title: A Teachable-Agent Arithmetic Game's Effects on Mathematics Understanding, Attitude and Self-efficacy

text: A teachable-agent arithmetic game is presented and evaluated in terms of student performance, attitude and self-eff icacy. An experimental prepost study design was used, enrolling 153 3 rd and 5 grade students in Sweden. The playing group showed significantly larger gains i math performance and self-efficacy beliefs, but not in general attitude towards math, compared to control groups. The contributions in relation to pr evious work include a novel educational game being evaluated, and an emphasis o n elf-efficacy in the study as a strong predictor of math achievements.


title: Video-based face recognition using ensemble of haar-like deep convolutional neural networks

text: Growing number of surveillance and biometric applications seek to recognize the face of individuals appearing in the viewpoint of video cameras. Systems for video-based FR can be subjected to challenging operational environments, where the appearance of faces captured with video cameras varies significantly due to changes in pose, illumination, scale, blur, expression, occlusion, etc. In particular, with still-to-video FR, a limited number of high-quality facial images are typically captured for enrollment of an individual to the system, whereas an abundance facial trajectories can be captured using video cameras during operations, under different viewpoints and uncontrolled conditions. This paper presents a deep learning architecture that can learn a robust facial representation for each target individual during enrollment, and then accurately compare the facial regions of interest (ROIs) extracted from a still reference image (of the target individual) with ROIs extracted from live or archived videos. An ensemble of deep convolutional neural networks (DCNNs) named HaarNet is proposed, where a trunk network first extracts features from the global appearance of the facial ROIs (holistic representation). Then, three branch networks effectively embed asymmetrical and complex facial features (local representations) based on Haar-like features. In order to increase the discriminativness of face representations, a novel regularized triplet-loss function is proposed that reduces the intra-class variations, while increasing the inter-class variations. Given the single reference still per target individual, the robustness of the proposed DCNN is further improved by fine-tuning the HaarNet with synthetically-generated facial still ROIs that emulate capture conditions found in operational environments. The proposed system is evaluated on stills and videos from the challenging COX Face and Chokepoint datasets according to accuracy and complexity. Experimental results indicate that the proposed method can significantly improve performance with respect to state-of-the-art systems for video-based FR.


title: Double Refinement Network for Efficient Indoor Monocular Depth Estimation

text: Monocular Depth Estimation is an important problem of Computer Vision that may be solved with Neural Networks and Deep Learning nowadays. Though recent works in this area have shown significant improvement in accuracy, state-of-the-art methods require large memory and time resources. The main purpose of this paper is to improve performance of the latest solutions with no decrease in accuracy. To achieve this, we propose a Double Refinement Network architecture. We evaluate the results using the standard benchmark RGB-D dataset NYU Depth v2. The results are equal to the current state-of-the-art, while frames per second rate of our approach is significantly higher (up to 15 times speedup per image with batch size 1), RAM per image is significantly lower.


title: The Possibility of an Epidemic Meme Analogy for Web Community Population Analysis

text: The aim of this paper is to discuss the possibility of understanding human social interaction in web communities by analogy with a disease propagation model from epidemiology. When an article is submitted by an individual to a social web service, it is potentially influenced by other participants. The submission sometimes starts a long and argumentative chain of articles, but often does not. This complex behavior makes management of server resources difficult and a more theoretical methodology is required. This paper tries to express these complex human dynamics by analogy with infection by a virus. In this first report, by fitting an epidemiological model to Bulletin Board System (BBS) logs in terms of a numerical triple, we show that the analogy is reasonable and beneficial because the analogy can estimate the community size despite the submitter’s information alone being observable.


title: Millimeter-wave Gbps Broadband Evolution towards 5G: Fixed Access and Backhaul

text: — As wireless communication evolves towards 5G, both fixed broadband and mobile broadband will play a crucial part in providing the Gbps infrastructure for a connected society. This paper proposes a Millimeter-wave Gbps Broadband (MGB) system as the solution to two critical problems in this evolution: last-mile access for fixed broadband and small cell backhaul for mobile broadband. The key idea is to use spectrum that is already available in the millimeter wave bands for fixed wireless access with optimized dynamic beamforming and massive MIMO infrastructure to achieve high capacity with wide area coverage. This paper explains the MGB concept and describes potential array architectures for realizing the system. Simulations demonstrate that with 500 MHz of bandwidth (at 39 GHz band) and 28 dBm transmission power (55 dBm EIRP), it is possible to provide more than 11 Gbps backhaul capacity for 96 small cells within 1-km radius.


title: Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment

text: Twitter is a microblogging website where users read and write millions of short messages on a variety of topics every day. This study uses the context of the German federal election to investigate whether Twitter is used as a forum for political deliberation and whether online messages on Twitter validly mirror offline political sentiment. Using LIWC text analysis software, we conducted a content analysis of over 100,000 messages containing a reference to either a political party or a politician. Our results show that Twitter is indeed used extensively for political deliberation. We find that the mere number of messages mentioning a party reflects the election result. Moreover, joint mentions of two parties are in line with real world political ties and coalitions. An analysis of the tweets’ political sentiment demonstrates close correspondence to the parties' and politicians’ political positions indicating that the content of Twitter messages plausibly reflects the offline political landscape. We discuss the use of microblogging message content as a valid indicator of political sentiment and derive suggestions for further research.


title: Science and society. Social media and the elections.

text: C R E D IT : J O E S U T IL F F /J O E S U T L IF F .C O M I n the United States, social media sites—such as Facebook, Twitter, and YouTube—are currently being used by two out of three people ( 1), and search engines are used daily ( 2). Monitoring what users share or search for in social media and on the Web has led to greater insights into what people care about or pay attention to at any moment in time. Furthermore, it is also helping segments of the world population to be informed, to organize, and to react rapidly. However, social media and search results can be readily manipulated, which is something that has been underappreciated by the press and the general public. In times of political elections, the stakes are high, and advocates may try to support their cause by active manipulation of social media. For example, altering the number of followers can affect a viewer’s conclusion about candidate popularity. Recently, it was noted that the number of followers for a presidential candidate in the United States surged by over 110 thousand within one single day, and analysis showed that most of these followers are unlikely to be real people ( 3). We can model propaganda efforts in graph-theoretic terms, as attempts to alter our “trust network”: Each of us keeps a mental trust network that helps us decide what and what not to believe ( 4). The nodes in this weighted network are entities that we are already familiar with (people, institutions, and ideas), and the arcs are our perceived connections between these entities. The weights on the nodes are values of trust and distrust that we implicitly assign to every entity we know. A propagandist is trying to make us alter connections and values in our trust network, i.e., trying to infl uence our perception about the candidates for the coming elections, and thus “help us” decide on candidates of their choice. The Web, as seen by search engines ( 5), is similarly a weighted network that is used to rank search results. The hyperlinks are considered “votes of support,” and the weights are a computed measurement of importance assigned to Web pages (the nodes in the graph). It is also the target of propaganda attacks, known as “Web spam” ( 6). A Web spammer is trying to alter the weighted Web network by adding connections and values that support his or her cause, aimed at affecting the search engine’s ranking decisions and thus the number of viewers who see the page and consider it important ( 4). “Google bomb” is a type of Web spam that is widely known and applicable to all major search engines today. Exploiting the descriptive power of anchor text (the phrase directly associated with a hyperlink), Web spammers create associations between anchor words or phrases and linked Web pages. These associations force a search engine to give high relevancy to results that would otherwise be unrelated, sending them to the “top 10” search results. A well-known Google bomb was the association of the phrase “miserable failure” with the Web page of President G. W. Bush initially and later with those of Michael Moore, Hillary Clinton, and Jimmy Carter ( 7). Another Google bomb associated candidate John Kerry with the word “waffl es” in 2004. A cluster of Google bombs was used in an effort to infl uence the 2006 congressional elections. Google has adjusted its ranking algorithm to defuse Google bombs on congressional candidates by restricting the selection of the top search results when querying their names ( 8). During the 2008 and 2010 elections, it proved impossible to launch any successful Google bombs on politicians, and it is hoped that the trend will continue. During the 2010 Massachusetts Special Election (MASEN) to fi ll the seat vacated by the death of Senator Ted Kennedy, we saw attempts to infl uence voters just before the elections, launched by out-of-state political groups ( 9). Propagandists exploited a loophole introduced by the feature of including real-time information from social networks in the “top 10” results of Web searches. They ensured that their message was often visible by repeatedly posting the same tweet. A third of all election-related tweets sent during the week before the 2010 MASEN were tweet repeats ( 9). All search engines have since reacted by moving real-time results out of the organic results (results selected purely by information retrieval algorithms) and into a separate search category. “Twitter bombs,” however, are likely to be launched within days of the elections. A Twitter bomb is the act of sending unsolicited replies to specifi c users via Twitter in order to get them to pay attention to one’s cause. Typically, it is done effectively by means of “bots,” short-lived programs that can send a large quantity of tweets automatically. Twitter is good at shutting most of them down because of their activity patterns and/or users’ complaints. However, bombers have used fake “replies” to spam real users who are not aware of their existence. For example, in the 2010 MASEN, political spammers created nine fake accounts that were used to send about 1000 tweets before being blocked by Twitter for spamming ( 9). Their messages were carefully focused, however, targeting users who in the previous hours were discussing the elections. With the retweeting help of similarly minded users, >60,000 Twitter accounts were reached within a day at essentially no cost. Twitter bombs, unfortunately, have become common practice. Department of Computer Science, Wellesley College, 106 Central Street, Wellesley, MA 02481, USA. E-mail: pmetaxas@wellesley. edu; emustafa@wellesley.edu US


title: Information network or social network?: the structure of the twitter follow graph

text: In this paper, we provide a characterization of the topological features of the Twitter follow graph, analyzing properties such as degree distributions, connected components, shortest path lengths, clustering coefficients, and degree assortativity. For each of these properties, we compare and contrast with available data from other social networks. These analyses provide a set of authoritative statistics that the community can reference. In addition, we use these data to investigate an often-posed question: Is Twitter a social network or an information network? The "follow" relationship in Twitter is primarily about information consumption, yet many follows are built on social ties. Not surprisingly, we find that the Twitter follow graph exhibits structural characteristics of both an information network and a social network. Going beyond descriptive characterizations, we hypothesize that from an individual user's perspective, Twitter starts off more like an information network, but evolves to behave more like a social network. We provide preliminary evidence that may serve as a formal model of how a hybrid network like Twitter evolves.


