Forward Thinking: Building Deep Random Forests

The success of deep neural networks has inspired many to wonder whether other learners could benefit from deep, layered architectures. We present a general framework called forward thinking for deep learning that generalizes the architectural flexibility and sophistication of deep neural networks while also allowing for (i) different types of learning functions in the network, other than neurons, and (ii) the ability to adaptively deepen the network as needed to improve results. This is done by training one layer at a time, and once a layer is trained, the input data are mapped forward through the layer to create a new learning problem. The process is then repeated, transforming the data through multiple layers, one at a time, rendering a new dataset, which is expected to be better behaved, and on which a final output layer can achieve good performance. In the case where the neurons of deep neural nets are replaced with decision trees, we call the result a Forward Thinking Deep Random Forest (FTDRF). We demonstrate a proof of concept by applying FTDRF on the MNIST dataset. We also provide a general mathematical formulation, called Forward Thinking that allows for other types of deep learning problems to be considered.


Exploiting Shallow Linguistic Information for Relation Extraction from Biomedical Literature

We propose an approach for extracting relations between entities from biomedical literature based solely on shallow linguistic information. We use a combination of kernel functions to integrate two different information sources: (i) the whole sentence where the relation appears, and (ii) the local contexts around the interacting entities. We performed experiments on extracting gene and protein interactions from two different data sets. The results show that our approach outperforms most of the previous methods based on syntactic and semantic information.


Oxidants, antioxidants, and the degenerative diseases of aging.

Metabolism, like other aspects of life, involves tradeoffs. Oxidant by-products of normal metabolism cause extensive damage to DNA, protein, and lipid. We argue that this damage (the same as that produced by radiation) is a major contributor to aging and to degenerative diseases of aging such as cancer, cardiovascular disease, immune-system decline, brain dysfunction, and cataracts. Antioxidant defenses against this damage include ascorbate, tocopherol, and carotenoids. Dietary fruits and vegetables are the principal source of ascorbate and carotenoids and are one source of tocopherol. Low dietary intake of fruits and vegetables doubles the risk of most types of cancer as compared to high intake and also markedly increases the risk of heart disease and cataracts. Since only 9% of Americans eat the recommended five servings of fruits and vegetables per day, the opportunity for improving health by improving diet is great.


A theoretical framework of a BIM-based multi-disciplinary collaboration platform

a r t i c l e i n f o Keywords: BIM BIM-server Multidisciplinary collaboration Operational technical requirements Support technical requirements Most complex projects in the Architecture, Engineering, and Construction (AEC) industries involve multidisciplinary collaboration and the exchange of large building data set. Traditionally, the collaboration efforts across the disciplines have been based on the frequent exchange of 2D drawings and documents. However, during the past decade, the widespread adoption of object-oriented Computer-aided Design (CAD) tools has generated more interests in Building Information Modelling (BIM). A number of BIM-compliant applications such as analysis tools, model checkers and facility management applications are being developed. This paper develops a theoretical framework of technical requirements for using BIM-server as a multidisciplinary collaboration platform. The methodologies that are used to develop the framework include focus group interviews (FGIs) with representatives from the diverse AEC disciplines, a case study of an Architectural project using a state-of-the-art BIM-server, and a critical review and analysis of current collaboration platforms that are available to the AEC industries. This paper concludes that greater emphasis should be placed on supporting technical requirements to facilitate technology management and implementation across disciplines. Their implications for user-centric technology development in design and construction industry are also discussed. Traditionally, the inter-disciplinary collaboration in the Architecture, Engineering, and Construction (AEC) industries has revolved around the exchange of 2D drawings and documents. Even though the separate design disciplines have been using 3D models and applications for visualization and design development, the collaboration practices have remained more or less 2D-based until recently. The widespread use and proliferation of object-oriented Computer-Aided Design (CAD) packages and increased constructability and level of automation in construction processes provide encouraging motives for the exchange of 3D data in the collaboration process. Building Information Modelling (BIM) is envisaged to play a significant role in this transformation. BIM is an advanced approach to object-oriented CAD, which extends the capability of traditional CAD approach by defining and applying intelligent relationships between the elements in the building model. BIM models include both geometric and non-geometric data such as object attributes and specifications. The built-in intelligence allows automated extraction of 2D drawings, documentation and other building information directly from the BIM model. This built-in intelligence also provides constraints that can reduce modelling errors and prevent technical flaws in the design, based on the rules encoded in the software [11,16,21]. Most recent CAD packages such as ArchiCAD …


A Maximum Entropy Model for Part-Of-Speech Tagging

This paper presents a statistical model which trains from a corpus annotated with Part Of Speech tags and assigns them to previously unseen text with state of the art accuracy The model can be classi ed as a Maximum Entropy model and simultaneously uses many contextual features to predict the POS tag Furthermore this paper demonstrates the use of specialized fea tures to model di cult tagging decisions discusses the corpus consistency problems discovered during the implementation of these features and proposes a training strategy that mitigates these problems


TnT -- A Statistical Part-of-Speech Tagger

Trigrams'n'Tags (TnT) is an efficient statistical part-of-speech tagger. Contrary to claims found elsewhere in the literature, we argue that a tagger based on Markov models performs at least as well as other current approaches, including the Maximum Entropy framework. A recent comparison has even shown that TnT performs significantly better for the tested corpora. We describe the basic model of TnT, the techniques used for smoothing and for handling unknown words. Furthermore, we present evaluations on two corpora.


Chunking with Support Vector Machines

We apply Support Vector Machines (SVMs) to identify English base phrases (chunks). SVMs are known to achieve high generalization performance even with input data of high dimensional feature spaces. Furthermore, by the Kernel principle, SVMs can carry out training with smaller computational overhead independent of their dimensionality. We apply weighted voting of 8 SVMsbased systems trained with distinct chunk representations. Experimental results show that our approach achieves higher accuracy than previous approaches.


WebCrow: A Web-Based System for Crossword Solving

Language games represent one of the most fascinating challenges of research in artificial intelligence. In this paper we give an overview of WebCrow, a system that tackles crosswords using the Web as a knowledge base. This appears to be a novel approach with respect to the available literature. It is also the first solver for nonEnglish crosswords and it has been designed to be potentially multilingual. Although WebCrow has been implemented only in a preliminary version, it already displays very interesting results reaching the performance of a human beginner: crosswords that are “easy” for expert humans are solved, within competition time limits, with 80% of correct words and over 90% of correct letters. Introduction Crosswords are probably the most played language puzzles worldwide. Until recently, the research in artificial intelligence focused its interest only on the NP-complete crossword generation problem (Mazlack 1976) (Ginsberg et al. 1990) which can now be solved in a few seconds for reasonable dimensions (Ginsberg 1993). Conversely, problems like solving crosswords from clues have been defined as AI-complete (Littman 2000) and are extremely challenging for machines since there is no closedworld assumption and they require human-level knowledge. Interestingly, AI has nowadays the opportunity to exploit a stable nucleus of technology, such as search engines, information retrieval and machine learning techniques, that can enable computers to enfold with semantics real-life concepts. We will present here a software system, called WebCrow, whose major assumption is to attack crosswords making use of the Web as its primary source of knowledge, being this an extremely rich and self-updating repository of human knowledge1. This paper gives a general overview of the modules and the subproblems present in the project. Copyright c © 2005, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. 1The WebCrow project, with the emphasis on the power coming from searching the Web, was briefly described in the Nature magazine(News-Nature 2004) and mentioned in the AAAI Web site concerning games and puzzles http://www.aaai.org/AITopics/html/crosswd.html To the best of our knowledge the only significant attempt reported in the literature to tackle this problem is the Proverb system, which has reached human-like performances (Keim, Shazeer, & Littman 1999). Unlike Proverb, WebCrow does not possess any knowledge-specific expert module nor a crossword database of great dimensions. WebCrow, instead, in order to stress the generality of its knowledge and language-independence, has in its core a module performing a special form of question answering on the Web that we shall call clue-answering. The second goal of the system, that is filling the crossword grid with the best set of candidate answers, has been tackled as a Probabilistic-CSP. Italian crosswords are structurally similar to Americans, but they differ in two aspects: two-letter words2 are allowed and, more importantly, they contain stronger and more ambiguous references to socio-cultural and political topics. The latter is a phenomenon especially present in newspapers and it introduces an additional degree of complexity in crossword solving, since it requires the possession of a knowledge that is extremely broad, fresh and also robust to volunteer vagueness and ambiguity. Neologisms and compound words are often present. We have collected 685 examples of Italian crosswords. The majority was obtained from two sources: the main Italian crossword magazine La Settimana Enigmistica and the main on-line newspaper’s crossword section La Repubblica. 60 puzzles were randomly chosen to form the experimental test suite. The remaining part constituted a database of clue-answer pairs used as an aid in the answering process. In this paper the challenge of WebCrow is to solve Italian crosswords within a 15 minutes time limit (running the program on a common laptop), as in real competitions. The version of WebCrow that is discussed here is preliminary but it has already given very promising results. WebCrow averaged around 70% words correct and 80% letters correct in the overall test set. On the examples that expert players consider “easy” WebCrow performed with 80% words correct (100% in one case) and over 90% letters correct. Architectural overview WebCrow has been designed as a modular and data-driven system. Two are the main sources that it applies to: Web 2Name initials, acronyms, abbreviations or portions of a word.


