title: Cavazza M.: The CaveUT System: Immersive Entertainment Based on a Game Engine

abstract: We describe recent developments in the CaveUT software, which supports immersive virtual reality installations based on the Unreal Tournament game engine. CaveUT implements several high-end VR features such as real-time stereoscopy with head and hand tracking. We demonstrate the use of CaveUT in the SAS Cube™, a PC-based CAVE™-like immersive four-screen display. One of the main advantages of the system is to support fully immersive VR while retaining the game engine's advanced features for interaction and behavioral (or AI) systems. We illustrate the use of CaveUT on two installations: an artistic VR installation and an immersive interactive storytelling system.


title: Random sampling with a reservoir

abstract: We introduce fast algorithms for selecting a random sample of n records without replacement from a pool of N records, where the value of N is unknown beforehand. The main result of the paper is the design and analysis of Algorithm Z; it does the sampling in one pass using constant space and in O ( n (1 + log( N/n ))) expected time, which is optimum, up to a constant factor. Several optimizations are studied that collectively improve the speed of the naive version of the algorithm by an order of magnitude. We give an efficient Pascal-like implementation that incorporates these modifications and that is suitable for general use. Theoretical and empirical results indicate that Algorithm Z outperforms current methods by a significant margin.


title: The asymptotic behaviors of a stage-structured autonomous predator-prey system with time delay

abstract: Abstract In this paper, we consider an autonomous predator–prey Lotka–Volterra system in which individuals in the population may belong to one of two classes: the immatures and the matures, the age to maturity is represented by a time delay. By using eigenvalue analysis, principal term analyze method, reduction to absurdity, and iterative method, we obtain some simple conditions for global asymptotic stability of the unique positive equilibrium point. Moreover, a condition that the prey population in the system get extinction and the predator population in the system get permanence will be obtained. Meanwhile the theorems extend the corresponding conclusions in which there have no two stage structures.


title: Modeling Tourist Movements: A Local Destination Analysis

abstract: Abstract Models depicting the spatial movement patterns of tourists within a destination are proposed in this paper. They are developed using an inductive approach based on urban transportation modeling and tourist behavior, to identify explanatory factors that could influence movements. Factors identified included a set of destination characteristics and a set of tourist characteristics that influence decisionmaking and behavior. These factors influence movement patterns in two ways, resulting in four types of territorial and three linear path models. Understanding the movement of tourists within a destination has practical applications for destination management, product development, and attraction marketing.


title: A Latent Source Model for Nonparametric Time Series Classification

abstract: For classifying time series, a nearest-neighbor approach is widely used in practice with performance often competitive with or better than more elaborate methods such as neural networks, decision trees, and support vector machines. We develop theoretical justification for the effectiveness of nearest-neighbor-like classification of time series. Our guiding hypothesis is that in many applications, such as forecasting which topics will become trends on Twitter, there aren't actually that many prototypical time series to begin with, relative to the number of time series we have access to, e.g., topics become trends on Twitter only in a few distinct manners whereas we can collect massive amounts of Twitter data. To operationalize this hypothesis, we propose a latent source model for time series, which naturally leads to a "weighted majority voting" classification rule that can be approximated by a nearest-neighbor classifier. We establish nonasymptotic performance guarantees of both weighted majority voting and nearest-neighbor classification under our model accounting for how much of the time series we observe and the model complexity. Experimental results on synthetic data show weighted majority voting achieving the same misclassification rate as nearest-neighbor classification while observing less of the time series. We then use weighted majority to forecast which news topics on Twitter become trends, where we are able to detect such "trending topics" in advance of Twitter 79% of the time, with a mean early advantage of 1 hour and 26 minutes, a true positive rate of 95%, and a false positive rate of 4%.


title: Continuum Differential Mechanisms and Their Applications in Gripper Designs

abstract: Differential mechanisms possess various forms and are applied in a wide spectrum of mechanical systems. This paper proposes to categorize differential mechanisms into kinematic differential mechanisms (KDMs) and continuum differential mechanisms (CDMs), and also introduces two CDMs. The working principle, structures, constructions, and kinetostatic analyses of the proposed planar and the spatial CDMs are elaborated upon. The planar CDM is compared with two widely used differential mechanisms made from pulleys and linkages to show its effectiveness using a simple construction. The spatial CDM could resolve one input into three outputs in a way different from two serially connected planar differential mechanisms. As demonstrated in experimentation, the spatial CDM could allow more isotropic distribution of the three outputs. The categorization, analyses, and applications of the CDMs might not only extend the IFToMM terminology on differential mechanisms, but also inspire more CDM designs with distinct responses and properties for adaptive mechanical systems.


title: CoinParty: Secure Multi-Party Mixing of Bitcoins

abstract: Bitcoin is a digital currency that uses anonymous cryptographic identities to achieve financial privacy. However, Bitcoin's promise of anonymity is broken as recent work shows how Bitcoin's blockchain exposes users to reidentification and linking attacks. In consequence, different mixing services have emerged which promise to randomly mix a user's Bitcoins with other users' coins to provide anonymity based on the unlinkability of the mixing. However, proposed approaches suffer either from weak security guarantees and single points of failure, or small anonymity sets and missing deniability. In this paper, we propose CoinParty a novel, decentralized mixing service for Bitcoin based on a combination of decryption mixnets with threshold signatures. CoinParty is secure against malicious adversaries and the evaluation of our prototype shows that it scales easily to a large number of participants in real-world network settings. By the application of threshold signatures to Bitcoin mixing, CoinParty achieves anonymity by orders of magnitude higher than related work as we quantify by analyzing transactions in the actual Bitcoin blockchain and is first among related approaches to provide plausible deniability.


A Parallel Algorithms on Nearest Neighbor Search


title: Can machine learning aid in delivering new use cases and scenarios in 5G?

abstract: 5G represents the next generation of communication networks and services, and will bring a new set of use cases and scenarios. These in turn will address a new set of challenges from the network and service management perspective, such as network traffic and resource management, big data management and energy efficiency. Consequently, novel techniques and strategies are required to address these challenges in a smarter way. In this paper, we present the limitations of the current network and service management and describe in detail the challenges that 5G is expected to face from a management perspective. The main contribution of this paper is presenting a set of use cases and scenarios of 5G in which machine learning can aid in addressing their management challenges. It is expected that machine learning can provide a higher and more intelligent level of monitoring and management of networks and applications, improve operational efficiencies and facilitate the requirements of the future 5G network.


title: Chinese culture and e-commerce: an exploratory study

abstract: Abstract Differing characteristics of local environments, both infrastructural and socio-economic, have created a significant level of variation in the acceptance and growth of e-commerce in different regions of the world. This paper focuses on the impact of these infrastructural and socio-economic factors on e-commerce development in China. The findings provide insights into the role of culture in e-commerce, and the factors that may impact a broader acceptance and development of e-commerce in China. In this paper, we present and discuss our findings, and identify changes that will be required for broader acceptance and diffusion of e-commerce in China. Cultural issues such as ‘socializing effect of commerce’, ‘transactional and institutional trust’, and ‘attitudes toward debt’ were determined to be the major impediments to e-commerce in China. However, our research also shows that, even though their means for payment are different, the most enlightened, able, and sophisticated consumers in China participate in e-commerce in the same frequencies as the mainstream e-commerce consumers in the US.


Investigation Correspondence between CMMI-DEV and ISO/IEC 15504


title: A unified approach to the design, measurement, and tuning of coupled-resonator filters

abstract: The concept of coupling coefficients has been a very useful one in the design of small-to-moderate bandwidth microwave filters. It is shown in this paper that the group delay of the input reflection coefficients of sequentially tuned resonators contains all the information necessary to design and tune filters, and that the group-delay value at the center frequency of the filter can be written quite simply in terms of the low-pass prototype values, the LC elements of a bandpass structure, and the coupling coefficients of the inverter coupled filter. This provides an easy method to measure the key elements of a filter, which is confirmed by results presented in this paper. It is also suggested that since the group delay of the reflection coefficient (i.e., the time taken for energy to get in and out of the coupled resonators) is easily measured, it is a useful conceptual alternative to coupling concepts.


title: Textures of optical flow for real-time anomaly detection in crowds

abstract: Automated visual surveillance of crowds is a rapidly growing area of research. In this paper we focus on motion representation for the purpose of abnormality detection in crowded scenes. We propose a novel visual representation called textures of optical flow. The proposed representation measures the uniformity of a flow field in order to detect anomalous objects such as bicycles, vehicles and skateboarders; and can be combined with spatial information to detect other forms of abnormality. We demonstrate that the proposed approach outperforms state-of-the-art anomaly detection algorithms on a large, publicly-available dataset.


title: Clustering financial time series: New insights from an extended hidden Markov model

abstract: In recent years, large amounts of financial data have become available for analysis. We propose exploring returns from 21 European stock markets by model-based clustering of regime switching models. These econometric models identify clusters of time series with similar dynamic patterns and moreover allow relaxing assumptions of existing approaches, such as the assumption of conditional Gaussian returns. The proposed model handles simultaneously the heterogeneity across stock markets and over time, i.e., time-constant and time-varying discrete latent variables capture unobserved heterogeneity between and within stock markets, respectively. The results show a clear distinction between two groups of stock markets, each one characterized by different regime switching dynamics that correspond to different expected return-risk patterns. We identify three regimes: the so-called bull and bear regimes, as well as a stable regime with returns close to 0, which turns out to be the most frequently occurring regime. This is consistent with stylized facts in financial econometrics.


title: Wearable Devices in Medical Internet of Things: Scientific Research and Commercially Available Devices

abstract: OBJECTIVES ::: Wearable devices are currently at the heart of just about every discussion related to the Internet of Things. The requirement for self-health monitoring and preventive medicine is increasing due to the projected dramatic increase in the number of elderly people until 2020. Developed technologies are truly able to reduce the overall costs for prevention and monitoring. This is possible by constantly monitoring health indicators in various areas, and in particular, wearable devices are considered to carry this task out. These wearable devices and mobile apps now have been integrated with telemedicine and telehealth efficiently, to structure the medical Internet of Things. This paper reviews wearable health care devices both in scientific papers and commercial efforts. ::: ::: ::: METHODS ::: MIoT is demonstrated through a defined architecture design, including hardware and software dealing with wearable devices, sensors, smart phones, medical application, and medical station analyzers for further diagnosis and data storage. ::: ::: ::: RESULTS ::: Wearables, with the help of improved technology have been developed greatly and are considered reliable tools for long-term health monitoring systems. These are applied in the observation of a large variety of health monitoring indicators in the environment, vital signs, and fitness. ::: ::: ::: CONCLUSIONS ::: Wearable devices are now used for a wide range of healthcare observation. One of the most important elements essential in data collection is the sensor. During recent years with improvement in semiconductor technology, sensors have made investigation of a full range of parameters closer to realization.


