achieving adaptive neighborhood for SLP. The first stage aims at correcting the possible corruptions in data and training an informative dictionary, and the second stage focuses on sparse coding. To deliver enhanced inter-class separation and intra-class compactness, we also present a mixed soft-similarity measure to evaluate the similarity/dissimilarity of constrained pairs using the sparse codes and outputted probabilistic values by SLP. Simulations on the synthetic and real datasets demonstrated the validity of our algorithms for data representation and image recognition, compared with other related state-of-the-art graph based semi-supervised techniques.


title: Manufacturing Analytics and Industrial Internet of Things

abstract: Over the last two decades, manufacturing across the globe has evolved to be more intel-ligent and data driven. In the age of industrial Internet of Things, a smart production unit can be perceived as a large connected industrial system of materials, parts, machines, tools, inventory, and logistics that can relay data and communicate with each other. While, traditionally, the focus has been on machine health and predictive maintenance, the manufacturing industry has also started focusing on analyzing data from the entire production line. These applications bring a new set of analytics challenges. Unlike tradi-tional data mining analysis, which consists of lean datasets (that is, datasets with few fea-tures), manufacturing has fat datasets. In addition, previous approaches to manufacturing analytics restricted themselves to small time periods of data. The latest advances in big data analytics allows researchers to do a deep dive into years of data. Bosch collects and utilizes all available information about its products to increase its understanding of complex linear and nonlinear relationships between parts, machines, and assembly lines. This helps in use cases such as the discovery of the root cause of internal defects. This article presents a case study and provides detail about challenges and approaches in data extraction, modeling, and visualization.


title: Spherical wavelets: efficiently representing functions on the sphere

abstract: Wavelets have proven to be powerful bases for use in numerical analysis and signal processing. Their power lies in the fact that they only require a small number of coefficients to represent general functions and large data sets accurately. This allows compression and efficient computations. Classical constructions have been limited to simple domains such as intervals and rectangles. In this paper we present a wavelet construction for scalar functions defined on the sphere. We show how biorthogonal wavelets with custom properties can be constructed with the lifting scheme. The bases are extremely easy to implement and allow fully adaptive subdivisions. We give examples of functions defined on the sphere, such as topographic data, bidirectional reflection distribution functions, and illumination, and show how they can be efficiently represented with spherical wavelets. CR


title: Adult ADHD and comorbid depression: A consensus-derived diagnostic algorithm for ADHD

abstract: OBJECTIVE ::: Many patients present to their physician with depression as their primary symptom. However, depression may mask other comorbid disorders. This article presents diagnostic criteria and treatment recommendations (and monitoring) pertaining to the diagnosis of adult attention deficit hyperactivity disorder (ADHD), which may be missed in patients who present with depressive symptoms, or major depressive disorder (MDD). Other co-occurring conditions such as anxiety, substance use, and bipolar disorder are briefly discussed. ::: ::: ::: METHODS ::: A panel of psychiatrist-clinicians with expertise in the area of child and adolescent ADHD and mood disorders, adult mood disorders, and adult ADHD was convened. A literature search for recommendations on the diagnosis and treatment of co-occurring conditions (MDD, anxiety symptoms, and substance use) with adult ADHD was performed. Based on this, and the panel's clinical expertise, the authors developed a diagnostic algorithm and recommendations for the treatment of adult ADHD with co-occurring MDD. ::: ::: ::: RESULTS ::: Little information exists to assist clinicians in diagnosing ADHD co-occurring with other disorders such as MDD. A three-step process was developed by the panel to aid in the screening and diagnosis of adult ADHD. In addition, comorbid MDD, bipolar disorder, anxiety symptoms, substance use and cardiovascular concerns regarding stimulant use are discussed. ::: ::: ::: CONCLUSION ::: This article provides clinicians with a clinically relevant overview of the literature on comorbid ADHD and depression and offers a clinically useful diagnostic algorithm and treatment suggestions.


title: A New Mobile Platform (RHyMo) for Smooth Movement on Rugged Terrain

abstract: This paper suggests a new performance metric for a mobile platform called as a posture variation index (PVI) to evaluate the smoothness of its movement, which is an important factor to predict undesired oscillations or shocks on a mobile platform while traveling on rugged terrain. The PVI consists of the height and pitch angle variations of center of mass of a mobile platform. By using the PVI, the movements of well-known mobile platforms are systematically analyzed. In order to ensure smooth movement as well as high terrainability on rugged terrain, a new mobile platform (RHyMo) is constructed on the basis of the kinematic and quasi-static analyses. The extensive experiments are carried out by using the RHyMo and the Rocker–Bogie platform on the artificial rugged terrain, which verify that in comparison with the Rocker–Bogie platform, the average and maximum height variations of RHyMo are reduced by 12.72% and 5.96%, respectively. Moreover, the average and maximum pitch angle variations of RHyMo are significantly reduced by 65.87% and 60.53%, respectively. The terrainability of RHyMo against a high step and steep stairs is improved with the help of the track mechanism installed in the front linkage.


title: A Combined System for Text Line Extraction and Handwriting Recognition in Historical Documents

abstract: Automated reading of historical handwriting is needed to search and browse ancient manuscripts in digital libraries based on their textual content. In this paper, we present a combined system for text localization and transcription in page images. It includes flexible learning-based methods for layout analysis and handwriting recognition, which were developed in the context of the Swiss research project HisDoc. A comprehensive experimental evaluation is provided for the medieval Parzival database, demonstrating a promising word recognition accuracy of 93.0% with closed vocabulary. In order to harmonize the evaluation of the two document analysis tasks, we introduce a novel evaluation measure for text line extraction that takes substitution, deletion, as well as insertion errors into account.


title: Catnap: energy proportional multiple network-on-chip

abstract: Multiple networks have been used in several processor implementations to scale bandwidth and ensure protocol-level deadlock freedom for different message classes. In this paper, we observe that a multiple-network design is also attractive from a power perspective and can be leveraged to achieve energy proportionality by effective power gating. Unlike a single-network design, a multiple-network design is more amenable to power gating, as its subnetworks (subnets) can be power gated without compromising the connectivity of the network. To exploit this opportunity, we propose the Catnap architecture which consists of synergistic subnet selection and power-gating policies. Catnap maximizes the number of consecutive idle cycles in a router, while avoiding performance loss due to overloading a subnet. We evaluate a 256-core processor with a concentrated mesh topology using synthetic traffic and 35 applications. We show that the average network power of a power-gating optimized multiple-network design with four subnets could be 44% lower than a bandwidth equivalent single-network design for an average performance cost of about 5%.


title: Knowledge nodes: the building blocks of a distributed approach to knowledge management

abstract: In this paper, we criticise the objectivistic approach that underlies most current sys- tems for Knowledge Management. We show that such an approach is incompatible with the very nature of what is to be managed (i.e., knowledge), and we argue that this may partially explain why most knowledge management systems are deserted by users. We propose a different ap- proach - called distributed knowledge management - in which subjective and social (in a word, contextual) aspects of knowledge are seriously taken into account. Finally, we present a general technological architecture in which these ideas are implemented by introducing the concept of knowledge node.


title: Compact and Scalable Graph Neighborhood Sketching

abstract: The all-distances sketch (ADS) has recently emerged as a promising paradigm of graph neighborhood sketching. An ADS is a probabilistic data structure that is defined for each vertex of a graph. ADSs facilitate accurate estimation of many useful indicators for network analysis with the guarantee of accuracy, and the ADSs for all the vertices in a graph can be computed in near-linear time. Because of these useful properties, ADS has attracted considerable attention. However, a critical drawback of ADS is its space requirement, which tends to be much larger than that of the graph itself. In the present study, we address this issue by designing a new graph sketching scheme, namely, sketch retrieval shortcuts (SRS). Although SRSs are more space-efficient than ADSs by an order of magnitude, an ADS of any vertex can be quickly retrieved from the SRSs. The retrieved ADSs can be used to estimate the aforementioned indicators in exactly the same manner as with plain ADSs, inheriting the same accuracy guarantee. Our experiments on real-world networks demonstrate the usefulness of SRSs as a practical back-end of large-scale graph data mining.


