title: Lexicon-enhanced sentiment analysis framework using rule-based classification scheme

abstract: With the rapid increase in social networks and blogs, the social media services are increasingly being used by online communities to share their views and experiences about a particular product, policy and event. Due to economic importance of these reviews, there is growing trend of writing user reviews to promote a product. Nowadays, users prefer online blogs and review sites to purchase products. Therefore, user reviews are considered as an important source of information in Sentiment Analysis (SA) applications for decision making. In this work, we exploit the wealth of user reviews, available through the online forums, to analyze the semantic orientation of words by categorizing them into +ive and -ive classes to identify and classify emoticons, modifiers, general-purpose and domain-specific words expressed in the public’s feedback about the products. However, the un-supervised learning approach employed in previous studies is becoming less efficient due to data sparseness, low accuracy due to non-consideration of emoticons, modifiers, and presence of domain specific words, as they may result in inaccurate classification of users’ reviews. Lexicon-enhanced sentiment analysis based on Rule-based classification scheme is an alternative approach for improving sentiment classification of users’ reviews in online communities. In addition to the sentiment terms used in general purpose sentiment analysis, we integrate emoticons, modifiers and domain specific terms to analyze the reviews posted in online communities. To test the effectiveness of the proposed method, we considered users reviews in three domains. The results obtained from different experiments demonstrate that the proposed method overcomes limitations of previous methods and the performance of the sentiment analysis is improved after considering emoticons, modifiers, negations, and domain specific terms when compared to baseline methods.


title: Fetal intracranial translucency and cisterna magna at 11 to 14 weeks: reference ranges and correlation with chromosomal abnormalities

abstract: Objective ::: ::: To measure the intracranial translucency (IT) and the cisterna magna (CM), to produce reference ranges and to examine the interobserver and intraobserver variability of those measurements. To examine the possible association of IT with chromosomal abnormalities. ::: ::: ::: ::: Methods ::: ::: Prospective study on pregnancies assessed at 11 to 14 weeks. IT was measured retrospectively in 17 cases with aneuploidy. ::: ::: ::: ::: Results ::: ::: To produce reference ranges, 465 fetuses were used. IT and CM correlated linearly with crown-rump-length (CRL) and were independent of maternal demographic characteristics and biochemical indices. IT had a weak positive correlation with nuchal translucency. For IT the intraclass correlation coefficient was 0.88 for intraobserver variability and 0.83 for interobserver variability. For CM the intraclass correlation coefficient was 0.95 for intraobserver variability and 0.84 for interobserver variability. The IT multiple of the median was significantly increased in the chromosomally abnormal fetuses (1.02 for the normal and 1.28 for the chromosomally abnormal fetuses, Mann Whitney p < 0.001). IT multiple of the median was a significant predictor of chromosomal abnormality (Receiver Operator Characteristic curve analysis: Area under the curve = 0.86, CI=0.76–0.96, p<0.001). ::: ::: ::: ::: Conclusion ::: ::: Intracranial translucency and CM can be measured reliably at the 11 to 14 weeks examination and the measurements are highly reproducible. IT appears to be increased in fetuses with chromosomal abnormalities. Copyright © 2011 John Wiley & Sons, Ltd.


ESOM-Maps: tools for clustering, visualization, and classification with Emergent SOM


Introduction to career counselling and coaching


title: Decentralized data offloading for mobile cloud computing based on game theory

abstract: In this paper, we propose two data offloading mechanisms among multiple mobile users based on game theory. We formulate the decentralized data offloading decision making problem as two decentralized data offloading game. We then design two decentralized data offloading mechanisms that can achieve Nash equilibriums of the game: multi-item auction (MIA) based data offloading and congestion game (COG) based data offloading. MIA can maximize the revenue of mobile operator (MO), while COG can minimize the payment of mobile subscribers (MSs). Moreover, both MIA and COG can improve cellular data offloading performance. Numerical results show that the proposed mechanisms can achieve efficient data offloading performance.


Facilitated Sensemaking: A Strategy and New Middle-Range Theory to Support Families of Intensive Care Unit Patients


title: Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena

abstract: The individual properties of visual objects, like form or color, are represented in different areas in our visual cortex. In order to perceive one coherent ob- ject, its features have to be bound together. This was found to be achieved in cat and monkey brains by tem- poral correlation of the firing rates of neurons which code the same object. This firing rate is predominantly observed in the gamma frequency range (approx. 30-80 Hz, mainly around 40 Hz). In addition, it has been shown in humans that stimuli which flicker at gamma frequencies are processed faster by our brains than when they flicker at different frequencies. These effects could be due to neural oscillators, which preferably oscillate at certain frequencies, so-called resonance frequencies. It is also known that neurons in visual cortex respond to flickering stimuli at the frequency of the flickering light. If neural oscillators exist with resonance frequencies, they should respond more strongly to stimulation with their resonance frequency. We performed an experiment, where ten human subjects were presented flickering light at frequencies from 1 to 100 Hz in 1-Hz steps. The event-related potentials exhibited steady-state oscilla- tions at all frequencies up to at least 90 Hz. Interestingly, the steady-state potentials exhibited clear resonance phe- nomena around 10, 20, 40 and 80 Hz. This could be a potential neural basis for gamma oscillations in binding experiments. The pattern of results resembles that of multiunit activity and local field potentials in cat visual cortex.


title: On Load Shedding in Complex Event Processing

abstract: Complex Event Processing (CEP) is a stream processing model that focuses on detecting event patterns in continuous event streams. While the CEP model has gained popularity in the research communities and commercial technologies, the problem of gracefully degrading performance under heavy load in the presence of resource constraints, or load shedding, has been largely overlooked. CEP is similar to “classical” stream data management, but addresses a substantially different class of queries. This unfortunately renders the load shedding algorithms developed for stream data processing inapplicable. In this paper we study CEP load shedding under various resource constraints. We formalize broad classes of CEP load-shedding scenarios as different optimization problems. We demonstrate an array of complexity results that reveal the hardness of these problems and construct shedding algorithms with performance guarantees. Our results shed some light on the difficulty of developing load-shedding algorithms that maximize utility.


title: Long-term Stabilization of Aqueous Doxycycline Formulations, in Mucoadhesive Hydrogels for Treatment of Oral Mucosal Conditions

abstract: The main aim of this work was to develop stable (>2 years) doxycycline formulation, at clinically relevant concentrations and using clinically relevant formulation. Doxycycline has a MMP- inhibitory effects that is important for the treatment of various oral mucosal conditions. Therefore, protecting doxycycline from degradation in aqueous formulation requires halting or prevention of oxidation and epimerisation of the active compound. Stabilizing excipients were intuitively put together to enhance the stability as a cumulative effort. A total of 30 hydrogels were compared with different types and concentrations of stability enhancing excipients, pH, storage temperatures (4, 25 and 40°C) and mucoadhesive polymers. The duration of the study was from day 1 and up to 58 months. The gelation temperature was adjusted below the actual body temperature. The complexation efficiency between the doxycycline and HPβCD was studied using the DSC, FTIR and XRPD. The majority of formulations at 4°C were highly stable by the end of 58 months and their stabilities were improved at all 3 temperatures. In conclusion, it is possible to prevent doxycycline from both oxidation and epimerization in an aqueous formulation, for up to 5 years.


title: A Roadmap for the Value-Loading Problem.

abstract: We analyze the value-loading problem. This is the problem of encoding moral values into an AI agent interacting with a complex environment. Like many before, we argue that this is both a major concern and an extremely challenging problem. Solving it will likely require years, if not decades, of multidisciplinary work by teams of top scientists and experts. Given how uncertain the timeline of human-level AI research is, we thus argue that a pragmatic partial solution should be designed as soon as possible. To this end, we propose a preliminary research program. This roadmap identifies several key steps. We hope that this will allow scholars, engineers and decision-makers to better grasp the upcoming difficulties, and to foresee how they can best contribute to the global effort.


title: Targeted Nonlinear Adversarial Perturbations in Images and Videos

abstract: We introduce a method for learning adversarial perturbations targeted to individual images or videos. The learned perturbations are found to be sparse while at the same time containing a high level of feature detail. Thus, the extracted perturbations allow a form of object or action recognition and provide insights into what features the studied deep neural network models consider important when reaching their classification decisions. From an adversarial point of view, the sparse perturbations successfully confused the models into misclassifying, although the perturbed samples still belonged to the same original class by visual examination. This is discussed in terms of a prospective data augmentation scheme. The sparse yet high-quality perturbations may also be leveraged for image or video compression.


title: A parallel and efficient approach to large scale clone detection

abstract: We propose a new token-based approach for large -scale code clone detection, which is based on a filtering heuristic that reduces the number of token comparisons when the two code blocks are compared. We also present a MapReduce based parallel algorithm that uses the filtering heuristic and scales to thousands of projects. The filtering heuristic is generic and can also be used in conjunction with other token-based approaches. In that context, we demonstrate how it can increase the retrieval speed and decrease the memory usage of the index-based approaches. In our experiments on 36 open source Java projects, we found that: i filtering reduces token comparisons by a factor of 10, and thus increasing the speed of clone detection by a factor of 1.5; ii the speed-up and scale-up of the parallel approach using filtering is near-linear on a cluster of 2-32 nodes for 150-2800 projects; and iii filtering decreases the memory usage of index-based approach by half and the search time by a factor of 5. Copyright © 2015 John Wiley & Sons, Ltd.