“I am active”: effects of a program to promote active aging


title: Optical Packet and Burst Switching Technologies for the Future Photonic Internet

abstract: This paper reviews advanced optical burst switching (OBS) and optical packet switching (OPS) technologies and discusses their roles in the future photonic Internet. Discussions include optoelectronic and optical systems technologies as well as systems integration into viable network elements (OBS and OPS routers). Optical label switching (OLS) offers a unified multiple-service platform with effective and agile utilization of the available optical bandwidth in support of voice, data, and multimedia services on the Internet Protocol. In particular, OLS routers with wavelength routing switching fabrics and parallel optical labeling allow forwarding of asynchronously arriving variable-length packets, bursts, and circuits. By exploiting contention resolution in wavelength, time, and space domains, the OLS routers can achieve high throughput without resorting to a store-and-forward method associated with large buffer requirements. Testbed demonstrations employing OLS edge routers show high-performance networking in support of multimedia and data communications applications over the photonic Internet with optical packets and bursts switched directly at the optical layer


title: Bladder cancer: clinical and pathological profile.

abstract: Bladder tumours represent a heterogeneous group of cancers. The natural history of these bladder cancers is that of recurrence of disease and progression to higher grade and stage disease. Furthermore, recurrence and progression rates of superficial bladder cancer vary according to several tumour characteristics, mainly tumour grade and stage. The most recent World Health Organization (WHO) classification of tumours of the urinary system includes urothelial flat lesions: flat hyperplasia, dysplasia and carcinoma in situ. The papillary lesions are broadly subdivided into benign (papilloma and inverted papilloma), papillary urothelial neoplasia of low malignant potential (PUNLMP) and non-invasive papillary carcinoma (low or high grade). The initial proposal of the 2004 WHO has been achieved, with most reports supporting that categories are better defined than in previous classifications. An additional important issue is that PUNLMP, the most controversial proposal of the WHO in 2004, has lower malignant behaviour than low-grade carcinoma. Whether PUNLMP remains a clinically useful category, or whether this category should be expanded to include all low-grade, stage Ta lesions (PUNLMP and low-grade papillary carcinoma) as a wider category of less aggressive tumours not labelled as cancer, needs to be discussed in the near future. This article summarizes the recent literature concerning important issues in the pathology and the clinical management of patients with bladder urothelial carcinoma. Emphasis is placed on clinical presentation, the significance of haematuria, macroscopic appearance (papillary, solid or mixed, single or multiple) and synchronous or metachronous presentation (field disease vs monoclonal disease with seeding), classification and microscopic variations of bladder cancer with clinical significance, TNM distribution and the pathological grading according to the 2004 WHO proposal.


title: From Simple Associations to Systematic Reasoning: a Connectionist Representation of Rules, Variables and Dynamic Bindings Using Temporal Synchrony

abstract: Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency – as though these inferences were a reflexive response of their cognitive apparatus. Furthermore, these inferences are drawn with reference to a large body of background knowledge. This remarkable human ability seems paradoxical given the complexity of reasoning reported by researchers in artificial intelligence. It also poses a challenge for cognitive science and computational neuroscience: How can a system of simple and slow neuronlike elements represent a large body of systemic knowledge and perform a range of inferences with such speed? We describe a computational model that takes a step toward addressing the cognitive science challenge and resolving the artificial intelligence paradox. We show how a connectionist network can encode millions of facts and rules involving n -ary predicates and variables and perform a class of inferences in a few hundred milliseconds. Efficient reasoning requires the rapid representation and propagation of dynamic bindings. Our model (which we refer to as SHRUTI) achieves this by representing (1) dynamic bindings as the synchronous firing of appropriate nodes, (2) rules as interconnection patterns that direct the propagation of rhythmic activity, and (3) long-term facts as temporal pattern-matching subnetworks. The model is consistent with recent neurophysiological evidence that synchronous activity occurs in the brain and may play a representational role in neural information processing. The model also makes specific psychologically significant predictions about the nature of reflexive reasoning. It identifies constraints on the form of rules that may participate in such reasoning and relates the capacity of the working memory underlying reflexive reasoning to biological parameters such as the lowest frequency at which nodes can sustain synchronous oscillations and the coarseness of synchronization.


title: Manifold Based Low-rank Regularization for Image Restoration and Semi-supervised Learning

abstract: Low-rank structures play important role in recent advances of many problems in image science and data science. As a natural extension of low-rank structures for data with nonlinear structures, the concept of the low-dimensional manifold structure has been considered in many data processing problems. Inspired by this concept, we consider a manifold based low-rank regularization as a linear approximation of manifold dimension. This regularization is less restricted than the global low-rank regularization, and thus enjoy more flexibility to handle data with nonlinear structures. As applications, we demonstrate the proposed regularization to classical inverse problems in image sciences and data sciences including image inpainting, image super-resolution, X-ray computer tomography (CT) image reconstruction and semi-supervised learning. We conduct intensive numerical experiments in several image restoration problems and a semi-supervised learning problem of classifying handwritten digits using the MINST data. Our numerical tests demonstrate the effectiveness of the proposed methods and illustrate that the new regularization methods produce outstanding results by comparing with many existing methods.


title: Massive MIMO: 10 Myths and One Grand Question

abstract: Wireless communications is one of the most successful technologies in modern years, given that an exponential growth rate in wireless traffic has been sustained for over a century (known as Cooperźs law). This trend will certainly continue, driven by new innovative applications; for example, augmented reality and the Internet of Things. Massive MIMO has been identified as a key technology to handle orders of magnitude more data traffic. Despite the attention it is receiving from the communication community, we have personally witnessed that Massive MIMO is subject to several widespread misunderstandings, as epitomized by following (fictional) abstract: źThe Massive MIMO technology uses a nearly infinite number of high-quality antennas at the base stations. By having at least an order of magnitude more antennas than active terminals, one can exploit asymptotic behaviors that some special kinds of wireless channels have. This technology looks great at first sight, but unfortunately the signal processing complexity is off the charts and the antenna arrays would be so huge that it can only be implemented in millimeter-wave bands.ź These statements are, in fact, completely false. In this overview article, we identify 10 myths and explain why they are not true. We also ask a question that is critical for the practical adoption of the technology and which will require intense future research activities to answer properly. We provide references to key technical papers that support our claims, while a further list of related overview and technical papers can be found at the Massive MIMO Info Point: http://massivemimo. eu


A Survey of Intelligent Visual Surveillance Systems


title: A new data mining methodology for generating new service ideas

abstract: To survive in today's market, decision makers including investors and their managerial teams should continuously attempt to realize the customers' unspoken needs and requirements by discovering their behavioral patterns. Discovering customers' patterns puts these decision makers in a better position in which higher qualified services can be designed and provided. Association rule mining is a well-known approach to discover these patterns. Although extracted rules could express customers' behaviors in an easy-to-understand way, the number of rules in real applications could be problematic. Moreover, the customers' comments are not usually considered for constructing/evaluating the rules. To tackle these issues, a system framework is proposed in this paper in which all association rules are clustered using a new similarity measure. For each cluster, a new type of graph is developed known as sub-graph in this paper. Each sub-graph has unique messages that can partially contribute to designing new services. Furthermore, for the first time, the real satisfaction levels are embedded into the association rules to enrich them in an innovative way. The main interesting point is that the satisfaction levels are only assessed for the overall system, not for current services. We also illustrate how our proposed methodology works through artificial and real datasets and also demonstrate the superiority of our proposed clustering algorithm compared to other popular methods.


title: Autism spectrum disorders and autistic traits: A decade of new twin studies

abstract: Researchers continue to pursue a better understanding of the symptoms, comorbidities, and causes of autism spectrum disorders. In this article we review more than 30 twin studies of autism spectrum disorders (ASDs) and autistic traits published in the last decade that have contributed to this endeavor. These twin studies have reported on the heritability of autism spectrum disorders and autistic traits in different populations and using different measurement and age groups. These studies have also stimulated debate and new hypotheses regarding why ASDs show substantial symptom heterogeneity, and what causes their comorbidity with intellectual disability, language delay, and other psychiatric disorders such as ADHD. These studies also reveal that the etiology of autism and autistic traits assessed in the general population is more similar than different, which contributes to the question of where the boundary lies between autism and typical development. Recent findings regarding molecular genetic and environmental causes of autism are discussed in the relation to these twin studies. Lastly, methodological assumptions of the twin design are given consideration, as well as issues of measurement. Future research directions are suggested to ensure that this decade is as productive as the last in attempting to disentangle the causes of autism spectrum disorders.