title: A generalized taxonomy of explanations styles for traditional and social recommender systems

abstract: Recommender systems usually provide explanations of their recommendations to better help users to choose products, activities or even friends. Up until now, the type of an explanation style was considered in accordance to the recommender system that employed it. This relation was one-to-one, meaning that for each different recommender systems category, there was a different explanation style category. However, this kind of one-to-one correspondence can be considered as over-simplistic and non generalizable. In contrast, we consider three fundamental resources that can be used in an explanation: users, items and features and any combination of them. In this survey, we define (i) the Human style of explanation, which provides explanations based on similar users, (ii) the Item style of explanation, which is based on choices made by a user on similar items and (iii) the Feature style of explanation, which explains the recommendation based on item features rated by the user beforehand. By using any combination of the aforementioned styles we can also define the Hybrid style of explanation. We demonstrate how these styles are put into practice, by presenting recommender systems that employ them. Moreover, since there is inadequate research in the impact of social web in contemporary recommender systems and their explanation styles, we study new emerged social recommender systems i.e. Facebook Connect explanations (HuffPo, Netflix, etc.) and geo-social explanations that combine geographical with social data (Gowalla, Facebook Places, etc.). Finally, we summarize the results of three different user studies, to support that Hybrid is the most effective explanation style, since it incorporates all other styles.


title: Diagnosis and management of teeth with vertical root fractures

abstract: Vertical fractures in teeth can present difficulties in diagnosis. There are, however, many specific clinical and radiographical signs which, when present, can alert clinicians to the existence of a fracture. In this review, the diagnosis of vertical root fractures is discussed in detail, and examples are presented of clinical and radiographic signs associated with these fractured teeth. Treatment alternatives are discussed for both posterior and anterior teeth.


title: Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases

abstract: Preserving individual privacy when publishing data is a problem that is receiving increasing attention. According to the fc-anonymity principle, each release of data must be such that each individual is indistinguishable from at least k - 1 other individuals. In this paper we study the problem of anonymity preserving data publishing in moving objects databases. We propose a novel concept of k-anonymity based on co-localization that exploits the inherent uncertainty of the moving object's whereabouts. Due to sampling and positioning systems (e.g., GPS) imprecision, the trajectory of a moving object is no longer a polyline in a three-dimensional space, instead it is a cylindrical volume, where its radius delta represents the possible location imprecision: we know that the trajectory of the moving object is within this cylinder, but we do not know exactly where. If another object moves within the same cylinder they are indistinguishable from each other. This leads to the definition of (k,delta) -anonymity for moving objects databases. We first characterize the (k, delta)-anonymity problem and discuss techniques to solve it. Then we focus on the most promising technique by the point of view of information preservation, namely space translation. We develop a suitable measure of the information distortion introduced by space translation, and we prove that the problem of achieving (k,delta) -anonymity by space translation with minimum distortion is NP-hard. Faced with the hardness of our problem we propose a greedy algorithm based on clustering and enhanced with ad hoc pre-processing and outlier removal techniques. The resulting method, named NWA (Never Walk .Alone), is empirically evaluated in terms of data quality and efficiency. Data quality is assessed both by means of objective measures of information distortion, and by comparing the results of the same spatio-temporal range queries executed on the original database and on the (k, delta)-anonymized one. Experimental results show that for a wide range of values of delta and k, the relative error introduced is kept low, confirming that NWA produces high quality (k, delta)-anonymized data.


title: Mathematical modeling and control of a hexacopter

abstract: Microcopters are emerging as a popular platform for Unmanned Aerial Vehicle (UAV). The purpose of this paper is to present the basic mathematical modeling of microcopters, which has been used to develop proper methods for stabilization and trajectory control. The microcopter taken into account consists of six rotors, with three pairs of counter-rotating fixedpitch blades. The microcopter is controlled by adjusting the angular velocities of the rotors which are spun by electric motors. It is assumed as a rigid body, so the differential equations of the microcopter dynamics can be derived from both the Newton-Euler and Euler-Lagrange equations. Euler-angle parametrization of three-dimensional rotations contains singular points in the coordinate space that can cause failure of both dynamical model and control. In order to avoid singularities, the rotations of the microcopter are parametrized in terms of quaternions. This choice has been made taking into consideration the linearity of quaternion formulation, their stability and efficiency.


title: Adaptive subgradient methods for online learning and stochastic optimization

abstract: We present a new family of subgradient methods that dynamically incorporate knowledge of the geometry of the data observed in earlier iterations to perform more informative gradient-based learning. Metaphorically, the adaptation allows us to find needles in haystacks in the form of very predictive but rarely seen features. Our paradigm stems from recent advances in stochastic optimization and online learning which employ proximal functions to control the gradient steps of the algorithm. We describe and analyze an apparatus for adaptively modifying the proximal function, which significantly simplifies setting a learning rate and results in regret guarantees that are provably as good as the best proximal function that can be chosen in hindsight. We give several efficient algorithms for empirical risk minimization problems with common and important regularization functions and domain constraints. We experimentally study our theoretical analysis and show that adaptive subgradient methods outperform state-of-the-art, yet non-adaptive, subgradient algorithms.


title: Protecting Poorly Chosen Secrets from Guessing Attacks

abstract: In a security system that allows people to choose their own passwords, people tend to choose passwords that can be easily guessed. This weakness exists in practically all widely used systems. Instead of forcing users to choose secrets that are likely to be difficult for them to remember, solutions that maintain user convenience and a high level of security at the same time are proposed. The basic idea is to ensure that data available to the attacker is sufficiently unpredictable to prevent an offline verification of whether a guess is successful or not. Common forms of guessing attacks are examined, examples of cryptographic protocols that are immune to such attacks are developed, and a systematic way to examine protocols to detect vulnerabilities to such attacks is suggested. >


title: A framework analysis of the open source software development paradigm

abstract: Open Source Software (OSS) has become the subject of much commercial interest of late. Certainly, OSS seems to hold much promise in addressing the core issues of the software crisis, namely that of software taking too long to develop, exceeding its budget, and not working very well. Indeed, there have been several examples of significant OSS success stories—the Linux operating system, the Apache web server, the BIND domain name resolution utility, to name but a few. However, little by way of rigorous academic research on OSS has been conducted to date. In this study, a framework was derived from two previous frameworks which have been very influential in the IS field, namely that of Zachman’s IS architecture (ISA) and Checkland’s CATWOE framework from Soft Systems Methodology (SSM). The resulting framework is used to analyze the OSS approach in detail. The potential future of OSS research is also discussed.


title: Microgrids: Hierarchical Control and an Overview of the Control and Reserve Management Strategies

abstract: The increasing share of distributed generation (DG) units in electrical power systems has a significant impact on the operation of the distribution networks, which are increasingly being confronted with congestion and voltage problems. This demands a coordinated approach for integrating DG in the network, allowing the DG units to actively contribute to frequency and voltage regulation. Microgrids can provide such coordination by aggregating DG, (controllable) loads, and storage in small-scale networks, which can operate in both grid-connected and islanded mode. In this article, the islanded operating condition is considered. As in the conventional networks, a hierarchical control structure can be implemented in islanded microgrids. In recent years, many different concepts for primary, secondary, and tertiary control of microgrids have been investigated. These controllers can be classified as either local or centralized. In this article, the hierarchical control for application in microgrids is discussed, and an overview of the control strategies is given with respect to the reserve provision by the DG units, loads, and storage equipment.


Cost-sensitive Deep Learning for Early Readmission Prediction at A Major Hospital


title: Universal prediction

abstract: This paper consists of an overview on universal prediction from an information-theoretic perspective. Special attention is given to the notion of probability assignment under the self-information loss function, which is directly related to the theory of universal data compression. Both the probabilistic setting and the deterministic setting of the universal prediction problem are described with emphasis on the analogy and the differences between results in the two settings.


title: Towards Robustness for Cyber-Physical Systems

abstract: While the importance of robustness in engineering design is well accepted, it is less clear how to design cyber-physical systems (CPS) for robustness. With the objective of developing a robustness theory for CPS, we introduce a notion of robustness for cyber systems inspired by existing notions of input-output stability in control theory. We show that the proposed notion of robustness captures two intuitive goals: bounded disturbances lead to bounded deviations from nominal behavior, and the effect of a sporadic disturbance disappears in finitely many steps. For cyber systems modeled as finite-state transducers, the proposed notion of robustness can be verified in pseudo-polynomial time. The synthesis problem, consisting of designing a controller enforcing robustness, can also be solved in pseudo-polynomial time.


title: Towards customized emotional design: An explorative study of user personality and user interface skin preferences

abstract: Two key elements in the area of cognitive ergonomics are user-system performance and the user similarity. However, with the introduction of skinnable user interfaces, a technology that gives the user interface a chameleon-like ability, elements such as aesthetic, fun, and especially user individuality and identity become more important. This paper presents two explorative studies on user personality in relation to skin preferences. In the studies participants were asked to rate their preference of a set of Windows Media Player skins and to complete the BIS/BAS and the IPIP-NEO personality inventories. The results of the first study suggest colour and similarity-attraction as two possible underlying factors for the correlations found between personality traits and skin preferences. The results of the second study partly confirm these findings, however not for similar personality traits and skin types correlations.


