title: Deep learning for fusing multi-sensor person-borne IED data

abstract: This paper presents the results from a feasibility study where an artificial neural network is applied to detect person-borne improvised explosive devices (IEDs) from imagery acquired using three different sensors; a radar array, an infrared (IR) camera, and a passive millimeter-wave camera. The data set was obtained from the U.S. Department of Homeland Security (DHS) Science and Technology Directorate (S&T), and consists of hundreds of images of human subjects concealing various simulated IEDs, and clutter objects, beneath different types of clothing. The network used for detection is a hybrid, where feature extraction is performed using a multi-layer convolutional neural network, also known as a deep learning network, and final classification performed using a support vector machine (SVM). The performance of the combined network is scored using receiver operating curves for each IED type and sensor configuration. The results demonstrate (i) that deep learning is effective at extracting useful information from sensor imagery, and (ii) that performance is boosted significantly by combining complementary data from different sensor types.


title: GarNet: A Two-Stream Network for Fast and Accurate 3D Cloth Draping

abstract: While Physics-Based Simulation (PBS) can accurately drape a 3D garment on a 3D body, it remains too costly for real-time applications, such as virtual try-on. By contrast, inference in a deep network, requiring a single forward pass, is much faster. Taking advantage of this, we propose a novel architecture to fit a 3D garment template to a 3D body. Specifically, we build upon the recent progress in 3D point cloud processing with deep networks to extract garment features at varying levels of detail, including point-wise, patch-wise and global features. We fuse these features with those extracted in parallel from the 3D body, so as to model the cloth-body interactions. The resulting two-stream architecture, which we call as GarNet, is trained using a loss function inspired by physics-based modeling, and delivers visually plausible garment shapes whose 3D points are, on average, less than 1 cm away from those of a PBS method, while running 100 times faster. Moreover, the proposed method can model various garment types with different cutting patterns when parameters of those patterns are given as input to the network.


title: The Smartphone-Based Offline Indoor Location Competition at IPIN 2016: Analysis and Future Work

abstract: This paper presents the analysis and discussion of the off-site localization competition track, which took place during the Seventh International Conference on Indoor Positioning and Indoor Navigation (IPIN 2016). Five international teams proposed different strategies for smartphone-based indoor positioning using the same reference data. The competitors were provided with several smartphone-collected signal datasets, some of which were used for training (known trajectories), and others for evaluating (unknown trajectories). The competition permits a coherent evaluation method of the competitors' estimations, where inside information to fine-tune their systems is not offered, and thus provides, in our opinion, a good starting point to introduce a fair comparison between the smartphone-based systems found in the literature. The methodology, experience, feedback from competitors and future working lines are described.


title: Using Kullback-Leibler distance for text categorization

abstract: A system that performs text categorization aims to assign appropriate categories from a predefined classification scheme to incoming documents. These assignments might be used for varied purposes such as filtering, or retrieval. This paper introduces a new effective model for text categorization with great corpus (more or less 1 million documents). Text categorization is performed using the Kullback-Leibler distance between the probability distribution of the document to classify and the probability distribution of each category. Using the same representation of categories, experiments show a significant improvement when the above mentioned method is used. KLD method achieve substantial improvements over the tfidf performing method.


title: Recycling misconceptions of perceived self-efficacy

abstract: This commentary addresses misconceptions concerning perceived self-efficacy contained in the article by Eastman and Marzillier. People who regard themselves as highly efficacious act, think, and feel differently from those who perceive themselves as inefficacious. Self-percepts of efficacy thus contribute significantly to performance accomplishments rather than residing in the host organism simply as inert predictors of behaviors to come. A substantial body of converging evidence is reviewed, lending validity to the proposition that perceived self-efficacy operates as one common mechanism through which diverse influences affect human action, thought, and affective arousal.


title: Diagnosing New York city's noises with ubiquitous data

abstract: Many cities suffer from noise pollution, which compromises people's working efficiency and even mental health. New York City (NYC) has opened a platform, entitled 311, to allow people to complain about the city's issues by using a mobile app or making a phone call; noise is the third largest category of complaints in the 311 data. As each complaint about noises is associated with a location, a time stamp, and a fine-grained noise category, such as "Loud Music" or "Construction", the data is actually a result of "human as a sensor" and "crowd sensing", containing rich human intelligence that can help diagnose urban noises. In this paper we infer the fine-grained noise situation (consisting of a noise pollution indicator and the composition of noises) of different times of day for each region of NYC, by using the 311 complaint data together with social media, road network data, and Points of Interests (POIs). We model the noise situation of NYC with a three dimension tensor, where the three dimensions stand for regions, noise categories, and time slots, respectively. Supplementing the missing entries of the tensor through a context-aware tensor decomposition approach, we recover the noise situation throughout NYC. The information can inform people and officials' decision making. We evaluate our method with four real datasets, verifying the advantages of our method beyond four baselines, such as the interpolation-based approach.


title: Analog Layout Generator for CMOS Circuits

abstract: In this paper, we present a new layout level automation tool for analog CMOS circuits, namely, analog layout generator (ALG). ALG is capable of generating individual or matched components as well as placement and routing. ALG takes performance considerations into account, optimizing the layout in each step. A distinguishing feature of the tool is primarily providing spectra of generation possibilities ranging from full custom to automatic generation. ALG is not only designed to work as a standalone tool but also implemented to be the final step of an analog automation flow. The flow supports circuit level specification in addition to layout level user specifications, so that it can be integrated into an analog automation system. Another feature of ALG is its interaction with a layout adviser tool, namely, YASA. YASA performs sensitivity simulations using a spicelike simulator providing sensitivities of performance parameters with respect to circuit parameters.


title: Comparison of IEEE AVB and AFDX

abstract: Avionics Full Duplex Ethernet (AFDX) is a well-established backbone network with wide usage in all current aircrafts, with its first usage onboard the Airbus A380. It was initially defined by Airbus and internationally standardized by ARINC (664 Part 7). The key features are 802.3 Ethernet compatibility, static routing based on a virtual link concept, reserved bandwidth with granted bandwidth allocation gaps, transparent redundancy and — most importantly — deterministic latency and jitter bounds. Beside several proprietary real time Ethernet solutions, an emerging open standard with support of major semiconductor companies, currently introduces guaranteed timing behavior with the focus on transportation of video and audio streams. Audio Video Bridging (AVB) is a set of IEEE standards (802.1AS, 802.1Qat, 802.1Qav, 802.1BA, 1722) developed under the Audio/Video Bridging Task group as part of 802.1, which defines protocol layers above the Ethernet MAC & LLC layers. AVB addresses low latency and highly synchronized playback of audio and video with low packet loss, low jitter and low latency. By using priority queuing (PQ) and credit-based shaper algorithms (CBQ), it aims at guaranteeing a maximum latency of 2 ms over 7 hops for the highest priority class, with the transmission of several time sensitive and best effort streams in parallel. This work introduces the basic principles of the new AVB standard in details, compares it with the properties of the AFDX standard, and determines if the latency determinism of AVB is suitable for Avionic's requirements and if the new mass market controllers and switches developed for AVB could be a cost-effective alternative for Commercial off-the-shelf (COTS) based low DAL (Design assurance Level) systems.


title: Folksonomy-Based Visual Ontology Construction and Its Applications

abstract: An ontology hierarchically encodes concepts and concept relationships, and has a variety of applications such as semantic understanding and information retrieval. Previous work for building ontologies has primarily relied on labor-intensive human contributions or focused on text-based extraction. In this paper, we consider the problem of automatically constructing a folksonomy-based visual ontology (FBVO) from the user-generated annotated images. A systematic framework is proposed consisting of three stages as concept discovery, concept relationship extraction, and concept hierarchy construction. The noisy issues of the user-generated tags are carefully addressed to guarantee the quality of derived FBVO. The constructed FBVO finally consists of 139 825 concept nodes and millions of concept relationships by mining more than 2.4 million Flickr images. Experimental evaluations show that the derived FBVO is of high quality and consistent with human perception. We further demonstrate the utility of the derived FBVO in applications of complex visual recognition and exploratory image search.


title: Test Anxiety and Academic Performance among Undergraduates: The Moderating Role of Achievement Motivation

abstract: This study investigated the moderating role of achievement motivation in the relationship between test anxiety and academic performance. Three hundred and ninety three participants (192 males and 201 females) selected from a public university in Ondo State, Nigeria using a purposive sampling technique, participated in the study. They responded to measures of test anxiety and achievement motivation. Three hypotheses were tested using moderated hierarchical multiple regression analysis. Results showed that test anxiety had a negative impact on academic performance (β = –.23; p < .05). Achievement motivation had a positive impact on academic performance (β = .38; p < .05). Also, achievement motivation significantly moderated the relationship between test anxiety and academic performance (β = .10; p < .01). These findings suggest that university management should design appropriate psycho-educational interventions that would enhance students’ achievement motivation.


title: Assessing Computational Thinking in Students' Game Designs

abstract: Designing games requires a complex sequence of planning and executing actions. This paper suggests that game design requires computational thinking, and discusses two methods for analyzing computational thinking in games designed by students in the visual programming language Scratch. We present how these two analyses produce different narratives of computational thinking for our case studies, and reflect on how we plan to move forward with our larger analysis.