Muscle glycogen synthesis after exercise: effect of time of carbohydrate ingestion.

The time of ingestion of a carbohydrate supplement on muscle glycogen storage postexercise was examined. Twelve male cyclists exercised continuously for 70 min on a cycle ergometer at 68% VO2max, interrupted by six 2-min intervals at 88% VO2max, on two separate occasions. A 25% carbohydrate solution (2 g/kg body wt) was ingested immediately postexercise (P-EX) or 2 h postexercise (2P-EX). Muscle biopsies were taken from the vastus lateralis at 0, 2, and 4 h postexercise. Blood samples were obtained from an antecubital vein before and during exercise and at specific times after exercise. Muscle glycogen immediately postexercise was not significantly different for the P-EX and 2P-EX treatments. During the first 2 h postexercise, the rate of muscle glycogen storage was 7.7 mumol.g wet wt-1.h-1 for the P-EX treatment, but only 2.5 mumol.g wet wt-1.h-1 for the 2P-EX treatment. During the second 2 h of recovery, the rate of glycogen storage slowed to 4.3 mumol.g wet wt-1.h-1 during treatment P-EX but increased to 4.1 mumol.g wet wt-1.h-1 during treatment 2P-EX. This rate, however, was still 45% slower (P less than 0.05) than that for the P-EX treatment during the first 2 h of recovery. This slower rate of glycogen storage occurred despite significantly elevated plasma glucose and insulin levels. The results suggest that delaying the ingestion of a carbohydrate supplement post-exercise will result in a reduced rate of muscle glycogen storage.


VERL: An Ontology Framework for Representing and Annotating Video Events

in large multimedia databases and make inferences about the data stream’s content. A human operator can specify gross indices, such as the date or hour, and perhaps the location and the activity (for example, a football game) at the time of capture. Finer-grain annotations require separating the video into meaningful segments such as shots and scenes. Many automated techniques and annotation formats for capturing this type of information exist. However, the descriptions they produce don’t define the data stream content adequately for semantic retrieval and reasoning. Such descriptions must be based on observable or inferrable events in the data streams. We describe a framework for video event representation and annotation that’s based on the definition of an ontology suitable for video content. An ontology consists of a specific vocabulary for describing a certain reality and a set of explicit assumptions regarding the vocabulary’s intended meaning.1 The Video Event Representation Language (VERL)2-4 describes an event ontology, and the Video Event Markup Language (VEML)4 lets us annotate instances of the events described in VERL (see the sidebar “Using VERL and VEML” on p. 78 for an example). Annotating video in VEML consists of describing instances of events and objects, in a previously defined ontology using VERL. Figure 1 illustrates the conceptual elements involved and their relationships: annotations draw on one or several domain-specific ontologies, defined according to VERL concepts and constructs. VEML incorporates extensible event and object type hierarchies rooted in VERL’s event and object concepts. The annotations also draw on VEML for content organization. Marking up data streams in a well-defined language rooted in an ontology enables nonambiguous data sharing among users. Furthermore, annotation data is accessible to automatic machine manipulation for indexing or inferencing. The framework we describe resulted from discussions at a series of workshops sponsored by the US government’s Advanced Research and Development Activity (ARDA). It therefore includes the intellectual contributions of many individuals. Currently, only a small community of researchers is using VERL and VEML; this article aims to publicize the framework to a broader community as its relevance depends on its widespread adoption. An earlier description can be found elsewhere.5


It’s All Fun and Games until Someone Annotates: Video Games with a Purpose for Linguistic Annotation

Annotated data is prerequisite for many NLP applications. Acquiring large-scale annotated corpora is a major bottleneck, requiring significant time and resources. Recent work has proposed turning annotation into a game to increase its appeal and lower its cost; however, current games are largely text-based and closely resemble traditional annotation tasks. We propose a new linguistic annotation paradigm that produces annotations from playing graphical video games. The effectiveness of this design is demonstrated using two video games: one to create a mapping from WordNet senses to images, and a second game that performs Word Sense Disambiguation. Both games produce accurate results. The first game yields annotation quality equal to that of experts and a cost reduction of 73% over equivalent crowdsourcing; the second game provides a 16.3% improvement in accuracy over current state-of-the-art sense disambiguation games with WordNet.


Natural Language Processing for Information Retrieval

The paper summarizes the essential properties of document retrieval and reviews both conventional practice and research findings, the latter suggesting that simple statistical techniques can be effective. It then considers the new opportunities and challenges presented by the user’s ability to search full text directly (rather than e.g. titles and abstracts), and suggests appropriate approaches to doing this, with a focus on the potential role of natural language processing. The paper also comments on possible connections with data and knowledge retrieval, and concludes by emphasizing the importance of rigorous performance testing.


Anticipating the Future by Constructing Human Activities using Object Affordances

An important aspect of human perception is anticipation and anticipating which activities will a human do next (and how to do them) in useful for many applications, for example, anticipation enables an assistive robot to plan ahead for reactive responses in the human environments. In this work, we present a constructive approach for generating various possible future human activities by reasoning about the rich spatial-temporal relations through object affordances. We represent each possible future using an anticipatory temporal conditional random field (ATCRF) where we sample the nodes and edges corresponding to future object trajectories and human poses from a generative model. We then represent the distribution over the potential futures using a set of constructed ATCRF particles. In extensive evaluation on CAD-120 human activity RGB-D dataset, for new subjects (not seen in the training set), we obtain an activity anticipation accuracy (defined as whether one of top three predictions actually happened) of 75.4%, 69.2% and 58.1% for an anticipation time of 1, 3 and 10 seconds respectively. 1


Constraint-Based Approaches for Balancing Bike Sharing Systems

In order to meet the users’ demand, bike sharing systems must be regularly rebalanced. The problem of balancing bike sharing systems (BBSS) is concerned with designing optimal tours and operating instructions for relocating bikes among stations to maximally comply with the expected future bike demands. In this paper, we tackle the BBSS by means of Constraint Programming: first, we introduce two novel constraint models for the BBSS including a smart branching strategy that focusses on the most promising routes. Second, in order to speed-up the search process, we incorporate both models in a Large Neighborhood Search (LNS) approach that is adapted to the respective CP model. Third, we perform an extensive computational evaluation on instances based on real-world data, where we see that the LNS approach outperforms the Branch & Bound approach and is competitive with other existing approaches.


Informative path planning for an autonomous underwater vehicle

We present a path planning method for autonomous underwater vehicles in order to maximize mutual information. We adapt a method previously used for surface vehicles, and extend it to deal with the unique characteristics of underwater vehicles. We show how to generate near-optimal paths while ensuring that the vehicle stays out of high-traffic areas during predesignated time intervals. In our objective function we explicitly account for the fact that underwater vehicles typically take measurements while moving, and that they do not have the ability to communicate until they resurface. We present field results from ocean trials on planning paths for a specific AUV, an underwater glider.


Memristive devices for computing: Beyond CMOS and beyond von Neumann

Traditional CMOS technology and its continuous down-scaling have been the driving force to improve performance of existing computer architectures. Today, however, both technology and computer architectures are facing challenges that make them incapable of delivering the growing computing performance requirement at pre-defined constraints. This forces the exploration of both novel architectures and technologies; not only to maintain the economic profit of technology scaling, but also to enable the computing architecture solutions for big-data and data-intensive applications. This paper discusses the emerging memristive device as a complement (or an alternative) to CMOS devices and shows how such devices enable novel computing paradigms that will solve the challenges of today's architectures for certain applications. The paper covers not only the potential of memristor devices in enabling novel memory technologies, logic design styles, and arithmetic operations, but also their potential in enabling in-memory computing and neuromorphic computing.


An Approach for Factory-Wide Control Utilizing Virtual Metrology

In the semiconductor manufacturing industry, market demands and technology trends drive manufacturers towards increases in wafer size and decreases in device size. Application of factory-wide advanced process control (APC) techniques down to the wafer-to-wafer (W2W) level control capability is becoming the only choice to cope with the demanding situation. However, one of the main limitations in undertaking W2W control is the nonavailability of timely metrology data at the wafer level. Recently virtual metrology (VM) techniques have been proposed to provide timely wafer level metrology data; they have the potential to be used in realizing W2W control. In this paper, the VM approach to W2W control on factory level is described. VM for an individual process is realized by utilizing the preprocess metrology and the process data from the underlying tools that is generally collected in real time for fault detection purposes. The VM implementation for factory-wide run-to-run control brings unique opportunities and issues to the forefront such as dealing with expected lower quality of VM data, coordination between VM modules of cascading processes for better prediction quality, flexibility of the factory-wide controller to accommodate lower quality VM data, dynamic adjustments to the target values of individual processes by the factory-wide controller when using VM data, and dealing with metrology delays at the factory level. Near and long-term solutions are presented to address these issues in order to allow VM to be used today and become an integral part of the factory-wide APC solution for W2W control.