title: VANET security: Issues, challenges and solutions

abstract: Vehicular Ad-hoc Network (VANET) is an infrastructure less network. It provides enhancement in safety related techniques and comfort while driving. It enables vehicles to share information regarding safety and traffic analysis. The scope of VANET application has increased with the recent advances in technology and development of smart cities across the world. VANET provide a self aware system that has major impact in enhancement of traffic services and in reducing road accidents. Information shared in this system is time sensitive and requires robust and quick forming network connections. VANET, being a wireless ad hoc network, serves this purpose completely but is prone to security attacks. Highly dynamic connections, sensitive information sharing and time sensitivity of this network, make it an eye-catching field for attackers. This paper represents a literature survey on VANET with primary concern of the security issues and challenges with it. Features of VANET, architecture, security requisites, attacker type and possible attacks in VANET are considered in this survey paper.


title: Social media recommendation based on people and tags

abstract: We study personalized item recommendation within an enterprise social media application suite that includes blogs, bookmarks, communities, wikis, and shared files. Recommendations are based on two of the core elements of social media - people and tags. Relationship information among people, tags, and items, is collected and aggregated across different sources within the enterprise. Based on these aggregated relationships, the system recommends items related to people and tags that are related to the user. Each recommended item is accompanied by an explanation that includes the people and tags that led to its recommendation, as well as their relationships with the user and the item. We evaluated our recommender system through an extensive user study. Results show a significantly better interest ratio for the tag-based recommender than for the people-based recommender, and an even better performance for a combined recommender. Tags applied on the user by other people are found to be highly effective in representing that user's topics of interest.


title: Text Summarization using Abstract Meaning Representation

abstract: With an ever increasing size of text present on the Internet, automatic summary generation remains an important problem for natural language understanding. In this work we explore a novel full-fledged pipeline for text summarization with an intermediate step of Abstract Meaning Representation (AMR). The pipeline proposed by us first generates an AMR graph of an input story, through which it extracts a summary graph and finally, generate summary sentences from this summary graph. Our proposed method achieves state-of-the-art results compared to the other text summarization routines based on AMR. We also point out some significant problems in the existing evaluation methods, which make them unsuitable for evaluating summary quality.


title: Care to Share?: Learning to Rank Personal Photos for Public Sharing

abstract: With mobile devices, users are taking ever-growing numbers of photos every day. These photos are uploaded to social sites such as Facebook and Flickr, often automatically. Yet, the portion of these uploaded photos being publicly shared is low, and on a constant decline. Deciding which photo to share takes considerable time and attention, and many users would rather forfeit the social interaction and engagement than sift through their piles of uploaded photos. In this paper, we introduce a novel task of recommending socially-engaging photos to their creators for public sharing. This will turn a tedious manual chore into a quick, software-assisted process. We provide extensive analysis over a large-scale dataset from the Flickr photo sharing website, which reveals some of the traits of photo sharing in such sites. Additionally, we present a ranking algorithm for the task that comprises three steps:(a) grouping of near-duplicate photos;(b) ranking the photos in each group by their "shareability"; and(c) ranking the groups by their likelihood to contain a shareable photo. A large-scale experiment allows us to evaluate our algorithm and show its benefits compared to competitive baselines and algorithmic alternatives.


title: A multifaceted approach to understanding the botnet phenomenon

abstract: The academic community has long acknowledged the existence of malicious botnets, however to date, very little is known about the behavior of these distributed computing platforms. To the best of our knowledge, botnet behavior has never been methodically studied, botnet prevalence on the Internet is mostly a mystery, and the botnet life cycle has yet to be modeled. Uncertainty abounds. In this paper, we attempt to clear the fog surrounding botnets by constructing a multifaceted and distributed measurement infrastructure. Throughout a period of more than three months, we used this infrastructure to track 192 unique IRC botnets of size ranging from a few hundred to several thousand infected end-hosts. Our results show that botnets represent a major contributor to unwanted Internet traffic - 27% of all malicious connection attempts observed from our distributed darknet can be directly attributed to botnet-related spreading activity. Furthermore, we discovered evidence of botnet infections in 11% of the 800,000 DNS domains we examined, indicating a high diversity among botnet victims. Taken as a whole, these results not only highlight the prominence of botnets, but also provide deep insights that may facilitate further research to curtail this phenomenon.


Logged In and Zoned Out : How Laptop Internet Use Relates to Classroom Learning


title: Label Refinement Network for Coarse-to-Fine Semantic Segmentation

abstract: We consider the problem of semantic image segmentation using deep convolutional neural networks. We propose a novel network architecture called the label refinement network that predicts segmentation labels in a coarse-to-fine fashion at several resolutions. The segmentation labels at a coarse resolution are used together with convolutional features to obtain finer resolution segmentation labels. We define loss functions at several stages in the network to provide supervisions at different stages. Our experimental results on several standard datasets demonstrate that the proposed model provides an effective way of producing pixel-wise dense image labeling.


title: Consensus algorithms and plug-and-play control for current sharing in DC microgrids

abstract: In this paper, we propose a secondary consensus-based control layer for current sharing and voltage balancing in DC microGrids (mGs). Differently from existing approaches based on droop control, we assume decentralized Plug-and-Play (PnP) controllers at the primary level as they provide voltage stabilization and their design complexity is independent of the mG size. We analyze the behavior of the closed-loop mG by approximating local primary control loops with either unitary gains or first-order transfer functions. Besides proving stability, current sharing and voltage balancing in the asymptotic regime, we describe how to design secondary controllers in a PnP fashion when distributed generation units are added or removed. Theoretical results are complemented by simulations using a 5-DGUs mG implemented in Simulink/PLECS.


Compressing LSTMs into CNNs.


title: The effect of diagenesis and fluid migration on rare earth element distribution in pore fluids of the northern Cascadia accretionary margin

abstract: Analytical challenges in obtaining high quality measurements of rare earth elements (REEs) from small pore fluid volumes have limited the application of REEs as deep fluid geochemical tracers. Using a recently developed analytical technique, we analyzed REEs from pore fluids collected from Sites U1325 and U1329, drilled on the northern Cascadia margin during the Integrated Ocean Drilling Program (IODP) Expedition 311, to investigate the REE behavior during diagenesis and their utility as tracers of deep fluid migration. These sites were selected because they represent contrasting settings on an accretionary margin: a ponded basin at the toe of the margin, and the landward Tofino Basin near the shelf's edge. REE concentrations of pore fluid in the methanogenic zone at Sites U1325 and U1329 correlate positively with concentrations of dissolved organic carbon (DOC) and alkalinity. Fractionations across the REE series are driven by preferential complexation of the heavy REEs. Simultaneous enrichment of diagenetic indicators (DOC and alkalinity) and of REEs (in particular the heavy elements Ho to Lu), suggests that the heavy REEs are released during particulate organic carbon (POC) degradation and are subsequently chelated by DOC. REE concentrations are greater at Site U1325, a site where shorter residence times of POC in sulfate-bearing redox zones may enhance REE burial efficiency within sulfidic and methanogenic sediment zones where REE release ensues. ::: ::: Cross-plots of La concentrations versus Cl, Li and Sr delineate a distinct field for the deep fluids (z > 75 mbsf) at Site U1329, and indicate the presence of a fluid not observed at the other sites drilled on the Cascadia margin. Changes in REE patterns, the presence of a positive Eu anomaly, and other available geochemical data for this site suggest a complex hydrology and possible interaction with the igneous Crescent Terrane, located east of the drilled transect.


title: Unsaturated and saturated saliency trends in fractional-slot concentrated-winding interior permanent magnet machines

abstract: This paper presents an investigation of trends in the unsaturated and saturated magnetic saliency values of interior permanent magnet (IPM) synchronous machines with fractional-slot concentrated windings (FSCW). This paper investigates alternative slot-pole combinations for FSCW-IPM machines, highlighting the key observation that the saliency of these machines is generally lower than their counterpart IPM machines with conventional distributed windings. The relative merits and challenges of FSCW-IPM machines are examined, with a focus on the contribution of reluctance torque to the total machine torque. A key design metric that proves useful in this discussion is “unsaturated saliency ratio”, defined as the ratio L q /L d in the limiting case of near zero stator current and “saturated saliency ratio”, defined as the ratio L q /L d at high stator current. An important objective is to use this parameter to help machine designers choose the most appropriate slot-pole configuration for an FSCW-IPM machine in order to achieve the desired performance requirements.


title: Three-phase NPC inverter using three-phase coupled inductor

abstract: A new NPC three-phase inverter with three-phase coupled inductor is presented in this paper. Initially the power stage diagram is shown and discussed. Their main characteristics are described and the more relevant waveforms, generated by simulation, are shown. Next, the employed modulation and converter operation are discussed and some relevant figures are presented. After that, the presence of the three-phase coupled inductor to provide significant reduction in harmonic distortion is discussed. Finally, experimental results obtained from the implemented prototype are presented and briefly discussed.


title: Finding Pareto Optimal Groups: Group-based Skyline

abstract: Skyline computation, aiming at identifying a set of skyline points that are not dominated by any other point, is particularly useful for multi-criteria data analysis and decision making. Traditional skyline computation, however, is inadequate to answer queries that need to analyze not only individual points but also groups of points. To address this gap, we generalize the original skyline definition to the novel group-based skyline (G-Skyline), which represents Pareto optimal groups that are not dominated by other groups. In order to compute G-Skyline groups consisting of k points efficiently, we present a novel structure that represents the points in a directed skyline graph and captures the dominance relationships among the points based on the first k skyline layers. We propose efficient algorithms to compute the first k skyline layers. We then present two heuristic algorithms to efficiently compute the G-Skyline groups: the point-wise algorithm and the unit group-wise algorithm, using various pruning strategies. The experimental results on the real NBA dataset and the synthetic datasets show that G-Skyline is interesting and useful, and our algorithms are efficient and scalable.


title: Hardening soft information sources

abstract: The web contains a large quantity of unstructured information. In many cases, it is possible to heuristically extract structured information, but the resulting databases are \soft": they contain inconsistencies and duplication, and lack unique, consistently-used object identi ers. Examples include large bibliographic databases harvested from raw scienti c papers or databases constructed by merging heterogeneous \hard" databases. Here we formally model a soft database as a noisy version of some unknown hard database. We then consider the hardening problem, i.e., the problem of inferring the most likely underlying hard database given a particular soft database. A key feature of our approach is that hardening is global | many sources of evidence for a given hard fact are taken into account. We formulate hardening as an optimization problem and give a nontrivial nearly linear time algorithm for nding a local optimum.


