title: Trajectory tracking control for a wheeled mobile robot using fuzzy logic controller

abstract: A trajectory tracking control for a wheeled mobile robot using fuzzy logic controller (FLC) is presented in this paper. The control algorithm based on the errors in postures of mobile robot which feed FLC, which generates correction signals for the left and right motor speeds. The control strategy is based on a feed forward velocity profile and the correcting signal in the velocity generated from the FLC according to the postures errors. Simulation and experimental results demonstrate the effectiveness of the proposed algorithm, which proved the good tracking results and showed the robustness of the algorithm against the uncertainties in the system model.


title: Engineering analysis for design optimisation of differential transformers

abstract: Design optimisation is a primary objective in computer-aided engineering. In this paper the optimisation of a linear variable differential transformer is used as an example. The study shows two typical features of computer-aided engineering: first that analysis techniques can be used to give measurements of system parameters that could not be achieved in a practical experiment; secondly that the design optimisation involves application-specific extensions to the basic techniques. Using the data from computer-evaluated measurements a transducer design which gives a linear output voltage is achieved.


title: Dual circularly-polarized stacked patch antenna for GPS/SDMB

abstract: This paper proposes stacked dual-patch CP antenna for GPS and SDMB services. The characteristic of CP at dual-frequency bands is achieved with a circular patch truncated corners with ears at diagonal direction. According to the dimensions of the truncated corners as well as spacing between centers of the two via-holes, the axial ratio of the CP antenna can be controlled. The good return loss results were obtained both at GPS and SDMB bands. The measured gains of the antenna system are 2.3 dBi and 2.4 dBi in GPS and SDMB bands, respectively. The measured axial ratio is slightly shifted frequencies due to diameter variation of via-holes and the spacing between lower patch and upper patch. The proposed low profile, low-cost fabrication, dual circularly polarization, and separated excitation ports make the proposed stacked antenna an applicable solution as a multi-functional antenna for GPS and SDMB operation on vehicle.


title: Computing the shortest path: A search meets graph theory

abstract: We propose shortest path algorithms that use A* search in combination with a new graph-theoretic lower-bounding technique based on landmarks and the triangle inequality. Our algorithms compute optimal shortest paths and work on any directed graph. We give experimental results showing that the most efficient of our new algorithms outperforms previous algorithms, in particular A* search with Euclidean bounds, by a wide margin on road networks and on some synthetic problem families.


title: Origin of CEO and Compensation Strategy: Differences between Insiders and Outsiders

abstract: Increasingly, U.S. firms are hiring their new CEOs from outside the firms. This study investigates the differences in compensation between outsider CEOs and insider CEOs from three dimensions: pay level, pay and performance link, and pay mix. Our analyses show: (1) outsider CEOs are paid more than insider CEOs, (2) pay and performance link is very weak for outsider CEOs, and (3) compensation package for outsider CEOs emphasizes the use of stock options. While several factors (e.g., firm size, firm performance, CEO tenure, ownership structure) influence insider CEOs' pay, firm size is the only determinant of outsider CEOs' pay. Our results suggest we will be able to understand CEO compensation more accurately if we analyze CEOs from different origins (insiders, outsiders, founders) separately.


title: Sources of Conflict Between Developers and Testers in Software Development.

abstract: Conflict between software developers and testers is inevitable. Prior work on such conflict has focused on conflict management but not on its prevention. We addressed the research question: What are the sources of interpersonal conflict between developers and testers in software development? To attempt to answer this, we collected and analyzed 50 developer-tester conflict scenarios from professional software developers and testers. Our results indicated that the sources of conflict fell into three major areas: process, people, and communication. Conflict sources are presented in a category-subcategory-description format. Implications for theory and practice in the proactive management of conflict sources are discussed.


title: Chimeric antigen receptor T‐cell therapy — assessment and management of toxicities

abstract: Immunotherapy using T cells genetically engineered to express a chimeric antigen receptor (CAR) is rapidly emerging as a promising new treatment for haematological and non-haematological malignancies. CAR-T-cell therapy can induce rapid and durable clinical responses, but is associated with unique acute toxicities, which can be severe or even fatal. Cytokine-release syndrome (CRS), the most commonly observed toxicity, can range in severity from low-grade constitutional symptoms to a high-grade syndrome associated with life-threatening multiorgan dysfunction; rarely, severe CRS can evolve into fulminant haemophagocytic lymphohistiocytosis (HLH). Neurotoxicity, termed CAR-T-cell-related encephalopathy syndrome (CRES), is the second most-common adverse event, and can occur concurrently with or after CRS. Intensive monitoring and prompt management of toxicities is essential to minimize the morbidity and mortality associated with this potentially curative therapeutic approach; however, algorithms for accurate and consistent grading and management of the toxicities are lacking. To address this unmet need, we formed a CAR-T-cell-therapy-associated TOXicity (CARTOX) Working Group, comprising investigators from multiple institutions and medical disciplines who have experience in treating patients with various CAR-T-cell therapy products. Herein, we describe the multidisciplinary approach adopted at our institutions, and provide recommendations for monitoring, grading, and managing the acute toxicities that can occur in patients treated with CAR-T-cell therapy.


title: MatchPad : Interactive Glyph-Based Visualization for Real-Time Sports Performance Analysis

abstract: Today real-time sports performance analysis is a crucial aspect of matches in many major sports. For example, in soccer and rugby, team analysts may annotate videos during the matches by tagging specific actions and events, which typically result in some summary statistics and a large spreadsheet of recorded actions and events. To a coach, the summary statistics (e.g., the percentage of ball possession) lacks sufficient details, while reading the spreadsheet is time-consuming and making decisions based on the spreadsheet in real-time is thereby impossible. In this paper, we present a visualization solution to the current problem in real-time sports performance analysis. We adopt a glyph-based visual design to enable coaching staff and analysts to visualize actions and events “at a glance”. We discuss the relative merits of metaphoric glyphs in comparison with other types of glyph designs in this particular application. We describe an algorithm for managing the glyph layout at different spatial scales in interactive visualization. We demonstrate the use of this technical approach through its application in rugby, for which we delivered the visualization software, MatchPad, on a tablet computer. The MatchPad was used by the Welsh Rugby Union during the Rugby World Cup 2011. It successfully helped coaching staff and team analysts to examine actions and events in detail whilst maintaining a clear overview of the match, and assisted in their decision making during the matches. It also allows coaches to convey crucial information back to the players in a visually-engaging manner to help improve their performance. © 2012 Wiley Periodicals, Inc.


title: Learning to Drive in a Day

abstract: We demonstrate the first application of deep reinforcement learning to autonomous driving. From randomly initialised parameters, our model is able to learn a policy for lane following in a handful of training episodes using a single monocular image as input. We provide a general and easy to obtain reward: the distance travelled by the vehicle without the safety driver taking control. We use a continuous, model-free deep reinforcement learning algorithm, with all exploration and optimisation performed on-vehicle. This demonstrates a new framework for autonomous driving which moves away from reliance on defined logical rules, mapping, and direct supervision. We discuss the challenges and opportunities to scale this approach to a broader range of autonomous driving tasks.


title: Offline Strangers, Online Friends: Bridging Classroom Gender Segregation with WhatsApp

abstract: Mobile Instant Messaging (MIM) apps such as WhatsApp are heralding new communication behaviors amongst students in peri-urban India. From an ethnographic study of a co-ed engineering college, we describe and analyze the role of WhatsApp in both engendering and balancing the ripples caused by cross gender communication. Through in-depth interviews and immersive participant observations in both physical as well as digital spaces, we show how WhatsApp emerges as the backbone of student interactions in a gender segregated academic environment. In analyzing social conditions and identifying structural features of WhatsApp that led to its bottoms up appropriation, our study presents possibilities for educators and designers aiming to create technology enabled collaborative spaces between people otherwise hemmed in by social norms from reaching out to one another.


title: Early Development of Functional Network Segregation Revealed by Connectomic Analysis of the Preterm Human Brain

abstract: Abstract Human brain functional networks are topologically organized with nontrivial connectivity characteristics such as small-worldness and densely linked hubs to support highly segregated and integrated information processing. However, how they emerge and change at very early developmental phases remains poorly understood. Here, we used resting-state functional MRI and voxel-based graph theory analysis to systematically investigate the topological organization of whole-brain networks in 40 infants aged around 31 to 42 postmenstrual weeks. The functional connectivity strength and heterogeneity increased significantly in primary motor, somatosensory, visual, and auditory regions, but much less in high-order default-mode and executive-control regions. The hub and rich-club structures in primary regions were already present at around 31 postmenstrual weeks and exhibited remarkable expansions with age, accompanied by increased local clustering and shortest path length, indicating a transition from a relatively random to a more organized configuration. Moreover, multivariate pattern analysis using support vector regression revealed that individual brain maturity of preterm babies could be predicted by the network connectivity patterns. Collectively, we highlighted a gradually enhanced functional network segregation manner in the third trimester, which is primarily driven by the rapid increases of functional connectivity of the primary regions, providing crucial insights into the topological development patterns prior to birth.


title: A model for gene therapy of human hereditary lymphedema

abstract: Primary human lymphedema (Milroy's disease), characterized by a chronic and disfiguring swelling of the extremities, is associated with heterozygous inactivating missense mutations of the gene encoding vascular endothelial growth factor C/D receptor (VEGFR-3). Here, we describe a mouse model and a possible treatment for primary lymphedema. Like the human patients, the lymphedema (Chy) mice have an inactivating Vegfr3 mutation in their germ line, and swelling of the limbs because of hypoplastic cutaneous, but not visceral, lymphatic vessels. Neuropilin (NRP)-2 bound VEGF-C and was expressed in the visceral, but not in the cutaneous, lymphatic endothelia, suggesting that it may participate in the pathogenesis of lymphedema. By using virus-mediated VEGF-C gene therapy, we were able to generate functional lymphatic vessels in the lymphedema mice. Our results suggest that growth factor gene therapy is applicable to human lymphedema and provide a paradigm for other diseases associated with mutant receptors.


