title: Network Innovation using OpenFlow: A Survey

abstract: OpenFlow is currently the most commonly deployed Software Defined Networking (SDN) technology. SDN consists of decoupling the control and data planes of a network. A software-based controller is responsible for managing the forwarding information of one or more switches; the hardware only handles the forwarding of traffic according to the rules set by the controller. OpenFlow is an SDN technology proposed to standardize the way that a controller communicates with network devices in an SDN architecture. It was proposed to enable researchers to test new ideas in a production environment. OpenFlow provides a specification to migrate the control logic from a switch into the controller. It also defines a protocol for the communication between the controller and the switches. As discussed in this survey paper, OpenFlow-based architectures have specific capabilities that can be exploited by researchers to experiment with new ideas and test novel applications. These capabilities include software-based traffic analysis, centralized control, dynamic updating of forwarding rules and flow abstraction. OpenFlow-based applications have been proposed to ease the configuration of a network, to simplify network management and to add security features, to virtualize networks and data centers and to deploy mobile systems. These applications run on top of networking operating systems such as Nox, Beacon, Maestro, Floodlight, Trema or Node.Flow. Larger scale OpenFlow infrastructures have been deployed to allow the research community to run experiments and test their applications in more realistic scenarios. Also, studies have measured the performance of OpenFlow networks through modelling and experimentation. We describe the challenges facing the large scale deployment of OpenFlow-based networks and we discuss future research directions of this technology.


title: Whippersnapper: A P4 Language Benchmark Suite

abstract: As P4 and its associated compilers move beyond relative immaturity, there is a need for common evaluation criteria. In this paper, we propose Whippersnapper, a set of benchmarks for P4. Rather than simply selecting a set of representative data-plane programs, the benchmark is designed from first principles, identifying and exploring key features and metrics. We believe the benchmark will not only provide a vehicle for comparing implementations and designs, but will also generate discussion within the larger community about the requirements for data-plane languages.


Protein Surface Representation and Comparison: New Approaches in Structural Proteomics


title: Data X-Ray: A Diagnostic Tool for Data Errors

abstract: A lot of systems and applications are data-driven, and the correctness of their operation relies heavily on the correctness of their data. While existing data cleaning techniques can be quite effective at purging datasets of errors, they disregard the fact that a lot of errors are systematic, inherent to the process that produces the data, and thus will keep occurring unless the problem is corrected at its source. In contrast to traditional data cleaning, in this paper we focus on data diagnosis: explaining where and how the errors happen in a data generative process. We develop a large-scale diagnostic framework called DATA X-RAY. Our contributions are three-fold. First, we transform the diagnosis problem to the problem of finding common properties among erroneous elements, with minimal domain-specific assumptions. Second, we use Bayesian analysis to derive a cost model that implements three intuitive principles of good diagnoses. Third, we design an efficient, highly-parallelizable algorithm for performing data diagnosis on large-scale data. We evaluate our cost model and algorithm using both real-world and synthetic data, and show that our diagnostic framework produces better diagnoses and is orders of magnitude more efficient than existing techniques.


title: Probabilistic linear discriminant analysis

abstract: Linear dimensionality reduction methods, such as LDA, are often used in object recognition for feature extraction, but do not address the problem of how to use these features for recognition. In this paper, we propose Probabilistic LDA, a generative probability model with which we can both extract the features and combine them for recognition. The latent variables of PLDA represent both the class of the object and the view of the object within a class. By making examples of the same class share the class variable, we show how to train PLDA and use it for recognition on previously unseen classes. The usual LDA features are derived as a result of training PLDA, but in addition have a probability model attached to them, which automatically gives more weight to the more discriminative features. With PLDA, we can build a model of a previously unseen class from a single example, and can combine multiple examples for a better representation of the class. We show applications to classification, hypothesis testing, class inference, and clustering, on classes not observed during training.


title: Traffic accident detection using random forest classifier

abstract: The Internet of Things (IoT) has been growing in recent years with the improvements in several different applications in the military, marine, intelligent transportation, smart health, smart grid, smart home and smart city domains. Although IoT brings significant advantages over traditional information and communication (ICT) technologies for Intelligent Transportation Systems (ITS), these applications are still very rare. Although there is a continuous improvement in road and vehicle safety, as well as improvements in IoT, the road traffic accidents have been increasing over the last decades. Therefore, it is necessary to find an effective way to reduce the frequency and severity of traffic accidents. Hence, this paper presents an intelligent traffic accident detection system in which vehicles exchange their microscopic vehicle variables with each other. The proposed system uses simulated data collected from vehicular ad-hoc networks (VANETs) based on the speeds and coordinates of the vehicles and then, it sends traffic alerts to the drivers. Furthermore, it shows how machine learning methods can be exploited to detect accidents on freeways in ITS. It is shown that if position and velocity values of every vehicle are given, vehicles' behavior could be analyzed and accidents can be detected easily. Supervised machine learning algorithms such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Random Forests (RF) are implemented on traffic data to develop a model to distinguish accident cases from normal cases. The performance of RF algorithm, in terms of its accuracy, was found superior to ANN and SVM algorithms. RF algorithm has showed better performance with 91.56% accuracy than SVM with 88.71% and ANN with 90.02% accuracy.


kvm: the Linux Virtual Machine Monitor


title: Ultra-High Input Impedance, Low Noise Integrated Amplifier for Noncontact Biopotential Sensing

abstract: Noncontact electrocardiogram/electroencephalogram/ electromyogram electrodes, which operate primarily through capacitive coupling, have been extensively studied for unobtrusive physiological monitoring. Previous implementations using discrete off-the-shelf amplifiers have been encumbered by the need for manually tuned input capacitance neutralization networks and complex dc-biasing schemes. We have designed and fabricated a custom integrated noncontact sensor front-end amplifier that fully bootstraps internal and external parasitic impedances. DC stability without the need for external large valued resistances is ensured by an ac bootstrapped, low-leakage, on-chip biasing network. The amplifier achieves, without neutralization, input impedance of 60 fF $\Vert$ 50 T $\Omega$ , input referred noise of 0.05 fA/ $\sqrt{\rm Hz}$ and 200 nV/ $\sqrt{\rm Hz}$ at 1 Hz, and power consumption of 1.5 $\mu$ A per channel at 3.3 V supply voltage. Stable frequency response is demonstrated below 0.05 Hz with electrode coupling capacitances as low as 0.5 pF.


title: Design of a compact planar MIMO antenna for LTE mobile application

abstract: Recently, the long-term evolution (LTE) is considered as one of the most promising 4th generation (4G) mobile standards to increase the capacity and speed of mobile handset networks [1]. In order to realize the LTE wireless communication system, the diversity and multiple-input multiple-output (MIMO) systems have been introduced [2]. In a MIMO mobile user terminal such as handset or USB dongle, at least two uncorrelated antennas should be placed within an extremely restricted space. This task becomes especially difficult when a MIMO planar antenna is designed for LTE band 13 (the corresponding wavelength is 390 mm). Due to the limited space available for antenna elements, the antennas are strongly coupled with each other and have narrow bandwidth.