title: Quasi-lumped suspended stripline filters and diplexers

abstract: This paper demonstrates a general concept and different types of filters and a diplexer realized in suspended stripline using quasi-lumped elements. Very small low-pass, bandpass, and high-pass filters with typically low loss are designed, fabricated, and tested; even transmission zeroes can easily be included into the design by additional coupling structures. Finally, a low-pass and high-pass filter are combined to form a diplexer of very small size.


title: Convolutional Neural Network Architecture for Geometric Matching

abstract: We address the problem of determining correspondences between two images in agreement with a geometric model such as an affine, homography or thin-plate spline transformation, and estimating its parameters. The contributions of this work are three-fold. First, we propose a convolutional neural network architecture for geometric matching. The architecture is based on three main components that mimic the standard steps of feature extraction, matching and simultaneous inlier detection and model parameter estimation, while being trainable end-to-end. Second, we demonstrate that the network parameters can be trained from synthetically generated imagery without the need for manual annotation and that our matching layer significantly increases generalization capabilities to never seen before images. Finally, we show that the same model can perform both instance-level and category-level matching giving state-of-the-art results on the challenging PF, TSS and Caltech-101 datasets.


title: IntegriDB: Verifiable SQL for Outsourced Databases

abstract: This paper presents IntegriDB, a system allowing a data owner to outsource storage of a database to an untrusted server, and then enable anyone to perform verifiable SQL queries over that database. Our system handles a rich subset of SQL queries, including multidimensional range queries, JOIN, SUM, MAX/MIN, COUNT, and AVG, as well as (limited) nestings of such queries. Even for tables with 105 entries, IntegriDB has small proofs (a few KB) that depend only logarithmically on the size of the database, low verification time (tens of milliseconds), and feasible server computation (under a minute). Efficient updates are also supported. We prove security of IntegriDB based on known cryptographic assumptions, and demonstrate its practicality and expressiveness via performance measurements and verifiable processing of SQL queries from the TPC-H and TPC-C benchmarks.


title: A Survey of FPGA-Based Hardware Implementation of ANNs

abstract: In this paper, the development of FPGA-based ANNs is presented. Field programmable gate arrays (FPGA) based artificial neural network (ANN) is now becoming a focus of ANN research. According to the parallelism and nature features of neural network (NN), the hardware implementation is superior comparing with software approach because it can take advantage of these characteristics. Furthermore, since FPGA is a digital device that owns reprogrammable properties and robust flexibility, many researchers have made great efforts on the realization of NN using FPGA technique. However, they encountered many problems during the process. Results of the researches are presented and discussed. The future development of FPGA implementation of ANN is also prospected. Firstly, the introduction of ANN and FPGA technique is briefly shown in this paper. Then ANN implementation with hardware, mainly FPGA, is presented and discussed


title: An Overview of Face Liveness Detection

abstract: Face recognition is a widely used biometric approach. Face recognition technology has developed rapidly in recent years and it is more direct, user friendly and convenient compared to other methods. But face recognition systems are vulnerable to spoof attacks made by non-real faces. It is an easy way to spoof face recognition systems by facial pictures such as portrait photographs. A secure system needs Liveness detection in order to guard against such spoofing. In this work, face liveness detection approaches are categorized based on the various types techniques used for liveness detection. This categorization helps understanding different spoof attacks scenarios and their relation to the developed solutions. A review of the latest works regarding face liveness detection works is presented. The main aim is to provide a simple path for the future development of novel and more secured face liveness detection approach.


title: Physical database design for relational databases

abstract: This paper describes the concepts used in the implementation of DBDSGN, an experimental physical design tool for relational databases developed at the IBM San Jose Research Laboratory. Given a workload for System R (consisting of a set of SQL statements and their execution frequencies), DBDSGN suggests physical configurations for efficient performance. Each configuration consists of a set of indices and an ordering for each table. Workload statements are evaluated only for atomic configurations of indices, which have only one index per table. Costs for any configuration can be obtained from those of the atomic configurations. DBDSGN uses information supplied by the System R optimizer both to determine which columns might be worth indexing and to obtain estimates of the cost of executing statements in different configurations. The tool finds efficient solutions to the index-selection problem; if we assume the cost estimates supplied by the optimizer are the actual execution costs, it finds the optimal solution. Optionally, heuristics can be used to reduce execution time. The approach taken by DBDSGN in solving the index-selection problem for multiple-table statements significantly reduces the complexity of the problem. DBDSGN's principles were used in the Relational Design Tool (RDT), an IBM product based on DBDSGN, which performs design for SQL/DS, a relational system based on System R. System R actually uses DBDSGN's suggested solutions as the tool expects because cost estimates and other necessary information can be obtained from System R using a new SQL statement, the EXPLAIN statement. This illustrates how a system can export a model of its internal assumptions and behavior so that other systems (such as tools) can share this model.


title: Visualizing Instagram: Tracing Cultural Visual Rhythms

abstract: Picture-taking has never been easier. We now use our phones to snap photos and instantly share them with friends, family and strangers all around the world. Consequently, we seek ways to visualize, analyze and discover concealed socio-cultural characteristics and trends in this ever-growing flow of visual information. How do we then trace global and local patterns from the analysis of visual planetary–scale data? What types of insights can we draw from the study of these massive visual materials? In this study we use Cultural Analytics visualization techniques for the study of approximately 550,000 images taken by users of the location-based social photo sharing application Instagram. By analyzing images from New York City and Tokyo, we offer a comparative visualization research that indicates differences in local color usage, cultural production rate, and varied hue’s intensities— all form a unique, local, ‘Visual Rhythm’: a framework for the analysis of location-based visual information flows.


title: Heuristic Feature Selection for Clickbait Detection

abstract: We study feature selection as a means to optimize the baseline clickbait detector employed at the Clickbait Challenge 2017. The challenge's task is to score the"clickbaitiness"of a given Twitter tweet on a scale from 0 (no clickbait) to 1 (strong clickbait). Unlike most other approaches submitted to the challenge, the baseline approach is based on manual feature engineering and does not compete out of the box with many of the deep learning-based approaches. We show that scaling up feature selection efforts to heuristically identify better-performing feature subsets catapults the performance of the baseline classifier to second rank overall, beating 12 other competing approaches and improving over the baseline performance by 20%. This demonstrates that traditional classification approaches can still keep up with deep learning on this task.


title: Scaling Queries over Big RDF Graphs with Semantic Hash Partitioning

abstract: Massive volumes of big RDF data are growing beyond the performance capacity of conventional RDF data management systems operating on a single node. Applications using large RDF data demand efficient data partitioning solutions for supporting RDF data access on a cluster of compute nodes. In this paper we present a novel semantic hash partitioning approach and implement a Semantic HAsh Partitioning-Enabled distributed RDF data management system, called Shape. This paper makes three original contributions. First, the semantic hash partitioning approach we propose extends the simple hash partitioning method through direction-based triple groups and direction-based triple replications. The latter enhances the former by controlled data replication through intelligent utilization of data access locality, such that queries over big RDF graphs can be processed with zero or very small amount of inter-machine communication cost. Second, we generate locality-optimized query execution plans that are more efficient than popular multi-node RDF data management systems by effectively minimizing the inter-machine communication cost for query processing. Third but not the least, we provide a suite of locality-aware optimization techniques to further reduce the partition size and cut down on the inter-machine communication cost during distributed query processing. Experimental results show that our system scales well and can process big RDF datasets more efficiently than existing approaches.


Transfer Learning for Deep Learning on Graph-Structured Data.


title: Real-time Robust Detection of Planar Regions in a Pair of Images

abstract: This work presents a method for segmenting image patches which correspond to planar regions in the scene. The method consists of an efficient and robust solution for detecting multiple planar regions in a global optimal sense. Moreover, in contrast with existing techniques which also work on intensity images, neither assumptions about the scene are made nor heuristic hypotheses are formulated. More specifically, the proposed method is based on a systematic, progressive voting procedure from the solution of a linear system, which exploits the two-view geometry. Hence, besides avoiding intermediary depth maps, the progressive mechanism together with such a convergence mapping drastically reduce the computational and storage complexities of the approach. Results from both synthetic and real-world scenes in different scenarios and under various kinds of strong noise confirm its effectiveness and robustness against large camera calibration errors and to the presence of outliers.


title: Unsupervised algorithms for non-intrusive load monitoring: An up-to-date overview

abstract: Research on Smart Grids has recently focused on the energy monitoring issue, with the objective to maximize the user consumption awareness in building contexts on one hand, and to provide a detailed description of customer habits to the utilities on the other. One of the hottest topic in this field is represented by Non-Intrusive Load Monitoring (NILM): it refers to those techniques aimed at decomposing the consumption aggregated data acquired at a single point of measurement into the diverse consumption profiles of appliances operating in the electrical system under study. The focus here is on unsupervised algorithms, which are the most interesting and of practical use in real case scenarios. Indeed, these methods rely on a sustainable amount of a-priori knowledge related to the applicative context of interest, thus minimizing the user intervention to operate, and are targeted to extract all information to operate directly from the measured aggregate data. This paper reports and describes the most promising unsupervised NILM methods recently proposed in the literature, by dividing them into two main categories: load classification and source separation approaches. An overview of the public available dataset used on purpose and a comparative analysis of the algorithms performance is provided, together with a discussion of challenges and future research directions.