title: Physical activity and cardiovascular risk factors in children.

abstract: BACKGROUND ::: A number of recent systematic reviews have resulted in changes in international recommendations for children's participation in physical activity (PA) for health. The World Health Authority (WHO) has recently released new recommendations. The WHO still recommends 60 min of moderate to vigorous physical activity (MVPA), but also emphasises that these minutes should be on top of everyday physical activities. Everyday physical activities total around 30 min of MVPA in the quintile of the least active children, which means that the new recommendations constitute more activity in total compared with earlier recommendations. ::: ::: ::: OBJECTIVE ::: To summarise evidence justifying new PA recommendation for cardiovascular health in children. ::: ::: ::: METHODS ::: The results of recent systematic reviews are discussed and supplemented with relevant literature not included in these reviews. PubMed was searched for the years 2006-2011 for additional topics not sufficiently covered by the reviews. ::: ::: ::: RESULTS ::: PA was associated with lower blood pressure and a healthier lipid blood profile in children. The association was stronger when a composite risk factor score was analysed, and the associations between physical fitness and cardiovascular disease (CVD) risk factors were even stronger. Muscle strength and endurance exercise each had an effect on blood lipids and insulin sensitivity even if the effect was smaller for muscle strength than for aerobic exercise. New evidence suggests possible effects of PA on C-reactive protein. ::: ::: ::: CONCLUSION ::: There is accumulating evidence that PA can have beneficial effects on the risk factors of CVD in children. Public health policy to promote PA in children, especially the most sedentary children, may be a key element to prevent the onset of CVD later in the children's lives.


title: Texture Classification Using Dense Micro-Block Difference

abstract: This paper is devoted to the problem of texture classification. Motivated by recent advancements in the field of compressive sensing and keypoints descriptors, a set of novel features called dense micro-block difference (DMD) is proposed. These features provide highly descriptive representation of image patches by densely capturing the granularities at multiple scales and orientations. Unlike most of the earlier work on local features, the DMD does not involve any quantization, thus retaining the complete information. We demonstrate that the DMD have dimensionality much lower than Scale Invariant Feature Transform (SIFT) and can be computed using integral image much faster than SIFT. The proposed features are encoded using the Fisher vector method to obtain an image descriptor, which considers high-order statistics. The proposed image representation is combined with the linear support vector machine classifier. Extensive experiments are conducted on five texture data sets (KTH-TIPS, UMD, KTH-TIPS-2a, Brodatz, and Curet) using standard protocols. The results demonstrate that our approach outperforms the state-of-the-art in texture classification.


title: Design and Simulation of UART Serial Communication Module Based on VHDL

abstract: UART (Universal Asynchronous Receiver Transmitter) is a kind of serial communication protocol; mostly used for short-distance, low speed, low-cost data exchange between computer and peripherals. During the actual industrial production, sometimes we do not need the full functionality of UART, but simply integrate its core part. UART includes three kernel modules which are the baud rate generator, receiver and transmitter. The UART implemented with VHDL language can be integrated into the FPGA to achieve compact, stable and reliable data transmission. It's significant for the design of SOC. The simulation results with Quartus II are completely consistent with the UART protocol.


title: Enhancing Semantic Segmentation for Robotics: The Power of 3-D Entangled Forests

abstract: We present a novel, fast, and compact method to improve semantic segmentation of three-dimensional (3-D) point clouds, which is able to learn and exploit common contextual relations between observed structures and objects. Introducing 3-D Entangled Forests (3-DEF), we extend the concept of entangled features for decision trees to 3-D point clouds, enabling the classifier not only to learn, which labels are likely to occur close to each other, but also in which specific geometric configuration. Operating on a plane-based representation of a point cloud, our method does not require a final smoothing step and achieves state-of-the-art results on the NYU Depth Dataset in a single inference step. This compactness in turn allows for fast processing times, a crucial factor to consider for online applications on robotic platforms. In a thorough evaluation, we demonstrate the expressiveness of our new 3-D entangled feature set and the importance of spatial context in the scope of semantic segmentation.


title: Appearance-from-motion: recovering spatially varying surface reflectance under unknown lighting

abstract: We present "appearance-from-motion", a novel method for recovering the spatially varying isotropic surface reflectance from a video of a rotating subject, with known geometry, under unknown natural illumination. We formulate the appearance recovery as an iterative process that alternates between estimating surface reflectance and estimating incident lighting. We characterize the surface reflectance by a data-driven microfacet model, and recover the microfacet normal distribution for each surface point separately from temporal changes in the observed radiance. To regularize the recovery of the incident lighting, we rely on the observation that natural lighting is sparse in the gradient domain. Furthermore, we exploit the sparsity of strong edges in the incident lighting to improve the robustness of the surface reflectance estimation. We demonstrate robust recovery of spatially varying isotropic reflectance from captured video as well as an internet video sequence for a wide variety of materials and natural lighting conditions.


title: A 1 V 2.2 mW 7 GHz CMOS Quadrature VCO Using Current-Reuse and Cross-Coupled Transformer-Feedback Technology

abstract: A current-reused quadrature voltage-controlled oscillator (CR-QVCO) is proposed with the cross-coupled transformer-feedback technology for the quadrature signal generation. This CR-QVCO has the advantages of low-voltage/low-power operation with an adequate phase noise performance. A compact differential three-port transformer, in which two half-circle secondary coils are carefully designed to optimize the effective turn ratio and the coupling factor, is newly constructed to satisfy the need of signal coupling and to save the area consumption simultaneously. The quadrature oscillator providing a center frequency of 7.128 GHz for the ultrawideband (UWB) frequency synthesizer use is demonstrated in a 0.18 mum RF CMOS technology. The oscillator core dissipates 2.2 mW from a 1 V supply and occupies an area of 0.48 mm2. A tuning range of 330 MHz (with a maximum control voltage of 1.8 V) can be achieved to stand the frequency shift caused by the process variation. The measured phase noise is -111.2 dBc/Hz at 1 MHz offset from the center frequency. The IQ phase error shown is less than 2deg. The calculated figure-of-merit (FOM) is 184.8 dB.


title: A Theory of Tonal Hierarchies in Music

abstract: One of the most pervasive structural principles found in music historically and cross-culturally is a hierarchy of tones. Certain tones serve as reference pitches; they are stable, repeated frequently, are emphasized rhythmically, and appear at structurally important positions in musical phrases. The details of the hierarchies differ across styles and cultures. Variation occurs in the particular intervals formed by pitches in the musical scale and the hierarchical levels assigned to pitches within the scale. This variability suggests that an explanation for how these hierarchies are formed cannot be derived from invariant acoustic facts, such as the harmonic structure (overtones) of complex tones. Rather, the evidence increasingly suggests that these hierarchies are products of cognition and, moreover, that they rely on fundamental psychological principles shared by other domains of perception and cognition.


title: Process and Deviation Exploration with Inductive Visual Miner

abstract: Process mining aims to extract information from recorded process data, which can be used to gain insights into the process. This requires applying a discovery algorithm and settings its parameters, after which the discovered process model should be evaluated. Both steps may need to be repeated several times until a satisfying model is found; we refer to this as process exploration. Existing commercial tools usually do not provide models having executable semantics, thereby disallowing for accurate map evaluation, while most academic tools lack features and by the repetitive nature of process exploration, their use is tedious. In this paper, we describe a novel process exploration tool: the Inductive visual Miner. It aims to bridge this gap between commercial and academic tools, by combining the executable semantics of academic tools with the exploration support of commercial tools. It also adds animation and deviation visualisation capabilities. Copyright © 2014 for this paper by its authors. Copying permitted for private and academic purposes.


title: Game-Theoretic Learning in Distributed Control

abstract: This work was supported by ONR Grant #N00014-17-1-2060 and NSF Grant #ECCS-1638214 and by funding from King Abdullah University of Science and Technology (KAUST).


title: Dynamic Patterns of Brain Structure–Behavior Correlation Across the Lifespan

abstract: Although the brain/behavior correlation is one of the premises of cognitive neuroscience, there is still no consensus about the relationship between brain measures and cognitive function, and only little is known about the effect of age on this relationship. We investigated the age-associated variations on the spatial patterns of cortical thickness correlates of four cognitive domains. We showed that the spatial distribution of the cortical thickness correlates of each cognitive domain is distinctive and depicts varying age-association differences across the adult lifespan. Specifically, the present study provides evidence that distinct cognitive domains are associated with unique structural patterns in three adulthood periods: Early, middle, and late adulthood. These findings suggest a dynamic interaction between multiple neural substrates supporting each cognitive domain across the adult lifespan.


