title: Low cost implementation of smart home automation

abstract: The fast growing world needs various technologies to improve quality of life. The Internet of Things is one such technology impacting human lives. The Internet of Things is a network exchanging the data/information between the devices having Internet connectivity, sensing and computing capabilities. This work uses the ultra low power consumption ESP8266 system on chip board operating 2.4 GHz ISM band for wireless connectivity. The relay circuitry is interfaced to ESP8266 NodeMCU board using the GPIO pins in order to control 220 V supply power outlet, wherein domestic appliances such as lights, fans, air conditioning system, and so on may be controlled remotely. The WLAN SSID & password allow only authorized persons from the home to control such appliances. The Android app has been designed and generated making use of the open source web application MIT App Inventor-2 to control the domestic appliances using any Android Smart phone. The Android app created provides graphical user interface supports two options to control appliances: (i) the user can use icons or graphical buttons created for respective home appliances and (ii) they can use specific voice commands to control those home appliances using Android Smart phone. The main objective of this work is to have a system which is cost effective, robust and also scalable.


title: Sleep and academic performance in later adolescence: results from a large population-based study

abstract: The aim of the current study was to assess the association between sleep duration and sleep patterns and academic performance in 16-19 year-old adolescents using registry-based academic grades. A large population-based study from Norway conducted in 2012, the youth@hordaland-survey, surveyed 7798 adolescents aged 16-19 years (53.5% girls). The survey was linked with objective outcome data on school performance. Self-reported sleep measures provided information on sleep duration, sleep efficiency, sleep deficit and bedtime differences between weekday and weekend. School performance [grade point average (GPA)] was obtained from official administrative registries. Most sleep parameters were associated with increased risk for poor school performance. After adjusting for sociodemographic information, short sleep duration and sleep deficit were the sleep measures with the highest odds of poor GPA (lowest quartile). Weekday bedtime was associated significantly with GPA, with adolescents going to bed between 22:00 and 23:00 hours having the best GPA. Also, delayed sleep schedule during weekends was associated with poor academic performance. The associations were somewhat reduced after additional adjustment for non-attendance at school, but remained significant in the fully adjusted models. In conclusion, the demonstrated relationship between sleep problems and poor academic performance suggests that careful assessment of sleep is warranted when adolescents are underperforming at school. Future studies are needed on the association between impaired sleep in adolescence and later functioning in adulthood.


title: Plasma Met-Enkephalin and Leu-Enkephalin in Chronic Renal Failure

abstract: Plasma met-enkephalin and leu-enkephalin has been measured in a group of 28 patients with chronic renal failure, to discover whether these opioids are affected by standard haemodialysis and haemofiltration. Met-enkephalin was markedly higher (P less than 0.001) in uraemic patients than in a group of 13 normal subjects, and was directly related to plasma creatinine (r = 0.60; P less than 0.01) and to plasma urea (r = 0.36; P = 0.06). In contrast, leu-enkephalin was suppressed in uraemic patients (P less than 0.001). Met-enkephalin fell slightly but significantly (P less than 0.02) after both haemodialysis and haemofiltration; however, on average it remained at concentrations four times higher than normal. No changes in plasma leu-enkephalin were observed after haemodialysis and haemofiltration. The cause(s) of the altered plasma concentrations of these opioid substances remains to be clarified.


title: From Vision to Action: Experiments and Models of Steering Control During Driving

abstract: Experienced drivers performed simple steering maneuvers in the absence of continuous visual input. Experiments conducted in a driving simulator assessed drivers' performance of lane corrections during brief visual occlusion and examined the visual cues that guide steering. The dependence of steering behavior on heading, speed, and lateral position at the start of the maneuver was measured. Drivers adjusted steering amplitude with heading and performed the maneuver more rapidly at higher speeds. These dependencies were unaffected by a 1.5-s visual occlusion at the start of the maneuver. Longer occlusions resulted in severe performance degradation. Two steering control models were developed to account for these findings. In the 1st, steering actions were coupled to perceptual variables such as lateral position and heading. In the 2nd, drivers pursued a virtual target in the scene. Both models yielded behavior that closely matches that of human drivers. Language: en


title: Loading of BMP-2-related peptide onto three-dimensional nano-hydroxyapatite scaffolds accelerates mineralization in critical-sized cranial bone defects

abstract: Extrusion free-forming, as a rapid prototyping technique, is extensively applied in fabricating ceramic material in bone tissue engineering. To improve the osteoinductivity of nano-hydroxyapatite (nHA) scaffold fabricated by extrusion free-forming, in this study, we incorporated a new peptide (P28) and optimized the superficial microstructure after shaping by controlling the sintering temperature. P28, a novel bone morphogenic protein 2 (BMP-2)-related peptide, was designed in this study. Analysis of the structure, physicochemical properties and release kinetics of P28 from nHA sintered at temperatures ranging from 1000 °C to 1400 °C revealed that nHA sintered at 1000 °C had higher porosity, preferable pore size and better capacity to control P28 release than that sintered at other temperatures. Moreover, the nHA scaffold sintered at 1000 °C with P28 showed improved adhesion, proliferation and osteogenic differentiation of MC3T3-E1 cells compared with scaffolds lacking P28 or BMP-2. In vivo, nHA scaffolds sintered at 1000 °C with P28 or BMP-2 induced greater bone regeneration in critical-sized rat cranial defects at 6 and 12 weeks post-implantation compared with scaffolds lacking P28 or BMP-2. Thus, nHA scaffolds sintered at 1000 °C and loaded with P28 may be excellent biomaterials for bone tissue engineering. Copyright © 2016 John Wiley & Sons, Ltd.


title: PoCoMo: projected collaboration using mobile devices

abstract: As personal projection devices become more common they will be able to support a range of exciting and unexplored social applications. We present a novel system and method that enables playful social interactions between multiple projected characters. The prototype consists of two mobile projector-camera systems, with lightly modified existing hardware, and computer vision algorithms to support a selection of applications and example scenarios. Our system allows participants to discover the characteristics and behaviors of other characters projected in the environment. The characters are guided by hand movements, and can respond to objects and other characters, to simulate a mixed reality of life-like entities.


title: Data Plane Programmability Beyond OpenFlow: Opportunities and Challenges for Network and Service Operations and Management

abstract: SDN and OpenFlow reshaped the way we configure forwarding devices and determine network behavior, by offering an open interface upon which apps like routing, monitoring, etc. can be built. SDN/OpenFlow helped break network "ossification" and unleash evolution, by enabling one to effectively think networking from top-down. It evidenced, however, a subtle but important problem: networking evolution is also hampered by the inability to change switch behavior, which forces one to build systems constrained from the bottom up. This scenario can change dramatically with the re-emergence of programmable data planes, and languages like POF and P4. Although recent, these languages proved to be very influential, powering several contributions towards flexible and customizable forwarding devices. In this paper, we survey the literature on data plane programmability, from early concepts (like Active Networks) to state-of-the-art solutions. We then dive into research questions that more recent work (especially P4) have uncovered: how to deploy and manage custom switch programs in large-scale networks? Can we improve services like monitoring and security with programmable data planes? Can we make network operation more dependable? As another contribution, we organize and discuss opportunities and challenges with potential to influence research in the field, from the perspective of network and service operations and management.


title: Using Convolutional Neural Networks to Recognize Rhythm Stimuli from Electroencephalography Recordings

abstract: Electroencephalography (EEG) recordings of rhythm perception might contain enough information to distinguish different rhythm types/genres or even identify the rhythms themselves. We apply convolutional neural networks (CNNs) to analyze and classify EEG data recorded within a rhythm perception study in Kigali, Rwanda which comprises 12 East African and 12 Western rhythmic stimuli - each presented in a loop for 32 seconds to 13 participants. We investigate the impact of the data representation and the pre-processing steps for this classification tasks and compare different network structures. Using CNNs, we are able to recognize individual rhythms from the EEG with a mean classification accuracy of 24.4% (chance level 4.17%) over all subjects by looking at less than three seconds from a single channel. Aggregating predictions for multiple channels, a mean accuracy of up to 50% can be achieved for individual subjects.