title: A new class of SIW filters based on periodically perforated dielectric substrate

abstract: A novel class of substrate integrated waveguide (SIW) filters based on perforations of the dielectric substrate is presented in this paper. The air-hole perforations allow the local modification of the characteristic impedance of the SIW as well as its cutoff frequency, thus permitting the easy design of filtering structures. Four-pole filters operating at 3.6 GHz, based on conventional and half-mode SIW, have been designed, experimentally validated, and discussed. The proposed approach provides high flexibility, low sensitivity to fabrication inaccuracy, and simple design rules.


title: Optimization of power and energy in FinFET based SRAM cell using adiabatic logic

abstract: Background/Objective: In the recent years, the instantaneous power consumption and the total energy dissipation of a circuit have become very important factors to be considered in complex VLSI system design solutions. The adiabatic logic is a technique, which is used to optimize the power dissipation and the energy recovery capability of these circuits, and this logic makes the VLSI circuits reuse the consumed power. This paper compares different adiabatic SRAM cell structures presented in the literature. Furthermore, the conventional SRAM cell and all the related designs are implemented using FinFET devices aiming at low power operation capability. Methods/Statistical analysis: The SRAM cell design is carried out in Cadence® EDA environment and power and energy values are estimated for the CMOS processes, namely, 180nm and 32nm followed by the 32nm FinFET technology. The two different technology libraries have been employed to identify the effects of the lower technology nodal effect on the power dissipation. The layouts for these SRAM cells have been drawn using Cadence® Assura tool. Findings: The results show that the power consumption of the FinFET based adiabatic SRAM cells (8T and 9T cell structure) is less than the conventional 6T CMOS based SRAM cell. Conclusion/improvement: In this paper, the conventional SRAM cell and the adiabatic SRAM cells with different technology nodes, namely, DSM and UDSM are compared for their power and energy values. The adiabatic logic displays lower power and energy consumption compared to those incurred by the conventional 6T CMOS SRAM cell. Furthermore, the FinFET device based circuits portrays advantages with its better control over the device channel and reduced short channel effects, resulting in reduced leakage power. The FinFET based SRAM cell employing the adiabatic logic incurs the minimum power and energy dissipation.


title: A Lightweight Authentication Mechanism for M2M Communications in Industrial IoT Environment

abstract: In the emerging industrial Internet of Things (IIoT) era, machine-to-machine (M2M) communication technology is considered as a key underlying technology for building IIoT environments, where devices (e.g., sensors, actuators, and gateways) are enabled to exchange information with each other in an autonomous way without human intervention. However, most of the existing M2M protocols that can be also used in the IIoT domain provide security mechanisms based on asymmetric cryptography resulting in high computational cost. As a consequence, the resource-constrained IoT devices are not able to support them appropriately and thus, many security issues arise for the IIoT environment. Therefore, lightweight security mechanisms are required for M2M communications in IIoT in order to reach its full potential. As a step toward this direction, in this paper, we propose a lightweight authentication mechanism, based only on hash and XOR operations, for M2M communications in IIoT environment. The proposed mechanism is characterized by low computational cost, communication, and storage overhead, while achieving mutual authentication, session key agreement, device’s identity confidentiality, and resistance against the following attacks: replay attack, man-in-the-middle attack, impersonation attack, and modification attack.


title: Big data: Old wine in new bottle?

abstract: Without question, astounding advances in storage, communication, and computation have propelled us into a new world, the world of Big Data. The Big Data enthusiasts believe that it is a revolution that will transform how we live, work, and think, and it will underpin new waves of innovation and productivity growth.


title: Stratospheric and tropospheric SSU/MSU temperature trends and compared to reanalyses and IPCC CMIP5 simulations in 1979–2005

abstract: Using the satellite temperature measurements from the Stratospheric Sounding Units (SSU) and Microwave Sounding Units (MSU including the advanced microwave sounding unit, AMSU) since 1979, the trends and uncertainties in the fifth Coupled Model Intercomparison Project (CMIP5) model simulations from the middle troposphere to the upper stratosphere (5–50 km) have been explored. The temperature trend discrepancies between the new generation reanalyses are investigated. Both the temporal character of the global mean temperature and the regional spatial pattern of the temperature trends are discussed. The results show that the CMIP5 model simulations reproduced common stratospheric cooling and tropospheric warming features although a significant discrepancy among the selected models was observed. For the temporal variation of the global mean temperature, the CMIP5 simulations reproduce the volcanic signal and were highly consistent with the SSU measurements in the upper stratosphere. In contrast, the CFSR and MERRA reanalyses (excluding ERA-I) exhibit a different result from the CMIP5 simulations. For the spatial variation of the temperature trends, the CMIP5 simulations displayed a different latitudinal-longitudinal pattern from SSU/MSU measurements in all six layers from the middle troposphere to the upper stratosphere. The CFSR reanalysis shows a good spatial correlation with satellite observations in the troposphere but poor spatial correlation in the stratosphere. The ERA-I and MERRA reanalyses have good spatial correlation in the upper stratosphere and an even better spatial correlation in the troposphere. Generally, the CMIP5 simulations significantly underestimated the stratospheric cooling in the tropics and substantially overestimated the cooling over the Antarctic in the MSU observations. The largest trend spread among the seven CMIP5 simulations is seen in both the south- and north-polar regions in the stratosphere and troposphere. The tropospheric spread values are generally smaller than the stratospheric spread values.


title: Image Based Visual Servoing: Estimated Image Jacobian by Using Fundamental Matrix VS Analytic Jacobian

abstract: This paper describes a comparative study of performance between the estimated image Jacobian that come from taking into account the geometry epipolar of a system of two cameras, and the well known analytic image Jacobian that is utilized for most applications in visual servoing. Image Based Visual Servoing architecture is used for controlling a 3 d.o.f. articular system using two cameras in eye to hand configuration. Tests in static and dynamic cases were carried out, and showed that the performance of estimated Jacobian by using the properties of the epipolar geometry is such as good and robust against noise as the analytic Jacobian. This fact is considered as an advantage because the estimated Jacobian does not need laborious previous work prior the control task in contrast to the analytic Jacobian does.


title: Design for 5G Mobile Network Architecture

abstract: In this paper we propose a new design solution for network architecture of future 5G mobile networks. The proposed design is based on user-centric mobile environment with many wireless and mobile technologies on the ground. In heterogeneous wireless environment changes in all, either new or older wireless technologies, is not possible, so each solution towards the next generation mobile and wireless networks should be implemented in the service stratum, while the radio access technologies belong to the transport stratum regarding the Next Generation Networks approach. In the proposed design the user terminal has possibility to change the Radio Access Technology - RAT based on certain criteria. For the purpose of transparent change of the RATs by the mobile terminal, we introduce so-called Policy-Router as node in the core network, which establishes IP tunnels to the mobile terminal via different available RATs to the terminal. The selection of the RAT is performed by the mobile terminal by using the proposed user agent for multi-criteria decision making based on the experience from the performance measurements performed by the mobile terminal. For the process of performance measurements we introduce the QoSPRO procedure for control information exchange between the mobile terminal and the Policy Router.


title: Real time vehicle speed prediction using a Neural Network Traffic Model

abstract: Prediction of the traffic information such as flow, density, speed, and travel time is important for traffic control systems, optimizing vehicle operations, and the individual driver. Prediction of future traffic information is a challenging problem due to many dynamic contributing factors. In this paper, various methodologies for traffic information prediction are investigated. We present a speed prediction algorithm, NNTM-SP (Neural Network Traffic Modeling-Speed Prediction) that trained with the historical traffic data and is capable of predicting the vehicle speed profile with the current traffic information. Experimental results show that the proposed algorithm gave good prediction results on real traffic data and the predicted speed profile shows that NNTM-SP correctly predicts the dynamic traffic changes.


title: Enhanced WiFi ToF indoor positioning system with MEMS-based INS and pedometric information

abstract: The most common technology for outdoor positioning is GNSS. It is commonly used together with inertial sensors to compensate for poor reception and to help determine outlier measurements. In dense areas and indoors, GPS performance degrades or is not available at all. In indoor environments WiFi is one of the most popular radios; it is not surprising therefore that WiFi is often used for positioning. Specifically, time-based range measurements are emerging as the leading WiFi indoor positioning technology. Because this technique is quite new, its coverage might be limited in the near future. In this paper we present a highly accurate indoor positioning system which is based on a new WiFi technology (protocol) [1] and on MEMS inertial sensors. This system fuses together WiFi time-of-flight (ToF) range measurements, INS-based position velocity and attitude measurements, and pedometric information. It harnesses the advantages of each of these components while compensating for their individual disadvantages. WiFi ToF typically exhibits good performance but suffers from outliers, coverage and dependency of Access Points (AP) deployment geometry (DoP). The INS solution is highly accurate but diverges quickly with time. Pedometric information (PDR) suffers from overall poor performance, inability to determine direction of movement (heading) and exhausting per-user calibration. Our solution uses WiFi ToF measurements and pedometric information to restrict the INS solution. We describe the INS model, the fusion model, and show exciting results from a real world environment.