title: A Low-Complexity Message-Passing Algorithm for Reduced Routing Congestion in LDPC Decoders

abstract: A low-complexity message-passing algorithm, called Split-Row Threshold, is used to implement low-density parity-check (LDPC) decoders with reduced layout routing congestion. Five LDPC decoders that are compatible with the 10GBASE-T standard are implemented using MinSum Normalized and MinSum Split-Row Threshold algorithms. All decoders are built using a standard cell design flow and include all steps through the generation of GDS II layout. An Spn = 16 decoder achieves improvements in area, throughput, and energy efficiency of 4.1 times, 3.3 times, and 4.8 times, respectively, compared to a MinSum Normalized implementation. Postlayout results show that a fully parallel Spn = 16 decoder in 65-nm CMOS operates at 195 MHz at 1.3 V with an average throughput of 92.8 Gbits/s with early termination enabled. Low-power operation at 0.7 V gives a worst case throughput of 6.5 Gbits/s-just above the 10GBASE-T requirement-and an estimated average power of 62 mW, resulting in 9.5 pj/bit. At 0.7 V with early termination enabled, the throughput is 16.6 Gbits/s, and the energy is 3.7 pJ/bit, which is 5.8× lower than the previously reported lowest energy per bit. The decoder area is 4.84 mm2 with a final postlayout area utilization of 97%.


title: OntoPop or how to annotate documents and populate ontologies from texts

abstract: This paper presents an innovative solution for annotating documents and populating domain ontologies in a single process. In fact, this process can be viewed as a translation of a linguistic representation of a text into a more formal representation format, being RDF statements or OWL instances. Thus, we provide the OntoPop methodology for mapping linguistic extractions with concepts of ontology thanks to knowledge acquisition rules. This methodology has been implemented in a platform integrating a IE tool and a commercial ontology repository. The platform is currently under evaluation on various real world applications. Nevertheless, OntoPop already raises interesting issues such as semantic heterogeneity that are discussed at the end of this article.


title: Foreign object detection in chest X-rays

abstract: Automatic analysis of chest X-ray images is one important approach for screening/identifying pulmonary diseases. The existence of foreign objects in the images hinders the performance of such processing. In this paper, we focus on one type of foreign objects that is often shown in the images of a large dataset of chest X-rays we are working on—the buttons on the gown that the patient is wearing. The method we propose involves four major steps: intensity normalization, low contrast image identification and enhancement, segmentation of lung regions, and button object extraction. Based on the characteristics of the button objects, we applied two methods for the step of button object extraction. One was based on the circular Hough transform; the other was based on the Viola-Jones algorithm. We tested and compared both methods using a ground truth dataset containing 505 button objects. The results demonstrate the effectiveness of the proposed method.


title: SHOG: spherical HOG descriptors for rotation invariant 3D object detection

abstract: We present a method for densely computing local spherical histograms of oriented gradients (SHOG) in volumetric images. The descriptors are based on the continuous representation of the orientation histograms in the harmonic domain, which we compute very efficiently via spherical tensor products and the Fast Fourier Transformation. Building upon these local spherical histogram representations, we utilize the Harmonic Filter to create a generic rotation invariant object detection system that benefits from both the highly discriminative representation of local image patches in terms of histograms of oriented gradients and an adaptable trainable voting scheme that forms the filter. We exemplarily demonstrate the effectiveness of such dense spherical 3D descriptors in a detection task on biological 3D images. In a direct comparison to existing approaches, our new filter reveals superior performance.


title: A Planar 4.5-GHz DC–DC Power Converter

abstract: In this paper, we present two DC-DC converters that operate at a microwave frequency. The first converter consists of a class-E switched-mode microwave amplifier, which performs the DC-AC conversion, and two half-wave diode rectifier outputs. The class-E MESFET amplifier has a minimum power-added efficiency of 86%, corresponding drain efficiency of 95%, and 120 mW of output power at 4.5 GHz. The diode rectifier has a maximum conversion efficiency of 98% and an overall efficiency of 83%. The second converter consists of a high-efficiency class-E oscillator and a diode rectifier. The class-E oscillator has a maximum efficiency of 57% and maximum output power of 725 mW. The DC-DC converter is planar and compact, with no magnetic components, and with a maximum overall DC-DC conversion efficiency of 64% for a DC input of 3 V, and the output voltage across a 87-/spl Omega/ load of 2.15 V.


“ Turn on , Tune in , Drop out ” : Anticipating student dropouts in Massive Open Online Courses


title: Designing and Implementing Corporate Social Responsibility

abstract: This article introduces an integrative framework of corporate social responsibility (CSR) design and implementation. A review of CSR literature—in particular with regard to design and implementation models—provides the background to develop a multiple case study. The resulting integrative framework, based on this multiple case study and Lewin’s change model, highlights four stages that span nine steps of the CSR design and implementation process. Finally, the study identifies critical success factors for the CSR process.


title: A fifth-order air-filled SIW filter for future 5G applications

abstract: This paper presents a fifth-order Tchebyshev bandpass filter based on air-filled substrate integrated waveguide (AFSIW) technology. The iris inductive coupling topology is chosen for filter implementation. The design concept is also introduced based on K-inverted model and Marcuvitz design flow. A comparison between the conventional dielectric-filled substrate integrated waveguide (DFSIW) and the improved AFSIW technology is presented in order to highlight the superiority in term of performance from AFSIW transmission lines. The filter has a relative bandwidth (BW) of 2.98% at 27.92 GHz. The insertion losses is smaller than −1.15 dB in the passband. This bandpass filter can be used for future 5G network or space applications.


title: GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function

abstract: Background:Most successful computational approaches for protein function prediction integrate multiple genomics and proteomics data sources to make inferences about the function of unknown proteins. The most accurate of these algorithms have long running times, making them unsuitable for real-time protein function prediction in large genomes. As a result, the predictions of these algorithms are stored in static databases that can easily become outdated. We propose a new algorithm, GeneMANIA, that is as accurate as the leading methods, while capable of predicting protein function in real-time.Results:We use a fast heuristic algorithm, derived from ridge regression, to integrate multiple functional association networks and predict gene function from a single process-specific network using label propagation. Our algorithm is efficient enough to be deployed on a modern webserver and is as accurate as, or more so than, the leading methods on the MouseFunc I benchmark and a new yeast function prediction benchmark; it is robust to redundant and irrelevant data and requires, on average, less than ten seconds of computation time on tasks from these benchmarks.Conclusion:GeneMANIA is fast enough to predict gene function on-the-fly while achieving state-of-the-art accuracy. A prototype version of a GeneMANIA-based webserver is available at http://morrislab.med.utoronto.ca/prototype.


title: Path difference learning for guitar fingering problem

abstract: In this paper we address the problem of mapping guitar music score into one of possible alternative fingering sequences on the fretboard grid. We use dynamic programming (DP) to model the decision process of a guitarist choosing the optimal fingering sequence. To estimate the DP cost functions based on examples of guitar fingering transcriptions (tablatures) we developed an original method named "path difference learning” employing a gradient descent search on the coefficients of the cost function. Features of the fingering alternatives that capture the essence of the mechanical difficulty and musical quality are used to reject impractical fingerings and thus reduce the DP search complexity. Our experiments for several classical guitar pieces showed consistent convergence of the path difference learning. The adaptation resulted in a significant decrease in error count compared to manually selecting the cost function weights.


title: Data integration driven ontology design, case study smart city

abstract: Methods to design of formal ontologies have been in focus of research since the early nineties when their importance and conceivable practical application in engineering sciences had been understood. However, often significant customization of generic methodologies is required when they are applied in tangible scenarios. In this paper, we present a methodology for ontology design developed in the context of data integration. In this scenario, a targeting ontology is applied as a mediator for distinct schemas of individual data sources and, furthermore, as a reference schema for federated data queries. The methodology has been used and evaluated in a case study aiming at integration of buildings' energy and carbon emission related data. We claim that we have made the design process much more efficient and that there is a high potential to reuse the methodology.


title: Best-order crossover for permutation-based evolutionary algorithms

abstract: Permutation-based encoding is used by many evolutionary algorithms dealing with combinatorial optimization problems. An important aspect of the evolutionary search process refers to the recombination process of existing individuals in order to generate new potentially better fit offspring leading to more promising areas of the search space. In this paper, we describe and analyze the best-order recombination operator for permutation-based encoding. The proposed operator uses genetic information from the two parents and from the best individual obtained up to the current generation. These sources of information are integrated to determine the best order of values in the new permutation. In order to evaluate the performance of best-order crossover, we address three well-known ?? ?? $\mathcal {NP}$ -hard optimization problems i.e. Travelling Salesman Problem, Vehicle Routing Problem and Resource-Constrained Project Scheduling Problem. For each of these problems, a set of benchmark instances is considered in a comparative analysis of the proposed operator with eight other crossover schemes designed for permutation representation. All crossover operators are integrated in the same standard evolutionary framework and using the same parameter setting to allow a comparison focused on the recombination process. Numerical results emphasize a good performance of the proposed crossover scheme which is able to lead to overall better quality solutions.


title: Review-Level Sentiment Classification with Sentence-Level Polarity Correction

abstract: We propose an effective technique to solving review-level sentiment classification problem by using sentence-level polarity correction. Our polarity correction technique takes into account the consistency of the polarities (positive and negative) of sentences within each product review before performing the actual machine learning task. While sentences with inconsistent polarities are removed, sentences with consistent polarities are used to learn state-of-the-art classifiers. The technique achieved better results on different types of products reviews and outperforms baseline models without the correction technique. Experimental results show an average of 82% F-measure on four different product review domains.