High Confidence Policy Improvement

We present a batch reinforcement learning (RL) algorithm that provides probabilistic guarantees about the quality of each policy that it proposes, and which has no hyper-parameters that require expert tuning. The user may select any performance lower-bound, ρ−, and confidence level, δ, and our algorithm will ensure that the probability that it returns a policy with performance below ρ− is at most δ. We then propose an incremental algorithm that executes our policy improvement algorithm repeatedly to generate multiple policy improvements. We show the viability of our approach with a simple gridworld and the standard mountain car problem, as well as with a digital marketing application that uses real world data.


UltraGesture: Fine-Grained Gesture Sensing and Recognition

With the rising of AR/VR technology and miniaturization of mobile devices, gesture is becoming an increasingly popular means of interacting with smart devices. Some pioneer ultrasound based human gesture recognition systems have been proposed. They mostly rely on low resolution Doppler Effect, and hence focus on whole hand motion and cannot deal with minor finger motions. In this paper, we present UltraGesture, a Channel Impulse Response (CIR) based ultrasonic finger motion perception and recognition system. CIR measurements can provide with 7 mm resolution, rendering it sufficient for minor finger motion recognition. UltraGesture encapsulates CIR measurements into an image, and builds a Convolutional Neural Network model to classify these images into different categories, which corresponding to distinct gestures. Our system runs on commercial speakers and microphones that already exist on most mobile devices without hardware modification. Our results show that UltraGesture achieves an average accuracy of greater than 97% for 12 gestures including finger click and rotation.


A classification of transaction processing systems

The problems in building a transaction processing system are discussed, and it is shown that the difficulties are a function of specific attributes of the underlying database system. A model of a transaction processing system is presented, and five system dimensions important in classifying transaction processing systems-the process, machine, heterogeneity, data, and site components-are introduced. The specific problems posed by various combinations of system characteristics are analyzed. The evolution of transaction processing systems are described in terms of the framework.<<ETX>>


Characterizing 3D Floating Gate NAND Flash

In this paper, we characterize a state-of-the-art 3D floating gate NAND flash memory through comprehensive experiments on an FPGA platform. Then, we present distinct observations on performance and reliability, such as operation latencies and various error patterns. We believe that through our work, novel 3D NAND flash-oriented designs can be developed to achieve better performance and reliability.


Web Mining Research: A Survey

With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. The Web mining research is at the cross road of research from several research communities, such as database, information retrieval, and within AI, especially the sub-areas of machine learning and natural language processing. However, there is a lot of confusions when comparing research efforts from different point of views. In this paper, we survey the research in the area of Web mining, point out some confusions regarded the usage of the term Web mining and suggest three Web mining categories. Then we situate some of the research with respect to these three categories. We also explore the connection between the Web mining categories and the related agent paradigm. For the survey, we focus on representation issues, on the process, on the learning algorithm, and on the application of the recent works as the criteria. We conclude the paper with some research issues.


SpaceTwist: Managing the Trade-Offs Among Location Privacy, Query Performance, and Query Accuracy in Mobile Services

In a mobile service scenario, users query a server for nearby points of interest but they may not want to disclose their locations to the service. Intuitively, location privacy may be obtained at the cost of query performance and query accuracy. The challenge addressed is how to obtain the best possible performance, subjected to given requirements for location privacy and query accuracy. Existing privacy solutions that use spatial cloaking employ complex server query processing techniques and entail the transmission of large quantities of intermediate result. Solutions that use transformation-based matching generally fall short in offering practical query accuracy guarantees. Our proposed framework, called SpaceTwist, rectifies these shortcomings for k nearest neighbor (kNN) queries. Starting with a location different from the user's actual location, nearest neighbors are retrieved incrementally until the query is answered correctly by the mobile terminal. This approach is flexible, needs no trusted middleware, and requires only well-known incremental NN query processing on the server. The framework also includes a server-side granular search technique that exploits relaxed query accuracy guarantees for obtaining better performance. The paper reports on empirical studies that elicit key properties of SpaceTwist and suggest that the framework offers very good performance and high privacy, at low communication cost.


Nonlinear controller design for a magnetic levitation device

Various applications of micro-robotic technology suggest the use of new actuator systems which allow motions to be realized with micrometer accuracy. Conventional actuation techniques such as hydraulic or pneumatic systems are no longer capable of fulfilling the demands of hi-tech micro-scale areas such as miniaturized biomedical devices and MEMS production equipment. These applications pose significantly different problems from actuation on a large scale. In particular, large scale manipulation systems typically deal with sizable friction, whereas micro manipulation systems must minimize friction to achieve submicron precision and avoid generation of static electric fields. Recently, the magnetic levitation technique has been shown to be a feasible actuation method for microscale applications. In this paper, a magnetic levitation device is recalled from the authors’ previous work and a control approach is presented to achieve precise motion control of a magnetically levitated object with sub-micron positioning accuracy. The stability of the controller is discussed through the Lyapunov method. Experiments are conducted and showed that the proposed control technique is capable of performing a positioning operation with rms accuracy of 16 lm over a travel range of 30 mm. The nonlinear control strategy proposed in this paper showed a significant improvement in comparison with the conventional control strategies for large gap magnetic levitation systems.


Motor control exercise for chronic non-specific low-back pain.

BACKGROUND
Non-specific low back pain (LBP) is a common condition. It is reported to be a major health and socioeconomic problem associated with work absenteeism, disability and high costs for patients and society. Exercise is a modestly effective treatment for chronic LBP. However, current evidence suggests that no single form of exercise is superior to another. Among the most commonly used exercise interventions is motor control exercise (MCE). MCE intervention focuses on the activation of the deep trunk muscles and targets the restoration of control and co-ordination of these muscles, progressing to more complex and functional tasks integrating the activation of deep and global trunk muscles. While there are previous systematic reviews of the effectiveness of MCE, recently published trials justify an updated systematic review.

OBJECTIVES
To evaluate the effectiveness of MCE in patients with chronic non-specific LBP.

SEARCH METHODS
We conducted electronic searches in CENTRAL, MEDLINE, EMBASE, five other databases and two trials registers from their inception up to April 2015. We also performed citation tracking and searched the reference lists of reviews and eligible trials.

SELECTION CRITERIA
We included randomised controlled trials (RCTs) that examined the effectiveness of MCE in patients with chronic non-specific LBP. We included trials comparing MCE with no treatment, another treatment or that added MCE as a supplement to other interventions. Primary outcomes were pain intensity and disability. We considered function, quality of life, return to work or recurrence as secondary outcomes. All outcomes must have been measured with a valid and reliable instrument.

DATA COLLECTION AND ANALYSIS
Two independent review authors screened the search results, assessed risk of bias and extracted the data. A third independent review author resolved any disagreement. We assessed risk of bias using the Cochrane Back and Neck (CBN) Review Group expanded 12-item criteria (Furlan 2009). We extracted mean scores, standard deviations and sample sizes from the included trials, and if this information was not provided we calculated or estimated them using methods recommended in the Cochrane Handbook. We also contacted the authors of the trials for any missing or unclear information. We considered the following time points: short-term (less than three months after randomisation); intermediate (at least three months but less than 12 months after randomisation); and long-term (12 months or more after randomisation) follow-up. We assessed heterogeneity by visual inspection of the forest plots, and by calculating the Chi(2) test and the I(2) statistic. We combined results in a meta-analysis expressed as mean difference (MD) and 95% confidence interval (CI). We assessed the overall quality of the evidence using the GRADE approach.

MAIN RESULTS
We included 29 trials (n = 2431) in this review. The study sample sizes ranged from 20 to 323 participants. We considered a total of 76.6% of the included trials to have a low risk of bias, representing 86% of all participants. There is low to high quality evidence that MCE is not clinically more effective than other exercises for all follow-up periods and outcomes tested. When compared with minimal intervention, there is low to moderate quality evidence that MCE is effective for improving pain at short, intermediate and long-term follow-up with medium effect sizes (long-term, MD -12.97; 95% CI -18.51 to -7.42). There was also a clinically important difference for the outcomes function and global impression of recovery compared with minimal intervention. There is moderate to high quality evidence that there is no clinically important difference between MCE and manual therapy for all follow-up periods and outcomes tested. Finally, there is very low to low quality evidence that MCE is clinically more effective than exercise and electrophysical agents (EPA) for pain, disability, global impression of recovery and quality of life with medium to large effect sizes (pain at short term, MD -30.18; 95% CI -35.32 to -25.05). Minor or no adverse events were reported in the included trials.

AUTHORS' CONCLUSIONS
There is very low to moderate quality evidence that MCE has a clinically important effect compared with a minimal intervention for chronic low back pain. There is very low to low quality evidence that MCE has a clinically important effect compared with exercise plus EPA. There is moderate to high quality evidence that MCE provides similar outcomes to manual therapies and low to moderate quality evidence that it provides similar outcomes to other forms of exercises. Given the evidence that MCE is not superior to other forms of exercise, the choice of exercise for chronic LBP should probably depend on patient or therapist preferences, therapist training, costs and safety.