title: DeSTNet: Densely Fused Spatial Transformer Networks

abstract: Modern Convolutional Neural Networks (CNN) are extremely powerful on a range of computer vision tasks. However, their performance may degrade when the data is characterised by large intra-class variability caused by spatial transformations. The Spatial Transformer Network (STN) is currently the method of choice for providing CNNs the ability to remove those transformations and improve performance in an end-to-end learning framework. In this paper, we propose Densely Fused Spatial Transformer Network (DeSTNet), which, to our best knowledge, is the first dense fusion pattern for combining multiple STNs. Specifically, we show how changing the connectivity pattern of multiple STNs from sequential to dense leads to more powerful alignment modules. Extensive experiments on three benchmarks namely, MNIST, GTSRB, and IDocDB show that the proposed technique outperforms related state-of-the-art methods (i.e., STNs and CSTNs) both in terms of accuracy and robustness.


title: The Missing Link - A Probabilistic Model of Document Content and Hypertext Connectivity

abstract: We describe a joint probabilistic model for modeling the contents and inter-connectivity of document collections such as sets of web pages or research paper archives. The model is based on a probabilistic factor decomposition and allows identifying principal topics of the collection as well as authoritative documents within those topics. Furthermore, the relationships between topics is mapped out in order to build a predictive model of link content. Among the many applications of this approach are information retrieval and search, topic identification, query disambiguation, focused web crawling, web authoring, and bibliometric analysis.


title: Shopping and Word-of-Mouth Intentions on Social Media

abstract: Social Media has been gaining popularity worldwide over the last years at an increasingly growing rate. Motivated by this fact, firms are piloting different approaches of promoting their products and services to consumers in order to capitalize on the prominence of such websites. However, there is much debate in the academic and business community about the potential of social media as a platform for marketing and commerce, and the viable strategies that could constitute them as a possible solution for future ventures. Research to date has been growing, with only a limited number of studies exploring the business potential of social media. The aim of this research is to elucidate how specific aspects of social media websites foster user intention to browse products, and the effect that this has in shaping purchasing and information sharing intentions. Utilitarian and Hedonic motivation theory provides the theoretical background on which we segregate the factors that contribute to product browsing on social media websites. Partial Least Squares (PLS) analysis is performed on data obtained from 165 social media users to test our research model. Outcomes indicate that specific aspects trigger Utilitarian (Convenience and Product Selection) and Hedonic (Idea and Adventure) motivations which in sequence impact user intention to browse products on such mediums. Finally, browsing intention is linked in a significantly positive manner with purchasing and word-of-mouth intention.


title: Face Recognition in Hyperspectral Images

abstract: Hyperspectral cameras provide useful discriminants for human face recognition that cannot be obtained by other imaging methods. We examine the utility of using near-infrared hyperspectral images for the recognition of faces over a database of 200 subjects. The hyperspectral images were collected using a CCD camera equipped with a liquid crystal tunable filter to provide 31 bands over the near-infrared (0.7 /spl mu/m-1.0 /spl mu/m). Spectral measurements over the near-infrared allow the sensing of subsurface tissue structure which is significantly different from person to person, but relatively stable over time. The local spectral properties of human tissue are nearly invariant to face orientation and expression which allows hyperspectral discriminants to be used for recognition over a large range of poses and expressions. We describe a face recognition algorithm that exploits spectral measurements for multiple facial tissue types. We demonstrate experimentally that this algorithm can be used to recognize faces over time in the presence of changes in facial pose and expression.


title: Diacritics Recognition Based Urdu Nastalique OCR System

abstract: Improvements and new developments in the field of Artificial Intelligence have opened new horizons in the advancement of machines that originally have limited intelligence. As compared to human brain, machines have already better computational speed and storage however there is still much room to improve the capability to acquire and process data and draw conclusions from it on its own. Optical Character Recognition (OCR) deals exclusively with printed designs and hand written text in nature. Plenty of developments have been made in OCR so far in recognition of Latin, Asian, Arabic and Western texts. As far as Urdu is concerned the work is almost non-existent when compared with the languages cited above. One of its main reasons is the use of extremely complex characters of Nastalique style in Urdu. A methodology for the recognition and processing of the diacritics of Nastalique script is presented in this research work. The proposed technique is effective in recognizing cursive texts with invariant font size of 48. A dataset of 6728 main Urdu Nastalique ligatures is used for the testing purposes which shows that this new technique has the capacity to recognize Nastalique ligatures by having an accuracy of 97.40%. The proposed research work also focuses to improve the existing base mark association process of the Urdu OCR system.


title: A feature study for classification-based speech separation at very low signal-to-noise ratio

abstract: Speech separation is a challenging problem at low signal-to-noise ratios (SNRs). Separation can be formulated as a classification problem. In this study, we focus on the SNR level of -5 dB in which speech is generally dominated by background noise. In such a low SNR condition, extracting robust features from a noisy mixture is crucial for successful classification. Using a common neural network classifier, we systematically compare separation performance of many monaural features. In addition, we propose a new feature called Multi-Resolution Cochleagram (MRCG), which is extracted from four cochlea-grams of different resolutions to capture both local information and spectrotemporal context. Comparisons using two non-stationary noises show a range of feature robustness for speech separation with the proposed MRCG performing the best. We also find that ARMA filtering, a post-processing technique previously used for robust speech recognition, improves speech separation performance by smoothing the temporal trajectories of feature dimensions.


title: Computer Vision based Automatic Scoring of shooting targets

abstract: Computer vision based scoring system can break the monopoly of other automatic scoring systems like shell shockwave amplitude system due to its ease of implementation and cost effectiveness. This paper presents a computer vision based automatic scoring method for the shooting targets. We perform morphological processing of the target image to thicken the boundaries of the bullet hits and then segment the target area by hysteresis thresholding. The impact of illumination variations is handled by adjustable thresholds. The bulls eye of the target is segmented by the help of distance transform to calculate the score inside the bulls eye. Thus, our method is capable of scoring inside and outside the bulls eye separately. The bullet hits are labeled after the segmentation of the target area and the overlapping bullets are also scored by defining a threshold pixel area for the bullet hits. The proposed algorithm is tested on 100 target images with varying number of bullets hit, resulting in bullet count accuracy of 98.3%.