Pacific Association for Computational Linguistics N-GRAM-BASED AUTHOR PROFILES FOR AUTHORSHIP ATTRIBUTION


title: Co-designing ambient-assisted interventions using digital interlocutors for people with dementia

abstract: Socially assistive robots (SAR) have been used to assist the care of persons with dementia (PwD). We propose to combine problematic behaviors detection and robot interventions in an Ambient-assisted Intervention System (AaIS). To achieve this long-term aim, our first step was to design and evaluate robot-based interventions. We describe the co-design process conducted with caregivers to define realistic application scenarios for a conversational robot, named Eva, to enact interventions. Most recommendations from the caregivers in the co-design session were implemented in Eva. We conducted two sessions of a music therapy intervention with the new version of Eva with six PwD at a geriatric residence. Preliminary results from the intervention showed that the residents enjoyed the sessions, they talked to Eva to request songs, answered questions made by the robot and sang with it.


title: Image Quality Assessment for Color Correction Based on Color Contrast Similarity and Color Value Difference

abstract: Color correction plays an important role in the image processing field. But substantial research on the assessment of color correction is still insufficient. In this paper, we present an image quality assessment metric for color correction. It assesses the color consistency between the reference and target/result images of color correction according to their color contrast similarity and color value difference. Both the average difference and difference span are considered during the assessment. To compensate for the scene difference between the reference and target/result images of color correction, we propose to use an image registration algorithm to build their matching relationship, upon which a matching image is built. The matching image has the same scene as the target image and the same color feature as the reference image, and thus the matching image is regarded as the real reference image of our color correction assessment. Furthermore, we combine a confidence map of the matching image and a saliency map of the target/result image as a weighting map for assessment, which helps to improve the consistency between the objective and subjective assessment results. The experimental results show that our color correction assessment metric has better correlation, accuracy, and monotonicity with users’ subjective scores than 19 state-of-the-art metrics.


title: A Systematic Literature Review of Success Factors and Barriers of Agile Software Development

abstract: Motivator and demotivator plays an important role in software industry. It encompasses software performance and productivity which are necessary for projects of Agile software development (ASD). Existing studies comprise of motivators and demotivators of ASD, which exist in dispersed form. That is why there is a need of a detailed systematic literature review to review the factors and sub-factors effecting motivators and demotivators in ASD. A comprehensive review is executed to gather the critical success factors of motivator and demotivator of Agile software development. Thus, the ongoing study classifies motivator and demotivator factors into four classes, i.e., people, organization, technical and process. However, sub-classification is also executed to clarify more of the motivators of agile. Along with this, motivator and demotivator of scrum process is also categorized to overview a clear vision.


UNDERSTANDING MICRO AND SMALL ENTERPRISE GROWTH microREPORT # 36


title: Radiology of Eating Disorders: A Pictorial Review

abstract: The spectrum of radiologic findings encountered in patients with severe eating disorders is reviewed, with emphasis on recognition of signs that may aid in diagnosis of previously undetected cases.


title: Episodic Memory , Semantic Memory , and Amnesia

abstract: Episodic memory and semantic memory are two types of declarative memory. There have been two principal views about how this distinction might be reflected in the organization of memory functions in the brain. One view, that episodic memory and semantic memory are both dependent on the integrity of medial temporal lobe and midline diencephalic structures, predicts that amnesic patients with medial temporal lobe/diencephalic damage should be proportionately impaired in both episodic and semantic memory. An alternative view is that the capacity for semantic memory is spared, or partially spared, in amnesia relative to episodic memory ability. This article reviews two kinds of relevant data: 1) case studies where amnesia has occurred early in childhood, before much of an individual's semantic knowledge has been acquired, and 2) experimental studies with amnesic patients of fact and event learning, remembering and knowing, and remote memory. The data provide no compelling support for the view that episodic and semantic memory are affected differently in medial temporal lobe/diencephalic amnesia. However, episodic and semantic memory may be dissociable in those amnesic patients who additionally have severe frontal lobe damage. Hippocampus 1998;8:205–211. Published 1998 Wiley-Liss, Inc.


title: Job-Shop Scheduling

abstract: Scheduling is the allocation of shared resources over time to competing activities, and has been the subject of a significant amount of literature in the operations research field. Emphasis has been on investigating machine scheduling problems where jobs represent activities and machines represent resources; each machine can process at most one job at a time. This chapter reviews a variety of GA applications to the JSSP. We begin our discussion by formulating the JSSP by a disjunctive graph. We then look at domain independent binary and permutation representations, followed by an active schedule representation with GT crossover and the genetic enumeration method. Section 7.7 discusses a method for integrating local optimisation directly into GAs. Section 7.8 discusses performance comparison using the well known Muth and Thompson benchmark and the more difficult ten tough problems.


title: Hybrid Automata: an algorithmic approach to the specification and verification of hybrid systems

abstract: We introduce the framework of hybrid automata as a model and specification language for hybrid systems. Hybrid automata can be viewed as a generalization of timed automata, in which the behavior of variables is governed in each state by a set of differential equations. We show that many of the examples considered in the workshop can be defined by hybrid automata. While the reachability problem is undecidable even for very restricted classes of hybrid automata, we present two semidecision procedures for verifying safety properties of piecewiselinear hybrid automata, in which all variables change at constant rates. The two procedures are based, respectively, on minimizing and computing fixpoints on generally infinite state spaces. We show that if the procedures terminate, then they give correct answers. We then demonstrate that for many of the typical workshop examples, the procedures do terminate and thus provide an automatic way for verifying their properties.


title: Artificial Intelligence and Data Mining Techniques in Medicine – Success Stories

abstract: Artificially Intelligent (AI) powered tools are able to deal with uncertain and incomplete data sets. Neural network classifiers have been successfully used for prediction purposes in many complex situations. Research shows that Al-based data mining tools have been also successfully used in many medical environments. This paper advances the understanding of the application of Artificial Intelligence and Data Mining tools to clinical data by demonstrating the potential of these techniques in complex clinical situations.


title: OVM: an ontology for vulnerability management

abstract: In order to reach the goals of the Information Security Automation Program (ISAP) [1], we propose an ontological approach to capturing and utilizing the fundamental concepts in information security and their relationship, retrieving vulnerability data and reasoning about the cause and impact of vulnerabilities. Our ontology for vulnerability management (OVM) has been populated with all vulnerabilities in NVD [2] with additional inference rules, knowledge representation, and data-mining mechanisms. With the seamless integration of common vulnerabilities and their related concepts such as attacks and countermeasures, OVM provides a promising pathway to making ISAP successful.


title: Stochastic Dual Ascent for Solving Linear Systems

abstract: We develop a new randomized iterative algorithm---stochastic dual ascent (SDA)---for finding the projection of a given vector onto the solution space of a linear system. The method is dual in nature: with the dual being a non-strongly concave quadratic maximization problem without constraints. In each iteration of SDA, a dual variable is updated by a carefully chosen point in a subspace spanned by the columns of a random matrix drawn independently from a fixed distribution. The distribution plays the role of a parameter of the method. Our complexity results hold for a wide family of distributions of random matrices, which opens the possibility to fine-tune the stochasticity of the method to particular applications. We prove that primal iterates associated with the dual process converge to the projection exponentially fast in expectation, and give a formula and an insightful lower bound for the convergence rate. We also prove that the same rate applies to dual function values, primal function values and the duality gap. Unlike traditional iterative methods, SDA converges under no additional assumptions on the system (e.g., rank, diagonal dominance) beyond consistency. In fact, our lower bound improves as the rank of the system matrix drops. Many existing randomized methods for linear systems arise as special cases of SDA, including randomized Kaczmarz, randomized Newton, randomized coordinate descent, Gaussian descent, and their variants. In special cases where our method specializes to a known algorithm, we either recover the best known rates, or improve upon them. Finally, we show that the framework can be applied to the distributed average consensus problem to obtain an array of new algorithms. The randomized gossip algorithm arises as a special case.


title: Managing and exploring media using semantic regions: a spatial interface supporting user-defined mental models

abstract: Computer users deal with large numbers of personal media objects such as images, audio clips, voice mails, video clips, web pages, emails, and various document files. Users often struggle to interpret, explore, arrange, and use personal media objects because of three major problems; an ever increasing amount of personal media data, rigid organizing metaphor, and difficulty in rapid data access. ::: With the progress of computer hardware and digitization technologies at the rate of Moore's law, users may generate, acquire, and store more and more personal media data on their personal machines. However, the means available for users to organize and customize their personal media information spaces are extremely poor and driven mostly by storage and distribution models, not users' needs. Furthermore, this rigid and system-oriented metaphor causes wide and deep file folder hierarchies and often forbids users to rapid data access from storage. ::: This dissertation uses a graphical mechanism for spatially organizing personal media data based on users' mental models and introduces an innovative interaction metaphor to apply users' mental models to personal media data. Semantic Regions introduces an innovative way to construct users' mental models by drawing regions on 2D space and specifying the semantics for each region so that users can apply various personal ontologies to personal media data using the fling-and-flock metaphor. The prototype application, MediaFinder, validates the usability of the interface, particularly in comparison with alternative approaches. Contributions of this dissertation include: (1) Semantic Regions provides a formal model of spatial and dynamic reorganization of personal media data based on users' mental models. It extends the concept from a system-oriented file management system to a user-oriented personal media management system by employing the semantics of personal media data. (2) The MediaFinder personal media management tool, which uses Semantic Regions, the fling-and-flock metaphor, and flexible categorization capabilities to explore and manage personal media data sets. MediaFinder's object-oriented architecture can easily be extended to support variants of the Semantic Regions model. (3) The design and implementation of interaction models that support the personal media management tasks such as organization, meaning extraction, navigation, search, indexing, and navigation. (4) Two usability studies provided preliminary insight into the utility of Semantic Regions and led to design improvements for the construction and operation of Semantic Regions. (5) A framework for extending the Semantic Regions model, including the descriptions of possible extensions.


title: Adaptive power management using reinforcement learning

abstract: System level power management must consider the uncertainty and variability that comes from the environment, the application and the hardware. A robust power management technique must be able to learn the optimal decision from past history and improve itself as the environment changes. This paper presents a novel online power management technique based on model-free constrained reinforcement learning (RL). It learns the best power management policy that gives the minimum power consumption for a given performance constraint without any prior information of workload. Compared with existing machine learning based power management techniques, the RL based learning is capable of exploring the trade-off in the power-performance design space and converging to a better power management policy. Experimental results show that the proposed RL based power management achieves 24% and 3% reduction in power and latency respectively comparing to the existing expert based power management.


title: The Impact of Information Security Awareness Training on Information Security Behaviour: The Case for Further Research

abstract: Information Security awareness initiatives are seen as critical to any information security programme. But, how do we determine the effectiveness of these awareness initiatives? We could get our employees to write a test afterwards to determine how well they understand the policies, but this does not show how it affects the employee’s on the job behaviour. Does awareness training have a direct influence on the security behaviour of individuals, and what is the direct benefit of awareness training? This paper represents a study in progress that aims to answer the question: to what extent does information security awareness training influence information security behaviour? Research carried out on information security has traditionally been slanted towards technical aspects of security, typically rooted in computer science and mathematics. Security was traditionally seen as a service to be provided and not something that was influenced by users. However, it was soon recognised that focusing on technical issues alone is inadequate. Technologies meant to provide security ultimately depend on the effective implementation and operation of these technologies by people. Thus awareness of policies is needed by all individuals in an organisation to ensure that policies are well understood and not misinterpreted. Some researchers have maintained that educating users is futile mainly because it is believed that it is difficult to teach users complex security issues and secondly, because security is seen as secondary by the user they will not pay enough attention to it. This paper reflects research in progress and discusses some of the problems with existing information security awareness research and proposes a model to be tested for examining the impact of information security awareness training on information security behaviour.