title: A 10 MHz BW 50 fJ/conv. continuous time ΔΣ modulator with high-order single opamp integrator using optimization-based design method

abstract: Recently, performances of ADCs are greatly improved. Especially, FOM of low resolution ADCs such as SAR-type has been drastically reduced. However, it is rather difficult to improve FOM of high resolution ADCs like ΔΣ-type.


title: Internet Use, Social Skills, and Adjustment

abstract: The present study investigated the extent to which inter-personal skills, personality, and emotional intelligence (EI) were related to the extent of usage of the Internet, as measured with the Internet Addiction Scale, on a sample of undergraduates. EI was assessed by performance measures derived from the identification and labeling of emotions as shown in pictures of facial expressions, and as interpreted from descriptions of social episodes. Use of the Internet was related to loneliness and adherence to idiosyncratic values (strong effects), and also to poorer balance between work and leisure and emotional intelligence (weaker effects). Big Five personality dimensions were also included in the study. No link was found between personality and usage of the Internet. Results suggest that frequent users tend to be lonely, to have deviant values, and to some extent to lack the emotional and social skills characteristic of high EI.


title: Multimedia Question Answering

abstract: By surveying recent research in multimedia question answering, this article explores QA's evolution from text to multimedia and identifies the challenges in the field.


title: Optogenetics: the age of light

abstract: The optogenetic revolution is transforming neuroscience. The dramatic recent progress in using light to both control and read out neural activity has highlighted the need for better probes, improved light delivery and more careful interpretation of results, which will all be required for optogenetics to fully realize its remarkable potential.


title: DOLORES: Deep Contextualized Knowledge Graph Embeddings

abstract: We introduce a new method DOLORES for learning knowledge graph embeddings that effectively captures contextual cues and dependencies among entities and relations. First, we note that short paths on knowledge graphs comprising of chains of entities and relations can encode valuable information regarding their contextual usage. We operationalize this notion by representing knowledge graphs not as a collection of triples but as a collection of entity-relation chains, and learn embeddings for entities and relations using deep neural models that capture such contextual usage. In particular, our model is based on Bi-Directional LSTMs and learn deep representations of entities and relations from constructed entity-relation chains. We show that these representations can very easily be incorporated into existing models to significantly advance the state of the art on several knowledge graph prediction tasks like link prediction, triple classification, and missing relation type prediction (in some cases by at least 9.5%).


title: A novel modification for a quadrotor design

abstract: Conventional quadrotor UAVs have crucial underactuation limitations. This appears in the coupling between the roll angle and the movement in y-direction, and between the pitch angle and the movement in x-direction. The quadrotor capability of hovering with either roll or pitch angle is restricted due to these limitations. In this paper, a novel design modification is proposed in order to increase the quadrotor degrees of freedom. Four additional rotations for the propellers grant overactuated system. It could be used to make either an oriented hovering without any translational movements or make horizontal movement with zero inclination angle, as followed in this work. A PID controller is used for the quadrotor control. Simulation results show the validity of the proposed control scheme.


title: Compilation of a preliminary checklist for the differential diagnosis of neurogenic stuttering

abstract: Background: Neurogenic stuttering (NS) is the most frequently occurring acquired form of stuttering in children and adults. This form of stuttering is primarily caused by neurological incidents. Owing to controversies with regard to similarities between developmental stuttering (DS) and NS symptomatology, differential diagnosis is problematic. Differential diagnosis will guide the appropriate management of persons who stutter (PWS). Objectives: The aim of this study was to describe and highlight the characteristics of NS in order to compile a preliminary checklist for accurate diagnosis and intervention. Method: An explorative, applied mixed method, multiple case study research design was followed. Purposive sampling was used to select four participants. A comprehensive assessment battery was compiled for data collection. Results: The results revealed a distinct pattern of core stuttering behaviours in NS, although discrepancies existed regarding stuttering severity and frequency. It was also found that DS and NS can co-occur. The case history and the core stuttering pattern are important considerations during differential diagnosis, as these are the only consistent characteristics in people with NS. Conclusion: It is unlikely that all the symptoms of NS are present in an individual. The researchers scrutinised the findings of this study and the findings of previous literature to compile a potentially workable checklist.


title: A data-driven kinematic model of the human hand with soft-tissue artifact compensation mechanism for grasp synergy analysis

abstract: This paper presents a methodology to accurately record human finger postures during grasping. The main contribution consists of a kinematic model of the human hand reconstructed via magnetic resonance imaging of one subject that (i) is fully parameterized and can be adapted to different subjects, and (ii) is amenable to in-vivo joint angle recordings via optical tracking of markers attached to the skin. The principal novelty here is the introduction of a soft-tissue artifact compensation mechanism that can be optimally calibrated in a systematic way. The high-quality data gathered are employed to study the properties of hand postural synergies in humans, for the sake of ongoing neuroscience investigations. These data are analyzed and some comparisons with similar studies are reported. After a meaningful mapping strategy has been devised, these data could be employed to define robotic hand postures suitable to attain effective grasps, or could be used as prior knowledge in lower-dimensional, real-time avatar hand animation.


title: Blindness Caused by Cosmetic Filler Injection

abstract: Summary:Vascular occlusion causing blindness is a rare yet greatly feared complication of the use of facial aesthetic fillers. The authors performed a review of the aesthetic literature to ascertain the reported cases of blindness and the literature reporting variations in the vascular anatomy of th


title: Design and fabrication of a pneumatic soft robotic gripper for delicate surgical manipulation

abstract: Soft compliant gripping is a promising way to protect soft tissues from the grip damage caused by the high stress points in delicate surgical manipulation. In this paper, a new soft robotic gripper is designed to minimize the risk of soft tissues damage due to the over-gripping force generated by the conventional forceps. This new soft robotic gripper consists of a 3D-printed hook retractor shell, a soft inflatable actuator and two small rods. The ability of compliant grip is achieved by the inflated soft pneumatic actuator. Two small rods are used to separate the inflatable actuator into three-fingers-like bloats which can firmly grip the soft tissues by multi-contacts between the tissues and the gripper when the air pressure is applied to the pneumatic channel. In addition, it can protect the tissues against the harmful contacts with the rigid shell. The hook structure allows scooping-up motion during delicate surgical manipulation. The gripping tests and pulling force sensing experiments are carried out to evaluate the performance of the proposed soft robotic gripper.


title: Distributed Newton Method for Large-Scale Consensus Optimization

abstract: In this paper, we propose a distributed Newton method for consensus optimization. Our approach outperforms state-of-the-art methods, including ADMM. The key idea is to exploit the sparsity of the dual Hessian and recast the computation of the Newton step as one of efficiently solving symmetric diagonally dominant linear equations. We validate our algorithm both theoretically and empirically. On the theory side, we demonstrate that our algorithm exhibits superlinear convergence within a neighborhood of optimality. Empirically, we show the superiority of this new method on a variety of machine learning problems. The proposed approach is scalable to very large problems and has a low communication overhead.


title: Investigating the querying and browsing behavior of advanced search engine users

abstract: One way to help all users of commercial Web search engines be more successful in their searches is to better understand what those users with greater search expertise are doing, and use this knowledge to benefit everyone. In this paper we study the interaction logs of advanced search engine users (and those not so advanced) to better understand how these user groups search. The results show that there are marked differences in the queries, result clicks, post-query browsing, and search success of users we classify as advanced (based on their use of query operators), relative to those classified as non-advanced. Our findings have implications for how advanced users should be supported during their searches, and how their interactions could be used to help searchers of all experience levels find more relevant information and learn improved searching strategies.


title: An Efficient Leave-One-Out Cross-Validation-Based Extreme Learning Machine (ELOO-ELM) With Minimal User Intervention

abstract: It is well known that the architecture of the extreme learning machine (ELM) significantly affects its performance and how to determine a suitable set of hidden neurons is recognized as a key issue to some extent. The leave-one-out cross-validation (LOO-CV) is usually used to select a model with good generalization performance among potential candidates. The primary reason for using the LOO-CV is that it is unbiased and reliable as long as similar distribution exists in the training and testing data. However, the LOO-CV has rarely been implemented in practice because of its notorious slow execution speed. In this paper, an efficient LOO-CV formula and an efficient LOO-CV-based ELM (ELOO-ELM) algorithm are proposed. The proposed ELOO-ELM algorithm can achieve fast learning speed similar to the original ELM without compromising the reliability feature of the LOO-CV. Furthermore, minimal user intervention is required for the ELOO-ELM, thus it can be easily adopted by nonexperts and implemented in automation processes. Experimentation studies on benchmark datasets demonstrate that the proposed ELOO-ELM algorithm can achieve good generalization with limited user intervention while retaining the efficiency feature.


title: Sentiment Identification in Code-Mixed Social Media Text

abstract: Sentiment analysis is the Natural Language Processing (NLP) task dealing with the detection and classification of sentiments in texts. While some tasks deal with identifying the presence of sentiment in the text (Subjectivity analysis), other tasks aim at determining the polarity of the text categorizing them as positive, negative and neutral. Whenever there is a presence of sentiment in the text, it has a source (people, group of people or any entity) and the sentiment is directed towards some entity, object, event or person. Sentiment analysis tasks aim to determine the subject, the target and the polarity or valence of the sentiment. In our work, we try to automatically extract sentiment (positive or negative) from Facebook posts using a machine learning approach.While some works have been done in code-mixed social media data and in sentiment analysis separately, our work is the first attempt (as of now) which aims at performing sentiment analysis of code-mixed social media text. We have used extensive pre-processing to remove noise from raw text. Multilayer Perceptron model has been used to determine the polarity of the sentiment. We have also developed the corpus for this task by manually labeling Facebook posts with their associated sentiments.