title: Advanced Persistent Threats - detection and defense

abstract: The term “Advanced Persistent Threat” refers to a well-organized, malicious group of people who launch stealthy attacks against computer systems of specific targets, such as governments, companies or military. The attacks themselves are long-lasting, difficult to expose and often use very advanced hacking techniques. Since they are advanced in nature, prolonged and persistent, the organizations behind them have to possess a high level of knowledge, advanced tools and competent personnel to execute them. The attacks are usually preformed in several phases - reconnaissance, preparation, execution, gaining access, information gathering and connection maintenance. In each of the phases attacks can be detected with different probabilities. There are several ways to increase the level of security of an organization in order to counter these incidents. First and foremost, it is necessary to educate users and system administrators on different attack vectors and provide them with knowledge and protection so that the attacks are unsuccessful. Second, implement strict security policies. That includes access control and restrictions (to information or network), protecting information by encrypting it and installing latest security upgrades. Finally, it is possible to use software IDS tools to detect such anomalies (e.g. Snort, OSSEC, Sguil).


title: Gameplaying for maths learning: cooperative or not?

abstract: This study investigated the effects of gameplaying on fifth-graders’ maths performance and attitudes. One hundred twenty five fifth graders were recruited and assigned to a cooperative Teams-Games-Tournament (TGT), interpersonal competitive or no gameplaying condition. A state standards-based maths exam and an inventory on attitudes towards maths were used for the pretest and posttest. The students’ gender, socio-economic status and prior maths ability were examined as the moderating variables and covariate. Multivariate analysis of covariance (MANCOVA) indicated that gameplaying was more effective than drills in promoting maths performance, and cooperative gameplaying was most effective for promoting positive maths attitudes regardless of students’ individual differences.


title: Auto Analysis of Customer Feedback using CNN and GRU Network

abstract: Analyzing customer feedback is the best way to channelize the data into new marketing strategies that benefit entrepreneurs as well as customers. Therefore an automated system which can analyze the customer behavior is in great demand. Users may write feedbacks in any language, and hence mining appropriate information often becomes intractable. Especially in a traditional feature-based supervised model, it is difficult to build a generic system as one has to understand the concerned language for finding the relevant features. In order to overcome this, we propose deep Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) based approaches that do not require handcrafting of features. We evaluate these techniques for analyzing customer feedback sentences in four languages, namely English, French, Japanese and Spanish. Our empirical analysis shows that our models perform well in all the four languages on the setups of IJCNLP Shared Task on Customer Feedback Analysis. Our model achieved the second rank in French, with an accuracy of 71.75% and third ranks for all the other languages.


An Overview on XML Semantic Disambiguation from Unstructured Text to Semi-Structured Data : Background , Applications , and Ongoing Challenges


title: ICDAR2005 page segmentation competition

abstract: This paper continues the authors' attempt to address the need for objective comparative evaluation of layout analysis methods in realistic circumstances. It describes the Page Segmentation Competition (modus operandi, dataset and evaluation criteria) held in the context of ICDAR2007 and presents the results of the evaluation of three candidate methods. The main objective of the competition was to compare the performance of such methods using scanned documents from commonly-occurring publications. The results indicate that although methods continue to mature, there is still a considerable need to develop robust methods that deal with everyday documents.


title: Generating Flexible Workloads for Graph Databases

abstract: Graph data management tools are nowadays evolving at a great pace. Key drivers of progress in the design and study of data intensive systems are solutions for synthetic generation of data and workloads, for use in empirical studies. Current graph generators, however, provide limited or no support for workload generation or are limited to fixed use-cases. Towards addressing these limitations, we demonstrate gMark, the first domain- and query language-independent framework for synthetic graph and query workload generation. Its novel features are: (i) fine-grained control of graph instance and query workload generation via expressive user-defined schemas; (ii) the support of expressive graph query languages, including recursion among other features; and, (iii) selectivity estimation of the generated queries. During the demonstration, we will showcase the highly tunable generation of graphs and queries through various user-defined schemas and targeted selectivities, and the variety of supported practical graph query languages. We will also show a performance comparison of four state-of-the-art graph database engines, which helps us understand their current strengths and desirable future extensions.


title: Learning detectors of malicious web requests for intrusion detection in network traffic

abstract: This paper proposes a generic classification system designed to detect security threats based on the behavior of malware samples. The system relies on statistical features computed from proxy log fields to train detectors using a database of malware samples. The behavior detectors serve as basic reusable building blocks of the multi-level detection architecture. The detectors identify malicious communication exploiting encrypted URL strings and domains generated by a Domain Generation Algorithm (DGA) which are frequently used in Command and Control (C&C), phishing, and click fraud. Surprisingly, very precise detectors can be built given only a limited amount of information extracted from a single proxy log. This way, the computational requirements of the detectors are kept low which allows for deployment on a wide range of security devices and without depending on traffic context such as DNS logs, Whois records, webpage content, etc. Results on several weeks of live traffic from 100+ companies having 350k+ hosts show correct detection with a precision exceeding 95% of malicious flows, 95% of malicious URLs and 90% of infected hosts. In addition, a comparison with a signature and rule-based solution shows that our system is able to detect significant amount of new threats.


title: A passive-mixer-first receiver with baseband-controlled RF impedance matching, ≪ 6dB NF, and ≫ 27dBm wideband IIP3

abstract: A software-defined radio (SDR) ideally allows all of the parameters of the radio to be programmed dynamically. SDRs so far have shown flexibility in bandwidth (BW) [1], local oscillator (LO) frequency [2-4], gain, and modulation type. However, the impedance an RF front-end presents to its antenna has proven difficult to tune [5]. Since antenna impedance can vary significantly across frequency and different environments, a software-controlled input impedance is desirable. Meanwhile, recent work suggests that connecting the antenna directly to a passive mixer without an RF LNA can provide significant benefits, such as extremely low power [6] or greatly increased tuning range and linearity [2]. This paper demonstrates a passive mixer-first SDR with an RF impedance that is controlled by baseband components whose impedance is reflected through the passive mixer to the RF port.


title: Face Sketch–Photo Synthesis and Retrieval Using Sparse Representation

abstract: Sketch-photo synthesis plays an important role in sketch-based face photo retrieval and photo-based face sketch retrieval systems. In this paper, we propose an automatic sketch-photo synthesis and retrieval algorithm based on sparse representation. The proposed sketch-photo synthesis method works at patch level and is composed of two steps: sparse neighbor selection (SNS) for an initial estimate of the pseudoimage (pseudosketch or pseudophoto) and sparse-representation-based enhancement (SRE) for further improving the quality of the synthesized image. SNS can find closely related neighbors adaptively and then generate an initial estimate for the pseudoimage. In SRE, a coupled sparse representation model is first constructed to learn the mapping between sketch patches and photo patches, and a patch-derivative-based sparse representation method is subsequently applied to enhance the quality of the synthesized photos and sketches. Finally, four retrieval modes, namely, sketch-based, photo-based, pseudosketch-based, and pseudophoto-based retrieval are proposed, and a retrieval algorithm is developed by using sparse representation. Extensive experimental results illustrate the effectiveness of the proposed face sketch-photo synthesis and retrieval algorithms.


title: Embedded Interaction: Interacting with the Internet of Things

abstract: The Internet of Things assumes that objects have digital functionality and can be identified and tracked automatically. The main goal of embedded interaction is to look at new opportunities that arise for interactive systems and the immediate value users gain. The authors developed various prototypes to explore novel ways for human-computer interaction (HCI), enabled by the Internet of Things and related technologies. Based on these experiences, they derive a set of guidelines for embedding interfaces into people's daily lives.


title: Communications and Signals Design for Wireless Power Transmission

abstract: Radiative wireless power transfer (WPT) is a promising technology to provide cost-effective and real-time power supplies to wireless devices. Although radiative WPT shares many similar characteristics with the extensively studied wireless information transfer or communication, they also differ significantly in terms of design objectives, transmitter/receiver architectures and hardware constraints, and so on. In this paper, we first give an overview on the various WPT technologies, the historical development of the radiative WPT technology and the main challenges in designing contemporary radiative WPT systems. Then, we focus on the state-of-the-art communication and signal processing techniques that can be applied to tackle these challenges. Topics discussed include energy harvester modeling, energy beamforming for WPT, channel acquisition, power region characterization in multi-user WPT, waveform design with linear and non-linear energy receiver model, safety and health issues of WPT, massive multiple-input multiple-output and millimeter wave enabled WPT, wireless charging control, and wireless power and communication systems co-design. We also point out directions that are promising for future research.