Treatment planning of the edentulous maxilla

The predictability of successful osseointegrated implant rehabilitation of the edentulous jaw as described by Branemark et al., introduced a new era of management for the edentulous predicament. Implant rehabilitation of the edentulous maxilla remains one of the most complex restorative challenges because of the number of variables that affect both the aesthetic and functional aspect of the prosthesis. Among the prosthesis designs used to treat the edentulous maxilla are fixed or removable implant-supported restorations. Since the aesthetic requirements and preoperative situation of each patient varies, considerable time must be spent on accurate diagnosis to ensure patient desires are satisfied and predictable outcomes are achieved. The purpose of this article is to compare the treatment options and prosthesis designs for the edentulous maxilla. Emphasis will be placed on diagnosis and treatment planning. Criteria will be given to guide the practitioner in deciding whether a fixed or removable restoration should be placed. This objective will be accomplished through the review of cases with regard to varying design considerations and factors that influence the decision-making process.


An Efficient Diagnosis System for Parkinson's Disease Using Kernel-Based Extreme Learning Machine with Subtractive Clustering Features Weighting Approach

A novel hybrid method named SCFW-KELM, which integrates effective subtractive clustering features weighting and a fast classifier kernel-based extreme learning machine (KELM), has been introduced for the diagnosis of PD. In the proposed method, SCFW is used as a data preprocessing tool, which aims at decreasing the variance in features of the PD dataset, in order to further improve the diagnostic accuracy of the KELM classifier. The impact of the type of kernel functions on the performance of KELM has been investigated in detail. The efficiency and effectiveness of the proposed method have been rigorously evaluated against the PD dataset in terms of classification accuracy, sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC), f-measure, and kappa statistics value. Experimental results have demonstrated that the proposed SCFW-KELM significantly outperforms SVM-based, KNN-based, and ELM-based approaches and other methods in the literature and achieved highest classification results reported so far via 10-fold cross validation scheme, with the classification accuracy of 99.49%, the sensitivity of 100%, the specificity of 99.39%, AUC of 99.69%, the f-measure value of 0.9964, and kappa value of 0.9867. Promisingly, the proposed method might serve as a new candidate of powerful methods for the diagnosis of PD with excellent performance.


Speaker-listener neural coupling underlies successful communication.

Verbal communication is a joint activity; however, speech production and comprehension have primarily been analyzed as independent processes within the boundaries of individual brains. Here, we applied fMRI to record brain activity from both speakers and listeners during natural verbal communication. We used the speaker's spatiotemporal brain activity to model listeners' brain activity and found that the speaker's activity is spatially and temporally coupled with the listener's activity. This coupling vanishes when participants fail to communicate. Moreover, though on average the listener's brain activity mirrors the speaker's activity with a delay, we also find areas that exhibit predictive anticipatory responses. We connected the extent of neural coupling to a quantitative measure of story comprehension and find that the greater the anticipatory speaker-listener coupling, the greater the understanding. We argue that the observed alignment of production- and comprehension-based processes serves as a mechanism by which brains convey information.


Practical Signal-Dependent Noise Parameter Estimation From a Single Noisy Image

The additive white Gaussian noise is widely assumed in many image processing algorithms. However, in the real world, the noise from actual cameras is better modeled as signal-dependent noise (SDN). In this paper, we focus on the SDN model and propose an algorithm to automatically estimate its parameters from a single noisy image. The proposed algorithm identifies the noise level function of signal-dependent noise assuming the generalized signal-dependent noise model and is also applicable to the Poisson-Gaussian noise model. The accuracy is achieved by improved estimation of local mean and local noise variance from the selected low-rank patches. We evaluate the proposed algorithm with both synthetic and real noisy images. Experiments demonstrate that the proposed estimation algorithm outperforms the state-of-the-art methods.


Human-Assisted Graph Search: It's Okay to Ask Questions

We consider the problem of human-assisted graph search: given a directed acyclic graph with some (unknown) target node(s), we consider the problem of finding the target node(s) by asking an omniscient human questions of the form “Is there a target node that is reachable from the current node?”. This general problem has applications in many domains that can utilize human intelligence, including curation of hierarchies, debugging workflows, image segmentation and categorization, interactive search and filter synthesis. To our knowledge, this work provides the first formal algorithmic study of the optimization of human computation for this problem. We study various dimensions of the problem space, providing algorithms and complexity results. We also compare the performance of our algorithm against other algorithms, for the problem of webpage categorization on a real taxonomy. Our framework and algorithms can be used in the design of an optimizer for crowdsourcing platforms such as Mechanical Turk.


Joint detection and decoding for MIMO systems with polar codes

As well known, the near-optimal K-best detection is popular in multiple-input and multiple-output (MIMO) systems. In this paper, we first propose the joint approaches of K-best detection and polar decoding. For joint detection and decoding (JDD) approach, both hard and soft decisions are considered. The simplified successive cancellation (SSC) decoding is exploited for hard decision, and the successive cancellation list (SCL) decoding is used as soft decision. The system setup for JDD is als o introduced, in which the modulation points across several channels are considered together. Simulation results have demonstrated the performance advantage of the JDD algorithms over the separated ones. For 1/2-rate polar codes, JDD schemes show 50% complexity reduction compared to the separated ones. Furthermore, by employing SSC hard decoding, the JDD algorithm is promising for high-throughput and low-complexity application s.


The Alignment Template Approach to Statistical Machine Translation

A phrase-based statistical machine translation approach the alignment template approach is described. This translation approach allows for general many-to-many relations between words. Thereby, the context of words is taken into account in the translation model, and local changes in word order from source to target language can be learned explicitly. The model is described using a log-linear modeling approach, which is a generalization of the often used source-channel approach. Thereby, the model is easier to extend than classical statistical machine translation systems. We describe in detail the process for learning phrasal translations, the feature functions used, and the search algorithm. The evaluation of this approach is performed on three different tasks. For the German-English speech Verbmobil task, we analyze the effect of various system components. On the French-English Canadian Hansards task, the alignment template system obtains significantly better results than a single-word-based translation model. In the Chinese-English 2002 National Institute of Standards and Technology (NIST) machine translation evaluation it yields statistically significantly better NIST scores than all competing research and commercial translation systems.


Adolescent Internet Addiction: Testing the Association Between Self-Esteem, the Perception of Internet Attributes, and Preference for Online Social Interactions

There is a general consensus that Internet addiction (IA) is mainly related to social aspects of the Web, especially among adolescents. The empirical link between poor social skills and IA is well documented; however, theoretical explanations for this relationship are less developed. One possibility is that people with poor social skills are especially prone to develop a preference for online social interaction (POSI), which, in turn, predicts problematic usage. This hypothesis has been tested for loneliness and social anxiety, but not for self-esteem (SE; one of the main antecedents of IA). Furthermore, the mediating role of the perceived relevance of some Internet features (e.g., anonymity) in the relationship between SE and POSI has never been investigated. A cross-sectional study was conducted with 257 adolescents. Using mediation analyses, we found evidence among females for the mediating role of (a) POSI in the relationship between SE and IA, and (b) the subjective relevance of some Internet features in the association between SE and POSI. No significant effects were found for males.


Automatic Detection of Calibration Grids in Time-of-Flight Images

It is convenient to calibrate time-of-flight cameras by established methods, using images of a chequerboard pattern. The low resolution of the amplitude image, however, makes it difficult to detect the board reliably. Heuristic detection methods, based on connected image-components, perform very poorly on this data. An alternative, geometrically-principled method is introduced here, based on the Hough transform. The projection of a chequerboard is represented by two pencils of lines, which are identified as oriented clusters in the gradient-data of the image. A projective Hough transform is applied to each of the two clusters, in axis-aligned coordinates. The range of each transform is properly bounded, because the corresponding gradient vectors are approximately parallel. Each of the two transforms contains a series of collinear peaks; one for every line in the given pencil. This pattern is easily detected, by sweeping a dual line through the transform. The proposed Hough-based method is compared to the standard OpenCV detection routine, by application to several hundred time-of-flight images. It is shown that the new method detects significantly more calibration boards, over a greater variety of poses, without any overall loss of accuracy. This conclusion is based on an analysis of both geometric and photometric error.


Reinforcement Learning with Self-Modifying Policies

A learner’s modifiable components are called its policy. An algorithm that modifies the policy is a learning algorithm. If the learning algorithm has modifiable components represented as part of the policy, then we speak of a self-modifying policy (SMP). SMPs can modify the way they modify themselves etc. They are of interest in situations where the initial learning algorithm itself can be improved by experience — this is what we call “learning to learn”. How can we force some (stochastic) SMP to trigger better and better self-modifications? The success-story algorithm (SSA) addresses this question in a lifelong reinforcement learning context. During the learner’s life-time, SSA is occasionally called at times computed according to SMP itself. SSA uses backtracking to undo those SMP-generated SMP-modifications that have not been empirically observed to trigger lifelong reward accelerations (measured up until the current SSA call — this evaluates the long-term effects of SMP-modifications setting the stage for later SMP-modifications). SMP-modifications that survive SSA represent a lifelong success history. Until the next SSA call, they build the basis for additional SMP-modifications. Solely by self-modifications our SMP/SSA-based learners solve a complex task in a partially observable environment (POE) whose state space is far bigger than most reported in the POE literature.


