title: Distributed Energy Resources (DER) object modeling with IEC 61850–7–420

abstract: The electrical networks are receiving more deployments of Distributed Energy Resources (DERs). In order to control the impact of DERs on existing networks, it is proposed to divide them into more manageable smaller sets which are called ‘microgrids’. Unlike conventional utility grids, microgrids include generators, storage devices and loads at all levels of the system. Power generation, distribution and consumption levels are not discrete and power flow may occur at any direction. At any point in time, the microgrid may become isolated from the utility grid and continue its operation under islanding conditions. Furthermore, some microgrids may have changing structures with alternative paths and the coupling point for a device or a part of the microgrid may change due to the altering conditions. In order to sustain a safe operation in such a versatile and dynamic structure which has numerous parameters and variables, a new management strategy that has communication lines is required. The need of an extensive communication between the microgrid components is widely accepted in the Power Engineering field. In an effort to standardize the communication methods and modeling IEC 61850 is issued by International Electrotechnical Commission. Currently, there are studies underway to extend this standard with IEC 61850–7–420 so that DERs are also modeled in a standard way. This paper focuses on the communication challenges in a microgrid system, a review of IEC 61850 and the implementation of IEC 61850–7–420 for DER modeling.


title: Towards automated exploit generation for embedded systems

abstract: Manual vulnerability discovery and exploit development on an executable are very challenging tasks for developers. Therefore, the automation of those tasks is becoming interesting in the field of software security. In this paper, we implement an approach of automated exploit generation for firmware of embedded systems by extending an existing dynamic analysis framework called Avatar. Embedded systems occupy a significant portion of the market but lack typical security features found on general purpose computers, making them prone to critical vulnerabilities. We discuss several techniques to automatically discover vulnerabilities and generate exploits for embedded systems, and evaluate our proposed approach by generating exploits for two vulnerable firmware written for a popular ARM Cortex-M3 microcontroller.


Correlation in an Intrusion Detection Process


title: Learning to Answer Biomedical Factoid & List Questions: OAQA at BioASQ 3B

abstract: This paper describes the CMU OAQA system evaluated in the BioASQ 3B Question Answering track. We first present a three-layered architecture, and then describe the components integrated for exact answer generation and retrieval. Using over 400 factoid and list questions from past BioASQ 1B and 2B tasks as background knowledge, we focus on how to learn to answer questions using a gold standard dataset of question-answer pairs, using supervised models for answer type prediction and candidate answer scoring. On the three test sets where the system was evaluated (3, 4, and 5), the official evaluation results have shown that the system achieves an MRR of .1615, .5155, .2727 for factoid questions, and an F-measure of .0969, .3168, .1875 for list questions, respectively; five of these scores were the highest reported among all participating systems.


title: Flexible Liquid Metal Alloy (EGaIn) Microstrip Patch Antenna

abstract: This paper describes a flexible microstrip patch antenna that incorporates a novel multi-layer construction consisting of a liquid metal (eutectic gallium indium) encased in an elastomer. The combined properties of the fluid and the elastomeric substrate result in a flexible and durable antenna that is well suited for conformal antenna applications. Injecting the metal into microfluidic channels provides a simple way to define the shape of the liquid, which is stabilized mechanically by a thin oxide skin that forms spontaneously on its surface. This approach has proven sufficient for forming simple, single layer antenna geometries, such as dipoles. More complex fluidic antennas, particularly those featuring large, co-planar sheet-like geometries, require additional design considerations to achieve the desired shape of the metal. Here, a multi-layer patch antenna is fabricated using specially designed serpentine channels that take advantage of the unique rheological properties of the liquid metal alloy. The flexibility of the resulting antennas is demonstrated and the antenna parameters are characterized through simulation and measurement in both the relaxed and flexed states.


title: HAND: A Hybrid Approach to Accelerate Non-contiguous Data Movement Using MPI Datatypes on GPU Clusters

abstract: An increasing number of MPI applications are being ported to take advantage of the compute power offered by GPUs. Data movement continues to be the major bottleneck on GPU clusters, more so when data is non-contiguous, which is common in scientific applications. The existing techniques of optimizing MPI data type processing, to improve performance of non-contiguous data movement, handle only certain data patterns efficiently while incurring overheads for the others. In this paper, we first propose a set of optimized techniques to handle different MPI data types. Next, we propose a novel framework (HAND) that enables hybrid and adaptive selection among different techniques and tuning to achieve better performance with all data types. Our experimental results using the modified DDTBench suite demonstrate up to a 98% reduction in data type latency. We also apply this data type-aware design on an N-Body particle simulation application. Performance evaluation of this application on a 64 GPU cluster shows that our proposed approach can achieve up to 80% and 54% increase in performance by using struct and indexed data types compared to the existing best design. To the best of our knowledge, this is the first attempt to propose a hybrid and adaptive solution to integrate all existing schemes to optimize arbitrary non-contiguous data movement using MPI data types on GPU clusters.


title: Acoustic noise mitigation of switched reluctance machines with windows in both stator and rotor poles

abstract: Switched Reluctance Machines (SRMs) have been studied by many researchers as an alternative to other types of electrical machines for use in electric and hybrid vehicle applications. SRMs are fault tolerant and have wide speed operating range. However, they suffer from several disadvantages including high vibration, acoustic noise and torque ripple. In this paper, placement of rectangular windows in both the rotor and stator poles is proposed to reduce the vibration and acoustic noise of SRMs. The position and the dimensions of the windows are optimized through Electromagnetic Finite Element Analysis (FEA). Multi-physics FEA is also performed to predict the vibration and acoustic noise of the optimized design. The results of this study confirm that placing windows in both the stator and the rotor of the SRMs can significantly reduce the acoustic noise compared to conventional SRMs.


title: The Impact Of Internet User Shopping Patterns and Demographics on Consumer Mobile Buying Behaviour

abstract: ABSTRACT In less than 15 years, the mobile phone has become an essential part of our daily lives. It is no longer a luxury item and has become one of the most commonly used daily consumer goods throughout the world. Despite its growing significance, there is still a lack of research work in many countries in this field. This study aims to make an approximation of the M-shopper profile and identify the variables influencing purchase behaviour. The methodology used to achieve these objectives is based on 7 focus groups and the analytical techniques of Chi- Square and logistic regression. Analysis of the results obtained from a representative sample of 2,104 Spanish Internet users shows that the M-commerce decision can be predicted according to consumer age, social class and behaviour patterns as user of non-store shopping channels. Keywords: Consumer behaviour; Direct shopping; Home Shopper; M-Commerce 1. Introduction Technological progress in the sphere of information and communication is encouraging the use and development of new shopping methods, leading to a rapid growth in non-store shopping as the individual can buy products/services without having to travel to retail outlets [Sharma and Sheth 2004; Thompson 1997]. This growth in non-store shopping and new trends in technology have facilitated the introduction of electronic marketing and promise to provide new ways of impacting and serving consumers in the future [Balasubramanian, Peterson and Jarvenpaa 2002; Reynolds 2000; Sivanad, Gesta and Sulep 2004]. Practically all products/services can be purchased quickly, conveniently and without moving from home [Davison, Dorrington and McCoy 1982; Eroglu, Machleit and Davies 2003; Rosenberg and Hirschman 1980; Sheth 1983]. Wireless Internet via mobile devices (WIMD) is leading the world into another spectrum of communications and means of conducting day-to-day business and life activities [Sivanad, 2004]. In the next years, more Internet transactions will be realized via mobile phones than fixed network devices. According to the MC Statistics viewed at epaynews. com, only 16% of 533 million Internet users were global wireless Internet users in the year 2001; however the percentage will soar to 57% of 1460 million Internet users in 2007. At present, online ticketing, reservation for flights and hotels or theater tickets are part of the most attractive WAP applications, since they bring consumers comfort, spontaneity and mobility [Buellingen and Woerter 2004]. In general, the different methods of direct shopping are still expected to grow but whereas some of the methods are showing rather low levels of growth, stagnation and even decline, in others the predicted growth rate is picking up speed. The most innovative methods maintain significant rates of growth [Sivanad 2004; Yang 2005] and at the same time there is a tendency to use them in conjunction with other sales systems as companies seek to complement sales methods and obtain synergies [Wu and Wang 2004]. Thus for example, the combination of Internet with other methods, such as the mobile phone or television will make it possible to optimise consumer convenience and increase their scope wider audience, in addition to overcoming some of the limitations which arise when using Internet alone as a shopping tool [Buellingen and Woerter 2004; Chiles and McMackin 1996; Jarvenpaa et al. 1999; Lohse and Spiller 1998; Swaminathan et al. 1999]. In Spain, the field study "Surfers on the net" [AIMC 2005] highlights the convergence of Internet with other media, and reveals that 45.4% of Internet users have sometimes watched television through Internet, 76.7% have sometimes used Internet to listen to the radio and 92.2% have sometimes consulted an e-newspaper. It should be emphasised that the public at home use not only Internet but also the new mobile technologies such as electronic diaries and mobile phones. According to the study "Surfers on the net" [AIMC 2005], 37. …


title: Circulate shifted OFDM chirp waveform diversity design with digital beamforming for MIMO SAR

abstract: Waveform diversity design has always been the key to multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) systems, and it is known that synthetic integral side lobe ratio (SISLR) is a more optimal indicator than the integral side lobe ratio (ISLR) to evaluate the orthogonality between different MIMO SAR waveforms. This paper presents proof that it is difficult to obtain the SISLR of an existing waveform such that it is sufficiently low to achieve high SNR for SAR imaging. Thus, it is necessary to find a way to separate these MIMO SAR waveforms in other domains, for example, a spatial domain by digital beamforming (DBF). Learning from Krieger’s idea of short-term shift-orthogonal waveforms and using joint time-frequency transforms (Gabor transform) to prove that for most existing SAR waveforms cyclic shift is a good operation with which to generate short-term shift-orthogonal waveforms. This paper presents the designs of four circulate shifted OFDM chirp waveforms, which have a much lower SISLR and retain all the advantages of the classical chirp waveform such as a large time-bandwidth product, constant amplitude, implementation simplicity, and good Doppler tolerance.