NONVANISHING OF L-SERIES AND THE COMBINATORIAL SIEVE


title: Unsupervised Segmentation of Color-Texture Regions in Images and Video

abstract: A method for unsupervised segmentation of color-texture regions in images and video is presented. This method, which we refer to as JSEG, consists of two independent steps: color quantization and spatial segmentation. In the first step, colors in the image are quantized to several representative classes that can be used to differentiate regions in the image. The image pixels are then replaced by their corresponding color class labels, thus forming a class-map of the image. The focus of this work is on spatial segmentation, where a criterion for "good" segmentation using the class-map is proposed. Applying the criterion to local windows in the class-map results in the "J-image," in which high and low values correspond to possible boundaries and interiors of color-texture regions. A region growing method is then used to segment the image based on the multiscale J-images. A similar approach is applied to video sequences. An additional region tracking scheme is embedded into the region growing process to achieve consistent segmentation and tracking results, even for scenes with nonrigid object motion. Experiments show the robustness of the JSEG algorithm on real images and video.


abstract: Intelligent agents are employed as the central characters in this new introductory text. Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI. Neural networks, genetic programming, computer vision, heuristic search, knowledge representation and reasoning, Bayes networks, planning, and language understanding are each revealed through the growing capabilities of these agents. The book provides a refreshing and motivating new synthesis of the field by one of AI's master expositors and leading researchers. Artificial Intelligence: A New Synthesis takes the reader on a complete tour of this intriguing new world of AI. ::: ::: * An evolutionary approach provides a unifying theme ::: * Thorough coverage of important AI ideas, old and new ::: * Frequent use of examples and illustrative diagrams ::: * Extensive coverage of machine learning methods throughout the text ::: * Citations to over 500 references ::: * Comprehensive index ::: ::: Table of Contents ::: ::: 1 Introduction ::: 2 Stimulus-Response Agents ::: 3 Neural Networks ::: 4 Machine Evolution ::: 5 State Machines ::: 6 Robot Vision ::: 7 Agents that Plan ::: 8 Uninformed Search ::: 9 Heuristic Search ::: 10 Planning, Acting, and Learning ::: 11 Alternative Search Formulations and Applications ::: 12 Adversarial Search ::: 13 The Propositional Calculus ::: 14 Resolution in The Propositional Calculus ::: 15 The Predicate Calculus ::: 16 Resolution in the Predicate Calculus ::: 17 Knowledge-Based Systems ::: 18 Representing Commonsense Knowledge ::: 19 Reasoning with Uncertain Information ::: 20 Learning and Acting with Bayes Nets ::: 21 The Situation Calculus ::: 22 Planning ::: 23 Multiple Agents ::: 24 Communication Among Agents ::: 25 Agent Architectures


title: Clustering XML documents by patterns

abstract: Now that the use of XML is prevalent, methods for mining semi-structured documents have become even more important. In particular, one of the areas that could greatly benefit from in-depth analysis of XML's semi-structured nature is cluster analysis. Most of the XML clustering approaches developed so far employ pairwise similarity measures. In this paper, we study clustering algorithms, which use patterns to cluster documents without the need for pairwise comparisons. We investigate the shortcomings of existing approaches and establish a new pattern-based clustering framework called XPattern, which tries to address these shortcomings. The proposed framework consists of four steps: choosing a pattern definition, pattern mining, pattern clustering, and document assignment. The framework's distinguishing feature is the combination of pattern clustering and document-cluster assignment, which allows to group documents according to their characteristic features rather than their direct similarity. We experimentally evaluate the proposed approach by implementing an algorithm called PathXP, which mines maximal frequent paths and groups them into profiles. PathXP was found to match, in terms of accuracy, other XML clustering approaches, while requiring less parametrization and providing easily interpretable cluster representatives. Additionally, the results of an in-depth experimental study lead to general suggestions concerning pattern-based XML clustering.


title: Backward-Forward Search for Manipulation Planning

abstract: In this paper we address planning problems in high-dimensional hybrid configuration spaces, with a particular focus on manipulation planning problems involving many objects. We present the hybrid backward-forward (HBF) planning algorithm that uses a backward identification of constraints to direct the sampling of the infinite action space in a forward search from the initial state towards a goal configuration. The resulting planner is probabilistically complete and can effectively construct long manipulation plans requiring both prehensile and nonprehensile actions in cluttered environments.


title: Using Text Mining Techniques to Identify Research Trends: A Case Study of Design Research

abstract: The research goal of this paper is to identify major academic branches and to detect research trends in design research using text mining techniques. In this paper, the information about scientific literature in design research isprocessed. A combination of clustering and bibliometric analysis led to shaping four academic branches and summarizing each academic branch. Then, research trends and the evolution for each academic branch are explored. We perform a two-dimensional text mining approach, including bibliometric and network analysis, in order to detect trends of major academic branches. Specifically, the bibliometric characterization aims to assess design research area outputs, while the network analysis intends to reveal research trends in each academic branch of design research and the evolution of core research themes.


title: Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology

abstract: This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents’ wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident’s feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment.


Global flush communication primitive for inter-process communication


title: The Innovative Communicator in the Diffusion Process

abstract: Both innovativeness and opinion leadership have been extensively investigated in past research. This article profiles the characteristics of “innovative communicators”—consumers who are both innova...


title: Robot programming by non-experts: Intuitiveness and robustness of One-Shot robot programming

abstract: General purpose robots are established tools in a variety of industrial applications. Current household robots, however, are limited in capabilities and purpose. One reason for this is the programming effort required to utilize a general purpose robot. A popular strategy to reduce programming effort is Programming by Demonstration (PbD). Usually PbD requires several tedious demonstrations to teach a robot some behavior or skill. Notably, there are just few approaches where a single demonstration suffices. Such One-Shot approaches reduce programming effort even further and allow non-experts to quickly and intuitively program a general purpose robot. In this work we evaluate intuitiveness and robustness (i.e. the dependability when executing non-expert generated programs) of a One-Shot PbD system. To this end, we carried out a user study with 34 participants. Our participants used kinesthetic programming to complete various tasks with a light-weight robot. We instructed users with standard methods, either by speech, by graphical tutorial or by video. During the study, structured questionnaires and an observation sheet collected user impressions: The effectiveness when solving a task, the efficiency and (mental) effort during programming, the attitude towards the robot, and finally the satisfaction with the entire system. User ratings on all aspects confirm the high intuitiveness of the investigated system. In contrast to other approaches, the study did not depend on a specific instructional material to achieve high intuitiveness ratings. Overall, our findings support the advantages of a One-Shot programming system and give important insights into general requirements for robot programming by non-experts. We conclude that the investigated One-Shot PbD approach forms a vital tool for the use of robots in private households.


title: Multi-Antenna Downlink Channels with Limited Feedback and User Selection

abstract: We analyze the sum-rate performance of a multi- antenna downlink system carrying more users than transmit antennas, with partial channel knowledge at the transmitter due to finite rate feedback. In order to exploit multiuser diversity, we show that the transmitter must have, in addition to directional information, information regarding the quality of each channel. Such information should reflect both the channel magnitude and the quantization error. Expressions for the SINR distribution and the sum-rate are derived, and tradeoffs between the number of feedback bits, the number of users, and the SNR are observed. In particular, for a target performance, having more users reduces feedback load.


title: End-to-End Conversion of HTML Tables for Populating a Relational Database

abstract: Automating the conversion of human-readable HTML tables into machine-readable relational tables will enable end-user query processing of the millions of data tables found on the web. Theoretically sound and experimentally successful methods for index-based segmentation, extraction of category hierarchies, and construction of a canonical table suitable for direct input to a relational database are demonstrated on 200 heterogeneous web tables. The methods are scalable: the program generates the 198 Access compatible CSV files in ~0.1s per table (two tables could not be indexed).