Does Twitter motivate involvement in politics? Tweeting, opinion leadership, and political engagement


title: Rule Based Machine Translation Combined with Statistical Post Editor for Japanese to English Patent Translation

text: Since sentences in patent texts are long, they are difficult to translate by a machine. Although statistical machine translation is one of the major streams of the field, long patent sentences are difficult to translate not using syntactic analysis. We propose the combination of a rule based method and a statistical method. It is a rule based machine translation (RMT) with a statistical based post editor (SPE). The evaluation by the NIST score shows RMT+SPE is more accurate than RMT only. Manual checks, however, show the outputs of RMT+SPE often have strange expressions in the target language. So we propose a new evaluation measure NMG (normalized mean grams). Although NMG is based on n-gram, it counts the number of words in the longest word sequence matches between the test sentence and the target language reference corpus. We use two reference corpora. One is the reference translation only the other is a large scaled target language corpus. In the former case, RMT+SPE wins in the later case, RMT wins.


title: Designing web usability

text: Where you can find the designing web usability easily? Is it in the book store? On-line book store? are you sure? Keep in mind that you will find the book in this site. This book is very referred for you because it gives not only the experience but also lesson. The lessons are very valuable to serve for you, that's not about who are reading this designing web usability book. It is about this book that will give wellness for all people from many societies.


title: Reliable Circuit Techniques for Low-Voltage Analog Design in Deep Submicron Standard CMOS: A Tutorial

text: We present in this paper an overview of circuit techniques dedicated to design reliable low-voltage (1-V and below) analog functions in deep submicron standard CMOS processes. The challenges of designing such low-voltage and reliable analog building blocks are addressed both at circuit and physical layout levels. State-ofthe-art circuit topologies and techniques (input level shifting, bulk and current driven, DTMOS), used to build main analog modules (operational amplifier, analog CMOS switches) are covered with the implementation of MOS capacitors.


title: Scrum Metrics for Hyperproductive Teams: How They Fly like Fighter Aircraft

text: Scrum Teams use lightweight tools like Story Points, the Burn down chart, and Team Velocity. While essential, these tools alone provide insufficient information to maintain a high energy state that yields Hyper productivity. More data is required, but data collection itself can slow Teams. This effect must be avoided when productivity is the primary marker of success. Here we describe nine metrics that can develop and sustain Hyper productive Teams -- Velocity, Work Capacity, Focus Factor, Percentage of Adopted Work, Percentage of Found Work, Accuracy of Estimation, Accuracy of Forecast, Targeted Value Increase, Success at Scale, and the Win/Loss Record of the Team. The unique contribution of this paper is to demonstrate how a light touch and lightweight strategy can be used to compare Teams with different Story Point reference scales.


title: A fast nearest neighbor search algorithm by nonlinear embedding

text: We propose an efficient algorithm to find the exact nearest neighbor based on the Euclidean distance for large-scale computer vision problems. We embed data points nonlinearly onto a low-dimensional space by simple computations and prove that the distance between two points in the embedded space is bounded by the distance in the original space. Instead of computing the distances in the high-dimensional original space to find the nearest neighbor, a lot of candidates are to be rejected based on the distances in the low-dimensional embedded space; due to this property, our algorithm is well-suited for high-dimensional and large-scale problems. We also show that our algorithm is improved further by partitioning input vectors recursively. Contrary to most of existing fast nearest neighbor search algorithms, our technique reports the exact nearest neighbor - not an approximate one - and requires a very simple preprocessing with no sophisticated data structures. We provide the theoretical analysis of our algorithm and evaluate its performance in synthetic and real data.


title: Nostalgia and the emotional tone and content of song lyrics.

text: Emotion and topic were manipulated in original song lyrics. Participants completed Batcho's and Holbrook's nostalgia surveys and rated 6 sets of lyrics for happiness, sadness, anger, nostalgia, meaning, liking, and relevance. Nostalgic lyrics were characterized by bittersweet reverie, loss of the past, identity, and meaning. Contrary to theories linking nostalgia to pathology, participants who scored high on Batcho's measure of personal nostalgia preferred happy lyrics, found them more meaningful, and related more closely to them. Consistent with theories relating nostalgia to social connectedness, high-nostalgia respondents preferred other-directed to solitary themes. Historical nostalgia was associated with relating more closely to sad lyrics.


title: Assistive Robotic Exoskeleton for Helping Limb Girdle Muscular Dystrophy

text: The Limb Girdle Muscular Dystrophy (LGMD) is a heterogeneous group of muscle diseases, this is manifested by a progressive loss of the strength of the muscles of the pelvic and shoulders girdles. Several devices that can compensate for the loss of some motors skills have been developed in support for the activities of patients presenting such dystrophy. Those devices include the pole or treadmill that can be used with an exoskeleton to improve the phase of displacement among other activities. This work shows a new mechanical design for a robotic-exoskeleton with seven movement axis, having as purpose to help people, with certain LGMD dystrophy or diseases characteristics, in generate movements of flexion/extension in the lower extremity joints and realize the gait cycle. All of the design movement's analysis is simulated in Solidworks® and OpenSim® software.


title: CNN-based modulation classification in the complicated communication channel

text: Machine Learning are gradually applied in Automatic Modulation Classification (AMC) domain these years. However, in the past researches, the experiment conditions are mostly idealized or simplified. This paper proposes a modified structure of Convolutional Neural Network (CNN), which is proved to have strong capability of classification. The wavelet denoising technology is exploited to restrain the high frequency noise for input signal. As an analysis of the feasibility of this method for complicated channel, we compare the proposed method with the commonly used AMC methods through a large number of signal sequences, taking consider of various distractors. The result shows that the method proposed outperforms on most rugged conditions.


title: An Integrated Bayesian Approach for Effective Multi-Truth Discovery

text: Truth-finding is the fundamental technique for corroborating reports from multiple sources in both data integration and collective intelligent applications. Traditional truth-finding methods assume a single true value for each data item and therefore cannot deal will multiple true values (i.e., the multi-truth-finding problem). So far, the existing approaches handle the multi-truth-finding problem in the same way as the single-truth-finding problems. Unfortunately, the multi-truth-finding problem has its unique features, such as the involvement of sets of values in claims, different implications of inter-value mutual exclusion, and larger source profiles. Considering these features could provide new opportunities for obtaining more accurate truth-finding results. Based on this insight, we propose an integrated Bayesian approach to the multi-truth-finding problem, by taking these features into account. To improve the truth-finding efficiency, we reformulate the multi-truth-finding problem model based on the mappings between sources and (sets of) values. New mutual exclusive relations are defined to reflect the possible co-existence of multiple true values. A finer-grained copy detection method is also proposed to deal with sources with large profiles. The experimental results on three real-world datasets show the effectiveness of our approach.


title: SLAM and Static Driver Verifier: Technology Transfer of Formal Methods inside Microsoft

text: A tutorial introduction to designs in unifying theories of programming p. 40 An integration of program analysis and automated theorem proving p. 67 Verifying controlled components p. 87 Efficient CSP[subscript Z] data abstraction p. 108 State/event-based software model checking p. 128 Formalising behaviour trees with CSP p. 148 Generating MSCs from an integrated formal specification language p. 168 UML to B : formal verification of object-oriented models p. 187 Software verification with integrated data type refinement for integer arithmetic p. 207


title: Replicator neural networks for universal optimal source coding.

text: Replicator neural networks self-organize by using their inputs as desired outputs; they internally form a compressed representation for the input data. A theorem shows that a class of replicator networks can, through the minimization of mean squared reconstruction error (for instance, by training on raw data examples), carry out optimal data compression for arbitrary data vector sources. Data manifolds, a new general model of data sources, are then introduced and a second theorem shows that, in a practically important limiting case, optimal-compression replicator networks operate by creating an essentially unique natural coordinate system for the manifold.


title: Coarse to Fine: Diffusing Categories in Wikipedia

text: Automatic taxonomy construction aims to build a categorization system without human efforts. Traditional textual pattern based methods extract hyponymy relation in raw texts. However, these methods usually yield low precision and recall. In this paper, we propose a method to automatically find diffusing attributes to a category from Wikipedia infoboxes. We use the diffusing attribute to diffuse a coarsegrained category into several fine-grained subcategories and generate a finer-grained taxonomy. Experiments show our method can find proper diffusing attributes to categories across various domains.