Big Data in Telco


title: Flame detection in video using hidden Markov models

abstract: This paper proposes a novel method to detect flames in video by processing the data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame flicker process is also detected by using a hidden Markov model. Markov models representing the flame and flame colored ordinary moving objects are used to distinguish flame flicker process from motion of flame colored moving objects. Spatial color variations in flame are also evaluated by the same Markov models, as well. These clues are combined to reach a final decision. False alarms due to ordinary motion of flame colored moving objects are greatly reduced when compared to the existing video based fire detection systems.


title: Human Capital Investment and Economic Growth in Nigeria

abstract: The role of human capital in the growth process of any nation is crucial. It is a major tool for poverty reduction. The main aim of this paper is to carry out an empirical investigation on the relationship between investment in education, health and economic growth in Nigeria, using time series data from 1982 to 2011. This paper employs trend analysis, the Johansen cointegration and ordinary least square technique. Empirical findings however indicate that there is a long-run relationship between government expenditure on education, health and economic growth. The variables: health and education expenditure, secondary and tertiary enrolment rate and gross fixed capital formation appear with the expected positive signs and are statistically significant (except government expenditure on education and primary enrolment rate). The findings of this work have strong implications on education and health policies and considering that they are of great debate in the country. Therefore, this study recommends that in order to accelerate growth and liberate Nigerians from the vicious cycle of poverty, the government should put in place policies geared towards massive investment in the education and health. Key words : Economic growth, Human Capital, Education, Health and Gross fixed capital formation.


title: A fully integrated 24-GHz phased-array transmitter in CMOS

abstract: This paper presents the first fully integrated 24-GHz phased-array transmitter designed using 0.18-/spl mu/m CMOS transistors. The four-element array includes four on-chip CMOS power amplifiers, with outputs matched to 50 /spl Omega/, that are each capable of generating up to 14.5 dBm of output power at 24 GHz. The heterodyne transmitter has a two-step quadrature up-conversion architecture with local oscillator (LO) frequencies of 4.8 and 19.2 GHz, which are generated by an on-chip frequency synthesizer. Four-bit LO path phase shifting is implemented in each element at 19.2 GHz, and the transmitter achieves a peak-to-null ratio of 23 dB with raw beam-steering resolution of 7/spl deg/ for radiation normal to the array. The transmitter can support data rates of 500 Mb/s on each channel (with BPSK modulation) and occupies 6.8 mm /spl times/ 2.1 mm of die area.


title: Measuring and Detecting Fast-Flux Service Networks

abstract: We present the first empirical study of fast-flux service networks (FFSNs), a newly emerging and still not widelyknown phenomenon in the Internet. FFSNs employ DNS to establish a proxy network on compromised machines through which illegal online services can be hosted with very high availability. Through our measurements we show that the threat which FFSNs pose is significant: FFSNs occur on a worldwide scale and already host a substantial percentage of online scams. Based on analysis of the principles of FFSNs, we develop a metric with which FFSNs can be effectively detected. Considering our detection technique we also discuss possible mitigation strategies.


title: A natural ZVS medium-power bidirectional DC-DC converter with minimum number of devices

abstract: This paper introduces a new bidirectional, isolated DC-DC converter. A typical application for this converter can be found in the auxiliary power supply of hybrid electric vehicles. A dual half-bridge topology has been developed to implement the required power rating using the minimum number of devices. Unified zero-voltage switching was achieved in either direction of power flow with neither a voltage-clamping circuit nor extra switching devices and resonant components. All these new features allow high power density, efficient power conversion, and compact packaging. Complete descriptions of operating principle and design guidelines are provided in this paper. An extended state-space averaged model is developed to predict large- and small-signal characteristics of the converter in either direction of power flow. A 1.6-kW prototype has been built and successfully tested under full power. The experimental results of the converter's steady-state operation confirm the soft-switching operation, simulation analysis, and the developed averaged model. The proposed converter is a good alternative to full-bridge isolated bidirectional DC-DC converter in medium-power applications.


title: Where and When to Pay Attention: The Neural Systems for Directing Attention to Spatial Locations and to Time Intervals as Revealed by Both PET and fMRI

abstract: Although attention is distributed across time as well as space, the temporal allocation of attention has been less well researched than its spatial counterpart. A temporal analog of the covert spatial orientation task [Posner MI, Snyder CRR, Davidson BJ (1980) Attention and the detection of signals. J Exp Psychol Gen 109:160–174] was developed to compare the neural systems involved in directing attention to spatial locations versus time intervals. We asked whether there exists a general system for allocating attentional resources, independent of stimulus dimension, or whether functionally specialized brain regions are recruited for directing attention toward spatial versus temporal aspects of the environment. We measured brain activity in seven healthy volunteers by using positron emission tomography (PET) and in eight healthy volunteers by using functional magnetic resonance imaging (fMRI). The task manipulated cued attention to spatial locations (S) and temporal intervals (T) in a factorial design. Symbolic central cues oriented subjects toward S only (left or right), toward T only (300 msec or 1500 msec), toward both S and T simultaneously, or provided no information regarding S or T. Subjects also were scanned during a resting baseline condition. Behavioral data showed benefits and costs for performance during temporal attention similar to those established for spatial attention. Brain-imaging data revealed a partial overlap between neural systems involved in the performance of spatial versus temporal orientation of attention tasks. Additionally, hemispheric asymmetries revealed preferential right and left parietal activation for spatial and temporal attention, respectively. Parietal cortex was activated bilaterally by attending to both dimensions simultaneously. This is the first direct comparison of the neural correlates of attending to spatial versus temporal cues.


title: Synthesis of surfactant-free SnS nanoplates in an aqueous solution

abstract: We report a synthetic route to produce surfactant-free SnS nanoplates with the Pbnm crystal structure. The process is quick and environmentally-friendly, accomplished under mild aqueous conditions. The synthesis involves two steps, formation of an intermediate tin oxide hydroxide (Sn6O4(OH)4) gel and its chemical transformation into SnS nanoplates in the presence of a sulfide precursor (Na2S). We found that addition of a small amount of PVP during the chemical transformation results in the formation of cubic SnS with the Cmcm crystal structure. We discuss the development of the crystal structures on the basis of density functional theory (DFT) calculations on the structure–energy relationship of SnS nanostructures. The optical properties of the SnS nanoplates and nanocubes are compared.


title: The Sudanese female face: normative craniofacial measurements and comparison with African-American and North American White females.

abstract: BACKGROUND ::: Ethnic differences in craniofacial dimensions and proportional relationships means that ethnicity-specific data is required for both diagnosis and treatment planning. This investigation establishes normative craniofacial anthropometric values for the Sudanese female (SF) population, compares with North American White (NAW) and African-American females (AA), and analyses the occurrence of neoclassical norms within the study sample. ::: ::: ::: METHODS ::: Standardized frontal and profile facial photographs were taken of 100 female participants. Five vertical and five horizontal anthropometric measurements were determined. ::: ::: ::: RESULTS ::: Significant differences were found between the 3 populations. The sample showed very poor compliance with the neoclassical norms. The upper face height of the SF was ∼ 20-mm taller than the AA faces (p < 0.001). Midface, nasal and lower face heights were also significantly taller, by 15-mm, 10-mm and 10-mm respectively (p < 0.001). SF showed significant reduction in bizygomatic width (∼ 30-mm), compared to NAW and AA (p < 0.001). Eye width was ∼ 5-mm less than the AA sample and 3-mm less than the NAW sample (p < 0.001). Mouth width was ∼ 5-mm less in SF than the other two groups (p < 0.001). The SF exhibited a reduced interalar width compared with AA (p < 0.001), but wider than the NAW (p < 0.001). ::: ::: ::: CONCLUSIONS ::: The average SF face is significantly taller and narrower than the AA or NAW female face. The neoclassical proportional norms appear to be unreliable guides in relation to the SF face, and should not be used in comparative diagnosis and treatment planning. Use of the normative anthropometric data presented in this article, both linear and proportional values, should be considered in the diagnosis and treatment planning of young adult female patients of Sudanese descent.