title: Pores and Ridges: Fingerprint Matching Using Level 3 Features

abstract: Fingerprint friction ridge details are generally described in a hierarchical order at three levels, namely, Level 1 (pattern), Level 2 (minutiae points) and Level 3 (pores and ridge shape). Although high resolution sensors (~1000 dpi) have become commercially available and have made it possible to reliably extract Level 3 features, most automated fingerprint identification systems (AFIS) employ only Level 1 and Level 2 features. As a result, increasing the scan resolution does not provide any matching performance improvement (1998). We develop a matcher that utilizes Level 3 features, including pores and ridge contours, for 1000 dpi fingerprint matching. Level 3 features are automatically extracted using wavelet transform and Gabor filters and are locally matched using the ICP algorithm. Our experiments on a median-sized database show that Level 3 features carry significant discriminatory information. EER values are reduced (relatively ~20%) when Level 3 features are employed in combination with Level 1 and 2 features


title: Autonomous Fruit Picking Machine: A Robotic Apple Harvester

abstract: This paper describes the construction and functionality of an Autonomous Fruit Picking Machine (AFPM) for robotic apple harvesting. The key element for the success of the AFPM is the integrated approach which combines state of the art industrial components with the newly designed flexible gripper. The gripper consist of a silicone funnel with a camera mounted inside. The proposed concepts guarantee adequate control of the autonomous fruit harvesting operation globally and of the fruit picking cycle particularly. Extensive experiments in the field validate the functionality of the AFPM.


title: Secure Coding: Building Security into the Software Development Life Cycle

abstract: Abstract Many of the security properties that are outlined repeatedly in the newer regulations and standards can easily be side-stepped. Too often the culprits are unsophisticated software development techniques, a lack of security- focused quality assurance, and scarce security training for software developers, software architects, and project managers. To meet future needs, opportunities, and threats associated with information security, security needs to be “baked in” to the overall systems development life-cycle process.


title: Filtering Spam by Using Factors Hyperbolic Tree

abstract: Most of current anti-spam techniques, like the Bayesian anti-spam algorithm, primarily use lexical matching for filtering unsolicited bulk E-mails (UBE) and unsolicited commercial E-mails (UCE). However, precision of spam filtering is usually low when the lexical matching algorithms are used in real dynamic environments. For example, an E-mail of refrigerator advertisements is useful for most families, but it is useless for Eskimos. The lexical matching anti-spam algorithms cannot distinguish such processed E-mails that are junk to most people but are useful for others. We propose a Factors Hyperbolic Tree (FHT) based algorithm that, unlike the lexical matching algorithms, handles spam filtering in a dynamic environment by considering various relevant factors. The new Ranked Term Frequency (RTF) algorithm is proposed to extract indicators from E-mails that are related to environmental factors. Type-1 and Type-2 fuzzy logic systems are used to evaluate the indicators and determine whether E-mails are spam based on the environmental factors. Additionally, weights of factors in a FHT database are continuously updated according to dynamic conditional factors in a real environment. Simulation results show that the FHT algorithm filters out spam with high precision. Furthermore, the FHT algorithm is more efficient than other methods when it filters E-mails with complex influencing factors. The main contribution of this paper is that the FHT based algorithm can filter E-mails based on influencing factors instead of matched words to allow dynamic filtering of spam E-mails.


Homomorphic Tallying for the Estonian Internet Voting System


title: A Multichannel Approach to Fingerprint Classification

abstract: Fingerprint classification provides an important indexing mechanism in a fingerprint database. An accurate and consistent classification can greatly reduce fingerprint matching time for a large database. We present a fingerprint classification algorithm which is able to achieve an accuracy better than previously reported in the literature. We classify fingerprints into five categories: whorl, right loop, left loop, arch, and tented arch. The algorithm uses a novel representation (FingerCode) and is based on a two-stage classifier to make a classification. It has been tested on 4000 images in the NIST-4 database. For the five-class problem, a classification accuracy of 90 percent is achieved (with a 1.8 percent rejection during the feature extraction phase). For the four-class problem (arch and tented arch combined into one class), we are able to achieve a classification accuracy of 94.8 percent (with 1.8 percent rejection). By incorporating a reject option at the classifier, the classification accuracy can be increased to 96 percent for the five-class classification task, and to 97.8 percent for the four-class classification task after a total of 32.5 percent of the images are rejected.


title: Deep neural networks for recognizing online handwritten mathematical symbols

abstract: This paper presents application of deep learning to recognize online handwritten mathematical symbols. Recently various deep learning architectures such as Convolution neural network (CNN), Deep neural network (DNN) and Long short term memory (LSTM) RNN have been applied to fields such as computer vision, speech recognition and natural language processing where they have been shown to produce state-of-the-art results on various tasks. In this paper, we apply max-out-based CNN and BLSTM to image patterns created from online patterns and to the original online patterns, respectively and combine them. We also compare them with traditional recognition methods which are MRF and MQDF by carrying out some experiments on CROHME database.


title: A Class-F CMOS Oscillator

abstract: An oscillator topology demonstrating an improved phase noise performance is proposed in this paper. It exploits the time-variant phase noise model with insights into the phase noise conversion mechanisms. The proposed oscillator is based on enforcing a pseudo-square voltage waveform around the LC tank by increasing the third-harmonic of the fundamental oscillation voltage through an additional impedance peak. This auxiliary impedance peak is realized by a transformer with moderately coupled resonating windings. As a result, the effective impulse sensitivity function (ISF) decreases thus reducing the oscillator's effective noise factor such that a significant improvement in the oscillator phase noise and power efficiency are achieved. A comprehensive study of circuit-to-phase-noise conversion mechanisms of different oscillators' structures shows the proposed class-F exhibits the lowest phase noise at the same tank's quality factor and supply voltage. The prototype of the class-F oscillator is implemented in TSMC 65-nm standard CMOS. It exhibits average phase noise of -136 dBc/Hz at 3 MHz offset from the carrier over 5.9-7.6 GHz tuning range with figure-of-merit of 192 dBc/Hz. The oscillator occupies 0.12 mm2 while drawing 12 mA from 1.25 V supply.


title: A Review of the Pinned Photodiode for CCD and CMOS Image Sensors

abstract: The pinned photodiode is the primary photodetector structure used in most CCD and CMOS image sensors. This paper reviews the development, physics, and technology of the pinned photodiode.


title: Maximum Power Point Tracking Scheme for PV Systems Operating Under Partially Shaded Conditions

abstract: Current-voltage and power-voltage characteristics of large photovoltaic (PV) arrays under partially shaded conditions are characterized by multiple steps and peaks. This makes the tracking of the actual maximum power point (MPP) [global peak (GP)] a difficult task. In addition, most of the existing schemes are unable to extract maximum power from the PV array under these conditions. This paper proposes a novel algorithm to track the global power peak under partially shaded conditions. The formulation of the algorithm is based on several critical observations made out of an extensive study of the PV characteristics and the behavior of the global and local peaks under partially shaded conditions. The proposed algorithm works in conjunction with a DC-DC converter to track the GP. In order to accelerate the tracking speed, a feedforward control scheme for operating the DC-DC converter is also proposed, which uses the reference voltage information from the tracking algorithm to shift the operation toward the MPP. The tracking time with this controller is about one-tenth as compared to a conventional controller. All the observations and conclusions, including simulation and experimental results, are presented.


title: Immersive Learning Technologies: Realism and Online Authentic Learning

abstract: THE DEVELOPMENT of immersive learning technologies in the form of virtual reality and advanced computer applications has meant that realistic creations of simulated environments are now possible. Such simulations have been used to great effect in training in the military, air force, and in medical training. But how realistic do problems need to be in education for effective learning to occur? Some authors and researchers argue that problems should be real, or that simulations should have ultrarealistic physical similarity to an actual context. This paper proposes that physical verisimilitude to real situations is of less importance in learning than “cognitive realism,” provided by immersing students in engaging and complex tasks. The paper presents a description of the theory and research that provide the foundations for this approach. Examples of courses employing cognitive, rather than physical, realism are presented together with the views of teachers, authors and instructional designers. Finally, the implications of this approach are discussed.


title: Design of low-pass filters with ultra-wide stopband using asymmetric DGS

abstract: A novel microstrip low-pass filter (LPF) with asymmetric defected ground structure (DGS) resonators is proposed. The asymmetric DGS is composed of a schematic Π-shaped DGS and is coupled to a T-shaped resonator at both terminators. By using this structure, the filter not only supports conventional DGS performances with a sharp rejection, but also exhibits an ultra-wide stopband compared with the traditional dumbbell-shaped DGS (DB-DGS) and the existing asymmetric DB-DGS. The successfully designed, implemented, and measured asymmetric DGS-LPF with 2 unit cells has a compact size of 36.8×12 mm2 and operates with an insertion loss of less than 2 dB from dc to 1.9 GHz. From 2.6 GHz to 6.3 GHz, the rejection is better than 30 dB. Using the Π-shaped resonator in the DGS-LPF design makes the LPF flexible in terms of controlling resonances, bandwidth, and the stopband. The proposed asymmetric DGS-LPF is easily integrated with radio-frequency/microwave circuitry at a low manufacturing cost, especially for wireless communication applications. In this letter, the design of the DGS-LPF is presented and discussed, as well as the improvements in the S-parameter response of the DGS-LPF.