title: Conditional Generators of Words Definitions

text: We explore recently introduced definition modeling technique that provided the tool for evaluation of different distributed vector representations of words through modeling dictionary definitions of words. In this work, we study the problem of word ambiguities in definition modeling and propose a possible solution by employing latent variable modeling and soft attention mechanisms. Our quantitative and qualitative evaluation and analysis of the model shows that taking into account words ambiguity and polysemy leads to performance improvement.


title: Probabilistic wavelet transform for partial discharge measurement of transformer

text: Partial discharge (PD) measurement provides a means for online monitoring and diagnosis of transformers. However, extensive interferences and noise can significantly jeopardize the measured PD signals and cause ambiguity in PD measurement interpretation. Necessary PD signal de-noising techniques need to be adopted and wavelet transform is one of such techniques. Mother wavelet selection, decomposition level determination and thresholding are important processes for effective PD extraction using wavelet transform. Various methods have been proposed in the literature to improve the above processes of wavelet transform. In these methods a single threshold is normally adopted at each decomposition level and a binary decision is made to indicate whether an extracted signal is PD signal or noise. However, in online PD measurements it is difficult to find a threshold, which can be used for extracting only PD signals without including any noise. As such, the signals determined by a single threshold cannot be assured as PD signals with certainty. To address the limitations caused by the single thresholding method in wavelet transform for PD signals extraction, this paper proposes quantile based multi-scale thresholding method at each decomposition level, which can thus provide probability indexes for the extracted signals evaluating the likelihood of these signals to be PD signals. To evaluate the proposed method, PD measurements have been conducted on both experimental PD models and inservice transformers at substation. The results are provided in the paper.


title: Comparisons of waist circumferences measured at 4 sites.

text:
BACKGROUND
Waist circumference (WC) is now accepted as a practical measure of adipose tissue distribution. Four body sites for WC measurements are commonly used, as follows: immediately below the lowest ribs (WC1), the narrowest waist (WC2), the midpoint between the lowest rib and the iliac crest (WC3), and immediately above the iliac crest (WC4).

OBJECTIVE
We sought to compare the magnitude and reliability of WC measured at these 4 sites in males and females.

DESIGN
WC was measured at each site 1 time in all subjects [49 males and 62 females, aged 7-83 y, with a body mass index (in kg/m(2)) of 9-43] and 3 times in a subgroup (n = 93) by one experienced observer using a heavy-duty inelastic tape. Body fat was measured in a subgroup (n = 74) with the use of dual-energy X-ray absorptiometry.

RESULTS
The mean values of WC were WC2 < WC1 < WC3 < WC4 (P < 0.01) in females and WC2 < WC1, WC3, and WC4 (P < 0.01) in males. For all 4 sites, measurement reproducibility was high, with intraclass correlation (r) values > 0.99. WC values were significantly correlated with fatness; correlations with trunk fat were higher than correlations with total body fat in both sexes.

CONCLUSIONS
WC values at the 4 commonly used anatomic sites differ in magnitude depending on sex, are highly reproducible, and are correlated with total body and trunk adiposity in a sex-dependent manner. These observations have implications for the use of WC measurements in clinical practice and patient-oriented research.


title: Making the Best of Mifare Classic Update

text: What would you do if you would be instructed to make a secure application built on the Mifare Classic? Arguably, due to the vulnerabilities shown in [5] and hinted at on [6], this is rather difficult and it may be easier to use to another chip. This document explores what the best is you can get, if the only option is Mifare Classic. We propose countermeasures against state restoration and against cloning. The effectiveness of these countermeasures depends on the absense of other vulnerabilities of


title: Reaching out: involving Users in Innovation Tasks through Social Media

text: Integrating social media into the innovation process can open up the potential for organizations to utilize the collective creativity of consumers from all over the world. The research in this paper sets out to identify how social media can facilitate innovation. By taking a Design Science Research approach this research presents the Social Media Innovation Method for matching innovation tasks with social media characteristics. This supports the selection of best suitable social media and can help organizations to achieve their innovation goals. At the core of the method is the honeycomb model which describes seven social media characteristics on three dimensions: audience, content and time. The method has been evaluated by using an approach called scenario walkthrough that is applied in a real-life spatial planning project. This research concludes that there is no one-size-fits-all answer to the question how social media can be of value for the innovation process. However, organizations that want to know how it can benefit their own innovation process can use the Social Media Innovation Method presented in this research as a way to provide an answer to that question, uniquely tailored to each innovation task for which social media is to be used.


title: A study on the bending mechanism of the flexible ureteroscope

text: Flexible ureteroscope is increasingly used for urologic treatment today. The key mechanism of the ureteroscope is the bending mechanism. It consists of a bending head and a control lever connected through a set of wire. The lighting and imaging optics as well as the surgical tools all go through the bending head. With the demand for improved performance and reduced size, more research on its design is still needed. In this paper, the modeling and simulation of an ureteroscope are presented, which pave the road for design optimization in the future.


title: Setting our bibliographic references free: towards open citation data

text: Purpose. Citation BLOCKIN BLOCKIN data BLOCKIN BLOCKIN needs BLOCKIN BLOCKIN to BLOCKIN BLOCKIN be BLOCKIN BLOCKIN recognized BLOCKIN BLOCKIN as BLOCKIN BLOCKIN a BLOCKIN BLOCKIN part BLOCKIN BLOCKIN of BLOCKIN BLOCKIN the BLOCKIN BLOCKIN Commons BLOCKIN BLOCKIN – BLOCKIN BLOCKIN those BLOCKIN BLOCKIN works BLOCKIN BLOCKIN that are freely and legally available for sharing – and placed in an open repository. Design/methodology/approach. The Open Citation Corpus is a new open repository of scholarly citation data, made available under a Creative Commons CC0 1.0 public domain dedication and encoded as Open Linked Data using the SPAR Ontologies. BLOCKIN Annotation Tools and CiTalO, to facilitate the semantic enhancement of the references in scholarly papers according to Semantic Publishing models and technologies. BLOCKIN publishers BLOCKIN BLOCKIN and BLOCKIN BLOCKIN institutions BLOCKIN BLOCKIN may BLOCKIN BLOCKIN freely BLOCKIN BLOCKIN build BLOCKIN BLOCKIN upon, BLOCKIN BLOCKIN enhance BLOCKIN BLOCKIN and reuse the open citation data for any purpose, without restriction under copyright or database law.


title: Mapping Images to Sentiment Adjective Noun Pairs with Factorized Neural Nets

text: We consider the visual sentiment task of mapping an image to an adjective noun pair (ANP) such as ”cute baby”. To capture the two-factor structure of our ANP semantics as well as to overcome annotation noise and ambiguity, we propose a novel factorized CNN model which learns separate representations for adjectives and nouns but optimizes the classification performance over their product. Our experiments on the publicly available SentiBank dataset show that our model significantly outperforms not only independent ANP classifiers on unseen ANPs and on retrieving images of novel ANPs, but also image captioning models which capture word semantics from co-occurrence of natural text; the latter turn out to be surprisingly poor at capturing the sentiment evoked by pure visual experience. That is, our factorized ANP CNN not only trains better from noisy labels, generalizes better to new images, but can also expands the ANP vocabulary on its own.


title: Pattern electrical stimulation of the human retina

text: Experiments were conducted to study if electrical stimulation of the retinal surface can elicit visual sensation in individuals blind from end-stage retinitis pigmentosa (RP) or age-related macular degeneration (AMD). Under local anesthesia, different stimulating electrodes were inserted through the eyewall and positioned over the surface of the retina. Subjects' psychophysical responses to electrical stimulation were recorded. Subjects perceived simple forms in response to pattern electrical stimulation of the retina. A non-flickering perception was created with stimulating frequencies between 40 and 50 Hz. The stimulation threshold was dependent on the targeted retinal area (macular versus extramacular).


title: Top-down facilitation of visual recognition.

text: Cortical analysis related to visual object recognition is traditionally thought to propagate serially along a bottom-up hierarchy of ventral areas. Recent proposals gradually promote the role of top-down processing in recognition, but how such facilitation is triggered remains a puzzle. We tested a specific model, proposing that low spatial frequencies facilitate visual object recognition by initiating top-down processes projected from orbitofrontal to visual cortex. The present study combined magnetoencephalography, which has superior temporal resolution, functional magnetic resonance imaging, and a behavioral task that yields successful recognition with stimulus repetitions. Object recognition elicited differential activity that developed in the left orbitofrontal cortex 50 ms earlier than it did in recognition-related areas in the temporal cortex. This early orbitofrontal activity was directly modulated by the presence of low spatial frequencies in the image. Taken together, the dynamics we revealed provide strong support for the proposal of how top-down facilitation of object recognition is initiated, and our observations are used to derive predictions for future research.


title: Models and Statistical Inference : The Controversy between Fisher and Neyman – Pearson

text: The main thesis of the paper is that in the case of modern statistics, the differences between the various concepts of models were the key to its formative controversies. The mathematical theory of statistical inference was mainly developed by Ronald A. Fisher, Jerzy Neyman, and Egon S. Pearson. Fisher on the one side and Neyman–Pearson on the other were involved often in a polemic controversy. The common view is that Neyman and Pearson made Fisher’s account more stringent mathematically. It is argued, however, that there is a profound theoretical basis for the controversy: both sides held conflicting views about the role of mathematical modelling. At the end, the influential programme of Exploratory Data Analysis is considered to be advocating another, more instrumental conception of models.