title: Stereoscopic image completion and depth recovery

abstract: In this paper, we have proposed a novel patch-based method for automatic completion of stereoscopic images and the corresponding depth/disparity maps simultaneously. The missing depths are estimated in local feature space and a patch distance metric is designed to take the appearance, depth gradients and depth inconsistency into account. To ensure the proper stereopsis, we first search for the proper stereoscopic patch in both left and right images according to the distance metric, and then iteratively refine the images. Our method is capable of dealing with general scenes including both frontal-parallel and non-frontal-parallel objects. Experimental results show that our method is superior to previous ones with better stereoscopically consistent content and more plausible completion.


title: Nerve Entrapment Syndromes as a Cause of Pain in the Hip, Groin and Buttock

abstract: In sports medicine, chronic hip, groin and buttock pain is a common diagnostic problem. Because of the complex anatomy of this region and the many potential neurological causes for pain, few sports clinicians have a detailed understanding of this problem. This paper discusses the clinical aspects of nerve entrapment syndromes related to sport and takes a regional approach in order to provide a diagnostic framework for the general sports physician. The various neurological syndromes are discussed and the surgical management elaborated in detail.For some specific conditions, such as the so-called ‘piriformis syndrome’, the pathophysiological understanding has changed since the early descriptions and now this particular diagnosis is often ascribed to almost any cause of buttock and/or hamstring symptoms. We discuss the nature of the origin of local symptoms and note that the often described symptoms are more likely due to compression of structures other then the sciatic nerve. Furthermore, the role of piriformis hypertrophy or anatomical nerve variations in the genesis of this syndrome must be questioned. We suggest renaming this the ‘deep gluteal syndrome’ to account for all of the observed phenomena.As sports medicine continues to develop a scientific basis, the role of nerve entrapments as the basis for chronic symptomatology is undergoing a new understanding and clinicians need to be aware of the diagnostic possibilities and be able to advise patients accordingly on the basis of scientific fact not anecdotal fiction.


title: A Wideband IM3 Cancellation Technique for CMOS ${\bf \Pi}$- and T-Attenuators

abstract: A wideband IM3 cancellation technique for CMOS attenuators is presented. With proper transistor width ratios, the dominant distortion currents of transistor switches cancel each other. As a result, a high IIP3 robust to PVT variations can be achieved without using large transistors. Two prototypes in a 0.16 μm standard bulk CMOS process are presented: a Π-attenuator with four discrete settings obtains +26 dBm IIP3 and +3 dBm 1 dB-compression point (CP) for 50 MHz to 5 GHz with only 0.0054 mm2 active area, and a similar T-attenuator system which obtains +27 dBm IIP3 and +13 dBm CP for 50 MHz to 5.6 GHz with only 0.0067 mm2 active area.


title: SECProv: Trustworthy and Efficient Provenance Management in the Cloud

abstract: The black-box nature of clouds introduces a lack of trusts in clouds. Since provenance can provide a complete history of an entity, trustworthy provenance management for data, application, or workflow can make the cloud more accountable. Current research on cloud provenance mainly focuses on collecting provenance records and trusting the cloud providers in managing the provenance records. However, a dishonest cloud provider can alter the provenance records, as the records are stored within the control of the cloud provider. To solve this problem, we first propose CloProv - a provenance model to capture the complete provenance of any type of entities in the cloud. We analyze the threats on the CloProv model considering collusion among malicious users and dishonest cloud providers. Based on the threat model, we propose a secure data provenance scheme - SECProv for cloud-based, multi-user, shared data storage systems. We integrate SECProv with the object storage module of an open source cloud framework - OpenStack Swift and analyze the efficiency of the proposed scheme.


title: Training Generative Adversarial Networks with Binary Neurons by End-to-end Backpropagation

abstract: We propose the BinaryGAN, a novel generative adversarial network (GAN) that uses binary neurons at the output layer of the generator. We employ the sigmoid-adjusted straight-through estimators to estimate the gradients for the binary neurons and train the whole network by end-to-end backpropogation. The proposed model is able to directly generate binary-valued predictions at test time. We implement such a model to generate binarized MNIST digits and experimentally compare the performance for different types of binary neurons, GAN objectives and network architectures. Although the results are still preliminary, we show that it is possible to train a GAN that has binary neurons and that the use of gradient estimators can be a promising direction for modeling discrete distributions with GANs. For reproducibility, the source code is available at https://github.com/salu133445/binarygan .


Identification of novel autism candidate regions through analysis of reported cytogenetic abnormalities associated with autism


title: Monte-Carlo Tree Search for the Game of 7 Wonders

abstract: Monte-Carlo Tree Search, and in particular with the Upper Confidence Bounds formula, has provided large improvements for AI in numerous games, particularly in Go, Hex, Havannah, Amazons and Breakthrough. In this work we study this algorithm on a more complex game, the game of "7 Wonders". This card game gathers together several known challenging properties, such as hidden information, multi-player and stochasticity. It also includes an inter-player trading system that induces a combinatorial search to decide which decisions are legal. Moreover, it is difficult to hand-craft an efficient evaluation function since the card values are heavily dependent upon the stage of the game and upon the other player decisions. We show that, in spite of the fact that "7 Wonders" is apparently not so related to classic abstract games, many known results still hold.


title: The functional neuroanatomy of language.

abstract: There has been substantial progress over the last several years in understanding aspects of the functional neuroanatomy of language. Some of these advances are summarized in this review. It will be argued that recognizing speech sounds is carried out in the superior temporal lobe bilaterally, that the superior temporal sulcus bilaterally is involved in phonological-level aspects of this process, that the frontal/motor system is not central to speech recognition although it may modulate auditory perception of speech, that conceptual access mechanisms are likely located in the lateral posterior temporal lobe (middle and inferior temporal gyri), that speech production involves sensory-related systems in the posterior superior temporal lobe in the left hemisphere, that the interface between perceptual and motor systems is supported by a sensory-motor circuit for vocal tract actions (not dedicated to speech) that is very similar to sensory-motor circuits found in primate parietal lobe, and that verbal short-term memory can be understood as an emergent property of this sensory-motor circuit. These observations are considered within the context of a dual stream model of speech processing in which one pathway supports speech comprehension and the other supports sensory-motor integration. Additional topics of discussion include the functional organization of the planum temporale for spatial hearing and speech-related sensory-motor processes, the anatomical and functional basis of a form of acquired language disorder, conduction aphasia, the neural basis of vocabulary development, and sentence-level/grammatical processing.


title: Aesthetics and credibility in web site design

abstract: Web sites often provide the first impression of an organization. For many organizations, web sites are crucial to ensure sales or to procure services within. When a person opens a web site, the first impression is probably made in a few seconds, and the user will either stay or move on to the next site on the basis of many factors. One of the factors that may influence users to stay or go is the page aesthetics. Another reason may involve a user's judgment about the site's credibility. This study explores the possible link between page aesthetics and a user's judgment of the site's credibility. Our findings indicate that when the same content is presented using different levels of aesthetic treatment, the content with a higher aesthetic treatment was judged as having higher credibility. We call this the amelioration effect of visual design and aesthetics on content credibility. Our study suggests that this effect is operational within the first few seconds in which a user views a web page. Given the same content, a higher aesthetic treatment will increase perceived credibility.


title: "They're all going out to something weird": Workflow, Legacy and Metadata in the Music Production Process

abstract: In this paper we use results from two ethnographic studies of the music production process to examine some key issues regarding how work is currently accomplished in studio production environments. These issues relate in particular to workflows and how metadata is adapted to the specific needs of specific parts of the process. We find that there can be significant tensions between how reasoning is applied to metadata at different stages of production and that this can lead to overheads where metadata has to be either changed or created anew to make the process work. On the basis of these findings we articulate some of the potential solutions we are now examining. These centre in particular upon the notions of Digital/Dynamic Musical Objects and flexible metadata shells.


title: Design of Null-Filling Antenna for Automotive Radar Using the Genetic Algorithm

abstract: A null-filling antenna for automotive radar is investigated using the genetic algorithm. The single column of the proposed antenna consists of four gap-coupled antenna elements and 14 direct-coupled antenna elements. The radiating elements are inclined 45 ° from the feedline. The proposed antenna has 19.67 dBi gain with a -20.19-dB sidelobe level in the elevation and a -25.08-dB sidelobe level in the azimuth direction. The blind zone is reduced from 19.03 to 2.38 m using the proposed antenna for sensing the obstacle. The proposed antenna is a good solution for both short- and long-range automotive radar.