title: Analog implementation of wavelet transform based on switched-current filter circuits

abstract: A general scheme for the analog implementations of wavelet transform using switched_current (SI) filter circuits is presented. SI circuits are well suited for this application since the dilation constant across different scales of the transform can be precisely implemented and controlled by both the transconductance ratios (W/L) and the clock frequency. The circuits are composed of analog filter banks whose impulse responses are the first derivative of Gaussian and its dilations. The transfer functions of the basic wavelet filter can be given by Pad approximation, which can decompose the transfer function into rational form so as to be conveniently implemented by SI circuits. Therefore, it can easily be applied to other filters with a prescribed impulse response. Simulated results indicated a good performance of wavelet transform.


title: Self-reported dependence on mobile phones in young adults: A European cross-cultural empirical survey

abstract: Background and aimsDespite many positive benefits, mobile phone use can be associated with harmful and detrimental behaviors. The aim of this study was twofold: to examine (a) cross-cultural patterns of perceived dependence on mobile phones in ten European countries, first, grouped in four different regions (North: Finland and UK; South: Spain and Italy; East: Hungary and Poland; West: France, Belgium, Germany, and Switzerland), and second by country, and (b) how socio-demographics, geographic differences, mobile phone usage patterns, and associated activities predicted this perceived dependence.MethodsA sample of 2,775 young adults (aged 18–29 years) were recruited in different European Universities who participated in an online survey. Measures included socio-demographic variables, patterns of mobile phone use, and the dependence subscale of a short version of the Problematic Mobile Phone Use Questionnaire (PMPUQ; Billieux, Van der Linden, & Rochat, 2008).ResultsThe young adults from the Northern and So...


How to conduct systematic reviews more expeditiously?


title: Location Privacy Violation via GPS-Agnostic Smart Phone Car Tracking

abstract: Smart phones nowadays are equipped with global positioning systems (GPS) chips to enable navigation and location-based services. A malicious app with the access to GPS data can easily track the person who carries the smart phone. People may disable the GPS module and turn it on only when necessary to protect their location privacy. However, in this paper, we demonstrate that an attacker is still able to track a person by using the embedded magnetometer sensor in victim's smart phone, even when the GPS module is disabled all the time. Moreover, this attack neither requests user permissions related to locations for installation, nor does its operation rely on wireless signals like WiFi positioning or suffer from signal propagation loss. Only the angles of a car's turning measured by the magnetometer sensor of a driver's smart phone are utilized. Without loss of generality, we focus on car tracking, since cars are popular transportation tools in developed countries, where smart phones are commonly used. Inspired by the intuition that a car may exhibit different turning angles at different road intersections, we find that an attacker can match car turning angles to a map to infer the actual path that the driver takes. We address technical challenges about car turn angle extraction, map database construction, and path matching algorithm design to make this attack practical and efficient. We also perform an evaluation using real-world driving paths to verify the relationship between the numbers of turns and the time cost of the matching algorithm. The results show that it is possible for attacker to precisely pinpoint the actual path when the driving path includes 11 turns or more. More simulations are performed to demonstrate the attack with lager selected local areas.


title: Joint Copying and Restricted Generation for Paraphrase

abstract: Many natural language generation tasks, such as abstractive summarization and text simplification, are paraphrase-orientated. In these tasks, copying and rewriting are two main writing modes. Most previous sequence-to-sequence (Seq2Seq) models use a single decoder and neglect this fact. In this paper, we develop a novel Seq2Seq model to fuse a copying decoder and a restricted generative decoder. The copying decoder finds the position to be copied based on a typical attention model. The generative decoder produces words limited in the source-specific vocabulary. To combine the two decoders and determine the final output, we develop a predictor to predict the mode of copying or rewriting. This predictor can be guided by the actual writing mode in the training data. We conduct extensive experiments on two different paraphrase datasets. The result shows that our model outperforms the state-of-the-art approaches in terms of both informativeness and language quality.


A CRD-WEL System for Chemical-disease Relations Extraction


title: Analog-DFE-based 16Gb/s SerDes in 40nm CMOS that operates across 34dB loss channels at Nyquist with a baud rate CDR and 1.2Vpp voltage-mode driver

abstract: In networking systems today data rates are increasing beyond 15Gb/s and yet the installed backplanes are made of low cost materials with losses in excess of 30dB at 7.5GHz. Standards, such as IEEE802.3ap-10GBASE-KR and OIF-CEI25G, are specifying SerDes requirements for channels with 25dB loss at Nyquist and this has driven the development of SerDes with 4 or 5 tap DFEs [1]. Until now, solutions for 34dB or more channel loss have been limited to 10.3Gb/s or below [2,3], whereas this paper describes an adaptive 14-tap DFE that achieves a 10−17 BER across a 34dB loss channel at 16Gb/s for a power of 235mW/lane. A baud-rate CDR technique is specifically developed that gives excellent locking characteristics and alignment for use with a speculative DFE together with an enhanced swing TX voltage mode driver.


title: Irony Detection with Attentive Recurrent Neural Networks

abstract: Automatic Irony Detection refers to making computer understand the real intentions of human behind the ironic language. Much work has been done using classic machine learning techniques applied on various features. In contrast to sophisticated feature engineering, this paper investigates how the deep learning can be applied to the intended task with the help of word embedding. Three different deep learning models, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Attentive RNN, are explored. It shows that the Attentive RNN achieves the state-of-the-art on Twitter datasets. Furthermore, with a closer look at the attention vectors generated by Attentive RNN, an insight into how the attention mechanism helps find out the linguistic clues of ironic utterances is provided.


title: Clinical importance of changes in chronic pain intensity measured on an 11-point numerical pain rating scale

abstract: Pain intensity is frequently measured on an 11-point pain intensity numerical rating scale (PI-NRS), where 0=no pain and 10=worst possible pain. However, it is difficult to interpret the clinical importance of changes from baseline on this scale (such as a 1- or 2-point change). To date, there are no data driven estimates for clinically important differences in pain intensity scales used for chronic pain studies. We have estimated a clinically important difference on this scale by relating it to global assessments of change in multiple studies of chronic pain. Data on 2724 subjects from 10 recently completed placebo-controlled clinical trials of pregabalin in diabetic neuropathy, postherpetic neuralgia, chronic low back pain, fibromyalgia, and osteoarthritis were used. The studies had similar designs and measurement instruments, including the PI-NRS, collected in a daily diary, and the standard seven-point patient global impression of change (PGIC), collected at the endpoint. The changes in the PI-NRS from baseline to the endpoint were compared to the PGIC for each subject. Categories of "much improved" and "very much improved" were used as determinants of a clinically important difference and the relationship to the PI-NRS was explored using graphs, box plots, and sensitivity/specificity analyses. A consistent relationship between the change in PI-NRS and the PGIC was demonstrated regardless of study, disease type, age, sex, study result, or treatment group. On average, a reduction of approximately two points or a reduction of approximately 30% in the PI-NRS represented a clinically important difference. The relationship between percent change and the PGIC was also consistent regardless of baseline pain, while higher baseline scores required larger raw changes to represent a clinically important difference. The application of these results to future studies may provide a standard definition of clinically important improvement in clinical trials of chronic pain therapies. Use of a standard outcome across chronic pain studies would greatly enhance the comparability, validity, and clinical applicability of these studies.


title: Improving the classification of newborn EEG time-frequency representations using a combined time-frequency signal and image approach

abstract: This paper presents new time-frequency (T-F) features to improve the classification of non-stationary signals such as EEG signals. Previous methods were based only on signal features that were derived from the instantaneous frequency and energies of EEG signals in different spectral sub-bands. This paper includes new features that are based on T-F image descriptors which are extracted from the T-F representation considered as an image, using T-F image processing techniques. The results obtained on newborn EEG data, show that the use of image related-features with signal based-features improve the performance of the newborn EEG seizure detection and classification when using multi-SVM classifiers. These results allow the possibility of improving health outcomes for sick babies by early intervention on the basis of the results of the classification of newborn EEG abnormalities.