title: Low-rank and Sparse Soft Targets to Learn Better DNN Acoustic Models

abstract: Conventional deep neural networks (DNN) for speech acoustic modeling rely on Gaussian mixture models (GMM) and hidden Markov model (HMM) to obtain binary class labels as the targets for DNN training. Subword classes in speech recognition systems correspond to context-dependent tied states or senones. The present work addresses some limitations of GMM-HMM senone alignments for DNN training. We hypothesize that the senone probabilities obtained from a DNN trained with binary labels can provide more accurate targets to learn better acoustic models. However, DNN outputs bear inaccuracies which are exhibited as high dimensional unstructured noise, whereas the informative components are structured and low-dimensional. We exploit principal component analysis (PCA) and sparse coding to characterize the senone subspaces. Enhanced probabilities obtained from low-rank and sparse reconstructions are used as soft-targets for DNN acoustic modeling, that also enables training with untranscribed data. Experiments conducted on AMI corpus shows 4.6% relative reduction in word error rate.


title: Working memory in children: tracing age differences and special educational needs to parameters of a formal model.

abstract: Parameters of a formal working-memory model were estimated for verbal and spatial memory updating of children. The model proposes interference though feature overwriting and through confusion of whole elements as the primary cause of working-memory capacity limits. We tested 2 age groups each containing 1 group of normal intelligence and 1 deficit group. For young children the deficit was developmental dyslexia; for older children it was a general learning difficulty. The interference model predicts less interference through overwriting but more through confusion of whole elements for the dyslexic children than for their age-matched controls. Older children exhibited less interference through confusion of whole elements and a higher processing rate than young children, but general learning difficulty was associated with slower processing than in the age-matched control group. Furthermore, the difference between verbal and spatial updating mapped onto several meaningful dissociations of model parameters.


title: XFT: Practical fault tolerance beyond crashes

abstract: Despite years of intensive research, Byzantine fault-tolerant (BFT) systems have not yet been adopted in practice. This is due to additional cost of BFT in terms of resources, protocol complexity and performance, compared with crash fault-tolerance (CFT). This overhead of BFT comes from the assumption of a powerful adversary that can fully control not only the Byzantine faulty machines, but at the same time also the message delivery schedule across the entire network, effectively inducing communication asynchrony and partitioning otherwise correct machines at will. To many practitioners, however, such strong attacks appear irrelevant. ::: ::: In this paper, we introduce cross fault tolerance or XFT, a novel approach to building reliable and secure distributed systems and apply it to the classical state-machine replication (SMR) problem. In short, an XFT SMR protocol provides the reliability guarantees of widely used asynchronous CFT SMR protocols such as Paxos and Raft, but also tolerates Byzantine faults in combination with network asynchrony, as long as a majority of replicas are correct and communicate synchronously. This allows the development of XFT systems at the price of CFT (already paid for in practice), yet with strictly stronger resilience than CFT -- sometimes even stronger than BFT itself. ::: ::: As a showcase for XFT, we present XPaxos, the first XFT SMR protocol, and deploy it in a geo-replicated setting. Although it offers much stronger resilience than CFT SMR at no extra resource cost, the performance of XPaxos matches that of the state-of-the-art CFT protocols.


title: International Accounting Standards and Accounting Quality

abstract: We examine whether application of International Accounting Standards is associated with higher accounting quality. The application of IAS reflects the combined effects of features of the financial reporting system, including standards, their interpretation, enforcement, and litigation. We find that firms applying IAS from 21 countries generally evidence less earnings management, more timely loss recognition, and more value relevance of accounting amounts than do a matched sample of firms applying non-US domestic standards. Differences in accounting quality between the two groups of firms in the period before the IAS firms adopt IAS do not account for the post-adoption differences. We also find that firms applying IAS generally evidence an improvement in accounting quality between the pre- and post-adoption periods. Although we cannot be sure that our findings are attributable to the change in the financial reporting system rather than to changes in firms' incentives and the economic environment, we include research design features to mitigate the effects of both.


Understanding a Proposed Model of Customer Loyalty Formation in B2C e-Commerce


title: In the blink of an eye: investigating latency perception during stylus interaction

abstract: While pen computing has become increasingly more popular, device responsiveness, or latency, still plagues such interaction. Although there have been advances in digitizer technology over the last few years, commercial end-to-end latencies are unfortunately similar to those found with touchscreens, i.e., 65 - 120 milliseconds. We report on a prototype stylus-enabled device, the High Performance Stylus System (HPSS), designed to display latencies as low as one millisecond while users ink or perform dragging tasks. To understand the role of latency while inking with a stylus, psychophysical just-noticeable difference experiments were conducted using the HPSS. While participants performed dragging and scribbling tasks, very low levels of latency could be discriminated, i.e., ~1 versus 2 milliseconds while dragging and ~7 versus 40 milliseconds while scribbling. The HPSS and our experimentation have provided further motivation for the implementation of latency saving measures in pen-based hardware and software systems.


title: Broca and Wernicke are dead, or moving past the classic model of language neurobiology

abstract: Abstract With the advancement of cognitive neuroscience and neuropsychological research, the field of language neurobiology is at a cross-roads with respect to its framing theories. The central thesis of this article is that the major historical framing model, the Classic “Wernicke-Lichtheim-Geschwind” model, and associated terminology, is no longer adequate for contemporary investigations into the neurobiology of language. We argue that the Classic model (1) is based on an outdated brain anatomy; (2) does not adequately represent the distributed connectivity relevant for language, (3) offers a modular and “language centric” perspective, and (4) focuses on cortical structures, for the most part leaving out subcortical regions and relevant connections. To make our case, we discuss the issue of anatomical specificity with a focus on the contemporary usage of the terms “Broca’s and Wernicke’s area”, including results of a survey that was conducted within the language neurobiology community. We demonstrate that there is no consistent anatomical definition of “Broca’s and Wernicke’s Areas”, and propose to replace these terms with more precise anatomical definitions. We illustrate the distributed nature of the language connectome, which extends far beyond the single-pathway notion of arcuate fasciculus connectivity established in Geschwind’s version of the Classic Model. By illustrating the definitional confusion surrounding “Broca’s and Wernicke’s areas”, and by illustrating the difficulty integrating the emerging literature on perisylvian white matter connectivity into this model, we hope to expose the limits of the model, argue for its obsolescence, and suggest a path forward in defining a replacement.


title: FREAK: Fast Retina Keypoint

abstract: A large number of vision applications rely on matching keypoints across images. The last decade featured an arms-race towards faster and more robust keypoints and association algorithms: Scale Invariant Feature Transform (SIFT)[17], Speed-up Robust Feature (SURF)[4], and more recently Binary Robust Invariant Scalable Keypoints (BRISK)[I6] to name a few. These days, the deployment of vision algorithms on smart phones and embedded devices with low memory and computation complexity has even upped the ante: the goal is to make descriptors faster to compute, more compact while remaining robust to scale, rotation and noise. To best address the current requirements, we propose a novel keypoint descriptor inspired by the human visual system and more precisely the retina, coined Fast Retina Keypoint (FREAK). A cascade of binary strings is computed by efficiently comparing image intensities over a retinal sampling pattern. Our experiments show that FREAKs are in general faster to compute with lower memory load and also more robust than SIFT, SURF or BRISK. They are thus competitive alternatives to existing keypoints in particular for embedded applications.


title: Reference values for the serum lipid profile of albino rats (Rattus norvegicus) of varied ages and sexes

abstract: Albino rats (Rattus norvegicus) are the most commonly used laboratory animals for studies on the effects of drugs, supplements, herbal extracts and diets on the serum lipid profile (SLP), yet there is no comprehensive set of reference values for the SLP of albino rats of varied ages and sexes in available literature. This study was designed to assay the SLP of albino rats of varied ages and sexes. A total of 480 Sprague Dawley albino rats were used for the study, composed of 240 females and 240 males. The age sets studied were rats of 4, 6, 8, 10, 12, 16, 20, 30, 40, and 50 weeks of age. For each age set, the SLP of 24 females and 24 males was assayed. Standard biochemical procedures were followed in the SLP assay after a 12-h overnight fast of the rats. Results showed that serum levels of total cholesterol (TC), high density lipoprotein cholesterol (HDLC) and low density lipoprotein cholesterol (LDLC) significantly rose (p 0.05) than that of the males. It was concluded that there were significant variations in the SLP of albino rats across the ages studied and also significant differences between the sexes at certain ages.


title: Sizing Up the Troll: A Quantitative Characterization of Moderator-Identified Trolling in an Online Forum

abstract: A few troublemakers often spoil online environments for everyone else. An extremely disruptive type of abuser is the troll, whose malicious activities are relatively non-obvious, and thus difficult to detect and contain -- particularly by automated systems. A growing corpus of qualitative research focuses on trolling, and differentiates it from other forms of abuse; however, its findings are not directly actionable into automated systems. On the other hand, quantitative research uses definitions of "troll" that mostly fail to capture what moderators and users consider trolling. We address this gap by giving a quantitative analysis of posts, conversations, and users, specifically sanctioned for trolling in an online forum. Although trolls (unlike most other abusers) hardly stand out in a conversation e.g. in terms of vocabulary, textit{how} they interact, rather than textit{what} they contribute, provides cues of their malicious intent.


title: Behavioral thermoregulation in mammals: a review.

abstract: In mammals, thermoregulation is a key feature in the maintenance of homeostasis. Thermoregulatory capacities are strongly related to energy balance and animals are constantly seeking to limit the energy costs of normothermia. In case of thermal changes, physiological mechanisms are enhanced, increasing rates of energy expenditure. However, behavioral adjustments are available for species to lower the autonomic work, and thus reduce the energy costs of thermoregulatory responses. Hence, thermogenesis-induced metabolic costs can be reduced during cold exposure, and hyperthermia associated to dehydration can be avoided during heat exposure. Hypothermia avoidance consists in a concomitant decrease in heat dissipation and increase in heat production. Inversely, heat exchange is enhanced and body heat production is reduced when avoiding hyperthermia. The different behavioral strategies available for mammal species to cope with both decreased and increased levels of ambient temperature are reviewed. Moreover, thermoregulation function is under the control of central, metabolic, energetic and endocrine systems, which induces that parameters such as hour of the day, season, gender or aging may affect thermoregulatory adjustments. Some examples will be given.