Theoretical analysis of three-dimensional polaritonic photonic crystals


title: Optimal Resource Allocation for Cellular Networks with MATLAB Instructions

abstract: This report presents a more detailed description of the algorithm and simulations published in papers [1, 2]. It includes a step by step description of the algorithm and included the corresponding flow chart. In addition, detailed instructions of the MATLAB code used to simulate the proposed allocation algorithm in [1, 2] is presented. The report starts with a brief motivation of resource allocation problem in wireless networks. Then, some of the prior related work on the subject are mentioned. Finally, we provide the details instructions on MATLAB functions used in our algorithm. More rigorous analysis and proofs of the problem and algorithm are present in [1, 2] and further discussions presented in [3, 4].


abstract: Recent studies indicate that e-government initiatives have not held their promise of improving government services. The majority of efforts to benchmark e-government have had central government as the unit of analysis. This study employs the MeGAP-3 (The Municipal E-Government Assessmen Project) assessment tool to assess the status of municipal e-government in the Agder region in southern Norway, an area with high Internet penetration and mature information and communication technology (ICT) use. MeGAP-3 proved effective in providing a relative positioning of these Norwegian municipalities, but we argue that country specific assessment indicators are needed to complement the tool and enable cross-country comparisons by relative scores. Surprisingly, the results show that the sophistication of local government web sites was fairly low. A series of qualitative interviews were con1 Flak et al.: Local E-Government in Norway: Current Status and Emerging Issues Published by AIS Electronic Library (AISeL), 2005 42 • L. S. Flak, D. H. Olsen & P. Wolcott ducted to explore the factors that shape the development of municipal egovernment. The evidence suggests that the dominant stakeholder in development is the bureaucratic administration rather than citizens or politicians. This group has a strong focus on internal efficiency and cost reduction. The majority of respondents report cost reduction as the major driver behind egovernment development. However we also identified a more citizen-centric approach that stresses the need for improving access and service quality for citizens. The study outlines a number of areas where further research will be needed to fully understand the development of e-government in Norway.


title: An implementers' view of systems engineering for systems of systems

abstract: This paper builds on and extends U.S. Department of Defense published guidance on systems engineering (SE) of systems of systems (SoS) by developing and presenting a view of SoS SE that translates the SoS SE core elements, their interrelationships, and SoS decision-making artifacts and information from a “trapeze” model to a more familiar and intuitive time-sequenced “wave” model representation. The information is thus rendered in a form more readily usable by SoS SE practitioners in the field and one that corresponds with incremental development approaches that are the norm for SoS capability evolution. The paper describes and motivates the development of the wave model, discusses its key characteristics, and provides examples of SoS efforts that reflect this view of SoS SE. Finally, the paper describes how the information critical to successful SoS SE is created, where it fits into the wave model, how it evolves over time, and in which artifacts the information is normally contained.


title: On a noncommutative analogue of the category of coherent sheaves on a projective scheme

abstract: An indicating recorder comprising a housing unit, an internal frame unit and an electric power source unit. The housing unit includes a single-swing front door and is made from a square prismatic housing formed by bending a thin plate. The internal frame unit is positioned in the vicinity of the inlet of the housing unit and is rotatable in a direction opposite the front door so as to be drawn out toward the front. The internal frame unit has left and right side plates and coupling beams between the two side plates, and principally comprises an indicating recorder mechanism having a chart feed mechanism, a recording pen mechanism, and other related instruments. The electric power source unit is located in the housing unit and is equipped with a power transformer and a terminal board unit, both of which are in a rear frame region to provide power supply and input signals. The wiring for the terminal board and the power transformer is run along a transverse frame and connected to the internal frame unit by a flexible zigzag wire holder.


title: Capturing Long-range Contextual Dependencies with Memory-enhanced Conditional Random Fields

abstract: Despite successful applications across a broad range of NLP tasks, conditional random fields ("CRFs"), in particular the linear-chain variant, are only able to model local features. While this has important benefits in terms of inference tractability, it limits the ability of the model to capture long-range dependencies between items. Attempts to extend CRFs to capture long-range dependencies have largely come at the cost of computational complexity and approximate inference. In this work, we propose an extension to CRFs by integrating external memory, taking inspiration from memory networks, thereby allowing CRFs to incorporate information far beyond neighbouring steps. Experiments across two tasks show substantial improvements over strong CRF and LSTM baselines.


title: Classification of EEG signals using fractional calculus and wavelet support vector machine

abstract: The behavior of many physical and biological processes and systems can be described satisfactorily by fractional order models. A new method, termed fractional linear prediction (FLP) based on fractional calculus, is used to model ictal and seizure-free EEG signals. Through numerical simulations it is demonstrated that, the EEG signal can be modeled accurately, by using a few integrals of fractional orders as basis functions. The parameters obtained from modeling are used for analysis and classification using support vector machines (SVM). It is found that improvements in classification accuracy is possible by using wavelet support vector machines using wavelet kernel functions such as Mexican hat wavelet and Morlet wavelet.


title: Data mining Techniques for Predicting Crop Productivity – A review article

abstract: In this paper an attempt has been made to review the research studies on application of data mining techniques in the field of agriculture. Some of the techniques, such asID3 algorithms, the k-means, the k nearest neighbor, artificial neural networks and support vector machines applied in the field of agriculture were presented. Data mining in application in agriculture is a relatively new approach for forecasting / predicting of agricultural crop/animal management. This article explores the applications of data mining techniques in the field of agriculture and allied sciences. Data mining is the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviours, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analysis offered by data mining move beyond the analysis of past events provided by retrospective tools typical of decision support systems. Agriculture and allied activities constitute the single largest component of India's gross domestic product, contributing nearly 25% of the total and nearly 60% of Indian population depends on this profession. Due to vagaries of climate factors the agricultural productivities in India are continuously decreasing over a decade. The reasons for this were studied mostly using regression analysis. In this paper an attempt has been made to compile the research findings of different researchers who used data. II. Application of Data Mining techniques in Agriculture Many techniques for learning rules and relationships automatically from diverse data sets were developed (14), to simplify the often tedious and error-prone process of acquiring knowledge from empirical data. While these techniques are plausible, theoretically well-founded, and perform well on more or less artificial test data sets, they depend on their ability to make sense of real-world data. This paper describes a project that is applying a range of machine learning strategies to problems in agriculture and horticulture. They briefly surveyed some of the techniques emerging from machine learning research, describe a software workbench for experimenting with a variety of techniques on real-world data sets, and describe a case study of dairy herd management in which culling rules were inferred from a medium-sized database of herd information. They also described a range of machine learning strategies to problems in agriculture and horticulture.


title: Human Quantitative Electroencephalographic and Schumann Resonance Exhibit Real-Time Coherence of Spectral Power Densities: Implications for Interactive Information Processing

abstract: Spectral Power Densities (SPD) within the ::: Quantitative Electroencephalographic (QEEGs) Profiles of 41 men and women ::: displayed repeated transient coherence with the first three modes (7 - 8 Hz, 13 ::: - 14 Hz, and 19 - 20 Hz) of the Schumann Resonance in real time. The duration ::: of the coherence was about 300 ms about twice per min. Topographical map clusters ::: indicated that the domain of maximum coherence was within the right caudal ::: hemisphere near the Parahippocampal gyrus. These clusters, associated with ::: shifts of about 2 μV, became stable about 35 to 45 ms after the onset of the ::: synchronizing event. During the first 10 to 20 ms, the isoelectric lines ::: shifted from clockwise to counterclockwise rotation. The results are consistent ::: with the congruence of the frequency, magnetic field intensity, voltage ::: gradient, and phase shifts that are shared by the human brain and the ::: earth-ionospheric spherical wave guide. Calculations indicated that under ::: certain conditions interactive information processing might occur for brief ::: periods. Natural and technology-based variables affecting the Schumann ::: parameters might be reflected in human brain activity, including modifications ::: of cognition and dream-related memory consolidation.


title: Iron loss analysis of interior permanent magnet synchronous motors variation of main loss factors due to driving condition

abstract: In this paper, the authors investigate the iron loss of interior permanent magnet motors driven by pulsewidth modulation (PWM) inverters from both results of the experiments and the finite-element analysis. In the analysis, the iron loss of the motor is decomposed into several components due to their origins, for instance, the fundamental field, carrier of the PWM inverter, slot ripples, and harmonic magnetomotive forces of the permanent magnet in order to clarify the main loss factors. The Fourier transformation and the finite-element method considering the carrier harmonics are applied to this calculation. The calculated iron loss is compared with the measurement at each driving condition. The measured and the calculated results agree well. It is clarified that the iron loss caused by the carrier of the PWM inverter is the largest component at low-speed condition under the maximum torque control, whereas the loss caused by the harmonic magnetomotive forces of the permanent magnet remarkably increase at high-speed condition under the flux-weakening control


title: Graphene-based materials: synthesis, characterization, properties, and applications.

abstract: Graphene, a two-dimensional, single-layer sheet of sp2 hybridized carbon atoms, has attracted tremendous attention and research interest, owing to its exceptional physical properties, such as high electronic conductivity, good thermal stability, and excellent mechanical strength. Other forms of graphene-related materials, including graphene oxide, reduced graphene oxide, and exfoliated graphite, have been reliably produced in large scale. The promising properties together with the ease of processibility and functionalization make graphene-based materials ideal candidates for incorporation into a variety of functional materials. Importantly, graphene and its derivatives have been explored in a wide range of applications, such as electronic and photonic devices, clean energy, and sensors. In this review, after a general introduction to graphene and its derivatives, the synthesis, characterization, properties, and applications of graphene-based materials are discussed.