Active learning for deep semantic parsing


title: Computer aided inbetweening

abstract: The production of inbetweens is a tedious task for animators and a complicated one for algorithms. In this paper, an algorithm for computer aided inbetweening and its integration in a pen-based graphical user interface are presented.The algorithm is layer-based, assuming an invariant layering order. It is applicable to animations in a style similar to paper cut out, in which the drawings on the cut-out pieces are inbetweened as well. The content of each key drawing is analysed and classified into strokes, chains of strokes and relations that hold among them. Rules decide what parts of different drawings may be matched. These rules specify allowed changes between relations in key drawings. A cost function based approach determines the correct matching of strokes. Generated animation paths between corresponding strokes determine the resulting inbetweens.To hold for possible mismatchings and to allow for artistic control over the results, the inbetweening algorithm is embedded in a free form graphic user interface. Thus artists are enabled to focus on the part of the inbetweening task computers are not able to solve.


title: Inter-finger coordination and postural synergies in robot hands via mechanical implementation of principal components analysis

abstract: Human hands employ characteristic patterns of actuation, or synergies, that contain much of the information required to describe an entire hand shape. In some cases, 80% or more of the total information can be described with only two scalar component values. Robotic hands, however, commonly only couple intra-finger joints, and rarely take advantage of this inter-finger coordination. In this paper, real-world data on a variety of human hand postures was collected using a data glove, and principal components analysis was used to calculate these synergies, resulting in what we call eigenpostures. A novel mechanism design is presented to combine the eigenpostures and drive a 17-degree-of-freedom 5-fingered robot hand. The hand uses only 2 DC motors to accurately recreate a wide range of hand shapes. We also present a design improvement that allows us to distinguish between high-precision and low-precision tasks, as well as greatly reduce overall error.


title: Fabrication of origami wheel using pattern embedded fabric and its application to a deformable mobile robot

abstract: The unique characteristics of origami to realize 3-D shape from 2-D patterns have been fascinating many researchers and engineers. This paper presents a fabrication of origami patterned fabric wheels that can deform and change the radius of the wheels. PVC segments are enclosed in the fabrics to build a tough and foldable structure. A special cable driven mechanism was designed to allow the wheels to deform while rotating. A mobile robot with two origami wheels has been built and tested to show that it can deform its wheels to overcome various obstacles.


title: Wordbank: an open repository for developmental vocabulary data.

abstract: The MacArthur-Bates Communicative Development Inventories (CDIs) are a widely used family of parent-report instruments for easy and inexpensive data-gathering about early language acquisition. CDI data have been used to explore a variety of theoretically important topics, but, with few exceptions, researchers have had to rely on data collected in their own lab. In this paper, we remedy this issue by presenting Wordbank, a structured database of CDI data combined with a browsable web interface. Wordbank archives CDI data across languages and labs, providing a resource for researchers interested in early language, as well as a platform for novel analyses. The site allows interactive exploration of patterns of vocabulary growth at the level of both individual children and particular words. We also introduce wordbankr , a software package for connecting to the database directly. Together, these tools extend the abilities of students and researchers to explore quantitative trends in vocabulary development.


title: BFS and Coloring-Based Parallel Algorithms for Strongly Connected Components and Related Problems

abstract: Finding the strongly connected components (SCCs) of a directed graph is a fundamental graph-theoretic problem. Tarjan's algorithm is an efficient serial algorithm to find SCCs, but relies on the hard-to-parallelize depth-first search (DFS). We observe that implementations of several parallel SCC detection algorithms show poor parallel performance on modern multicore platforms and large-scale networks. This paper introduces the Multistep method, a new approach that avoids work inefficiencies seen in prior SCC approaches. It does not rely on DFS, but instead uses a combination of breadth-first search (BFS) and a parallel graph coloring routine. We show that the Multistep method scales well on several real-world graphs, with performance fairly independent of topological properties such as the size of the largest SCC and the total number of SCCs. On a 16-core Intel Xeon platform, our algorithm achieves a 20X speedup over the serial approach on a 2 billion edge graph, fully decomposing it in under two seconds. For our collection of test networks, we observe that the Multistep method is 1.92X faster (mean speedup) than the state-of-the-art Hong et al. SCC method. In addition, we modify the Multistep method to find connected and weakly connected components, as well as introduce a novel algorithm for determining articulation vertices of biconnected components. These approaches all utilize the same underlying BFS and coloring routines.


title: Recognition and identification of the clothes in the photo or video using neural networks

abstract: We introduce our solution of the problem of clothing recognition and identification. The cause of taking convolutional neural network as a mathematic model for the further research was the result of an experimental survey of methods of keypoints detection and computing their descriptors. The convolutional neural network model was developed and trained on the Apparel Classification with Style clothing image dataset. The training image dataset was expanded with the help of augmentation methods. The computational experiment aimed at determination of neural network efficiency and stability proved its productivity. Moreover, we developed clothing color recognition, which let us perform clothing identification by 2 parameters: type and color.


title: Thalamocortical dysrhythmia detected by machine learning

abstract: Thalamocortical dysrhythmia (TCD) is a model proposed to explain divergent neurological disorders. It is characterized by a common oscillatory pattern in which resting-state alpha activity is replaced by cross-frequency coupling of low- and high-frequency oscillations. We undertook a data-driven approach using support vector machine learning for analyzing resting-state electroencephalography oscillatory patterns in patients with Parkinson’s disease, neuropathic pain, tinnitus, and depression. We show a spectrally equivalent but spatially distinct form of TCD that depends on the specific disorder. However, we also identify brain areas that are common to the pathology of Parkinson’s disease, pain, tinnitus, and depression. This study therefore supports the validity of TCD as an oscillatory mechanism underlying diverse neurological disorders.