Advantages of power system state estimation using Phasor Measurement Units


title: Passive Bistatic Radar (PBR) using FM radio illuminators of opportunity

abstract: We present a system characterisation of a passive bistatic radar (PBR). The system under investigation exploits dasiailluminators of opportunitypsila, which in this case are commercial, non-cooperative, VHF FM broadcast transmissions. The PBR under investigation demonstrates the detection of large passenger-jet aircraft in the airspace over greater London. FM based PBRs such as the type examined here have been shown to be effective at remote sensing of auroral turbulence, density irregularities in the E-region and F-region of the ionosphere and meteor trails. Detection of aircraft is an important step in attempting to remotely sense outer-atmospheric phenomena as aircraft scattered power is of a comparable magnitude and similar Doppler frequency to the natural phenomena described. This paper also analyses the merits of FM broadcast transmissions for use as radar signals. A system characterisation with performance predictions is presented. The results show that target detections have been achieved to ranges in excess of 70 km (bistatic range). Multiple broadcast channels from two different transmitters of opportunity enhance the case for deciding whether or not targets are present. Air-truth data provided by a mode S/ADS-B IFF receiver is used for comparison with the 2-D bistatic results.


title: A Compact Ramp-Based Soft-Start Circuit for Voltage Regulators

abstract: A fully integrated soft-start circuit for voltage regulators is presented in this brief. A soft-start strategy based on a linearly ramped-up reference is adopted to prevent massive in-rush currents through the power device during the start-up phase of the regulator. The strategy is realized by a compact on-chip circuit, which requires no external components and has minimal transistor overhead, thereby minimizing the implementation area and cost of the overall regulator. The proposed soft-start circuit has been implemented in a 0.35-mum high-voltage complementary metal-oxide-semiconductor technology as part of a linear regulator controller for automotive applications. The proposed soft-start circuit occupies 0.026 mm2 on silicon, which corresponds to about one fifteenth of the total area of the linear regulator controller.


title: Recover Missing Sensor Data with Iterative Imputing Network

abstract: Sensor data has been playing an important role in machine learning tasks, complementary to the human-annotated data that is usually rather costly. However, due to systematic or accidental mis-operations, sensor data comes very often with a variety of missing values, resulting in considerable difficulties in the follow-up analysis and visualization. Previous work imputes the missing values by interpolating in the observational feature space, without consulting any latent (hidden) dynamics. In contrast, our model captures the latent complex temporal dynamics by summarizing each observation's context with a novel Iterative Imputing Network, thus significantly outperforms previous work on the benchmark Beijing air quality and meteorological dataset. Our model also yields consistent superiority over other methods in cases of different missing rates.


title: Identifying rhetorical questions in social media

abstract: Social media provides a platform for seeking information from a large user base. Information seeking in social media, however, occurs simultaneously with users expressing their viewpoints by making statements. Rhetorical questions have the form of a question but serve the function of a statement and might mislead platforms assisting information seeking in social media. It becomes difficult to identify rhetorical questions as they are not syntactically different from other questions. In this paper, we develop a framework to identify rhetorical questions by modeling the motivations of the users to post them. We focus on one motivation of the users drawing from linguistic theories, to implicitly convey a message. We develop a framework from this motivation to identify rhetorical questions in social media and evaluate the framework using questions posted on Twitter. This is the first framework to model the motivations for posting rhetorical questions to identify them on social


title: Fusion algorithm based on fuzzy neural networks

abstract: The problem of optimal fusion of several predictive machine learning regression models is considered. The method of combining different predictive models based on additive fuzzy systems is presented. The framework of model fusion based on fuzzy neural networks is described and the appropriate algorithms are derived. Learning process justifications and the requirement of the separate fusion set are discussed. The presented models are supported with the real world application example of robotic hand control.


title: Simple, Robust Autonomous Grasping in Unstructured Environments

abstract: The inherent uncertainty associated with unstructured grasping tasks makes establishing a successful grasp difficult. Traditional approaches to this problem involve hands that are complex, fragile, require elaborate sensor suites, and are difficult to control. In this paper, we demonstrate a novel autonomous grasping system that is both simple and robust. The four-fingered hand is driven by a single actuator, yet can grasp objects spanning a wide range of size, shape, and mass. The hand is constructed using polymer-based shape deposition manufacturing, with joints formed by elastomeric flexures and actuator and sensor components embedded in tough rigid polymers. The hand has superior robustness properties, able to withstand large impacts without damage and capable of grasping objects in the presence of large positioning errors. We present experimental results showing that the hand mounted on a three degree of freedom manipulator arm can reliably grasp 5 cm-scale objects in the presence of positioning error of up to 100% of the object size and 10 cm-scale objects in the presence of positioning error of up to 33% of the object size, while keeping acquisition contact forces low.


title: An Energy Efficient Multichannel MAC Protocol for Cognitive Radio Ad Hoc Networks

abstract: This paper presents a TDMA based energy efficient cognitive radio multichannel medium access control (MAC) protocol called ECR-MAC for wireless ad hoc networks. ECR- MAC requires only a single half-duplex radio transceiver on each node that integrates the spectrum sensing at physical (PHY) layer and the packet scheduling at MAC layer. In addition to explicit frequency negotiation which is adopted by conventional multichannel MAC protocols, ECR-MAC introduces lightweight explicit time negotiation. This two-dimensional negotiation enables ECR-MAC to exploit the advantage of both multiple channels and TDMA, and achieve aggressive power savings by allowing nodes that are not involved in communication to go into doze mode. The IEEE 802.11 standard allows for the use of multiple channels available at the PHY layer, but its MAC protocol is designed only for a single channel. A single channel MAC protocol does not work well in a multichannel environment, because of the multichannel hidden terminal problem. The proposed energy efficient ECR-MAC protocol allows secondary users (SUs) to identify and use the unused frequency spectrum in a way that constrains the level of interference to the primary users (PUs). Extensive simulation results show that our proposed ECR-MAC protocol successfully exploits multiple channels and significantly improves network performance by using the licensed spectrum band opportunistically and protects QoS provisioning over cognitive radio ad hoc networks.


title: Using cluster-based logic blocks and timing-driven packing to improve FPGA speed and density

abstract: In 1999, most commercial FPGAs, like the Altera Flex and Xilinx Virtex FPGAs already had cluster-based logic blocks. However, the modeling and evaluation of these sorts of architectures was still in its infancy. In the previous year, Betz had shown that cluster-based logic blocks led to improved density. The real advantage of clustered-based logic blocks, though, was speed, as this paper demonstrates. In doing so, this paper opened up an entirely new research area, setting the framework for numerous packing algorithms that have become a fundamental part of any FPGA CAD flow.


title: Correlation Bandwidth and Delay Spread Multipath Propagation Statistics for 910-MHz Urban Mobile Radio Channels

abstract: Distributions of delay spread and correlation bandwidth at 0.9 and 0.5 correlation for Gaussian wide-sense stationary uncorrelated scattering (GWSSUS) channels associated with 100 small-scale areas at different locations within a 2 × 2.5 km region of New York City are presented. For delay spread the maximum value observed was 3\frac{1}{2};\mu s and l0 percent of the areas exceeded 2\frac{1}{2}\mu s; for correlation bandwidth at 0.9 correlation the minimum was 20 kHz and 10 percent of the areas were less than 30 kHz; for correlation bandwidth at 0.5 correlation the minimum was 55 kHz and 10 percent of the areas were less than 130 kHz. The region is representative of the heavily built-up areas of many large cities in the United States.


title: Differentiating anxiety forms and their role in academic performance from primary to secondary school

abstract: Introduction ::: Individuals with high levels of mathematics anxiety are more likely to have other forms of anxiety, such as general anxiety and test anxiety, and tend to have some math performance decrement compared to those with low math anxiety. However, it is unclear how the anxiety forms cluster in individuals, or how the presence of other anxiety forms influences the relationship between math anxiety and math performance. ::: ::: ::: Method ::: We measured math anxiety, test anxiety, general anxiety and mathematics and reading performance in 1720 UK students (year 4, aged 8–9, and years 7 and 8, aged 11–13). We conducted latent profile analysis of students’ anxiety scores in order to examine the developmental change in anxiety profiles, the demographics of each anxiety profile and the relationship between profiles and academic performance. ::: ::: ::: Results ::: Anxiety profiles appeared to change in specificity between the two age groups studied. Only in the older students did clusters emerge with specifically elevated general anxiety or academic anxiety (test and math anxiety). Our findings suggest that boys are slightly more likely than girls to have elevated academic anxieties relative to their general anxiety. Year 7/8 students with specifically academic anxiety show lower academic performance than those who also have elevated general anxiety. ::: ::: ::: Conclusions ::: There may be a developmental change in the specificity of anxiety and gender seems to play a strong role in determining one’s anxiety profile. The anxiety profiles present in our year 7/8 sample, and their relationships with math performance, suggest a bidirectional relationship between math anxiety and math performance.