Speaker-sensitive dual memory networks for multi-turn slot tagging

In multi-turn dialogs, natural language understanding models can introduce obvious errors by being blind to contextual information. To incorporate dialog history, we present a neural architecture with Speaker-Sensitive Dual Memory Networks which encode utterances differently depending on the speaker. This addresses the different extents of information available to the system — the system knows only the surface form of user utterances while it has the exact semantics of system output. We performed experiments on real user data from Microsoft Cortana, a commercial personal assistant. The result showed a significant performance improvement over the state-of-the-art slot tagging models using contextual information.


Integrated force and distance sensing using elastomer-embedded commodity proximity sensors

We describe a combined force and distance sensor using a commodity infrared distance sensor embedded in a transparent elastomer with applications in robotic manipulation. Prior to contact, the sensor works as a distance sensor (0–10 cm), whereas after contact the material doubles as a spring, with force proportional to the compression of the elastomer (0– 5 N). We describe its principle of operation and design parameters, including polymer thickness, mixing ratio, and emitter current, and show that the sensor response has an inflection point at contact that is independent of an object’s surface properties. We then demonstrate how two arrays of eight sensors, each mounted on a standard Baxter gripper, can be used to (1) improve gripper alignment during grasping, (2) determine contact points with objects, and (3) obtain crude 3D models that can serve to determine possible grasp locations.


Accurate Synchronization of Speech and EGG Signal Using Phase Information

Synchronization of speech and corresponding Electroglottographic (EGG) signal is very helpful for speech processing research and development. During simultaneous recording of speech and EGG signals, the speech signal will be delayed by the duration corresponding to the speech wave propagation from the glottis to the microphone relative to the EGG signal. Even in same session of recording, the delay between the speech and the EGG signals is varying due to the natural movement of speaker’s head and movement of microphone in case MIC is held by hand. To study and model the information within glottal cycles, precise synchronization of speech and EGG signals is of utmost necessity. In this work, we propose a method for synchronization of speech and EGG signals based on the glottal activity information present in the signals. The performance of the proposed method is demonstrated by estimation of delay between the two signals (speech signals and corresponding EGG signals) and synchronizing these signals by compensating the estimated delay. The CMU-Arctic database consist of simultaneous recording of the speech and the EGG signals is used for the evaluation of the proposed method.


Wormhole Attack Detection in Wireless Networks

Wormhole attacks can destabilize or disable wireless sensor networks. In a typical wormhole attack, the attacker receives packets at one point in the network, forwards them through a wired or wireless link with less latency than the network links, and relays them to another point in the network. We first propose a centralized algorithm to detect wormholes and show its correctness rigorously. For the distributed wireless network, we proposes DAWN, Distributed detection Algorithm against Wormhole in wireless Network coding systems, by exploring the change of the flow directions of the innovative packets caused by wormholes. We find that the robustness depends on the node density in the network, and prove a necessary condition to achieve collision-resistance. Our solutions only depend on the local information that can be obtained from regular network coding protocols, and thus the overhead that our algorithms introduce is acceptable for most applications.


Polygraph: automatically generating signatures for polymorphic worms

It is widely believed that content-signature-based intrusion detection systems (IDS) are easily evaded by polymorphic worms, which vary their payload on every infection attempt. In this paper, we present Polygraph, a signature generation system that successfully produces signatures that match polymorphic worms. Polygraph generates signatures that consist of multiple disjoint content substrings. In doing so, Polygraph leverages our insight that for a real-world exploit to function properly, multiple invariant substrings must often be present in all variants of a payload; these substrings typically correspond to protocol framing, return addresses, and in some cases, poorly obfuscated code. We contribute a definition of the polymorphic signature generation problem; propose classes of signature suited for matching polymorphic worm payloads; and present algorithms for automatic generation of signatures in these classes. Our evaluation of these algorithms on a range of polymorphic worms demonstrates that Polygraph produces signatures for polymorphic worms that exhibit low false negatives and false positives.


Are We Ready for Driver-less Vehicles? Security vs. Privacy- A Social Perspective

At this moment Automous cars are probably the biggest and most talked about technology in the Robotics Reserach Community. In spite of great technolgical advances over past few years a full fledged autonomous car is still far from reality. This article talks about the existing system and discusses the possibility of a Computer Vision enabled driving being superior than the LiDar based system. A detailed overview of privacy violations that might arise from autonomous driving has been discussed in detail both from a technical as well as legal perspective. It has been proved through evidence and arguments that efficient and accurate estimation and efficient solution of the contraint satisfaction problem adressed in the case of autonomous cars are negatively correlated with the preserving the privacy of the user. It is a very difficult trade-off since both are very important aspects and has to be taken into account. The fact that one cannot compromise with the safety issues of the car makes it inevidable to run into serious privacy concerns that might have adverse social and political effects.


Image-based Localization with Spatial LSTMs

In this work we propose a new CNN+LSTM architecture for camera pose regression for indoor and outdoor scenes. CNNs allow us to learn suitable feature representations for localization that are robust against motion blur and illumination changes. We make use of LSTM units on the CNN output in spatial coordinates in order to capture contextual information. This substantially enlarges the receptive field of each pixel leading to drastic improvements in localization performance. We provide extensive quantitative comparison of CNN-based vs SIFT-based localization methods, showing the weaknesses and strengths of each. Furthermore, we present a new large-scale indoor dataset with accurate ground truth from a laser scanner. Experimental results on both indoor and outdoor public datasets show our method outperforms existing deep architectures, and can localize images in hard conditions, e.g., in the presence of mostly textureless surfaces.


Communication issues in requirements elicitation: a content analysis of stakeholder experiences

The gathering of stakeholder requirements comprises an early, but continuous and highly critical stage in system development. This phase in development is subject to a large degree of error, influenced by key factors rooted in communication problems. This pilot study builds upon an existing theory-based categorisation of these problems through presentation of a four-dimensional framework on communication. Its structure is validated through a content analysis of interview data, from which themes emerge, that can be assigned to the dimensional categories, highlighting any problematic areas. The paper concludes with a discussion on the utilisation of the framework for requirements elicitation exercises.


Game Theory in Signal Processing and Communications

1Department of Mobile Communications, School of Electrical Engineering and Computer Sciences, Technical University of Berlin, Berlin, Germany 2Wireless Networking, Signal Processing and Security Lab, Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USA 3Division of Communication Systems, Department of Electrical Engineering (ISY), Linköping University, SE-581 83 Linköping, Sweden 4Communications Laboratory, Faculty of Electrical Engineering and Information Technology, Dresden University of Technology, 01062 Dresden, Germany


Exercise motion classification from large-scale wearable sensor data using convolutional neural networks

The ability to accurately identify human activities is essential for developing automatic rehabilitation and sports training systems. In this paper, large-scale exercise motion data obtained from a forearm-worn wearable sensor are classified with a convolutional neural network (CNN). Time-series data consisting of accelerometer and orientation measurements are formatted as images, allowing the CNN to automatically extract discriminative features. A comparative study on the effects of image formatting and different CNN architectures is also presented. The best performing configuration classifies 50 gym exercises with 92.1% accuracy.


Long-range GPS-denied aerial inertial navigation with LIDAR localization

Despite significant progress in GPS-denied autonomous flight, long-distance traversals (> 100 km) in the absence of GPS remain elusive. This paper demonstrates a method capable of accurately estimating the aircraft state over a 218 km flight with a final position error of 27 m, 0.012% of the distance traveled. Our technique efficiently captures the full state dynamics of the air vehicle with semi-intermittent global corrections using LIDAR measurements matched against an a priori Digital Elevation Model (DEM). Using an error-state Kalman filter with IMU bias estimation, we are able to maintain a high-certainty state estimate, reducing the computation time to search over a global elevation map. A sub region of the DEM is scanned with the latest LIDAR projection providing a correlation map of landscape symmetry. The optimal position is extracted from the correlation map to produce a position correction that is applied to the state estimate in the filter. This method provides a GPS-denied state estimate for long range drift-free navigation. We demonstrate this method on two flight data sets from a full-sized helicopter, showing significantly longer flight distances over the current state of the art.


EbbRT : Elastic Building Block Runtime-overview Schatzberg


Modeling of Item-Difficulty for Ontology-based MCQs

Multiple choice questions (MCQs) that can be generated from a domain ontology can significantly reduce human effort & time re quired for authoring & administering assessments in an e-Learning envir o ment. Even though there are various methods for generating MCQs from ontologi es, methods for determining the difficulty-levels of such MCQs are less explor ed. In this paper, we study various aspects and factors that are involved in deter mining the difficultyscore of an MCQ, and propose an ontology-based model for the p rediction. This model characterizes the difficulty values associated with t he s em and choice set of the MCQs, and describes a measure which combines both the s cor s. Furthermore, the notion of assigning difficultly-scores based on th e skill level of the test taker is utilized for predicating difficulty-score of a stem . We studied the effectiveness of the predicted difficulty-scores with the help of a psychometric model from the Item Response Theory, by involving real-students a d domain experts. Our results show that, the predicated difficulty-levels of t he MCQs are having high correlation with their actual difficulty-levels.


Kirner’s deformity and clinodactyly in one family

Clinodactyly and Kirner’s deformity are generally regarded as independent anomalies. We present the first family in which both deformities are present. This concurrence raises the question as to whether these deformities are variable expressions of the same genetic disorder. However, because a conclusive genetic test could not be performed, coincidence cannot be excluded.