title: Assessing Landscape Functions with Broad-Scale Environmental Data: Insights Gained from a Prototype Development for Europe

abstract: We examine the advantages and disadvantages of a methodological framework designed to analyze the poorly understood relationships between the ecosystem properties of large portions of land, and their capacities (stocks) to provide goods and services (flows). These capacities (stocks) are referred to as landscape functions. The core of our assessment is a set of expert- and literature-driven binary links, expressing whether specific land uses or other environmental properties have a supportive or neutral role for given landscape functions. The binary links were applied to the environmental properties of 581 administrative units of Europe with widely differing environmental conditions and this resulted in a spatially explicit landscape function assessment. To check under what circumstances the binary links are able to replace complex interrelations, we compared the landscape function maps with independently generated continent-wide assessments (maps of ecosystem services or environmental parameters/indicators). This rigorous testing revealed that for 9 out of 15 functions the straightforward binary links work satisfactorily and generate plausible geographical patterns. This conclusion holds primarily for production functions. The sensitivity of the nine landscape functions to changes in land use was assessed with four land use scenarios (IPCC SRES). It was found that most European regions maintain their capacity to provide the selected services under any of the four scenarios, although in some cases at other locations within the region. At the proposed continental scale, the selected input parameters are thus valid proxies which can be used to assess the mid-term potential of landscapes to provide goods and services.


title: Embedding Words and Senses Together via Joint Knowledge-Enhanced Training

abstract: Word embeddings are widely used in Natural ::: Language Processing, mainly due to ::: their success in capturing semantic information ::: from massive corpora. However, ::: their creation process does not allow the ::: different meanings of a word to be automatically ::: separated, as it conflates them ::: into a single vector. We address this issue ::: by proposing a new model which learns ::: word and sense embeddings jointly. Our ::: model exploits large corpora and knowledge ::: from semantic networks in order to ::: produce a unified vector space of word ::: and sense embeddings. We evaluate the ::: main features of our approach both qualitatively ::: and quantitatively in a variety of ::: tasks, highlighting the advantages of the ::: proposed method in comparison to stateof- ::: the-art word- and sense-based models.


title: A Q-Learning strategy for LTE mobility Load Balancing

abstract: Cellular radio networks are seldom uniformly loaded. This motivates the need for Load Balancing (LB), as has been defined in the LTE Self-Organization standard. It is expected that on overload, a serving cell (S-cell) initiates LB to transfer some of its edge users to its neighbor cells so called target cells, by adjusting the Cell Individual Offset (CIO) parameter. In this work, we have proposed a reactive LB algorithm that adjusts the CIOs between the S-cell and all its neighbors by a fixed step φ. Our results show that the best φ depends on the load conditions in both the S-cell and its neighbors as well as on the S-cell's user distribution. We then propose a Q-Learning (QL) algorithm that learns the best φ values to apply for different load conditions and demonstrate that the QL based algorithm performs better than the best fixed φ algorithm in virtually all scenarios.


title: An Efficient and Scalable Approach to CNN Queries in a Road Network

abstract: A continuous search in a road network retrieves the objects which satisfy a query condition at any point on a path. For example, return the three nearest restaurants from all locations on my route from point s to point e. In this paper, we deal with NN queries as well as continuous NN queries in the context of moving objects databases. The performance of existing approaches based on the network distance such as the shortest path length depends largely on the density of objects of interest. To overcome this problem, we propose UNICONS (a uni que con tinuous s earch algorithm) for NN queries and CNN queries performed on a network. We incorporate the use of precomputed NN lists into Dijkstra's algorithm for NN queries. A mathematical rationale is employed to produce the final results of CNN queries. Experimental results for real-life datasets of various sizes show that UNICONS outperforms its competitors by up to 3.5 times for NN queries and 5 times for CNN queries depending on the density of objects and the number of NNs required.


title: An encryption based on DNA cryptography and steganography

abstract: The data security is a challenging issue nowadays with the increase of information capacity and its transmission rate. The most common and widely used techniques in the data security fields are cryptography and steganography. The combination of cryptography and steganography methods provides more security to the data. Now, DNA (Deoxyribonucleic Acid) is explored as a new carrier for data security since it achieves maximum protection and powerful security with high capacity and low modification rate. A new data security method can be developed by taking the advantages of DNA based AES (Advanced Encryption Standard) cryptography and DNA steganography. This new technique will provide multilayer security to the secret message. Here the secret message is first encoded to DNA bases then DNA based AES algorithm is applied to it. Finally the encrypted DNA will be concealed in another DNA sequence. This hybrid technique provides triple layer security to the secret message.


title: Multi-scale salient object detection with pyramid spatial pooling

abstract: Salient object detection is a challenging task in complex compositions depicting multiple objects of different scales. Albeit the recent progress thanks to the convolutional neural networks, the state-of-the-art salient object detection methods still fall short to handle such real-life scenarios. In this paper, we propose a new method called MP-SOD that exploits both Multi-Scale feature fusion and Pyramid spatial pooling to detect salient object regions in varying sizes. Our framework consists of a front-end network and two multi-scale fusion modules. The front-end network learns an end-to-end mapping from the input image to a saliency map, where a pyramid spatial pooling is incorporated to aggregate rich context information from different spatial receptive fields. The multi-scale fusion module integrates saliency cues across different layers, that is from low-level detail patterns to high-level semantic information by concatenating feature maps, to segment out salient objects with multiple scales. Extensive experimental results on eight benchmark datasets demonstrate the superior performance of our method compared with existing methods.


title: Simple Realizations of Current-Mode and Voltage-Mode PID, PI and PD Controllers

abstract: This paper presents new circuits for realizing both current-mode and voltage-mode proportional-integral- derivative (PID), proportional-derivative (PD) and proportional-integral (PI) controllers employing second- generation current conveyors (CCIIs) as active elements. All of the proposed PID, PI and PD controllers have grounded passive elements and adjustable parameters. The controllers employ reduced number of active and passive components with respect to the traditional op-amp-based PID, PI and PD controllers. A closed loop control system using the proposed PID controller is designed and simulated with SPICE. I. INTRODUCTION The proportional-integral-derivative (PID), proportional- integral (PI) and proportional-derivative (PD) controllers with adjustable parameters are widely used in many industrial control systems, and usually have been implemented with operational amplifiers (op-amps). PID controller realizations using op-amps employ excessive number of active and passive components. Current conveyors, especially plus-type second-generation current conveyors (CCII+s) have larger bandwidth and dynamic range, and also greater linearity than op-amps (1). Employing only CCII+s simplifies the configuration thus gyrator (2), synthetic floating inductances (3-4), voltage- mode filter (5) and transfer function synthesis (6-7) employing only CCII+s have been reported in the literature. Despite of several circuits such as derivative, integral, proportion and summer constructed with op-amps (8-9) that can be used to implement PID, PI and PD controllers, current conveyors have not been used to realize PID, PD and PI controllers in the literature so far. In this paper, novel current-mode and voltage-mode PID, PI and PD controllers employing CCII+s as active elements are proposed. In the proposed current-mode controllers, output currents are taken from high impedance terminals of the CCII+s. Also input voltages are applied to the Y-terminals of the CCII+s in the presented voltage- mode PID, PI and PD controllers. These properties make the proposed circuit ideal for using in cascaded systems. All the proposed controller realizations employ only grounded passive components, and do not require passive element matching so the circuits are suitable for integration. The PID, PI and PD parameters can be selected independently. Also simulations are performed using SPICE program to verify the theoretical analyzes.