title: Learning long-term dependencies in NARX recurrent neural networks

abstract: It has previously been shown that gradient-descent learning algorithms for recurrent neural networks can perform poorly on tasks that involve long-term dependencies, i.e. those problems for which the desired output depends on inputs presented at times far in the past. We show that the long-term dependencies problem is lessened for a class of architectures called nonlinear autoregressive models with exogenous (NARX) recurrent neural networks, which have powerful representational capabilities. We have previously reported that gradient descent learning can be more effective in NARX networks than in recurrent neural network architectures that have "hidden states" on problems including grammatical inference and nonlinear system identification. Typically, the network converges much faster and generalizes better than other networks. The results in this paper are consistent with this phenomenon. We present some experimental results which show that NARX networks can often retain information for two to three times as long as conventional recurrent neural networks. We show that although NARX networks do not circumvent the problem of long-term dependencies, they can greatly improve performance on long-term dependency problems. We also describe in detail some of the assumptions regarding what it means to latch information robustly and suggest possible ways to loosen these assumptions.


title: Efficient Computation of the Skyline Cube

abstract: Skyline has been proposed as an important operator for multi-criteria decision making, data mining and visualization, and user-preference queries. In this paper. we consider the problem of efficiently computing a SKYCUBE, which consists of skylines of all possible non-empty subsets of a given set of dimensions. While existing skyline computation algorithms can be immediately extended to computing each skyline query independently, such "shared-nothing" algorithms are inefficient. We develop several computation sharing strategies based on effectively identifying the computation dependencies among multiple related skyline queries. Based on these sharing strategies, two novel algorithms, Bottom-Up and Top-Down algorithms, are proposed to compute SKYCUBE efficiently. Finally, our extensive performance evaluations confirm the effectiveness of the sharing strategies. It is shown that new algorithms significantly outperform the naive ones.


title: Local-global ranking for facial expression intensity estimation

abstract: Facial action units provide an objective characterization of facial muscle movements. Automatic estimation of facial action unit intensities is a challenging problem given individual differences in neutral face appearances and the need to generalize across different pose, illumination and datasets. In this paper, we introduce the Local-Global Ranking method as a novel alternative to direct prediction of facial action unit intensities. Our method takes advantage of the additional information present in videos and image collections of the same person (e.g. a photo album). Instead of trying to estimate facial expression intensities independently for each image, our proposed method performs a two-stage ranking: a local pair-wise ranking followed by a global ranking. The local ranking is designed to be accurate and robust by making a simple 3-class comparison (higher, equal, or lower) between randomly sampled pairs of images. We use a Bayesian model to integrate all these pair-wise rankings and construct a global ranking. Our Local-Global Ranking method shows state-of-the-art performance on two publicly-available datasets. Our cross-dataset experiments also show better generalizability.


title: Pose Guided Person Image Generation

abstract: This paper proposes the novel Pose Guided Person Generation Network (PG$^2$) that allows to synthesize person images in arbitrary poses, based on an image of that person and a novel pose. Our generation framework PG$^2$ utilizes the pose information explicitly and consists of two key stages: pose integration and image refinement. In the first stage the condition image and the target pose are fed into a U-Net-like network to generate an initial but coarse image of the person with the target pose. The second stage then refines the initial and blurry result by training a U-Net-like generator in an adversarial way. Extensive experimental results on both 128$\times$64 re-identification images and 256$\times$256 fashion photos show that our model generates high-quality person images with convincing details.


title: Citations and annotations in classics: old problems and new perspectives

abstract: Annotations played a major role in Classics since the very beginning of the discipline. Some of the first attested examples of philological work, the so-called scholia, were in fact marginalia, namely comments written at the margins of a text. Over the centuries this kind of scholarship evolved until it became a genre on its own, the classical commentary, thus moving away from the text with the result that philologists had to devise a solution to linking together the commented and the commenting text. The solution to this problem is the system of canonical citations, a special kind of bibliographic references that are at the same time very precise and highly interoperable. In this paper we present HuCit, an ontology that models in depth the semantics of canonical citations. We discuss how it can be used to a) support the automatic extraction of canonical citations from texts and b) to publish them in machine-readable format on the Semantic Web. Finally, we describe how HuCit's machine-generated citation data can also be expressed as annotations by using the Open Annotation Collaboration (OAC) ontology, to the aim of increasing reuse and semantic interoperability.


title: Rate of Return to Pitching and Performance After Tommy John Surgery in Major League Baseball Pitchers

abstract: BACKGROUND ::: Medial ulnar collateral ligament (UCL) reconstruction is a common procedure performed on Major League Baseball (MLB) pitchers in the United States. ::: ::: ::: PURPOSE ::: To determine (1) the rate of return to pitching (RTP) in the MLB after UCL reconstruction, (2) the RTP rate in either the MLB and minor league combined, (3) performance after RTP, and (4) the difference in the RTP rate and performance between pitchers who underwent UCL reconstruction and matched controls without UCL injuries. ::: ::: ::: STUDY DESIGN ::: Cohort study; Level of evidence, 3. ::: ::: ::: METHODS ::: Major League Baseball pitchers with symptomatic medial UCL deficiency who underwent UCL reconstruction were evaluated. All player, elbow, and surgical demographic data were analyzed. Controls matched by age, body mass index, position, handedness, and MLB experience and performance were selected from the MLB during the same years as those undergoing UCL reconstruction. An "index year" was designated for controls, analogous to the UCL reconstruction year in cases. Return to pitching and performance measures in the MLB were compared between cases and controls. Student t tests were performed for analysis of within-group and between-group variables, respectively. ::: ::: ::: RESULTS ::: A total of 179 pitchers with UCL tears who underwent reconstruction met the inclusion criteria and were analyzed. Of these, 148 pitchers (83%) were able to RTP in the MLB, and 174 pitchers were able to RTP in the MLB and minor league combined (97.2%), while only 5 pitchers (2.8%) were never able to RTP in either the MLB or minor league. Pitchers returned to the MLB at a mean 20.5 ± 9.72 months after UCL reconstruction. The length of career in the MLB after UCL reconstruction was 3.9 ± 2.84 years, although 56 of these patients were still currently actively pitching in the MLB at the start of the 2013 season. The revision rate was 3.9%. In the year before UCL reconstruction, pitching performance declined significantly in the cases versus controls in the number of innings pitched, games played, and wins and the winning percentage (P < .05). After surgery, pitchers showed significantly improved performance versus before surgery (fewer losses, a lower losing percentage, lower earned run average [ERA], threw fewer walks, and allowed fewer hits, runs, and home runs) (P < .05). Comparisons between cases and controls for the time frame after UCL reconstruction (cases) or the index year (controls) demonstrated that cases had significantly (P < .05) fewer losses per season and a lower losing percentage. In addition, cases had a significantly lower ERA and allowed fewer walks and hits per inning pitched. ::: ::: ::: CONCLUSION ::: There is a high rate of RTP in professional baseball after UCL reconstruction. Performance declined before surgery and improved after surgery. When compared with demographic-matched controls, patients who underwent UCL reconstruction had better results in multiple performance measures. Reconstruction of the UCL allows for a predictable and successful return to the MLB.


title: Fabrication of ceramic components with hierarchical porosity

abstract: This article reviews different methodologies for the fabrication of monolithic ceramic components possessing multiscale porosity, i.e., with pores ranging from a few nanometers to several hundred microns. Two main strategies have been discussed: (a) the assembling of micro/mesoporous materials into components possessing also macropores; (b) the addition of micro/mesoporosity to macroporous, cellular monoliths. Both routes include one-pot and multi-step processing routes, and yield components with different properties in terms, for instance, of specific surface area values, mechanical strength, and permeability to fluids. The wide range of processing approaches available enable the fabrication of components with very varied morphology, suitable for a variety of industrial applications.


title: Individualness and Determinantal Point Processes for Pedestrian Detection

abstract: In this paper, we introduce individualness of detection candidates as a complement to objectness for pedestrian detection. The individualness assigns a single detection for each object out of raw detection candidates given by either object proposals or sliding windows. We show that conventional approaches, such as non-maximum suppression, are sub-optimal since they suppress nearby detections using only detection scores. We use a determinantal point process combined with the individualness to optimally select final detections. It models each detection using its quality and similarity to other detections based on the individualness. Then, detections with high detection scores and low correlations are selected by measuring their probability using a determinant of a matrix, which is composed of quality terms on the diagonal entries and similarities on the off-diagonal entries. For concreteness, we focus on the pedestrian detection problem as it is one of the most challenging problems due to frequent occlusions and unpredictable human motions. Experimental results demonstrate that the proposed algorithm works favorably against existing methods, including non-maximal suppression and a quadratic unconstrained binary optimization based method.


title: Lexical Chaining for Measuring Discourse Coherence Quality in Test-taker Essays

abstract: This paper presents an investigation of lexical chaining (Morris and Hirst, 1991) for measuring discourse coherence quality in test-taker essays. We hypothesize that attributes of lexical chains, as well as interactions between lexical chains and explicit discourse elements, can be harnessed for representing coherence. Our experiments reveal that performance achieved by our new lexical chain features is better than that of previous discourse features used for this task, and that the best system performance is achieved when combining lexical chaining features with complementary discourse features, such as those provided by a discourse parser based on rhetorical structure theory, and features that reflect errors in grammar, word usage, and mechanics.