title: High efficiency buck-boost converter with reduced average inductor current (RAIC) technique

abstract: This paper presents a buck-boost converter with high-efficiency and smooth-transition to extend the battery life of portable devices. The new control topology minimizes the switching and conduction losses at the same time even four switches are used. Therefore, over a wide input voltage range, the proposed buck-boost converter with minimum switching loss like the buck or boost converter can reduce the conduction loss through the use of the reduced average inductor current (RAIC) level. Especially, the converter offers good line/load regulation and thus provides a smooth and stable output voltage without being affected by the decreasing battery voltage. Experimental results show that the output voltage is regulated for the whole battery life time and the output transition is very smooth during mode transition. Besides, the output voltage ripple and the average inductor current are effectively minimized by the proposed control scheme. The peak efficiency is 97% and output voltage ripple is reduced from 100mV to within 10mV.


title: Challenges of Big Data Analysis

abstract: Big Data bring new opportunities to modern society and challenges to data scientists. On the one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This paper gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogenous assumptions in most statistical methods for Big Data cannot be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions.


title: Relation Extraction : A Survey

abstract: With the advent of the Internet, large amount of digital text is generated everyday in the form of news articles, research publications, blogs, question answering forums and social media. It is important to develop techniques for extracting information automatically from these documents, as lot of important information is hidden within them. This extracted information can be used to improve access and management of knowledge hidden in large text corpora. Several applications such as Question Answering, Information Retrieval would benefit from this information. Entities like persons and organizations, form the most basic unit of the information. Occurrences of entities in a sentence are often linked through well-defined relations; e.g., occurrences of person and organization in a sentence may be linked through relations such as employed at. The task of Relation Extraction (RE) is to identify such relations automatically. In this paper, we survey several important supervised, semi-supervised and unsupervised RE techniques. We also cover the paradigms of Open Information Extraction (OIE) and Distant Supervision. Finally, we describe some of the recent trends in the RE techniques and possible future research directions. This survey would be useful for three kinds of readers - i) Newcomers in the field who want to quickly learn about RE; ii) Researchers who want to know how the various RE techniques evolved over time and what are possible future research directions and iii) Practitioners who just need to know which RE technique works best in various settings.


Vorndam V. Rapid detection and typing of dengue viruses from clinical samples by using reverse transcriptase-polymerase chain reaction


title: You Can Run but You Can't Read: Preventing Disclosure Exploits in Executable Code

abstract: Code reuse attacks allow an adversary to impose malicious behavior on an otherwise benign program. To mitigate such attacks, a common approach is to disguise the address or content of code snippets by means of randomization or rewriting, leaving the adversary with no choice but guessing. However, disclosure attacks allow an adversary to scan a process - even remotely - and enable her to read executable memory on-the-fly, thereby allowing the just-in time assembly of exploits on the target site. In this paper, we propose an approach that fundamentally thwarts the root cause of memory disclosure exploits by preventing the inadvertent reading of code while the code itself can still be executed. We introduce a new primitive we call Execute-no-Read (XnR) which ensures that code can still be executed by the processor, but at the same time code cannot be read as data. This ultimately forfeits the self-disassembly which is necessary for just-in-time code reuse attacks (JIT-ROP) to work. To the best of our knowledge, XnR is the first approach to prevent memory disclosure attacks of executable code and JIT-ROP attacks in general. Despite the lack of hardware support for XnR in contemporary Intel x86 and ARM processors, our software emulations for Linux and Windows have a run-time overhead of only 2.2% and 3.4%, respectively.


title: Obscuro: A Bitcoin Mixer using Trusted Execution Environments

abstract: Bitcoin provides only pseudo-anonymous transactions, which can be exploited to link payers and payees -- defeating the goal of anonymous payments. To thwart such attacks, several Bitcoin mixers have been proposed, with the objective of providing unlinkability between payers and payees. However, existing Bitcoin mixers can be regarded as either insecure or inefficient. We present Obscuro, a highly efficient and secure Bitcoin mixer that utilizes trusted execution environments (TEEs). With the TEE's confidentiality and integrity guarantees for code and data, our mixer design ensures the correct mixing operations and the protection of sensitive data (i.e., private keys and mixing logs), ruling out coin theft and address linking attacks by a malicious service provider. Yet, the TEE-based implementation does not prevent the manipulation of inputs (e.g., deposit submissions, blockchain feeds) to the mixer, hence Obscuro is designed to overcome such limitations: it (1) offers an indirect deposit mechanism to prevent a malicious service provider from rejecting benign user deposits; and (2) scrutinizes blockchain feeds to prevent deposits from being mixed more than once (thus degrading anonymity) while being eclipsed from the main blockchain branch. In addition, Obscuro provides several unique anonymity features (e.g., minimum mixing set size guarantee, resistant to dropping user deposits) that are not available in existing centralized and decentralized mixers. Our prototype of Obscuro is built using Intel SGX and we demonstrate its effectiveness in Bitcoin Testnet. Our implementation mixes 1000 inputs in just 6.49 seconds, which vastly outperforms all of the existing decentralized mixers.


title: Comprehensive Two-Level Analysis of Static and Dynamic RBAC Constraints with UML and OCL

abstract: Organizations with stringent security requirements like banks or hospitals frequently adopt role-based access control (RBAC) principles to simplify their internal permission management. Authorization constraints represent a fundamental advanced RBAC concept enabling precise restrictions on access rights. Thereby, the complexity of the resulting security policies increases so that tool support for comfortable creation and adequate validation is required. We propose a new approach to developing and analyzing RBAC policies using UML for modeling RBAC core concepts and OCL to realize authorization constraints. Dynamic (i. e., time-dependent) constraints, their visual representation in UML and their analysis are of special interest. The approach results in a domain-specific language for RBAC which is highly configurable and extendable with respect to new RBAC concepts and classes of authorization constraints and allows the developer to validate RBAC policies in an effective way. The approach is supported by a UML and OCL validation tool.


title: Occupational stress and turnover intention: implications for nursing management.

abstract: BACKGROUND ::: The main purpose of this study was to explore the status of occupational stress among hospital nurses in Isfahan, Iran. It also aimed to examine the relationship between nurses' occupational stress and their intention to leave the hospital. ::: ::: ::: METHODS ::: The study employed a cross-sectional research design. A validated questionnaire was used to collect data from 296 nurses. Respondents were asked to rate the intensity of 30 common occupational stressors using a five-point scale. ::: ::: ::: RESULTS ::: A third of hospital nurses rated their occupational stress high. The major sources of stress were inadequate pay, inequality at work, too much work, staff shortage, lack of promotion, job insecurity and lack of management support. More than 35% of nurses stated that they are considering leaving the hospital, if they could find another job opportunity. Occupational stress was positively associated with nurses' turnover intentions. ::: ::: ::: CONCLUSION ::: Hospital managers should develop and apply appropriate policies and strategies to reduce occupational stress and consequently nurses' turnover intention.


Institutions and entrepreneurship in China: three regions, three tales


title: Antecedents and Impact of Knowledge Management Capability in Public Organizations

abstract: Public organizations are increasingly embracing knowledge management (KM) tools and practices. However, there is a lack of understanding and empirical research in the public context to offer relevant insights. This study examines how KM influences public organizations’ performance through the concept of KM capability. KM capability represents an organization’s ability to capture, share, apply, and create knowledge. The salient resources influencing KM capability are identified and their direct and interaction effects are examined. The effect of KM capability on organizational performance is also assessed. Findings from a survey of 101 public organizations indicate that the physical KM resources of KM technology support and non-IT KM investments significantly influence the level of KM capability. Physical KM resources also have stronger effect on KM capability when the organizational KM resource of senior management championship exists. In contrast, the effects of physical KM resources are weakened by centralized and formalized organizational structure. Implications of these findings for KM research and practice in public organizations are discussed.


title: Derivation of GFDM based on OFDM principles

abstract: This paper starts with presenting a fundamental principle based on which the celebrated orthogonal frequency division multiplexing (OFDM) waveform is constructed. It then extends the same principle to construct the newly introduced generalized frequency division multiplexing (GFDM) signals. This novel derivation sheds light on some interesting properties of GFDM. In particular, our derivation seamlessly leads to an implementation of GFDM transmitter which has significantly lower complexity than what has been reported so far. Our derivation also facilitates a trivial understanding of how GFDM (similar to OFDM) can be applied in MIMO channels.


title: Low-dimensional procedure for the characterization of human faces

abstract: A method is presented for the representation of (pictures of) faces. Within a specified framework the representation is ideal. This results in the characterization of a face, to within an error bound, by a relatively low-dimensional vector. The method is illustrated in detail by the use of an ensemble of pictures taken for this purpose.


title: Unsupervised author disambiguation using Dempster–Shafer theory

abstract: The name ambiguity problem presents many challenges for scholar finding, citation analysis and other related research fields. To attack this issue, various disambiguation methods combined with separate disambiguation features have been put forward. In this paper, we offer an unsupervised Dempster---Shafer theory (DST) based hierarchical agglomerative clustering algorithm for author disambiguation tasks. Distinct from existing methods, we exploit the DST in combination with Shannon's entropy to fuse various disambiguation features and come up with a more reliable candidate pair of clusters for amalgamation in each iteration of clustering. Also, some solutions to determine the convergence condition of the clustering process are proposed. Depending on experiments, our method outperforms three unsupervised models, and achieves comparable performances to a supervised model, while does not prescribe any hand-labelled training data.