A Genetic Algorithm-Based Heterogeneous Random Subspace Ensemble Model for Bankruptcy Prediction


title: A driver fatigue recognition model based on information fusion and dynamic Bayesian network

abstract: We propose a driver fatigue recognition model based on the dynamic Bayesian network, information fusion and multiple contextual and physiological features. We include features such as the contact physiological features (e.g., ECG and EEG), and apply the first-order Hidden Markov Model to compute the dynamics of the Bayesian network at different time slices. The experimental validation shows the effectiveness of the proposed system; also it indicates that the contact physiological features (especially ECG and EEG) are significant factors for inferring the fatigue state of a driver.


title: Investigation on the Impact of Organizational Culture on Organization Innovation

abstract: Innovation is an inherent part of organizational culture (OC). Innovation is the process to develop and improve products, processes and markets, with the aim to aggregate value. Define innovation as the successful implementation of creative ideas within an organization. The Purpose of this paper is to examine the relationship between organizational culture and innovation. However, without good capability of organizational learning (OL), an organization cannot retain important organizational cultural practices. Therefore, we will examine the effects of organizational culture on innovation directly and through organizational learning. This study expects to clarify the result that indicates that OL has a full mediation effect on OC and OI. However, there are still some confusing relations between OC and OL. It is important for both innovation and organizational culture professionals to understand the systemic relationship between these concepts and the value that it can generate in respect of creating and maintaining sustainable competitive advantage for organizations. In this article, the research model is proposed.


title: MyBehavior: automatic personalized health feedback from user behaviors and preferences using smartphones

abstract: Mobile sensing systems have made significant advances in tracking human behavior. However, the development of personalized mobile health feedback systems is still in its infancy. This paper introduces MyBehavior, a smartphone application that takes a novel approach to generate deeply personalized health feedback. It combines state-of-the-art behavior tracking with algorithms that are used in recommendation systems. MyBehavior automatically learns a user's physical activity and dietary behavior and strategically suggests changes to those behaviors for a healthier lifestyle. The system uses a sequential decision making algorithm, Multi-armed Bandit, to generate suggestions that maximize calorie loss and are easy for the user to adopt. In addition, the system takes into account user's preferences to encourage adoption using the pareto-frontier algorithm. In a 14-week study, results show statistically significant increases in physical activity and decreases in food calorie when using MyBehavior compared to a control condition.


title: Minimizing the Total Weighted Completion Time of Coflows in Datacenter Networks

abstract: Communications in datacenter jobs (such as the shuffle operations in MapReduce applications) often involve many parallel flows, which may be processed simultaneously. This highly parallel structure presents new scheduling challenges in optimizing job-level performance objectives in data centers. Chowdhury and Stoica introduced the coflow abstraction to capture these communication patterns, and recently Chowdhury et al. developed effective heuristics to schedule coflows. In this paper, we consider the problem of efficiently scheduling coflows with release dates so as to minimize the total weighted completion time, which has been shown to be strongly NP-hard. Our main result is the first polynomial-time deterministic approximation algorithm for this problem, with an approximation ratio of 67/3, and a randomized version of the algorithm, with a ratio of 9+16√2/3. Our results use techniques from both combinatorial scheduling and matching theory, and rely on a clever grouping of coflows. We also run experiments on a Facebook trace to test the practical performance of several algorithms, including our deterministic algorithm. Our experiments suggest that simple algorithms provide effective approximations of the optimal, and that our deterministic algorithm has near-optimal performance.


title: Two-shot SVBRDF capture for stationary materials

abstract: Material appearance acquisition usually makes a trade-off between acquisition effort and richness of reflectance representation. In this paper, we instead aim for both a light-weight acquisition procedure and a rich reflectance representation simultaneously, by restricting ourselves to one, but very important, class of appearance phenomena: texture-like materials. While such materials' reflectance is generally spatially varying, they exhibit self-similarity in the sense that for any point on the texture there exist many others with similar reflectance properties. We show that the texturedness assumption allows reflectance capture using only two images of a planar sample, taken with and without a headlight flash. Our reconstruction pipeline starts with redistributing reflectance observations across the image, followed by a regularized texture statistics transfer and a non-linear optimization to fit a spatially-varying BRDF (SVBRDF) to the resulting data. The final result describes the material as spatially-varying, diffuse and specular, anisotropic reflectance over a detailed normal map. We validate the method by side-by-side and novel-view comparisons to photographs, comparing normal map resolution to sub-micron ground truth scans, as well as simulated results. Our method is robust enough to use handheld, JPEG-compressed photographs taken with a mobile phone camera and built-in flash.


title: Explanation feedback is better than correct answer feedback for promoting transfer of learning.

abstract: Among the many factors that influence the efficacy of feedback on learning, the information contained in the feedback message is arguably the most important. One common assumption is that there is a benefit to increasing the complexity of the feedback message beyond providing the correct answer. Surprisingly, studies that have manipulated the content of the feedback message in order to isolate the unique effect of greater complexity have failed to support this assumption. However, the final test in most of these studies consisted of a repetition of the same questions from the initial test. The present research investigated whether feedback that provides an explanation of the correct answer promotes superior transfer of learning to new questions. In 2 experiments, subjects studied prose passages and then took an initial short-answer test on concepts from the text. After each question, they received correct answer feedback, explanation feedback, or no feedback (Experiment 1 only). Two days later, subjects returned for a final test that consisted of both repeated questions and new inference questions. The results showed that correct answer feedback and explanation feedback led to equivalent performance on the repeated questions, but explanation feedback produced superior performance on the new inference questions.


title: At the Roots of Dictionary Compression: String Attractors

abstract: A well-known fact in the field of lossless text compression is that high-order entropy is a weak model when the input contains long repetitions. Motivated by this fact, decades of research have generated myriads of so-called dictionary compressors: algorithms able to reduce the text’s size by exploiting its repetitiveness. Lempel-Ziv 77 is one of the most successful and well-known tools of this kind, followed by straight-line programs, run-length Burrows-Wheeler transform, macro schemes, collage systems, and the compact directed acyclic word graph. In this paper, we show that these techniques are different solutions to the same, elegant, combinatorial problem: to find a small set of positions capturing all distinct text’s substrings. We call such a set a string attractor. We first show reductions between dictionary compressors and string attractors. This gives the approximation ratios of dictionary compressors with respect to the smallest string attractor and allows us to uncover new asymptotic relations between the output sizes of different dictionary compressors. We then show that the k-attractor problem — deciding whether a text has a size-t set of positions capturing all substrings of length at most k — is NP-complete for k≥ 3. This, in particular, includes the full string attractor problem. We provide several approximation techniques for the smallest k-attractor, show that the problem is APX-complete for constant k, and give strong inapproximability results. To conclude, we provide matching lower and upper bounds for the random access problem on string attractors. The upper bound is proved by showing a data structure supporting queries in optimal time. Our data structure is universal: by our reductions to string attractors, it supports random access on any dictionary-compression scheme. In particular, it matches the lower bound also on LZ77, straight-line programs, collage systems, and macro schemes, and therefore essentially closes (at once) the random access problem for all these compressors.


title: Documents topic classification model in social networks using classifiers voting system

abstract: Topic model uncovers abstract topics within texts documents, which is an essential task in text analysis in social networks. However, identifying topics in text documents in social networks is challenging since the texts are short, unlabeled, and unstructured. For this reason, we propose a topic classification system regarding the features of text documents in social networks. The proposed system is based on several machine-learning algorithms and voting system. The accuracy of the system has been tested using text documents that were classified into three topics. The experiment results show that the proposed system guarantees high accuracy rates in documents topic classification.