title: Learning how to match fresco fragments

text: One of the main problems faced during reconstruction of fractured archaeological artifacts is sorting through a large number of candidate matches between fragments to find the relatively few that are correct. Previous computer methods for this task provided scoring functions based on a variety of properties of potential matches, including color and geometric compatibility across fracture surfaces. However, they usually consider only one or at most a few properties at once, and therefore provide match predictions with very low precision. In this article, we investigate a machine learning approach that computes the probability that a match is correct based on the combination of many features. We explore this machine learning approach for ranking matches in three different sets of fresco fragments, finding that classifiers based on many match properties can be significantly more effective at ranking proposed matches than scores based on any single property alone. Our results suggest that it is possible to train a classifier on match properties in one dataset and then use it to rank predicted matches in another dataset effectively. We believe that this approach could be helpful in a variety of cultural heritage reconstruction systems.


title: Boosting the margin: A new explanation for the effectiveness of voting methods

text: One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated classifier usually does not increase as its size becomes very large, and often is observed to decrease even after the training error reaches zero. In this paper, we show that this phenomenon is related to the distribution of margins of the training examples with respect to the generated voting classification rule, where the margin of an example is simply the difference between the number of correct votes and the maximum number of votes received by any incorrect label. We show that techniques used in the analysis of Vapnik’s support vector classifiers and of neural networks with small weights can be applied to voting methods to relate the margin distribution to the test error. We also show theoretically and experimentally that boosting is especially effective at increasing the margins of the training examples. Finally, we compare our explanation to those based on the bias-variance decomposition.


title: Matching pursuits with time-frequency dictionaries

text: We introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order to best match the signal structures. Matching pursuits are general procedures to compute adaptive signal representations. With a dictionary of Gabor functions a matching pursuit defines an adaptive time-frequency transform. We derive a signal energy distribution in the time-frequency plane, which does not include interference terms, unlike Wigner and Cohen class distributions. A matching pursuit isolates the signal structures that are coherent with respect to a given dictionary. An application to pattern extraction from noisy signals is described. We compare a matching pursuit decomposition with a signal expansion over an optimized wavepacket orthonormal basis, selected with the algorithm of Coifman and Wickerhauser.


title: Support-Vector Networks

text: The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.


title: Self-Oscillating Flyback Converter for Mobile Batteries Charging Applications

text: The self-oscillating flyback converter is a popular circuit for cost-sensitive applications due to its simplicity and low component count. This paper presents self-oscillating fly-back converter for mobile batteries charging applications. The converter is designed to step-down the high voltage input to an acceptable level from 220 VAC to 7VDC. By adding indictor and capacitor the power factor and input currant increased, and became 500mA, where the old input currant was 250mA, Diode was connected between Emitter and collector, so the output voltage was improved, and has less ripple, in addition to the soft switching was obtained, simulation model is developed by using MULTISIM software. The operation principle of this converter is described using some operating waveforms based on simulation results and a detailed circuit operation analysis. Analytical results are verified by laboratory experiment. With an input of 220 VAC, 500mA and an output of 7V DC, the efficiency of circuit was75%.


title: Efficient and Low Latency Detection of Intruders in Mobile Active Authentication

text: Active authentication (AA) refers to the problem of continuously verifying the identity of a mobile device user for the purpose of securing the device. We address the problem of quickly detecting intrusions with lower false detection rates in mobile AA systems with higher resource efficiency. Bayesian and MiniMax versions of the quickest change detection (QCD) algorithms are introduced to quickly detect intrusions in mobile AA systems. These algorithms are extended with an update rule to facilitate low-frequency sensing which leads to low utilization of resources. Effectiveness of the proposed framework is demonstrated using three publicly available unconstrained face and touch gesture-based AA datasets. It is shown that the proposed QCD-based intrusion detection methods can perform better than many state-of-the-art AA methods in terms of latency and low false detection rates. Furthermore, it is shown that employing the proposed resource-efficient extension further improves the performance of the QCD-based setup.


title: Don't Be Lazy, Be Consistent: Postgres-R, A New Way to Implement Database Replication

text: \ N\| N\wOEN^|< e\ eNNN¡  ^  N¢X\£<¤ N¥N ¦||§N| ̈\<©RN\dV\a | aN ¦|<§ « \\NX¡;aUN\ O¢X\ N¬  ^  \NW \0\|aUNN@®u7 | a^NN¡e¤o  O¤ \ a \ aN\`¦||a |\ ̄ 0§N WJ§N|\§X a ¢N<N <£  | 7 §Xa  | \| O « aO¤ I J O¢X¦ \X§° a |± aNN 20\NO¢NN¡¤@\ O¤3 O¤¬ a\O3 ^\X \¦| \| §X ^9| §N\^\\NU| &&N¦  \a\w\£<\Na4e ́ * WXjaμ§N \N*  N¤  \<§N¦|  ` N| |a  ¦|NaUN<\\| |·¶[\u3 _ ̧ 1 oeR®u¦|\  O¢<μaN9N»<^N\|aUN<\¦[\N ¦|\  O¢ | @\ a a <£1⁄4j\ a& aN  0¦||a |\1⁄43 \|  O \N ¦||§N\| §NJ^4O¢μaN <£  μa \ a    μ\ \X§° a |± aNN


title: A neural network students' performance prediction model (NNSPPM)

text: In the academic industry, students' early performance prediction is important to academic communities so that strategic intervention can be planned before students reach the final semester. This paper presents a study on Artificial Neural Network (ANN) model development in predicting academic performance of engineering students. Cumulative Grade Point Average (CGPA) was used to measure the academic achievement at semester eight. The study was conducted at the Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), Malaysia. Students' results for the fundamental subjects in the first semester were used as independent variables or input predictor variables while CGPA at semester eight was used as the output or the dependent variable. The study was done for two different entry points namely Matriculation and Diploma intakes. Performances of the models were measured using the coefficient of Correlation R and Mean Square Error (MSE). The outcomes from the study showed that fundamental subjects at semester one and three have strong influence in the final CGPA upon graduation.


Deep Image Prior


title: OpenCL: A Parallel Programming Standard for Heterogeneous Computing Systems

text: The OpenCL standard offers a common API for program execution on systems composed of different types of computational devices such as multicore CPUs, GPUs, or other accelerators.


title: E-LOYALTY IS NOT ALL ABOUT TRUST , PRICE ALSO MATTERS : EXTENDING EXPECTATION-CONFIRMATION THEORY IN BOOKSELLING WEBSITES

text: Identifying factors that influence customers’ e-loyalty is paramount for practitioners and academics to develop successful marketing strategies and behavioral models. Online bookselling is a rapid growing industry in the UK, where e-loyalty models have yet to reach a conclusive argument. This paper aims to explore factors influencing customers’ e-loyalty to five online bookselling websites in the UK by testing a theoretical model, based on expectation-confirmation theory. A quantitative approach was employed using questionnaires; the sample consisted of 290 respondents (50% males, age range: 18 to over 54). The questionnaire was pretested and confirmatory factor analysis was performed to assess the measurement model. Structural equation modeling and MANOVA were employed to examine the association between latent constructs. Eleven hypotheses were formulated examining different independent variables in the theoretical model; results showed a significant direct and positive association between satisfaction and e-loyalty. Web design affected e-loyalty significantly on all bookselling websites, perceived value was a significant predictor of satisfaction while price notably influenced e-trust development. Etrust was not associated with e-loyalty. This study introduces new variables that affect e-loyalty as well as illuminates new associations between existing factors – perceived value, price, and trust are new aspects which practitioners and academics should take into account for marketing strategies and behavioral models, respectively. Hence, managers will likely increase customer satisfaction and loyalty by improving web design and balancing quality with price, predisposing positively customer’s attitudes towards the website.


title: A Storage Solution for Massive IoT Data Based on NoSQL

text: Storage is an important research direction of the data management of the Internet of Things. Massive and heterogeneous data of the Internet of Things brings the storage huge challenges. Based on the analysis of the IoT data characteristics, this paper proposed a storage management solution called IOTMDB based on NoSQL as current storage solutions are not well support storing massive and heterogeneous IoT data. Besides, the storage strategies about expressing and organizing IoT data in a uniform manner were proposed, some evaluations were carried out. Furthermore, we not only just concerned about descripting the data itself, but also cared for sharing of the data, so a data sharing mechanism based on ontology was proposed. Finally, index was studied and a set of query syntaxes to meet the needs of different kinds of IoT queries based NoSQL was given.


title: Automatic Definition of Modular Neural Networks

text: This paper illustrates an artiicial developmental system that is a computationally ee-cient technique for the automatic generation of complex Artiicial Neural Networks (ANN). Artiicial developmental system can develop a graph grammar into a modular ANN made of a combination of more simple subnetworks. A genetic algorithm is used to evolve coded grammars that generates ANNs for controlling a six-legged robot locomotion. A mechanism for the automatic deenition of sub-neural networks is incorporated. Using this mechanism, the genetic algorithm can automatically decompose a problem into subproblems, generate a subANN for solving the subproblem, and instantiate copies of this subANN to build a higher level ANN that solves the problem. We report some simulation results showing that the same problem cannot be solved if the mechanism for automatic deenition of sub-networks is suppressed. We support our argumentation with pictures describing the steps of development, how ANN structures are evolved, and how the ANNs compute.matic deention of sub-neural networks.


title: Analyzing the transitional region in low power wireless links

text: The wireless sensor networks community, has now an increased understanding of the need for realistic link layer models. Recent experimental studies have shown that real deployments have a "transitional region" with highly unreliable links, and that therefore the idealized perfect-reception-within-range models used in common network simulation tools can be very misleading. In this paper, we use mathematical techniques from communication theory to model and analyze the low power wireless links. The primary contribution of this work is the identification of the causes of the transitional region, and a quantification of their influence. Specifically, we derive expressions for the packet reception rate as a function of distance, and for the width of the transitional region. These expressions incorporate important channel and radio parameters such as the path loss exponent and shadowing variance of the channel; and the modulation and encoding of the radio. A key finding is that for radios using narrow-band modulation, the transitional region is not an artifact of the radio non-ideality, as it would exist even with perfect-threshold receivers because of multi-path fading. However, we hypothesize that radios with mechanisms to combat multi-path effects, such as spread-spectrum and diversity techniques, can reduce the transitional region.


title: A New Approach Layered Architecture Based Clustering for Prolong Life of Wireless Sensor Network (WSN)

text: Sensor nodes are the tiny particles which have to rely on limited power of energy. Sensor nodes with limited battery power are deployed to gather information in wireless environment throughout the field. Due to the small amount of energy in a node of sensor web, energy balancing among nodes is quite important. Secondly, due to lack of energy in a particular node we are also concerned to the power saving of a sensor node. We are also concerned here to prolong the life of sensor node so that it can sense the field for longer period of time. All these features are the prime necessity to introduce a new energy efficient routing algorithm in this paper.


