Measuring Computer Performance: A Practitioner's Guide

A practitioner's guide Measuring computer performance sets out the fundamental techniques used in analyzing and understanding the performance of computer systems. Throughout the book, the emphasis is on practical methods of measurement, simulation and analytical modeling. The author discusses performance metrics and provides detailed coverage of the strategies used in benchmark programs. He gives intuitive explanations of the key statistical tools needed to interpret measured performance data. He also describes the generaìdesign of experiments' technique, and shows how the maximum amount of information can be obtained for the minimum effort. The book closes with a chapter on the technique of queueing analysis. Appendices listing common probability distributions and statistical tables are included, along with a glossary of important technical terms. This practically oriented book will be of great interest to anyone who wants a detailed, yet intuitive, understanding of computer systems performance analysis.


Two hearts in three-quarter time : How to waltz the social media / viral marketing dance

The concept of viral marketing has been discussed in the literature for over 15 years, since Jeffrey Rayport first introduced the term in 1996. However, the more widespread use of social media has recently pushed this idea to a whole new level. We provide insight into the relationship between social media and viral marketing, and illustrate the six steps executives should take in order to dance the social media/viral marketing waltz. We define viral marketing as electronic word-of-mouth whereby some form of marketing message related to a company, brand, or product is transmitted in an exponentially growing wayâ€”often through the use of social media applications. We consider the three conditions that need to be fulfilled to create a viral marketing epidemic (i.e., giving the right message to the right messengers in the right environment) and present four different groups of social media viral marketing campaigns (nightmares, strokes-of-luck, homemade issues, and triumphs). We conclude Purchase Export Previous article Next article Check if you have access through your login credentials or your institution.


A 5MHz, 24V-to-1.2V, AO2T current mode buck converter with one-cycle transient response and sensorless current detection for medical meters

With a stringent input-to-output conversion ratio (CR) of 20 (24V input and 1.2V output) for a DC-DC converter, two stage cascaded architectures are easy to implement, but suffer from poor efficiency and doubled number of power components. This paper presents a single-stage solution with a proposed Adaptive ON-OFF Time (AO2T) control. In steady state, the control works as an adaptive ON-time valley current mode control to accommodate the large CR. During load transient periods, both ON- and OFF-time are adaptively adjustable to instantaneous load change, in order to accomplish fast load transient response within one switching cycle. To facilitate the high speed current mode control, a sensorless current detection circuit is also proposed. Operating at 5MHz, the converter achieves a maximum efficiency of 89.8% at 700mA and an efficiency of 85% at 2A full load. During a load current slew rate of 1.8A/200ns, the undershoot/overshoot voltages at VO are 23mV and 37mV respectively.


Metamaterial-Substrate Antenna Array for MIMO Communication System

We demonstrate how a magnetic permeability enhanced metamaterial can enhance the antenna array of a multiple-input multiple-output(MIMO) communication system. The performance of a rectangular patch antenna array on a metamaterial substrate was studied relative to a similar array constructed on a conventional FR4 substrate. Differently spaced arrays were analytically compared using array correlation coefficients and mean effective gain as performance metrics. Achievable channel capacity were obtained through channel measurements made on a MIMO testbed. While results show that arrays on conventional FR4 substrates have higher capacity due to gain and efficiency factors, arrays can be made smaller, and have less mutual coupling and correlation coefficients, when using a metamaterial substrate, but the antenna built on the metamaterial substrate can be made more efficient through the use of better host materials. This was reflected in the analysis of both antenna arrays normalized to remove efficiency and gain differences where they showed similar performances. Hence, metamaterial substrates are a cost-effective solution when antenna miniaturization is a key design criteria compared to conventional substrates that achieve the same miniaturization factor without significantly sacrificing performance.


The Simpler The Better: A Unified Approach to Predicting Original Taxi Demands based on Large-Scale Online Platforms

Taxi-calling apps are gaining increasing popularity for their efficiency in dispatching idle taxis to passengers in need. To precisely balance the supply and the demand of taxis, online taxicab platforms need to predict the Unit Original Taxi Demand (UOTD), which refers to the number of taxi-calling requirements submitted per unit time (e.g., every hour) and per unit region (e.g., each POI). Predicting UOTD is non-trivial for large-scale industrial online taxicab platforms because both accuracy and flexibility are essential. Complex non-linear models such as GBRT and deep learning are generally accurate, yet require labor-intensive model redesign after scenario changes (e.g., extra constraints due to new regulations). To accurately predict UOTD while remaining flexible to scenario changes, we propose LinUOTD, a unified linear regression model with more than 200 million dimensions of features. The simple model structure eliminates the need of repeated model redesign, while the high-dimensional features contribute to accurate UOTD prediction. We further design a series of optimization techniques for efficient model training and updating. Evaluations on two large-scale datasets from an industrial online taxicab platform verify that LinUOTD outperforms popular non-linear models in accuracy. We envision our experiences to adopt simple linear models with high-dimensional features in UOTD prediction as a pilot study and can shed insights upon other industrial large-scale spatio-temporal prediction problems.


KeLP: a Kernel-based Learning Platform for Natural Language Processing

Kernel-based learning algorithms have been shown to achieve state-of-the-art results in many Natural Language Processing (NLP) tasks. We present KELP, a Java framework that supports the implementation of both kernel-based learning algorithms and kernel functions over generic data representation, e.g. vectorial data or discrete structures. The framework has been designed to decouple kernel functions and learning algorithms: once a new kernel function has been implemented it can be adopted in all the available kernelmachine algorithms. The platform includes different Online and Batch Learning algorithms for Classification, Regression and Clustering, as well as several Kernel functions, ranging from vector-based to structural kernels. This paper will show the main aspects of the framework by applying it to different NLP tasks.


Linear Machine Decision Trees

This article presents an algorithm for inducing multiclass decision trees with multivariate tests at internal decision nodes. Each test is constructed by training a linear machine and eliminating variables in a controlled manner. Empirical results demonstrate that the algorithm builds small accurate trees across a variety of tasks.


Biological network analysis and comparison: mining new biological knowledge

The mechanisms underlying life machinery are still not completely understood. Something is known, something is “probably” known, other things are still unknown. Scientists all over the world are working very hard to clarify the processes regulating the cell life cycle and bioinformaticians try to support them by developing specialized automated tools. Within the plethora of applications devoted to the study of life mechanisms, tools for the analysis and comparison of biological networks are catching the attention of many researchers. It is interesting to investigate why.


The Function of Fiction is the Abstraction and Simulation of Social Experience.

Fiction literature has largely been ignored by psychology researchers because its only function seems to be entertainment, with no connection to empirical validity. We argue that literary narratives have a more important purpose. They offer models or simulations of the social world via abstraction, simplification, and compression. Narrative fiction also creates a deep and immersive simulative experience of social interactions for readers. This simulation facilitates the communication and understanding of social information and makes it more compelling, achieving a form of learning through experience. Engaging in the simulative experiences of fiction literature can facilitate the understanding of others who are different from ourselves and can augment our capacity for empathy and social inference.


Machine Learning to Balance the Load in Parallel Branch-and-Bound

We describe in this paper a new approach to parallelize branchand-bound on a certain number of processors. We propose to split the optimization of the original problem into the optimization of several subproblems that can be optimized separately with the goal that the amount of work that each processor carries out is balanced between the processors, while achieving interesting speedups. The main innovation of our approach consists in the use of machine learning to create a function able to estimate the difficulty (number of nodes) of a subproblem of the original problem. We also present a set of features that we developed in order to characterize the encountered subproblems. These features are used as input of the function learned with machine learning in order to estimate the difficulty of a subproblem. The estimates of the numbers of nodes are then used to decide how to partition the original optimization tree into a given number of subproblems, and to decide how to distribute them among the available processors. The experiments that we carry out show that our approach succeeds in balancing the amount of work between the processors, and that interesting speedups can be achieved with little effort.


Cervical radiculopathy.

Cervical radiculopathy is the result of irritation and/or compression of nerve root as it exits the cervical spine. Pain is a common presenting symptom and may be accompanied by motor or sensory deficits in areas innervated by the affected nerve root. Diagnosis is suggested by history and corresponding physical examination findings. Confirmation is achieved with MRI. A multimodal approach to treatment helps patients improve. Medications may be used to alleviate symptoms and manage pain. Physical therapy and manipulation may improve neck discomfort. Guided corticosteroid injections and selected nerve blocks may help control nerve root pain. Most patients improve with a conservative, nonoperative treatment course.


Flower species recognition system using convolution neural networks and transfer learning

Automatic identification and recognition of medicinal plant species in environments such as forests, mountains and dense regions is necessary to know about their existence. In recent years, plant species recognition is carried out based on the shape, geometry and texture of various plant parts such as leaves, stem, flowers etc. Flower based plant species identification systems are widely used. While modern search engines provide methods to visually search for a query image that contains a flower, it lacks in robustness because of the intra-class variation among millions of flower species around the world. Hence in this proposed research work, a Deep learning approach using Convolutional Neural Networks (CNN) is used to recognize flower species with high accuracy. Images of the plant species are acquired using the built-in camera module of a mobile phone. Feature extraction of flower images is performed using a Transfer Learning approach (i.e. extraction of complex features from a pre-trained network). A machine learning classifier such as Logistic Regression or Random Forest is used on top of it to yield a higher accuracy rate. This approach helps in minimizing the hardware requirement needed to perform the computationally intensive task of training a CNN. It is observed that, CNN combined with Transfer Learning approach as feature extractor outperforms all the handcrafted feature extraction methods such as Local Binary Pattern (LBP), Color Channel Statistics, Color Histograms, Haralick Texture, Hu Moments and Zernike Moments. CNN combined with Transfer Learning approach yields impressive Rank-1 accuracies of 73.05%, 93.41% and 90.60% using OverFeat, Inception-v3 and Xception architectures, respectively as Feature Extractors on FLOWERS102 dataset.