title: Factors associated with nutritional status of infants and young children in Somali Region, Ethiopia: a cross- sectional study

abstract: Inadequate nutrition during the first two years of life may lead to childhood morbidity and mortality, as well as inadequate brain development. Infants are at increased risk of malnutrition by six months, when breast milk alone is no longer sufficient to meet their nutritional requirements. However the factors associated with nutritional status of infants after 6 months of age have received little attention in pastoralist communities of Ethiopia. Therefore this study aimed to identify the factors associated with nutritional status of infants and young children (6–23 months) in Filtu town, Somali Region, Ethiopia. A cross-sectional community-based study was conducted. Simple random sampling was employed to select 214 infants for the study. Univariable and multivariable logistic regressions models were used in the statistical analysis. The strength of association was measured by odds ratios with 95 % confidence intervals. Both the crude (COR) and adjusted odds ratios (AOR) are reported. The prevalence of wasting, stunting and underweight among infants and young children were 17.5 % (95 % CI: 12.91-23.22), 22.9 % (95 % CI: 17.6-28.9) and 19.5 % (95 % CI: 14.58-25.3) respectively. The multivariable logistic regression model showed that breastfeeding was independently associated with reduced odds of wasting (AOR = 0.38(95 % CI: 0.14-0.99)). Diarrhea in the past 15 days (AOR = 2.13 (95 % CI: 1.55-4.69)) was also associated with increased odds for wasting. The independent predictors of reduced odds for stunting were dietary diversity score ≥4 (AOR = 0.45(95 % CI: 0.21-0.95)) and introduction of complementary feeding at 6 months (AOR = 0.25 (95 % CI: 0.09-0.66)). Bottle feeding was associated with increased odds of stunting (AOR = 3.83 (95 % CI: 1.69-8.67)). Breastfeeding was associated with reduced odds of underweight (AOR = 0.24 (95 % CI: 0.1-0.59)), while diarrheal disease in the past 15 days was associated with increased odds of underweight (AOR = 3.54 (95 % CI: 1.17-7.72)). Under nutrition is a public health problem among infants and young children in Filtu town, Somali region Ethiopia. Breastfeeding was associated with lower odds of wasting and underweight while diarrheal disease was associated with higher odds of wasting and underweight. Low dietary diversity scores, inappropriate age of complementary feeding initiation and bottle feeding were identified to be significant predictors of stunting. Those factors should be considered for any intervention aimed to reduce under nutrition among infants and young children in Filitu town, Somali region, Ethiopia.


title: A network forensics tool for precise data packet capture and replay in cyber-physical systems

abstract: Network data packet capture and replay capabilities are basic requirements for forensic analysis of faults and securityrelated anomalies, as well as for testing and development. Cyber-physical networks, in which data packets are used to monitor and control physical devices, must operate within strict timing constraints, in order to match the hardware devices' characteristics. Standard network monitoring tools are unsuitable for such systems because they cannot guarantee to capture all data packets, may introduce their own traffic into the network, and cannot reliably reproduce the original timing of data packets. Here we present a high-speed network forensics tool specifically designed for capturing and replaying data traffic in Supervisory Control and Data Acquisition systems. Unlike general-purpose "packet capture" tools it does not affect the observed network's data traffic and guarantees that the original packet ordering is preserved. Most importantly, it allows replay of network traffic precisely matching its original timing. The tool was implemented by developing novel user interface and back-end software for a special-purpose network interface card. Experimental results show a clear improvement in data capture and replay capabilities over standard network monitoring methods and general-purpose forensics solutions.


title: Re-space-ing place: "place" and "space" ten years on

abstract: In the ten years since the distinction between "place" and "space" emerged as a consideration for CSCW researchers and designers, the concepts have proven useful across a range of domains. In that same period of time, wireless and mobile technologies have given us new sites at which to examine the issues of space, practice, and mobility. These changes suggest that it might be fruitful to re-examine the issues of place and space in light of recent developments. In particular, the nature of space and spatiality deserve further consideration.


title: Development and initial validation of an implicit measure of humility relative to arrogance

abstract: Data from two studies describe the development of an implicit measure of humility and support the idea that dispositional humility is a positive quality with possible benefits. In Study 1, 135 college students completed Humility and Self-Esteem Implicit Association Tests (IATs) and several self-report measures of personality self-concept. Fifty-four participants also completed the Humility IAT again approximately 2 weeks later and their humility was rated by close acquaintances. The Humility IAT was found to be internally and temporally consistent. Implicit humility correlated with self-reported humility relative to arrogance, implicit self-esteem, and narcissism (inversely). Humility was not associated with self-reported low selfesteem, pessimism, or depression. In fact, self-reported humility relative to arrogance correlated positively with self-reported self-esteem, gratitude, forgiveness, spirituality, and general health. In addition, self-reported humility and acquaintancerated humility correlated positively; however, implicit humility and acquaintance-rated humility were not strongly associated. In Study 2, to examine the idea that humility might be associated with increased academic performance, we examined actual course grades of 55 college students who completed Humility and Self-Esteem IATs. Implicit humility correlated positively with higher actual course grades when narcissism, conscientiousness, and implicit self-esteem were simultaneously controlled. Implications and future research directions are discussed.


title: The Restricted Isometry Property and Its Implications for Compressed Sensing

abstract: Abstract It is now well-known that one can reconstruct sparse or compressible signals accurately from a very limited number of measurements, possibly contaminated with noise. This technique known as “compressed sensing” or “compressive sampling” relies on properties of the sensing matrix such as the restricted isometry property . In this Note, we establish new results about the accuracy of the reconstruction from undersampled measurements which improve on earlier estimates, and have the advantage of being more elegant. To cite this article: E.J. Candes, C. R. Acad. Sci. Paris, Ser. I 346 (2008).


title: Reducing BCI calibration effort in RSVP tasks using online weighted adaptation regularization with source domain selection

abstract: Rapid serial visual presentation based brain-computer interface (BCI) system relies on single-trial classification of event-related potentials. Because of large individual differences, some labeled subject-specific data are needed to calibrate the classifier for each new subject. This paper proposes an online weighted adaptation regularization (OwAR) algorithm to reduce the online calibration effort, and hence to increase the utility of the BCI system. We show that given the same number of labeled subject-specific training samples, OwAR can significantly improve the online calibration performance. In other words, given a desired classification accuracy, OwAR can significantly reduce the number of labeled subject-specific training samples. Furthermore, we also show that the computational cost of OwAR can be reduced by more than 50% by source domain selection, without a statistically significant sacrifice of classification performance.


title: Force-closure workspace analysis of cable-driven parallel mechanisms

abstract: A cable-driven parallel mechanism (CDPM) possesses a number of promising advantages over the conventional rigid-link mechanisms, such as simple and light-weight mechanical structure, high-loading capacity, and large reachable workspace. However, the formulations and results obtained for the rigid-link mechanisms cannot be directly applied to CDPMs due to the unilateral property of cables. This paper focuses on the workspace analysis of fully restrained positioning mechanisms. Because the cable tension is the most essential issue to constrain the moving platform, the force-closure workspace is mainly studied. A general approach is proposed to check the force-closure condition. This condition is expressed in terms of the convex hull which encloses the origin. However, such a condition is formulated and expressed in high dimensions. To simplify the analysis, a recursive dimension reduction algorithm is proposed to check convex hulls in one dimension spaces. This algorithm is verified through simulation results of various CDPMs.


title: Expert System for Automatic Analysis of Facial Expressions

abstract: Abstract This paper discusses our expert system called Integrated System for Facial Expression Recognition (ISFER), which performs recognition and emotional classification of human facial expression from a still full-face image. The system consists of two major parts. The first one is the ISFER Workbench, which forms a framework for hybrid facial feature detection. Multiple feature detection techniques are applied in parallel. The redundant information is used to define unambiguous face geometry containing no missing or highly inaccurate data. The second part of the system is its inference engine called HERCULES, which converts low level face geometry into high level facial actions, and then this into highest level weighted emotion labels.


title: Attention Allocation Aid for Visual Search

abstract: This paper outlines the development and testing of a novel, feedback-enabled attention allocation aid (AAAD), which uses real-time physiological data to improve human performance in a realistic sequential visual search task. Indeed, by optimizing over search duration, the aid improves efficiency, while preserving decision accuracy, as the operator identifies and classifies targets within simulated aerial imagery. Specifically, using experimental eye-tracking data and measurements about target detectability across the human visual field, we develop functional models of detection accuracy as a function of search time, number of eye movements, scan path, and image clutter. These models are then used by the AAAD in conjunction with real time eye position data to make probabilistic estimations of attained search accuracy and to recommend that the observer either move on to the next image or continue exploring the present image. An experimental evaluation in a scenario motivated from human supervisory control in surveillance missions confirms the benefits of the AAAD.


title: Disability management after stroke: its medical aspects for workplace accommodation.

abstract: PURPOSE ::: Return to work (RTW) after stroke is one of the critical issues for both employer and employee. Early RTW is a manifestation of social restoration for the disabled stroke as well as an effective way to reduce social costs related stroke. ::: ::: ::: METHOD ::: This paper discusses the medical problems referred to RTW after stroke for workplace accommodation. Reviewing the literature, factors influencing RTW after stroke are addressed. ::: ::: ::: RESULTS/CONCLUSION ::: The process of RTW is extremely individual in each case, and affected by multiple factors. Therefore, it is necessary to individually evaluate precise impact of each factor on RTW.