Decision support systems and its role in developing the universities strategic management: Islamic university in Gaza as a case study


title: Development of an atrophic acne scar risk assessment tool

abstract: Background ::: Acne is a chronic dermatological disease predominantly afflicting young adults and is often associated with the development of scars. Acne scarring is usually avoidable when acne is managed early and effectively. However, acne patients often fail to seek early treatment. New and innovative tools to raise awareness are needed. ::: ::: Objective ::: This study presents the development and assessment of a tool aiming to assess the risk of atrophic acne scars. ::: ::: Methods ::: A systematic literature review of clinical risk factors for acne scars, a Delphi-like survey of dermatological experts in acne and secondary data analysis, were conducted to produce an evidence-based risk assessment tool. The tool was assessed both with a sample of young adults with and without scars and was assessed via a database cross-validation. ::: ::: Results ::: A self-administered tool for risk assessment of developing atrophic acne scars in young adults was developed. It is a readily comprehensible and practical tool for population education and for use in medical practices. It comprises of four risk factors: worst ever severity of acne, duration of acne, family history of atrophic acne scars and lesion manipulation behaviours. It provides a dichotomous outcome: lower vs. higher risk of developing scars, thereby categorizing nearly two-thirds of the population correctly, with sensitivity of 82% and specificity of 43%. ::: ::: Conclusion ::: The present tool was developed as a response to current challenges in acne scar prevention. A potential benefit is to encourage those at risk to self-identify and to seek active intervention of their acne. In clinical practice, we expect this tool may help clinicians identify patients at risk of atrophic acne scarring and underscore their requirement for rapid and effective acne treatment.


title: Graph mining: Laws, generators, and algorithms

abstract: How does the Web look? How could we tell an abnormal social network from a normal one? These and similar questions are important in many fields where the data can intuitively be cast as a graph; examples range from computer networks to sociology to biology and many more. Indeed, any M : N relation in database terminology can be represented as a graph. A lot of these questions boil down to the following: “How can we generate synthetic but realistic graphs?” To answer this, we must first understand what patterns are common in real-world graphs and can thus be considered a mark of normality/realism. This survey give an overview of the incredible variety of work that has been done on these problems. One of our main contributions is the integration of points of view from physics, mathematics, sociology, and computer science. Further, we briefly describe recent advances on some related and interesting graph problems.


Exploring the Aesthetic Range for Humanoid Robots


title: A Wideband Balanced-to-Unbalanced Coupled-Line Power Divider

abstract: A compact wideband balanced-to-unbalanced (BTU) out-of-phase power divider (PD) is proposed in this letter. This novel circuit essentially consists of three pairs of cascaded coupled lines and a grounded resistor for output isolation. By using the even- (odd-) mode method analysis and the traditional transmission-line theory, closed-form design equations for equal power division with out of phase from one differential input to two unbalanced outputs, high output isolation and good common-mode suppression are obtained, simultaneously. In addition, an experimental PD is designed and fabricated. The good performance and the consistency between the simulated and measured results verify our design theory.


title: Eight-Week Remote Monitoring Using a Freely Worn Device Reveals Unstable Gait Patterns in Older Fallers

abstract: Objectives: Develop algorithms to detect gait impairments remotely using data from freely worn devices during long-term monitoring. Identify statistical models that describe how gait performances are distributed over several weeks. Determine the data window required to reliably assess an increased propensity for falling. Methods: 1085 days of walking data were collected from eighteen independent-living older people (mean age 83 years) using a freely worn pendant sensor (housing a triaxial accelerometer and pressure sensor). Statistical distributions from several accelerometer-derived gait features (encompassing quantity, exposure, intensity, and quality) were compared for those with and without a history of falling. Results: Participants completed more short walks relative to long walks, as approximated by a power law. Walks less than 13.1 s comprised 50% of exposure to walking-related falls. Daily-life cadence was bimodal and step-time variability followed a log-normal distribution. Fallers took significantly fewer steps per walk and had relatively more exposure from short walks and greater mode of step-time variability. Conclusions: Using a freely worn device and wavelet-based analysis tools allowed long-term monitoring of walks greater than or equal to three steps. In older people, short walks constitute a large proportion of exposure to falls. To identify fallers, mode of variability may be a better measure of central tendency than mean of variability. A week's monitoring is sufficient to reliably assess the long-term propensity for falling. Significance: Statistical distributions of gait performances provide a reference for future wearable device development and research into the complex relationships between daily-life walking patterns, morbidity, and falls.


title: The working principle of an Arduino

abstract: In this paper, we analyze the working principle of an arduino. These days many people try to use the arduino because it makes things easier due to the simplified version of C++ and the already made Arduino microcontroller(atmega328 microcontroller [1]) that you can programme, erase and reprogramme at any given time. In this paper we will discuss the hardware components used in the arduino board, the software used to programme it (Arduino board) with the guide on how to write and construct your own projects, and a couple of examples of an arduino project, This will give you the overall view of an arduino uno, that after reading this paper you will get the basic concept and use of an arduino uno.


title: Flavor network and the principles of food pairing

abstract: The cultural diversity of culinary practice, as illustrated by the variety of regional cuisines, raises the question of whether there are any general patterns that determine the ingredient combinations used in food today or principles that transcend individual tastes and recipes. We introduce a flavor network that captures the flavor compounds shared by culinary ingredients. Western cuisines show a tendency to use ingredient pairs that share many flavor compounds, supporting the so-called food pairing hypothesis. By contrast, East Asian cuisines tend to avoid compound sharing ingredients. Given the increasing availability of information on food preparation, our data-driven investigation opens new avenues towards a systematic understanding of culinary practice.


title: PCC: Re-architecting Congestion Control for Consistent High Performance

abstract: TCP and its variants have suffered from surprisingly poor performance for decades. We argue the TCP family has little hope of achieving consistent high performance due to a fundamental architectural deficiency: hardwiring packet-level events to control responses. We propose Performance-oriented Congestion Control (PCC), a new congestion control architecture in which each sender continuously observes the connection between its actions and empirically experienced performance, enabling it to consistently adopt actions that result in high performance. We prove that PCC converges to a stable and fair equilibrium. Across many real-world and challenging environments, PCC shows consistent and often 10× performance improvement, with better fairness and stability than TCP. PCC requires no router hardware support or new packet format.


title: Survey of Solid-State Fault Current Limiters

abstract: This paper presents a comparative survey of research activities and emerging technologies of solid-state fault current limiters for power distribution systems.


title: Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising

abstract: Most of the existing denoising algorithms are developed for grayscale images, while it is not a trivial work to extend them for color image denoising because the noise statistics in R, G, B channels can be very different for real noisy images. In this paper, we propose a multi-channel (MC) optimization model for real color image denoising under the weighted nuclear norm minimization (WNNM) framework. We concatenate the RGB patches to make use of the channel redundancy, and introduce a weight matrix to balance the data fidelity of the three channels in consideration of their different noise statistics. The proposed MC-WNNM model does not have an analytical solution. We reformulate it into a linear equality-constrained problem and solve it with the alternating direction method of multipliers. Each alternative updating step has closed-form solution and the convergence can be guaranteed. Extensive experiments on both synthetic and real noisy image datasets demonstrate the superiority of the proposed MC-WNNM over state-of-the-art denoising methods.


title: Dual Modes of Natural Selection on Upstream Open Reading Frames

abstract: Upstream open reading frames (uORFs) are common features of eukaryotic genes, occurring in 10%-25% of 5' leader sequences. Upstream ORFs that have been subjected to experimental analysis have been generally found to decrease translational efficiency of the downstream coding sequence. Previous investigations of uORFs in mammals and yeast have detected uORFs conserved over long evolutionary distances, prompting speculation about the nature and cause of the natural selection underlying such conservation. We have analyzed uORFs in the basidiomycetous fungal pathogen Cryptococcus neoformans to discern the properties of this purifying selection. We find that uORFs in the Cryptococcus species complex are conserved at twice the expected rate, and we report 122 uORFs that are conserved among all four sequenced Cryptococcus strains. A significantly greater proportion of uORF losses occur via direct mutation to the uORF start codon than expected. This observation suggests that mutational disruption of a uORF that leaves the start codon intact may be selectively disadvantageous, perhaps because of the risk of premature translation initiation. Accounting for this constrained mode of loss and comparing the relative conservation of uORFs between the 5' leader and control sequences enables us to calculate that at least a third of uORFs may be conserved for their effects on translational efficiency. The remaining fraction may be conserved either by chance or as a result of selective pressure to prevent premature translation initiation from the uORF start codon. We find that the majority of conserved uORFs do not exhibit codon usage bias or conservation at the amino acid level, and therefore they do not likely encode bioactive peptides. Our analysis suggests that uORFs are an important and underappreciated mechanism of post-transcriptional gene regulation in eukaryotes.


title: AfRob: The affordance network ontology for robots

abstract: AfNet, The Affordance Network is an open affordance computing initiative that provides affordance knowledge ontologies for common household articles in terms of affordance features using surface forms termed as afbits (affordance bits). AfNet currently offers 68 base affordance features (25 structural, 10 material, 33 grasp), providing over 200 object category definitions in terms of 4000 afbits. Symbol grounding algorithms for these affordance features enable recognition of objects in visual (RGB-D) data. While AfNet is built as a generic visual knowledge ontology for recognition, it is well suited for deployment on domestic robots. In this paper, we describe AfRob, an extension of AfNet for robotic applications. AfRob builds upon AfNet by imbibing semantic context and mapping for holistic recognition and manipulation of objects in domestic environments. AfRob also offers modules to enable robots to interact and grasp objects through the generation of grasp affordances. The paper also details the inference mechanisms that adapt AfNet for robots in domestic contexts. Results demonstrate the efficiency of the affordance driven approach to holistic visual processing.