Deep learning of binary hash codes for fast image retrieval

Approximate nearest neighbor search is an efficient strategy for large-scale image retrieval. Encouraged by the recent advances in convolutional neural networks (CNNs), we propose an effective deep learning framework to generate binary hash codes for fast image retrieval. Our idea is that when the data labels are available, binary codes can be learned by employing a hidden layer for representing the latent concepts that dominate the class labels. The utilization of the CNN also allows for learning image representations. Unlike other supervised methods that require pair-wised inputs for binary code learning, our method learns hash codes and image representations in a point-wised manner, making it suitable for large-scale datasets. Experimental results show that our method outperforms several state-of-the-art hashing algorithms on the CIFAR-10 and MNIST datasets. We further demonstrate its scalability and efficacy on a large-scale dataset of 1 million clothing images.


Describing objects by their attributes

We propose to shift the goal of recognition from naming to describing. Doing so allows us not only to name familiar objects, but also: to report unusual aspects of a familiar object (“spotty dog”, not just “dog”); to say something about unfamiliar objects (“hairy and four-legged”, not just “unknown”); and to learn how to recognize new objects with few or no visual examples. Rather than focusing on identity assignment, we make inferring attributes the core problem of recognition. These attributes can be semantic (“spotty”) or discriminative (“dogs have it but sheep do not”). Learning attributes presents a major new challenge: generalization across object categories, not just across instances within a category. In this paper, we also introduce a novel feature selection method for learning attributes that generalize well across categories. We support our claims by thorough evaluation that provides insights into the limitations of the standard recognition paradigm of naming and demonstrates the new abilities provided by our attribute-based framework.


Style Finder : Fine-Grained Clothing Style Recognition and Retrieval

With the rapid proliferation of smartphones and tablet computers, search has moved beyond text to other modalities like images and voice. For many applications like Fashion, visual search offers a compelling interface that can capture stylistic visual elements beyond color and pattern that cannot be as easily described using text. However, extracting and matching such attributes remains an extremely challenging task due to high variability and deformability of clothing items. In this paper, we propose a fine-grained learning model and multimedia retrieval framework to address this problem. First, an attribute vocabulary is constructed using human annotations obtained on a novel finegrained clothing dataset. This vocabulary is then used to train a fine-grained visual recognition system for clothing styles. We report benchmark recognition and retrieval results on Women’s Fashion Coat Dataset and illustrate potential mobile applications for attribute-based multimedia retrieval of clothing items and image annotation.


Efficient Object Category Recognition Using Classemes

We introduce a new descriptor for images which allows the construction of efficient and compact classifiers with good accuracy on object category recognition. The descriptor is the output of a large number of weakly trained object category classifiers on the image. The trained categories are selected from an ontology of visual concepts, but the intention is not to encode an explicit decomposition of the scene. Rather, we accept that existing object category classifiers often encode not the category per se but ancillary image characteristics; and that these ancillary characteristics can combine to represent visual classes unrelated to the constituent categories’ semantic meanings. The advantage of this descriptor is that it allows object-category queries to be made against image databases using efficient classifiers (efficient at test time) such as linear support vector machines, and allows these queries to be for novel categories. Even when the representation is reduced to 200 bytes per image, classification accuracy on object category recognition is comparable with the state of the art (36% versus 42%), but at orders of magnitude lower computational cost.


Low-Profile Dual-Wideband Inverted-T Open Slot Antenna for the LTE/WWAN Tablet Computer With a Metallic Frame

An inverted-T open slot (ITOS) antenna with a low profile suitable for the LTE/WWAN tablet computer with a metallic frame is presented. With a low profile of 7 mm above the top edge of the device ground plane of the tablet computer, the ITOS antenna can provide two wide operating bands of 698-960 and 1710-2690 MHz to cover the LTE/WWAN operation. The ITOS antenna shows a simple structure and can fit in the narrow region between the metallic frame and the display panel of the tablet computer. The lower and higher operating bands of the antenna can be, respectively, controlled by the low-band and high-band feeds thereof. The low-band feed excites the antenna's longer slot arm to obtain a wide lower band covering the 698-960 MHz band. The high-band feed excites the antenna's shorter slot arm to achieve a wide higher band covering the 1710-2690 MHz band. Working principle of the low-profile ITOS antenna for generating the dual-wideband operation is described. Simulated and experimental results of the antenna are presented and discussed.


Locational marginal pricing basics for restructured wholesale power markets

Although Locational Marginal Pricing (LMP) plays an important role in many restructured wholesale power markets, the detailed derivation of LMPs as actually used in industry practice is not readily available. This lack of transparency greatly hinders the efforts of researchers to evaluate the performance of these markets. In this paper, different AC and DC optimal power flow (OPF) models are presented to help understand the derivation of LMPs. As a byproduct of this analysis, the paper provides a rigorous explanation of the basic LMP and LMP-decomposition formulas (neglecting real power losses) presented without derivation in the business practice manuals of the U.S. Midwest Independent System Operator (MISO).


On the O(1=k) convergence of asynchronous distributed alternating Direction Method of Multipliers

We consider a network of agents that are cooperatively solving a global optimization problem, where the objective function is the sum of privately known local objective functions of the agents and the decision variables are coupled via linear constraints. Recent literature focused on special cases of this formulation and studied their distributed solution through either subgradient based methods with O(1/√k) rate of convergence (where k is the iteration number) or Alternating Direction Method of Multipliers (ADMM) based methods, which require a synchronous implementation and a globally known order on the agents. In this paper, we present a novel asynchronous ADMM based distributed method for the general formulation and show that it converges at the rate O (1=k).


Joint Optimization of Resource Provisioning in Cloud Computing

Cloud computing exploits virtualization to provision resources efficiently. Increasingly, Virtual Machines (VMs) have high bandwidth requirements; however, previous research does not fully address the challenge of both VM and bandwidth provisioning. To efficiently provision resources, a joint approach that combines VMs and bandwidth allocation is required. Furthermore, in practice, demand is uncertain. Service providers allow the reservation of resources. However, due to the dangers of over- and under-provisioning, we employ stochastic programming to account for this risk. To improve the efficiency of the stochastic optimization, we reduce the problem space with a scenario tree reduction algorithm, that significantly increases tractability, whilst remaining a good heuristic. Further we perform a sensitivity analysis that finds the tolerance of our solution to parameter changes. Based on historical demand data, we use a deterministic equivalent formulation to find that our solution is optimal and responds well to changes in parameter values. We also show that sensitivity analysis of prices can be useful for both users and providers in maximizing cost efficiency.


Psychophysiology of False Memories in a Deese-Roediger-McDermott Paradigm with Visual Scenes

Remembering something that has not in fact been experienced is commonly referred to as false memory. The Deese-Roediger-McDermott (DRM) paradigm is a well-elaborated approach to this phenomenon. This study attempts to investigate the peripheral physiology of false memories induced in a visual DRM paradigm. The main research question is whether false recognition is different from true recognition in terms of accompanying physiological responses.Sixty subjects participated in the experiment, which included a study phase with visual scenes each showing a group of interrelated items in social contexts. Subjects were divided into an experimental group undergoing a classical DRM design and a control group without DRM manipulation. The control group was implemented in order to statistically control for possible biases produced by memorability differences between stimulus types. After a short retention interval, a pictorial recognition phase was conducted in the manner of a Concealed Information Test. Simultaneous recordings of electrodermal activity, respiration line length, phasic heart rate, and finger pulse waveform length were used. Results yielded a significant Group by Item Type interaction, showing that true recognition is accompanied by greater electrodermal activity than false recognition.Results are discussed in the light of Sokolov's Orienting Reflex, the Preliminary Process Theory and the Concealed Information Test. Implications and restrictions of the introduced design features are critically discussed. This study demonstrates the applicability of measures of peripheral physiology to the field of false memory research.


Making business sense of the Internet.

For managers in large, well-established businesses, the Internet is a tough nut to crack. It is very simple to set up a Web presence and very difficult to create a Web-based business model. Established businesses that over decades have carefully built brands and physical distribution relationships risk damaging all they have created when they pursue commerce through the Net. Still, managers can't avoid the impact of electronic commerce on their businesses. They need to understand the opportunities available to them and recognize how their companies may be vulnerable if rivals seize those opportunities first. Broadly speaking, the Internet presents four distinct types of opportunities. First, it links companies directly to customers, suppliers, and other interested parties. Second, it lets companies bypass other players in an industry's value chain. Third, it is a tool for developing and delivering new products and services to new customers. Fourth, it will enable certain companies to dominate the electronic channel of an entire industry or segment, control access to customers, and set business rules. As he elaborates on these four points, the author gives established companies a systematic way to sort through the risks and rewards of doing business in cyberspace.