EMNLP 2018 The 6th BioASQ Workshop A challenge on large-scale biomedical semantic indexing and question answering


title: Gravity-independent mobility and drilling on natural rock using microspines

abstract: To grip rocks on the surfaces of asteroids and comets, and to grip the cliff faces and lava tubes of Mars, a 250 mm diameter omni-directional anchor is presented that utilizes a hierarchical array of claws with suspension flexures, called microspines, to create fast, strong attachment. Prototypes have been demonstrated on vesicular basalt and a'a lava rock supporting forces in all directions away from the rock. Each anchor can support >160 N tangent, >150 N at 45°, and >180 N normal to the surface of the rock. A two-actuator selectively-compliant ankle interfaces these anchors to the Lemur IIB robot for climbing trials. A rotary percussive drill was also integrated into the anchor, demonstrating self-contained rock coring regardless of gravitational orientation. As a harder-than-zero-g proof of concept, 20mm diameter boreholes were drilled 83 mm deep in vesicular basalt samples, retaining a 12 mm diameter rock core in 3–6 pieces while in an inverted configuration, literally drilling into the ceiling.


title: MuJoCo: A physics engine for model-based control

abstract: We describe a new physics engine tailored to model-based control. Multi-joint dynamics are represented in generalized coordinates and computed via recursive algorithms. Contact responses are computed via efficient new algorithms we have developed, based on the modern velocity-stepping approach which avoids the difficulties with spring-dampers. Models are specified using either a high-level C++ API or an intuitive XML file format. A built-in compiler transforms the user model into an optimized data structure used for runtime computation. The engine can compute both forward and inverse dynamics. The latter are well-defined even in the presence of contacts and equality constraints. The model can include tendon wrapping as well as actuator activation states (e.g. pneumatic cylinders or muscles). To facilitate optimal control applications and in particular sampling and finite differencing, the dynamics can be evaluated for different states and controls in parallel. Around 400,000 dynamics evaluations per second are possible on a 12-core machine, for a 3D homanoid with 18 dofs and 6 active contacts. We have already used the engine in a number of control applications. It will soon be made publicly available.


title: Embodied Artificial Intelligence

abstract: This book is devoted to the rapidly growing highly interdisciplinary field of embodied artificial intelligence involving researchers from areas as diverse as computer science, engineering, cognitive science, neuroscience, biology, ...


Improving WalkSAT By Effective Tie-Breaking and Efficient Implementation


title: Functional Programming with Bananas, Lenses, Envelopes and Barbed Wire

abstract: We develop a calculus for lazy functional programming based on recursion operators associated with data type definitions. For these operators we derive various algebraic laws that are useful in deriving and manipulating programs. We shall show that all example functions in Bird and Wadler's Introduction to Functional Programming can be expressed using these operators.


title: Scalable Nonlinear Learning with Adaptive Polynomial Expansions

abstract: Can we effectively learn a nonlinear representation in time comparable to linear learning? We describe a new algorithm that explicitly and adaptively expands higher-order interaction features over base linear representations. The algorithm is designed for extreme computational efficiency, and an extensive experimental study shows that its computation/prediction tradeoff ability compares very favorably against strong baselines.


title: Design of Fractional Order PID controller for dc motor using Genetic Algorithm

abstract: Design of fractional order PID (FOPID) controller for DC motor is proposed in this paper. A FOPID (PI λ D μ ) is a PID controller whose derivative and integral orders are fractional numbers rather than integers. Design stage of such controller consists of determining six parameters – proportional constant (K p ), integral constant (K i ), derivative constant (K d ), filter time constant (τ d ), integral order (λ) and derivative order (μ). The proposed approach poses the problem as designing a DC motor speed controller on the concept of fixed structure robust controller and mixed sensitivity H ∞ method. The uncertainty caused by the parameter changes of motor resistance, motor inductance and load are formulated as multiplicative uncertainty weight, which are used in the objective function in the design. Genetic Algorithm (GA) and Simulated Annealing (SA) are employed to carry out the aforementioned design procedure. Comparisons are made with a PID with derivative first order filter controller and it is shown that the proposed FOPID controller can highly improve the system robustness with respect to model uncertainties. The comparison of PID and FOPID controllers is also been done on the basis of Time Domain Performance index i.e. ISE (Integral of Square Error).


title: Advocating for Action Design Research on IT Value Creation in Healthcare

abstract: Today there is mixed evidence that health IT decreases costs and/or improves care quality in the US. Some of the same factors that have driven delays in realizing the benefits from IT investments in other industries (i.e., time consuming process changes) are apparent in the U.S. healthcare industry, which is only now digitizing its fundamental patient data, the electronic health record. The healthcare industry itself is in transition and new IT may not provide full benefit unless it is accompanied with a restructuring of healthcare delivery. Traditional ex post approaches to measuring IT value will limit the ability of healthcare IT value researchers to add value to practice now especially as government incentives in the US drive significant investment. But generalizing results from traditional IT value research to the healthcare setting is risky due to differences between healthcare and other industries. I advocate for action design research that uses existing theory as a foundation, but adapts it to the specific unique characteristics of this industry. By actively participating in the design and evaluation of new socio-technical systems, IT value researchers can generate grounded theory to explain value creation in healthcare while influencing practice now.


title: SafeGPU: Contract- and Library-Based GPGPU for Object-Oriented Languages

abstract: Abstract Using GPUs as general-purpose processors has revolutionized parallel computing by providing, for a large and growing set of algorithms, massive data-parallelization on desktop machines. An obstacle to their widespread adoption, however, is the difficulty of programming them and the low-level control of the hardware required to achieve good performance. This paper proposes a programming approach, SafeGPU, that aims to make GPU data-parallel operations accessible through high-level libraries for object-oriented languages, while maintaining the performance benefits of lower-level code. The approach provides data-parallel operations for collections that can be chained and combined to express compound computations, with data synchronization and device management all handled automatically. It also integrates the design-by-contract methodology, which increases confidence in functional program correctness by embedding executable specifications into the program text. We present a prototype of SafeGPU for Eiffel, and show that it leads to modular and concise code that is accessible for GPGPU non-experts, while still providing performance comparable with that of hand-written CUDA code. We also describe our first steps towards porting it to C#, highlighting some challenges, solutions, and insights for implementing the approach in different managed languages. Finally, we show that runtime contract-checking becomes feasible in SafeGPU, as the contracts can be executed on the GPU.


title: Transformation of generalized chebyshev lowpass filter prototype to Suspended Stripline Structure highpass filter for wideband communication systems

abstract: This paper presents the transformation of generalized Chebyshev lowpass filter prototype to highpass filter using Suspended Stripline Structure (SSS) technology. The study involves circuit analysis to determine generalized Chebyshev responses with a transmission zero at finite frequency. The transformation of the highpass filter from the lowpass filter prototype provides a cutoff frequency of 3.1 GHz with a return loss better than -20 dB. The design is simulated on a Roger Duroid RO4350 with a dielectric constant, er of 3.48 and a thickness of 0.168 mm. The simulation performance results show promising results that could be further examined during the experimental works. This class of generalized Chebyshev highpass filter with finite transmission zero would be useful in any RF/ microwave communication systems particularly in wideband applications where the reduction of overall physical volume and weight as well as cost very important, while maintaining its excellent performance.


title: Pilon Fracture: A Case Report of a 45-Year-Old Dental Technician

abstract: Pilon fractures are complex and difficult-to-treat fractures of the lower extremity that account for about 1% of all lower extremity fractures and up to 10% of tibial fractures. The injury is caused by high energy axial load either from motor vehicle accidents or a fall from height. The treatment of these fractures has caused controversy among surgeons due to mixed outcomes. Here we report a case of pilon fracture in a 45 year old male patient who has sustained the injury as a result of a fall from a height of approximately 12 feet. We describe why it is absolutely crucial that the patient is treated with external fixation initially and evaluate its merits and drawbacks as well as ways to minimize the complications associated with external fixation of open intra-articular distal tibial fractures.


title: Aggregate Interference Analysis for Underlay Cognitive Radio Networks

abstract: This paper investigates the aggregate interference on a primary user caused by a random number of cognitive radio transmitters distributed in a finite ring. A composite model involving path loss, Rayleigh fading, and shadowing is considered. The exact closed-form moment generating function and an accurate approximation are derived. The aggregate interference is shown to be accurately approximated by a Gamma distribution. The exact outage and an asymptotic approximation are derived.


Children’s eye movements during listening: Developmental evidence for a constraint-based theory of sentence processing


title: The Mario AI Championship 2009-2012

abstract: We give a brief overview of the Mario AI Championship, a series of competitions based on an open source clone of the seminal platform game Super Mario Bros. The competition has four tracks. The gameplay and learning tracks resemble traditional reinforcement learning competitions, the Level generation track focuses on the generation of entertaining game levels, and the Turing Test track focuses on humanlike game-playing behavior. We also outline some lessons learned from the competition and its future. The article is written by the four organizers of the competition.


title: The unfamiliar face effect on forensic craniofacial reconstruction and recognition.

abstract: Previous research into the reliability of forensic craniofacial reconstruction (CFR) has focused primarily on the accuracy of reconstructed faces from European or African ancestry skulls. Moreover, the recognition of CFR in relation to the experience and ancestry of the practitioners and the assessors has not been previously considered. The cross-race effect is a recognised phenomenon in psychology studies, where familiar ancestry faces are recognised more readily than unfamiliar ancestry faces, but there is a paucity of research addressing the relationship between the accuracy of reconstructed faces and the familiarity with this ancestry by the practitioners/assessors. The aims of this research were to investigate whether 'unfamiliar-race effect' has any influence on the accuracy of CFR and to evaluate how much the correct recognition rate of CFR is affected by the cross-race effect. Eight CFRs from three ancestry groups were produced by experienced practitioners in order to explore the aims. The results demonstrated that practitioners produced more recognisable CFRs using skulls from a familiar ancestry than skulls from unfamiliar ancestries.