The dynamics of standardization : Three perspectives on standards in organization studies


title: The Fraud Management Lifecycle Theory : A Holistic Approach to Fraud Management

abstract: Fraud losses impact every business. Caveat Emptor, let the buyer beware, tells half the story; Caveat Venditor, let the seller beware, tells the rest. Fraud costs are passed on to society through increased customer inconvenience, opportunity costs, unnecessarily high prices, and criminal activities funded by the fraudulent gains. In short, fraud is rampant. This study developed a theoretical framework for the Fraud Management Lifecycle, examined numerous significant lifecycle stage interactions, and evaluated the lifecycle in five industries with significant economic crime. The Fraud Management Lifecycle is dynamic, evolving, and adaptive. The eight stages are: Deterrence, Prevention, Detection, Mitigation, Analysis, Policy, Investigation, and Prosecution. Effective fraud management requires a balance in the competing and complementary actions within the Fraud Management Lifecycle.


title: Tracing the lineage of view data in a warehousing environment

abstract: We consider the view data lineage problem in a warehousing environment: For a given data item in a materialized warehouse view, we want to identify the set of source data items that produced the view item. We formally define the lineage problem, develop lineage tracing algorithms for relational views with aggregation, and propose mechanisms for performing consistent lineage tracing in a multisource data warehousing environment. Our result can form the basis of a tool that allows analysts to browse warehouse data, select view tuples of interest, and then “drill-through” to examine the exact source tuples that produced the view tuples of interest.


title: High Target Visibility Analysis

abstract: Abstract : This paper presents an approach to visibility analysis for a target located high over a terrain made of discrete elevation points. This analysis is a special subset of a general terrain intervisibility analysis problem. It is an efficient algorithm based on a variation of the 'line-of sight' approach, being k-time faster than the ordinary approach, where k is the ratio of the highest terrain elevation to the target altitude.


title: Hierarchical Recurrent Neural Networks for Long-Term Dependencies

abstract: We have already shown that extracting long-term dependencies from sequential data is difficult, both for determimstic dynamical systems such as recurrent networks, and probabilistic models such as hidden Markov models (HMMs) or input/output hidden Markov models (IOHMMs). In practice, to avoid this problem, researchers have used domain specific a-priori knowledge to give meaning to the hidden or state variables representing past context. In this paper, we propose to use a more general type of a-priori knowledge, namely that the temporal dependencies are structured hierarchically. This implies that long-term dependencies are represented by variables with a long time scale. This principle is applied to a recurrent network which includes delays and multiple time scales. Experiments confirm the advantages of such structures. A similar approach is proposed for HMMs and IOHMMs.


title: Classifying imbalanced data using a bagging ensemble variation (BEV)

abstract: In many applications, data collected are highly skewed where data of one class clearly dominates data from the other classes. Most existing classification systems that perform well on balanced data give very poor performance on imbalanced data, especially for the minority class data. Existing work on improving the quality of classification on imbalanced data include over-sampling, under-sampling, and methods that make modifications to the existing classification systems. This paper discusses the BEV system for classifying imbalanced data. The system is developed based on the ideas from the "Bagging" classification ensemble. The motivation behind the scheme is to maximally use the minority class data without creating synthetic data or making changes to the existing classification systems. Experimental results using real world imbalanced data show the effectiveness of the system.


CDN-on-Demand: An affordable DDoS Defense via Untrusted Clouds


title: An exact algorithm for the capacitated vertex p-center problem

abstract: We develop a simple and practical exact algorithm for the problem of locating p facilities and assigning clients to them within capacity restrictions in order to minimize the maximum distance between a client and the facility to which it is assigned (capacitated p-center). The algorithm iteratively sets a maximum distance value within which it tries to assign all clients, and thus solves bin-packing or capacitated concentrator location subproblems using off-the-shelf optimization software. Computational experiments yield promising results.


Bayesian Nonnegative Matrix Factorization with Stochastic Variational Inference


title: A Novel Simple and Compact Microstrip-Fed Circularly Polarized Wide Slot Antenna With Wide Axial Ratio Bandwidth for C-Band Applications

abstract: This communication presents a design of a new compact circularly polarized (CP) slot antenna fed by a microstrip feedline. The 3-dB axial ratio band can be achieved by simply protruding a horizontal stub from the ground plane toward the center of the wide slot (WS) and then feeding the WS with a microstrip feedline positioned to the side of the WS, underneath the protruded stub. The feedline and metallic stub are perpendicular to each other, and they resemble a T shape when viewed from the top. The proposed antenna is fabricated with an area of $25 \times 25\;{\rm mm}^{2}$ . Measurement results show that the antenna attains an ${\rm S}_{11}\le-10\;{\rm dB}$ impedance matching bandwidth of 90.2%, from 3.5 to 9.25 GHz, and a broadband 3 dB-AR bandwidth of 40%, ranging from 4.6 to 6.9 GHz. A peak gain of 0.8–4.5 dBi is achieved within the AR band. The proposed antenna is suitable for circular polarization applications in C band.


title: A Surgical Palpation Probe With 6-Axis Force/Torque Sensing Capability for Minimally Invasive Surgery

abstract: A novel surgical palpation probe installing a miniature 6-axis force/torque (F/T) sensor is presented for robot-assisted minimally invasive surgery. The 6-axis F/T sensor is developed by using the capacitive transduction principle and a novel capacitance sensing method. The sensor consists of only three parts, namely a sensing printed circuit board, a deformable part, and a base part. The simple configuration leads to simpler manufacturing and assembly processes in conjunction with high durability and low weight. In this study, a surgical instrument installed with a surgical palpation probe is implemented. The 6-axis F/T sensing capability of the probe has been experimentally validated by comparing it with a reference 6-axis F/T sensor. Finally, a vivo tissue palpation task is performed in a simulated surgical environment with an animal organ and a relatively hard simulated cancer buried under the surface of the organ.


title: Effects of in-season short-term plyometric training on jumping and agility performance of basketball players

abstract: The purpose of this investigation was to examine the effects of in-season plyometric training program on power and agility performance in young male basketball players. Twenty intermediate basketball players (age 20.1 ± 1.3 years; height 181.1 ± 8.5 cm; body mass 78.8 ± 5 kg) from Division I province team volunteered to participate in this study and were randomly divided into two groups: plyometric training (PL; n = 10) and control group (CG; n = 10). Plyometric training took place twice weekly for 6 weeks including three sets of 15 repetitions of depth jump (from 45-cm box height), vertical jump, and standing long jump, in addition to regular basketball practice of the team. Vertical jump (VJ), standing long jump (SLJ), 4 × 9-m shuttle run, agility t test (ATT), and Illinois Agility Test (IAT) were measured at pre- and post-training. The PL group showed significant improvement (P < 0.05) in VJ (10.21 ± 2.72 cm), SLJ (21.15 ± 8.10 cm), 4 × 9-m shuttle run (0.62 ± 0.28 s), ATT (1.16 ± 0.57 s), and IAT (1.17 ± 0.65 s) after a 6-week training period and compared to CG. It can be concluded that a 6-week in-season plyometric training program has positive effects for improving power and agility performance in young male basketball players and this study provides support for coaches and basketball players who use this training method during competitive phase.


title: Policy-based non-interactive outsourcing of computation using multikey FHE and CP-ABE

abstract: We consider the problem of outsourced computation that operates on encrypted inputs supplied by multiple independent parties. To facilitate fine-grained access control, it would be desirable if each party could encrypt her input under an appropriate access policy. Moreover, a party should only be authorized to decrypt the result of a computation performed on a set of encrypted inputs if his credentials satisfy the composition of all input policies. There has been limited success so far achieving homomorphic encryption in the functional setting; that is, for primitives such as Ciphertext-Policy Attribute Based Encryption (CP-ABE) and Identity Based Encryption (IBE). We introduce a new primitive that captures homomorphic encryption with support for access policies and policy composition. We then present a generic construction using CP-ABE and multikey Fully-Homomorphic encryption (FHE). Furthermore, we show that a CP-ABE scheme that is homomorphic for circuits of polylogarithmic depth in some parameter m implies a CP-ABE scheme that is homomorphic for circuits of arity m and unbounded depth.


title: How anywhere computing just killed your phone-based two-factor authentication

abstract: Exponential growth in smartphone usage combined with recent advances in mobile technology is causing a shift in (mobile) app behavior: application vendors no longer restrict their apps to a single platform, but rather add synchronization options that allow users to conveniently switch from mobile to PC or vice versa in order to access their services. This process of integrating apps among multiple platforms essentially removes the gap between them. Current, state of the art, mobile phone-based two-factor authentication (2FA) mechanisms, however, heavily rely on the existence of such separation. They are used in a variety of segments (such as consumer online banking services or enterprise secure remote access) to protect against malware. For example, with 2FA in place, attackers should no longer be able to use their PC-based malware to instantiate fraudulent banking transactions.


title: Fine-grained analysis of explicit and implicit sentiment in financial news articles

abstract: In the financial domain, news has an impact on the stock markets.Most sentiment analysis methods are coarse-grained and focus on explicit sentiment.Such a method is insufficient to detect topic-specific sentiment in financial news articles.We propose a novel fine-grained method that detects explicit and implicit sentiment.This is a viable method for topic-specific sentiment analysis in financial news text. This paper focuses on topic-specific and more specifically company-specific sentiment analysis in financial newswire text. This application is of great use to researchers in the financial domain who study the impact of news (media) on the stock markets.We investigate the viability of a new fine-grained sentiment annotation scheme. Most of the current approaches to sentiment analysis focus on the detection of explicit sentiment. As news text often contains implicit sentiment, i.e. factual information implying positive or negative sentiment, our approach aims to identify both explicit and implicit sentiment. Furthermore, this sentiment is analyzed on a fine-grained level by detecting the topic of the sentiment, as sentiment is not always expressed towards the topics one is interested in.In order to test our approach, we assembled a corpus of company-specific news articles, which was manually labeled by four annotators to create a gold standard. We compare the results of our method to the performance of two coarse-grained baseline systems: a lexicon-based approach and a supervised machine learning approach that makes use of lexical features. Our fine-grained approach outperforms both baselines, and its output shows substantial to almost perfect agreement with the gold standard sentiment labels. Using our annotation scheme, we are able to filter out irrelevant sentiment expressions and detect explicit and implicit sentiment in a reliable way.