title: Serum Gastrin in Chronic Gastritis

abstract: Fasting gastrin levels in serum were measured in 49 patients with different types of chronic gastritis and in matched controls. In 15 patients with established pernicious anaemia the mean (± S.E. of mean) level of gastrin was greatly raised (699 ± 99 pg/ml). In 17 patients with chronic atrophic gastritis, seropositive for parietal cell antibody but with adequate vitamin-B 12 absorption, the level was also raised (476 ± 74 pg/ml). By contrast, in “simple” atrophic gastritis seronegative for parietal cell antibody the gastrin levels were significantly lower for both diffuse atrophic gastritis (129 ± 31 pg/ml) and multifocal gastritis (14 ± 4 pg/ml). These levels were similar to those in the controls (46 ± 7 pg/ml). The mechanism of the raised gastrin levels remains uncertain, but neither achlorhydria nor in vivo action of the parietal cell antibody wholly accounted for the hypergastrinaemia. We conclude that hypergastrinaemia is characteristic of gastritis associated with autoimmune reactions to gastric antigens and pernicious anaemia and that a raised serum gastrin is a useful marker of the type of gastritis that tends to progress to the gastric lesion of pernicious anaemia. The findings suggest that this type of gastritis is an essentially different disease from “simple” atrophic gastritis, and the differences in gastrin levels may be due to sparing of the antral mucosa in the autoimmune type but not in “simple” gastritis.


title: Alzheimer's disease diagnostics by a 3D deeply supervised adaptable convolutional network.

abstract: Early diagnosis is playing an important role in preventing progress of the Alzheimer's disease (AD). This paper proposes to improve the prediction of AD with a deep 3D Convolutional Neural Network (3D-CNN), which can show generic features capturing AD biomarkers extracted from brain images, adapt to different domain datasets, and accurately classify subjects with improved fine-tuning method. The 3D-CNN is built upon a convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans for source domain. Fully connected upper layers of the 3D-CNN are then fine-tuned for each task-specific AD classification in target domain. In this paper, deep supervision algorithm is used to improve the performance of already proposed 3D Adaptive CNN. Experiments on the ADNI MRI dataset without skull-stripping preprocessing have shown that the proposed 3D Deeply Supervised Adaptable CNN outperforms several proposed approaches, including 3D-CNN model, other CNN-based methods and conventional classifiers by accuracy and robustness. Abilities of the proposed network to generalize the features learnt and adapt to other domains have been validated on the CADDementia dataset.


title: Factors Influencing Business Process Standardization: A Multiple Case Study

abstract: Organizations make determined efforts to standardize their business processes in order to gain performance improvements. Having integrated and standardized data as well as processes reduces costs, improves collaboration and eases decision making for managers. Higher quality levels can be reached by successfully implemented standard processes. But due to the wide variety of business process types several challenges for the standardization of business processes exist. The diversity of business processes makes business process management (BPM) a cumbersome and complex challenge. Therefore, the main aim of this paper is to identify business process standardization success factors. As a first step, we use the categorization criteria offered by Lillrank as a starting point for our research and develop a theoretical research model of business process standardization success. Afterwards we conduct a preliminary test of our model in two case studies at two German companies.


title: An Approach to Blind Source Separation Based on Temporal Structure of Speech Signals

abstract: Abstract In this paper, we introduce a new technique for blind source separation of speech signals. We focus on the temporal structure of the signals. The idea is to apply the decorrelation method proposed by Molgedey and Schuster in the time–frequency domain. Since we are applying separation algorithm on each frequency separately, we have to solve the amplitude and permutation ambiguity properly to reconstruct the separated signals. For solving the amplitude ambiguity, we use the matrix inversion and for the permutation ambiguity, we introduce a method based on the temporal structure of speech signals. We show some results of experiments with both artificially controlled data and speech data recorded in the real environment.


title: Relational grammars for interactive design

abstract: This paper describes an approach to interactive design tasks based on relational grammars. Relational grammars extend traditional one-dimensional string languages to higher dimensions through user-supplied domain relations. Design support takes the form of graphic inferences on partial input during design interaction with the user. Because of the nature of the rule definitions, design elements can be roughly 'sketched' without concern for their specific details. During the interaction process, the system installs constraints, adds default attributes, and/or builds higher-level composite structures out of the original input. An example scenario of the use of this formalism is presented supporting the task of document design. A general algorithm for the integration of grammar-based parsing in an interactive design environment is presented. >


title: The Development of Attention Systems and Working Memory in Infancy

abstract: In this paper, we review research and theory on the development of attention and working memory in infancy using a developmental cognitive neuroscience framework. We begin with a review of studies examining the influence of attention on neural and behavioral correlates of an earlier developing and closely related form of memory (i.e., recognition memory). Findings from studies measuring attention utilizing looking measures, heart rate, and event-related potentials (ERPs) indicate significant developmental change in sustained and selective attention across the infancy period. For example, infants show gains in the magnitude of the attention related response and spend a greater proportion of time engaged in attention with increasing age (Richards & Turner, 2001). Throughout infancy, attention has a significant impact on infant performance on a variety of tasks tapping into recognition memory; however, this approach to examining the influence of infant attention on memory performance has yet to be utilized in research on working memory. In the second half of the paper, we review research on working memory in infancy focusing on studies that provide insight into the developmental timing of significant gains in working memory as well as research and theory related to neural systems potentially involved in working memory in early development. We also examine issues related to measuring and distinguishing between working memory and recognition memory in infancy. To conclude, we discuss relations between the development of attention systems and working memory.


title: Prest: An Intelligent Software Metrics Extraction, Analysis and Defect Prediction Tool.

abstract: Test managers use intelligent predictors to increase testing efficiency and to decide on when to stop testing. However, those predictors would be impractical to use in an industry setting, unless measurement and prediction processes are automated. Prest as an open source tool aims to address this problem. Compared to other open source prediction and analysis tools Prest is unique that it collects source code metrics and call graphs in 5 different programming languages, and performs learning based defect prediction and analysis. So far Prest in real life industry projects helped companies to achieve an average of 32% efficiency increase in testing effort.


title: Bidirectional bridgeless PFC with reduced input current distortion and switching loss using gate skipping technique

abstract: Bidirectional bridgeless PFC is an advantageous topology for high power application among many bridgeless PFCs due to small EMI filter size and no reverse recovery problem. However due to the large junction capacitance of secondary leg's diodes, the dead angle is shown in the zero-crossing area. Thus, the input current distortion is intensified, and the unnecessary switching loss decreases the efficiency. In this paper, a simple gate skipping technique is implemented to the zero-crossing area of the bidirectional bridgeless PFC. As a result, the unnecessary switching loss of boost switches and the input current distortion are reduced. The improvement of the proposed method is verified by experimental results with the high line input and 800W(400V/2A) output prototype.


title: Cybersecurity Strategies: The QuERIES Methodology

abstract: QuERIES offers a novel multidisciplinary approach to quantifying risk associated with security technologies resulting in investment-efficient cybersecurity strategies. R esearchers can use the QuERIES methodology to rigorously determine, for the first time, appropriate investment levels and strategies for the protection of intellectual property in complex systems. As a result, it can have a significant and immediate impact on the protection of critical IP, including weapons systems and chip designs, complex computer software, and databases containing personal and financial information. In this paper, initial testing of QuERIES in small-scale, realistic scenarios, were performed with results that suggest the methodology can significantly improve risk assessments in complex systems under attack by rational and capable adversaries. Such systems include software, hardware, and data critical to national security and industrial competitiveness. Consequently, it is believed that QuERIES has wide applicability within both the DoD and private sectors.


title: Chinese interpreting studies: structural determinants of MA students’ career choices

abstract: During the last 30 years, the growth of the interpreting industry in China has been outstanding. Increasing economic and political collaboration has driven the demand for interpreters to bridge the linguistic and cultural divides that exist between China and the West. With the creation of master's and bachelor's degrees in interpreting and translation all over China, hundreds of graduates from various universities have since undertaken distinctly different career paths. Using an exhaustive corpus of Masters' theses and a combination of logistic regression and Targeted Maximum Likelihood Estimation to establish causalities, this paper focuses on some of the structural determinants of graduate students' career choices. The paper examines to what extent university affiliations, thesis advisors, research methodology and thesis content influence the choice to pursue an academic career. The research reveals that graduating from a top university makes students less likely to become academics, and studying under a top advisor does not necessarily increase an individual's chances of securing an academic post. By contrast, writers of empirical theses or ones that are about training are more likely to enter the academic sphere.


title: Pathologizing Possession: An Essay on Mind, Self, and Experience in Dissociation

abstract: In this paper, critique the classic psychoanalytic anthropological construal of dissociative spirit possession as a pathological phenomenon. I review some of the relevant theoretical and ethnographic literature on this subject but focus on the work of two prominent psychoanalytic anthropologists to explore divergent views of the psychological nature of pathological and religious experience. Emphasis is placed on the necessity for taking into account the culture specific factors that shape dissociative possession, particularly with regard to spiritual experiences. I also move beyond this view to an embodiment approach that is useful for analyzing the experiential ground of spirit possession, and thus for providing insight into how particular individual and cultural realities are constructed through dissociation. Key words: dissociation, embodiment, possession, psychoanalytic anthropology, self ::: ::: "Perception is never an absolute revelation of 'what is' " -A.l. Hallovvell (1955: 84)