Hybrid Association Rule Learning and Process Mining for Fraud Detection


title: Triboelectric nanogenerator based on vertical contact separation mode for energy harvesting

abstract: Triboelectric effect works on the principle of triboelectrification and electrostatic induction. This principle is used to generate voltage by converting mechanical energy into electrical energy. This paper presents the charging behavior of different capacitors by rubbing of two different materials using mechanical motion. The numerical and simulation modeling, describes the charging performance of a TENG with a bridge rectifier. It is also demonstrated that a 10 μF capacitor can be charged to a maximum of 24.04 volt in 300 seconds and it is also provide 2800 μJ/cm3 maximum energy density. Such system can be used for ultralow power electronic devices, biomedical devices and self-powered appliances etc.


title: Privacy Protection for Wireless Medical Sensor Data

abstract: In recent years, wireless sensor networks have been widely used in healthcare applications, such as hospital and home patient monitoring. Wireless medical sensor networks are more vulnerable to eavesdropping, modification, impersonation and replaying attacks than the wired networks. A lot of work has been done to secure wireless medical sensor networks. The existing solutions can protect the patient data during transmission, but cannot stop the inside attack where the administrator of the patient database reveals the sensitive patient data. In this paper, we propose a practical approach to prevent the inside attack by using multiple data servers to store patient data. The main contribution of this paper is securely distributing the patient data in multiple data servers and employing the Paillier and ElGamal cryptosystems to perform statistic analysis on the patient data without compromising the patients’ privacy.


abstract: Zolt n Dornyei Language Teaching / Volume 31 / Issue 03 / July 1998, pp 117  135 DOI: 10.1017/S026144480001315X, Published online: 12 June 2009 Link to this article: http://journals.cambridge.org/abstract_S026144480001315X How to cite this article: Zolt n Dornyei (1998). Motivation in second and foreign language learning. Language Teaching, 31, pp 117135 doi:10.1017/S026144480001315X Request Permissions : Click here


title: Improving calibration of thermal stereo cameras using heated calibration board

abstract: Calibration of stereo cameras is important for accurate 3D reconstructions. For standard color cameras there are many available tools and algorithms for accurate calibration, such as detecting corners of chessboard patterns on planar calibration boards. When viewed in thermal imagery, these chessboard patterns are difficult to detect due to uniform temperature between the white and black squares. Previous techniques involve creating a custom calibration board using multiple materials. One abandoned approach uses a printed chessboard heated by a flood lamp which results in blurry, hard to detect corners that are only visible for a short period of time. We propose improvements to this approach to make it more reliable by using an iterative pre-processing technique to enhance contrast and a glazed finish ceramic tile backing to retain heat longer. We present results which show our calibration board retains heat to reliably detect corners for over 10 minutes; our method performs well in real calibration trials.


title: Integration of fMRI and simultaneous EEG: towards a comprehensive understanding of localization and time-course of brain activity in target detection

abstract: Abstract fMRI and EEG are complimentary methods for the analysis of brain activity since each method has its strength where the other one has limits: The spatial resolution is thus in the range of millimeters with fMRI and the time resolution is in the range of milliseconds with EEG. For a comprehensive understanding of brain activity in target detection, nine healthy subjects (age 24.2 ± 2.9) were investigated with simultaneous EEG (27 electrodes) and fMRI using an auditory oddball paradigm. As a first step, event-related potentials, measured inside the scanner, have been compared with the potentials recorded in a directly preceding session in front of the scanner. Attenuated amplitudes were found inside the scanner for the earlier N1/P2 component but not for the late P300 component. Second, an independent analysis of the localizations of the fMRI activations and the current source density as revealed by low resolution electromagnetic tomography (LORETA) has been done. Concordant activations were found in most regions, including the temporoparietal junction (TPJ), the supplementary motor area (SMA)/anterior cingulate cortex (ACC), the insula, and the middle frontal gyrus, with a mean Euclidean distance of 16.0 ± 6.6 mm between the BOLD centers of gravity and the LORETA-maxima. Finally, a time-course analysis based on the current source density maxima was done. It revealed different time-course patterns in the left and right hemisphere with earlier activations in frontal and parietal regions in the right hemisphere. The results suggest that the combination of EEG and fMRI permits an improved understanding of the spatiotemporal dynamics of brain activity.


title: A hybrid bat algorithm

abstract: Swarm intelligence is a very powerful technique appropriate to optimization. In this paper, we present a new swarm intelligence algorithm, which is based on the bat algorithm. Bat algorithm has been hybridized with differential evolution strategies. This hybridization showed very promising results on standard benchmark functions and also significantly improved the original bat algorithm.


title: A framework for efficient network anomaly intrusion detection with features selection

abstract: An intrusion Detection System (IDS) provides alarts against intrusion attacks where a traditional firewall fails. Machine learning algorithms aim to detect anomalies using supervised and unsupervised approaches. Features selection techniques identify important features and remove irrelevant and redundant attributes to reduce the dimensionality of feature space. This paper presents a features selection framework for efficient network anomaly detection using different machine learning classifiers. The framework applies different strategies by using filter and wrapper features selection methodologies. The aim of this framework is to select the minimum number of features that achieve the highest accuracy. UNSW-NB15 dataset is used in the experimental results to evaluate the proposed framework. The results show that by using 18 features from one of the filter ranking methods and applying J48 as a classifier, an accuracy of 88% is achieved.


title: Compression in wireless sensor networks: A survey and comparative evaluation

abstract: Wireless sensor networks (WSNs) are highly resource constrained in terms of power supply, memory capacity, communication bandwidth, and processor performance. Compression of sampling, sensor data, and communications can significantly improve the efficiency of utilization of three of these resources, namely, power supply, memory and bandwidth. Recently, there have been a large number of proposals describing compression algorithms for WSNs. These proposals are diverse and involve different compression approaches. It is high time that these individual efforts are put into perspective and a more holistic view taken. In this article, we take a step in that direction by presenting a survey of the literature in the area of compression and compression frameworks in WSNs. A comparative study of the various approaches is also provided. In addition, open research issues, challenges and future research directions are highlighted.


title: A 5GHz Double-Sideband Radar Sensor Chip in 0.18 $\mu$m CMOS for Non-Contact Vital Sign Detection

abstract: A 5 GHz double-sideband radar sensor chip for non-contact vital sign detection was designed and fabricated in UMC 0.18 mum mixed-mode/radio frequency CMOS process. The transceiver was designed with a differential architecture. Measurements show that it has over 1 GHz tuning range, and can successfully detect both the respiration and the heartbeat signals of a human subject. It is also shown that the differential architecture has the advantage of reducing local oscillation leakage. The 5 GHz double-sideband system can avoid null detection point by frequency tuning.


title: RECON: a reciprocal recommender for online dating

abstract: The reciprocal recommender is a class of recommender system that is important for several tasks where people are both the subjects and objects of the recommendation. Some examples are: job recommendation, mentor-mentee matching, and online dating. Despite the importance of this type of recommender, our work is the first to distinguish it and define its properties. We have implemented RECON, a reciprocal recommender for online dating, and have evaluated it on a large dataset from a major Australian dating website. We investigated the predictive power gained by taking account of reciprocity, finding that it is substantial, for example it improved the success rate of the top ten recommendations from 23% to 42% and also improved the recall at the same time. We also found reciprocity to help with the cold start problem obtaining a success rate of 26% for the top ten recommendations for new users. We discuss the implications of these results for broader uses of our approach for other reciprocal recommenders.


title: A molecular phylogeny of marine amphipods in the herbivorous family Ampithoidae

abstract: Ampithoid amphipods dominate invertebrate assemblages associated with shallow-water macroalgae and seagrasses worldwide and represent the most species-rich family of herbivorous amphipod known. To generate the first molecular phylogeny of this family, we sequenced 35 species from 10 genera at two mitochondrial genes [the cytochrome c oxidase subunit I (COI) and the large subunit of 16 s (LSU)] and two nuclear loci [sodium–potassium ATPase (NAK) and elongation factor 1-alpha (EF1)], for a total of 1453 base pairs. All 10 genera are embedded within an apparently monophyletic Ampithoidae (Amphitholina, Ampithoe, Biancolina, Cymadusa, Exampithoe, Paragrubia, Peramphithoe, Pleonexes, Plumithoe, Pseudoamphithoides and Sunamphitoe). Biancolina was previously placed within its own superfamily in another suborder. Within the family, single-locus trees were generally poor at resolving relationships among genera. Combined-locus trees were better at resolving deeper nodes, but complete resolution will require greater taxon sampling of ampithoids and closely related outgroup species, and more molecular characters. Despite these difficulties, our data generally support the monophyly of Ampithoidae, novel evolutionary relationships among genera, several currently accepted genera that will require revisions via alpha taxonomy and the presence of cryptic species.


title: Snapback and Postsnapback Saturation of Pseudomorphic High-Electron Mobility Transistor Subject to Transient Overstress

abstract: The snapback and postsnapback saturation characteristics in a pseudomorphic high-electron mobility transistor (PHEMT) subject to electrostatic discharge (ESD) transient overstress are studied. This is undertaken, for the first time, via transmission line pulsing (TLP)-like 2-D device simulations and benchmarked against TLP measurements. Physical mechanisms underlying the postsnapback behavior and ESD-induced failure are identified and discussed by analyzing TLP-like simulation results rather than extrapolating dc-like numerical simulation data.