title: Factors associated with late fetal mortality

abstract: Perinatal mortality has been decreasing in Europe thanks to a reduction in neonatal mortality. The causes of fetal mortality remain poorly studied. The objective was to determine the late fetal mortality rate in Spain in 2015 and the associated factors. A cross-sectional study was performed using data regarding births in 2015 in Spain extracted from the National Institute of Statistics. Single births at 28 or more weeks of pregnancy were included. The sample comprised 340,371 births. Sociodemographic, obstetrical and neonatal variables were analyzed using univariate and multivariate logistic regression (MLR), with the fetal mortality from 28 weeks of pregnancy as the dependent variable. The total number of late fetal deaths was 884 (2.6 × 1000). The MLR model showed that the following factors were associated with late fetal mortality: birth before 37 weeks of pregnancy (OR 13.1); weight of the newborn < 2500 g (OR 3.22) and ≥ 4000 g (OR 3.36); low training level (OR 2.28); and others, such as African origin, maternal age ≥ 35 years, primiparity and mothers who were single. The rate of late fetal mortality in Spain has not decreased and has remained at the same level as in 2010. This result is related to prematurity, low birth weight, macrosomia and sociodemographic factors, such as low maternal preparation, mothers of African origin, age ≥ 35 years and mothers who are single. It is necessary to improve the quality and accessibility of prenatal care and the early detection of risk factors.


title: HUmanoid Robotic Leg via pneumatic muscle actuators : implementation and control

abstract: In this article, a HUmanoid Robotic Leg (HURL) via the utilization of pneumatic muscle actuators (PMAs) is presented. PMAs are a pneumatic form of actuation possessing crucial attributes for the im ...


title: Canonical Correlation Inference for Mapping Abstract Scenes to Text

abstract: We describe a technique for structured prediction, based on canonical correlation analysis. Our learning algorithm finds two projections for the input and the output spaces that aim at projecting a given input and its correct output into points close to each other. We demonstrate our technique on a language-vision problem, namely the problem of giving a textual description to an "abstract scene".


title: Identifying licensing of jar archives using a code-search approach

abstract: Free and open source software strongly promotes the reuse of source code. Some open source Java components/libraries are distributed as jar archives only containing the bytecode and some additional information. For whoever wanting to integrate this jar in her own project, it is important to determine the license(s) of the code from which the jar archive was produced, as this affects the way that such component can be used.


title: A new stereo matching method for RAW image data based on improved SGBM

abstract: Traditionally, stereo vision algorithms are performed after color image processing pipeline, which includes demosaicing, color correction, white balance, etc. Color image processing pipeline possibly causes a loss of key information and introduces artifacts into final output image, which may lead stereo matching accuracy decreased. Hence, we implemented stereo matching algorithm on RAW data before color image processing pipeline to improve binocular stereo vision system matching accuracy. We proposed using RAW data in stereo matching enhance the robustness and accuracy of binocular stereo vision algorithm. Our approach focus on the first stage of many stereo algorithms: stereo matching. We approach the problem by using improved SGBM (Semi-Global Block Matching) algorithm. The proposed algorithm is tested on RAW image pairs captured by stereo camera system, and the experiments indicates that the algorithm is effective.


title: Rotational invariance in the three-dimensional lattice Boltzmann method is dependent on the choice of lattice

abstract: The lattice Boltzmann method has recently gained popularity as a tool for simulating complex fluid flows. It uses discrete sets of velocity vectors, or lattices, to create a reduced model of the molecular dynamics of a continuum fluid. While several lattices are believed to behave isotropically, there are reports of qualitatively incorrect results. However, thus far, the reason as to why a lack of isotropy occurs is not known. Based on the hypothesis that lower order lattices may not display rotational invariance, this study tests the isotropy of the D3Q15, D3Q19 and D3Q27 lattices by performing simulations at intermediate Reynolds numbers (50-500) and low Knudsen number ( =250 the D3Q15 and D3Q19 lattices produced different results depending on the plane of the lattice with which the flow was aligned. Lattice planes with fewer than six velocity vectors consistently produced results which were qualitatively different from the planes with six or more velocity vectors. These errors were not observed at Re = 50 or when a D3Q27 lattice was used. They appeared to be independent of grid density, collision operator and Ma. This suggests that the lattices which contain these planes are not fully isotropic and therefore do not properly replicate the behavior of a real fluid in this particular situation, notably downstream from the expansion. Predictions made using these models in more complex geometries may therefore be affected by the orientation of the lattice. When using LBM in CFD simulation (including validation) this study highlights the need for caution to ensure that the solution obtained is independent of the lattice orientation throughout the domain.


title: Linear feature-based models for information retrieval

abstract: There have been a number of linear, feature-based models proposed by the information retrieval community recently. Although each model is presented differently, they all share a common underlying framework. In this paper, we explore and discuss the theoretical issues of this framework, including a novel look at the parameter space. We then detail supervised training algorithms that directly maximize the evaluation metric under consideration, such as mean average precision. We present results that show training models in this way can lead to significantly better test set performance compared to other training methods that do not directly maximize the metric. Finally, we show that linear feature-based models can consistently and significantly outperform current state of the art retrieval models with the correct choice of features.


title: High-order Graph-based Neural Dependency Parsing

abstract: In this work, we present a novel way of using neural network for graph-based dependency parsing, which fits the neural network into a simple probabilistic model and can be furthermore generalized to high-order parsing. Instead of the sparse features used in traditional methods, we utilize distributed dense feature representations for neural network, which give better feature representations. The proposed parsers are evaluated on English and Chinese Penn Treebanks. Compared to existing work, our parsers give competitive performance with much more efficient inference.


title: A Novel Method for Unsupervised Anomaly Detection Using Unlabelled Data

abstract: Most current intrusion detection methods cannot process large amounts of audit data for real-time operation. In this paper, anomaly network intrusion detection method based on principal component analysis (PCA) for data reduction and fuzzy adaptive resonance theory (fuzzy ART) for classifier is presented. Moreover, PCA is applied to reduce the high dimensional data vectors and distance between a vector and its projection onto the subspace reduced is used for anomaly detection. Using a set of benchmark data from KDD (knowledge discovery and data mining) competition designed by DARPA for demonstrate to detection intrusions. Experimental results show the proposed model can classify the network connections with satisfying performance.