6 Data Mining Using RFM Analysis


title: GRfid: A Device-Free RFID-Based Gesture Recognition System

abstract: Gesture recognition has emerged recently as a promising application in our daily lives. Owing to low cost, prevalent availability, and structural simplicity, RFID shall become a popular technology for gesture recognition. However, the performance of existing RFID-based gesture recognition systems is constrained by unfavorable intrusiveness to users, requiring users to attach tags on their bodies. To overcome this, we propose GRfid, a novel device-free gesture recognition system based on phase information output by COTS RFID devices. Our work stems from the key insight that the RFID phase information is capable of capturing the spatial features of various gestures with low-cost commodity hardware. In GRfid, after data are collected by hardware, we process the data by a sequence of functional blocks, namely data preprocessing, gesture detection, profiles training, and gesture recognition, all of which are well-designed to achieve high performance in gesture recognition. We have implemented GRfid with a commercial RFID reader and multiple tags, and conducted extensive experiments in different scenarios to evaluate its performance. The results demonstrate that GRfid can achieve an average recognition accuracy of $96.5$ and $92.8$ percent in the identical-position and diverse-positions scenario, respectively. Moreover, experiment results show that GRfid is robust against environmental interference and tag orientations.


title: Endocrine Treatment of Transsexual Persons:An Endocrine Society Clinical Practice Guideline

abstract: Objective: The aim was to formulate practice guidelines for endocrine treatment oftranssexual persons. Evidence: This evidence-based guideline was developed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system to describe the strength of recommendations and the quality of evidence, which was low or very low. Consensus Process: Committees and members of The Endocrine Society, European Society of Endocrinology, European Society for Paediatric Endocrinology, Lawson Wilkins Pediatric Endocrine Society, and World Professional Association for Transgender Health commented on preliminary drafts of these guidelines. Conclusions: Transsexual persons seeking to develop the physical characteristics of the desired gender require a safe, effective hormone regimen that will 1) suppress endogenous hormone secretion determined by the person’s genetic/biologic sex and 2) maintain sex hormone levels within the normal range for the person’s desired gender. A mental health professional ...


title: Combining modality specific deep neural networks for emotion recognition in video

abstract: In this paper we present the techniques used for the University of Montreal's team submissions to the 2013 Emotion Recognition in the Wild Challenge. The challenge is to classify the emotions expressed by the primary human subject in short video clips extracted from feature length movies. This involves the analysis of video clips of acted scenes lasting approximately one-two seconds, including the audio track which may contain human voices as well as background music. Our approach combines multiple deep neural networks for different data modalities, including: (1) a deep convolutional neural network for the analysis of facial expressions within video frames; (2) a deep belief net to capture audio information; (3) a deep autoencoder to model the spatio-temporal information produced by the human actions depicted within the entire scene; and (4) a shallow network architecture focused on extracted features of the mouth of the primary human subject in the scene. We discuss each of these techniques, their performance characteristics and different strategies to aggregate their predictions. Our best single model was a convolutional neural network trained to predict emotions from static frames using two large data sets, the Toronto Face Database and our own set of faces images harvested from Google image search, followed by a per frame aggregation strategy that used the challenge training data. This yielded a test set accuracy of 35.58%. Using our best strategy for aggregating our top performing models into a single predictor we were able to produce an accuracy of 41.03% on the challenge test set. These compare favorably to the challenge baseline test set accuracy of 27.56%.


title: Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition

abstract: Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) architecture that has been designed to address the vanishing and exploding gradient problems of conventional RNNs. Unlike feedforward neural networks, RNNs have cyclic connections making them powerful for modeling sequences. They have been successfully used for sequence labeling and sequence prediction tasks, such as handwriting recognition, language modeling, phonetic labeling of acoustic frames. However, in contrast to the deep neural networks, the use of RNNs in speech recognition has been limited to phone recognition in small scale tasks. In this paper, we present novel LSTM based RNN architectures which make more effective use of model parameters to train acoustic models for large vocabulary speech recognition. We train and compare LSTM, RNN and DNN models at various numbers of parameters and configurations. We show that LSTM models converge quickly and give state of the art speech recognition performance for relatively small sized models.


title: Activity-Driven Clock Design

abstract: In this paper, we investigate reducing the power consumption of a synchronous digital system by minimizing the total power consumed by the clock signals. We construct activity-driven clock trees wherein sections of the clock tree are turned off by gating the clock signals. Since gating the clock signal implies that additional control signals and gates are needed, there exists a tradeoff between the amount of clock tree gating and the total power consumption of the clock tree. We exploit similarities in the switching activity of the clocked modules to reduce the number of clock gates. Assuming a given switching activity of the modules, we propose three novel activity-driven problems: a clock tree construction problem, a clock gate insertion problem, and a zero-skew clock gate insertion problem. The objective of these problems is to minimize the system's power consumption by constructing an activity-driven clock tree. We propose an approximation algorithm based on recursive matching to solve the clock tree construction problem. We also propose an exact algorithm employing the dynamic programming paradigm to solve the gate insertion problems. Finally, we present experimental results that verify the effectiveness of our approach. This paper is a step in understanding how high-level decisions (e.g., behavioral design) can affect a low-level design (e.g., clock design).


title: Fully complex extreme learning machine

abstract: Recently, a new learning algorithm for the feedforward neural network named the extreme learning machine (ELM) which can give better performance than traditional tuning-based learning methods for feedforward neural networks in terms of generalization and learning speed has been proposed by Huang et al. In this paper, we first extend the ELM algorithm from the real domain to the complex domain, and then apply the fully complex extreme learning machine (C-ELM) for nonlinear channel equalization applications. The simulation results show that the ELM equalizer significantly outperforms other neural network equalizers such as the complex minimal resource allocation network (CMRAN), complex radial basis function (CRBF) network and complex backpropagation (CBP) equalizers. C-ELM achieves much lower symbol error rate (SER) and has faster learning speed.


title: An Instructional Unit for Teaching Project Management Tools Aligned with PMBOK

abstract: Project Management (PM) tools are necessary to manage contemporary software projects. Computer professionals need to know how to use these tools, and higher-education computer courses teach their usage. Therefore, educators often adopt general usage tools, such as MS Project. However, as such tools lack didactic features, educational PM tools have been developed. Yet, previous studies have shown that even instructional units (IU) based on such educational PM tools do not cover the entire PM process as defined by PMBOK. This article presents an instructional unit based on an open-source educational PM tool, dotProject+, using the tool to support learning a large part of the PM process. We describe the instructional design process used to develop the IU, as well as the evaluation of the IU in a series of case studies. The results indicate that this IU contributes to student learning and assists teachers.


title: An optimization technique for reducing the PAPR in MIMO-OFDM technique by using FPPC scheme

abstract: In this practical world, MIMO (Multiple Input Multiple Output) system with OFDM technology plays a competent role in wireless transmission. It will transmit high speed data in delay scattering surroundings through multicarrier modulation that reduces multipath attenuation, in order that the spectral efficiency of the complete system is exaggerated. It realize its manner in digital acoustic and video broadcasting and fourth generation cellular systems. The obstacle during this approach is high peak to average power ratio (PAPR) that thunderous disturbs the quality of power amplifiers. There are unit varied modes to attenuate PAPR. In this survey, we tend to space unit close to switch views regarding selective mapping (SLM), clipping & filtering and partial transmits sequence (PTS) techniques to minimize the results of PAPR. In this paper we are exchanging our views about flexible peak power cancellation for reducing PAPR to a great extent.


title: Gait phase detection based on non-contact capacitive sensing: Preliminary results

abstract: Gait phase detection is essential to the control of lower-limb exoskeletons. In this paper, we present a non-contact capacitive sensing strategy for gait phase detection to replace foot pressure sensors. The designed capacitance sensing system can record signals of human muscle contraction from the leg. The electrodes are non-contact with the skin, which are fixed on the particularly designed cuffs. To evaluate the performance of the capacitance sensing on gait phase detection, two experiments are conducted on healthy subjects. With selected features and sliding window classification method, the proposed method obtains 98.3% average accuracy with the sensing cuff on the shank and 96.5% accuracy with the sensing cuff on the thigh for level walking tasks. The system also accurately recognizes the gait events (largest error rate smaller than 0.6%) when walking speed changes. The preliminary results indicate that the proposed sensing strategy is a promising solution to provide useful gait information for exoskeleton control.