Precisely recommending relevant items from massive candidates to a large number of users is an indispensable yet computationally expensive task in many online platforms (e.g., Amazon.com and Netflix.com). A promising way is to project users and items into a Hamming space and then recommend items via Hamming distance. However, previous studies didn't address the cold-start challenges and couldn't make the best use of preference data like implicit feedback. To fill this gap, we propose a Discrete Content-aware Matrix Factorization (DCMF) model, 1) to derive compact yet informative binary codes at the presence of user/item content information; 2) to support the classification task based on a local upper bound of logit loss; 3) to introduce an interaction regularization for dealing with the sparsity issue. We further develop an efficient discrete optimization algorithm for parameter learning. Based on extensive experiments on three real-world datasets, we show that DCFM outperforms the state-of-the-arts on both regression and classification tasks.


title: Application of Representation Theory to Dual-Mode Microwave Bandpass Filters

text: This paper discusses the physics behind the operation of dual-mode bandpass filters from a field theoretical point of view. It is argued that the two degenerate modes of the empty dual-mode cavity, commonly taken as the vertical and horizontal polarizations, become nonphysical when coupling and tuning elements are inserted. Instead, the original degenerate modes are rotated, or modified in a complex way, to generate two new modes whose characteristics depend on the coupling and tuning elements. It is shown that the tuning elements, as placed in existing dual-mode filter designs, act as both tuning and coupling elements. A working dual-mode filter can be designed with only ldquotuningrdquo elements present. A physical representation of dual-mode filters in terms of the eigenresonances of the dual-mode cavities, with the tuning and coupling elements present, is introduced. Two fourth-order dual-mode rectangular cavity filters with the same response in the passband and its vicinity are also presented to demonstrate the similar role played by ldquotuningrdquo and coupling elements in dual-mode cavities. The first filter uses only ldquotuningrdquo elements, while the second is based only on ldquocouplingrdquo elements.


title: Indra: A Word Embedding and Semantic Relatedness Server

text: In recent years word embedding/distributional semantic models evolved to become a fundamental component in many natural language processing (NLP) architectures due to their ability of capturing and quantifying semantic associations at scale. Word embedding models can be used to satisfy recurrent tasks in NLP such as lexical and semantic generalisation in machine learning tasks, finding similar or related words and computing semantic relatedness of terms. However, building and consuming specific word embedding models require the setting of a large set of configurations, such as corpus-dependant parameters, distance measures as well as compositional models. Despite their increasing relevance as a component in NLP architectures, existing frameworks provide limited options in their ability to systematically build, parametrise, compare and evaluate different models. To answer this demand, this paper describes INDRA, a multi-lingual word embedding/distributional semantics framework which supports the creation, use and evaluation of word embedding models. In addition to the tool, INDRA also shares more than 65 pre-computed models in 14 languages.


title: Association Rule Mining: A Survey

text: Data mining [Chen et al. 1996] is the process of extracting interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from large information repositories such as: relational database, data warehouses, XML repository, etc. Also data mining is known as one of the core processes of Knowledge Discovery in Database (KDD). Many people take data mining as a synonym for another popular term, Knowledge Discovery in Database (KDD). Alternatively other people treat Data Mining as the core process of KDD. The KDD processes are shown in Figure 1 [Han and Kamber 2000]. Usually there are three processes. One is called preprocessing, which is executed before data mining techniques are applied to the right data. The preprocessing includes data cleaning, integration, selection and transformation. The main process of KDD is the data mining process, in this process different algorithms are applied to produce hidden knowledge. After that comes another process called postprocessing, which evaluates the mining result according to users’ requirements and domain knowledge. Regarding the evaluation results, the knowledge can be presented if the result is satisfactory, otherwise we have to run some or all of those processes again until we get the satisfactory result. The actually processes work as follows. First we need to clean and integrate the databases. Since the data source may come from different databases, which may have some inconsistences and duplications, we must clean the data source by removing those noises or make some compromises. Suppose we have two different databases, different words are used to refer the same thing in their schema. When we try to integrate the two sources we can only choose one of them, if we know that they denote the same thing. And also real world data tend to be incomplete and noisy due to the manual input mistakes. The integrated data sources can be stored in a database, data warehouse or other repositories. As not all the data in the database are related to our mining task, the second process is to select task related data from the integrated resources and transform them into a format that is ready to be mined. Suppose we want to find which items are often purchased together in a supermarket, while the database that records the purchase history may contains customer ID, items bought, transaction time, prices, number of each items and so on, but for this specific task we only need items bought. After selection of relevant data, the database that we are going to apply our data mining techniques to will be much smaller, consequently the whole process will be


title: Mobile robot positioning: Sensors and techniques

text: Exact knowledge of the position of a vehicle is a fundamental problem in mobile robot applications. In search for a solution, researchers and engineers have developed a variety of systems, sensors, and techniques for mobile robot positioning. This paper provides a review of relevant mobile robot positioning technologies. The paper defines seven categories for positioning systems: 1. Odometry; 2. Inertial Navigation; 3. Magnetic Compasses; 4. Active Beacons; 5. Global Positioning Systems; 6. Landmark Navigation; and 7. Model Matching. The characteristics of each category are discussed and examples of existing technologies are given for each category. The field of mobile robot navigation is active and vibrant, with more great systems and ideas being developed continuously. For this reason the examples presented in this paper serve only to represent their respective categories, but they do not represent a judgment by the authors. Many ingenious approaches can be found in the literature, although, for reasons of brevity, not all could be cited in this paper. 1) (Corresponding Author) The University of Michigan, Advanced Technologies Lab, 1101 Beal Avenue, Ann Arbor, MI 48109-2110, Ph.: 313-763-1560, Fax: 313-944-1113. Email: johannb@umich.edu 2) Naval Command, Control, and Ocean Surveillance Center, RDT&E Division 5303, 271 Catalina Boulevard, San Diego, CA 92152-5001, Email: Everett@NOSC.MIL 3) The University of Michigan, Advanced Technologies Lab, 1101 Beal Avenue, Ann Arbor, MI 481092110, Email: Feng@engin.umich.edu 4) The University of Michigan, Dept. of Nuclear Engineering and Radiological Sciences, 239 Cooley Bldg., Ann Arbor, MI 48109, Email: dkw@umich.edu


title: On the role of linguistic descriptions of data in the building of natural language generation systems

text: This paper explores the current state of the task of generating easily understandable information from data for people using natural language, which is currently addressed by two independent research fields: the natural language generation field — and, more specifically, the data-to-text sub-field — and the linguistic descriptions of data field. Both approaches are explained in a detailed description which includes: i) a methodological revision of both fields including basic concepts and definitions, models and evaluation procedures; ii) the most relevant systems, use cases and real applications described in the literature. Some reflections about the current state and future trends of each field are also provided, followed by several remarks that conclude by hinting at some potential points of mutual interest and convergence between both fields. © 2015 Elsevier B.V. All rights reserved.


title: A Survey on Ambient-Assisted Living Tools for Older Adults

text: In recent years, we have witnessed a rapid surge in assisted living technologies due to a rapidly aging society. The aging population, the increasing cost of formal health care, the caregiver burden, and the importance that the individuals place on living independently, all motivate development of innovative-assisted living technologies for safe and independent aging. In this survey, we will summarize the emergence of `ambient-assisted living” (AAL) tools for older adults based on ambient intelligence paradigm. We will summarize the state-of-the-art AAL technologies, tools, and techniques, and we will look at current and future challenges.


title: Behavioral Patterns of Older Adults in Assisted Living

text: In this paper, we examine at-home activity rhythms and present a dozen of behavioral patterns obtained from an activity monitoring pilot study of 22 residents in an assisted living setting with four case studies. Established behavioral patterns have been captured using custom software based on a statistical predictive algorithm that models circadian activity rhythms (CARs) and their deviations. The CAR was statistically estimated based on the average amount of time a resident spent in each room within their assisted living apartment, and also on the activity level given by the average n.umber of motion events per room. A validated in-home monitoring system (IMS) recorded the monitored resident's movement data and established the occupancy period and activity level for each room. Using these data, residents' circadian behaviors were extracted, deviations indicating anomalies were detected, and the latter were correlated to activity reports generated by the IMS as well as notes of the facility's professional caregivers on the monitored residents. The system could be used to detect deviations in activity patterns and to warn caregivers of such deviations, which could reflect changes in health status, thus providing caregivers with the opportunity to apply standard of care diagnostics and to intervene in a timely manner.