title: DeepWeak: Reasoning common software weaknesses via knowledge graph embedding

abstract: Common software weaknesses, such as improper input validation, integer overflow, can harm system security directly or indirectly, causing adverse effects such as denial-of-service, execution of unauthorized code. Common Weakness Enumeration (CWE) maintains a standard list and classification of common software weakness. Although CWE contains rich information about software weaknesses, including textual descriptions, common sequences and relations between software weaknesses, the current data representation, i.e., hyperlined documents, does not support advanced reasoning tasks on software weaknesses, such as prediction of missing relations and common consequences of CWEs. Such reasoning tasks become critical to managing and analyzing large numbers of common software weaknesses and their relations. In this paper, we propose to represent common software weaknesses and their relations as a knowledge graph, and develop a translation-based, description-embodied knowledge representation learning method to embed both software weaknesses and their relations in the knowledge graph into a semantic vector space. The vector representations (i.e., embeddings) of software weaknesses and their relations can be exploited for knowledge acquisition and inference. We conduct extensive experiments to evaluate the performance of software weakness and relation embeddings in three reasoning tasks, including CWE link prediction, CWE triple classification, and common consequence prediction. Our knowledge graph embedding approach outperforms other description- and/or structure-based representation learning methods.


title: Analog Computation by DNA Strand Displacement Circuits

abstract: DNA circuits have been widely used to develop biological computing devices because of their high programmability and versatility. Here, we propose an architecture for the systematic construction of DNA circuits for analog computation based on DNA strand displacement. The elementary gates in our architecture include addition, subtraction, and multiplication gates. The input and output of these gates are analog, which means that they are directly represented by the concentrations of the input and output DNA strands, respectively, without requiring a threshold for converting to Boolean signals. We provide detailed domain designs and kinetic simulations of the gates to demonstrate their expected performance. On the basis of these gates, we describe how DNA circuits to compute polynomial functions of inputs can be built. Using Taylor Series and Newton Iteration methods, functions beyond the scope of polynomials can also be computed by DNA circuits built upon our architecture.


title: Investigation on Co-Combustion Kinetics of Anthracite and Waste Plastics by Thermogravimetric Analysis

abstract: Abstract In order to effectively recycle resource for the benefit of the global environment, the utilization of waste plastics as auxiliary injectant for blast furnaces is becoming increasingly important. Combustion kinetics of plastics-coal blends with 0, 10%, 20% and 40% waste plastics (WP) are investigated separately by thermogravimetric analysis (TGA) from ambient temperature to 900 °C in air atmosphere. These blends are combusted at the heating rates of 5, 10 and 20 °C/min. The results indicate that, with the increase of waste plastics content, the combustion processes of blends could be divided into one stage, two stages and three stages. The waste plastics content and heating rates have important effects on the main combustion processes of blends. With the increase of waste plastics content, the ignition temperature and the final combustion temperature of blends tend to decrease, while the combustion reaction becomes fiercer. With the increase of the heating rate, the ignition temperature, the mass loss rate of the peaks and the final combustion temperature of blends combustion tend to increase. The Flynn-Wall-Ozawa (FWO) iso-conversional method is used for the kinetic analysis of the main combustion process. The results indicate that, when the waste plastics content varied from 0 to 40%, the values of activation energy increase from 126. 05 to 184. 12 kJ /mol.


title: VisualBackProp: Efficient Visualization of CNNs for Autonomous Driving

abstract: This paper proposes a new method, that we call VisualBackProp, for visualizing which sets of pixels of the input image contribute most to the predictions made by the convolutional neural network (CNN). The method heavily hinges on exploring the intuition that the feature maps contain less and less irrelevant information to the prediction decision when moving deeper into the network. The technique we propose is dedicated for CNN-based systems for steering self-driving cars and is therefore required to run in real-time. This makes the proposed visualization method a valuable debugging tool which can be easily used during both training and inference. We justify our approach with theoretical arguments and confirm that the proposed method identifies sets of input pixels, rather than individual pixels, that collaboratively contribute to the prediction. We utilize the proposed visualization tool in the NVIDIA neural-network-based end-to-end learning system for autonomous driving, known as PilotNet. We demonstrate that VisualBackProp determines which elements in the road image most influence PilotNet's steering decision and indeed captures relevant objects on the road. The empirical evaluation furthermore shows the plausibility of the proposed approach on public road video data as well as in other applications and reveals that it compares favorably to the layer-wise relevance propagation approach, i.e. it obtains similar visualization results and achieves order of magnitude speed-ups.


Action Recognition with the Augmented MoCap Data using Neural Data Translation


title: Anxiety impairs decision-making: Psychophysiological evidence from an Iowa Gambling Task

abstract: Using the Iowa Gambling Task (IGT) and psychophysiological correlates of emotional responses (i.e., heart rate and skin conductance), we investigate the effects of trait anxiety (TA) on decision-making. We find that high TA is associated with both impaired decision-making and increased anticipatory physiological (somatic) responses prior to advantageous trials. For both high and low TA, skin conductance responses preceding advantageous trials predict decisions. At the same time, somatic responses to choice outcomes reflect differences between high and low TA sensitivities to punishments and rewards. The pattern of impaired decision-making and increased somatic markers that we find in high TA may have important implications for neuropsychological decision theory. In particular, it offers an example of defective modulation of somatic signals, coupled with disrupted discrimination of advantageous and disadvantageous choices.


title: An “independent visual background” reduced balance disturbance envoked by visual scene motion: implication for alleviating simulator sickness

abstract: Simulator sickness (SS) / virtual environment (VE) sickness is expected to become increasingly troublesome as VE technology evolves [20]. Procedures to alleviate SS / VE sickness have been of limited value [12]. This paper investigated a possible procedure to reduce SS and VE sickness. Postural disturbance was evoked by visual scene motion at different frequencies. Differences in disturbance were examined as a function of simultaneous exposure to an “independent visual background” (IVB). Eight subjects were tested at two scene motion frequencies and three different IVB conditions using a within-subjects design. An expected statistically significant interaction between IVB condition and frequency was observed. For low frequency scene movements, subjects exhibited less balance disturbance when the IVB was presented. We suggest that an IVB may alleviate disturbance when conflicting visual and inertial cues are likely to result in simulator or VE sickness.


title: Texture based classification of arecanut

abstract: Crop grading is one of the important stages in crop management. The different grades can be done by classification. In this paper, we propose the texture based grading of arecanut. Different texture features are extracted from arecanut by applying approaches such as Wavelet, Gabor, Local Binary Pattern (LBP), Gray Level Difference Matrix (GLDM) and Gray Level Co-Occurrence Matrix (GLCM) features. For classification Nearest Neighbor (NN) classifier is used. Experimentation conducted using a dataset of 700 images of 7 classes to demonstrate the proposed model's performance. 91.43% of classification rate is achieved with Gabor wavelet features.


title: gSVD++: supporting implicit feedback on recommender systems with metadata awareness

abstract: This paper proposes a recommender algorithm denominated "gSVD++" which exploits implicit feedback from users by considering not only the latent space of factors describing the user and item, but also the available metadata associated to the content. Such descriptions are an important source to construct a user profile containing relevant and meaningful information about his/her preferences. The method is evaluated on the MovieLens dataset, being compared against other approaches reported in the literature. The results show the effectiveness of incorporating metadata awareness into a latent factor model.


title: Statistical Analysis of Changes for Synthesizing Realistic Test Models

abstract: Tools and methods in the context of Model-Driven Engineering have to be evaluated and tested. Unfortunately, adequate test models are scarcely available in many application domains, and available models often lack required properties. Test model generators have been proposed recently to overcome this deficiency. Their basic principle is to synthesize test models by controlled application of edit operations from a given set of edit operation definitions. If test models are created by randomly selecting edit operations, then they become quite unnatural and do not exhibit realworld characteristics; generated sequences of edit operation should rather be similar to realistic model evolution. To this end, we have reverse-engineered a carefully selected set of open-source Java projects to class diagrams and computed the differences between subsequent revisions in terms of various edit operations, including generic low-level graph edit operations and high-level edit operations such as model refactorings. Finally, we statistically analyzed the distribution of the frequency of these edit operations. We have checked the fitness of 60 distributions in order to correctly represent the statistical properties. Only four distributions have been able to adequately describe the observed evolution. The successful distributions are being used to configure our model generator in order to produce more realistic test models.


title: Cognitive control and brain resources in major depression: An fMRI study using the n-back task

abstract: Abstract Several neuroimaging studies have reported ‘hypofrontality’ in depressed patients performing a cognitive challenge compared to control subjects. Hypofrontality in depression is likely associated with an impaired behavioral performance. It is unclear whether this impaired performance is the consequence or the cause of hypofrontality. Consequently, we proposed to compare the cerebral activity of depressed patients and healthy subjects while controlling for the level of performance. Ten individuals meeting DSM-IV criteria for Major Depression and 10 healthy controls were tested with a verbal version of the n-back task during fMRI scanning. The working memory load was manipulated across the experiment (1,2,3-back) to increase the cognitive demands. fMRI data were acquired on a 1.5-T GE scanner and analyzed using SPM99 software. We did not find any difference between groups in both performance and reaction times for each level of complexity of the n-back task. Depressed patients and control subjects showed bilateral activation of the lateral prefrontal cortex, anterior cingulate and parietal cortex. Activation of these regions was modulated by the complexity of the task. Within this n-back neural network, depressed patients showed greater activation of the lateral prefrontal cortex and the anterior cingulate compared to healthy subjects. This study provides evidence that depressed patients need greater activation within the same neural network to maintain a similar level of performance as controls during a working memory task. Our findings suggest that depression may impair the cognitive capacity of depressed patients by recruiting more brain resources than controls during cognitive control.


title: Quality-Specific Hand Vein Recognition System

abstract: Vein images generally appear darker with low contrast, which require contrast enhancement during preprocessing to design satisfactory hand vein recognition system. However, the modification introduced by contrast enhancement (CE) is reported to bring side effects through pixel intensity distribution adjustments. Furthermore, the inevitable results of fake vein generation or information loss occur and make nearly all vein recognition systems unconvinced. In this paper, a “CE-free” quality-specific vein recognition system is proposed, and three improvements are involved. First, a high-quality lab-vein capturing device is designed to solve the problem of low contrast from the view of hardware improvement. Then, a high quality lab-made database is established. Second, CFISH score, a fast and effective measurement for vein image quality evaluation, is proposed to obtain quality index of lab-made vein images. Then, unsupervised $K$ -means with optimized initialization and convergence condition is designed with the quality index to obtain the grouping results of the database, namely, low quality (LQ) and high quality (HQ). Finally, discriminative local binary pattern (DLBP) is adopted as the basis for feature extraction. For the HQ image, DLBP is adopted directly for feature extraction, and for the LQ one. CE_DLBP could be utilized for discriminative feature extraction for LQ images. Based on the lab-made database, rigorous experiments are conducted to demonstrate the effectiveness and feasibility of the proposed system. What is more, an additional experiment with PolyU database illustrates its generalization ability and robustness.