Investigation of multilingual deep neural networks for spoken term detection

The development of high-performance speech processing systems for low-resource languages is a challenging area. One approach to address the lack of resources is to make use of data from multiple languages. A popular direction in recent years is to use bottleneck features, or hybrid systems, trained on multilingual data for speech-to-text (STT) systems. This paper presents an investigation into the application of these multilingual approaches to spoken term detection. Experiments were run using the IARPA Babel limited language pack corpora (~10 hours/language) with 4 languages for initial multilingual system development and an additional held-out target language. STT gains achieved through using multilingual bottleneck features in a Tandem configuration are shown to also apply to keyword search (KWS). Further improvements in both STT and KWS were observed by incorporating language questions into the Tandem GMM-HMM decision trees for the training set languages. Adapted hybrid systems performed slightly worse on average than the adapted Tandem systems. A language independent acoustic model test on the target language showed that retraining or adapting of the acoustic models to the target language is currently minimally needed to achieve reasonable performance.


Channel selection based on multichannel cross-correlation coefficients for distant speech recognition

In theory, beamforming performance can be improved by using as many microphones as possible, but in practice it has been shown that using all possible channels does not always improve speech recognition performance [1, 2, 3, 4, 5]. In this work, we present a new channel selection method in order to increase the computational efficiency of beamforming for distant speech recognition (DSR) without sacrficing performance.


Key Risk Indicators – Their Role in Operational Risk Management and Measurement

In comparison with other operational risk management and measurement tools such as loss data (internal, public and consortium), risk-and-control assessments, capital allocation and performance measurement, key risk indicators (KRIs) remain one of the outstanding action items on most firms’ to-do lists, along with scenario analysis. KRIs are not new, but, up until recently, attempts to implement such programmes have often been characterised as less than effective. We believe that there are emerging best practices for KRI programmes that overcome some of the traditional challenges in KRI implementations. Further, we believe that investment in KRI programmes will reap many benefits, including the ability to clearly convey risk appetite, optimise risk and return, and improve the likelihood of achieving primary business goals through more effective operational risk management. In this chapter, we will seek to demystify KRIs, understand the basic fundamentals in identifying, specifying, selecting and implementing quality indicators, and consider how to monitor and report on them, in conjunction with other useful operational risk management information, to create powerful management reporting. We will also examine the potential for more advanced applications of KRIs, touching on the measurement of risk for correlation purposes, and the potential for composite indicators as well as KRI benchmarking. Finally, we will overview an industry KRI 10


Measuring problem video game playing in adolescents.

AIMS
Some researchers suggest that for some people, video game playing is an addictive behaviour similar to substance dependence. Our aim was to design and validate a scale to measure the problems associated with the apparently addictive use of all types of video games and video game systems, because there is no instrument at the present time that can be used for this purpose.

DESIGN
We reviewed the DSM-IV criteria for substance dependence and for pathological gambling, as well as the literature on the addictions in order to design a short scale (PVP; problem video game playing) that is quick and easy to apply.

PARTICIPANTS
The scale was administered to 223 Spanish adolescents aged between 13 and 18 years. The study was carried out in Granada and Algeciras, Spain.

FINDINGS
Psychometric analyses show that the PVP seems to be unidimensional and has acceptable internal consistency (Cronbach's alpha) at 0.69. The pattern of associations between the scale scores and alternative measures of problem play supports its construct validity (higher total scores in the scale were associated with higher frequency of play, mean and longest times per session, self and parents' perception of playing to excess, and scores in the Severity of Dependence Scale).

CONCLUSIONS
Our results confirm that the excessive use of video games is associated with a number of problems which resemble a dependence syndrome, and the PVP appears as a useful instrument for the measurement of such problems.


A mathematical theory of communication: Meaning, information, and topology


Markerless tracking using planar structures in the scene

We describea markerlesscamera tracking systemfor augmentedreality thatoperatesin environmentswhich contain oneor more planes. This is a commonspecialcase, which we showsignificantlysimplifiestracking. The result is a practical, reliable, vision-basedtracker. Furthermore, thetrackedplaneimposesa natural referenceframe, sothat thealignmentof thereal andvirtual coordinatesystemsis rather simplerthan would be the casewith a general structure-and-motionsystem.Multiple planescan be tracked, and additional data such as 2D point tracks are easilyincorporated.


Planar object recognition using projective shape representation

We describe a model based recognition system, called LEWIS, for the identification of planar objects based on a projectively invariant representation of shape. The advantages of this shape description include simple model acquisition (direct from images), no need for camera calibration or object pose computation, and the use of index functions. We describe the feature construction and recognition algorithms in detail and provide an analysis of the combinatorial advantages of using index functions. Index functions are used to select models from a model base and are constructed from projective invariants based on algebraic curves and a canonical projective coordinate frame. Examples are given of object recognition from images of real scenes, with extensive object libraries. Successful recognition is demonstrated despite partial occlusion by unmodelled objects, and realistic lighting conditions.


Scholarly use of social media and altmetrics: A review of the literature

Social media has become integrated into the fabric of the scholarly communication system in fundamental ways: principally through scholarly use of social media platforms and the promotion of new indicators on the basis of interactions with these platforms. Research and scholarship in this area has accelerated since the coining and subsequent advocacy for altmetrics—that is, research indicators based on social media activity. This review provides an extensive account of the state-of-the art in both scholarly use of social media and altmetrics. The review consists of two main parts: the first examines the use of social media in academia, examining the various functions these platforms have in the scholarly communication process and the factors that affect this use. The second part reviews empirical studies of altmetrics, discussing the various interpretations of altmetrics, data collection and methodological limitations, and differences according to platform. The review ends with a critical discussion of the implications of this transformation in the scholarly communication system.


Classification of Acceleration Data for Biometric Gait Recognition on Mobile Devices

Ubiquitous mobile devices like smartphones and tablets are often not secured against unauthorized access as the users tend to not use passwords because of convenience reasons. Therefore, this study proposes an alternative user authentication method for mobile devices based on gait biometrics. The gait characteristics are captured using the built-in accelerometer of a smartphone. Various features are extracted from the measured accelerations and utilized to train a support vector machine (SVM). Among the extracted features are the Meland Bark-frequency cepstral coefficients (MFCC, BFCC) which are commonly used in speech and speaker recognition and have not been used for gait recognition previously. The proposed approach showed competitive recognition performance, yielding 5.9% FMR at 6.3% FNMR in a mixedday scenario.


Attributed scattering centers for SAR ATR

High-frequency radar measurements of man-made targets are dominated by returns from isolated scattering centers, such as corners and flat plates. Characterizing the features of these scattering centers provides a parsimonious, physically relevant signal representation for use in automatic target recognition (ATR). In this paper, we present a framework for feature extraction predicated on parametric models for the radar returns. The models are motivated by the scattering behaviour predicted by the geometrical theory of diffraction. For each scattering center, statistically robust estimation of model parameters provides high-resolution attributes including location, geometry, and polarization response. We present statistical analysis of the scattering model to describe feature uncertainty, and we provide a least-squares algorithm for feature estimation. We survey existing algorithms for simplified models, and derive bounds for the error incurred in adopting the simplified models. A model order selection algorithm is given, and an M-ary generalized likelihood ratio test is given for classifying polarimetric responses in spherically invariant random clutter.


The role of prediction algorithms in the MavHome smart home architecture

The goal of the Managing an Adaptive Versatile Home (MavHome) project is to create a home that acts as a rational agent. The agent seeks to maximize inhabitant comfort and minimize operation cost. In order to achieve these goals, the agent must be able to predict the mobility patterns and device usages of the inhabitants. In this article, we introduce the MavHome project and its underlying architecture. The role of prediction algorithms within the architecture is discussed, and three prediction algorithms that are central to home operations are presented. We demonstrate the effectiveness of these algorithms on synthetic and/or actual smart home data. THE ROLE OF PREDICTION ALGORITHMS IN THE MAVHOME SMART HOME ARCHITECTURE


Surface Aesthetics in Tip Rhinoplasty: A Step-by-Step Guide.

Tip rhinoplasty is a key component of aesthetic rhinoplasty. An understanding of the correlation between tip surface aesthetics and the underlying anatomic structures enables proper identification and correction of tip abnormalities. Surface aesthetics of the attractive nose are created by certain lines, shadows, and highlights with specific proportions and breakpoints. In this Featured Operative Technique, the authors describe a stepwise process for tip rhinoplasty that conceptualizes aesthetic subunits as geometric polygons to define the existing deformity, the operative plan, and the aesthetic goals. Tip rhinoplasty is described in detail, from initial markings through incisions and dissection. The autorim graft concept is explained, and lateral crural steal and footplate setback techniques are described for the attainment of symmetric domes with correct lateral crural resting angles. Methods in columellar reconstruction are described, including creating the columella (C') breakpoint and the infralobular caudal contour graft. The principal author (B.Ç.) has applied these techniques to 257 consecutive "polygon rhinoplasties" over the past 3 years.


Multiuser MIMO-OFDM for Next-Generation Wireless Systems

This overview portrays the evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station's or radio port's coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment in multiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems.