Combinatorial Creativity for Procedural Content Generation via Machine Learning.


title: Jointly optimizing word representations for lexical and sentential tasks with the C-PHRASE model

abstract: We introduce C-PHRASE, a distributional semantic model that learns word representations by optimizing context prediction for phrases at all levels in a syntactic tree, from single words to full sentences. C-PHRASE outperforms the state-of-theart C-BOW model on a variety of lexical tasks. Moreover, since C-PHRASE word vectors are induced through a compositional learning objective (modeling the contexts of words combined into phrases), when they are summed, they produce sentence representations that rival those generated by ad-hoc compositional models.


title: Cloud Computing Research and Development Trend

abstract: With the development of parallel computing, distributed computing, grid computing, a new computing model appeared. The concept of computing comes from grid, public computing and SaaS. It is a new method that shares basic framework. The basic principles of cloud computing is to make the computing be assigned in a great number of distributed computers, rather then local computer or remoter server. The running of the enterprise’s data center is just like Internet. This makes the enterprise use the resource in the application that is needed, and access computer and storage system according to the requirement. This article introduces the background and principle of cloud computing, the character, style and actuality. This article also introduces the application field the merit of cloud computing, such as, it do not need user’s high level equipment, so it reduces the user’s cost. It provides secure and dependable data storage center, so user needn’t do the awful things such storing data and killing virus, this kind of task can be done by professionals. It can realize data share through different equipments. It analyses some questions and hidden troubles, and puts forward some solutions, and discusses the future of cloud computing. Cloud computing is a computing style that provide power referenced with IT as a service. Users can enjoy the service even he knows nothing about the technology of cloud computing and the professional knowledge in this field and the power to control it.


title: Why Doesn’t Jane Protect Her Privacy?

abstract: End-to-end encryption has been heralded by privacy and security researchers as an effective defence against dragnet surveillance, but there is no evidence of widespread end-user uptake. We argue that the non-adoption of end-to-end encryption might not be entirely due to usability issues identified by Whitten and Tygar in their seminal paper “Why Johnny Can’t Encrypt”. Our investigation revealed a number of fundamental issues such as incomplete threat models, misaligned incentives, and a general absence of understanding of the email architecture. From our data and related research literature we found evidence of a number of potential explanations for the low uptake of end-to-end encryption. This suggests that merely increasing the availability and usability of encryption functionality in email clients will not automatically encourage increased deployment by email users. We shall have to focus, first, on building comprehensive end-user mental models related to email, and email security. We conclude by suggesting directions for future research.


title: Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting

abstract: Road networks are very important features in geospatial databases. Even though high-resolution optical satellite images have already been acquired for more than a decade, tools for automated extraction of road networks from these images are still rare. One consequence of this is the need for manual interaction which, in turn, is time and cost intensive. In this paper, a multi-stage approach is proposed which integrates structural, spectral, textural, as well as contextual information of objects to extract road networks from very high resolution satellite images. Highlights of the approach are a novel linearity index employed for the discrimination of elongated road segments from other objects and customized tensor voting which is utilized to fill missing parts of the network. Experiments are carried out with different datasets. Comparison of the achieved results with the results of seven state-of-the-art methods demonstrated the efficiency of the proposed approach.


title: Approximate Steepest Coordinate Descent

abstract: We propose a new selection rule for the coordinate selection in coordinate descent methods for huge-scale optimization. The efficiency of this novel scheme is provably better than the efficiency of uniformly random selection, and can reach the efficiency of steepest coordinate descent (SCD), enabling an acceleration of a factor of up to $n$, the number of coordinates. In many practical applications, our scheme can be implemented at no extra cost and computational efficiency very close to the faster uniform selection. Numerical experiments with Lasso and Ridge regression show promising improvements, in line with our theoretical guarantees.


title: ProgME: towards programmable network measurement

abstract: Traffic measurements provide critical input for a wide range of network management applications, including traffic engineering, accounting, and security analysis. Existing measurement tools collect traffic statistics based on some predetermined, inflexible concept of "flows." They do not have sufficient built-in intelligence to understand the application requirements or adapt to the traffic conditions. Consequently, they have limited scalability with respect to the number of flows and the heterogeneity of monitoring applications. We present ProgME, a Programmable MEasurement architecture based on a novel concept of flowset--an arbitrary set of flows defined according to application requirements and/or traffic conditions. Through a simple flowset composition language, ProgME can incorporate application requirements, adapt itself to circumvent the scalability challenges posed by the large number of flows, and achieve a better application-perceived accuracy. The modular design of ProgME enables it to exploit the surging popularity of multicore processors to cope with 7-Gb/s line rate. ProgME can analyze and adapt to traffic statistics in real time. Using sequential hypothesis test, ProgME can achieve fast and scalable heavy hitter identification.


title: Microwave Flash Sintering of Inkjet-Printed Silver Tracks on Polymer Substrates

abstract: Microwave flash sintering of inkjet printed colloidal silver dispersions on thin polymer substrates was studied as a function of the antenna area and initial resistance. The presence of conductive antennae promotes nanoparticle sintering in predried ink lines. For dried nanoparticle inks connected to antennae, sintering times of 1 s are sufficient to obtain pronounced nanoparticle sintering and conductivities between 10 and 34% compared to bulk silver. © 2009 WILEY-VCH Verlag GmbH & Co. KGaA.


title: Bayesian VoxDRN: A Probabilistic Deep Voxelwise Dilated Residual Network for Whole Heart Segmentation from 3D MR Images

abstract: In this paper, we propose a probabilistic deep voxelwise dilated residual network, referred as Bayesian VoxDRN, to segment the whole heart from 3D MR images. Bayesian VoxDRN can predict voxelwise class labels with a measure of model uncertainty, which is achieved by a dropout-based Monte Carlo sampling during testing to generate a posterior distribution of the voxel class labels. Our method has three compelling advantages. First, the dropout mechanism encourages the model to learn a distribution of weights with better data-explanation ability and prevents over-fitting. Second, focal loss and Dice loss are well encapsulated into a complementary learning objective to segment both hard and easy classes. Third, an iterative switch training strategy is introduced to alternatively optimize a binary segmentation task and a multi-class segmentation task for a further accuracy improvement. Experiments on the MICCAI 2017 multi-modality whole heart segmentation challenge data corroborate the effectiveness of the proposed method.


title: Physical Primitive Decomposition

abstract: Objects are made of parts, each with distinct geometry, physics, functionality, and affordances. Developing such a distributed, physical, interpretable representation of objects will facilitate intelligent agents to better explore and interact with the world. In this paper, we study physical primitive decomposition---understanding an object through its components, each with physical and geometric attributes. As annotated data for object parts and physics are rare, we propose a novel formulation that learns physical primitives by explaining both an object's appearance and its behaviors in physical events. Our model performs well on block towers and tools in both synthetic and real scenarios; we also demonstrate that visual and physical observations often provide complementary signals. We further present ablation and behavioral studies to better understand our model and contrast it with human performance.


title: The prevalence of neck pain in the world population: a systematic critical review of the literature

abstract: The objective of this study was to determine the prevalence of neck pain (NP) in the world population and to identify areas of methodological variation between studies. A systematic search was conducted in five databases (MEDLINE, EMBASE, CINAHL, OSH-ROM, and PsycINFO), followed by a screening of reference lists of relevant papers. Included papers were extracted for information and each paper was given a quality score. Mean prevalence estimates were calculated for six prevalence periods (point, week, month, 6 months, year, and lifetime), and considered separately for age, gender, quality score, response rate, sample size, anatomical definition, geography, and publication year. Fifty-six papers were included. The six most commonly reported types of prevalence were point, week, month, 6 months, year, and lifetime. Except for lifetime prevalence, women reported more NP than men. For 1-year prevalence, Scandinavian countries reported more NP than the rest of Europe and Asia. Prevalence estimates were not affected by age, quality score, sample size, response rate, and different anatomical definitions of NP. NP is a common symptom in the population. As expected, the prevalence increases with longer prevalence periods and generally women have more NP than men. At least for 1-year prevalence Scandinavian countries report higher mean estimates than the rest of Europe and Asia. The quality of studies varies greatly but is not correlated with the prevalence estimates. Design varies considerably and standardisation is needed in future studies.


title: Design and In Vitro Interference Test of Microwave Noninvasive Blood Glucose Monitoring Sensor

abstract: A design of a microwave noninvasive continuous blood glucose monitoring sensor and its interference test results are presented. The novelty of the proposed sensor is that it comprises two spatially separated split-ring resonators, where one interacts with the change in glucose level of a sample under test while the other ring is used as a reference. The reference ring has a slightly different resonant frequency and is desensitized to the sample owing to its location, thus allowing changes in temperature to be calibrated out. From an oral glucose tolerance test with two additional commercially available sensors (blood strip and continuous glucose monitor) in parallel, we obtained encouraging performance for our sensor comparable with those of the commercial sensors. The effects of endogenous interferents common to all subjects, i.e., common sugars, vitamins (ascorbic acid), and metabolites (uric acid) have also been investigated by using a large Franz cell assembly. From the interference test, it is shown that the change in sensor response is dominated by changes in glucose level for concentrations relevant to blood, and the effects of interferents are negligible in comparison.


title: Battery Equalization Using Bi-directional Cuk Converter in DCVM Operation

abstract: A systematic approach to the analysis and design of a bi-directional Cuk converter for the cell voltage balancing control of a series-connected battery string is presented in this paper. The proposed individual cell equalizers (ICE) are designed to operate at discontinuous-capacitor-voltage mode (DCVM) to achieve the zero-voltage turn-off switching (ZVS) for reducing the switching loss of the bi-directional DC/DC converters. Simulation and experimental results show that the proposed battery equalization scheme can not only enhance the bi-directional battery equalization performance, but also can reduce the switching loss during the equalization period. The switch power is significantly reduced by about 52.8% from MOSFETs and the equalization efficiency can be improved by 68-72% using the proposed DCVM ZVS battery equalizer under the specified cell equalization process


title: Describing Textures in the Wild

abstract: Patterns and textures are key characteristics of many natural objects: a shirt can be striped, the wings of a butterfly can be veined, and the skin of an animal can be scaly. Aiming at supporting this dimension in image understanding, we address the problem of describing textures with semantic attributes. We identify a vocabulary of forty-seven texture terms and use them to describe a large dataset of patterns collected "in the wild". The resulting Describable Textures Dataset (DTD) is a basis to seek the best representation for recognizing describable texture attributes in images. We port from object recognition to texture recognition the Improved Fisher Vector (IFV) and Deep Convolutional-network Activation Features (DeCAF), and show that surprisingly, they both outperform specialized texture descriptors not only on our problem, but also in established material recognition datasets. We also show that our describable attributes are excellent texture descriptors, transferring between datasets and tasks, in particular, combined with IFV and DeCAF, they significantly outperform the state-of-the-art by more than 10% on both FMD and KTH-TIPS-2b benchmarks. We also demonstrate that they produce intuitive descriptions of materials and Internet images.