A principal axis transformation for non-hermitian matrices


title: Patterns, Dungeons and Generators

abstract: This paper analyses dungeons, of the varieties commonly found in role-playing games, into several sets of design patterns at different levels of abstraction. The analysis focuses on mechanical patterns that could be either straightforwardly instantiated or recognized by a well-defined process. At the most concrete level a set of fundamental components were identified, followed by a long list of micropatterns which can be directly instantiated. Shorter lists of meso- and macro-patterns, which can be identified mechanically, are also identified. The direct motivation for this analysis is to find building blocks and objectives for a search-based procedural dungeon generator, however we believe the analysis can be useful for understanding this class of game artifacts in general. In particular, the constraints on patterns being instantiable or recognizable leads to a stricter pattern analysis than many other attempts at analyzing game design.


title: Semantics + filtering + search = twitcident. exploring information in social web streams

abstract: Automatically filtering relevant information about a real-world incident from Social Web streams and making the information accessible and findable in the given context of the incident are non-trivial scientific challenges. In this paper, we engineer and evaluate solutions that analyze the semantics of Social Web data streams to solve these challenges. We introduce Twitcident, a framework and Web-based system for filtering, searching and analyzing information about real-world incidents or crises. Given an incident, our framework automatically starts tracking and filtering information that is relevant for the incident from Social Web streams and Twitter particularly. It enriches the semantics of streamed messages to profile incidents and to continuously improve and adapt the information filtering to the current temporal context. Faceted search and analytical tools allow people and emergency services to retrieve particular information fragments and overview and analyze the current situation as reported on the Social Web. We put our Twitcident system into practice by connecting it to emergency broadcasting services in the Netherlands to allow for the retrieval of relevant information from Twitter streams for any incident that is reported by those services. We conduct large-scale experiments in which we evaluate (i) strategies for filtering relevant information for a given incident and (ii) search strategies for finding particular information pieces. Our results prove that the semantic enrichment offered by our framework leads to major and significant improvements of both the filtering and the search performance. A demonstration is available via: http://wis.ewi.tudelft.nl/twitcident/


title: Male crickets alter the relative expression of cuticular hydrocarbons when exposed to different acoustic environments

abstract: Males often display signals from more than one sensory modality when producing courtship displays. In crickets, males produce a courtship song that conveys information about male quality. Although less well studied, male crickets also produce chemical cues that are used in mate choice. Plasticity in chemical cues has been recently reported, but empirical evidence for signal-mediated plasticity in these cues is primarily lacking. We tested whether exposure to acoustic sexual signals in the Australian field cricket, Teleogryllus oceanicus, affects the expression of another sexual signal, cuticular hydrocarbons. We found that the expression of male cuticular hydrocarbons was affected by acoustic experience; in the absence of singing males, males increased the relative concentration of a number of cuticular hydrocarbons that females have previously been shown to find attractive. Our results demonstrate that social cues relating to acoustic signals can stimulate a plastic change in cuticular hydrocarbon expression.


title: Development of adjustable knee joint for walking assistance devices

abstract: This paper presents an adjustable knee mechanism for walking assistance devices for the elderly to provide physical gait assistance. The adjustable knee mechanism can assist in flexion/extension motions of the knee joint and compensate for the transitional movements of the knee in the sagittal plane as well as aligning the frontal plane. In order to compensate for the center of rotation, the proposed adjustable knee mechanism is implemented by connecting several rolling cams with unique and precisely calculated contact geometries. The key idea of the proposed mechanism is to realize linear motion and accurate torque transmission by a pulley method using the rolling joint in which the distance of the center between the rolling cams is changed. The proposed articulated joint can effectively deliver the torque required for assistance while adapting to the joint motion of the wearer. This paper describes the mechanical design of this knee mechanism and its implementation on a wearable robot and in preliminary experiments. The performance of the proposed mechanism was verified by simulations and experiments.


title: Leadership = Communication? The Relations of Leaders’ Communication Styles with Leadership Styles, Knowledge Sharing and Leadership Outcomes

abstract: Purpose ::: The purpose of this study was to investigate the relations between leaders’ communication styles and charismatic leadership, human-oriented leadership (leader’s consideration), task-oriented leadership (leader’s initiating structure), and leadership outcomes.


title: Large-Pose Face Alignment via CNN-Based Dense 3D Model Fitting

abstract: Large-pose face alignment is a very challenging problem in computer vision, which is used as a prerequisite for many important vision tasks, e.g, face recognition and 3D face reconstruction. Recently, there have been a few attempts to solve this problem, but still more research is needed to achieve highly accurate results. In this paper, we propose a face alignment method for large-pose face images, by combining the powerful cascaded CNN regressor method and 3DMM. We formulate the face alignment as a 3DMM fitting problem, where the camera projection matrix and 3D shape parameters are estimated by a cascade of CNN-based regressors. The dense 3D shape allows us to design pose-invariant appearance features for effective CNN learning. Extensive experiments are conducted on the challenging databases (AFLW and AFW), with comparison to the state of the art.


title: Table of Contents Recognition and Extraction for Heterogeneous Book Documents

abstract: Existing work on book table of contents (TOC) recognition has been almost all on small size, application-dependent, and domain-specific datasets. However, TOC of books from different domains differ significantly in their visual layout and style, making TOC recognition a challenging problem for a large scale collection of heterogeneous books. We observed that TOCs can be placed into three basic styles, namely "flat", "ordered", and "divided", giving insights into how to achieve effective TOC parsing. As such, we propose a new TOC recognition approach which adaptively decides the most appropriate TOC parsing rules based on the classification of these three TOC styles. Evaluation on large number, over 25,000, of book documents from various domains demonstrates its effectiveness and efficiency.


title: Federated Online Learning to Rank with Evolution Strategies

abstract: Online Learning to Rank is a powerful paradigm that allows to train ranking models using only online feedback from its users.In this work, we consider Federated Online Learning to Rank setup (FOLtR) where on-mobile ranking models are trained in a way that respects the users' privacy. We require that the user data, such as queries, results, and their feature representations are never communicated for the purpose of the ranker's training. We believe this setup is interesting, as it combines unique requirements for the learning algorithm: (a) preserving the user privacy, (b) low communication and computation costs, (c) learning from noisy bandit feedback, and (d) learning with non-continuous ranking quality measures. We propose a learning algorithm FOLtR-ES that satisfies these requirements. A part of FOLtR-ES is a privatization procedure that allows it to provide ε-local differential privacy guarantees, i.e. protecting the clients from an adversary who has access to the communicated messages. This procedure can be applied to any absolute online metric that takes finitely many values or can be discretized to a finite domain. Our experimental study is based on a widely used click simulation approach and publicly available learning to rank datasets MQ2007 and MQ2008. We evaluate FOLtR-ES against offline baselines that are trained using relevance labels, linear regression model and RankingSVM. From our experiments, we observe that FOLtR-ES can optimize a ranking model to perform similarly to the baselines in terms of the optimized online metric, Max Reciprocal Rank.


title: A comparison of classification algorithms for verbose queries detection using BabelNet

abstract: Users of search engines interact with the system using different size and type of queries. Current search engines perform well with keyword queries but are not for verbose queries which are too long, detailed, or are expressed in more words than are needed. The detection of verbose queries may help search engines to get pertinent results. To accomplish this goal it is important to make some appropriate preprocessing techniques in order to improve classifiers effectiveness. In this paper, we propose to use BabelNet as knowledge base in the preprocessing step and then make a comparative study between different algorithms to classify queries into two classes, verbose or succinct. Our Experimental results are conducted using the TREC Robust Track as data set and different classifiers such as, decision trees probabilistic methods, rule-based methods, instance-based methods, SVM and neural networks.


title: Diversity is All You Need: Learning Skills without a Reward Function

abstract: Intelligent creatures can explore their environments and learn useful skills without supervision. In this paper, we propose DIAYN ('Diversity is All You Need'), a method for learning useful skills without a reward function. Our proposed method learns skills by maximizing an information theoretic objective using a maximum entropy policy. On a variety of simulated robotic tasks, we show that this simple objective results in the unsupervised emergence of diverse skills, such as walking and jumping. In a number of reinforcement learning benchmark environments, our method is able to learn a skill that solves the benchmark task despite never receiving the true task reward. We show how pretrained skills can provide a good parameter initialization for downstream tasks, and can be composed hierarchically to solve complex, sparse reward tasks. Our results suggest that unsupervised discovery of skills can serve as an effective pretraining mechanism for overcoming challenges of exploration and data efficiency in reinforcement learning.