Machine learning for attack vector identification in malicious source code

As computers and information technologies become ubiquitous throughout society, the security of our networks and information technologies is a growing concern. As a result, many researchers have become interested in the security domain. Among them, there is growing interest in observing hacker communities for early detection of developing security threats and trends. Research in this area has often reported hackers openly sharing cybercriminal assets and knowledge with one another. In particular, the sharing of raw malware source code files has been documented in past work. Unfortunately, malware code documentation appears often times to be missing, incomplete, or written in a language foreign to researchers. Thus, analysis of such source files embedded within hacker communities has been limited. Here we utilize a subset of popular machine learning methodologies for the automated analysis of malware source code files. Specifically, we explore genetic algorithms to resolve questions related to feature selection within the context of malware analysis. Next, we utilize two common classification algorithms to test selected features for identification of malware attack vectors. Results suggest promising direction in utilizing such techniques to help with the automated analysis of malware source code.


Emergency Response Information System Interoperability: Development of Chemical Incident Response Data Model

Special Issue


USER INTERFACE DESIGN OF VOICE CONTROLLED CONSUMER ELECTRONICS

Today speech recognition of a small vocabulary can be realized so costeffectively that the technology can penetrate into consumer electronics. But, as first applications that failed on the market show, it is by no means obvious how to incorporate voice control in a user interface. This paper addresses the issue of how to design a voice control so that the user perceives it as a benefit. User interface guidelines that are adapted or specific to voice control are presented. Then the process of designing a voice control in the user-centred approach is described. By means of two examples, the car stereo and the telephone answering machine, it is shown how this is turned into practice.


Reliability of Capacitors for DC-Link Applications in Power Electronic Converters—An Overview

DC-link capacitors are an important part in the majority of power electronic converters which contribute to cost, size and failure rate on a considerable scale. From capacitor users' viewpoint, this paper presents a review on the improvement of reliability of dc link in power electronic converters from two aspects: 1) reliability-oriented dc-link design solutions; 2) conditioning monitoring of dc-link capacitors during operation. Failure mechanisms, failure modes and lifetime models of capacitors suitable for the applications are also discussed as a basis to understand the physics-of-failure. This review serves to provide a clear picture of the state-of-the-art research in this area and to identify the corresponding challenges and future research directions for capacitors and their dc-link applications.


And Lead Us (Not) into Persuasion…? Persuasive Technology and the Ethics of Communication

The paper develops ethical guidelines for the development and usage of persuasive technologies (PT) that can be derived from applying discourse ethics to this type of technologies. The application of discourse ethics is of particular interest for PT, since 'persuasion' refers to an act of communication that might be interpreted as holding the middle between 'manipulation' and 'convincing'. One can distinguish two elements of discourse ethics that prove fruitful when applied to PT: the analysis of the inherent normativity of acts of communication ('speech acts') and the Habermasian distinction between 'communicative' and 'strategic rationality' and their broader societal interpretation. This essay investigates what consequences can be drawn if one applies these two elements of discourse ethics to PT.


Mapping Large Spatial Flow Data with Hierarchical Clustering

It is challenging to map large spatial flow data due to the problem of occlusion and cluttered display, where hundreds of thousands of flows overlap and intersect each other. Existing flow mapping approaches often aggregate flows using predetermined high-level geographic units (e.g. states) or bundling partial flow lines that are close in space, both of which cause a significant loss or distortion of information and may miss major patterns. In this research, we developed a flow clustering method that extracts clusters of similar flows to avoid the cluttering problem, reveal abstracted flow patterns, and meanwhile preserves data resolution as much as possible. Specifically, our method extends the traditional hierarchical clustering method to aggregate and map large flow data. The new method considers both origins and destinations in determining the similarity of two flows, which ensures that a flow cluster represents flows from similar origins to similar destinations and thus minimizes information loss during aggregation. With the spatial index and search algorithm, the new method is scalable to large flow data sets. As a hierarchical method, it generalizes flows to different hierarchical levels and has the potential to support multi-resolution flow mapping. Different distance definitions can be incorporated to adapt to uneven spatial distribution of flows and detect flow clusters of different densities. To assess the quality and fidelity of flow clusters and flow maps, we carry out a case study to analyze a data set of 243,850 taxi trips within an urban area.


GOTCHA password hackers!

We introduce GOTCHAs (Generating panOptic Turing Tests to Tell Computers and Humans Apart) as a way of preventing automated offline dictionary attacks against user selected passwords. A GOTCHA is a randomized puzzle generation protocol, which involves interaction between a computer and a human. Informally, a GOTCHA should satisfy two key properties: (1) The puzzles are easy for the human to solve. (2) The puzzles are hard for a computer to solve even if it has the random bits used by the computer to generate the final puzzle --- unlike a CAPTCHA [44]. Our main theorem demonstrates that GOTCHAs can be used to mitigate the threat of offline dictionary attacks against passwords by ensuring that a password cracker must receive constant feedback from a human being while mounting an attack. Finally, we provide a candidate construction of GOTCHAs based on Inkblot images. Our construction relies on the usability assumption that users can recognize the phrases that they originally used to describe each Inkblot image --- a much weaker usability assumption than previous password systems based on Inkblots which required users to recall their phrase exactly. We conduct a user study to evaluate the usability of our GOTCHA construction. We also generate a GOTCHA challenge where we encourage artificial intelligence and security researchers to try to crack several passwords protected with our scheme.


A Formal Model for a System's Attack Surface

Practical software security metrics and measurements are essential to the development of secure software [18]. In this paper, we propose to use a software system’s attack surface measurement as an indicator of the system’s security; the larger the attack surface, the more insecure the system. We formalize the notion of a system’s attack surface using an I/O automata model of the system [15] and define a quantitative measure of the attack surface in terms of three kinds of resources used in attacks on the system: methods, channels, and data. We demonstrate the feasibility of our approach by measuring the attack surfaces of two open source FTP daemons and two IMAP servers. Software developers can use our attack surface measurement method in the software development process and software consumers can use the method in their decision making process. This research was sponsored by the US Army Research Office (ARO) under contract no. DAAD190210389, SAP Labs, LLC under award no. 1010751, and the Software Engineering Institute. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the U.S. government or any other entity. Report Documentation Page Form Approved


Combining Big Data and Thick Data Analyses for Understanding Youth Learning Trajectories in a Summer Coding Camp

In this paper we explore how to assess novice youths' learning of programming in an open-ended, project-based learning environment. Our goal is to combine analysis of frequent, automated snapshots of programming (e.g., "big" data) within the "thick" social context of kids? learning for deeper insights into their programming trajectories. This paper focuses on the first stage of this endeavor: the development of exploratory quantitative measures of youths? learning of computer science concepts. Analyses focus on kids? learning in a series of three Scratch Camps where 64 campers aged 10-13 used Scratch 2.0 to make a series of creative projects over 30 hours in five days. In the discussion we consider the highlights of the insights-and blind spots-of each data source with regard to youths' learning.


Link Prediction in Bipartite Networks-Predicting Yelp Reviews

In this paper, we aim to predict new user reviews on businesses in the Yelp social network. We formulate this as a network link prediction problem by modeling Yelp dataset as a bipartite network between users and businesses. We implement link prediction algorithms with various proximity metrics, thoroughly evaluate the effectiveness of each algorithm and conclude that Delta, AdamicAdar and Common Neighbors algorithms perform the best in precision.


Construction and evaluation of a robust multifeature speech/music discriminator

We report on the construction of a real-time computer system capable of distinguishing speech signals from music signals over a wide range of digital audio input. We have examined 13 features intended to measure conceptually distinct properties of speech and/or music signals, and combined them in several multidimensional classification frameworks. We provide extensive data on system performance and the cross-validated training/test setup used to evaluate the system. For the datasets currently in use, the best classifier classifies with 5.8% error on a frame-by-frame basis, and 1.4% error when integrating long (2.4 second) segments of sound.


Globally Optimal Crowdsourcing Quality Management

We study crowdsourcing quality management, that is, given worker responses to a set of tasks, our goal is to jointly estimate the true answers for the tasks, as well as the quality of the workers. Prior work on this problem relies primarily on applying ExpectationMaximization (EM) on the underlying maximum likelihood problem to estimate true answers as well as worker quality. Unfortunately, EM only provides a locally optimal solution rather than a globally optimal one. Other solutions to the problem (that do not leverage EM) fail to provide global optimality guarantees as well. In this paper, we focus on filtering, where tasks require the evaluation of a yes/no predicate, and rating, where tasks elicit integer scores from a finite domain. We design algorithms for finding the global optimal estimates of correct task answers and worker quality for the underlying maximum likelihood problem, and characterize the complexity of these algorithms. Our algorithms conceptually consider all mappings from tasks to true answers (typically a very large number), leveraging two key ideas to reduce, by several orders of magnitude, the number of mappings under consideration, while preserving optimality. We also demonstrate that these algorithms often find more accurate estimates than EM-based algorithms. This paper makes an important contribution towards understanding the inherent complexity of globally optimal crowdsourcing quality management.