Events can be defined as "something that occurs at specific place and time associated with some specific actions". In general, events extracted from news articles and social networks are used to map the information from web to the various phenomena that occur in our physical world. One of the main steps to perform this relationship is the use of machine learning algorithms for event classification, which has received great attention in the web document engineering field in recent years. Traditional machine learning algorithms are based on vector space model representations and supervised classification. However, events are composed of multiple representations such as textual data, temporal information, geographic location and other types of metadata. All these representations are poorly represented together in a vector space model. Moreover, supervised classification requires the labeling of a significant sample of events to construct a training set for learning process, thereby hampering the practical application of event classification. In this paper, we propose a method called TECHN (Transductive Event Classification through Heterogeneous Networks), which considers event metadata as different objects in an heterogeneous network. Besides, the TECHN method has the ability to automatically learn which types of network objects (event metadata) are most efficient in the classification task. In addition, our TECHN method is based on a transductive classification that considers both labeled events and a vast amount of unlabeled events. The experimental results show that TECHN method obtains promising results, especially when we consider different weights of importance for each type of event metadata and a small set of labeled events.


Executive Functions Predict the Success of Top-Soccer Players

While the importance of physical abilities and motor coordination is non-contested in sport, more focus has recently been turned toward cognitive processes important for different sports. However, this line of studies has often investigated sport-specific cognitive traits, while few studies have focused on general cognitive traits. We explored if measures of general executive functions can predict the success of a soccer player. The present study used standardized neuropsychological assessment tools assessing players' general executive functions including on-line multi-processing such as creativity, response inhibition, and cognitive flexibility. In a first cross-sectional part of the study we compared the results between High Division players (HD), Lower Division players (LD) and a standardized norm group. The result shows that both HD and LD players had significantly better measures of executive functions in comparison to the norm group for both men and women. Moreover, the HD players outperformed the LD players in these tests. In the second prospective part of the study, a partial correlation test showed a significant correlation between the result from the executive test and the numbers of goals and assists the players had scored two seasons later. The results from this study strongly suggest that results in cognitive function tests predict the success of ball sport players.


Dataset Augmentation in Feature Space

Dataset augmentation, the practice of applying a wide array of domain-specific transformations to synthetically expand a training set, is a standard tool in supervised learning. While effective in tasks such as visual recognition, the set of transformations must be carefully designed, implemented, and tested for every new domain, limiting its re-use and generality. In this paper, we adopt a simpler, domain-agnostic approach to dataset augmentation. We start with existing data points and apply simple transformations such as adding noise, interpolating, or extrapolating between them. Our main insight is to perform the transformation not in input space, but in a learned feature space. A re-kindling of interest in unsupervised representation learning makes this technique timely and more effective. It is a simple proposal, but to-date one that has not been tested empirically. Working in the space of context vectors generated by sequence-to-sequence models, we demonstrate a technique that is effective for both static and sequential data.


Guidelines for Financial Forecasting with Neural Networks

Neural networks are good at classification, forecasting and recognition. They are also good candidates of financial forecasting tools. Forecasting is often used in the decision making process. Neural network training is an art. Trading based on neural network outputs, or trading strategy is also an art. We will discuss a seven-step neural network forecasting model building approach in this article. Pre and post data processing/analysis skills, data sampling, training criteria and model recommendation will also be covered in this article. 1. Forecasting with Neural Networks Forecasting is a process that produces a set of outputs by given a set of variables. The variables are normally historical data. Basically, forecasting assumes that future occurrences are based, at least in part, on presently observable or past events. It assumes that some aspects of the past patterns will continue into the future. Past relationships can then be discovered through study and observation. The basic idea of forecasting is to find an approximation of mapping between the input and output data in order to discover the implicit rules governing the observed movements. For instance, the forecasting of stock prices can be described in this way. Assume that i u represents today's price, i v represents the price after ten days. If the prediction of a stock price after ten days could be obtained using today's stock price, then there should be a functional mapping i u to i v , where ) ( i i i u v Γ = . Using all ( i u , i v ) pairs of historical data, a general function () Γ which consists of () i Γ could be obtained, that is ) (u v Γ = . More generally, u which consists of more information in today's price could be used in function () Γ . As NNs are universal approximators, we can find a NN simulating this () Γ function. The trained network is then used to predict the movements for the future. NN based financial forecasting has been explored for about a decade. Many research papers are published on various international journals and conferences proceedings. Some companies and institutions are also claiming or marketing the so called advanced forecasting tools or models. Some research results of financial forecasting found in references. For instance, stock trading system[4], stock forecasting [6, 22], foreign exchange rates forecasting [15, 24], option prices [25], advertising and sales volumes [13]. However, Callen et al. [3] claim that NN models are not necessarily superior to linear time series models even when the data are financial, seasonal and nonlinear. 2. Towards a Better Robust Financial Forecasting Model In working towards a more robust financial forecasting model, the following issues are worth examining. First, instead of emphasizing on the forecasting accuracy only, other financial criteria should be considered. Current researchers tend to use goodness of fit or similar criteria to judge or train their models in financial domain. In terms of mathematical calculation this approach is a correct way in theory. As we understand that a perfect forecasting is impossible in reality. No model can achieve such an ideal goal. Under this constraint, seeking a perfect forecasting is not our aim. We can only try to optimize our imperfect forecasts and use other yardsticks to give the most realistic measure. Second, there should be adequate organization and processing of forecasting data. Preprocessing and proper sampling of input data can have impact on the forecasting performance. Choice of indicators as inputs through sensitivity analysis could help to eliminate redundant inputs. Furthermore, NN forecasting results should be used wisely and effectively. For example, as the forecast is not perfect, should we compare the NN output with the previous forecast or with the real data especially when price levels are used as the forecasting targets? Third, a trading system should be used to decide on the best tool to use. NN is not the single tool that can be used for financial forecasting. We also cannot claim that it is the best forecasting tool. In fact, people are still not aware of which kind of time series is the most suitable for NN applications. To conduct post forecasting analysis will allow us to find out the suitability of models and series. We may then conclude that a certain kind of models should be used for a certain kind of time series. Training or building NN models is a trial and error procedure. Some researchers are not willing to test more on their data set [14]. If there is a system that can help us to formalize these tedious exploratory procedures, it will certainly be of great value to financial forecasting. Instead of just presenting one successful experiment, possibility or confidence level can be applied to the outputs. Data are partitioned into several sets to find out the particular knowledge of this time series. As stated by David Wolpert and William Macready about their No-Free-Lunch theorems [28], averaged over all problems, all search algorithms perform equally. Just experimenting on a single data set, a NN model which outperforms other models can be found. However, for another data set one model which outperforms NN model can also be found according to No-Free-Lunch theorems. To avoid such a case of one model outperforming others, we partition the data set into several sub data sets. The recommended NN models are those that outperform other models for all sub time horizons. In other words, only those models incorporated with enough local knowledge can be used for future forecasting. It is very important and necessary to emphasize these three issues here. Different criteria exist for the academics and the industry. In academics, sometime people seek for the accuracy towards 100%. While in industry a guaranteed 60% accuracy is typically aimed for. In addition, profit is the eventual goal of practitioners, so a profit oriented forecasting model may fit their needs. Cohen [5] surveyed 150 papers in the proceedings of the 8th National Conference on artificial intelligence. He discovered that only 42% of the papers reported that a program had run on more than one example; just 30% demonstrated performance in some way; a mere 21% framed hypotheses or made predictions. He then concluded that the methodologies used were incomplete with respect to the goals of designing and analyzing AI system. Tichy [20] showed that in a very large study of over 400 research articles in computer science. Over 40% of the articles are about new designs and the models completely lack experimental data. In a recent IEEE computer journal, he also points out 16 excuses to avoid experimentation for computer scientists [21]. What he is talking is true and not a joke. Prechelt [14] showed that the situation is not better in the NN literature. Out of 190 papers published in wellknown journals dedicated to NNs, 29% did not employ even a single realistic or real learning problem. Only 8% of the articles presented results for more than one problem using real world data. To build a NN forecasting we need sufficient experiments. To test only for one market or just for one particular time period means nothing. It will not lead to a robust model based on manually, trial-and-error, or ad hoc experiments. More robust model is needed but not only in one market or for one time period. Because of the lack of industrial models and because failures in academic research are not published, a single person or even a group of researchers will not gain enough information or experiences to build up a good forecasting model. It is obvious that an automated system dealing with NN models building is necessary. 3. Steps of NN Forecasting: The Art of NN Training As NN training is an art, many searchers and practitioners have worked in the field to work towards successful prediction and classification. For instance, William Remus and Marcus O'connor [16] suggest some principles for NN prediction and classification are of critical importance in the chapter, “Principles of Forecasting” in “A Handbook for Researchers and Practitioners”: • Clean the data prior to estimating the NN model. • Scale and deseasonalize data prior to estimating the model. • Use appropriate methods to choose the right starting point. • Use specialized methods to avoid local optima. • Expand the network until there is no significant improvement in fit. • Use pruning techniques when estimating NNs and use holdout samples when evaluating NNs. • Take care to obtain software that has in-built features to avoid NN disadvantages. • Build plausible NNs to gain model acceptance by reducing their size. • Use more approaches to ensure that the NN model is valid. With the authors' experience and sharing from other researchers and practitioners, we propose a seven-step approach for NN financial forecasting model building. The seven steps are basic components of the automated system and normally involved in the manual approach. Each step deals with an important issue. They are data preprocessing, input and output selection, sensitive analysis, data organization, model construction, post analysis and model recommendation. Step 1. Data Preprocessing A general format of data is prepared. Depending on the requirement, longer term data, e.g. weekly, monthly data may also be calculated from more frequently sampled time series. We may think that it makes sense to use as frequent data sampling as possible for experiments. However, researchers have found that increasing observation frequency does not always help to improve the accuracy of forecasting [28]. Inspection of data to find outliers is also important as outliers make it difficult for NNs and other forecasting models to model the true underlying functional. Although NNs have been shown to be universal approximators, it had been found that NNs had difficulty modeling seasonal patterns in time series [11].When a time series contains significant seasonality, the data