title: mccallum~justresearch.com Building Domain-Specific Search Engines with Machine Learning Techniques

abstract: Domain-specific search engines are growing in popularity because they offer increased accuracy and extra functionality not possible with the general, Web-wide search engines. For example, www.campsearch.com allows complex queries by age-group, size, location and cost over .summer camps. Unfortunately these domain-specific search engines are difficult and timeconsuming to maintain. This paper proposes the use of machine learning techniques to greatly automate the creation and maintenance of domain-specific search engines. We describe new research in reinforcement learning, information extraction and text classification that enables efficient spidering, identifying informative text segments, and populating topic hierarchies. Using these techniques, we have built a demonstration system: a search engine for computer science research papers. It already contaius over 50,000 papers and is publicly available at ~w. cora.justres earch, com.


title: On-Line Fingerprint Verification

abstract: Fingerprint verification is one of the most reliable personal identification methods. However, manual fingerprint verification is incapable of meeting today's increasing performance requirements. An automatic fingerprint identification system (AFIS) is needed. This paper describes the design and implementation of an online fingerprint verification system which operates in two stages: minutia extraction and minutia matching. An improved version of the minutia extraction algorithm proposed by Ratha et al. (1995), which is much faster and more reliable, is implemented for extracting features from an input fingerprint image captured with an online inkless scanner. For minutia matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the correspondences between minutiae in the input image and the stored template without resorting to exhaustive search and has the ability of adaptively compensating for the nonlinear deformations and inexact pose transformations between fingerprints. The system has been tested on two sets of fingerprint images captured with inkless scanners. The verification accuracy is found to be acceptable. Typically, a complete fingerprint verification procedure takes, on an average, about eight seconds on a SPARC 20 workstation. These experimental results show that our system meets the response time requirements of online verification with high accuracy.


title: Ant-colony algorithms for permutation flowshop scheduling to minimize makespan / total flowtime of jobs

abstract: The problem of scheduling in permutation flowshops is considered with the objective of minimizing the makespan, followed by the consideration of minimization of total flowtime of jobs. Two ant-colony optimization algorithms are proposed and analyzed for solving the permutation flowshop scheduling problem. The first algorithm extends the ideas of the ant-colony algorithm by Stuetzle [Proceedings of the 6th European Congress on Intelligent Techniques and Soft Computing (EUFIT 98),vol. 3,Verlag Mainz,Aachen,Germany,1998,p. 1560],called max–min ant system (MMAS), by incorporating the summation rule suggested by Merkle and Middendorf [Proceedings of the EvoWorkshops 2000, Lecture Notes in Computer Science No. 1803,Springer-Verlag,Berlin,2000,p. 287] and a newly proposed local search technique. The second ant-colony algorithm is newly developed. The proposed ant-colony algorithms have been applied to 90 benchmark problems taken from Taillard [European Journal of Operational Research 64 (1993) 278]. First,a comparison of the solutions yielded by the MMAS and the two ant-colony algorithms developed in this paper,with the heuristic solutions given by Taillard [European Journal of Operational Research 64 (1993) 278] is undertaken with respect to the minimization of makespan. The comparison shows that the two proposed ant-colony algorithms perform better,on an average,than the MMAS. Subsequently,by considering the objective of minimizing the total flowtime of jobs,a comparison of solutions yielded by the proposed ant-colony algorithms with the best heuristic solutions known for the benchmark problems,as published in an extensive study by Liu and Reeves [European Journal of Operational Research 132 (2001) 439],is carried out. The comparison shows that the proposed ant-colony algorithms are clearly superior to the heuristics analyzed by Liu and Reeves. For 83 out of 90 problems considered,better solutions have been found by the two proposed ant-colony algorithms,as compared to the solutions reported by Liu and Reeves. 2003 Elsevier B.V. All rights reserved.


title: Local Rademacher Complexities

abstract: We propose new bounds on the error of learning algorithms in terms of a data-dependent notion of complexity. The estimates we establish give optimal rates and are based on a local and empirical version of Rademacher averages, in the sense that the Rademacher averages are computed from the data, on a subset of functions with small empirical error. We present some applications to classification and prediction with convex function classes, and with kernel classes in particular.


title: Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation

abstract: Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. This study presents a hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-LSTM) for short-term load forecasting. The extreme gradient boosting-based weighted k-means algorithm is used to evaluate the similarity between the forecasting and historical days. The EMD method is employed to decompose the SD load to several intrinsic mode functions (IMFs) and residual. Separated LSTM neural networks were also employed to forecast each IMF and residual. Lastly, the forecasting values from each LSTM model were reconstructed. Numerical testing demonstrates that the SD-EMD-LSTM method can accurately forecast the electric load.


title: Unhelpful thoughts and beliefs linked to social anxiety in stuttering: development of a measure

abstract: Background: Those who stutter have a proclivity to social anxiety. Yet, to date, there is no comprehensive measure of thoughts and beliefs about stuttering that represent the cognitions associated with that anxiety.Aims: The present paper describes the development of a measure to assess unhelpful thoughts and beliefs about stuttering.Methods & Procedures: The Unhelpful Thoughts and Beliefs about Stuttering (UTBAS) self‐report measure contains 66 items that assess the frequency of unhelpful thoughts and beliefs. Items were constructed from a comprehensive file audit of all stuttering cases seen in a cognitive–behavior therapy based treatment programme over a ten‐year period.Outcomes & Results: Preliminary investigations indicate that the UTBAS has high levels of test–retest reliability (r = 0.89) and internal consistency (Chronbach's alpha = 0.98). It has good known‐groups validity, being able to discriminate between stuttering and non‐stuttering participants on items that contain no reference to stutterin...


title: Concept Development and Design of a Spherical Wheel Motor (SWM)

abstract: This paper presents the design concept, models, and open-loop control of a particular form of a variable- reluctance spherical motor (VRSM), referred here as a spherical wheel motor (SWM). Unlike existing spherical motors where design focuses have been on controlling the three degrees of freedom (DOF) angular displacements, the SWM offers a means to control the orientation of a continuously rotating shaft in an open-loop (OL) fashion. We provide a formula for deriving different switching sequences (full step and fractional step) for a specified current magnitude and pole configurations. The concept feasibility of an OL controlled SWM has been experimentally demonstrated on a prototype that has 8 rotor permanent-magnet (PM) pole-pairs and 10 stator electromagnet (EM) pole-pairs.


title: On Pilot-Symbol-Assisted Carrier Synchronization for DVB-S2 Systems

abstract: We consider the problem of carrier synchronization in future 2nd-generation satellite digital video broadcasting (DVB-S2) receivers. In this scenario, this task is made harder by the complexity constraints, related to the use of consumer-grade equipment. Making use of the distributed pilot symbols of the DVB-S2 standard, low-complexity techniques for fine frequency estimation and for detection in the presence of a strong phase noise, typical of consumer-grade equipment, will be proposed. The performance of the described algorithms will be analysed in detail through computer simulations.


title: Bird detection in audio: A survey and a challenge

abstract: Many biological monitoring projects rely on acoustic detection of birds. Despite increasingly large datasets, this detection is often manual or semi-automatic, requiring manual tuning/postprocessing. We review the state of the art in automatic bird sound detection, and identify a widespread need for tuning-free and species-agnostic approaches. We introduce new datasets and an IEEE research challenge to address this need, to make possible the development of fully automatic algorithms for bird sound detection.


title: The optical character recognition of Urdu-like cursive scripts

abstract: We survey the optical character recognition (OCR) literature with reference to the Urdu-like cursive scripts. In particular, the Urdu, Pushto, and Sindhi languages are discussed, with the emphasis being on the Nasta'liq and Naskh scripts. Before detaining the OCR works, the peculiarities of the Urdu-like scripts are outlined, which are followed by the presentation of the available text image databases. For the sake of clarity, the various attempts are grouped into three parts, namely: (a) printed, (b) handwritten, and (c) online character recognition. Within each part, the works are analyzed par rapport a typical OCR pipeline with an emphasis on the preprocessing, segmentation, feature extraction, classification, and recognition. HighlightsA literature review of the Nasta'liq and Naskh cursive script OCR.The peculiarities and challenges are described a priori.Printed, handwritten and online OCR efforts are being explored.Analyses based on the stages of a typical OCR pipeline.


title: Learning to Generate Chairs with Generative Adversarial Nets

abstract: Generative adversarial networks (GANs) has gained tremendous popularity lately due to an ability to reinforce quality of its predictive model with generated objects and the quality of the generative model with and supervised feedback. GANs allow to synthesize images with a high degree of realism. However, the learning process of such models is a very complicated optimization problem and certain limitation for such models were found. It affects the choice of certain layers and nonlinearities when designing architectures. In particular, it does not allow to train convolutional GAN models with fully-connected hidden layers. In our work, we propose a modification of the previously described set of rules, as well as new approaches to designing architectures that will allow us to train more powerful GAN models. We show the effectiveness of our methods on the problem of synthesizing projections of 3D objects with the possibility of interpolation by class and view point.