title: Pattern Of Skin Prick Allergy Test Results In Enugu

abstract: Allergic rhinitis is a global health problem and is increasing in prebbalence up to 10 – 20% over the last half century. Both allergic rhinitis and vasomotorhinitis are the most common health problems encountered in the Ear Nose and Throat Clinic in Nigeria and they afflict all age groups in any modern society. ::: Aim ::: In this study we report on pattern of allergy prick skin test results found among atopic patients attending the department of otorhinolargngology of the University of Nigeria Teaching Hospital Enugu and Hansa Clinics, Enugu and propose ways of minimizing the exposure of the population to allergens. ::: Material and method ::: 76 patients of which 46 were males and 30 females with signs and symptoms of allergic rhinitis attending the ENT clinics were randomly recruited for the study. The patients' age ranged from > 9 – 48 years with a male to female ratio of 1.5:1. Complete medical, family and social history was obtained. The “Allergo Ganzer” Allergy test kit (Germany) was used for skin prick allergy test on each patient. ::: Result ::: The reactivities to test allergens were found to be in 44.4% patients positive to house dust mites I & II, 27.2% to house dust, 22% to grass, 16.6% to cereal and trees respectively and only 8.3% to mould. ::: Conclusion ::: House dust mites and house dust were the commonest implicated allergens, both accounting for 72.1% of all positive reactivity in our study. The need for avoidance of house rugs and carpets in our homes and offices and the development of local prick test solutions with inclusion of tropical allergens should be emphasized. Keywords : Allergic rhinitis, allergens, house dust mites, house dust Nigeria. Nigerian Journal of Otorhinolaryngology Vol. 3 (2) 2006: pp. 65-71


title: High-Dimensional Feature Selection by Feature-Wise Non-Linear Lasso

abstract: The goal of supervised feature selection is to find a subset of input features that are responsible for predicting output values. The least absolute shrinkage and selection operator (Lasso) allows computationally efficient feature selection based on linear dependency between input features and output values. In this paper, we consider a feature-wise kernelized Lasso for capturing non-linear input-output dependency. We first show that, with particular choices of kernel functions, non-redundant features with strong statistical dependence on output values can be found in terms of kernel-based independence measures such as the Hilbert-Schmidt independence criterion (HSIC). We then show that the globally optimal solution can be efficiently computed; this makes the approach scalable to high-dimensional problems. The effectiveness of the proposed method is demonstrated through feature selection experiments for classification and regression with thousands of features.


title: Person Re-Identification Ranking Optimisation by Discriminant Context Information Analysis

abstract: Person re-identification is an open and challenging problem in computer vision. Existing re-identification approaches focus on optimal methods for features matching (e.g., metric learning approaches) or study the inter-camera transformations of such features. These methods hardly ever pay attention to the problem of visual ambiguities shared between the first ranks. In this paper, we focus on such a problem and introduce an unsupervised ranking optimization approach based on discriminant context information analysis. The proposed approach refines a given initial ranking by removing the visual ambiguities common to first ranks. This is achieved by analyzing their content and context information. Extensive experiments on three publicly available benchmark datasets and different baseline methods have been conducted. Results demonstrate a remarkable improvement in the first positions of the ranking. Regardless of the selected dataset, state-of-the-art methods are strongly outperformed by our method.


title: Optimal downlink and uplink user association in backhaul-limited HetNets

abstract: Operators, struggling to continuously add capacity and upgrade their architecture to keep up with data traffic increase, are turning their attention to denser deployments that improve spectral efficiency. Denser deployments make the problem of user association challenging, and much work has been devoted to finding algorithms that strike a tradeoff between user quality of service (QoS), and network-wide performance (load-balancing). Nevertheless, the majority of these algorithms typically consider only the radio access part, and ignore the backhaul topology and potential capacity limitations. Backhaul constraints are emerging as a key performance bottleneck in future heterogeneous networks, partly due to the continuous improvement of the radio interface, and partly due to the need for inexpensive backhaul links to reduce CAPEX/OPEX. To this end, we propose an analytical framework for user association that jointly considers radio access and backhaul performance. We derive an algorithm that takes into account spectral efficiency, base station load, backhaul link capacities and topology, and uplink and downlink traffic demand, and prove it converges to an optimal solution. We then use extensive simulations to study the impact of (i) backhaul capacity limitations and (ii) backhaul topology on key performance metrics.


title: Evaluation of Multi-view 3D Reconstruction Software

abstract: A number of software solutions for reconstructing 3D models from multi-view image sets have been released in recent years. Based on an unordered collection of photographs, most of these solutions extract 3D models using structure-from-motion SFM algorithms. In this work, we compare the resulting 3D models qualitatively and quantitatively. To achieve these objectives, we have developed different methods of comparison for all software solutions. We discuss the perfomance and existing drawbacks. Particular attention is paid to the ability to create printable 3D models or 3D models usable for other applications.


Performance Overview, Comprehensive Assessment and Review of Image Segmentation Techniques for Natural Images


title: Issues in predicting and explaining usage behaviors with the technology acceptance model and the theory of planned behavior when usage is mandatory

abstract: Within certain industries there is an increasing prevalence toward computerizing work practices and mandating specific tasks performed using an Information System (IS). This trend is unlikely to diminish the need for stakeholders, involved in the implementation of an IS, to identify the determinants of successful use. Yet, to date, there is a paucity of research that has considered the issues specific to predicting and explaining user behavior in these situations. In this paper, we identify the relevant issues necessary for applying the technology acceptance model and the theory of planned behavior to the prediction and explanation of mandated IS usage, and we assess the value of these models to the task. The results of a longitudinal study conducted in a hospital setting are presented and we suggest the direction that future research might take.


Graph similarity and matching


title: Brain size as an explanation of national differences in IQ, longevity, and other life-history variables.

abstract: Abstract Brain size provides a causal mechanism for why national differences in intelligence correlate with life-history variables such as longevity, health, parental care, and fecundity. Brain size correlates .40 with general intelligence within human populations, .91 with IQ across ten human population groups, and .60–.90 with longevity, fecundity, and infant mortality in non-human animals (just as IQ does within and across nations, albeit often with lower values). Brain size is central to a suite of life-history variables arising during the course of evolution. Traits need to be harmonized, not work independently of each other. The question Wicherts et al. do not ask is – what causes national differences in their preferred theory of “developmental status?” Heritable brain-power is the answer. A life-history theory perspective on heritable brain-power also explains the social-class/longevity paradox within nations. Any theory which explains differences at the individual, national, and cross-national level deserves to be taken very seriously.


title: Customer profile classification using transactional data

abstract: Customer profiles are by definition made up of factual and transactional data. It is often the case that due to reasons such as high cost of data acquisition and/or protection, only the transactional data are available for data mining operations. Transactional data, however, tend to be highly sparse and skewed due to a large proportion of customers engaging in very few transactions. This can result in a bias in the prediction accuracy of classifiers built using them towards the larger proportion of customers with fewer transactions. This paper investigates an approach for accurately and confidently grouping and classifying customers in bins on the basis of the number of their transactions. The experiments we conducted on a highly sparse and skewed real-world transactional data show that our proposed approach can be used to identify a critical point at which customer profiles can be more confidently distinguished.


title: A High Gain Input-Parallel Output-Series DC/DC Converter With Dual Coupled Inductors

abstract: High voltage gain dc-dc converters are required in many industrial applications such as photovoltaic and fuel cell energy systems, high-intensity discharge lamp (HID), dc back-up energy systems, and electric vehicles. This paper presents a novel input-parallel output-series boost converter with dual coupled inductors and a voltage multiplier module. On the one hand, the primary windings of two coupled inductors are connected in parallel to share the input current and reduce the current ripple at the input. On the other hand, the proposed converter inherits the merits of interleaved series-connected output capacitors for high voltage gain, low output voltage ripple, and low switch voltage stress. Moreover, the secondary sides of two coupled inductors are connected in series to a regenerative capacitor by a diode for extending the voltage gain and balancing the primary-parallel currents. In addition, the active switches are turned on at zero current and the reverse recovery problem of diodes is alleviated by reasonable leakage inductances of the coupled inductors. Besides, the energy of leakage inductances can be recycled. A prototype circuit rated 500-W output power is implemented in the laboratory, and the experimental results shows satisfactory agreement with the theoretical analysis.


title: Synthesizing information systems knowledge: A typology of literature reviews

abstract: Abstract In this article we develop a typology of review types and provide a descriptive insight into the most common reviews found in top IS journals. Our assessment reveals that the number of IS reviews has increased over the years. The majority of the 139 reviews are theoretical in nature, followed by narrative reviews, meta-analyses, descriptive reviews, hybrid reviews, critical reviews, and scoping reviews. Considering the calls for IS research to develop a cumulative tradition, we hope more review articles will be published in the future and encourage researchers who start a review to use our typology to position their contribution.