Developing an Enterprise Business Intelligence Capability: The Norfolk Southern Journey


title: A Comb-Line Antenna Modified for Wideband Circular Polarization

abstract: A sequential rotation technique is newly applied to a circularly polarized (CP) comb-line antenna. The antenna is analyzed using the moment method. First, a novel loop antenna is proposed as a CP radiation element. Next, the loop element is used for a comb-line antenna to radiate a CP wave. This reference antenna shows a bandwidth of 3% for a 3-dB axial-ratio criterion. Last, the comb-line antenna is modified for wideband CP radiation. It is both numerically and experimentally revealed that the CP wave bandwidth is wider than that of the reference antenna by a factor of five.


title: How software process improvement helped Motorola

abstract: Many organizations are using or considering the Capability Maturity Model as a vehicle for software process improvement. But does the CMM provide real benefits? The authors offer metrics and data that show the results of Motorola's CMM usage.


title: Compact thermal modeling for temperature-aware design

abstract: Thermal design in sub-100nm technologies is one of the major challenges to the CAD community. In this paper, we first introduce the idea of temperature-aware design. We then propose a compact thermal model which can be integrated with modern CAD tools to achieve a temperature-aware design methodology. Finally, we use the compact thermal model in a case study of microprocessor design to show the importance of using temperature as a guideline for the design. Results from our thermal model show that a temperature-aware design approach can provide more accurate estimations, and therefore better decisions and faster design convergence.


title: Learning Edge Representations via Low-Rank Asymmetric Projections

abstract: We propose a new method for embedding graphs while preserving directed edge information. Learning such continuous-space vector representations (or embeddings) of nodes in a graph is an important first step for using network information (from social networks, user-item graphs, knowledge bases, etc.) in many machine learning tasks. Unlike previous work, we (1) explicitly model an edge as a function of node embeddings, and we (2) propose a novel objective, the graph likelihood, which contrasts information from sampled random walks with non-existent edges. Individually, both of these contributions improve the learned representations, especially when there are memory constraints on the total size of the embeddings. When combined, our contributions enable us to significantly improve the state-of-the-art by learning more concise representations that better preserve the graph structure. We evaluate our method on a variety of link-prediction task including social networks, collaboration networks, and protein interactions, showing that our proposed method learn representations with error reductions of up to 76% and 55%, on directed and undirected graphs. In addition, we show that the representations learned by our method are quite space efficient, producing embeddings which have higher structure-preserving accuracy but are 10 times smaller.


title: An Emotion Recognition System for Mobile Applications

abstract: Emotion-aware mobile applications have been increasing due to their smart features and user acceptability. To realize such an application, an emotion recognition system should be in real time and highly accurate. As a mobile device has limited processing power, the algorithm in the emotion recognition system should be implemented using less computation. In this paper, we propose an emotion recognition with high performance for mobile applications. In the proposed system, facial video is captured by an embedded camera of a smart phone. Some representative frames are extracted from the video, and a face detection module is applied to extract the face regions in the frames. The Bandlet transform is realized on the face regions, and the resultant subband is divided into non-overlapping blocks. Local binary patterns’ histograms are calculated for each block, and then are concatenated over all the blocks. The Kruskal–Wallis feature selection is applied to select the most dominant bins of the concatenated histograms. The dominant bins are then fed into a Gaussian mixture model-based classifier to classify the emotion. Experimental results show that the proposed system achieves high recognition accuracy in a reasonable time.


abstract: Do English and Mandarin Speakers Think Differently About Time? Lera Boroditsky (lera@stanford.edu) Department of Psychology, 450 Serra Mall, Bldg. 420 Stanford, CA 94305 USA bounded events, unidirectional change, etc.) appear to be universal across cultures and languages. However, there are many aspects of our concept of time that are not observable in the world. For example, does time move horizontally or vertically? Does it move forward or back, left or right, up or down? Does it move past us, or do we move through it? All of these aspects are left unspecified in our experience with the world. They are however, specified in our language —most often through spatial metaphors. Across languages, people use spatial metaphors to talk about time. Whether they are looking forward to a brighter tomorrow, proposing theories ahead of their time, or falling behind schedule, they rely on terms from the domain of space to talk about time (Clark, 1973; Lehrer, 1990; Traugott, 1978). Those aspects of time that are not constrained by our physical experience with time are free to vary across languages and our conceptions of them may be shaped by the way we choose to talk about them. This article focuses on one such aspect of time and examines whether different ways of talking about time lead to different ways of thinking about it. Abstract Do the languages we speak shape the ways we think? Boroditsky, (2001) demonstrated that speakers of English and Mandarin think differently about time. This work has recently been brought into question (January & Kako, 2007; Chen, 2007). Here I present new evidence that again demonstrates a difference between English and Mandarin speakers’ construals of time. Both languages use horizontal and vertical spatial language to talk about time. For example, in English we might say that the best is ahead of us, or we may move a meeting up. In English, vertical metaphors are relatively infrequent and horizontal metaphors predominate. In Mandarin, both horizontal and vertical metaphors are frequent. Importantly, vertical metaphors are much more frequent in Mandarin than they are in English. The new evidence once again suggests that Mandarin speakers don’t just talk about time vertically more frequently than do English speakers, they also think about time vertically more frequently than do English speakers. Keywords: Language and Thought, Space, Time, Metaphor, Mandarin Introduction Time is a topic of central importance in our culture. According to the latest Oxford dictionary, the word “time” is the most frequently used noun in English, with other temporal words like “year” and “day” also placing in the top 10. How does our understanding of the domain of time develop? What are the ingredients of our mental representations of time? Certainly some elements of time are apparent in our experience with the world. From experience, we might be able to learn that each moment in time only happens once, that we can only be in one place at one time, that we can never go back, and that many aspects of our experience are not permanent (i.e., faculty meetings are not everlasting, but rather begin and end at certain times). In other words, our experience dictates that time is a phenomenon in which we, the observer, experience continuous unidirectional change that may be marked by appearance and disappearance of objects and events. These aspects of conceptual time should be universal across cultures and languages. Indeed, this appears to be the case. In order to capture the sequential order of events, time is generally conceived as a one- dimensional, directional entity. Across languages, the spatial terms imported to talk about time are also one-dimensional, directional terms such as ahead/ behind or up / down rather than multidimensional or symmetric terms such as narrow/ wide or left / right (Clark, 1973; Traugott, 1978). Aspects of time that are extractable from world experience (temporally Time in English In English, we predominantly use front / back terms to talk about time. We can talk about the good times ahead of us or the hardships behind us. We can move meetings forward, push deadlines back, and eat dessert before we are done with our vegetables. On the whole, the terms used to order events are the same as those used to describe asymmetric horizontal spatial relations (e.g., ‘‘he took three steps forward’’ or ‘‘the dumpster is behind the store’’). Time in Mandarin Chinese In Mandarin, front / back spatial metaphors for time are also common (Scott, 1989). Mandarin speakers use the spatial morphemes qian (‘‘front’’) and hou (‘‘back’’) to talk about time. What makes Mandarin interesting for present purposes is that Mandarin speakers also systematically use vertical metaphors to talk about time (Scott, 1989). The spatial morphemes shang (‘‘up’’) and xia (‘‘down’’) are frequently used to talk about the order of events, weeks, months, semesters, and more. Earlier events are said to be shang or ‘‘up,’’ and later events are said to be xia or ‘‘down.’’ Although in English vertical spatial terms can also be used to talk about time (e.g., ‘‘hand down knowledge from generation to generation’’ or ‘‘the meeting was coming up’’), these uses are not nearly as common or systematic as


title: Twelve Common Mistakes in Pilonidal Sinus Care

abstract: Healing of pilonidal sinus wounds (PSWs) by secondary intention requires an average of 2 to 6 months, but delayed healing may require 1 to 2 years or even longer. Characteristically, these midline wounds are in the natal cleft of the buttocks or sacrococcygeal area of the back. These PSWs have costly financial consequences to the healthcare system and negatively affect the quality of life of the individual with the wound. This article contains an evidence-based literature review supplemented by the clinical expert opinion of the authors. Twelve leading mistakes in assessment and treatment have been identified with appropriate solutions to optimize patient outcomes. A case study is included to illustrate the common clinical challenges with strategies to optimize healing.


title: IoT enabled environmental monitoring system for smart cities

abstract: A smart city enables the effective utilization of resources and better quality of services to the citizens. To provide services such as air quality management, weather monitoring and automation of homes and buildings in a smart city, the basic parameters are temperature, humidity and CO 2 . This paper presents a customised design of an Internet of Things (IoT) enabled environment monitoring system to monitor temperature, humidity and CO2. In developed system, data is sent from the transmitter node to the receiver node. The data received at the receiver node is monitored and recorded in an excel sheet in a personal computer (PC) through a Graphical User Interface (GUI), made in LabVIEW. An Android application has also been developed through which data is transferred from LabVIEW to a smartphone, for monitoring data remotely. The results and the performance of the proposed system is discussed.