Backing Rich Credentials with a Blockchain PKI ∗

This is the second of a series of papers describing the results of a project whose goal was to identify five remote identity proofing solutions that can be used as alternatives to knowledge-based verification. This paper describes the second solution, which makes use of a rich credential adapted for use on a blockchain and backed by a blockchain PKI. A rich credential, also used in Solution 1, allows the subject to identify him/herself to a remote verifier with which the subject has no prior relationship by presenting verification factors including possession of a private key, knowledge of a password, and possession of one or more biometric features, with selective disclosure of attributes and selective presentation of verification factors. In Solution 2 the issuer is a bank and the biometric verification factor is speaker recognition, which can be combined with face recognition to defeat voice morphing. The paper describes in detail the concept of a blockchain PKI, and shows that it has remarkable advantages over a traditional PKI, notably the fact that revocation checking is performed on the verifier’s local copy of the blockchain without requiring CRLs or OCSP.


Advanced Message Queuing Protocol

The advanced message queuing protocol (AMQP) working group's goal is to create an open standard for an interoperable enterprise-scale asynchronous messaging protocol. AMQP is finally addressing the lack of enterprise messaging interoperability standards. This relatively simple yet compellingly powerful enterprise messaging protocol is thus poised to open up a bright new era for enterprise messaging


Taking Empowerment to the Next Level : A Multiple-Level Model of Empowerment , Performance , and Satisfaction

Most research to date has approached employee empowerment as an individual-level phenomenon. In this study we proposed a work-unit-level construct, empowerment climate, and tested a multiple-level model integrating macro and micro approaches to empowerment. Empowerment climate was shown to be empirically distinct from psychological empowerment and positively related to manager ratings of work-unit performance. A cross-level mediation analysis using hierarchical linear modeling showed that psychological empowerment mediated the relationships between empowerment climate and individual performance and job satisfaction.


Lattice-based motion planning for a general 2-trailer system

Motion planning for a general 2-trailer system poses a hard problem for any motion planning algorithm and previous methods have lacked any completeness or optimality guarantees. In this work we present a lattice-based motion planning framework for a general 2-trailer system that is resolution complete and resolution optimal. The solution will satisfy both differential and obstacle imposed constraints and is intended either as a part of an autonomous system or as a driver support system to automatically plan complicated maneuvers in backward and forward motion. The proposed framework relies on a precomputing step that is performed offline to generate a finite set of kinematically feasible motion primitives. These motion primitives are then used to create a regular state lattice that can be searched for a solution using standard graph-search algorithms. To make this graph-search problem tractable for real-time applications a novel parametrization of the reachable state space is proposed where each motion primitive moves the system from and to a selected set of circular equilibrium configurations. The approach is evaluated over three different scenarios and impressive real-time performance is achieved.


Labelling strategies for hierarchical multi-label classification techniques

Many hierarchical multi-label classification systems predict a real valued score for every (instance, class) couple, with a higher score reflecting more confidence that the instance belongs to that class. These classifiers leave the conversion of these scores to an actual label set to the user, who applies a cut-off value to the scores. The predictive performance of these classifiers is usually evaluated using threshold independent measures like precision-recall curves. However, several applications require actual label sets, and thus an automatic labelling strategy. In this article, we present and evaluate different alternatives to perform the actual labelling in hierarchical multi-label classification. We investigate the selection of both single and multiple thresholds. Despite the existence of multiple threshold selection strategies in non-hierarchical multi-label classification, they can not be applied directly to the hierarchical context. The proposed strategies are implemented within two main approaches: optimisation of a certain performance measure of interest (such as F-measure or hierarchical loss), and simulating training set properties (such as class distribution or label cardinality) in the predictions. We assess the performance of the proposed labelling schemes on 10 datasets from different application domains. Our results show that selecting multiple thresholds may result in ∗Corresponding author. Tel : +32(0)9 331 36 93 Fax : +32(0)9 221 76 73 Email addresses: Isaac.Triguero@irc.vib-UGent.be (Isaac Triguero), Celine.Vens@kuleuven-kulak.be (Celine Vens) Preprint submitted to Elsevier January 29, 2016 an efficient and effective solution for hierarchical multi-label problems.


Building a Robust Implementation of Bearing-only Inertial SLAM for a UAV

This paper presents the on-going design and implementation of a robust inertial sensor based Simultaneous Localisation And Mapping (SLAM) algorithm for an Unmanned Aerial Vehicle (UAV) using bearing-only observations. A single colour vision camera is used to observe the terrain from which image points corresponding to features in the environment are extracted. The SLAM algorithm estimates the complete 6-DoF motion of the UAV along with the three-dimensional position of the features in the environment. An Extended Kalman Filter (EKF) approach is used where a technique of delayed initialisation is performed to initialise the 3D position of features from bearing-only observations. Data association is achieved using a multi-hypothesis innovation gate based on the spatial uncertainty of each feature. Results are presented by running the algorithm off-line using inertial sensor and vision data collected during a flight test of a UAV.


Normative rational agents-A BDI approach

This paper proposes an approach on how to accommodate norms to an already existing architecture of rational agents. Starting from the famous BDI model, an extension of the BDI execution loop will be presented; it will address such issues as norm instantiation and norm internalization, with a particular emphasis on the problem of norm consistency. A proposal for the resolution of conflicts between newly occurring norms, on one side, and already existing norms or mental states, on the other side, will be described. While it is fairly difficult to imagine an evaluation for the proposed architecture, a challenging scenario inspired form the science-fiction literature will be used to give the reader an intuition of how the proposed approach will deal with situations of normative conflicts.


Temporal QoS-aware web service recommendation via non-negative tensor factorization

With the rapid growth of Web Service in the past decade, the issue of QoS-aware Web service recommendation is becoming more and more critical. Since the Web service QoS information collection work requires much time and effort, and is sometimes even impractical, the service QoS value is usually missing. There are some work to predict the missing QoS value using traditional collaborative filtering methods based on user-service static model. However, the QoS value is highly related to the invocation context (e.g., QoS value are various at different time). By considering the third dynamic context information, a Temporal QoS-aware Web Service Recommendation Framework is presented to predict missing QoS value under various temporal context. Further, we formalize this problem as a generalized tensor factorization model and propose a Non-negative Tensor Factorization (NTF) algorithm which is able to deal with the triadic relations of user-service-time model. Extensive experiments are conducted based on our real-world Web service QoS dataset collected on Planet-Lab, which is comprised of service invocation response-time and throughput value from 343 users on 5817 Web services at 32 time periods. The comprehensive experimental analysis shows that our approach achieves better prediction accuracy than other approaches.


Multi-finger and whole hand gestural interaction techniques for multi-user tabletop displays

Recent advances in sensing technology have enabled a new generation of tabletop displays that can sense multiple points of input from several users simultaneously. However, apart from a few demonstration techniques [17], current user interfaces do not take advantage of this increased input bandwidth. We present a variety of multifinger and whole hand gestural interaction techniques for these displays that leverage and extend the types of actions that people perform when interacting on real physical tabletops. Apart from gestural input techniques, we also explore interaction and visualization techniques for supporting shared spaces, awareness, and privacy. These techniques are demonstrated within a prototype room furniture layout application, called RoomPlanner.


Introduction to Modern Information Retrieval

Come with us to read a new book that is coming recently. Yeah, this is a new coming book that many people really want to read will you be one of them? Of course, you should be. It will not make you feel so hard to enjoy your life. Even some people think that reading is a hard to do, you must be sure that you can do it. Hard will be felt when you have no ideas about what kind of book to read. Or sometimes, your reading material is not interesting enough.


Cyberguide: A mobile context-aware tour guide

Future computing environments will free the user from the constraints of the desktop. Applications for a mobile environment should take advantage of contextual information, such as position, to o er greater services to the user. In this paper, we present the Cyberguide project, in which we are building prototypes of a mobile context-aware tour guide. Knowledge of the user's current location, as well as a history of past locations, are used to provide more of the kind of services that we come to expect from a real tour guide. We describe the architecture and features of a variety of Cyberguide prototypes developed for indoor and outdoor use on a number of di erent hand-held platforms. We also discuss the general research issues that have emerged in our context-aware applications development in a mobile environment.