title: Clustering of time series data - a survey

text: Time series clustering has been shown effective in providing useful information in various domains. There seems to be an increased interest in time series clustering as part of the effort in temporal data mining research. To provide an overview, this paper surveys and summarizes previous works that investigated the clustering of time series data in various application domains. The basics of time series clustering are presented, including general-purpose clustering algorithms commonly used in time series clustering studies, the criteria for evaluating the performance of the clustering results, and the measures to determine the similarity/dissimilarity between two time series being compared, either in the forms of raw data, extracted features, or some model parameters. The past researchs are organized into three groups depending upon whether they work directly with the raw data either in the time or frequency domain, indirectly with features extracted from the raw data, or indirectly with models built from the raw data. The uniqueness and limitation of previous research are discussed and several possible topics for future research are identified. Moreover, the areas that time series clustering have been applied to are also summarized, including the sources of data used. It is hoped that this review will serve as the steppingstone for those interested in advancing this area of research. 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.


title: Miniature Ultra-Wideband Power Divider Using Bridged T-Coils

text: In this work, a miniature ultra-wideband (UWB) power divider (PD) is proposed. By implementing the transmission lines of a two-stage Wilkinson PD using bridged T-coils, very compact size with no reduction in bandwidth can be achieved. Specifically, a proposed UWB two-way PD with a center frequency f0=5.5 GHz is implemented using the commercial GaAs pHEMT process. The circuit size without pads is only 1.45 mm × 0.84 mm, which is about 0.027λ0 × 0.016λ0 at f0. The fractional bandwidth for 15-dB input/output return loss and isolation is 110%, and the in-band insertion loss is within 1.3 ± 0.36 dB.


title: Using Syntactic Features for Phishing Detection

text: This paper reports on the comparison of the subject and object of verbs in their usage between phishing emails and legitimate emails. The purpose of this research is to explore whether the syntactic structures and subjects and objects of verbs can be distinguishable features for phishing detection. To achieve the objective, we have conducted two series of experiments: the syntactic similarity for sentences, and the subject and object of verb comparison. The results of the experiments indicated that both features can be used for some verbs, but more work has to be done for others. Keywords—phishing detection; syntactic similarity; parse tree path.


title: LTE-advanced in 3GPP Rel -13/14: an evolution toward 5G

text: As the fourth generation (4G) LTE-Advanced network becomes a commercial success, technologies for beyond 4G and 5G are being actively investigated from the research perspective as well as from the standardization perspective. While 5G will integrate the latest technology breakthroughs to achieve the best possible performance, it is expected that LTE-Advanced will continue to evolve, as a part of 5G technologies, in a backward compatible manner to maximize the benefit from the massive economies of scale established around the 3rd Generation Partnership Project (3GPP) LTE/LTE-Advanced ecosystem from Release 8 to Release 12. In this article we introduce a set of key technologies expected for 3GPP Release 13 and 14 with a focus on air interface aspects, as part of the continued evolution of LTE-Advanced and as a bridge from 4G to 5G.


title: Search-Oriented Conversational AI (SCAI)

text: The aim of SCAI@ICTIR2017 is to bring together IR and AI communities to instigate future direction of search-oriented conversational systems. We identified the number of research areas related to conversational AI which is of actual interest to both communities and which have not been fully explored yet. We think it's beneficial to exchange our visions. We solicit the paper submissions and more importantly proposals for panel discussions where researchers can exchange opinions and experiences. We believe that the proposed workshop is relevant to ICTIR since we look for novel contributions to search-oriented conversational systems which are a new and promising area.


title: Fabrication of extended-release patient-tailored prednisolone tablets via fused deposition modelling (FDM) 3D printing.

text: Rapid and reliable tailoring of the dose of controlled release tablets to suit an individual patient is a major challenge for personalized medicine. The aim of this work was to investigate the feasibility of using a fused deposition modelling (FDM) based 3D printer to fabricate extended release tablet using prednisolone loaded poly(vinyl alcohol) (PVA) filaments and to control its dose. Prednisolone was loaded into a PVA-based (1.75 mm) filament at approximately 1.9% w/w via incubation in a saturated methanolic solution of prednisolone. The physical form of the drug was assessed using differential scanning calorimetry (DSC) and X-ray powder diffraction (XRPD). Dose accuracy and in vitro drug release patterns were assessed using HPLC and pH change flow-through dissolution test. Prednisolone loaded PVA filament demonstrated an ability to be fabricated into regular ellipse-shaped solid tablets using the FDM-based 3D printer. It was possible to control the mass of printed tablet through manipulating the volume of the design (R(2) = 0.9983). On printing tablets with target drug contents of 2, 3, 4, 5, 7.5 and 10mg, a good correlation between target and achieved dose was obtained (R(2) = 0.9904) with a dose accuracy range of 88.7-107%. Thermal analysis and XRPD indicated that the majority of prednisolone existed in amorphous form within the tablets. In vitro drug release from 3D printed tablets was extended up to 24h. FDM based 3D printing is a promising method to produce and control the dose of extended release tablets, providing a highly adjustable, affordable, minimally sized, digitally controlled platform for producing patient-tailored medicines.


title: Leave-one-out error and stability of learning algorithms with applications

text: The leave-one-out error is an important statistical estimator of the performance of a learning algorithm. Unlike the empirical error, it is almost unbiased and is frequently used for model selection. We review attempts aiming at justifying the use of leave-one-out error in Machine Learning. We especially focus on the concept of stability of a learning algorithm and show how this can be used to formally link leaveone-out error to the generalization error. Stability has also motivated recent work on averaging techniques similar to bagging which we briefly summarize in the paper. The ideas we develop are illustrated in some details in the context of kernel-based learning algorithms.


title: Distortion correction formulas for pre-objective dual galvanometer laser scanning.

text: For many applications a laser beam needs to be scanned across a plane. Several methods exist to accomplish this.One method, dual galvanometer pre-objective scanning, is particularly well suited to precise laser marking applications. The laser beam impinges on a first mirror mounted on a galvanometer motor, then on a second mirror with its rota tion axis parallel to the laser axis, and passes then through the flat-field objective lens, which focuses the beam onto the image plane. Figure 1 shows a photograph of the SILAMATIK laser marker. This letter investigates what amount of distortion is intro duced and how it can be corrected electronically. We as sume that the lens is axial symmetric. The geometry is that of Fig. 2. Say the first mirror is rotated a mechanical angle a/2 from its rest position and the second mirror an angle 6/2. The direction vector lR of the beam that falls on the lens is


title: Improving recommendation lists through topic diversification

text: In this work we present topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spectrum of interests. Though being detrimental to average accuracy, we show that our method improves user satisfaction with recommendation lists, in particular for lists generated using the common item-based collaborative filtering algorithm.Our work builds upon prior research on recommender systems, looking at properties of recommendation lists as entities in their own right rather than specifically focusing on the accuracy of individual recommendations. We introduce the intra-list similarity metric to assess the topical diversity of recommendation lists and the topic diversification approach for decreasing the intra-list similarity. We evaluate our method using book recommendation data, including offline analysis on 361, !, 349 ratings and an online study involving more than 2, !, 100 subjects.


title: Healthy Menus Recommendation: Optimizing the Use of the Pantry

text: We are often unable to plan menus ahead, thus making poor and unhealthy choices of meals. Besides healthy, one may want menus in which ingredients harmonize and cover well the available ingredients in the pantry. In this paper, we propose a novel multi-objective-based recommender of menus that features an optimal balance between nutritional aspects, harmony and coverage of available ingredients. We conduct experiments on real-world and synthetic datasets and show that our approach achieves the desired levels of nutrients, harmonization and coverage of ingredients.


title: Learning a convolutional neural network for non-uniform motion blur removal

text: In this paper, we address the problem of estimating and removing non-uniform motion blur from a single blurry image. We propose a deep learning approach to predicting the probabilistic distribution of motion blur at the patch level using a convolutional neural network (CNN). We further extend the candidate set of motion kernels predicted by the CNN using carefully designed image rotations. A Markov random field model is then used to infer a dense non-uniform motion blur field enforcing motion smoothness. Finally, motion blur is removed by a non-uniform deblurring model using patch-level image prior. Experimental evaluations show that our approach can effectively estimate and remove complex non-uniform motion blur that is not handled well by previous approaches.


title: A Simple Compact Reconfigurable Slot Antenna With a Very Wide Tuning Range

text: A simple and compact slot antenna with a very wide tuning range is proposed. A 25 mm (roughly equal to λH/8, where λH corresponds to the highest frequency of the tuning range) open slot is etched at the edge of the ground. To achieve the tunability, only two lumped elements, namely, a PIN diode and a varactor diode are used in the structure. By switching the PIN diode placed at the open end of the slot, the slot antenna can resonate as a standard slot (when the switch is on) or a half slot (when the switch is off). Continuous tuning over a wide frequency range in those two modes can be achieved by adjusting the reverse bias (giving different capacitances) of the varactor diode loaded in the slot. Through optimal design, the tuning bands of the two modes are stitched together to form a very wide tuning range. The fabricated prototype has a tuning frequency range from 0.42 GHz to 1.48 GHz with Sll better than -10 dB, giving a frequency ratio (fR = fu/fL) of 3.52:1. The measured full spherical radiation patterns show consistent radiation characteristics of the proposed antenna within the whole tuning range.