A hybrid automated trading system based on multi-objective grammatical evolution


title: An Extragradient-Based Alternating Direction Method for Convex Minimization

abstract: In this paper, we consider the problem of minimizing the sum of two convex functions subject to linear linking constraints. The classical alternating direction type methods usually assume that the two convex functions have relatively easy proximal mappings. However, many problems arising from statistics, image processing and other fields have the structure that while one of the two functions has easy proximal mapping, the other function is smoothly convex but does not have an easy proximal mapping. Therefore, the classical alternating direction methods cannot be applied. To deal with the difficulty, we propose in this paper an alternating direction method based on extragradients. Under the assumption that the smooth function has a Lipschitz continuous gradient, we prove that the proposed method returns an $\epsilon$-optimal solution within $O(1/\epsilon)$ iterations. We apply the proposed method to solve a new statistical model called fused logistic regression. Our numerical experiments show that the proposed method performs very well when solving the test problems. We also test the performance of the proposed method through solving the lasso problem arising from statistics and compare the result with several existing efficient solvers for this problem; the results are very encouraging indeed.


title: Tutorial: Big Data Analytics: Concepts, Technologies, and Applications

abstract: We have entered the big data era. Organizations are capturing, storing, and analyzing data that has high volume, velocity, and variety and comes from a variety of new sources, including social media, machines, log files, video, text, image, RFID, and GPS. These sources have strained the capabilities of traditional relational database management systems and spawned a host of new technologies, approaches, and platforms. The potential value of big data analytics is great and is clearly established by a growing number of studies. The keys to success with big data analytics include a clear business need, strong committed sponsorship, alignment between the business and IT strategies, a fact-based decision-making culture, a strong data infrastructure, the right analytical tools, and people skilled in the use of analytics. Because of the paradigm shift in the kinds of data being analyzed and how this data is used, big data can be considered to be a new, fourth generation of decision support data management. Though the business value from big data is great, especially for online companies like Google and Facebook, how it is being used is raising significant privacy concerns.


title: Feature Selection with Attributes Clustering by Maximal Information Coefficient

abstract: Abstract Feature selection is usually a separate procedure which can not benefit from result of the data exploration. In this paper, we propose a unsupervised feature selection method which could reuse a specific data exploration result. Furthermore, our algorithm follows the idea of clustering attributes and combines two state-of-the-art data analyzing methods, that's maximal information coefficient and affinity propagation. Classification problems with different classifiers were tested to validation our method and others. Data experiments result exhibits our unsupervised algorithm is comparable with classical feature selection methods and even outperforms some supervised learning algorithms. Data simulation with one credit dataset of our own from a bank of China shows the capability of our method for real world application.


title: Constructions of hamiltonian graphs with bounded degree and diameter O (log n)

abstract: Token ring topology has been frequently used in the design of distributed loop computer networks and one measure of its performance is the diameter. We propose an algorithm for constructing hamiltonian graphs with $n$ vertices and maximum degree $\Delta$ and diameter $O (\log n)$, where $n$ is an arbitrary number. The number of edges is asymptotically bounded by $(2 - \frac{1}{\Delta - 1} - \frac{(\Delta - 2)^2}{(\Delta - 1)^3}) n$. In particular, we construct a family of hamiltonian graphs with diameter at most $2 \lfloor \log_2 n \rfloor$, maximum degree 3 and at most $1+11n/8$ edges.


Normal automorphisms of discrete groups


title: © Rinton Press SYSTEM ARCHITECTURE OF A WIRELESS BODY AREA SENSOR NETWORK FOR UBIQUITOUS HEALTH MONITORING

abstract: Recent technological advances in sensors, low-power microelectronics and miniaturization, and wireless networking enabled the design and proliferation of wireless sensor networks capable of autonomously monitoring and controlling environments. One of the most promising applications of sensor networks is for human health monitoring. A number of tiny wireless sensors, strategically placed on the human body, create a wireless body area network that can monitor various vital signs, providing real-time feedback to the user and medical personnel. The wireless body area networks promise to revolutionize health monitoring. However, designers of such systems face a number of challenging tasks, as they need to address often quite conflicting requirements for size, operating time, precision, and reliability. ::: ::: In this paper we present hardware and software architecture of a working wireless sensor network system for ambulatory health status monitoring. The system consists of multiple sensor nodes that monitor body motion and heart activity, a network coordinator, and a personal server running on a personal digital assistant or a personal computer.


Active Pin 1 is a key target of all-trans retinoic acid in acute promyelocytic leukemia and breast cancer


title: Towards more systematicTwitteranalysis: metrics for tweeting activities

abstract: Twitter is an important and influential social media platform, but much research into its uses remains centred around isolated cases – e.g. of events in political communication, crisis communication, or popular culture, often coordinated by shared hashtags (brief keywords, prefixed with the symbol ‘#’). In particular, a lack of standard metrics for comparing communicative patterns across cases prevents researchers from developing a more comprehensive perspective on the diverse, sometimes crucial roles which hashtags play in Twitter-based communication. We address this problem by outlining a catalogue of widely applicable, standardised metrics for analysing Twitter-based communication, with particular focus on hashtagged exchanges. We also point to potential uses for such metrics, presenting an indication of what broader comparisons of diverse cases can achieve.


title: Dietary prebiotics: current status and new definition

abstract: In November 2008, a group of scientists met at the 6th Meeting of the International Scientific Association of Probiotics and Prebiotics (ISAPP) in London, Ontario, Canada, to discuss the functionality of prebiotics. As a result of this, it was concluded that the prebiotic field is currently dominated by gastrointestinal events. However, in the future, it may be the case that other mixed microbial ecosystems may be modulated by a prebiotic approach, such as the oral cavity, skin and the urogenital tract. Therefore, a decision was taken to build upon the current prebiotic status and define a niche for ‘dietary prebiotics’. This review is co-authored by the working group of ISAPP scientists and sets the background for defining a dietary prebiotic as ‘‘a selectively fermented ingredient that results in specific changes in the composition and/or activity of the gastrointestinal microbiota, thus conferring benefit(s) upon host health’’.


title: SegEM: Efficient Image Analysis for High-Resolution Connectomics

abstract: Progress in electron microscopy-based high-resolution connectomics is limited by data analysis throughput. Here, we present SegEM, a toolset for efficient semi-automated analysis of large-scale fully stained 3D-EM datasets for the reconstruction of neuronal circuits. By combining skeleton reconstructions of neurons with automated volume segmentations, SegEM allows the reconstruction of neuronal circuits at a work hour consumption rate of about 100-fold less than manual analysis and about 10-fold less than existing segmentation tools. SegEM provides a robust classifier selection procedure for finding the best automated image classifier for different types of nerve tissue. We applied these methods to a volume of 44 × 60 × 141 μm(3) SBEM data from mouse retina and a volume of 93 × 60 × 93 μm(3) from mouse cortex, and performed exemplary synaptic circuit reconstruction. SegEM resolves the tradeoff between synapse detection and semi-automated reconstruction performance in high-resolution connectomics and makes efficient circuit reconstruction in fully-stained EM datasets a ready-to-use technique for neuroscience.


abstract: ABSTRACT ::: ::: AndroSAT: Security Analysis Tool for Android Applications ::: ::: With about 1.5 million Android device activations per day and billions of applications installation from Google Play, Android is becoming one of the most widely used operating systems for smartphones and tablets. ::: ::: Besides typical personal usages, Android mobile devices are also being integrated into enterprises, government organizations, and military networks. Consequently, these devices hold valuable sensitive information which makes them face the same level of malicious attacks that have targeted the desktop environments over the past three decades. ::: ::: In this thesis, we present AndroSAT, a Security Analysis Tool for Android applications. The developed framework allows us to efficiently experiment with different security aspects of Android apps through the integration of (i) a static analysis module that scans Android apps for malicious patterns. The static analysis process involves several steps such as n-gram analysis of dex files, de-compilation of the app, pattern search, and analysis of the AndroidManifest file; (ii) a dynamic analysis sandbox that executes Android apps in a controlled virtual environment which logs low-level interactions with the operating system. ::: The effectiveness of the developed framework is confirmed by testing it on popular apps collected from F-Droid, and malware samples obtained from a third party and the Android Malware Genome Project dataset. As a case study, we show how the analysis reports obtained from AndroSAT can be used for studying the frequency of use of different Android permissions and dynamic operations and detection of Android malware.


title: Discriminant Locally Linear Embedding With High-Order Tensor Data

abstract: Graph-embedding along with its linearization and kernelization provides a general framework that unifies most traditional dimensionality reduction algorithms. From this framework, we propose a new manifold learning technique called discriminant locally linear embedding (DLLE), in which the local geometric properties within each class are preserved according to the locally linear embedding (LLE) criterion, and the separability between different classes is enforced by maximizing margins between point pairs on different classes. To deal with the out-of-sample problem in visual recognition with vector input, the linear version of DLLE, i.e., linearization of DLLE (DLLE/L), is directly proposed through the graph-embedding framework. Moreover, we propose its multilinear version, i.e., tensorization of DLLE, for the out-of-sample problem with high-order tensor input. Based on DLLE, a procedure for gait recognition is described. We conduct comprehensive experiments on both gait and face recognition, and observe that: 1) DLLE along its linearization and tensorization outperforms the related versions of linear discriminant analysis, and DLLE/L demonstrates greater effectiveness than the linearization of LLE; 2) algorithms based on tensor representations are generally superior to linear algorithms when dealing with intrinsically high-order data; and 3) for human gait recognition, DLLE/L generally obtains higher accuracy than state-of-the-art gait recognition algorithms on the standard University of South Florida gait database.