title: Poultry and policy in Ghana: Lessons from the periphery of an agricultural policy system

abstract: We analyze poultry‐related policy documents from Ghana and ask how the problems identified and the actions proposed have changed over time. The analysis highlights the fact of limited and poor quality data, and associated uncertainty, ambiguity and poor specification of both problems and interventions. It also points to a long‐term commitment to intensive commercial production as the preferred pathway to deliver more animal protein. However, in the face of persistent feed constraints and economic liberalization, imports of frozen chicken have sky‐rocketed since the early 2000s, and a new pathway has come to dominate. We discuss this outcome and what it says more generally about policy around minor crops and livestock species.


title: Latent Semantic Word Sense Induction and Disambiguation

abstract: In this paper, we present a unified model for the automatic induction of word senses from text, and the subsequent disambiguation of particular word instances using the automatically extracted sense inventory. The induction step and the disambiguation step are based on the same principle: words and contexts are mapped to a limited number of topical dimensions in a latent semantic word space. The intuition is that a particular sense is associated with a particular topic, so that different senses can be discriminated through their association with particular topical dimensions; in a similar vein, a particular instance of a word can be disambiguated by determining its most important topical dimensions. The model is evaluated on the semeval-2010 word sense induction and disambiguation task, on which it reaches state-of-the-art results.


title: A Study of the Effects of Immersion on Shortterm Spatial Memory

abstract: The goal of the study reported in the paper was to determine whether the level of immersion of a virtual environment has a significant effect on users' short-term spatial memory. The study involved forty-four (44) subjects age 18-44. The participants were asked to navigate a virtual 3D world and find a goal object located in the environment. 22 subjects performed the task using a non-immersive system (i.e. desktop computer); 22 subjects performed the task using a partially immersive platform (i.e. a Fish- tank system). The time required by the subjects to complete the finding task was recorded and used as a measure of the effect of immersion on short-term spatial memory. Although the study has several limitations, findings show that the level of immersion affects spatial memory: a higher level of immersion appears to improve short-term spatial memory.


title: Semi-supervised Learning by Entropy Minimization

abstract: We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regularization, which enables to incorporate unlabeled data in the standard supervised learning. Our approach includes other approaches to the semi-supervised problem as particular or limiting cases. A series of experiments illustrates that the proposed solution benefits from unlabeled data. The method challenges mixture models when the data are sampled from the distribution class spanned by the generative model. The performances are definitely in favor of minimum entropy regularization when generative models are misspecified, and the weighting of unlabeled data provides robustness to the violation of the "cluster assumption". Finally, we also illustrate that the method can also be far superior to manifold learning in high dimension spaces.


title: The Human Brain Project: Social and Ethical Challenges

abstract: Focusing on the Human Brain Project, I discuss some social and ethical challenges raised by such programs of research: the possibility of a unified knowledge of "the brain," balancing privacy and the public good, dilemmas of "dual use," brain-computer interfaces, and "responsible research and innovation" in governance of emerging technologies.


title: Gender and Sentencing: What We Know and Don't Know from Empirical Research

abstract: Many claims are made about gender differences ::: in sentencing. I review what we know and don't ::: know from empirical research on judicial paternalism ::: and the more lenient treatment of women compared ::: to men. I then challenge several arguments made by ::: llene Nagel and Barry Johnson1 on equal treatment, ::: special treatment, and gender-neutrality in sentenc ::: ing policies. I conclude by suggesting the need to ::: contemplate women as sentencing subjects


title: A Review of Technical Approaches to Realizing Near-Field Communication Mobile Payments

abstract: This article describes and compares four approaches to storing payment keys and executing payment applications on mobile phones via near-field communication at the point of sale. Even though the comparison hinges on security--specifically, how well the keys and payment application are protected against misuse--other criteria such as hardware requirements, availability, management complexity, and performance are also identified and discussed.


title: Influence Maximization in Trajectory Databases

abstract: In this paper, we study a novel problem of influence maximization in trajectory databases that is very useful in precise location-aware advertising. It finds $k$ best trajectories to be attached with a given advertisement and maximizes the expected influence among a large group of audience. We show that the problem is NP-hard and propose both exact and approximate solutions to find the best set of trajectories. In the exact solution, we devise an expansion-based framework that enumerates trajectory combinations in a best-first manner and propose three types of upper bound estimation techniques to facilitate early termination. In addition, we propose a novel trajectory index to reduce the influence calculation cost. To support large $k$ , we propose a greedy solution with an approximation ratio of (1 − 1/e), whose performance is further optimized by a new proposed cluster-based method. We also propose a threshold method that can support any approximation ratio $\epsilon \in (0,1]$ . In addition, we extend our problem to support the scenario when there are a group of advertisements. In our experiments, we use real datasets to construct user profiles, motion patterns, and trajectory databases. The experimental results verified the efficiency of our proposed methods.


title: OntoBayes: An Ontology-Driven Uncertainty Model

abstract: This paper describes an ontology-driven model, which integrates Bayesian networks (BN) into the Ontology Web Language (OWL) to preserve the advantages of both. This model makes use of probability and dependency-annotated OWL to represent uncertain information in BN structures. These extensions enhance knowledge representation in OWL and enable agents to act under uncertainty and complex structured open environments at the same time. This paper presents the underlying principles and scratches the surface of the decision theoretic agent system design based on "OntoBayes"


title: Combining Open Source Annotators for Entity Linking through Weighted Voting

abstract: An English entity linking (EL) workflow is presented, which combines the annotations of five public open source EL services. The annotations are combined through a weighted voting scheme inspired by the ROVER method , which had not been previously tested on EL outputs. The combined results improved over each individual system's results, as evaluated on four different golden sets.


title: Emergent Translation in Multi-Agent Communication

abstract: While most machine translation systems to date are trained on large parallel corpora, humans learn language in a different way: by being grounded in an environment and interacting with other humans. In this work, we propose a communication game where two agents, native speakers of their own respective languages, jointly learn to solve a visual referential task. We find that the ability to understand and translate a foreign language emerges as a means to achieve shared goals. The emergent translation is interactive and multimodal, and crucially does not require parallel corpora, but only monolingual, independent text and corresponding images. Our proposed translation model achieves this by grounding the source and target languages into a shared visual modality, and outperforms several baselines on both word-level and sentence-level translation tasks. Furthermore, we show that agents in a multilingual community learn to translate better and faster than in a bilingual communication setting.


title: DeepPrior++: Improving Fast and Accurate 3D Hand Pose Estimation

abstract: DeepPrior [18] is a simple approach based on Deep Learning that predicts the joint 3D locations of a hand given a depth map. Since its publication early 2015, it has been outperformed by several impressive works. Here we show that with simple improvements: adding ResNet layers, data augmentation, and better initial hand localization, we achieve better or similar performance than more sophisticated recent methods on the three main benchmarks (NYU, ICVL, MSRA) while keeping the simplicity of the original method. Our new implementation is available at https://github.com/moberweger/deep-prior-pp.


title: On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses

abstract: Neural networks are known to be vulnerable to adversarial examples. In this note, we evaluate the two white-box defenses that appeared at CVPR 2018 and find they are ineffective: when applying existing techniques, we can reduce the accuracy of the defended models to 0%.


title: ANALYSIS OF VITAL SIGNS MONITORING USING AN IR-UWB RADAR

abstract: Ultra-wide Band (UWB) technology is a new, useful and safe technology in the fleld of wireless body networks. This paper focuses on the feasibility of estimating vital signs | speciflcally breathing rate and heartbeat frequency | from the spectrum of recorded waveforms, using an impulse-radio (IR) UWB radar. To this end, an analytical model is developed to perform and interpret the spectral analysis. Both the harmonics and the intermodulation between respiration and heart signals are addressed. Simulations have been performed to demonstrate how they afiect the detection of vital signs and also to analyze the in∞uence of the pulse waveform. A fllter to cancel out breathing harmonics is also proposed to improve heart rate detection. The results of the experiments are presented under difierent scenarios which demonstrate the accuracy of the proposed technique for determining respiration and heartbeat rates. It has been shown that an IR-UWB radar can meet the requirements of typical biomedical applications such as non-invasive heart and respiration rate monitoring.