title: Learning from Multiple Outlooks

abstract: We propose a novel problem formulation of learning a single task when the data are provided in different feature spaces. Each such space is called an outlook, and is assumed to contain both labeled and unlabeled data. The objective is to take advantage of the data from all the outlooks to better classify each of the outlooks. We devise an algorithm that computes optimal affine mappings from different outlooks to a target outlook by matching moments of the empirical distributions. We further derive a probabilistic interpretation of the resulting algorithm and a sample complexity bound indicating how many samples are needed to adequately find the mapping. We report the results of extensive experiments on activity recognition tasks that show the value of the proposed approach in boosting performance.


title: Accountability of AI Under the Law: The Role of Explanation

abstract: The ubiquity of systems using artificial intelligence or "AI" has brought increasing attention to how those systems should be regulated. The choice of how to regulate AI systems will require care. AI systems have the potential to synthesize large amounts of data, allowing for greater levels of personalization and precision than ever before|applications range from clinical decision support to autonomous driving and predictive policing. That said, common sense reasoning [McCarthy, 1960] remains one of the holy grails of AI, and there exist legitimate concerns about the intentional and unintentional negative consequences of AI systems [Bostrom, 2003, Amodei et al., 2016, Sculley et al., 2014]. ::: ::: There are many ways to hold AI systems accountable. In this work, we focus on one: explanation. Questions about a legal right to explanation from AI systems was recently debated in the EU General Data Protection Regulation [Goodman and Flaxman, 2016, Wachter et al., 2017], and thus thinking carefully about when and how explanation from AI systems might improve accountability is timely. Good choices about when to demand explanation can help prevent negative consequences from AI systems, while poor choices may not only fail to hold AI systems accountable but also hamper the development of much-needed beneficial AI systems. ::: ::: Below, we briefly review current societal, moral, and legal norms around explanation, and then focus on the different contexts under which explanation is currently required under the law. We find that there exists great variation around when explanation is demanded, but there also exists important consistencies: when demanding explanation from humans, what we typically want to know is how and whether certain input factors affected the final decision or outcome. ::: ::: These consistencies allow us to list the technical considerations that must be considered if we desired AI systems that could provide kinds of explanations that are currently required of humans under the law. Contrary to popular wisdom of AI systems as indecipherable black boxes, we find that this level of explanation should often be technically feasible but may sometimes be practically onerous|there are certain aspects of explanation that may be simple for humans to provide but challenging for AI systems, and vice versa. As an interdisciplinary team of legal scholars, computer scientists, and cognitive scientists, we recommend that for the present, AI systems can and should be held to a similar standard of explanation as humans currently are; in the future we may wish to hold an AI to a different standard.


title: Science Graph for characterizing the recent scientific landscape using Paragraph Vectors

abstract: Maps of science representing the structure of science can help us understand science and technology (ST however, navigating the recent scientific landscape is still challenging, since conventional inter-citation and co-citation analysis has difficulty in applying to ongoing projects and recently published papers. Therefore, in order to characterize what is being attempted in the current scientific landscape, this paper proposes a content-based method of locating research projects in a multi-dimensional space using word/paragraph embedding techniques. Specifically, for addressing an unclustered problem associated with paragraph vectors, we introduce cluster vectors based on the information entropies of concepts in an S&T thesaurus. In addition, we propose three approaches to find the semantics of project relationships. The experimental results show that the proposed method successfully formed a clustered graph from 25,607 project descriptions from the 7th Framework Programme of EU from 2006 to 2016. Finally, we evaluated the distances and semantics of the project relationships and identified significant relationships from the graph.


title: The Impact of Human Resource Management Practices on Turnover, Productivity, and Corporate Financial Performance

abstract: This study comprehensively evaluated the links between systems of High Performance Work Practices and firm performance. Results based on a national sample of nearly one thousand firms indicate that these practices have an economically and statistically significant impact on both intermediate employee outcomes (turnover and productivity) and short- and long-term measures of corporate financial performance. Support for predictions that the impact of High Performance Work Practices on firm performance is in part contingent on their interrelationships and links with competitive strategy was limited. The impact of human resource management (HRM) policies and prac


title: Bad Data Injection Attack and Defense in Electricity Market using Game Theory Study

abstract: Applications of cyber technologies improve the quality of monitoring and decision making in smart grid. These cyber technologies are vulnerable to malicious attacks, and compromising them can have serious technical and economical problems. This paper specifies the effect of compromising each measurement on the price of electricity, so that the attacker is able to change the prices in the desired direction (increasing or decreasing). Attacking and defending all measurements are impossible for the attacker and defender, respectively. This situation is modeled as a zero sum game between the attacker and defender. The game defines the proportion of times that the attacker and defender like to attack and defend different measurements, respectively. From the simulation results based on the PJM 5 Bus test system, we can show the effectiveness and properties of the studied game.


title: Design and fabrication of modified fractal antenna for UWB applications

abstract: A modified fractal antenna for bandwidth enhancement is proposed in this paper. The proposed antenna has stair-slots on the ground plane and cross square fractal element as radiating element. The overall size of the proposed antenna measures 31.35 mm × 18.7 mm × 1.6 mm. The proposed fractal UWB antenna is simulated using CST Microwave Studio. The antenna has broad impedance bandwidth of 8.1 GHz from 2.6 GHz to 10.7 GHz at 8 dB reference level of return loss. The antenna is designed on a low cost FR-4 substrate. The proposed antenna is fabricated and the results are validated using measurements. The proposed antenna may be a useful tool for UWB communication systems.


title: OTA linearity enhancement technique for high frequency applications with IM3 below -65 dB

abstract: A technique to enhance the linearity of continuous-time operational transconductance amplifiers (OTA)-C filters working at high frequencies is proposed. Each OTA consumes 10.5 mW and the transconductance can be tuned from 70 to 160 /spl mu/A/V while the IM3 remains below -70 dB up to 50 MHz for a 1.3-V/sub pp/ differential input. For a 20-MHz low-pass second-order filter implementation, the measured IM3 with an input voltage of 1.3 V/sub pp/ is below - 65 dB. The supply voltage is 3.3 V. Experimental results of the circuit, fabricated in a standard CMOS 0.35-/spl mu/m technology, are presented.


title: DENSER Cities: A System for Dense Efficient Reconstructions of Cities

abstract: This paper is about the efficient generation of dense, colored models of city-scale environments from range data and in particular, stereo cameras. Better maps make for better understanding; better understanding leads to better robots, but this comes at a cost. The computational and memory requirements of large dense models can be prohibitive. We provide the theory and the system needed to create city-scale dense reconstructions. To do so, we apply a regularizer over a compressed 3D data structure while dealing with the complex boundary conditions this induces during the data-fusion stage. We show that only with these considerations can we swiftly create neat, large, "well behaved" reconstructions. We evaluate our system using the KITTI dataset and provide statistics for the metric errors in all surfaces created compared to those measured with 3D laser. Our regularizer reduces the median error by 40% in 3.4 km of dense reconstructions with a median accuracy of 6 cm. For subjective analysis, we provide a qualitative review of 6.1 km of our dense reconstructions in an attached video. These are the largest dense reconstructions from a single passive camera we are aware of in the literature.


title: Compressing term positions in web indexes

abstract: Large search engines process thousands of queries per second on billions of pages, making query processing a major factor in their operating costs. This has led to a lot of research on how to improve query throughput, using techniques such as massive parallelism, caching, early termination, and inverted index compression. We focus on techniques for compressing term positions in web search engine indexes. Most previous work has focused on compressing docID and frequency data, or position information in other types of text collections. Compression of term positions in web pages is complicated by the fact that term occurrences tend to cluster within documents but not across document boundaries, making it harder to exploit clustering effects. Also, typical access patterns for position data are different from those for docID and frequency data. We perform a detailed study of a number of existing and new techniques for compressing position data in web indexes. We also study how to efficiently access position data for ranking functions that take proximity features into account.


title: Recurrent dislocation of the patella

abstract: Abstract 1. 1. Recurrent slipping or dislocation of the patella is due to congenital and acquired defects or a combination of both. 2. 2. Chronic slipping of the patella may result, after many years, in a degenerative arthritis of the knee. 3. 3. Conservative treatment in palliative. 4. 4. Operative treatment should be done early to avoid arthritic changes and to prevent accidents to the patient which may occur as the result of falling. 5. 5. The method of procedure followed out should not be limited to one method, otherwise fractures are bound to result. The underlying condition which is responsible for the dislocation must be sought for and corrected in so far as possible.


title: Joint user knowledge and matrix factorization for recommender systems

abstract: Currently, most of the existing recommendation methods treat social network users equally, which assume that the effect of recommendation on a user is decided by the user’s own preferences and social influence. However, a user’s own knowledge in a field has not been considered. In other words, to what extent does a user accept recommendations in social networks need to consider the user’s own knowledge or expertise in the field. In this paper, we propose a novel matrix factorization recommendation algorithm based on integrating social network information such as trust relationships, rating information of users and users’ own knowledge. Specifically, since we cannot directly measure a user’s knowledge in the field, we first use a user’s status in a social network to indicate a user’s knowledge in a field, and users’ status is inferred from the distributions of users’ ratings and followers across fields or the structure of domain-specific social network. Then, we model the final rating of decision-making as a linear combination of the user’s own preferences, social influence and user’s own knowledge. Experimental results on real world data sets show that our proposed approach generally outperforms the state-of-the-art recommendation algorithms that do not consider the knowledge level difference between the users.