title: A computer forensic method for detecting timestamp forgery in NTFS

abstract: In this paper, we present a computer forensic method for detecting timestamp forgeries in the Windows NTFS file system. It is difficult to know precisely that the timestamps have been changed by only examining the timestamps of the file itself. If we can find the past timestamps before any changes to the file are made, this can act as evidence of file time forgery. The log records operate on files and leave large amounts of information in the $LogFile that can be used to reconstruct operations on the files and also used as forensic evidence. Log record with 0x07/0x07 opcode in the data part of Redo/Undo attribute has timestamps which contain past-and-present timestamps. The past-and-present timestamps can be decisive evidence to indicate timestamp forgery, as they contain when and how the timestamps were changed. We used file time change tools that can easily be found on Internet sites. The patterns of the timestamp change created by the tools are different compared to those of normal file operations. Seven file operations have ten timestamp change patterns in total by features of timestamp changes in the $STANDARD_INFORMATION attribute and the $FILE_NAME attribute. We made rule sets for detecting timestamp forgery based on using difference comparison between changes in timestamp patterns by the file time change tool and normal file operations. We apply the forensic rule sets for ''.txt'', ''.docx'' and ''.pdf'' file types, and we show the effectiveness and validity of the proposed method. The importance of this research lies in the fact that we can find the past time in $LogFile, which gives decisive evidence of timestamp forgery. This makes the timestamp active evidence as opposed to simply being passive evidence.


title: Comparative 1H NMR chemical shift study on the stacking interaction of pyrimidine with purine and 6-methylpurine

abstract: Abstract The pyrimidine-purine and pyrimidine-6-methylpurine cross-interactions were compared by measuring the mutually induced concentration-dependent changes in proton chemical shifts in deuterium oxide at 35°C. The chemical shift vs. concentration profiles were analysed using a competitive dimer model. The equilibrium constants (0.41 ± 0.06 and 0.74 ± 0.05 l mol −1 for the pyrimidine-purine and pyrimidine-6-methylpurine hetero-associations, respectively) and the dimer shifts imply that the methylation of purine significantly influences the stacking interaction between the parent molecules of nucleic acid bases and thus their plane-to-plane association structure.


title: An OCR System with OCRopus for Scientific Documents Containing Mathematical Formulas

abstract: This paper describes the installation of a mathematical formula recognition module into an open source OCR system: OCRopus. In particular we consider the identification of inline formulas utilizing existing modules. Text lines including math formulas are first processed using a N-gram language model to reduce the number of formula candidates by thresholding the conditional probability of words. Then the formula candidates are classified into formulas and texts by SVM using geometric features associated with the bounding boxes of symbols.


title: Sleep: An Open-Source Python Software for Visualization, Analysis, and Staging of Sleep Data

abstract: We introduce Sleep, a new Python open-source graphical user interface (GUI) dedicated to visualization, scoring and analyses of sleep data. Among its most prominent features are: 1) Dynamic display of polysomnographic data, spectrogram, hypnogram and topographic maps with several customizable parameters, 2) Implementation of several automatic detection of sleep features such as spindles, K-complexes, slow waves and rapid eye movements, 3) Implementation of practical signal processing tools such as re-referencing or filtering, and 4) Display of main descriptive statistics including publication-ready tables and figures. The software package supports loading and reading raw EEG data from a standard file formats such as European Data Format, in addition to a range of commercial data formats. Most importantly, Sleep is built on top of the VisPy library, which provides GPU-based fast and high-level visualization. As a result, it is capable of efficiently handling and displaying large sleep datasets. Sleep is freely available (http://visbrain.org/sleep) and comes with sample datasets and an extensive documentation. Novel functionalities will continue to be added and open-science community efforts are expected to enhance the capacities of this module.


title: Rethinking Summarization and Storytelling for Modern Social Multimedia

abstract: Traditional summarization initiatives have been focused on specific types of documents such as articles, reviews, videos, image feeds, or tweets, a practice which may result in pigeonholing the summarization task in the context of modern, content-rich multimedia collections. Consequently, much of the research to date has revolved around mostly toy problems in narrow domains and working on single-source media types. We argue that summarization and story generation systems need to refocus the problem space in order to meet the information needs in the age of user-generated content in different formats and languages. Here we create a framework for flexible multimedia storytelling. Narratives, stories, and summaries carry a set of challenges in big data and dynamic multi-source media that give rise to new research in spatial-temporal representation, viewpoint generation, and explanation.


title: HARASSMENT BASED ON SEX: PROTECTING SOCIAL STATUS IN THE CONTEXT OF GENDER HIERARCHY.

abstract: I conceptualize sex-based harassment as behavior that derogates an individual based on sex. I propose that sex-based harassment is fundamentally motivated by the harasser's desire to protect or enhance his or her own sex-based status, a desire that stems from the fact that social status is stratified by a system of gender hierarchy. This theory explains currently identified forms of sexual harassment and predicts others, including nonsexual harassment between women.


title: Real-time 3D scene layout from a single image using Convolutional Neural Networks

abstract: We consider the problem of understanding the 3D layout of indoor corridor scenes from a single image in real time. Identifying obstacles such as walls is essential for robot navigation, but also challenging due to the diversity in structure, appearance and illumination of real-world corridor scenes. Many current single-image methods make Manhattan-world assumptions, and break down in environments that do not meet this mold. They also may require complicated hand-designed features for image segmentation or clear boundaries to form certain building models. In addition, most cannot run in real time.


title: An epsilon negative metamaterial antenna with extended bandwidth

abstract: In this paper, a design and simulation study of a new epsilon-negative metamaterial antenna consisting of a squared shaped complementary split ring resonator (CSRR), shorting via and a stub is presented. The antenna resonates at 2.95 GHz with impedance bandwidth of 17.5%. The overall size of the antenna is 0.36λo × 0.33λo with the size of the main antenna radiating element of 0.15λo × 0.15λo at 2.95 GHz. A prototype has been fabricated and tested to validate the design. Peak realized gain of 2.19 dBi is obtained at resonance frequency. Simulated and measured results are presented and compared.


title: Reconstruction, Binding Theory, and the Interpretation of Chains

abstract: This article investigates interactions between the scope of QPs and the restrictions imposed by binding theory. It presents new evidence that Condition C applies at (and only at) LF and demonstrates that this condition can serve as a powerful tool for distinguishing among various claims regarding the nature of LF and the inventory of semantic mechanisms. The conclusions reached are these: (1) Scope reconstruction is represented in the syntax (semantic type-shifting operations are very limited). (2) Ā-chains have the following properties: (a) Scope reconstruction results from deleting the head of the chain and interpreting a copy at the tail. (b) Non-scope-reconstruction results from interpreting the head of the chain with a copy of the restrictor at the tail (unless this option is impossible, as in antecedent-contained deletion, in which case the copy is changed to a variable as in standard notations). (c) VP adjunction is an intermediate landing site. (3) A-chains are different in a way that at the momen...


title: Small-Size LTE/WWAN Printed Loop Antenna With an Inductively Coupled Branch Strip for Bandwidth Enhancement in the Tablet Computer

abstract: The technique of using an inductively coupled branch strip for bandwidth enhancement of a simple printed loop antenna to achieve small size yet multiband operation to cover the LTE/WWAN bands (704-960 and 1710-2690 MHz) in the tablet computer is presented. The antenna's metal pattern occupies a small area of 10 × 34.5 mm 2 and is printed on a thin FR4 substrate. The branch strip is coupled to the loop antenna through a chip inductor. In the antenna's lower band, the branch strip can cause an additional resonance excited near a resonant mode contributed by the printed loop antenna and thereby greatly widens the antenna's low-band bandwidth to cover the 704-960 MHz band for the LTE700/GSM850/900 operations. While in the antenna's higher band, the chip inductor provides a high inductance and limits the excitation of the branch strip, thereby making the branch strip have negligible effects on the high-band operation. In this case, with the printed loop antenna excited in the higher band, the antenna can generate two higher order resonant modes to form a wide operating band for the GSM1800/1900/UMTS/LTE2300/2500 operations (1710-2690 MHz). Details of the proposed antenna are described, and the obtained results of the antenna are presented and discussed.


title: Customer Oriented Business Process Improvement Methodlogy for Public Sector Organizations

abstract: The globalization of businesses and the germination of customer satisfaction importance induced a profound transfer from transitional to relational marketing, together with the introduction of relationship marketing concept. To surmount this transition, business process improvement is acknowledged as one of the prevailing methods to conduct changes in organizations. Concerning the achievement of process superiority and the superlative services to customers, practitioners are advised to benefit the customeroriented BPI methodologies. While they have their roots in private sectors and manufacturing organizations, recently they are widely used in public sectors as well. Since the essential essence of BPI in public sectors is enhancing the satisfaction of customers, the existing approaches mainly focus on cost reduction and policy-based process introduction. Sifting through organizations’ processes, diagnosing befitting tools, and evaluating techniques can be a daunting task. The available literature on business process improvement in public sectors is full with recommendations on how to do improvements and the methodologies, but what is lacking is a customer-oriented BPI guideline together with necessary tools and technologies for each step. Following the mentioned gap, this paper proposes a customer-oriented methodology to conduct process improvement smoothly in public sectors besides the necessary tools and techniques for each phase.


title: Probabilistic image segmentation with closedness constraints

abstract: We propose a novel graphical model for probabilistic image segmentation that contributes both to aspects of perceptual grouping in connection with image segmentation, and to globally optimal inference with higher-order graphical models. We represent image partitions in terms of cellular complexes in order to make the duality between connected regions and their contours explicit. This allows us to formulate a graphical model with higher-order factors that represent the requirement that all contours must be closed. The model induces a probability measure on the space of all partitions, concentrated on perceptually meaningful segmentations. We give a complete polyhedral characterization of the resulting global inference problem in terms of the multicut polytope and efficiently compute global optima by a cutting plane method. Competitive results for the Berkeley segmentation benchmark confirm the consistency of our approach.