title: SAWSDL: Semantic Annotations for WSDL and XML Schema

abstract: Web services are important for creating distributed applications on the Web. In fact, they're a key enabler for service-oriented architectures that focus on service reuse and interoperability. The World Wide Web Consortium (W3C) has recently finished work on two important standards for describing Web services the Web Services Description Language (WSDL) 2.0 and Semantic Annotations for WSDL and XML Schema (SAWSDL). Here, the authors discuss the latter, which is the first standard for adding semantics to Web service descriptions.


title: Evolution of magnetic therapy from alternative to traditional medicine.

abstract: Static or electromagnetic fields have been used for centuries to control pain and other biologic problems, but scientific evidence of their effect had not been gathered until recently. This article explores the value of magnetic therapy in rehabilitation medicine in terms of static magnetic fields and time varying magnetic fields (electromagnetic). A historical review is given and the discussion covers the areas of scientific criteria, modalities of magnetic therapy, mechanisms of the biologic effects of magnetic fields, and perspectives on the future of magnetic therapy.


title: A Comparison of Borderline and Schizophrenic Patients for Childhood Life Events and Parent-Child Relationships*

abstract: Fifteen patients with DSM-III diagnoses of borderline personality disorder and 14 schizophrenics were administered two questionnaires by an interviewer blind to the diagnoses. The questionnaires were the Childhood Life Events and Family Characteristics Questionnaire and the Parental Bonding Instrument. A scale to monitor denial was also included.Borderlines, when compared with schizophrenics, reported having significantly more childhood sexual and physical abuse, more early separation from their mothers, more paternal criminality, and also more paternal overprotection and less maternal care. When compared with normal population data, they received significantly less care and were significantly more overprotected by both parents.


title: How will NFV/SDN transform service provider opex?

abstract: Software defined networking and network function virtualization are widely deemed two critical pillars of the future service provider network. The expectation for significant operations cost savings from network programmability, open APIs, and operations automation is frequently mentioned as one of the primary benefits of the NFV/SDN vision. Intuitively, the flexibility and simplification values attributed to NFV/SDN lead the industry to the conclusion that operating expenses will decrease. This article provides a view into the operational costs of a typical service provider and discusses how the NFV/SDN attributes can be expected to influence the business equation. The drivers of OPEX change, the directionality of the change, and the categories of OPEX most affected based on our analysis from interactions with a number of service providers worldwide are presented in a structured analysis.


title: Modified Getsinger's model for accurate determination of effective permittivity dispersion in multilayered microstrip lines

abstract: Getsinger's Longitudinal Section Electric model describing the propagation characteristics of a single layer microstrip line is modified to cater for ‘n’ layers. An accurate closed-form expression for two and three layered structures is presented and validated against full-wave numerical results. The computed effective permittivity for a randomly selected two and three layer dielectric microstrip line is presented. In the selected examples, for the modified Getsinger model, the dispersion of effective permittivity is within 2–3% relative error for two-layer and within 6–7% relative error for three-layer dielectric microstrip lines for frequency up to 50 GHz. In contrast, for the same structures, the relative errors in previously reported works are above 10% for two layers and above 15% for three-layer dielectric substrates.


title: Time-varying Hurst exponent for US stock markets

abstract: In this work, the dynamical behavior of the US stock markets is characterized on the basis of the temporal variations of the Hurst exponent estimated with detrended fluctuation analysis (DFA) over moving windows for the historical Dow Jones (1928–2007) and the S&P-500 (1950–2007) daily indices. According to the results drawn: (i) the Hurst exponent displays an erratic dynamics with some episodes alternating low and high persistent behavior, (ii) the major breakthrough of the long-term trend of the scaling behavior occurred in 1972, at the end of the Bretton Woods system, when the Hurst exponent shifted form a positive to a negative long-term trend. Other effects, such as the 1987 crisis and the emergence of anti-correlated behavior in the recent two years, are also discussed.


title: Solar PV array fed water pumping using BLDC motor drive with boost-buck converter

abstract: A boost-buck (BB) DC-DC converter is proposed for solar photovoltaic (SPV) array fed water pumping system using a permanent magnet brushless DC (BLDC) motor drive. To design a BB converter with suitable voltage control, DC-DC boost and buck converters are cascaded such that it accomplishes the purpose of maximum power point tracking (MPPT) and soft starting of the BLDC motor. The BB converter exhibits the advantages of both the boost and buck converters and interestingly emerges as a solution to problems associated with these converters in SPV applications. The good switch utilization, high efficiency, non-inverting output voltage and low stress on power devices are the features of BB converter. This paper deals with the starting, dynamic and steady state performances under varying atmospheric conditions and examines the effectiveness of the BLDC motor with the proposed BB converter for SPV based water pumping. Simulated results using MATLAB/Simulink followed by the experimental validation have demonstrated the suitability of this drive for SPV based water pumping system.


title: Efficient ATPG for design validation based on partitioned state exploration histories

abstract: This paper introduces a new concept of state partitioning and state/transition exploration histories to generate test stimulus for the purpose of design validation. With our new state partitioning, during vector generation, state and transition exploration histories for each state group are maintained by dynamically constructing partial state transition graphs (STGs) for all state groups. By limiting a maximum size any state group can be, maintaining the complete state and transition exploration histories for each state group is feasible even for very large sequential circuits. While such histories are being collected, test vectors are generated using extracted spectral information from existing tests and genetic algorithm (GA) is used to explore new scenarios that are not in the histories. Experiments showed that much higher design error coverages together with smaller test sets are achieved with very short execution times.


title: The Adjusted Winner Procedure: Characterizations and Equilibria

abstract: The Adjusted Winner procedure is an important mechanism proposed by Brams and Taylor for fairly allocating goods between two agents. It has been used in practice for divorce settlements and analyzing political disputes. Assuming truthful declaration of the valuations, it computes an allocation that is envy-free, equitable and Pareto optimal. We show that Adjusted Winner admits several elegant characterizations, which further shed light on the outcomes reached with strategic agents. We find that the procedure may not admit pure Nash equilibria in either the discrete or continuous variants, but is guaranteed to have e-Nash equilibria for each e > 0. Moreover, under informed tiebreaking, exact pure Nash equilibria always exist, are Pareto optimal, and their social welfare is at least 3/4 of the optimal.


title: Exploring Success Factors of Virtual Communities : The Perspectives of Members and Operators

abstract: Virtual communities have been the focus of research for some time. However, although many studies have provided recommendations on how to build, extend, and manage virtual communities, few have verified the success factors they have considered essential for virtual communities. Conclusions made regarding basic preferences and distinct priorities of different stakeholders in virtual communities have not been empirically substantiated. Building on previous work on success factors of virtual communities, in this article, we present a ranking of success factors discussed in the literature based on an online survey among operators and members of virtual communities in the German speaking Internet. Consequently, we identified and analyzed incongruencies between members and operators. This research gains first empirically validated insights into success factors for establishing and managing virtual communities. We derived recommendations for operators of virtual communities on the basis of the findings, and we present an agenda for future research in the field.


title: User-guided white balance for mixed lighting conditions

abstract: Proper white balance is essential in photographs to eliminate color casts due to illumination. The single-light case is hard to solve automatically but relatively easy for humans. Unfortunately, many scenes contain multiple light sources such as an indoor scene with a window, or when a flash is used in a tungsten-lit room. The light color can then vary on a per-pixel basis and the problem becomes challenging at best, even with advanced image editing tools. We propose a solution to the ill-posed mixed light white balance problem, based on user guidance. Users scribble on a few regions that should have the same color, indicate one or more regions of neutral color, and select regions where the current color looks correct. We first expand the provided scribble groups to more regions using pixel similarity and a robust voting scheme. We formulate the spatially varying white balance problem as a sparse data interpolation problem in which the user scribbles and their extensions form constraints. We demonstrate that our approach can produce satisfying results on a variety of scenes with intuitive scribbles and without any knowledge about the lights.


title: Finding Implicit Features in Consumer Reviews for Sentiment Analysis

abstract: With the explosion of e-commerce shopping, customer reviews on the Web have become essential in the decision making process for consumers. Much of the research in this field focuses on explicit feature extraction and sentiment extraction. However, implicit feature extraction is a relatively new research field. Whereas previous works focused on finding the correct implicit feature in a sentence, given the fact that one is known to be present, this research aims at finding the right implicit feature without this pre-knowledge. Potential implicit features are assigned a score based on their co-occurrence frequencies with the words of a sentence, with the highest-scoring one being assigned to that sentence. To distinguish between sentences that have an implicit feature and the ones that do not, a threshold parameter is introduced, filtering out potential features whose score is too low. Using restaurant reviews and product reviews, the threshold-based approach improves the F1-measure by 3.6 and 8.7 percentage points, respectively.