title: Cuba: healthcare and the revolution.

abstract: This paper depicts Cuba as a relic of the Cold War Its coverage of healthcare demonstrates steadfastness and success in surmounting hurdles of complacency and disregard to socialized medicine - an extension of Soviet patronage and third world alliances. The literature relays a mission of inclusivity underpinned by political ideology and a conviction to humanity. With the aid ofendorsements, it speaks to contrasts and critiques in service and results by reflecting on the delivery offree healthcare for all Cuban citizens and its impression on the eradication of numerous diseases, reduced mortality rate and increased life expectancy. Punished by the longest trade embargo in modern history, the regime is in possession of limited resources to expedite remedy to its subjects. Such, much to the dislike of the authorities, elevates elements of distinction in association with the dispensation of service and drugs demonstrated by an evolving two-tier system for the disenfranchised and privileged clientele while simultaneously impacting the maintenance of facilities and equipment. Consequently, it recognizes harsh ramifications attributed to compliance with ideology and subtle adjustments to withstand external exertion. The Cuban replica is currently a tale of sorts awaiting a comprehensible definition for future generations.


title: Voxel-based global illumination

abstract: Computing a global illumination solution in real-time is still an open problem. We introduce Voxel-based Global Illumination (VGI), a scalable technique that ranges from real-time near-field illumination to interactive global illumination solutions. To obtain a voxelized scene representation, we introduce a new atlas-based boundary voxelization algorithm and an extension to a fast ray-voxel intersection test. Similar to screen-space illumination methods, VGI is independent of the scene complexity. Using voxels for indirect visibility enables real-time near-field illumination without the screen-space artifacts of alternative methods. Furthermore, VGI can be extended to interactive, multi-bounce global illumination solutions like path tracing and instant radiosity.


title: Abstractive and Extractive Text Summarization using Document Context Vector and Recurrent Neural Networks

abstract: Sequence to sequence (Seq2Seq) learning has recently been used for abstractive and extractive summarization. In current study, Seq2Seq models have been used for eBay product description summarization. We propose a novel Document-Context based Seq2Seq models using RNNs for abstractive and extractive summarizations. Intuitively, this is similar to humans reading the title, abstract or any other contextual information before reading the document. This gives humans a high-level idea of what the document is about. We use this idea and propose that Seq2Seq models should be started with contextual information at the first time-step of the input to obtain better summaries. In this manner, the output summaries are more document centric, than being generic, overcoming one of the major hurdles of using generative models. We generate document-context from user-behavior and seller provided information. We train and evaluate our models on human-extracted-golden-summaries. The document-contextual Seq2Seq models outperform standard Seq2Seq models. Moreover, generating human extracted summaries is prohibitively expensive to scale, we therefore propose a semi-supervised technique for extracting approximate summaries and using it for training Seq2Seq models at scale. Semi-supervised models are evaluated against human extracted summaries and are found to be of similar efficacy. We provide side by side comparison for abstractive and extractive summarizers (contextual and non-contextual) on same evaluation dataset. Overall, we provide methodologies to use and evaluate the proposed techniques for large document summarization. Furthermore, we found these techniques to be highly effective, which is not the case with existing techniques.


title: Using transcranial direct-current stimulation (tDCS) to understand cognitive processing

abstract: Noninvasive brain stimulation methods are becoming increasingly common tools in the kit of the cognitive scientist. In particular, transcranial direct-current stimulation (tDCS) is showing great promise as a tool to causally manipulate the brain and understand how information is processed. The popularity of this method of brain stimulation is based on the fact that it is safe, inexpensive, its effects are long lasting, and you can increase the likelihood that neurons will fire near one electrode and decrease the likelihood that neurons will fire near another. However, this method of manipulating the brain to draw causal inferences is not without complication. Because tDCS methods continue to be refined and are not yet standardized, there are reports in the literature that show some striking inconsistencies. Primary among the complications of the technique is that the tDCS method uses two or more electrodes to pass current and all of these electrodes will have effects on the tissue underneath them. In this tutorial, we will share what we have learned about using tDCS to manipulate how the brain perceives, attends, remembers, and responds to information from our environment. Our goal is to provide a starting point for new users of tDCS and spur discussion of the standardization of methods to enhance replicability.


title: Exposure to Whole-Body Vibration and Mechanical Shock: A Field Study of Quad Bike Use in Agriculture

abstract: OBJECTIVES ::: The purpose of this study was to determine exposure to whole-body vibration (WBV) and mechanical shock in rural workers who use quad bikes and to explore how personal, physical, and workplace characteristics influence exposure. ::: ::: ::: METHODS ::: A seat pad mounted triaxial accelerometer and data logger recorded full workday vibration and shock data from 130 New Zealand rural workers. Personal, physical, and workplace characteristics were gathered using a modified version of the Whole Body Vibration Health Surveillance Questionnaire. WBVs and mechanical shocks were analysed in accordance with the International Standardization for Organization (ISO 2631-1 and ISO 2631-5) standards and are presented as vibration dose value (VDV) and mechanical shock (S(ed)) exposures. ::: ::: ::: RESULTS ::: VDV(Z) consistently exceeded European Union (Guide to good practice on whole body vibration. Directive 2002/44/EC on minimum health and safety, European Commission Directorate General Employment, Social Affairs and Equal Opportunities. 2006) guideline exposure action thresholds with some workers exceeding exposure limit thresholds. Exposure to mechanical shock was also evident. Increasing age had the strongest (negative) association with vibration and shock exposure with body mass index (BMI) having a similar but weaker effect. Age, daily driving duration, dairy farming, and use of two rear shock absorbers created the strongest multivariate model explaining 33% of variance in VDV(Z). Only age and dairy farming combined to explain 17% of the variance for daily mechanical shock. Twelve-month prevalence for low back pain was highest at 57.7% and lowest for upper back pain (13.8%). ::: ::: ::: CONCLUSIONS ::: Personal (age and BMI), physical (shock absorbers and velocity), and workplace characteristics (driving duration and dairy farming) suggest that a mix of engineered workplace and behavioural interventions is required to reduce this level of exposure to vibration and shock.


A Dataset for Research of Traditional Chinese Painting and Calligraphy Images


title: Unsupervised Segmentation of Cervical Cell Images Using Gaussian Mixture Model

abstract: Cervical cancer is one of the leading causes of cancer death in women. Screening at early stages using the popular Pap smear test has been demonstrated to reduce fatalities significantly. Cost effective, automated screening methods can significantly improve the adoption of these tests worldwide. Automated screening involves image analysis of cervical cells. Gaussian Mixture Models (GMM) are widely used in image processing for segmentation which is a crucial step in image analysis. In our proposed method, GMM is implemented to segment cell regions to identify cellular features such as nucleus, cytoplasm while addressing shortcomings of existing methods. This method is combined with shape based identification of nucleus to increase the accuracy of nucleus segmentation. This enables the algorithm to accurately trace the cells and nucleus contours from the pap smear images that contain cell clusters. The method also accounts for inconsistent staining, if any. The results that are presented shows that our proposed method performs well even in challenging conditions.


title: Monopole Antenna With Inkjet-Printed EBG Array on Paper Substrate for Wearable Applications

abstract: In this letter, a novel electromagnetic band-gap structure (EBG) with single-ring resonators is inkjet-printed on the commercially available photo paper using conductive nano-silver ink. The printed EBG array is placed above a copper sheet, forming an artificial magnetic conductor (AMC) reflector at the designed frequency range (2.4 ~ 2.5 GHz). A microstrip monopole antenna is backed with the designed AMC reflector and is tested in free space and in contact with a human phantom. The antenna gain of a conventional microstrip monopole on human phantom is as low as -9 dBi. The gain of the proposed AMC backed monopole, measured on a human phantom is 0.95 dBi. The measurements demonstrate superior performance of the proposed monopole with EBG array compared to a conventional microstrip monopole antenna when they are considered for wearable applications.


title: CONSUMER SOCIAL COGNITION Is the Implicit Association Test a Valid and Valuable Measure of Implicit Consumer Social Cognition?

abstract: This article discusses the need for more satisfactory implicit measures in consumer psychology and assesses the theoretical foundations, validity, and value of the Implicit Association Test (IAT) as a measure of implicit consumer social cognition. Study 1 demonstrates the IAT's sensitivity to explicit individual differences in brand attitudes, ownership, and usage frequency, and shows their correlations with lAT-based measures of implicit brand attitudes and brand relationship strength. In Study 2, the contrast between explicit and implicit measures of attitude toward the ad for sportswear advertisements portraying African American (Black) and European American (White) athlete–spokespersons revealed different patterns of responses to explicit and implicit measures in Black and White respondents. These were explained in terms of self-presentation biases and system justification theory. Overall, the results demonstrate that the IAT enhances our understanding of consumer responses, particularly when consumers are either unable or unwilling to identify the sources of influence on their behaviors or opinions.