title: Review: A state-of the-art survey of TOPSIS applications

abstract: Multi-Criteria Decision Aid (MCDA) or Multi-Criteria Decision Making (MCDM) methods have received much attention from researchers and practitioners in evaluating, assessing and ranking alternatives across diverse industries. Among numerous MCDA/MCDM methods developed to solve real-world decision problems, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) continues to work satisfactorily across different application areas. In this paper, we conduct a state-of-the-art literature survey to taxonomize the research on TOPSIS applications and methodologies. The classification scheme for this review contains 266 scholarly papers from 103 journals since the year 2000, separated into nine application areas: (1) Supply Chain Management and Logistics, (2) Design, Engineering and Manufacturing Systems, (3) Business and Marketing Management, (4) Health, Safety and Environment Management, (5) Human Resources Management, (6) Energy Management, (7) Chemical Engineering, (8) Water Resources Management and (9) Other topics. Scholarly papers in the TOPSIS discipline are further interpreted based on (1) publication year, (2) publication journal, (3) authors' nationality and (4) other methods combined or compared with TOPSIS. We end our review paper with recommendations for future research in TOPSIS decision-making that is both forward-looking and practically oriented. This paper provides useful insights into the TOPSIS method and suggests a framework for future attempts in this area for academic researchers and practitioners.


title: Microstructure examination and microhardness of friction stir welded joint of (AA7020-O) after PWHT

abstract: Abstract This paper studies the microstructure and microhardness of the welded joints of the annealed aluminum alloy AA7020-O produced by friction stir welding (FSW) technique. The material was applied to post weld heat treatment (solution treatment and artificially aged, T6). The traverse speed and the rotational speed are the most important process parameters of FSW, and have great influence on the heat input of the welding operation which governs the welded joints quality. To investigate their effect, the welding operation was performed using three traverse speeds, 20, 40 and 60 mm/s with two rotational speeds of 1125 and 1400 rpm, and other welding parameters were kept constant to produce comparable joints. It was found that the two rotational speeds are accepted with lower traverse speeds to produce sound joints. Microstructure of the welded joints was significantly affected by the FSW process parameters, and slight effect was reported for the grain size. Microhardness examination showed high weld joint quality with respect to the base metal hardness, which proves the reprecipitation of the hardening phase in the weld zone. The microhardness profile was strongly dependant on the rotational speed, and the average values of the joints hardness have increased with the decrease in the rotational speed, where it have been slightly affected by the welding speed.


title: Real-Time Multi-human Tracking Using a Probability Hypothesis Density Filter and Multiple Detectors

abstract: The Probability Hypothesis Density (PHD) filter is a multi-object Bayes filter which has recently attracted a lot of interest in the tracking community mainly for its linear complexity and its ability to deal with high clutter especially in radar/sonar scenarios. In the computer vision community however, underlying constraints are different from radar scenarios and have to be taken into account when using the PHD filter. In this article, we propose a new tree-based path extraction algorithm for a Gaussian Mixture PHD filter in Computer Vision applications. We also investigate how an additional benefit can be achieved by using a second human detector and justify an approximation for multiple sensors in low-clutter scenarios.


title: Effect of metal line width on electromigration of BEOL Cu interconnects

abstract: Electromigration reliability of BEOL Cu interconnects with various metal line widths and via sizes has been studied. EM lifetime significantly improves from minimum width to three times the minimum width, and then saturates. In addition, the EM lifetime of the wide metal lines was not dependent on via size. The proposed mechanism for EM lifetime improvement is larger grains in wider lines leading to a suppression of grain boundary diffusion. Cu grain size and Cu drift velocity were correlated to the EM lifetime behavior, saturating at the same line width, and kinetic studies found activation energies consistent with grain boundary diffusion.


title: Anchored Neighborhood Regression for Fast Example-Based Super-Resolution

abstract: Recently there have been significant advances in image up scaling or image super-resolution based on a dictionary of low and high resolution exemplars. The running time of the methods is often ignored despite the fact that it is a critical factor for real applications. This paper proposes fast super-resolution methods while making no compromise on quality. First, we support the use of sparse learned dictionaries in combination with neighbor embedding methods. In this case, the nearest neighbors are computed using the correlation with the dictionary atoms rather than the Euclidean distance. Moreover, we show that most of the current approaches reach top performance for the right parameters. Second, we show that using global collaborative coding has considerable speed advantages, reducing the super-resolution mapping to a precomputed projective matrix. Third, we propose the anchored neighborhood regression. That is to anchor the neighborhood embedding of a low resolution patch to the nearest atom in the dictionary and to precompute the corresponding embedding matrix. These proposals are contrasted with current state-of-the-art methods on standard images. We obtain similar or improved quality and one or two orders of magnitude speed improvements.


title: Twin Learning for Similarity and Clustering: A Unified Kernel Approach

abstract: Many similarity-based clustering methods work in two separate steps including similarity matrix computation and subsequent spectral clustering. However, similarity measurement is challenging because it is usually impacted by many factors, e.g., the choice of similarity metric, neighborhood size, scale of data, noise and outliers. Thus the learned similarity matrix is often not suitable, let alone optimal, for the subsequent clustering. In addition, nonlinear similarity often exists in many real world data which, however, has not been effectively considered by most existing methods. To tackle these two challenges, we propose a model to simultaneously learn cluster indicator matrix and similarity information in kernel spaces in a principled way. We show theoretical relationships to kernel k-means, k-means, and spectral clustering methods. Then, to address the practical issue of how to select the most suitable kernel for a particular clustering task, we further extend our model with a multiple kernel learning ability. With this joint model, we can automatically accomplish three subtasks of finding the best cluster indicator matrix, the most accurate similarity relations and the optimal combination of multiple kernels. By leveraging the interactions between these three subtasks in a joint framework, each subtask can be iteratively boosted by using the results of the others towards an overall optimal solution. Extensive experiments are performed to demonstrate the effectiveness of our method.


title: A Blockchain-Based Authentication and Security Mechanism for IoT

abstract: The existing identity authentication of IoT devices mostly depends on an intermediary institution, i.e., a CA server, which suffers from the single-point-failure attack. Even worse, the critical data of authenticated devices can be tampered by inner attacks without being identified. To address these issues, we utilize blockchain technology, which serves as a secure tamper-proof distributed ledger to IoT devices. In the proposed method, we assign a unique ID for each individual device and record them into the blockchain, so that they can authenticate each other without a central authority. We also design a data protection mechanism by hashing significant data (i.e. firmware) into the blockchain where any state changes of the data can be detected immediately. Finally, we implement a prototype based on an open source blockchain platform Hyperledger Fabric to verify the proposed system.


title: Task offloading decision in fog computing system

abstract: Fog computing is an emerging paradigm of cloud computing which to meet the growing computation demand of mobile application. It can help mobile devices to overcome resource constraints by offloading the computationally intensive tasks to cloud servers. The challenge of the cloud is to minimize the time of data transfer and task execution to the user, whose location changes owing to mobility, and the energy consumption for the mobile device. To provide satisfactory computation performance is particularly challenging in the fog computing environment. In this paper, we propose a novel fog computing model and offloading policy which can effectively bring the fog computing power closer to the mobile user. The fog computing model consist of remote cloud nodes and local cloud nodes, which is attached to wireless access infrastructure. And we give task offloading policy taking into account executi+on, energy consumption and other expenses. We finally evaluate the performance of our method through experimental simulations. The experimental results show that this method has a significant effect on reducing the execution time of tasks and energy consumption of mobile devices.


title: Portable Exoskeleton Glove With Soft Structure for Hand Assistance in Activities of Daily Living

abstract: This paper presents a new portable exoskeleton glove developed for assistance in activities of daily living (ADL). The proposed glove is soft, adjustable, allows unconstrained motion of the wrist, and leaves the palm free for object grasping and manipulation. In the proposed design, both flexion and extension motions of the thumb and three fingers (excluding the little finger) are supported by respective bidirectional actuators. The kinematics of the device were derived and are presented along with an experimental evaluation. The developed glove system is compact and light, with the weights of the wearable part and the whole system being 250 and 340 g, respectively. The maximum pinch force provided by the device was experimentally measured to be 16 N, which gives the proposed device one of the highest power-to-weight ratio among existing portable devices for hand ADL assistance. The usability of the developed device was verified in a series of indoor and outdoor tests. The glove allowed test subjects to hold and manipulate versatile objects with diameters up to 90 mm and weighing 300 g. When powered by a conventional 3000-mAh battery, the glove could operate continuously performing grasping task for 4 h.


title: 5G security: Analysis of threats and solutions

abstract: 5G will provide broadband access everywhere, entertain higher user mobility, and enable connectivity of massive number of devices (e.g. Internet of Things (IoT)) in an ultrareliable and affordable way. The main technological enablers such as cloud computing, Software Defined Networking (SDN) and Network Function Virtualization (NFV) are maturing towards their use in 5G. However, there are pressing security challenges in these technologies besides the growing concerns for user privacy. In this paper, we provide an overview of the security challenges in these technologies and the issues of privacy in 5G. Furthermore, we present security solutions to these challenges and future directions for secure 5G systems.


title: DC link voltage sensorless control of a three-phase boost power factor correction rectifier

abstract: This paper presents an innovative and simple approach of controlling a three-phase boost-type rectifier without using an output DC link voltage sensor, whose information is one of the most importantly governing factor of stability and regulation of the converter. Though all the traditional PFC control techniques require the feedback signals from input voltage, input current and output voltage sensors to ensure the stability of the converter, reducing a DC voltage sensor is theoretically feasible and implementable without affecting stability of the system, as proposed in this manuscript. The proposed control method incorporates the prediction of the output voltage from the fluctuations of other state variables and preceding switching state information from converter dynamics. In order to validate and perform a proof-of-concept verification to the proposed control strategy, a 2 kW three-phase boost PFC prototype is designed and developed. The experimental results show that an input power factor of 0.995, a total harmonic distortion (THD) as low as 2.1%, a conversion efficiency of 97.8% and a tightly regulated DC link voltage with 1% ripple can be achieved.