title: Cellular and synaptic mechanisms of nicotine addiction.

text: The tragic health effects of nicotine addiction highlight the importance of investigating the cellular mechanisms of this complex behavioral phenomenon. The chain of cause and effect of nicotine addiction starts with the interaction of this tobacco alkaloid with nicotinic acetylcholine receptors (nAChRs). This interaction leads to activation of reward centers in the CNS, including the mesoaccumbens DA system, which ultimately leads to behavioral reinforcement and addiction. Recent findings from a number of laboratories have provided new insights into the biologic processes that contribute to nicotine self-administration. Examination of the nAChR subtypes expressed within the reward centers has identified potential roles for these receptors in normal physiology, as well as the effects of nicotine exposure. The high nicotine sensitivity of some nAChR subtypes leads to rapid activation followed in many cases by rapid desensitization. Assessing the relative importance of these molecular phenomena in the behavioral effects of nicotine presents an exciting challenge for future research efforts.


title: A survey of point-based POMDP solvers

text: The past decade has seen a significant breakthrough in research on solving partially observable Markov decision processes (POMDPs). Where past solvers could not scale beyond perhaps a dozen states, modern solvers can handle complex domains with many thousands of states. This breakthrough was mainly due to the idea of restricting value function computations to a finite subset of the belief space, permitting only local value updates for this subset. This approach, known as point-based value iteration, avoids the exponential growth of the value function, and is thus applicable for domains with longer horizons, even with relatively large state spaces. Many extensions were suggested to this basic idea, focusing on various aspects of the algorithm—mainly the selection of the belief space subset, and the order of value function updates. In this survey, we walk the reader through the fundamentals of point-based value iteration, explaining the main concepts and ideas. Then, we survey the major extensions to the basic algorithm, discussing their merits. Finally, we include an extensive empirical analysis using well known benchmarks, in order to shed light on the strengths and limitations of the various approaches.


Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps


Smartphone Addiction in University Students and Its Implication for Learning


title: Design and Implementation of a Wearable Gas Sensor Network for Oil and Gas Industry Workers

text: Industrial environment usually involves some types of hazardous substances including toxic and/or flammable gases. Accidental gas leakage can cause potential dangers to a plant, its employees and surrounding neighborhoods. Around 64% of accidents that happen in the oil fields are due to combustibles and/or toxic gases. The safety plan of most industries includes measures to reduce risk to humans and plants by incorporating early-warning devices, such as gas detectors. Most existing tools for monitoring gases are stationary and incapable of accurately measuring individual exposures that depend on personal lifestyles and environment. This paper provides a design and implementation of a wearable gas sensor network by building sensor nodes with wireless communication modules which communicate their data along the network. The system is designed to be flexible, low cost, low maintenance and with accurate performance to detect toxic gases in a timely fashion to warn employees before an existence of a disaster.


title: Estimating relatedness via data compression

text: We show that it is possible to use data compression on independently obtained hypotheses from various tasks to algorithmically provide guarantees that the tasks are sufficiently related to benefit from multitask learning. We give uniform bounds in terms of the empirical average error for the true average error of the n hypotheses provided by deterministic learning algorithms drawing independent samples from a set of n unknown computable task distributions over finite sets.


title: Sharing Personal Content Online: Exploring Channel Choice and Multi-Channel Behaviors

text: People share personal content online with varied audiences, as part of tasks ranging from conversational-style content sharing to collaborative activities. We use an interview- and diary-based study to explore: 1) what factors impact channel choice for sharing with particular audiences; and 2) what behavioral patterns emerge from the ability to combine or switch between channels. We find that in the context of different tasks, participants match channel features to selective-sharing and other task-based needs, shaped by recipient attributes and communication dynamics. Participants also combine multiple channels to create composite sharing features or reach broader audiences when one channel is insufficient. We discuss design implications of these channel dynamics.


title: Comparing Task Models for User Interface Design

text: Many task models, task analysis methods, and supporting tools have been introduced in the literature and are widely used in practice. With this comes need to understand their scopes and their differences. This chapter provides a thorough review of selected, significant task models along with their method and supporting tools. For this purpose, a meta-model of each task model is expressed as an Entity-Relationship-Attribute schema (ERA) and discussed. This leads to a comparative analysis of task models according to aims and goals, discipline, concepts and relationships, expressiveness of static and dynamic structures. Following is discussion of the model with respect to developing life cycle steps, tool support, advantages, and shortcomings. This comparative analysis provides a reference framework against which task models can be understood with respect to each other. The appreciation of the similarities and the differences allows practioners to identify a task model that fits a situation’s given requirements. It shows how a similar concept or relationship translates in different task usage models.


title: Regression from patch-kernel

text: In this paper, we present a patch-based regression framework for addressing the human age and head pose estimation problems. Firstly, each image is encoded as an ensemble of orderless coordinate patches, the global distribution of which is described by Gaussian mixture models (GMM), and then each image is further expressed as a specific distribution model by Maximum a Posteriori adaptation from the global GMM. Then the patch-kernel is designed for characterizing the Kullback-Leibler divergence between the derived models for any two images, and its discriminating power is further enhanced by a weak learning process, called inter-modality similarity synchronization. Finally, kernel regression is employed for ultimate human age or head pose estimation. These three stages are complementary to each other, and jointly minimize the regression error. The effectiveness of this regression framework is validated by three experiments: 1) on the YAMAHA aging database, our solution brings a more than 50% reduction in age estimation error compared with the best reported results; 2) on the FG-NET aging database, our solution based on raw image features performs even better than the state-of-the-art algorithms which require fine face alignment for extracting warped appearance features; and 3) on the CHIL head pose database, our solution significantly outperforms the best one reported in the CLEAR07 evaluation.


title: Stochastic Pooling for Regularization of Deep Convolutional Neural Networks

text: We introduce a simple and effective method for regularizing large convolutional neural networks. We replace the conventional deterministic pooling operations with a stochastic procedure, randomly picking the activation within each pooling region according to a multinomial distribution, given by the activities within the pooling region. The approach is hyper-parameter free and can be combined with other regularization approaches, such as dropout and data augmentation. We achieve state-of-the-art performance on four image datasets, relative to other approaches that do not utilize data augmentation.


title: Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree

text: Abstract We seek to improve deep neural networks by generalizing the pooling operations that play a central role in current architectures. We pursue a careful exploration of approaches to allow pooling to learn and to adapt to complex and variable patterns. The two primary directions lie in (1) learning a pooling function via (two strategies of) combining of max and average pooling, and (2) learning a pooling function in the form of a tree-structured fusion of pooling filters that are themselves learned. In our experiments every generalized pooling operation we explore improves performance when used in place of average or max pooling. We experimentally demonstrate that the proposed pooling operations provide a boost in invariance properties relative to conventional pooling and set the state of the art on several widely adopted benchmark datasets; they are also easy to implement, and can be applied within various deep neural network architectures. These benefits come with only a light increase in computational overhead during training (ranging from 5% to 15% in timing experiments) and a very modest increase in the number of model parameters. For example, using 45 additional parameters, we improve AlexNet performance on ImageNet by 6% relative (top-5, single-view).


title: A survey of frequent subgraph mining algorithms

text: Graph mining is an important research area within the domain of data mining. The field of study concentrates on the identification of frequent subgraphs within graph data sets. The research goals are directed at: (i) effective mechanisms for generating candidate subgraphs (without generating duplicates) and (ii) how best to process the generated candidate subgraphs so as to identify the desired frequent subgraphs in a way that is computationally efficient and procedurally effective. This paper presents a survey of current research in the field of frequent subgraph mining, and proposed solutions to address the main research issues.


title: Notions of Video Game Addiction and Their Relation to Self-Reported Addiction Among Players of World of Warcraft

text: In this study, 438 players of the online video game, World of Warcraft, completed a survey about video game addiction and answered an open-ended question about behaviors they considered characteristic of video game addiction. Responses were coded and correlated with players’ self-reports of being addicted to games and scores on a modified video game addiction scale. The behaviors most frequently mentioned as characteristic of addiction included playing a lot and games’ interfering with other activities, especially socializing or work. Few players mentioned such signs of addiction as withdrawal symptoms or tolerance, and some thought it was not possible to become addicted to video games. Self-reported addiction to video games correlated positively with perceptions that video game addiction involved playing a lot or playing to escape problems, and correlated negatively with perceptions that addiction involved games’ interfering with other activities or not being able to stop play. Implications for assessment are discussed.


title: Consideration of 10kW in-wheel type axial-gap motor using ferrite permanent magnets

text: In general, the in-wheel type permanent magnet synchronous motor (PMSM) for electric city commuters uses powerful rare earth permanent magnets (PMs). However, the employment of the rare earth PMs should be reduced due to high prices. Therefore, it is important to develop an in-wheel PMSM that does not use the rare earth PMs. We proposed 5kW in-wheel type axial gap motor that can generate high torque density and has features such as a coreless rotor structure with ferrite PMs and a reduction gearbox in the inner side of the stator. And we have presented details of experimental results on a prototype. So, in this paper, in order to achieve further higher power output, we considered a 10 kW in-wheel motor with the proposed structure. The motor with 10-kW output was examined by means of using 3D-FEM and experimental results in order to attain the further higher power as a rare earth free motor.