RDF* and SPARQL*: An Alternative Approach to Annotate Statements in RDF


title: Connecting the Dots: A Review of Resting Connectivity MRI Studies in Attention-Deficit/Hyperactivity Disorder

abstract: Psychopathology is increasingly viewed from a circuit perspective in which a disorder stems not from circumscribed anomalies in discrete brain regions, but rather from impairments in distributed neural networks. This focus on neural circuitry has rendered resting state functional connectivity MRI (rs-fcMRI) an increasingly important role in the elucidation of pathophysiology including attention-deficit/hyperactivity disorder (ADHD). Unlike many other MRI techniques that focus on the properties of discrete brain regions, rs-fcMRI measures the coherence of neural activity across anatomically disparate brain regions, examining the connectivity and organization of neural circuits. In this review, we explore the methods available to investigators using rs-fcMRI techniques, including a discussion of their relative merits and limitations. We then review findings from extant rs-fcMRI studies of ADHD focusing on neural circuits implicated in the disorder, especially the default mode network, cognitive control network, and cortico-striato-thalamo-cortical loops. We conclude by suggesting future directions that may help advance subsequent rs-fcMRI research in ADHD.


title: An Architecture to Support the Collection of Big Data in the Internet of Things

abstract: The Internet of Things (IoT) relies on physical objects interconnected between each others, creating a mesh of devices producing information. In this context, sensors are surrounding our environment (e.g., cars, buildings, smartphones) and continuously collect data about our living environment. Thus, the IoT is a prototypical example of Big Data. The contribution of this paper is to define a software architecture supporting the collection of sensor-based data in the context of the IoT. The architecture goes from the physical dimension of sensors to the storage of data in a cloud-based system. It supports Big Data research effort as its instantiation supports a user while collecting data from the IoT for experimental or production purposes. The results are instantiated and validated on a project named SMARTCAMPUS, which aims to equip the SophiaTech campus with sensors to build innovative applications that supports end-users.


title: A versatile multichannel direct-synthesized electrical stimulator for FES applications

abstract: Functional electrical stimulation (FES) has been applied to restore or maintain the muscle activity of paralyzed patients who suffer from spinal cord injuries and related neural impairments for several decades. In this paper, a direct-synthesized arbitrary waveform stimulator for multichannel FES applications is described. A novel element-envelope method is proposed for flexible waveform generation and implemented by a digital signal processor based system. We also designed an output circuit that can provide bi-phasic, voltage-controlled, constant-current outputs while keeping high-voltage compliance and wide signal bandwidth. High time-resolution and arbitrary stimulating waveforms can thus be synthesized, and more flexible closed-loop feedback controllers can be achieved. The proposed stimulator can be considered as a full-featured electrical stimulator for various FES applications with its flexibility in pattern generation and feedback processing capabilities.


title: Bowenoid papulosis and invasive Bowen's disease: a multidisciplinary approach

abstract: Elena Campione1, Chiara Centonze2, Laura Diluvio1, Augusto Orlandi3, Cesidio Cipriani4, Alessandro Di Stefani3, Emilio Piccione2, Sergio Chimenti1 and Luca Bianchi1 Departments of 1Dermatology, 2Gynaecology and 3Anatomic Pathology, University of Rome, Tor Vergata, Viale Oxford, 81, IT-00133 Rome, and 4Department of Nuclear Medicine, S. Eugenio Hospital, Rome, Italy. E-mail: campioneelena@hotmail.com Accepted Nov 7, 2011; Epub ahead of print Jun 21, 2012


title: A gamified system for learning enterprise resource planning systems: Investigating the user experience

abstract: A dire need for Enterprise Resource Planning (ERP) skills globally has led to the adoption of ERP courses into higher education programs. Gamification is proposed as a possible e-learning strategy that can engage and motivate students to learn introductory ERP concepts. This paper explores the design of a gamified system for learning ERP systems and the impact on the user experience of students. A gamified system, LearnERP, was developed that exposes students to the introductory concepts of ERP systems by means of four mini-games. An exploratory qualitative study was performed of students' perceptions of the user experience of the LearnERP system. Results revealed that the students enjoyed using the system and found it fun, motivating and stimulating. Some drawbacks to the system related to confusion regarding instructions of how to play the games.


title: High performance two-dimensional phase unwrapping on GPUs

abstract: Phase unwrapping is an important procedure in digital image and signal processing, and has been widely used in many fields, such as optical and microwave interferometry, magnetic resonance imaging, synthetic aperture radar, adaptive optics. Phase unwrapping is a time consuming process with large amount of calculations and complicated data dependency. A number of algorithms with different features have been developed to solve this problem. Among all of them, Goldstein's algorithm is one of the most widely used algorithms, and has been included in some standard libraries (such as MATLAB). In this paper we propose an innovative implementation of Goldstein's algorithm on GPU. Several important approaches and optimizations are proposed for the GPU algorithm. For example, by introducing a localmatching step, we were able to parallelize the branchcut step efficiently, getting much better performance than existing work. With a cascaded propagation model, another important operation in the algorithm, floodfill, is able to make good use of the computing power of GPU. We tested our GPU algorithm on NVIDIA C2050 and K20 GPUs, and achieved speedup of up to 781 and 896 over the CPU implementation respectively. To the best of our knowledge, this is the best performance of unwrap ever achieved on GPUs.


title: The Interpretation of Libet's Results on the Timing of Conscious Events: A Commentary

abstract: Abstract A commentary on articles by Klein, Pockett, and Trevena and Miller, in this issue, is given. Average shift in the point of subjective equality (PSE), calculated by Klein on Libet's data, and corresponding change in mean shift, calculated by Libet et al. (1983), may be “corrected,” taking as a reference point the end of the minimum train duration. Values obtained, if significant, indicate a latency for conscious sensation of the skin stimulus of at least 230 ms. Pockett's main conclusions are favored, but her explanation of peripheral–lemniscal couplings is found to be unconvincing. Trevena and Miller's article unsuccessfully tries to rescue a dualist interactionist view. Libet's method of timing intentions is thoroughly criticized.


title: Universal Denoising Networks : A Novel CNN-based Network Architecture for Image Denoising

abstract: We design a novel network architecture for learning discriminative image models that are employed to efficiently tackle the problem of grayscale and color image denoising. Based on the proposed architecture, we introduce two different variants. The first network involves convolutional layers as a core component, while the second one relies instead on non-local filtering layers and thus it is able to exploit the inherent non-local self-similarity property of natural images. As opposed to most of the existing neural networks, which require the training of a specific model for each considered noise level, the proposed networks are able to handle a wide range of different noise levels, while they are very robust when the noise degrading the latent image does not match the statistics of the one used during training. The latter argument is supported by results that we report on publicly available images corrupted by unknown noise and which we compare against solutions obtained by alternative state-of-the-art methods. At the same time the introduced networks achieve excellent results under additive white Gaussian noise (AWGN), which are comparable to the current state-of-the-art network, while they depend on a more shallow architecture with the number of trained parameters being one order of magnitude smaller. These properties make the proposed networks ideal candidates to serve as sub-solvers on restoration methods that deal with general inverse imaging problems such as deblurring, demosaicking, superresolution, etc.


title: User Association for Load Balancing in Heterogeneous Cellular Networks

abstract: For small cell technology to significantly increase the capacity of tower-based cellular networks, mobile users will need to be actively pushed onto the more lightly loaded tiers (corresponding to, e.g., pico and femtocells), even if they offer a lower instantaneous SINR than the macrocell base station (BS). Optimizing a function of the long-term rate for each user requires (in general) a massive utility maximization problem over all the SINRs and BS loads. On the other hand, an actual implementation will likely resort to a simple biasing approach where a BS in tier j is treated as having its SINR multiplied by a factor Aj ≥ 1, which makes it appear more attractive than the heavily-loaded macrocell. This paper bridges the gap between these approaches through several physical relaxations of the network-wide association problem, whose solution is NP hard. We provide a low-complexity distributed algorithm that converges to a near-optimal solution with a theoretical performance guarantee, and we observe that simple per-tier biasing loses surprisingly little, if the bias values Aj are chosen carefully. Numerical results show a large (3.5x) throughput gain for cell-edge users and a 2x rate gain for median users relative to a maximizing received power association.


title: Iterative Hard Thresholding for Compressed Sensing

abstract: Compressed sensing is a technique to sample compressible signals below the Nyquist rate, whilst still allowing near optimal reconstruction of the signal. In this paper we present a theoretical analysis of the iterative hard thresholding algorithm when applied to the compressed sensing recovery problem. We show that the algorithm has the following properties (made more precise in the main text of the paper)