Interacting with Paper on the Digital Desk

real world can be overcome in VR, and physical tools can. be m a d e obsolete by more flexible, virtual alternatives'.. Physical tools can be hard to replace , however. At one time it seemed that paper might become obsolete, for example, and visionaries predicted the "paperlless office" would dominate within a few years. But the trouble is that people like paper. It is easier to read than a screen [5], it is cheap, universally accepted , tactile, and portable. According to some studies, paperwork in the office has increased by a l~actor of six since 1970, and is now growing at 20% annually [14]. Like electronic documents, paper has properties that people just cannot seem to give up, making it resilient in the face of computer-based alternatives [10]. Consequently, we have two desks: one for "paper pushing" and the other for "pixel pushing." Although activities on the two desk.s are often related, the two are quite isolated from each other. Printers and scanners provide a way for documents to move back ;and forth between desk-tops, but this conversion process is inconvenient. Wang's Freestyle system [3], for example, was. a "paper-less office" system that attempted to coexist with partially paper-based processes. A key factor necessary for adoption of that system was to minimize the printing and scanning re-RoE.ALL o quired, because too many of these tasks would cause the process to revert entirely back to paper. Trade-offs between electronic and paper documents can make the choice of medium difficult, but imagine if we did not have to choose, and we had a space where documents could be both paper and electronic at the same time. Instead of putting the user in the virtual world of the computer , we could do the opposite: add the computer to the real world of the user and create a Computer Augmented Environment for paper (see guest editors' introduction.). Instead of replacing paper with computers, we could enhance paper with computation. The Xerox PaperWorks product [8] takes a step in this direction with its fax-based paper user interface (UI) to a storage and retrieval system. With this system, ordinary paper forms are enhanced to control a PC through a fax machine. These paper documents gain some properties of electronic documents, but fax machines are slow compared to computer screens. Response time is limited by the delay it takes to scan and print a …


Keeping track of time: evidence for episodic-like memory in great apes

Episodic memory, as defined by Tulving, can be described in terms of behavioural elements (what, where and when information) but it is also accompained by an awareness of one’s past (chronesthesia) and a subjective conscious experience (autonoetic awareness). Recent experiments have shown that corvids and rodents recall the where, what and when of an event. This capability has been called episodic-like memory because it only fulfils the behavioural criteria for episodic memory. We tested seven chimpanzees, three orangutans and two bonobos of various ages by adapting two paradigms, originally developed by Clayton and colleagues to test scrub jays. In Experiment 1, subjects were fed preferred but perishable food (frozen juice) and less preferred but non-perishable food (grape). After the food items were hidden, subjects could choose one of them either after 5 min or 1 h. The frozen juice was still available after 5 min but melted after 1 h and became unobtainable. Apes chose the frozen juice significantly more after 5 min and the grape after 1 h. In Experiment 2, subjects faced two baiting events happening at different times, yet they formed an integrated memory for the location and time of the baiting event for particular food items. We also included a memory task that required no temporal encoding. Our results showed that apes remember in an integrated fashion what, where and when (i.e., how long ago) an event happened; that is, apes distinguished between different events in which the same food items were hidden in different places at different times. The temporal control of their choices was not dependent on the familiarity of the platforms where the food was hidden. Chimpanzees’ and bonobos’ performance in the temporal encoding task was age-dependent, following an inverted U-shaped distribution. The age had no effect on the performance of the subjects in the task that required no temporal encoding.


DESH: Database evaluation system with hibernate ORM framework

Relational databases have been the predominant choice for back-ends in enterprise applications for several decades. JDBC - a Java API - that is used for developing such applications and persisting data on the back-end requires enormous time and effort. JDBC makes the application logic to become tightly coupled with the database and consequently is inadequate for building enterprise applications that need to adopt to dynamic requirements. Hence, ORM frameworks such as Hibernate, became prominent. However, even with ORM, the relational back-end often faces a drawback of lack of scalability and flexibility. In this context, NoSQL databases are increasingly gaining popularity. Existing research works have either benchmarked Hibernate with an RDBMS or with one of the NoSQL databases. However, it has not been attempted in the literature to test both an RDBMS and a NoSQL database for their performance within the context of a single application developed using the features of Hibernate. This kind of a study will provide an insight that using Hibernate ORM solution would help us to develop database-independent applications and how much performance gain can be achieved when an application is ported from an RDBMS back-end to a NoSQL database backend. The objective of this work is to develop a business application using Hibernate framework that can individually communicate with an RDBMS as well as a specific NoSQL database and to evaluate the performance of both these databases.


Neural Approaches to Conversational AI

This tutorial surveys neural approaches to conversational AI that were developed in the last few years. We group conversational systems into three categories: (1) question answering agents, (2) task-oriented dialogue agents, and (3) social bots. For each category, we present a review of state-of-the-art neural approaches, draw the connection between neural approaches and traditional symbolic approaches, and discuss the progress we have made and challenges we are facing, using specific systems and models as case studies.


Towards Twitter hashtag recommendation using distributed word representations and a deep feed forward neural network

Hashtags are useful for categorizing and discovering content and conversations in online social networks. However, assigning hashtags requires additional user effort, hampering their widespread adoption. Therefore, in this paper, we introduce a novel approach for hashtag recommendation, targeting English language tweets on Twitter. First, we make use of a skip-gram model to learn distributed word representations (word2vec). Next, we make use of the distributed word representations learned to train a deep feed forward neural network. We test our deep neural network by recommending hashtags for tweets with user-assigned hashtags, using Mean Squared Error (MSE) as the objective function. We also test our deep neural network by recommending hashtags for tweets without user-assigned hashtags. Our experimental results show that the proposed approach recommends hashtags that are specific to the semantics of the tweets and that preserve the linguistic regularity of the tweets. In addition, our experimental results show that the proposed approach is capable of generating hashtags that have not been seen before.


Review of Charge Pump Topologies for Micro Energy Harvesting Systems

Corresponding Author: Michelle Lim Sern Mi Institute of Microengineering and Nanoelectronics (IMEN), UKM, 43600 Bangi, Selangor, Malaysia Email: mlsm_2002@yahoo.com Abstract: This paper reviews CMOS based charge pump topologies used within autonomous embedded micro-systems. These charge pump structures have evolved from its simplistic diode-tied, single-branches with major threshold drops to exponential type, dual-branches with sophisticated gate and substrate control for lower voltage operation. Published charge pumps are grouped based on architecture, operation principles and pump optimization techniques with their pros and cons compared and results contrasted. The various charge pump topologies and schemes used are considered based on pumping efficiency, power efficiency, charge transferability, circuit complexity, pumping capacitors, form factor and minimum supply voltages with an optimum load. This article concludes with an overview of suitable techniques and recommendations that will aid a designer in selecting the most suitable charge pump topology especially for low ambient micro energy harvesting applications.


Private Query on Encrypted Data in Multi-user Settings

Searchable encryption schemes allow users to perform keyword based searches on an encrypted database. Almost all existing such schemes only consider the scenario where a single user acts as both the data owner and the querier. However, most databases in practice do not just serve one user; instead, they support search and write operations by multiple users. In this paper, we systematically study searchable encryption in a practical multi-user setting. Our results include a set of security notions for multi-user searchable encryption as well as a construction which is provably secure under the newly introduced security notions.


SMIT: Stochastic Multi-Label Image-to-Image Translation

Cross-domain mapping has been a very active topic in recent years. Given one image, its main purpose is to translate it to the desired target domain, or multiple domains in the case of multiple labels. This problem is highly challenging due to three main reasons: (i) unpaired datasets, (ii) multiple attributes, and (iii) the multimodality associated with the translation. Most of the existing state-of-theart has focused only on two reasons, i.e. producing disentangled representations from unpaired datasets in a one-toone domain translation or producing multiple unimodal attributes from unpaired datasets. In this work, we propose a joint framework of diversity and multi-mapping image-toimage translations, using a single generator to conditionally produce countless and unique fake images that hold the underlying characteristics of the source image. Extensive experiments over different datasets demonstrate the effectiveness of our proposed approach with comparisons to the state-of-the-art in both multi-label and multimodal problems. Additionally, our method is able to generalize under different scenarios: continuous style interpolation, continuous label interpolation, and multi-label mapping.


Instagram #Instasad?: Exploring Associations Among Instagram Use, Depressive Symptoms, Negative Social Comparison, and Strangers Followed

As the use and influence of social networking continues to grow, researchers have begun to explore its consequences for psychological well-being. Some research suggests that Facebook use can have negative consequences for well-being. Instagram, a photo-sharing social network created in 2010, has particular characteristics that may make users susceptible to negative consequences. This study tested a theoretically grounded moderated meditation model of the association between Instagram use and depressive symptoms through the mechanism of negative social comparison, and moderation by amount of strangers one follows. One hundred and seventeen 18-29 year olds completed online questionnaires containing demographics, frequency of Instagram use, amount of strangers followed on Instagram, the Center for Epidemiological Resources Scale for Depression, and the Social Comparison Rating Scale. Instagram use was marginally positively associated with depressive symptoms, and positive social comparison was significantly negatively associated with depressive symptoms. Amount of strangers followed moderated the associations of Instagram use with social comparison (significantly) and depressive symptoms (marginally), and further significantly moderated the indirect association of Instagram use with depressive symptoms through social comparison. Findings generally suggest that more frequent Instagram use has negative associations for people who follow more strangers, but positive associations for people who follow fewer strangers, with social comparison and depressive symptoms. Implications of negative associations of social networking for people who follow strangers and the need for more research on Instagram use given its increasing popularity are explored.