title: A Personalized Markov Clustering and Deep Learning Approach for Arabic Text Categorization

text: Text categorization has become a key research field in the NLP community. However, most works in this area are focused on Western languages ignoring other Semitic languages like Arabic. These languages are of immense political and social importance necessitating robust categorization techniques. In this paper, we present a novel three-stage technique to efficiently classify Arabic documents into different categories based on the words they contain. We leverage the significance of root-words in Arabic and incorporate a combination of Markov clustering and Deep Belief Networks to classify Arabic words into separate groups (clusters). Our approach is tested on two public datasets giving a F-Measure of 91.02%.


title: Algorithms on strings, trees, and sequences

text: Linear-Time Construction of Suffix Trees We will present two methods for constructing suffix trees in detail, Ukkonen’s method and Weiner’s method. Weiner was the first to show that suffix trees can be built in linear time, and his method is presented both for its historical importance and for some different technical ideas that it contains. However, lJkkonen’s method is equally fast and uses far less space (i.e., memory) in practice than Weiner’s method Hence Ukkonen is the method of choice for most problems requiring the construction of a suffix tree. We also believe that Ukkonen’s method is easier to understand. Therefore, it will be presented first A reader who wishes to study only one method is advised to concentrate on it. However, our development of Weiner’s method does not depend on understanding Ukkonen’s algorithm, and the two algorithms can be read independently (with one small shared section noted in the description of Weiner’s method).


title: Enterprise Software System Integration : An Architectural Perspective

text: The present thesis is concerned with enterprise software systems of companies in the Swedish electricity industry, an industry that for the past few years has been exposed to a fairly tumultuous change process as a consequence of legislative reforms. Previously, the business operations of electric utilities – as well as those of most companies in the computerized world – were supported by a number of isolated software systems performing specific tasks. In recent years, these systems have been extended, and more importantly, integrated into a company-wide system in its own right, in this thesis referred to as the enterprise software system. As enterprise software systems have evolved, so has a need for strategies, methods and techniques for their management. Enterprise software system management involves a number of concerns; of these concerns, software integration is one of the most prominent. The discipline of software architecture is concerned with the modeling of large-scale structures of software systems. While the generated models are employed for a number of purposes , their perhaps most significant function is to serve as a base for reasoning about the represented system. Several methods for analysis of architectures have been proposed in recent years, and although software architecture analysis has displayed considerable success for a number of systems, enterprise software systems have to a large extent been ignored. In the empirical context of the Scandinavian electricity industry, this thesis explores the applicability of software architecture analysis to enterprise software system integration. Two conceptually different architectural analysis methods – deduction-and induction-based approaches – are considered, as well as the engineering process in which architectural analysis is performed. As a result of the investigations, the thesis proposes a modified process for architectural analysis, presents an evaluation of deduction-based analysis methods , and proposes an adaptation of induction-based analysis methods to the enterprise software system context.


title: Wireless Sensor Network for Precise Agriculture Monitoring

text: Precision Agriculture Monitor System (PAMS) is an intelligent system which can monitor the agricultural environments of crops and provides service to farmers. PAMS based on the wireless sensor network (WSN) technique attracts increasing attention in recent years. The purpose of such systems is to improve the outputs of crops by means of managing and monitoring the growth period. This paper presents the design of a WSN for PAMS, shares our real-world experience, and discusses the research and engineering challenges in implementation and deployments.


title: Synthesis and Application of Monolayer Semiconductors (June 2015)

text: Recently, semiconducting monolayers, such as MoS2 and WSe2, have been highlighted for their spin-valley coupling, diverse band structures, bendability, and excellent optoelectronic performances. With a subnanometer thickness of atomic layers, the transition metal dichalcogenides (TMDc) atomic layers demonstrate a significant photoresponse, considerable absorption to incident sunlight and favorable transport performances, leading to applications in the electronic circuit requiring low stand-by power, diverse optoelectronic devices, and next-generation nanoelectronics. Therefore, the class of monolayer TMDc offers a burgeoning field in materials science, fundamental physics, and optoelectronics. A feasible synthetic process to realize controlled synthesis of large area and high quality of TMDc monolayers is in demands. In this review, we will introduce the progress on synthesis and applications of the TMDc atomic layers.


title: Human skin as data transmission medium for improved privacy and usability in wearable electronics

text: Wearable electronics have multiple benefits in medical applications, such as feedback, monitoring and prevention. The number of these devices in circulation is rapidly growing, emphasizing increasing problems in their use caused by one of two standard communication types - wireless communications and wired communications. These problems include obtrusiveness and inconvenient usage, limited bandwidth, privacy and security issues and relatively high energy consumption. Wireless communications are less secure, with a more limited bandwidth but more unobtrusive and convenient than wired ones. On the other hand, wired communications don’t need so many batteries, thereby devices can be charged more rarely and provide more privacy. A third, emerging technology for wearable sensor network communication using the body itself as communication medium tries to solve these problems. The body coupled communications benefit from the communication privacy of the wired networks, while not requiring obtrusive wiring. Unfortunately this type of communication requires complex signal processing for reliable communications as the signal changes depending on the body parts connected. To solve this problem and allow more rapid adoption of body coupled communications in wearable electronics we develop a transfer function for calculating the parameters of human skin as data transmission medium, allowing more usable and privacy oriented communications between the wearable nodes. Additionally this has the potential of reducing the energy consumption of wearable devices making them more practical to use for a prolonged period of time as well as opening new applications as secure personal data exchange using touch.


title: Gender and Smile Classification Using Deep Convolutional Neural Networks

text: Facial gender and smile classification in unconstrained environment is challenging due to the invertible and large variations of face images. In this paper, we propose a deep model composed of GNet and SNet for these two tasks. We leverage the multi-task learning and the general-to-specific fine-tuning scheme to enhance the performance of our model. Our strategies exploit the inherent correlation between face identity, smile, gender and other face attributes to relieve the problem of over-fitting on small training set and improve the classification performance. We also propose the tasks-aware face cropping scheme to extract attribute-specific regions. The experimental results on the ChaLearn 16 FotW dataset for gender and smile classification demonstrate the effectiveness of our proposed methods.


title: Evidence-Based Psychological Assessment.

text: In recent years there has been increasing emphasis on evidence-based practice in psychology (EBPP), and as is true in most health care professions, the primary focus of EBPP has been on treatment. Comparatively little attention has been devoted to applying the principles of EBPP to psychological assessment, despite the fact that assessment plays a central role in myriad domains of empirical and applied psychology (e.g., research, forensics, behavioral health, risk management, diagnosis and classification in mental health settings, documentation of neuropsychological impairment and recovery, personnel selection and placement in organizational contexts). This article outlines the central elements of evidence-based psychological assessment (EBPA), using the American Psychological Association's tripartite definition of EBPP as integration of the best available research with clinical expertise in the context of patient characteristics, culture, and preferences. After discussing strategies for conceptualizing and operationalizing evidence-based testing and evidence-based assessment, 6 core skills and 3 meta-skills that underlie proficiency in psychological assessment are described. The integration of patient characteristics, culture, and preferences is discussed in terms of the complex interaction of patient and assessor identities and values throughout the assessment process. A preliminary framework for implementing EBPA is offered, and avenues for continued refinement and growth are described.


title: Very low-voltage fully differential amplifier for switched-capacitor applications

text: A fully differential opamp suitable for very-low voltage switched-capacitor circuits in standard CMOS technologies is introduced. The proposed two stage opamp needs a simple low voltage CMFB switched-capacitor circuit only for the second stage. Due to the reduced supply voltage, the CMFB circuit is implemented using boot-strapped switches. Minor modifications allow to use chop-per stabilization for flicker noise reduction. Two different compensation schemes are discussed and compared using an example for 1V operation of the amplifier.


title: Optimization of 6Slots-7Poles & 12Slots-14Poles flux-switching permanent magnet machines for plug-in HEV

text: Plug-in hybrid electric vehicle (PHEV) is independent of the internal combustion engine but acts as the auxiliary unit mostly on the electric drive system. Main while, the Flux-switching permanent magnet machine (FSPMM) has been studied in terms of structure with its operation principle been analyzed. To achieve a higher torque with a corresponding high power density and lower torque ripple, FSPMM with 6Slots-7Poles and 12Slots-14Poles, according investigations, are optimized based on their objective functions. Moreover, it analyzes several typical performance curves, such as cogging torque, flux linkage and back-EMF. Finally, the torque and corresponding power, efficiency and rotor mechanical strength analysis of optimized designed are obtained. The results indicate that FSPMM is a viable candidate for PHEV and also has good mechanical robustness with strong flux-weakening ability.


title: Magnetic Equivalent Circuit Modeling of the AC Homopolar Machine for Flywheel Energy Storage

text: This paper develops a magnetic equivalent circuit model suitable to the design and optimization of the synchronous ac homopolar machine. The ac homopolar machine is of particular interest in the application of grid-based flywheel energy storage, where it has the potential to significantly reduce self-discharge associated with magnetic losses. The ac homopolar machine features both axial and radial magnetizing flux paths, which requires finite element analysis to be conducted in 3-D. The computation time associated with 3-D finite element modeling is highly prohibitive in the design process. The magnetic equivalent circuit model developed in this paper is shown to be a viable alternative for calculating several design performance parameters and has a computation time which is orders of magnitude less than that of 3-D finite element analysis. Results obtained from the developed model are shown to be in good agreement with finite element and experimental results for varying levels of saturation.