On Identifying Cited Texts for Citances and Classifying Their Discourse Facets by Classification Techniques


title: Diagnose crashing faults on production software

abstract: Software crashes are severe manifestations of software faults. Especially, software crashes in production software usually result in bad user experiences. Therefore, crashing faults mostly are required to be fixed with a high priority. Diagnosing crashing faults on production software is non-trivial, due to the characteristics of production environment. In general, it is required to address two major challenges. First, crash reports in production software are usually numerous, since production software is used by a large number of end users in various environments and configurations. Especially, a single fault may manifest as different crash reports, which makes the prioritizing debugging and understanding faults difficult. Second, deployed software is required to run with minimal overhead and cannot afford a heavyweight instrumentation approach to collect program execution information. Furthermore, end users require that the logged information should not reveal sensitive production data. This thesis contributes for developing crashing fault diagnosis tools that can be used in production environment.


title: A Smart Contract for Boardroom Voting with Maximum Voter Privacy

abstract: We present the first implementation of a decentralised and self-tallying internet voting protocol with maximum voter privacy using the Blockchain. The Open Vote Network is suitable for boardroom elections and is written as a smart contract for Ethereum. Unlike previously proposed Blockchain e-voting protocols, this is the first implementation that does not rely on any trusted authority to compute the tally or to protect the voter’s privacy. Instead, the Open Vote Network is a self-tallying protocol, and each voter is in control of the privacy of their own vote such that it can only be breached by a full collusion involving all other voters. The execution of the protocol is enforced using the consensus mechanism that also secures the Ethereum blockchain. We tested the implementation on Ethereum’s official test network to demonstrate its feasibility. Also, we provide a financial and computational breakdown of its execution cost.


title: Event-based Moving Object Detection and Tracking

abstract: Event-based vision sensors, such as the Dynamic Vision Sensor (DVS), are ideally suited for real-time motion analysis. The unique properties encompassed in the readings of such sensors provide high temporal resolution, superior sensitivity to light and low latency. These properties provide the grounds to estimate motion efficiently and reliably in the most sophisticated scenarios, but these advantages come at a price - modern event-based vision sensors have extremely low resolution, produce a lot of noise and require the development of novel algorithms to handle the asynchronous event stream. This paper presents a new, efficient approach to object tracking with asynchronous cameras. We present a novel event stream representation which enables us to utilize information about the dynamic (temporal)component of the event stream. The 3D geometry of the event stream is approximated with a parametric model to motion-compensate for the camera (without feature tracking or explicit optical flow computation), and then moving objects that don't conform to the model are detected in an iterative process. We demonstrate our framework on the task of independent motion detection and tracking, where we use the temporal model inconsistencies to locate differently moving objects in challenging situations of very fast motion.


title: Seamless Outdoors-Indoors Localization Solutions on Smartphones: Implementation and Challenges

abstract: The demand for more sophisticated Location-Based Services (LBS) in terms of applications variety and accuracy is tripling every year since the emergence of the smartphone a few years ago. Equally, smartphone manufacturers are mounting several wireless communication and localization technologies, inertial sensors as well as powerful processing capability, to cater to such LBS applications. A hybrid of wireless technologies is needed to provide seamless localization solutions and to improve accuracy, to reduce time to fix, and to reduce power consumption. The review of localization techniques/technologies of this emerging field is therefore important. This article reviews the recent research-oriented and commercial localization solutions on smartphones. The focus of this article is on the implementation challenges associated with utilizing these positioning solutions on Android-based smartphones. Furthermore, the taxonomy of smartphone-location techniques is highlighted with a special focus on the detail of each technique and its hybridization. The article compares the indoor localization techniques based on accuracy, utilized wireless technology, overhead, and localization technique used. The pursuit of achieving ubiquitous localization outdoors and indoors for critical LBS applications such as security and safety shall dominate future research efforts.


title: CDSchecker: checking concurrent data structures written with C/C++ atomics

abstract: Writing low-level concurrent software has traditionally required intimate knowledge of the entire toolchain and often has involved coding in assembly. New language standards have extended C and C++ with support for low-level atomic operations and a weak memory model, enabling developers to write portable and efficient multithreaded code. Developing correct low-level concurrent code is well-known to be especially difficult under a weak memory model, where code behavior can be surprising. Building reliable concurrent software using C/C++ low-level atomic operations will likely require tools that help developers discover unexpected program behaviors. In this paper we present CDSChecker, a tool for exhaustively exploring the behaviors of concurrent code under the C/C++ memory model. We develop several novel techniques for modeling the relaxed behaviors allowed by the memory model and for minimizing the number of execution behaviors that CDSChecker must explore. We have used CDSChecker to exhaustively unit test several concurrent data structure implementations on specific inputs and have discovered errors in both a recently published C11 implementation of a work-stealing queue and a single producer, single consumer queue implementation.


title: MobiBee: a mobile treasure hunt game for location-dependent fingerprint collection

abstract: Constructing a location-dependent fingerprint map is one of the essential steps for fingerprint-based indoor localization. A comprehensive site survey is time-consuming, labor-intensive, and subjects to environmental changes. In this work, we develop a mobile participatory game, MobiBee, to collect fingerprints with the help of quick response (QR) codes (posted on walls or pillars as location tags). Various incentive strategies, including monetary, entertainment and competition, are utilized. The location marker is deliberately designed to ensure that fingerprints are collected at the targeted locations in a stationary manner. Interestingly, a few instances of QR code forgery have been identified during the game play, which exposes the vulnerability of using QR code in indoor localization. A fraud detection mechanism is hence proposed. Experiments show that the proposed approach can accurately detect the QR code forgery and improve indoor localization accuracy.


title: Full length article: LTE-Advanced and the evolution to Beyond 4G (B4G) systems

abstract: Cellular networks have been undergoing an extraordinarily fast evolution in the past years. With commercial deployments of Release 8 (Rel-8) Long Term Evolution (LTE) already being carried out worldwide, a significant effort is being put forth by the research and standardization communities on the development and specification of LTE-Advanced. The work started in Rel-10 by the Third Generation Partnership Project (3GPP) had the initial objective of meeting the International Mobile Telecommunications-Advanced (IMT-Advanced) requirements set by the International Telecommunications Union (ITU) which defined fourth generation (4G) systems. However, predictions based on the wireless traffic explosion in recent years indicate a need for more advanced technologies and higher performance. Hence, 3GPP's efforts have continued through Rel-11 and now Rel-12. This paper provides a state-of-the-art comprehensive view on the key enabling technologies for LTE-Advanced systems. Already consolidated technologies developed for Rel-10 and Rel-11 are reviewed while novel approaches and enhancements currently under consideration for Rel-12 are also discussed. Technical challenges for each of the main areas of study are pointed out as an encouragement for the research community to participate in this collective effort.


Integrative Literature Reviews : Guidelines and Examples


title: An efficient surface intersection algorithm based on lower-dimensional formulation

abstract: We present an efficient algorithm to compute the intersection of algebraic and NURBS surfaces. Our approach is based on combining the marching methods with the algbraic formulation. In particular, we propose and matrix computations. We present algorithms to compute a start point on each component of the intersection curve (both open and closed components), detect the presence of singularities, and find all the curve branches near the singularity. We also suggest methods to compute the step size during tracing to prevent component jumping. The algorithm runs an order of magnitude faster than previously published robust algorithms. The complexity of the algorithm is output sensitive.


title: Two secret sharing schemes based on Boolean operations

abstract: Traditional secret sharing schemes involve complex computation. A visual secret sharing (VSS) scheme decodes the secret without computation, but each shadow is m times as big as the original. Probabilistic VSS solved the computation complexity and space complexity problems at once. In this paper we propose a probabilistic (2,n) scheme for binary images and a deterministic (n,n) scheme for grayscale images. Both use simple Boolean operations and both have no pixel expansion. The (2,n) scheme provides a better contrast and significantly smaller recognized areas than other methods. The (n,n) scheme gives an exact reconstruction.


title: Future developments trend for Ku and Ka antenna for satcom on the move

abstract: This document presents the state-of-the-art of TeS Ku band antennas for mobile satellite communications on board high speed trains and ground vehicles, and his evolution in terms of Ku band antenna performances improvement and upgrade to Ka band terminals.


title: ‘Stop Fake Hate Profiles on Facebook’: Challenges for crowdsourced activism on social media

abstract: This research examines how activists mobilise against fake hate profiles on Facebook. Based on six months of participant observation, this paper demonstrates how Danish Facebook users organised to combat fictitious Muslim profiles that spurred hatred against ethnic minorities. Crowdsourced action by Facebook users is insufficient as a form of sustainable resistance against fake hate profiles. A viable solution would require social media companies, such as Facebook, to take responsibility in the struggle against fake content used for political manipulation.


title: High-speed 3D image acquisition using coded structured light projection

abstract: In various application fields, high-speed cameras are used to analyze high-speed phenomena. Coded structured light projection methods have been proposed for acquiring three-dimensional images. Most of them are not suitable for measuring high-speed phenomena because the errors are caused when the measured objects move due to light projection of multiple patterns. In this paper, we propose a new coded structured light projection method that can select not only a temporal encoding but also a spatial encoding adaptively for obtaining three-dimensional images at a high-speed frame rate. We also develop an evaluation system that uses a DLP projector and an off-line high-speed video camera, and verify the effectiveness of the proposed method by showing the obtained three-dimensional shapes for moving objects.