Coping Mechanisms of Nurses Facing the Deathof Pediatric Patients. : A Literature Review


title: Decentralized Consensus Algorithm with Delayed and Stochastic Gradients

abstract: We analyze the convergence of decentralized consensus algorithm with delayed gradient information across the network. The nodes in the network privately hold parts of the objective function and collaboratively solve for the consensus optimal solution of the total objective while they can only communicate with their immediate neighbors. In real-world networks, it is often difficult and sometimes impossible to synchronize the nodes, and therefore they have to use stale gradient information during computations. We show that, as long as the random delays are bounded in expectation and a proper diminishing step size policy is employed, the iterates generated by decentralized gradient descent method converge to a consensual optimal solution. Convergence rates of both objective and consensus are derived. Numerical results on a number of synthetic problems and real-world seismic tomography datasets in decentralized sensor networks are presented to show the performance of the method.


title: Faces engage us: photos with faces attract more likes and comments on Instagram

abstract: Photos are becoming prominent means of communication online. Despite photos' pervasive presence in social media and online world, we know little about how people interact and engage with their content. Understanding how photo content might signify engagement, can impact both science and design, influencing production and distribution. One common type of photo content that is shared on social media, is the photos of people. From studies of offline behavior, we know that human faces are powerful channels of non-verbal communication. In this paper, we study this behavioral phenomena online. We ask how presence of a face, it's age and gender might impact social engagement on the photo. We use a corpus of 1 million Instagram images and organize our study around two social engagement feedback factors, likes and comments. Our results show that photos with faces are 38% more likely to receive likes and 32% more likely to receive comments, even after controlling for social network reach and activity. We find, however, that the number of faces, their age and gender do not have an effect. This work presents the first results on how photos with human faces relate to engagement on large scale image sharing communities. In addition to contributing to the research around online user behavior, our findings offer a new line of future work using visual analysis.


title: Integrated suspended stripline structure (SSS) with J-shape defected stripline structure (DSS) to remove undesired signals in wideband applications

abstract: An integrated suspended stripline structure with defected stripline structure to produce simultaneous bandpass and notch response is presented and implemented in this paper. The bandpass filter was designed with cascading lowpass and highpass and produced the finite transmission zeros. The integration of SSS bandpass filter with DSS produced high selectivity and attenuation of notch response at resonant frequency of 5.2 GHz and Q-factor of 34.67. The bandpass filter was designed at wideband frequency from 3.1 GHz to 6 GHz with a fractional bandwidth of about 66.67%. The insertion was around 0.1 dB and the return loss was better than 18 dB. The group delay was less than 0.8 ns in the passband, except for the notch response. The design was simulated and fabricated on a Roger Duroid RO4350 with a dielectric constant, e r of 3.48 and a thickness of 0.508 mm. The simulation performance showed promising results, which could be further examined in future experimental works. This new class of generalized Chebyshev bandpass filter with DSS is useful to remove any undesired signal in any wideband communication system, particularly in civilian and military radar applications.


title: Modular Framework Kinematic and Fuzzy Reward Reinforcement Learning Analysis of a Radially Symmetric Six-Legged Robot

abstract: Hexapod Robots gives us the ability to study walking robots without facing problems such as stability in many aspects. It has a great deal of flexibility in movement even if a leg becomes malfunctioned. Radially symmetric (hexagonal) hexapods have more flexibility in movement than rectangular leg alignments. Because of symmetry they can move in any direction and time efficiently. Inverse kinematic problem of this kind of hexapods is solved through a modular mobile view considering six degrees of freedom for the trunk. Then typical tripod and wave gaits are analyzed and simulated through the presented formulation. In Reinforcement Learning algorithm for walking it is important how to make reward signal with respect to robot's actions and states. A fuzzy approach is presented and analyzed to generate reward signals. It is shown that the presented fuzzy system generates more considerable accurate rewards with better performance than functional rewards which are used in walking learning problems. (Mohammadali Shahriari*, Kambiz Ghaemi Osguie, Amir Ali Akbar Khayyat Modular Framework Kinematic And Fuzzy Reward Reinforcement Learning Analysis Of A Radially Symmetric Six-Legged Robot. Life Sci J 2013;10(8s):120-129) (ISSN:1097-8135). http://www.lifesciencesite.com. 15


title: A High-Linearity 76–85-GHz 16-Element 8-Transmit/8-Receive Phased-Array Chip With High Isolation and Flip-Chip Packaging

abstract: An SiGe transmit-receive phased-array chip has been developed for automotive radar applications at 76-84 GHz. The chip is based on an all-RF beamforming approach and contains 8-transmit channels, 8-receive channels, and a complete built-in-self-test system. Two high-linearity quadrature mixers with an input P1 dB of +2.5 dBm are used and allow simultaneous sum and difference patterns in the receive mode. The chip operates in either a narrowband frequency-modulated continuous-wave (FMCW) mode or a wideband mode with > 2-GHz bandwidth. A high-linearity design results in an input P1 dB of -10 dBm (per channel), a system noise figure of 16-18 dB, and a transmit power is 4-5 dBm (per channel). The chip uses a controlled collapse chip connection (C4) bumping process and is flip-chipped on a low-cost printed-circuit board, and results in > 50-dB isolation between the transmit and receive chains. To our knowledge, this work represents the state-of-the-art in terms of complexity at millimeter-wave frequencies and with simultaneous transmit and receive operation for high-performance FMCW radars.


title: Event Driven Web Video Summarization by Tag Localization and Key-Shot Identification

abstract: With the explosive growth of web videos on the Internet, it becomes challenging to efficiently browse hundreds or even thousands of videos. When searching an event query, users are often bewildered by the vast quantity of web videos returned by search engines. Exploring such results will be time consuming and it will also degrade user experience. In this paper, we present an approach for event driven web video summarization by tag localization and key-shot mining. We first localize the tags that are associated with each video into its shots. Then, we estimate the relevance of the shots with respect to the event query by matching the shot-level tags with the query. After that, we identify a set of key-shots from the shots that have high relevance scores by exploring the repeated occurrence characteristic of key sub-events. Following the scheme in [6] and [22], we provide two types of summaries, i.e., threaded video skimming and visual-textual storyboard. Experiments are conducted on a corpus that contains 60 queries and more than 10 000 web videos. The evaluation demonstrates the effectiveness of the proposed approach.


A Real – Time Irrigation Control System for Precision Agriculture using WSN in Indian Agricultural Sectors


title: Efficient implementation of a cubic-convolution based image scaling engine

abstract: In video applications, real-time image scaling techniques are often required. In this paper, an efficient implementation of a scaling engine based on 4×4 cubic convolution is proposed. The cubic convolution has a better performance than other traditional interpolation kernels and can also be realized on hardware. The engine is designed to perform arbitrary scaling ratios with an image resolution smaller than 2560×1920 pixels and can scale up or down, in horizontal or vertical direction. It is composed of four functional units and five line buffers, which makes it more competitive than conventional architectures. A strict fixed-point strategy is applied to minimize the quantization errors of hardware realization. Experimental results show that the engine provides a better image quality and a comparatively lower hardware cost than reference implementations.


title: Methods and Tools of Digital Triage in Forensic Context: Survey and Future Directions

abstract: Digital triage is the first investigative step of the forensic examination. The digital triage comes in two forms, live triage and post-mortem triage. The primary goal of the live triage is a rapid extraction of an intelligence from the potential sources. The live triage raises legitimate concerns. The post-mortem triage is conducted in the laboratory and its main goal is ranking of the seized devices for the possible existence of the relevant evidence. The digital triage has the potential to quickly identify items that are likely to contain the evidential data. Therefore, it is a solution to the problem of case backlogs. However, existing methods and tools of the digital triage have limitations, especially, in the forensic context. Nevertheless, we have no better solution for the time being. In this paper, we critically review published research works and the proposed solutions for digital triage. The review is divided into four sections as follows: live triage, post-mortem triage, mobile device triage, and triage tools. We conclude that many challenges are awaiting for the developers in creating methods and tools of digital triage in order to keep pace with the development of new technologies.


title: Automated Assessment of the Quality of Peer Reviews using Natural Language Processing Techniques

abstract: A review is textual feedback provided by a reviewer to the author of a submitted version. Peer reviews are used in academic publishing and in education to assess student work. While reviews are important to e-commerce sites like Amazon and e-bay, which use them to assess the quality of products and services, our work focuses on academic reviewing. We seek to help reviewers improve the quality of their reviews. One way to measure review quality is through metareview or review of reviews. We develop an automated metareview software that provides rapid feedback to reviewers on their assessment of authors’ submissions. To measure review quality, we employ metrics such as: review content type, review relevance, review’s coverage of a submission, review tone, review volume and review plagiarism (from the submission or from other reviews). We use natural language processing and machine-learning techniques to calculate these metrics. We summarize results from experiments to evaluate our review quality metrics: review content, relevance and coverage, and a study to analyze user perceptions of importance and usefulness of these metrics. Our approaches were evaluated on data from Expertiza and the Scaffolded Writing and Rewriting in the Discipline (SWoRD) project, which are two collaborative web-based learning applications.