Processing capacity defined by relational complexity: implications for comparative, developmental, and cognitive psychology.

Working memory limits are best defined in terms of the complexity of the relations that can be processed in parallel. Complexity is defined as the number of related dimensions or sources of variation. A binary relation has one argument and one source of variation; its argument can be instantiated in only one way at a time. A binary relation has two arguments, two sources of variation, and two instantiations, and so on. Dimensionality is related to the number of chunks, because both attributes on dimensions and chunks are independent units of information of arbitrary size. Studies of working memory limits suggest that there is a soft limit corresponding to the parallel processing of one quaternary relation. More complex concepts are processed by "segmentation" or "conceptual chunking." In segmentation, tasks are broken into components that do not exceed processing capacity and can be processed serially. In conceptual chunking, representations are "collapsed" to reduce their dimensionality and hence their processing load, but at the cost of making some relational information inaccessible. Neural net models of relational representations show that relations with more arguments have a higher computational cost that coincides with experimental findings on higher processing loads in humans. Relational complexity is related to processing load in reasoning and sentence comprehension and can distinguish between the capacities of higher species. The complexity of relations processed by children increases with age. Implications for neural net models and theories of cognition and cognitive development are discussed.


What causes hidradenitis suppurativa?

Hidradenitis suppurativa (HS)--a rather common, very chronic and debilitating inflammatory skin appendage disorder with a notoriously underestimated burden of disease--has long been a playground for the high priests of nomenclature: Ask a bunch of eminent dermatologists and skin pathologists to publicly share their thoughts on what causes HS, and they will soon get entrenched in a heated debate on whether this historical term is a despicable misnomer. Fortunately, the recently founded Hidradenitis Suppurativa Foundation (HSF; http://www.hs-foundation.org), to which EXP DERMATOL serves as home journal, has broken with this unproductive tradition and has encouraged publication of the current CONTROVERSIES feature. This is exclusively devoted to discussing the pathobiology of this chronic neutrophilic folliculitis of unknown origin. Although traces of terminological bickering remain visible, it does the HS experts in our virtual debate room credit that they engage in a constructive and comprehensive dissection of potential pathogenesis pathways that may culminate in the clinical picture we know under the competing terms HS or acne inversa. These experts sketch more often complementary than mutually exclusive pathogenesis scenarios, and the outlines of a conceivable consensus on the many open pathobiology questions begin to emerge in these CONTROVERSIES. Hopefully, this heralds a welcome new tradition: to get to the molecular heart of HS pathogenesis, which can only be achieved by a renaissance of solid basic HS research, as the key to developing more effective HS therapy.


Achievable cases in an asynchronous environment

The paper deals with achievability of fault tolerant goals in a completely asynchronous distributed system. Fischer, Lynch, and Paterson [FLP] proved that in such a system "nontrivial agreement" cannot be achieved even in the (possible) presence of a single "benign" fault. In contrast, we exhibit two pairs of goals that are achievable even in the presence of up to t ≪ n/2 faulty processors, contradicting the widely held assumption that no nontrivial goals are attainable in such a system. The first pair deals with renaming processors so as to reduce the size of the initial name space. When only uniqueness is required of the new names, we present a lower bound of n + 1 on the size of the new name space, and a renaming algorithm which establishes an upper bound of n + t. In case the new names are required also to preserve the original order, a tight bound of 2t(n- t + 1) - 1 is obtained. The second pair of goals deals with the multi-slot critical section problem. We present algorithms for controlled access to a critical section. As for the number of slots required, a tight bound of t + 1 is proved in case the slots are identical. In the case of distinct slots the upper bound is 2t + 1.


Steerable Higher Order Mode Dielectric Resonator Antenna With Parasitic Elements for 5G Applications

This paper presents the findings of a steerable higher order mode (TE<inline-formula> <tex-math notation="LaTeX">$^{\mathrm {y}}_{1\delta 3}$ </tex-math></inline-formula>) dielectric resonator antenna with parasitic elements. The beam steering was successfully achieved by switching the termination capacitor on the parasitic element. In this light, all of the dielectric resonator antennas (DRAs) have the same dielectric permittivity similar to that of ten and excited by a <inline-formula> <tex-math notation="LaTeX">$50\Omega $ </tex-math></inline-formula> microstrip with a narrow aperture. The effect of the mutual coupling on the radiation pattern and the reflection coefficient, as well as the array factor, was investigated clearly using MATLAB version 2014b and ANSYS HFSS version 16. As the result, the antenna beam of the proposed DRA array managed to steer from −32° to +32° at 15 GHz. Furthermore, the measured antenna array showed the maximum gain of 9.25 dBi and the reflection coefficients which are less than −10 dB with the bandwidth more than 1.3 GHz, which is viewed as desirable for device-to-device communication in 5G Internet of Things applications.


Autonomous UAV Surveillance in Complex Urban Environments

We address the problem of multi-UAV-based surveillance in complex urban environments with occlusions. The problem consists of controlling the flight of UAVs with on-board cameras so that the coverage and recency of the information about the designated area is maximized. In contrast to the existing work, sensing constraints due to occlusions and UAV motion constraints are modeled realistically and taken into account. We propose a novel \emph{occlusion-aware} surveillance algorithm based on a decomposition of the surveillance problem into a variant of the 3D art gallery problem and an instance of traveling salesman problem for Dubins vehicles. The algorithm is evaluated on the high-fidelity \textsc{AgentFly} UAV simulation testbed which accurately models all constraints and effects involved. The results confirm the importance of occlusion-aware flight path planning, in particular in the case of narrow-street areas and low UAV flight altitudes.


Spatial augmented reality — A tool for 3D data visualization

This paper presents a proposal for the use of Spatial Augmented Reality as a tool for 3D data visualizations. The use of traditional display technologies such as LCD monitors provides a fish tank metaphor for the user, i.e. the information is behind a plane of glass. The use of VR technologies allows the user to be immersed in the 3D volume and remove this fish tank problem, but can limit the set of techniques that allow users to interact with spatial data. Spatial Augmented Reality employed in conjunction with a highresolution monitor provides an elegant blend of spatial reasoning, tangible interaction, and detailed information viewing. This paper purposes a range of usages for SAR in 3D visualizations.


Learning deep semantic attributes for user video summarization

This paper presents a Semantic Attribute assisted video SUMmarization framework (SASUM). Compared with traditional methods, SASUM has several innovative features. Firstly, we use a natural language processing tool to discover a set of keywords from an image and text corpora to form the semantic attributes of visual contents. Secondly, we train a deep convolution neural network to extract visual features as well as predict the semantic attributes of video segments which enables us to represent video contents with visual and semantic features simultaneously. Thirdly, we construct a temporally constrained video segment affinity matrix and use a partially near duplicate image discovery technique to cluster visually and semantically consistent video frames together. These frame clusters can then be condensed to form an informative and compact summary of the video. We will present experimental results to show the effectiveness of the semantic attributes in assisting the visual features in video summarization and our new technique achieves state-of-the-art performance.


Infants’ Metaphysics: The Case of Numerical Identity

Adults conceptualize the world in terms of enduring physical objects. Sortal concepts provide conditions of individuation (establishing the boundaries of objects) and numerical identity (establishing whether an object is the same one as one encountered at some other time). In the adult conceptual system, there are two roughly hierarchical levels of object sortals. Most general is the sortal bounded physical object itself, for which spatiotemporal properties provide the criteria for individuation and identity. More specific sortals, such as dog or car, rely on additional types of properties to provide criteria for individuation and identity. We conjecture that young infants might represent only the general sortal, object, and construct more specific sortals later (the Object-first Hypothesis). This is closely related to Bower's (1974) conjecture that infants use spatiotemporal information to trace identity before they use property information. Five studies using the visual habituation paradigm were conducted to address the Object-first Hypothesis. In these studies, 10-month-old infants were able to use spatiotemporal information but failed to use property/kind information to set up representations of numerically distinct individuals, thus providing empirical evidence for the Object-first Hypothesis. Finally, infants succeed at object individuation in terms of more specific sortals by 12 months. The relation between success at our task and early noun comprehension is discussed.


The Google effect in doctoral theses

It is often said that successive generations of researchers face an increasing educational burden due to knowledge accumulation. On the other hand, technological advancement over time can improve the productivity of researchers and even change their cognitive processes. This paper presents a longitudinal study (2004–2011) of citation behavior in doctoral theses at the Massachusetts Institute of Technology’s Department of Electrical Engineering and Computer Science. It is found that the number of references cited has increased over the years. At the same time, there has been a decrease in the length of time in the doctoral program and a relative constancy in the culture of the department. This suggests that students are more productive in facing an increased knowledge burden, and indeed seem to encode prior literature as transactive memory to a greater extent, as evidenced by the greater use of older literature.