Human Behavior and the Principle of Least Effort: An Introduction to Human Ecology


title: BioSpec: A Biophysically-Based Spectral Model of Light Interaction with Human Skin

abstract: Despite the notable progress in physically-based rendering, there is still a long way to go before we can automatically generate predictable images of biological materials. In this paper, we address an open problem in this area, namely the spectral simulation of light interaction with human skin. We propose a novel biophysicallybased model that accounts for all components of light propagation in skin tissues, namely surface reflectance, subsurface reflectance and transmittance, and the biological mechanisms of light absorption by pigments in these tissues. The model is controlled by biologically meaningful parameters, and its formulation, based on standard Monte Carlo techniques, enables its straightforward incorporation into realistic image synthesis frameworks. Besides its biophysically-based nature, the key difference between the proposed model and the existing skin models is its comprehensiveness, i.e., it computes both spectral (reflectance and transmittance) and scattering (bidirectional surface-scattering distribution function) quantities for skin specimens. In order to assess the predictability of our simulations, we evaluate their accuracy by comparing results from the model with actual skin measured data. We also present computer generated images to illustrate the flexibility of the proposed model with respect to variations in the biological input data, and its applicability not only in the predictive image synthesis of different skin tones, but also in the spectral simulation of medical conditions.


title: Learning ontologies from natural language texts

abstract: Research on ontology is becoming increasingly widespread in the computer science community. The major problems in building ontologies are the bottleneck of knowledge acquisition and time-consuming construction of various ontologies for various domains/ applications. Meanwhile moving toward automation of ontology construction is a solution.We proposed an automatic ontology building approach. In this approach, the system starts from a small ontology kernel and constructs the ontology through text understanding automatically. The kernel contains the primitive concepts, relations and operators to build an ontology. The features of our proposed model are being domain/application independent, building ontologies upon a small primary kernel, learning words, concepts, taxonomic and non-taxonomic relations and axioms and applying a symbolic, hybrid ontology learning approach consisting of logical, linguistic based, template driven and semantic analysis methods.Hasti is an ongoing project to implement and test the automatic ontology building approach. It extracts lexical and ontological knowledge from Persian (Farsi) texts.In this paper, at first, we will describe some ontology engineering problems, which motivated our approach. In the next sections, after a brief description of Hasti, its features and its architecture, we will discuss its components in detail. In each part, the learning algorithms will be described. Then some experimental results will be discussed and at last, we will have an overview of related works and will introduce a general framework to compare ontology learning systems and will compare Hasti with related works according to the framework.


title: RATT: Rapid Annotation Transfer Tool

abstract: Second-generation sequencing technologies have made large-scale sequencing projects commonplace. However, making use of these datasets often requires gene function to be ascribed genome wide. Although tool development has kept pace with the changes in sequence production, for tasks such as mapping, de novo assembly or visualization, genome annotation remains a challenge. We have developed a method to rapidly provide accurate annotation for new genomes using previously annotated genomes as a reference. The method, implemented in a tool called RATT (Rapid Annotation Transfer Tool), transfers annotations from a high-quality reference to a new genome on the basis of conserved synteny. We demonstrate that a Mycobacterium tuberculosis genome or a single 2.5 Mb chromosome from a malaria parasite can be annotated in less than five minutes with only modest computational resources. RATT is available at http://ratt.sourceforge.net.


title: Dynamic and Kinematic Models and Control for Differential Drive Mobile Robots

abstract: The two-wheel differential drive mobile robots, are one of the simplest and most used structures in mobile robotics applications, it consists of a chassis with two fixed and in-line with each other electric motors. This paper presents new models for differential drive mobile robots and some considerations regarding design, modeling and control solutions. The presented models are to be used to help in facing the two top challenges in developing mechatronic mobile robots system; early identifying system level problems and ensuring that all design requirements are met, as well as, to simplify and accelerate Mechatronics mobile robots design process, including proper selection, analysis, integration and verification of the overall system and sub-systems performance throughout the development process.


title: Enhancing recurrent neural network-based language models by word tokenization

abstract: Different approaches have been used to estimate language models from a given corpus. Recently, researchers have used different neural network architectures to estimate the language models from a given corpus using unsupervised learning neural networks capabilities. Generally, neural networks have demonstrated success compared to conventional n-gram language models. With languages that have a rich morphological system and a huge number of vocabulary words, the major trade-off with neural network language models is the size of the network. This paper presents a recurrent neural network language model based on the tokenization of words into three parts: the prefix, the stem, and the suffix. The proposed model is tested with the English AMI speech recognition dataset and outperforms the baseline n-gram model, the basic recurrent neural network language models (RNNLM) and the GPU-based recurrent neural network language models (CUED-RNNLM) in perplexity and word error rate. The automatic spelling correction accuracy was enhanced by approximately 3.5% for Arabic language misspelling mistakes dataset.


title: Automatic Arabic text summarization: a survey

abstract: This survey investigates several research studies that have been conducted in the field of Arabic text summarization. Specifically, it addresses summarization and evaluation methods, as well as the corpora used in those studies. The literature in this field is fairly limited and relatively new compared to the available literature on other languages, such as English. Therefore, there exists a great opportunity for further research in Arabic text summarization. In addition, one of the largest problems in Arabic summarization was the absence of Arabic gold standard summaries, although this situation is beginning to change, especially with the inclusion of Arabic language as a part of the corpora and tasks in the TAC 2011 MultiLing Pilot and ACL 2013 MultiLing Workshop. Finally, providing the required corpora and adopting them in Arabic summarization studies is an essential demand.


title: Pegasos: primal estimated sub-gradient solver for SVM

abstract: We describe and analyze a simple and effective stochastic sub-gradient descent algorithm for solving the optimization problem cast by Support Vector Machines (SVM). We prove that the number of iterations required to obtain a solution of accuracy $${\epsilon}$$ is $${\tilde{O}(1 / \epsilon)}$$, where each iteration operates on a single training example. In contrast, previous analyses of stochastic gradient descent methods for SVMs require $${\Omega(1 / \epsilon^2)}$$ iterations. As in previously devised SVM solvers, the number of iterations also scales linearly with 1/λ, where λ is the regularization parameter of SVM. For a linear kernel, the total run-time of our method is $${\tilde{O}(d/(\lambda \epsilon))}$$, where d is a bound on the number of non-zero features in each example. Since the run-time does not depend directly on the size of the training set, the resulting algorithm is especially suited for learning from large datasets. Our approach also extends to non-linear kernels while working solely on the primal objective function, though in this case the runtime does depend linearly on the training set size. Our algorithm is particularly well suited for large text classification problems, where we demonstrate an order-of-magnitude speedup over previous SVM learning methods.