Building Poker Agent Using Reinforcement Learning with Neural Networks


title: A Priori Estimates for Two-layer Neural Networks

abstract: New estimates for the population risk are established for two-layer neural networks. These estimates are nearly optimal in the sense that the error rates scale in the same way as the Monte Carlo error rates. They are equally effective in the over-parametrized regime when the network size is much larger than the size of the dataset. These new estimates are a priori in nature in the sense that the bounds depend only on some norms of the underlying functions to be fitted, not the parameters in the model, in contrast with most existing results which are a posteriori in nature. Using these a priori estimates, we provide a perspective for understanding why two-layer neural networks perform better than kernel methods.


title: SWOT ANALYSIS OF REQUIREMENTS ENGINEERING FOR WEB APPLICATIONS

abstract: Given the complexity of Web Applications, Requirements Engineering is a critical initial stage, but often poorly executed & handled with less relevance. The major consequences are inadequate software architectures, “Unforeseen” problems, viz., budget overruns & production delay, and low user acceptance. Web Applications (Web Apps) require a more extensive and detailed Requirements Engineering (RE) process due to the number of stakeholders involved, the diversity of the requirements, rapidly changing technology and environment (user needs), lack of communication with users of application & the high risk possibility. This study primarily highlights the SWOT Analysis of RE for Web Apps for evaluating the internal potential and limitations and the probable/likely opportunities and threats from the external environment. It provides a basis of solutions to the meta-problem of RE for Web Apps giving a better perspective on the tools and techniques that will be needed to develop a more efficient and cohesive application.


title: Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs

abstract: We describe the first sub-quadratic sampling algorithm for the Multiplicative Attribute Graph Model (MAGM) of Kim and Leskovec (2010). We exploit the close connection between MAGM and the Kronecker Product Graph Model (KPGM) of Leskovec et al. (2010), and show that to sample a graph from a MAGM it suffices to sample small number of KPGM graphs and \emph{quilt} them together. Under a restricted set of technical conditions our algorithm runs in $O((\log_2(n))^3 |E|)$ time, where $n$ is the number of nodes and $|E|$ is the number of edges in the sampled graph. We demonstrate the scalability of our algorithm via extensive empirical evaluation; we can sample a MAGM graph with 8 million nodes and 20 billion edges in under 6 hours.


title: Eddy-Current Sensing Principle in Inertial Sensors

abstract: The eddy-current displacement sensing principle is, to the best of our knowledge, not yet used in inertial sensors. The main reasons for this are the important performance limitations of the existing eddy-current sensor solutions, such as low sensitivity, poor stability, high-power consumption, and bulkiness. Our novel high-frequency eddy-current displacement sensor (ECDS), however, has significantly improved performance with respect to these limitations and allows the use of planar, stable coils, making it a viable candidate for the use in inertial sensors. An implementation example of an ECDSbased inertial sensor with a bandwidth of 370 Hz and a noise floor of 13 μg/√Hz is proposed. Although not yet competitive with state-of-the-art inertial sensors, it performs better than other types of inductive accelerometers and offers the inherent advantages of ECDSs, such as insensitivity to the environment.


title: Automatic detection of body parts in x-ray images

abstract: The number of digital images that needs to be acquired, analyzed, classified, stored and retrieved in the medical centers is exponentially growing with the advances in medical imaging technology. Accordingly, medical image classification and retrieval has become a popular topic in the recent years. Despite many projects focusing on this problem, proposed solutions are still far from being sufficiently accurate for real-life implementations. Interpreting medical image classification and retrieval as a multi-class classification task, in this work, we investigate the performance of five different feature types in a SVM-based learning framework for classification of human body X-Ray images into classes corresponding to body parts. Our comprehensive experiments show that four conventional feature types provide performances comparable to the literature with low per-class accuracies, whereas local binary patterns produce not only very good global accuracy but also good class-specific accuracies with respect to the features used in the literature.


title: Understanding the relationship between ICT and education means exploring innovation and change

abstract: This paper sets the debates of the other papers in the context of a benchmark in time that is after four decades of deliberations about the relationship between ICT and education. The environment for and deliberations about this relationship has been one of rapid change in perspectives, most characterised as a shift of focus from the technology to learning. While contemplating the future however, a number of enduring issues remain, including understanding how learning occurs, learning with or about the technology, and the role of the teacher and professional development. The papers address these issues, as well as exploring national policies and software learning environments; all reflect on the growing complexity of the situation and the conundrum of failure to achieve `lift-off.' Key phrases for debates in the future include lifelong learning, the commonplace of ICT in society, the digital world of the young, and the digital ethical dilemmas. In addition I suggest that the key word of change could be paid more attention.


title: The genetic basis of non-syndromic intellectual disability: a review

abstract: Intellectual disability (ID), also referred to as mental retardation (MR), is frequently the result of genetic mutation. Where ID is present together with additional clinical symptoms or physical anomalies, there is often sufficient information available for the diagnosing physician to identify a known syndrome, which may then educe the identification of the causative defect. However, where co-morbid features are absent, narrowing down a specific gene can only be done by 'brute force' using the latest molecular genetic techniques. Here we attempt to provide a systematic review of genetic causes of cases of ID where no other symptoms or co-morbid features are present, or non-syndromic ID. We attempt to summarize commonalities between the genes and the molecular pathways of their encoded proteins. Since ID is a common feature of autism, and conversely autistic features are frequently present in individuals with ID, we also look at possible overlaps in genetic etiology with non-syndromic ID.


title: Habits, action sequences and reinforcement learning

abstract: It is now widely accepted that instrumental actions can be either goal-directed or habitual; whereas the former are rapidly acquired and regulated by their outcome, the latter are reflexive, elicited by antecedent stimuli rather than their consequences. Model-based reinforcement learning (RL) provides an elegant description of goal-directed action. Through exposure to states, actions and rewards, the agent rapidly constructs a model of the world and can choose an appropriate action based on quite abstract changes in environmental and evaluative demands. This model is powerful but has a problem explaining the development of habitual actions. To account for habits, theorists have argued that another action controller is required, called model-free RL, that does not form a model of the world but rather caches action values within states allowing a state to select an action based on its reward history rather than its consequences. Nevertheless, there are persistent problems with important predictions from the model; most notably the failure of model-free RL correctly to predict the insensitivity of habitual actions to changes in the action‐reward contingency. Here, we suggest that introducing model-free RL in instrumental conditioning is unnecessary, and demonstrate that reconceptualizing habits as action sequences allows model-based RL to be applied to both goal-directed and habitual actions in a manner consistent with what real animals do. This approach has significant implications for the way habits are currently investigated and generates new experimental predictions. The problem with habits There is now considerable evidence from studies of instrumental conditioning in rats and humans that the performance of rewardrelated actions reflects the interaction of two learning processes, one controlling the acquisition of goal-directed actions, and the other of habits (Dickinson, 1994; Balleine & O’Doherty, 2010). This evidence suggests that, in the case of goal-directed actions, action selection is governed by encoding the association between the action and its consequences or outcome. In contrast, the performance of habitual actions depends on forming an association between an action and antecedent stimuli, rather than its consequences. As a consequence, being stimulus bound, habitual actions are relatively inflexible or reflexive, and are not immediately influenced by manipulations involving the outcome. They differ from goal-directed actions, therefore, in two ways: (i) in their sensitivity to changes in the value of the outcome (Adams, 1982); and (ii) in their sensitivity to changes in the causal relationship between the action and outcome delivery (Dickinson et al., 1998).


title: DWT and MFCCs based Feature Extraction Methods for Isolated Word Recognition

abstract: A new method for feature extraction is presented in this paper for speech recognition using a combination of discrete wavelet transform (DWT) and mel Frequency Cepstral Coefficients (MFCCs). The objective of this method is to enhance the performance of the proposed method by introducing more features from the signal. The performance of the Wavelet-based mel Frequency Cepstral Coefficients method is compared to mel Frequency Cepstral Coefficients based method for features extraction. Wavelet transform is applied to the speech signal where the input speech signal is decomposed into various frequency channels using the properties of wavelet transform. then Mel-Frequency Cepstral Coefficients (MFCCs) of the wavelet channels are calculated. A new set of features can be generated by concatenating both features. The speech signals are sampled directly from the microphone. Neural Networks (NN) are used in the proposed methods for classification. The proposed method is implemented for 15 male speakers uttering 10 isolated words each which are the digits from zero to nine. each digit is repeated 15 times.


title: Rule extraction from trained adaptive neural networks using artificial immune systems

abstract: Although artificial neural network (ANN) usually reaches high classification accuracy, the obtained results sometimes may be incomprehensible. This fact is causing a serious problem in data mining applications. The rules that are derived from ANN are needed to be formed to solve this problem and various methods have been improved to extract these rules. Activation function is critical as the behavior and performance of an ANN model largely depends on it. So far there have been limited studies with emphasis on setting a few free parameters in the neuron activation function. ANN's with such activation function seem to provide better fitting properties than classical architectures with fixed activation function neurons [Xu, S., & Zhang, M. (2005). Data mining - An adaptive neural network model for financial analysis. In Proceedings of the third international conference on information technology and applications]. In this study a new method that uses artificial immune systems (AIS) algorithm has been presented to extract rules from trained adaptive neural network. Two real time problems data were investigated for determining applicability of the proposed method. The data were obtained from University of California at Irvine (UCI) machine learning repository. The datasets were obtained from Breast Cancer disease and ECG data. The proposed method achieved accuracy values 94.59% and 92.31% for ECG and Breast Cancer dataset, respectively. It has been observed that these results are one of the best results comparing with results obtained from related previous studies and reported in UCI web sites.