CRADLE: An Online Plan Recognition Algorithm for Exploratory Domains

In exploratory domains, agents’ behaviors include switching between activities, extraneous actions, and mistakes. Such settings are prevalent in real world applications such as interaction with open-ended software, collaborative office assistants, and integrated development environments. Despite the prevalence of such settings in the real world, there is scarce work in formalizing the connection between high-level goals and low-level behavior and inferring the former from the latter in these settings. We present a formal grammar for describing users’ activities in such domains. We describe a new top-down plan recognition algorithm called CRADLE (Cumulative Recognition of Activities and Decreasing Load of Explanations) that uses this grammar to recognize agents’ interactions in exploratory domains. We compare the performance of CRADLE with state-of-the-art plan recognition algorithms in several experimental settings consisting of real and simulated data. Our results show that CRADLE was able to output plans exponentially more quickly than the state-of-the-art without compromising its correctness, as determined by domain experts. Our approach can form the basis of future systems that use plan recognition to provide real-time support to users in a growing class of interesting and challenging domains.


On the Optimality of the Simple Bayesian Classifier under Zero-One Loss

The simple Bayesian classifier is known to be optimal when attributes are independent given the class, but the question of whether other sufficient conditions for its optimality exist has so far not been explored. Empirical results showing that it performs surprisingly well in many domains containing clear attribute dependences suggest that the answer to this question may be positive. This article shows that, although the Bayesian classifier's probability estimates are only optimal under quadratic loss if the independence assumption holds, the classifier itself can be optimal under zero-one loss (misclassification rate) even when this assumption is violated by a wide margin. The region of quadratic-loss optimality of the Bayesian classifier is in fact a second-order infinitesimal fraction of the region of zero-one optimality. This implies that the Bayesian classifier has a much greater range of applicability than previously thought. For example, in this article it is shown to be optimal for learning conjunctions and disjunctions, even though they violate the independence assumption. Further, studies in artificial domains show that it will often outperform more powerful classifiers for common training set sizes and numbers of attributes, even if its bias is a priori much less appropriate to the domain. This article's results also imply that detecting attribute dependence is not necessarily the best way to extend the Bayesian classifier, and this is also verified empirically.


Learning Bayesian Networks is NP-Complete

Algorithms for learning Bayesian networks from data have two components: a scoring metric and a search procedure. The scoring metric computes a score reeecting the goodness-of-t of the structure to the data. The search procedure tries to identify network structures with high scores. Heckerman et al. (1995) introduce a Bayesian metric, called the BDe metric, that computes the relative posterior probability of a network structure given data. In this paper, we show that the search problem of identifying a Bayesian network|among those where each node has at most K parents|that has a relative posterior probability greater than a given constant is NP-complete, when the BDe metric is used. 12.1 Introduction Recently, many researchers have begun to investigate methods for learning Bayesian networks. Many of these approaches have the same basic components: a scoring metric and a search procedure. The scoring metric takes a database of observed cases D and a network structure B S , and returns a score reeecting the goodness-of-t of the data to the structure. A search procedure generates networks for evaluation by the scoring metric. These approaches use the two components to identify a network structure or set of structures that can be used to predict future events or infer causal relationships. Cooper and Herskovits (1992)|herein referred to as CH|derive a Bayesian metric, which we call the BD metric, from a set of reasonable assumptions about learning Bayesian networks containing only discrete variables. Heckerman et al. (1995)|herein referred to as HGC|expand upon the work of CH to derive a new metric, which we call the BDe metric, which has the desirable property of likelihood equivalence. Likelihood equivalence says that the data cannot help to discriminate equivalent structures. We now present the BD metric derived by CH. We use B h S to denote the hypothesis that B S is an I-map of the distribution that generated the database. 2 Given a belief-network structure B S , we use i to denote the parents of x i. We use r i to denote the number of states of variable x i , and q i = Q x l 2 i r l to denote the number of instances of i. We use the integer j to index these instances. That is, we write i = j to denote the observation of the jth instance of the parents of x i. 1996 Springer-Verlag. 2 There is an …


Probabilistic reasoning in intelligent systems - networks of plausible inference

Probabilistic methods to create the areas, of computational tools. But I needed to get canned, bayesian networks worked recently strongly. Recently I tossed this book was published. In intelligent systems is researchers in, ai operations research excellence award for graduate. Too concerned about how it i've been. Apparently daphne koller and learning structures evidential reasoning. Pearl is a language for i've. Despite its early publication date it, is not great give the best references.


Expert Systems and Probabilistic Network Models

Follow up what we will offer in this article about expert systems and probabilistic network models. You know really that this book is coming as the best seller book today. So, when you are really a good reader or you're fans of the author, it does will be funny if you don't have this book. It means that you have to get this book. For you who are starting to learn about something new and feel curious about this book, it's easy then. Just get this book and feel how this book will give you more exciting lessons.


Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches

The naïve Bayes classifier is built on the assumpti on of conditional independence between the attributes given the class. The algorithm has been shown to be surprisingly robust to obvious violations of this condition, but it is natural to ask if it is possib le to further improve the accuracy by relaxing this assumption. We examine an approach where naïve Bayes is augmented by the addition of correlation a rcs between attributes. We explore two methods for finding the set of augmenting arcs, a greedy hillclimbing search, and a novel, more computationally efficient algorithm that we call SuperParent. We compare these methods to TAN; a state-of the-art distribution-based approach to finding the augmenti ng arcs.


A novel three-phase utility interface minimizing line current harmonics of high-power telecommunications rectifier modules

Based on the combination of a three-phase diode bridge and a dc/dc boost converter, a new three-phase threeswitch three-level pulsewidth modulated (PWM) rectifier system is developed. It can be characterized by sinusoidal mains current consumption, controlled output voltage, and low-blocking voltage stress on the power transistors. The application could be, e.g., for feeding the dc link of a telecommunications power supply module. The stationary operational behavior, the control of the mains currents, and the control of the output voltage are analyzed. Finally, the stresses on the system components are determined by digital simulation and compared to the stresses in a conventional six-switch two-level PWM rectifier system.


Event Detection in Crowded Videos

Real-world actions occur often in crowded, dynamic environments. This poses a difficult challenge for current approaches to video event detection because it is difficult to segment the actor from the background due to distracting motion from other objects in the scene. We propose a technique for event recognition in crowded videos that reliably identifies actions in the presence of partial occlusion and background clutter. Our approach is based on three key ideas: (1) we efficiently match the volumetric representation of an event against oversegmented spatio-temporal video volumes; (2) we augment our shape-based features using flow; (3) rather than treating an event template as an atomic entity, we separately match by parts (both in space and time), enabling robustness against occlusions and actor variability. Our experiments on human actions, such as picking up a dropped object or waving in a crowd show reliable detection with few false positives.


Accelerated singular value thresholding for matrix completion

Recovering a large matrix from a small subset of its entries is a challenging problem arising in many real world applications, such as recommender system and image in-painting. These problems can be formulated as a general matrix completion problem. The Singular Value Thresholding (SVT) algorithm is a simple and efficient first-order matrix completion method to recover the missing values when the original data matrix is of low rank. SVT has been applied successfully in many applications. However, SVT is computationally expensive when the size of the data matrix is large, which significantly limits its applicability. In this paper, we propose an Accelerated Singular Value Thresholding (ASVT) algorithm which improves the convergence rate from O(1/N) for SVT to O(1/N2), where N is the number of iterations during optimization. Specifically, the dual problem of the nuclear norm minimization problem is derived and an adaptive line search scheme is introduced to solve this dual problem. Consequently, the optimal solution of the primary problem can be readily obtained from that of the dual problem. We have conducted a series of experiments on a synthetic dataset, a distance matrix dataset and a large movie rating dataset. The experimental results have demonstrated the efficiency and effectiveness of the proposed algorithm.


Bot Classification for Real-Life Highly Class-Imbalanced Dataset

Botnets are networks formed with a number of machines infected by malware called bots. Detection of these malicious networks is becoming a major concern as they pose a serious threat to the network security. Most of the research on bot detection is based on particular botnet characteristics which fail to detect other types of botnets and bots. Furthermore, there are very few bot detection methods that considered real-life class-imbalanced dataset. A dataset is class-imbalanced if there are significantly more instances in one class than the other classes. In this paper, we develop three generic features to detect different types of bots regardless of their botnet characteristics. We develop five classification models based on those features to classify bots from a large, real-life, class-imbalanced network dataset. Results show that our methodology can detect bots more accurately than the existing methods. Experimental results also demonstrate that the developed methodology can successfully detect bots when the proportion of bots to normal activity is very small. We also provide a performance comparison of our methodology with a recent study on bot detection in a real-life, large, imbalanced dataset.


A 1.8-GHz CMOS Power Amplifier Using a Dual-Primary Transformer With Improved Efficiency in the Low Power Region

A 1.8-GHz CMOS power amplifier for a polar transmitter is implemented with a 0.18- RF CMOS process. The matching components, including the input and output transformers, were integrated. A dual-primary transformer is proposed in order to increase the efficiency in the low power region of the amplifier. The loss induced by the matching network for the low-output power region is minimized using the dual-primary transformer. The amplifier achieved a power-added efficiency of 40.7% at a maximum output power of 31.6 dBm. The dynamic range was 34 dB for a supply voltage that ranged from 0.5 to 3.3 V. The low power efficiency was 32% at the output power of 16 dBm.