title: Wittgenstein, Locality and Rules

abstract: This paper attempts to give some substance to local holism, a picture that seems to fit Wittgenstein's analysis of the working of language. Any form of holism has to solve the well-known paradox of semantic holism, as discussed in philosophy of language: if meaning is holistic there is no possibility of sharing meaning. In the paper I show different answer s to this paradox, from atomism to different forms of molecularism and holism. In this quick overview, I try to check to what extent the different answers to the paradox are coherent with the main ideas of later//the second// Wittgenstein. Wittgenstein seems to be far from the global holistic thinker he is often considered to be; his ideas are more akin to a form of local holis m. However, for local holism to work, even in Wittgenstein's case we need some general restrictions given by rules to navigate across contexts.


title: α lean TAP: A Declarative Theorem Prover for First-Order Classical Logic

abstract: We present α lean TA P , adeclarative tableau-based theorem prover written as a purerelation. Like lean TA P , on which it is based,α lean TA P can prove groundtheorems in first-order classical logic. Since it is declarative,α lean TA P generates theorems and accepts non-ground theorems and proofs. The lack ofmode restrictions also allows the user to provide guidance inproving complex theorems and to ask the prover to instantiatenon-ground parts of theorems. We present a complete implementationof α lean TA P , beginning with atranslation of lean TA P intoα Kanren, an embedding of nominal logic programmingin Scheme. We then show how to use a combination of tagging andnominal unification to eliminate the impure operators inheritedfrom lean TA P , resulting in a purely declarativetheorem prover.


title: Color names learning using convolutional neural networks

abstract: In this paper, we propose a two-stage CNN-based framework to learn color names from web images, aiming to predict color names for tiny image patches. To deal with the noisy labels widespread in web images, we propose a self-supervised CNN (SS-CNN) model in the first stage. The SS-CNN model is trained on image patches with their own color histograms as supervision information. Thus its outputs are able to reflect the color characteristics of images without the influence of the noisy labels. In the second stage, we finetune the SS-CNN model to learn the mapping from image patches to color names, where the patch labels are inherited from its father images. Besides, sample selection is imported iteratively in turns with the finetuning process, which helps filtering out some noisy samples and further improves the model accuracy. Our model shows high representation ability to colors and achieves better performance of color naming compared with the state-of-the-art methods.


title: 1.3 V 20 ps time-to-digital converter for frequency synthesis in 90-nm CMOS

abstract: We propose and demonstrate a 20-ps time-to-digital converter (TDC) realized in 90-nm digital CMOS. It is used as a phase/frequency detector and charge pump replacement in an all-digital phase-locked loop for a fully-compliant Global System for Mobile Communications (GSM) transceiver. The TDC core is based on a pseudodifferential digital architecture that makes it insensitive to nMOS and pMOS transistor mismatches. The time conversion resolution is equal to an inverter propagation delay, which is the finest logic-level regenerative timing in CMOS. The TDC is self calibrating with the estimation accuracy better than 1%. It additionally serves as a CMOS process strength estimator for analog circuits in this large system-on-chip. Measured integral nonlinearity is 0.7 least significant bits. The TDC consumes 5.3 mA raw and 1.3 mA with power management from a 1.3-V supply.


title: Learning crop models for vision-based guidance of agricultural robots

abstract: This paper describes a vision-based method of guiding autonomous vehicles within crop rows in agricultural fields where the crop rows are challenging to detect or their appearance is not known a-priori. The location of the crop rows is estimated with an SVM regression algorithm using colour, texture and 3D structure descriptors from a forward facing stereo camera pair. Our system rapidly learns a model online with minimal user input, and then uses this model to track crop rows. Results demonstrate our method is able to learn and track a wide variety of crops with an RMS error of less than 3cm. We also present online control results demonstrating our system autonomously steering a robot for 3km.


title: Adaptive neural control for self-organized locomotion and obstacle negotiation of quadruped robots

abstract: Many quadruped robots have been developed to imitate their biological counterparts, several of which show excellent performance. However, the biological neural control mechanisms responsible for self-organized adaptive quadruped locomotion remain elusive. By drawing lessons from biological findings and using an artificial neural approach, we simulated a mammal-like quadruped robot and used it as our simulation platform to investigate and develop neural control mechanisms. In this study, we proposed an adaptive neural control network that can autonomously generate self-organized emergent locomotion with adaptability for the robot. The control network consists of three main components: Decoupled neural central pattern generator circuits (one for each leg), sensory feedback adaptation with dual-rate learning, and multiple neural reflex mechanisms. Simulation results show that the robot can perform quadruped-like gaits in a self-organized manner and adapt its gait to negotiate an obstacle. In addition, this work also suggests that the tight combination of the body-environment interaction and adaptive neural control, guided by sensory feedback adaptation and neural reflexes, is a powerful approach to better understand and solve self-organized adaptive coordination problems in quadruped locomotion.


title: Critical Success Factors for Online Distance Learning in Higher Education: A Review of the Literature

abstract: The aim of this paper is to specify the Critical Success Factors (CSFs) for Online Distance Learning (ODL) in Higher Education (HE). Research methodology was analyzing and synthesizing the literature review. The literatures were reviewed to determine items relevant to online learning success as implementation, criteria and indicator. A total of 19 papers, published during 2000-2012, were selected from Chulalongkorn University reference databases. Data analysis method was using one of the popular analysis techniques for qualitative research works or the content analysis. The results on the CSFs for ODL can be grouped into 5 factors: (1) institutional management, (2) learning environment, (3) instructional design, (4) services support and (5) course evaluation. Each of these 5 factors includes several important elements that can assist to enhance efficiency of online learning courses in higher education institutions. It is a concrete approach to lead functions of an online institute or course in all levels to the same directions for achieving the success of the institute‘s vision, and make staffs and executives know what they have to do for the success of online distance learning.


title: Analysis of workload influence on dependability

abstract: The authors consider a general, analytic approach to the study of workload effects on computer system dependability, where the faults considered are transient and the dependability measure in question is the time to failure, T/sub f/. Under these conditions, workload plays two roles with opposing effects: it can help detect/correct a correctable fault, or it can cause the system to fail by activating an uncorrectable fault. As a consequence, the overall influence of workload on T/sub f/ is difficult to evaluate intuitively. To examine this in more formal terms, the authors establish a Markov renewal process model that represents the interaction among workload and fault accumulation ins systems for which fault tolerance can be characterized by fault margins. Using this model, they consider some specific examples and show how the probabilistic nature of T/sub f/ can be formulated directly in terms of parameters regarding workload, fault arrivals, and fault margins. >


title: Pattern Matching for k-Track Permutations

abstract: Given permutations \(\tau \) and \(\pi \), the permutation pattern (PP) problem is to decide whether \(\pi \) occurs in \(\tau \) as an order-isomorphic subsequence. Although an FPT algorithm is known for PP parameterized by the size of the pattern \(|\pi |\) [Guillemot and Marx 2014], the high complexity of this algorithm makes it impractical for most instances. In this paper we approach the PP problem from k-track permutations, i.e. those permutations that are the union of k increasing patterns or, equivalently, those permutation that avoid the decreasing pattern \((k+1) k \ldots 1\). Recently, k-track permutations have been shown to be central combinatorial objects in the study of the PP problem. Indeed, the PP problem is NP-complete when \(\pi \) is 321-avoiding and \(\tau \) is 4321-avoiding but is solvable in polynomial-time if both \(\pi \) and \(\tau \) avoid 321. We propose and implement an exact algorithm, FPT for parameters k and \(|\pi |\), which allows to solve efficiently some large instances.


title: A pragmatic android malware detection procedure

abstract: Abstract The academic security research community has studied the Android malware detection problem extensively. Machine learning methods proposed in previous work typically achieve high reported detection performance on fixed datasets. Some of them also report reasonably fast prediction times. However, most of them are not suitable for real-world deployment because requirements for malware detection go beyond these figures of merit. In this paper, we introduce several important requirements for deploying Android malware detection systems in the real world. One such requirement is that candidate approaches should be tested against a stream of continuously evolving data. Such streams of evolving data represent the continuous flow of unknown file objects received for categorization, and provide more reliable and realistic estimate of detection performance once deployed in a production environment. As a case study we designed and implemented an ensemble approach for automatic Android malware detection that meets the real-world requirements we identified. Atomic Naive Bayes classifiers used as inputs for the Support Vector Machine ensemble are based on different APK feature categories, providing fast speed and additional reliability against the attackers due to diversification. Our case study with several malware families showed that different families are detected by different atomic classifiers. To the best of our knowledge, our work contains the first publicly available results generated against evolving data streams of nearly 1 million samples with a model trained over a massive sample set of 120,000 samples.


title: Spatial Aliasing in Spherical Microphone Arrays

abstract: Performance of microphone arrays at the high-frequency range is typically limited by aliasing, which is a result of the spatial sampling process. This paper presents analysis of aliasing for spherical microphone arrays, which have been recently studied for a range of applications. The paper presents theoretical analysis of spatial aliasing for various sphere sampling configurations, showing how high-order spherical harmonic coefficients are aliased into the lower orders. Spatial antialiasing filters on the sphere are then introduced, and the performance of spatially constrained filters is compared to that of the ideal antialiasing filter. A simulation example shows how the effect of aliasing on the beam pattern can be reduced by the use of the antialiasing filters