Systematic Literature Review of Software Process Capability / Maturity Models


title: Automatic Object Segmentation by Quantum Cuts

abstract: In this study, the link between quantum mechanics and graph-cuts is exploited and a novel saliency map generation and salient object segmentation method is proposed based on the ground state solution of a modified Hamiltonian. First, the graph representation of certain quantum mechanical operators is studied. This reveals strong connections with widely used graph-cut algorithms while quantum mechanical constraints exhibit crucial advantages over the existing graph-cut algorithms. Furhtermore, concepts such as potential field helps solving a particular singularity problem related to laplacian matrices. In the proposed approach, the ground state (wave function) corresponding to a sub-atomic particle of a modified Hamiltonian operator corresponds to a particular optimization problem, the solution of which yields the salient object segmentation in a digital image. This approach provides a parameter-free -hence dataset independent-, unsupervised and fully automatic saliency map generation, which outperforms many existing state-of-the-art algorithms. The results of the proposed salient object extraction method exhibit such a promising accuracy that pushes the frontier in this field to the borders of the input-driven processing only - without the use of "object knowledge" aided by long-term human memory and intelligence. Furthermore, with the novel technologies for measuring a quantum wave function, the proposed method has a unique potential: Salient object segmentation in an actual physical setup in nano-scale. Such an unprece-dendent property will not only produce segmentation results instantaneously, but may be a unique opportunity to achieve accurate object segmentation in real-time for the massive visual repositories of today's "Big Data".


title: The many dimensions of poverty in Albania: income, wealth and perceptions

abstract: This paper aims at assessing poverty in Albania through the use of an asset index whose effectiveness is compared with consumption in explaining differences in results of health and educational outcomes. Firstly, an asset index is constructed by the use of factor analysis and principal component techniques; then, two probit models are estimated assessing enrolment rate for secondary education and chronic disability in Albania using the asset index as an independent variable to compare its effectiveness with expenditures. The World Bank LSMS Survey of 2002 is used in the analysis.


title: Logical, Ontological and Cognitive Aspects of Object Types and Cross-World Identity with Applications to the Theory of Conceptual Spaces

abstract: Types are fundamental for conceptual domain modeling and knowledge representation in computer science. Frequently, monadic types used in domain models have as their instances objects (endurants, continuants), i.e., entities persisting in time that experience qualitative changes while keeping their numerical identity. In this paper, I revisit a philosophically and cognitively well-founded theory of object types and propose a system of modal logics with restricted quantification designed to formally characterize the distinctions and constraints proposed by this theory. The formal system proposed also addresses the limitations of classical (unrestricted extensional) modal logics in differentiating between types that represent mere properties (or attributions) ascribed to individual objects from types that carry a principle of identity for those individuals (the so-called sortal types). Finally, I also show here how this proposal can complement the theory of conceptual spaces by offering an account for kind-supplied principles of cross-world identity. The account addresses an important criticism posed to conceptual spaces in the literature and is in line with a number of empirical results in the literature of cognitive psychology.


title: Internet of Things: Security challenges for next generation networks

abstract: Internet of Things(IoT) is the next big boom in the networking field. The vision of IoT is to connect daily used objects (which have the ability of sensing and actuation) to the Internet. This may or may or may not involve human. IoT field is still maturing and has many open issues. We build up on the security issues. As the devices have low computational power and low memory the existing security mechanisms (which are a necessity) should also be optimized accordingly or a clean slate approach needs to be followed. This is a survey paper to focus on the security aspects of IoT. We further also discuss the open challenges in this field.


title: Classification features for attack detection in collaborative recommender systems

abstract: Collaborative recommender systems are highly vulnerable to attack. Attackers can use automated means to inject a large number of biased profiles into such a system, resulting in recommendations that favor or disfavor given items. Since collaborative recommender systems must be open to user input, it is difficult to design a system that cannot be so attacked. Researchers studying robust recommendation have therefore begun to identify types of attacks and study mechanisms for recognizing and defeating them. In this paper, we propose and study different attributes derived from user profiles for their utility in attack detection. We show that a machine learning classification approach that includes attributes derived from attack models is more successful than more generalized detection algorithms previously studied.


title: Distributed learning of deep neural network over multiple agents

abstract: In domains such as health care and finance, shortage of labeled data and computational resources is a critical issue while developing machine learning algorithms. To address the issue of labeled data scarcity in training and deployment of neural network-based systems, we propose a new technique to train deep neural networks over several data sources. Our method allows for deep neural networks to be trained using data from multiple entities in a distributed fashion. We evaluate our algorithm on existing datasets and show that it obtains performance which is similar to a regular neural network trained on a single machine. We further extend it to incorporate semi-supervised learning when training with few labeled samples, and analyze any security concerns that may arise. Our algorithm paves the way for distributed training of deep neural networks in data sensitive applications when raw data may not be shared directly.


title: Optimized index structures for querying RDF from the Web

abstract: Storing and querying resource description framework (RDF) data is one of the basic tasks within any semantic Web application. A number of storage systems provide assistance for this task. However, current RDF database systems do not use optimized indexes, which results in a poor performance behavior for querying RDF. In this paper we describe optimized index structures for RDF, show how to process and evaluate queries based on the index structure, describe a lightweight adaptable implementation in Java, and provide a performance comparison with existing RDF databases.


title: Enhanced fog detection and free-space segmentation for car navigation

abstract: Free-space detection is a primary task for car navigation. Unfortunately, classical approaches have difficulties in adverse weather conditions, in particular in daytime fog. In this paper, a solution is proposed thanks to a contrast restoration approach on images grabbed by an in-vehicle camera. The proposed method improves the state-of-the-art in several ways. First, the segmentation of the fog region of interest is better segmented thanks to the computation of the shortest routes maps. Second, the fog density as well as the position of the horizon line is jointly computed. Then, the method restores the contrast of the road by only assuming that the road is flat and, at the same time, detects the vertical objects. Finally, a segmentation of the connected component in front of the vehicle gives the free-space area. An experimental validation was carried out to foresee the effectiveness of the method. Different results are shown on sample images extracted from video sequences acquired from an in-vehicle camera. The proposed method is complementary to existing free-space area detection methods relying on color segmentation and stereovision.


title: Multi-agent Q-learning for autonomous D2D communication

abstract: This paper is devoted to autonomous device-to-device (D2D) communication in cellular networks. The aim of each D2D pair is to maximize its throughput subject to the minimum signal-to-interference-plus-noise ratio (SINR) constraints. This problem is represented by a stochastic non-cooperative game where the players (D2D pairs) have no prior information on the availability and quality of selected channels. Therefore, each player in this game becomes a “learner” which explores all of its possible strategies based on the locally-observed throughput and state (defined by the channel quality). Consequently, we propose a multi-agent Q-learning algorithm based on the players' “beliefs” about the strategies of their counterparts and show its implementation in a Long Term Evolution - Advanced (LTE-A) network. As follows from simulations, the algorithm achieves a near-optimal performance after a small number of iterations.


title: A 420 fW self-regulated 3T voltage reference generator achieving 0.47%/V line regulation from 0.4-to-1.2 V

abstract: This paper presents a constant with temperature (CWT) voltage reference generator (VRG) that utilizes only regular transistors in standard CMOS technology. By operating an NMOS transistor as a self-biased current source driving a diode-connected PMOS transistor in the subthreshold region, a sub-pW, temperature stabilized voltage can be generated. To improve line regulation, a self-regulation NMOS is added, creating a 3-transistor (3T) VRG. Measurement results of 38 samples fabricated in 65nm CMOS reveal an average line regulation of 0.47%/V from 0.4 to 1.2 V and an average temperature coefficient of 252.2 ppm/°C, all at an average power consumption of 420 fW at 20° C, thereby enabling a near-zero-power, temperature- and line-stable low-cost voltage reference for next-generation Internet of Things applications.


title: From here to AGI: A roadmap to the realization of human-level artificial general intelligence

abstract: A practical roadmap to human-level artificial general intelligence is outlined, leading from the current situation in which complex AGI-oriented cognitive architectures remain partially implemented and inadequately tested, to a future in which AGI systems are deployed to carry out a variety of practical tasks currently only achievable via humans. The roadmap involves simultaneous development of proto-AGI applications in a number of different areas: videogame agent control, mobile robotics, genomics, natural language dialogue, theorem proving and program learning. The simultaneous pursuit of diverse application areas using a single AGI architecture is proposed as critical to maintaining generality of intelligence throughout the development process. While acknowledging the profound difficulty of time estimation for a project of this complexity, it is projected that the roadmap outlined could potentially be completed in a decade or less.


title: DUAL-BAND AND WIDEBAND DESIGN OF A PRINTED DIPOLE ANTENNA INTEGRATED WITH DUAL-BAND BALUN

abstract: A dual-band printed dipole antenna with integrated balun feed is given in this paper. First, the fork-shaped slot is etched on the arms of the printed dipole antenna to achieve the dual-band operation with resonances at WLAN bands. The radiating element without balun is optimized and operates at 2.4 GHz (2180–2750MHz) and 5.2GHz (5040 MHz–5480MHz) where return loss is less than −10 dB. In order to get a larger bandwidth, a modified Marchand balun is introduced for dual-band operation, which can provide two resonances in each band to enhance impedance bandwidth. By co-designing the radiating element with the dual-band balun, an antenna covering 2150–2750 MHz and 5050–6230 MHz bands has been achieved. The design equations for modified balun have been presented and agreement between calculation and measurement is good.