title: Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges

abstract: The existing state-of-the-art in wireless sensor networks for agricultural applications is reviewed thoroughly.The existing WSNs are analyzed with respect to communication and networking technologies, standards, and hardware.The prospects and problems of the existing framework are discussed with case studies for global and Indian scenarios.Few futuristic applications are presented highlighting the factors for improvements for the existing scenarios. The advent of Wireless Sensor Networks (WSNs) spurred a new direction of research in agricultural and farming domain. In recent times, WSNs are widely applied in various agricultural applications. In this paper, we review the potential WSN applications, and the specific issues and challenges associated with deploying WSNs for improved farming. To focus on the specific requirements, the devices, sensors and communication techniques associated with WSNs in agricultural applications are analyzed comprehensively. We present various case studies to thoroughly explore the existing solutions proposed in the literature in various categories according to their design and implementation related parameters. In this regard, the WSN deployments for various farming applications in the Indian as well as global scenario are surveyed. We highlight the prospects and problems of these solutions, while identifying the factors for improvement and future directions of work using the new age technologies.


title: A Multimodal In-Car Dialogue System That Tracks The Driver's Attention

abstract: When a passenger speaks to a driver, he or she is co-located with the driver, is generally aware of the situation, and can stop speaking to allow the driver to focus on the driving task. In-car dialogue systems ignore these important aspects, making them more distracting than even cell-phone conversations. We developed and tested a "situationally-aware" dialogue system that can interrupt its speech when a situation which requires more attention from the driver is detected, and can resume when driving conditions return to normal. Furthermore, our system allows driver-controlled resumption of interrupted speech via verbal or visual cues (head nods). Over two experiments, we found that the situationally-aware spoken dialogue system improves driving performance and attention to the speech content, while driver-controlled speech resumption does not hinder performance in either of these two tasks


title: Syllable-based acoustic modeling with CTC-SMBR-LSTM

abstract: We explore the feasibility of training long short-term memory (LSTM) recurrent neural networks (RNNs) with syllables, rather than phonemes, as outputs. Syllables are a natural choice of linguistic unit for modeling the acoustics of languages such as Mandarin Chinese, due to the inherent nature of the syllable as an elemental pronunciation construct and the limited size of the syllable set for such languages (around 1400 syllables for Mandarin). Our models are trained with Connectionist Temporal Classification (CTC) and state-level minimum Bayes risk (sMBR) loss using asynchronous stochastic gradient descent (ASGD) utilizing a parallel computation infrastructure for large-scale training. Our acoustic models operate on feature frames computed every 30ms, which makes them well suited for modeling syllables rather than phonemes, which can have a shorter duration. Additionally, when compared to wordlevel modeling, syllables have the advantage of avoiding out-of-vocabulary (OOV) model outputs. Our experiments on a Mandarin voice search task show that syllable-output models can perform better than context-independent (CI) phone-output models, and can give similar performance as our state-of-the-art context-dependent (CD) models. Additionally, decoding with syllable-output models is substantially faster than with CI models or with CD models. We demonstrate that these improvements are maintained when the model is trained to recognize both Mandarin syllables and English phonemes.


title: Secret-Sharing Schemes: A Survey

abstract: A secret-sharing scheme is a method by which a dealer distributes shares to parties such that only authorized subsets of parties can reconstruct the secret. Secret-sharing schemes are an important tool in cryptography and they are used as a building box in many secure protocols, e.g., general protocol for multiparty computation, Byzantine agreement, threshold cryptography, access control, attribute-based encryption, and generalized oblivious transfer. ::: ::: In this survey, we describe the most important constructions of secret-sharing schemes; in particular, we explain the connections between secret-sharing schemes and monotone formulae and monotone span programs. We then discuss the main problem with known secret-sharing schemes - the large share size, which is exponential in the number of parties. We conjecture that this is unavoidable. We present the known lower bounds on the share size. These lower bounds are fairly weak and there is a big gap between the lower and upper bounds. For linear secret-sharing schemes, which is a class of schemes based on linear algebra that contains most known schemes, super-polynomial lower bounds on the share size are known. We describe the proofs of these lower bounds. We also present two results connecting secret-sharing schemes for a Hamiltonian access structure to the NP vs. coNP problem and to a major open problem in cryptography - constructing oblivious-transfer protocols from one-way functions.


The State of the Art in Semantic Representation


title: Event Grounding from Multimodal Social Network Fusion

abstract: This paper studies the problem of extracting real world event information from social media streams. Although existing work focuses on event signals of bursty mentions extracted from a single-source of textual streams, these signals are likely to be noisy due to ambiguous occurrences of individual mentions. To extract accurate event signals, we propose a framework capable of "grounding" mentions to unique event using multiple social networks with complementary strength. We show that our framework jointly using multiple sources outperforms state-of-the-arts using publicly available datasets.


title: Copulas for information retrieval

abstract: In many domains of information retrieval, system estimates of document relevance are based on multidimensional quality criteria that have to be accommodated in a unidimensional result ranking. Current solutions to this challenge are often inconsistent with the formal probabilistic framework in which constituent scores were estimated, or use sophisticated learning methods that make it difficult for humans to understand the origin of the final ranking. To address these issues, we introduce the use of copulas, a powerful statistical framework for modeling complex multi-dimensional dependencies, to information retrieval tasks. We provide a formal background to copulas and demonstrate their effectiveness on standard IR tasks such as combining multidimensional relevance estimates and fusion of results from multiple search engines. We introduce copula-based versions of standard relevance estimators and fusion methods and show that these lead to significant performance improvements on several tasks, as evaluated on large-scale standard corpora, compared to their non-copula counterparts. We also investigate criteria for understanding the likely effect of using copula models in a given retrieval scenario.


title: A simple approach for finding the globally optimal Bayesian network structure

abstract: We study the problem of learning the best Bayesian network structure with respect to a decomposable score such as BDe, BIC or AIC. This problem is known to be NP-hard, which means that solving it becomes quickly infeasible as the number of variables increases. Nevertheless, in this paper we show that it is possible to learn the best Bayesian network structure with over 30 variables, which covers many practically interesting cases. Our algorithm is less complicated and more efficient than the techniques presented earlier. It can be easily parallelized, and offers a possibility for efficient exploration of the best networks consistent with different variable orderings. In the experimental part of the paper we compare the performance of the algorithm to the previous state-of-the-art algorithm. Free source-code and an online-demo can be found at http://b-course.hiit.fi/bene.


title: Lagrangian duality in 3D SLAM: Verification techniques and optimal solutions

abstract: State-of-the-art techniques for simultaneous localization and mapping (SLAM) employ iterative nonlinear optimization methods to compute an estimate for robot poses. While these techniques often work well in practice, they do not provide guarantees on the quality of the estimate. This paper shows that Lagrangian duality is a powerful tool to assess the quality of a given candidate solution. Our contribution is threefold. First, we discuss a revised formulation of the SLAM inference problem. We show that this formulation is probabilistically grounded and has the advantage of leading to an optimization problem with quadratic objective. The second contribution is the derivation of the corresponding Lagrangian dual problem. The SLAM dual problem is a (convex) semidefinite program, which can be solved reliably and globally by off-the-shelf solvers. The third contribution is to discuss the relation between the original SLAM problem and its dual. We show that from the dual problem, one can evaluate the quality (i.e., the suboptimality gap) of a candidate SLAM solution, and ultimately provide a certificate of optimality. Moreover, when the duality gap is zero, one can compute a guaranteed optimal SLAM solution from the dual problem, circumventing non-convex optimization. We present extensive (real and simulated) experiments supporting our claims and discuss practical relevance and open problems.


title: Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior

abstract: reporting systems have emerged as an important risk management mechanism in online trading communities. However, the predictive value of these systems can be compromised in situations where conspiring buyers intentionally give unfair ratings to sellers or, where sellers discriminate on the quality of service they provide to different buyers. This paper proposes and evaluates a set of mechanisms, which eliminate, or significantly reduce the negative effects of such fraudulent behavior. The proposed mechanisms can be easily integrated into existing online reputation systems in order to safeguard their reliability in the presence of potentially deceitful buyers and sellers.


title: Small Cell Lung Carcinoma (SCLC) A Clinicopathologic Study of 100 Cases With Surgical Specimens

abstract: Separation of small cell lung carcinoma (SCLC) from nonsmall cell lung carcinoma (NSCLC) is a critical distinction to be made in the diagnosis of lung cancer. However, the diagnosis of SCLC is most commonly made on small biopsies and cytologic specimens, and practicing pathologists may not be familiar with all its morphologic guises and frequent combination with NSCLC elements, which may be seen in larger specimens. Following the most recent WHO classification of lung tumors and with the hope of identifying prognostic markers, we examined in detail the histology of 100 surgical biopsies or resections with a diagnosis of SCLC from the AFIP and pathology panel of the International Association for the Study of Lung Cancer (IASLC). Multiple clinical and histologic features were studied by Kaplan-Meier analysis. Neuroendocrine architectural patterns, including nested and trabecular growth, with peripheral palisading and rosette formation were common in SCLC. Necrosis and apoptotic debris was prominent in all cases, but crush artifact was infrequent. Cell size in surgical biopsy specimens appears larger than in bronchoscopic biopsy specimens and occasional cells may show prominent nucleoli and vesicular nuclear chromatin, but this does not preclude the diagnosis of SCLC. A high percentage of cases (28%) showed combinations with NSCLC, with large cell carcinoma the most common, followed by adenocarcinoma and squamous cell carcinoma. Because of the frequency of a few scattered large cells in SCLC, we arbitrarily recommend that at least 10% of the tumor show large cell carcinoma before subclassification as combined SC/LC. However, combined SCLC is easily recognized if the additional component consists of other NSCLC


title: A Two-Iteration Clustering Method to Reveal Unique and Hidden Characteristics of Items Based on Text Reviews

abstract: This paper presents a new method for extracting unique features of items based on their textual reviews. The method is built of two similar iterations of applying a weighting scheme and then clustering the resultant set of vectors. In the first iteration, restaurants of similar food genres are grouped together into clusters. The second iteration reduces the importance of common terms in each such cluster, and highlights those that are unique to each specific restaurant. Clustering the restaurants again, now according to their unique features, reveals very interesting connections between the restaurants.