W-Band Large-Scale High-Gain Planar Integrated Antenna Array

This communication presents a 32 × 32 high-gain patch array antenna fed by the substrate integrated waveguide (SIW) structure at W-band. The array antenna consists of two layers to achieve a compact topology, which allows for mass production using a standard PCB fabrication process. The wideband feeding network is placed in the bottom layer while the radiating patches are on the top layer. This configuration also resolves the trade-off between gain and bandwidth of conventional SIW array antennas. Measured gain of the 32 × 32 antenna array is within the range 28.81-29.97 dBi in the working bandwidth of 91-97 GHz. Measured impedance bandwidth covers the same frequency band for . The cross-polarization of the antenna array is less than 40 dB at the beam direction. Good agreement between the simulated and measured results validates our design.


Position and Orientation Estimation Through Millimeter-Wave MIMO in 5G Systems

Millimeter-wave (mm-wave) signals and large antenna arrays are considered enabling technologies for future 5G networks. While their benefits for achieving high-data rate communications are well-known, their potential advantages for accurate positioning are largely undiscovered. We derive the Cramér-Rao bound (CRB) on position and rotation angle estimation uncertainty from mm-wave signals from a single transmitter, in the presence of scatterers. We also present a novel two-stage algorithm for position and rotation angle estimation that attains the CRB for average to high signal-to-noise ratio. The algorithm is based on multiple measurement vectors matching pursuit for coarse estimation, followed by a refinement stage based on the space-alternating generalized expectation maximization algorithm. We find that accurate position and rotation angle estimation is possible using signals from a single transmitter, in either line-of-sight, non-line-of-sight, or obstructed-line-of-sight conditions.


Enhancing the TED-LIUM Corpus with Selected Data for Language Modeling and More TED Talks

In this paper, we present improvements made to the TED-LIUM corpus we released in 2012. These enhancements fall into two categories. First, we describe how we filtered publicly available monolingual data and used it to estimate well-suited language models (LMs), using open-source tools. Then, we describe the process of selection we applied to new acoustic data from TED talks, providing additions to our previously released corpus. Finally, we report some experiments we made around these improvements.


Blockchain challenges and opportunities: a survey

Copyright © 2017 Inderscience Enterprises Ltd. 2 Blockchain has numerous benefits such as decentralization, persistency, anonymity and auditability. There is a wide spectrum of blockchain applications ranging from cryptocurrency, financial services, risk management, Internet of Things to public and social services. Although a number of studies focus on using the blockchain technology in various application aspects, there is no comprehensive survey on the blockchain technology in both technological and application perspectives. To fill this gap, we conduct a comprehensive survey on the blockchain technology. In particular, this paper gives the blockchain taxonomy, introduces typical blockchain consensus algorithms, reviews blockchain applications and discusses technical challenges as well as recent advances in tackling the challenges. Moreover, this paper also points out the future directions in the blockchain technology.


A Real Time Audio Fingerprinting System for Advertisement Tracking and Reporting in FM Radio

In this paper we present a system designed to detect, identify and track commercial segments transmitted by a radio station. The program is entirely written in Visual C++ and uses state of the art audio fingerprinting technologies to achieve a great performance, being able to operate several times faster than real time while keeping a moderate computational load.


Detecting and discriminating behavioural anomalies

This paper aims to address the problem of anomaly detection and discrimination in complex behaviours, where anomalies are subtle and difficult to detect owing to the complex temporal dynamics and correlations among multiple objects’ behaviours. Specifically, we decompose a complex behaviour pattern according to its temporal characteristics or spatial-temporal visual contexts. The decomposed behaviour is then modelled using a cascade of Dynamic Bayesian Networks (CasDBNs). In contrast to existing standalone models, the proposed behaviour decomposition and cascade modelling offers distinct advantage in simplicity for complex behaviour modelling. Importantly, the decomposition and cascade structure map naturally to the structure of complex behaviour, allowing for a more effective detection of subtle anomalies in surveillance videos. Comparative experiments using both indoor and outdoor data are carried out to demonstrate that, in addition to the novel capability of discriminating different types of anomalies, the proposed framework outperforms existing methods in detecting durational anomalies in complex behaviours and subtle anomalies that are difficult to detect when objects are viewed in isolation. & 2010 Elsevier Ltd. All rights reserved.


Mobile Virtual Network Admission Control and Resource Allocation for Wireless Network Virtualization: A Robust Optimization Approach

Wireless network virtualization is a promising technology in next generation wireless networks. In this paper, motivated by the experience of user equipment (UE) admission control in traditional wireless networks, we propose a novel concept of mobile virtual network (MVN) admission control for wireless virtualization. By limiting the number of MVNs embedded in the physical network, MVN admission control can effectively guarantee quality of service (QoS) experienced by users of MVNs and maximize the utilization of the physical networks at the same time. Specifically, we propose a two-stage MVN embedding mechanism that can decouple short-term physical resource allocation from long-term admission control and resource leasing. With recent advances in robust optimization, we formulate the MVN admission control problem as a robust optimization problem. Both the long-term admission control and short- term resource allocation problems are transformed to convex problems, which can be solved efficiently. Simulation results are presented to show the effectiveness of the proposed scheme.


The Experience of Nature A Psychological Perspective


A novel framework for SMS spam filtering is introduced in this paper to prevent mobile phone users from unsolicited SMS messages. The framework makes use of two distinct feature selection approaches based on information gain and chi-square metrics to find out discriminative features representing SMS messages. The discriminative feature subsets are then employed in two different Bayesian-based classifiers, so that SMS messages are categorized as either spam or legitimate. Moreover, the paper introduces a real-time mobile application for Android™ based mobile phones utilizing the proposed spam filtering scheme, as well. Hence, SMS spam messages are silently filtered out without disturbing phone users. Effectiveness of the filtering framework is evaluated on a large SMS message collection including legitimate and spam messages. Following the evaluation, remarkably accurate classification results are obtained for both spam and legitimate messages.


Forecasting stock price directional movements using technical indicators: Investigating window size effects on one-step-ahead forecasting

Accurate forecasting of directional changes in stock prices is important for algorithmic trading and investment management. Technical analysis has been successfully used in financial forecasting and recently researchers have explored the optimization of parameters for technical indicators. This study investigates the relationship between the window size used for calculating technical indicators and the accuracy of one-step-ahead (variable steps) forecasting. The directions of the future price movements are predicted using technical analysis and machine learning algorithms. Results show a correlation between window size and forecasting step size for the Support Vector Machines approach but not for the other approaches.


Scene Graph Generation by Belief RNNs

Understanding and describing the scene beyond an image has great value. It is a 1 step forward from recognizing individual objects and their relationships in isolation. 2 In addition, Scene graph is a crucial step towards a deeper understanding of a visual 3 scene. We present a method in an end-to-end model that given an image generates 4 a scene graph including nodes that corresponds to the objects in the image and 5 an edges corresponds to relationship between objects. Our main contribution is 6 introducing a novel deep structure prediction module Belief RNNs that performs 7 learning on a large graphs in a very efficient and generic way. 8


A new Design Criteria for Hash-Functions

The most common way of constructing a hash function (e.g., SHA-1) is to iterate a compression function on the input message. The compression function is usually designed from scratch or made out of a block-cipher. In this paper, we introduce a new security notion for hash-functions, stronger than collision-resistance. Under this notion, the arbitrary length hash function H must behave as a random oracle when the fixed-length building block is viewed as an ideal primitive. This enables to eliminate all possible generic attacks against iterative hash-functions. In this paper, we show that the current design principle behind hash functions such as SHA-1 and MD5 — the (strengthened) Merkle-Damg̊ard transformation — does not satisfy this security notion. We provide several constructions that provably satisfy this notion; those new constructions introduce minimal changes to the plain Merkle-Damg̊ard construction and are easily implementable in practice. This paper is a modified version of a paper to appear at Crypto 2005.


Ordinary magic. Resilience processes in development.

The study of resilience in development has overturned many negative assumptions and deficit-focused models about children growing up under the threat of disadvantage and adversity. The most surprising conclusion emerging from studies of these children is the ordinariness of resilience. An examination of converging findings from variable-focused and person-focused investigations of these phenomena suggests that resilience is common and that it usually arises from the normative functions of human adaptational systems, with the greatest threats to human development being those that compromise these protective systems. The conclusion that resilience is made of ordinary rather than extraordinary processes offers a more positive outlook on human development and adaptation, as well as direction for policy and practice aimed at enhancing the development of children at risk for problems and psychopathology.


An automated framework for generating variable-accuracy battery models from datasheet information

Models based on an electrical circuit equivalent have become the most popular choice for modeling the behavior of batteries, thanks to their ease of co-simulation with other parts of a digital system. Such circuit models are actually model templates: the specific values of their electrical elements must be derived by the analysis of the specific battery devices to be modeled. This process requires either to measure the battery characteristics or to derive them from the datasheet. In the latter case, however, very often not all information are available and the model fitting becomes then unfeasible.
 In this paper we present a methodology for deriving, in a semi-automatic way, circuit equivalent battery models solely from data available in a battery datasheet. In order to account for the different amount of information available, we introduce the concept of "level" of a model, so that models with different accuracy can be derived depending on the available data. The methodology requires only minimal intervention by the designer and it automatically generates MATLAB models once the required data for the corresponding model level are transcribed from the datasheet.
 Simulation results show that our methodology allows to accurately reconstruct the information reported in the datasheet as well as to derive missing ones.