COMPLEMENTARY MATERIAL - Detailed analysis of case studies


title: Auditory Beat Stimulation and its Effects on Cognition and Mood States

abstract: Auditory beat stimulation may be a promising new tool for the manipulation of cognitive processes and the modulation of mood states. Here, we aim to review the literature examining the most current applications of auditory beat stimulation and its targets. We give a brief overview of research on auditory steady-state responses and its relationship to auditory beat stimulation (ABS). We have summarized relevant studies investigating the neurophysiological changes related to ABS and how they impact upon the design of appropriate stimulation protocols. Focusing on binaural-beat stimulation, we then discuss the role of monaural- and binaural-beat frequencies in cognition and mood states, in addition to their efficacy in targeting disease symptoms. We aim to highlight important points concerning stimulation parameters and try to address why there are often contradictory findings with regard to the outcomes of ABS.


title: Revisiting Recurrent Networks for Paraphrastic Sentence Embeddings

abstract: We consider the problem of learning general-purpose, paraphrastic sentence embeddings, revisiting the setting of Wieting et al. (2016b). While they found LSTM recurrent networks to underperform word averaging, we present several developments that together produce the opposite conclusion. These include training on sentence pairs rather than phrase pairs, averaging states to represent sequences, and regularizing aggressively. These improve LSTMs in both transfer learning and supervised settings. We also introduce a new recurrent architecture, the Gated Recurrent Averaging Network, that is inspired by averaging and LSTMs while outperforming them both. We analyze our learned models, finding evidence of preferences for particular parts of speech and dependency relations.


title: ABASH: finding bugs in bash scripts

abstract: This paper describes the design and implementation of A BASH , a tool for statically analyzing programs written in the bash scripting language. Although it makes no formal guarantees against missed errors or spurious warnings (largely due to the highly dynamic nature of bash scripts), A BASH is useful for detecting certain common program errors that may lead to security vulnerabilities. In experiments with 49 bash scripts taken from popular Internet repositories, A BASH was able to identify 20 of them as containing bugs of varying severity while yielding only a reasonable number of spurious warnings on both these scripts and the generally bug-free initialization scripts of the Ubuntu Linux distribution. A BASH works by performing abstract interpretation of a bash script via an abstract semantics that accounts for shell variable expansion. The analysis is also parameterized by a collection of signatures that describe external program interfaces (for Unix commands, etc.), yielding an easily configurable and extensible framework for finding bugs in bash scripts


title: Modelling total weighted vibration of a trailer seat pulled by a two-wheel tractor consumed diesel–biodiesel fuel blends using ANFIS methodology

abstract: The application of adaptive neural-fuzzy inference system (ANFIS) to predict the total weighted vibration acceleration of a trailer seat pulled by a two-wheel tractor is set out in the present paper. The vibration acceleration signals were obtained in a field experiment using a 9.5 kW two-wheel tractor. Three accelerometers were installed at three orthogonal directions according to ISO 2631-1 standard on the trailer seat for measuring and recording the vibration acceleration signals. The two-wheel tractor consumed diesel–biodiesel fuel blends, and the engine speed and gear ratio were varied to cover normal range of operation in transportation conditions on asphalt rural road. The digital recorded vibration acceleration signals in time domain were converted to the frequency domain using Fast Fourier Transform algorithm, and the one-third octave frequency bands were obtained. The one-third octave frequencies were weighted according to the ISO standard. Altogether, 100 patterns were generated for training and evaluation of ANFIS. The input parameters of ANFIS were the tractor engine speed, transmission gear ratio of the tractor and fuel blend type, and the output parameter was the trailer seat total weighted vibration acceleration. The ANFIS structure was designed based on the grid partition, fuzzy c-means clustering and subtractive clustering. The results revealed that the ANFIS with subtractive clustering method was the best for accurate prediction of the total weighted vibration acceleration of the trailer seat. The number of rules, root-mean-square error (RMSE) of train, RMSE of test, correlation coefficient (R) of train and R of test values for the optimized structure were 5, 0.1479, 0.2141, 0.9175 and 0.8804, respectively.


title: Coherence and Coreference*

abstract: Coherence in conversations and in texts can be partially characterized by a set of coherence relations, motivated ultimately by the speaker's or writer's need to be understood. In this paper, formal definitions are given for several coherence relations, based on the operations of an inference system; that is, the relations between successive portions of a discourse are characterized in terms of the inferences that can be drawn from each. In analyzing a discourse, it is frequently the case that we would recognize it as coherent, in that it would satisfy the formal definition of some coherence relation, if only we could assume certain noun phrases to be coreferential. In such cases, we will simply assume the identity of the entities referred to, in what might be called a “petty conversational implicature,” thereby solving the coherence and coreference problems simultaneously. Three examples of different kinds of reference problems are presented. In each, it is shown how the coherence of the discourse can be recognized, and how the reference problems are solved, almost as a by-product, by means of these petty conversational implicatures.


title: An integrated algorithm of incremental and robust PCA

abstract: Principal component analysis (PCA) is a well-established technique in image processing and pattern recognition. Incremental PCA and robust PCA are two interesting problems with numerous potential applications. However, these two issues have only been separately addressed in the previous studies. In this paper, we present a novel algorithm for incremental and robust PCA by seamlessly integrating the two issues together. The proposed algorithm has the advantages of both incremental PCA and robust PCA. Moreover, unlike most M-estimation based robust algorithms, it is computational efficient. Experimental results on dynamic background modelling are provided to show the performance of the algorithm with a comparison to the conventional batch-mode and nonrobust algorithms.


title: Learning Pose-Aware Models for Pose-Invariant Face Recognition in the Wild

abstract: We propose a method designed to push the frontiers of unconstrained face recognition in the wild with an emphasis on extreme out-of-plane pose variations. Existing methods either expect a single model to learn pose invariance by training on massive amounts of data or else normalize images by aligning faces to a single frontal pose. Contrary to these, our method is designed to explicitly tackle pose variations. Our proposed Pose-Aware Models (PAM) process a face image using several pose-specific, deep convolutional neural networks (CNN). 3D rendering is used to synthesize multiple face poses from input images to both train these models and to provide additional robustness to pose variations at test time. Our paper presents an extensive analysis of the IARPA Janus Benchmark A (IJB-A), evaluating the effects that landmark detection accuracy, CNN layer selection, and pose model selection all have on the performance of the recognition pipeline. It further provides comparative evaluations on IJB-A and the PIPA dataset. These tests show that our approach outperforms existing methods, even surprisingly matching the accuracy of methods that were specifically fine-tuned to the target dataset. Parts of this work previously appeared in [1] and [2] .


title: Modeling of Agent Cognition in Extensive Games via Artificial Neural Networks

abstract: The decision-making process, which is regarded as cognitive and ubiquitous, has been exploited in diverse fields, such as psychology, economics, and artificial intelligence. This paper considers the problem of modeling agent cognition in a class of game-theoretic decision-making scenarios called extensive games. We present a novel framework in which artificial neural networks are incorporated to simulate agent cognition regarding the structure of the underlying game and the goodness of the game situations therein. An algorithmic procedure is investigated to describe the process for solving games with cognition, and then, a new equilibrium concept is proposed as a refinement of the classical one—subgame perfect equilibrium—by involving players’ cognitive reasoning. Moreover, a series of results concerning the computational complexity, soundness, and completeness of the algorithm, as well as the existence of an equilibrium solution, is obtained. This framework, which is shown to be general enough to model the way in which AlphaGo plays Go, may offer a means for bridging the gap between theoretical models and practical problem-solving.


title: Sockets for Limb Prostheses: A Review of Existing Technologies and Open Challenges

abstract: In the prosthetics field, one of the most important bottlenecks is still the human-machine interface, namely the socket. Indeed, a large number of amputees still rejects prostheses or points out a low satisfaction level, due to a sub-optimal interaction between the socket and the residual limb tissues. The aim of this paper is to describe the main parameters (displacements, stress, volume fluctuations and temperature) affecting the stump-socket interface and reducing the comfort/stability of limb prostheses. In this review, a classification of the different socket types proposed in the literature is reported, together with an analysis of advantages and disadvantages of the different solutions, from multiple viewpoints. The paper then describes the technological solutions available to face an altered distribution of stresses on the residual limb tissues, volume fluctuations affecting the stump overtime and temperature variations affecting the residual tissues within the socket. The open challenges in this research field are highlighted and the possible future routes are discussed, towards the ambitious objective of achieving an advanced socket able to self-adapt in real-time to the complex interplay of factors affecting the stump, during both static and dynamic tasks.


title: Comparative Study on Novel Dual Stator Radial Flux and Axial Flux Permanent Magnet Motors With Ferrite Magnets for Traction Application

abstract: This paper proposes two novel traction motors with ferrite magnets for hybrid electric vehicles (HEVs), which have competitive torque density and efficiency as well as operating range with respect to a referenced rare earth magnet motor employed in the third-generation Toyota Prius, a commercialized HEV. The two proposed traction motors, named as dual stator radial flux permanent magnet motor (DSRFPMM) and dual stator axial flux permanent magnet motor (DSAFPMM), adopt the same design concept, which incorporates the unaligned arrangement of two stators together with the use of spoke-type magnet array and phase-group concentrated-coil windings for the purpose of increasing torque density and reducing torque ripple. A finite element method is utilized for predicting the main characteristics, such as back electromotive force, cogging torque, electromagnetic torque, iron loss, and efficiency in both of the proposed motors. Moreover, a comparative study between the proposed DSRFPMM and DSAFPMM is performed under the same operating condition. As a result, it is demonstrated that both of the proposed ferrite permanent magnet motors could be good alternatives for traction application, replacing the rare earth magnet motors.