title: DigSig: Run-time Authentication of Binaries at Kernel Level Axelle Apvrille – Trusted Logic

abstract: This paper presents a Linux kernel module, DigSig, which helps system administrators control Executable and Linkable Format (ELF) binary execution and library loading based on the presence of a valid digital signature. By preventing attackers from replacing libraries and sensitive, privileged system daemons with malicious code, DigSig increases the difficulty of hiding illicit activities such as access to compromised systems.DigSig provides system administrators with an efficient tool which mitigates the risk of running malicious code at run time. This tool adds extra functionality previously unavailable for the Linux operating system: kernel level RSA signature verification with caching and revocation of signatures.


title: Transformational change and business process reengineering (BPR): Lessons from the British and Dutch public sector

abstract: Abstract Facilitated by electronic government, public agencies are looking for transformational change by making a radical improvement. At first glance, this development is similar to the business process re-engineering (BPR) movement in the private sector. While policy makers and practitioners in the public sector have branded their recent improvements as BPR, the academic and research community have thus far eluded from making any comparisons. This has left a vacuum in terms of understanding the complexity of the challenges facing e-Government re-engineering and resultant change in public agencies. The aim of this paper is to translate the BPR movement findings to the field of e-Government induced change in the public sector. BPR characteristics and challenges are derived using normative literature and compared with two cases of public sector transformation in the UK and Netherlands. The results of these cases show that e-Government-induced change requires a plan for a radical improvement which, in contrast to BPR, is obtained by incremental steps and has a high level of participation. The findings offer policy makers valuable insights into the complexities and possible strategies that may need to be followed in order to succeed in e-Government implementation.


Wigner distribution function applied to partially coherent light


title: African Americans and High Blood Pressure: The Role of Stereotype Threat

abstract: We examined the effect of stereotype threat on blood pres- sure reactivity. Compared with European Americans, and African Americans under little or no stereotype threat, African Americans un- der stereotype threat exhibited larger increases in mean arterial blood pressure during an academic test, and performed more poorly on diffi- cult test items. We discuss the significance of these findings for under- standing the incidence of hypertension among African Americans.


title: SSDH: Semi-supervised Deep Hashing for Large Scale Image Retrieval

abstract: Hashing methods have been widely used for efficient similarity retrieval on large scale image database. Traditional hashing methods learn hash functions to generate binary codes from hand-crafted features, which achieve limited accuracy since the hand-crafted features cannot optimally represent the image content and preserve the semantic similarity. Recently, several deep hashing methods have shown better performance because the deep architectures generate more discriminative feature representations. However, these deep hashing methods are mainly designed for supervised scenarios, which only exploit the semantic similarity information, but ignore the underlying data structures. In this paper, we propose the semi-supervised deep hashing approach, to perform more effective hash function learning by simultaneously preserving semantic similarity and underlying data structures. The main contributions are as follows: 1) We propose a semi-supervised loss to jointly minimize the empirical error on labeled data, as well as the embedding error on both labeled and unlabeled data, which can preserve the semantic similarity and capture the meaningful neighbors on the underlying data structures for effective hashing. 2) A semi-supervised deep hashing network is designed to extensively exploit both labeled and unlabeled data, in which we propose an online graph construction method to benefit from the evolving deep features during training to better capture semantic neighbors. To the best of our knowledge, the proposed deep network is the first deep hashing method that can perform hash code learning and feature learning simultaneously in a semi-supervised fashion. Experimental results on five widely-used data sets show that our proposed approach outperforms the state-of-the-art hashing methods.


Word Embeddings and Convolutional Neural Network for Arabic Sentiment Classification


title: Ultrasensitive and Selective Nitrogen Dioxide Sensor Based on Self-Assembled Graphene/Polymer Composite Nanofibers

abstract: Reduced graphene oxide (rGO) sheets were self-assembled onto the surfaces of electrospun polymer nanofibers to form an ultrathin coating. These rGO/polymer composite nanofibers were used to fabricate nitrogen dioxide (NO2) sensor. This sensor can be performed at room temperature, and it exhibited a high sensitivity of 1.03 ppm–1 with excellent selectivity and good reversibility. Furthermore, the limit of detection was experimentally measured to be as low as 150 ppb, and this value is much lower than the threshold exposure limit proposed by American Conference of Governmental Industrial Hygienists (200 ppb).


The changing Chinese culture and business behaviour


title: Methodological Review: A review of causal inference for biomedical informatics

abstract: Causality is an important concept throughout the health sciences and is particularly vital for informatics work such as finding adverse drug events or risk factors for disease using electronic health records. While philosophers and scientists working for centuries on formalizing what makes something a cause have not reached a consensus, new methods for inference show that we can make progress in this area in many practical cases. This article reviews core concepts in understanding and identifying causality and then reviews current computational methods for inference and explanation, focusing on inference from large-scale observational data. While the problem is not fully solved, we show that graphical models and Granger causality provide useful frameworks for inference and that a more recent approach based on temporal logic addresses some of the limitations of these methods.


title: Analytical model for LLC resonant converter with variable duty-cycle control

abstract: In LLC resonant converters, the variable duty-cycle control is usually combined with a variable frequency control to widen the gain range, improve the light-load efficiency, or suppress the inrush current during start-up. However, a proper analytical model for the variable duty-cycle controlled LLC converter is still not available due to the complexity of operation modes and the nonlinearity of steady-state equations. This paper makes the efforts to develop an analytical model for the LLC converter with variable duty-cycle control. All possible operation models and critical operation characteristics are identified and discussed. The proposed model enables a better understanding of the operation characteristics and fast parameter design of the LLC converter, which otherwise cannot be achieved by the existing simulation based methods and numerical models. The results obtained from the proposed model are in well agreement with the simulations and the experimental verifications from a 500-W prototype.


title: Exploring a national cybersecurity exercise for universities

abstract: In cybersecurity competitions, participants either create new or protect preconfigured information systems and then defend these systems against attack in a real-world setting. Institutions should consider important structural and resource-related issues before establishing such a competition. Critical infrastructures increasingly rely on information systems and on the Internet to provide connectivity between systems. Maintaining and protecting these systems requires an education in information warfare that doesn't merely theorize and describe such concepts. A hands-on, active learning experience lets students apply theoretical concepts in a physical environment. Craig Kaucher and John Saunders found that even for management-oriented graduate courses in information assurance, such an experience enhances the students' understanding of theoretical concepts. Cybersecurity exercises aim to provide this experience in a challenging and competitive environment. Many educational institutions use and implement these exercises as part of their computer science curriculum, and some are organizing competitions with commercial partners as capstone exercises, ad hoc hack-a-thons, and scenario-driven, multiday, defense-only competitions. Participants have exhibited much enthusiasm for these exercises, from the DEFCON capture-the-flag exercise to the US Military Academy's Cyber Defense Exercise (CDX). In February 2004, the US National Science Foundation sponsored the Cyber Security Exercise Workshop aimed at harnessing this enthusiasm and interest. The educators, students, and government and industry representatives attending the workshop discussed the feasibility and desirability of establishing regular cybersecurity exercises for postsecondary-level students. This article summarizes the workshop report.


title: Database synchronization model for mobile devices

abstract: With the increasing number of mobile devices it is necessary to create adapted solutions for this type of devices that respect their limited processing capacities, memory and bandwidth. We propose a synchronization model based on message digests to synchronize relational databases between mobile devices and a server, in order to minimize the data transferred between the device and the server and to minimize the processing done by the mobile device. The approach consists in a synchronization model where the amount of data to be exchanged has been reduced to the minimum. In order to achieve the model above a registry is done in the device with the new, the modified or removed content, which purpose is to send only the essential data for synchronization from the device to the server. Regarding the synchronization from the server to the device, the modifications are calculated and sent to the device. This model is independent of proprietary solutions and can be adapted to any system or platform, often involving software development to the mobile application and the application server. The results of testing the model show that the proposed model can achieve better synchronization times than the competitor models, while always fulfilling the set objectives.


title: Unified Chassis Control for Rollover Prevention and Lateral Stability

abstract: This paper describes a unified chassis control (UCC) strategy to prevent vehicle rollover and improve vehicle lateral stability. A rollover index (RI), which indicates impending rollover, and a model-based roll state estimator are introduced to detect potential rollover. An RI/lateral stability-based rollover mitigation (ROM) controller is designed to reduce the danger of rollover without loss of vehicle lateral stability by integrating electronic stability control (ESC) and continuous damping control (CDC). Simulation results indicate that significant improvement in vehicle rollover prevention and lateral stability can be expected from the proposed UCC system.


title: Mind matters: Cognitive and physical effects of aging self-stereotypes. Commentaries. Author's reply

abstract: In the first part of this article, a wide range of research is drawn upon to describe the process by which aging stereotypes are internalized in younger individuals and then become self-stereotypes when individuals reach old age. The second part consists of a review of the author's cross-cultural, experimental, and longitudinal research that examines the cognitive and physical effects of aging self-stereotypes. The final section presents suggestions for future research relating to aging self-stereotypes.