Spatial Reference in Linguistic Human-Robot Interaction: Iterative, Empirically Supported Development of a Model of Projective Relations

The aim of our research is to enable spontaneous and efficient spatial reference to objects in human-robot interaction situations. This paper presents the iterative, empirically based design of a robotic system that uses a computational model for identifying objects on the basis of a range of spatial reference systems. The efficiency of the system is evaluated by two successive empirical studies involving uninformed users. The linguistic analysis points to the striking variability in speakers’ spontaneous strategies and preferences, and it motivates a number of modifications of the computational model.


Mobile Media Use, Multitasking and Distractibility

Portable media devices are ubiquitous and their use has become a core component of many people’s daily experience, but to what effect? In this paper, the authors review research on the ways in which media use and multitasking relate to distraction, distractibility and impulsivity. They review recent research on the effects of media multitasking on driving, walking, work, and academic performance. The authors discuss earlier research concerning the nature of media’s impact on attention and review cognitive and neuropsychological findings on the effects of divided attention. Research provides clear evidence that mobile media use is distracting, with consequences for safety, efficiency and learning. Greater use of media is correlated with higher levels of trait impulsivity and distractibility, but the direction of causality has not been established. Individuals may become more skilled at media multitasking over time, but intervention is currently required to improve the safe and effective use of mobile media. DOI: 10.4018/ijcbpl.2012070102 16 International Journal of Cyber Behavior, Psychology and Learning, 2(3), 15-29, July-September 2012 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. and neuropsychological research on the effects of divided attention. Finally, we explore how high levels of media multitasking might alter our general responses to experience in ways that are marked by changes in distractibility and impulsivity. THE DISTRACTING EFFECTS OF MULTITASKING WITH MOBILE MEDIA


Multileaved Comparisons for Fast Online Evaluation

Evaluation methods for information retrieval systems come in three types: offline evaluation, using static data sets annotated for relevance by human judges; user studies, usually conducted in a lab-based setting; and online evaluation, using implicit signals such as clicks from actual users. For the latter, preferences between rankers are typically inferred from implicit signals via interleaved comparison methods, which combine a pair of rankings and display the result to the user. We propose a new approach to online evaluation called multileaved comparisons that is useful in the prevalent case where designers are interested in the relative performance of more than two rankers. Rather than combining only a pair of rankings, multileaved comparisons combine an arbitrary number of rankings. The resulting user clicks then give feedback about how all these rankings compare to each other. We propose two specific multileaved comparison methods. The first, called team draft multileave, is an extension of team draft interleave. The second, called optimized multileave, is an extension of optimized interleave and is designed to handle cases where a large number of rankers must be multileaved. We present experimental results that demonstrate that both team draft multileave and optimized multileave can accurately determine all pairwise preferences among a set of rankers using far less data than the interleaving methods that they extend.


A tensorized logic programming language for large-scale data

We introduce a new logic programming language T-PRISM based on tensor embeddings. Our embedding scheme is a modification of the distribution semantics in PRISM, one of the state-of-the-art probabilistic logic programming languages, by replacing distribution functions with multidimensional arrays, i.e., tensors. T-PRISM consists of two parts: logic programming part and numerical computation part. The former provides flexible and interpretable modeling at the level of first order logic, and the latter part provides scalable computation utilizing parallelization and hardware acceleration with GPUs. Combing these two parts provides a remarkably wide range of high-level declarative modeling from symbolic reasoning to deep learning. To embody this programming language, we also introduce a new semantics, termed tensorized semantics, which combines the traditional least model semantics in logic programming with the embeddings of tensors. In T-PRISM, we first derive a set of equations related to tensors from a given program using logical inference, i.e., Prolog execution in a symbolic space and then solve the derived equations in a continuous space by TensorFlow. Using our preliminary implementation of T-PRISM, we have successfully dealt with a wide range of modeling. We have succeeded in dealing with real large-scale data in the declarative modeling. This paper presents a DistMult model for knowledge graphs using the FB15k and WN18 datasets.


X-band microstrip narrowband BPF composed of split ring resonator

The paper deals with the development of X-band microstrip narrowband bandpass filter (BPF) composed of split ring resonator (SRR). The proposed filter is designed on a grounded 0.508mm thick Roger RT/Duroid® 5880 dielectric substrate with the dimension of 25mm length and 20mm width. Some parametric studies to achieve an optimum design of microstrip narrowband BPF are investigated prior realization by varying the physical parameters of SRR including the width of rings and the gap between rings. From the experimental characterization, it shows that the realized microstrip narrowband BPF has a center frequency of 9.02 GHz with the values of return loss and insertion loss of 15.231 dB and 2.768 dB, respectively. The measured result is 20MHz lower than the simulated one which has center frequency of 9 GHz with the values of return loss and insertion loss of 15.025 dB and 2.242 dB, respectively.


Enhanced Modelling and Performance in Braided Pneumatic Muscle Actuators

Pneumatic technology has been successfully applied for over two millennia. Even today, pneumatic cylinder based technology forms the keystone of many manufacturing processes where there is a need for simple, high-speed, low-cost, reliable motion. But when the system requires accurate control of position, velocity or acceleration profiles, these actuators form a far from satisfactory solution. Braided pneumatic muscle actuators (pMAs) form an interesting development of the pneumatic principle offering even higher power/weight performance, operation in a wide range of environments and accurate control of position, motion and force. This technology provides an interesting and potentially very successful alternative actuation source for robots as well as other applications. However, there are difficulties with this approach due to the following. (i) Modeling errors. Models of the force response are still nonoptimal and for good results these models are highly complex, which makes accurate design difficult. (ii) Low bandwidth—the bandwidth of the actuator—link assemblies are often considered to be too low for practical success in many applications, particularly robotics. In this paper we address these limitations and show how the performance in each area can be enhanced with overall improvements in the response and utility of the braided pMAs. KEY WORDS—actuators, braided pneumatic muscle actuators, McKibben muscles


High Dimensional Semiparametric Gaussian Copula Graphical Models

We propose a semiparametric approach called the nonparanormal SKEPTIC for efficiently and robustly estimating high-dimensional undirected graphical models. To achieve modeling flexibility, we consider the nonparanormal graphical models proposed by Liu, Lafferty and Wasserman [J. Mach. Learn. Res. 10 (2009) 2295–2328]. To achieve estimation robustness, we exploit nonparametric rank-based correlation coefficient estimators, including Spearman’s rho and Kendall’s tau. We prove that the nonparanormal SKEPTIC achieves the optimal parametric rates of convergence for both graph recovery and parameter estimation. This result suggests that the nonparanormal graphical models can be used as a safe replacement of the popular Gaussian graphical models, even when the data are truly Gaussian. Besides theoretical analysis, we also conduct thorough numerical simulations to compare the graph recovery performance of different estimators under both ideal and noisy settings. The proposed methods are then applied on a large-scale genomic data set to illustrate their empirical usefulness. The R package huge implementing the proposed methods is available on the Comprehensive R Archive Network: http://cran.r-project.org/.


Chapter 3 Recent Developments in Auxiliary Particle Filtering

State space models (SSMs; sometimes termed hidden Markov models, particularly in the discrete case) are very popular statistical models for time series. Such models describe the trajectory of some system of interest as an unobserved E-valued Markov chain, known as the signal process. Let X1 ∼ ν and Xn|(Xn−1 = xn−1) ∼ f(·|xn−1) denote this process. Indirect observations are available via an observation process, {Yn}n∈N. Conditional upon Xn, Yn is independent of the remainder of the observation and signal processes, with Yn|(Xn = xn) ∼ g(·|xn). For any sequence {zn}n∈N, we write zi:j = (zi, zi+1, ..., zj). In numerous applications, we are interested in estimating, recursively in time, an analytically intractable sequence of posterior distributions {p (x1:n| y1:n)}n∈N, of the form:


Fast time series classification using numerosity reduction

Many algorithms have been proposed for the problem of time series classification. However, it is clear that one-nearest-neighbor with Dynamic Time Warping (DTW) distance is exceptionally difficult to beat. This approach has one weakness, however; it is computationally too demanding for many realtime applications. One way to mitigate this problem is to speed up the DTW calculations. Nonetheless, there is a limit to how much this can help. In this work, we propose an additional technique, numerosity reduction, to speed up one-nearest-neighbor DTW. While the idea of numerosity reduction for nearest-neighbor classifiers has a long history, we show here that we can leverage off an original observation about the relationship between dataset size and DTW constraints to produce an extremely compact dataset with little or no loss in accuracy. We test our ideas with a comprehensive set of experiments, and show that it can efficiently produce extremely fast accurate classifiers.


Blind Source Separation in nonminimum-phase systems based on filter decomposition

This paper focuses on the causality problem in the task of Blind Source Separation (BSS) of speech signals in nonminimum-phase mixing channels. We propose a new algorithm for solving this problem using filter decomposition approach. Our proposed algorithm uses an integrated cost function in which independence criterion is defined in frequency-domain. The parameters of demixing system are derived in time-domain, so the algorithm has the benefits of both time and frequency-domain approaches. Compared to the previous work in this framework, our proposed algorithm is the extension of filter decomposition idea in multi-channel blind deconvolution to the problem of blind source separation of speech signals. The proposed method is capable of dealing with both minimum-phase and nonminimum-phase mixing situations. Simulation results show considerable improvement in separating speech signals specially when the mixing system is nonminimum-phase.