title: Why We Watch the News: A Dataset for Exploring Sentiment in Broadcast Video News

abstract: We present a multimodal sentiment study performed on a novel collection of videos mined from broadcast and cable television news programs. To the best of our knowledge, this is the first dataset released for studying sentiment in the domain of broadcast video news. We describe our algorithm for the processing and creation of person-specific segments from news video, yielding 929 sentence-length videos, and are annotated via Amazon Mechanical Turk. The spoken transcript and the video content itself are each annotated for their expression of positive, negative or neutral sentiment. Based on these gathered user annotations, we demonstrate for news video the importance of taking into account multimodal information for sentiment prediction, and in particular, challenging previous text-based approaches that rely solely on available transcripts. We show that as much as 21.54% of the sentiment annotations for transcripts differ from their respective sentiment annotations when the video clip itself is presented. We present audio and visual classification baselines over a three-way sentiment prediction of positive, negative and neutral, as well as person-dependent versus person-independent classification influence on performance. Finally, we release the News Rover Sentiment dataset to the greater research community.


title: Recent advances on multicue object tracking: a survey

abstract: The performance of single cue object tracking algorithms may degrade due to complex nature of visual world and environment challenges. In recent past, multicue object tracking methods using single or multiple sensors such as vision, thermal, infrared, laser, radar, audio, and RFID are explored to a great extent. It was acknowledged that combining multiple orthogonal cues enhance tracking performance over single cue methods. The aim of this paper is to categorize multicue tracking methods into single-modal and multi-modal and to list out new trends in this field via investigation of representative work. The categorized works are also tabulated in order to give detailed overview of latest advancement. The person tracking datasets are analyzed and their statistical parameters are tabulated. The tracking performance measures are also categorized depending upon availability of ground truth data. Our review gauges the gap between reported work and future demands for object tracking.


Learning Stable Linear Dynamical Systems Learning Stable Linear Dynamical Systems


title: FlexiGAN: An End-to-End Solution for FPGA Acceleration of Generative Adversarial Networks

abstract: Generative Adversarial Networks (GANs) are among the frontiers of deep networks. GANs consist of two models, a generative model and a discriminative model. While the discriminative model uses the conventional convolution operator, the generative model is fundamentally different per its use of the transposed convolution operator. Unlike the conventional convolution, the transposed convolution initially inserts a large number of zeros in its input. This zero-insertion leads to a large number of inconsequential operations and creates different patterns of computation across the sliding windows. The inconsequential operations along with the variation in computation patterns lead to signicant resource underutilization when evaluated using conventional convolution hardware. This paper introduces FlexiGAN, an end-to-end solution, from high-level GAN specication to an optimized synthesizable FPGA accelerator. FlexiGAN framework is coupled with a novel architecture that aims to harness the benets of both MIMD and SIMD execution models. The proposed architecture separated data retrieval and data processing units at the nest granularity of each compute engine. Leveraging the separation between data retrieval and data processing units in the compute engines, we introduce a succinct set of operations that enable us to signicantly reduce the on-chip memory usage, which is generally scarce in FPGAs. We evaluate our end-to-end solution across various GANs from machine learning literature. FlexiGAN provides 2.4 higher performance than an optimized conventional convolution design. In addition, FlexiGAN, on average, yields 2.8 (up to 3.7) improvements in Performance-per-Watt over a high-end GPU. These results indicate that FlexiGAN is an effective initial step towards providing an end-to-end solution for accelerating GANs


title: BP neural network control of single inverted pendulum

abstract: The BP neural network can combine with Levenberg-Marquardt algorithm, which effectively overcomes the disadvantages of traditional neural network such as slow convergence speed, always converging to local minimum point and poor stability. Neural network capture the data of inverted pendulum with LQR optimal control method, set up multilayer forward feedback neural network. We through matlab simulate the control system to get the each state variable and control response curve of Single inverted pendulum. Experimental results show that the improved BP neural network can successfully control the inverted pendulum and have faster speed of control response and good stability than LQR optimal control.


title: Delving into Transferable Adversarial Examples and Black-box Attacks

abstract: An intriguing property of deep neural networks is the existence of adversarial examples, which can transfer among different architectures. These transferable adversarial examples may severely hinder deep neural network-based applications. Previous works mostly study the transferability using small scale datasets. In this work, we are the first to conduct an extensive study of the transferability over large models and a large scale dataset, and we are also the first to study the transferability of targeted adversarial examples with their target labels. We study both non-targeted and targeted adversarial examples, and show that while transferable non-targeted adversarial examples are easy to find, targeted adversarial examples generated using existing approaches almost never transfer with their target labels. Therefore, we propose novel ensemble-based approaches to generating transferable adversarial examples. Using such approaches, we observe a large proportion of targeted adversarial examples that are able to transfer with their target labels for the first time. We also present some geometric studies to help understanding the transferable adversarial examples. Finally, we show that the adversarial examples generated using ensemble-based approaches can successfully attack Clarifai.com, which is a black-box image classification system.


title: Effectual recommendations using artificial algae algorithm and fuzzy c-mean

abstract: Abstract Recommender systems play a significant role in e-commerce applications. The primary motive of a recommender system is to recommend some items or products to the users based on their previous ratings of other products in the online environment. In this article, we presented a hybrid collaborative filtering based recommender system that improved the accuracy of the recommendations. In our work, we adopted fuzzy c-mean (FCM) and a recent bio-inspired approach, which is artificial algae algorithm (AAA). We have used advanced multilevel Pearson correlation coefficient (PCC) to find the similarity between two users. Moreover, we discovered the rating which the user will most likely give to the movies which he has not given any ratings yet. By applying above-mentioned procedures, the quality of the recommendations is improved significantly. The proposed system succeeded to provide recommendations of better quality and accuracy when compared to other alternatives. We have experimented and evaluated our proposed recommender system on four real data sets: Movielens 100,000, Movielens 1 million, Jester and Epinion. We concluded that our proposed recommender system delivered better recommendations for all four datasets. The efficiency of the system was estimated by evaluation metrics such as mean absolute error (MAE), precision and recall and showed impressive results. This proposed system delivered best results as compared to our previous work (Katarya and Verma, 2016) [1].


title: What Enterprise Architecture Can Bring for Digital Transformation: An Exploratory Study

abstract: Nowadays, CEOs from most of the industries investigate digitalization opportunities. Customer preferences and behavior are driving enterprise technology choices, and internal organizational change is essential to maintain focus on the customer in today's digital world. To the enterprise architect, such initiatives sound like a perfect application of Enterprise Architecture (EA). Despite the ongoing research in academia, the benefits and the role of EA management in digital context are still a topic of lively discussions, and there is a gap in research on how to leverage EA for digital transformation. This paper explores the concepts of digital transformation and EA based on selected publications drawn from the literature and discusses the potential role of EA in supporting digital transformation. We also sketch an approach based on the TOGAF framework to position EA as a problem solving tool for digital transformation initiatives.


title: Neem and other Botanical insecticides: Barriers to commercialization

abstract: In spite of the wide recognition that many plants possess insecticidal properties, only a handful of pest control products directly obtained from plants, i.e., botanical insecticides, are in use in developed countries. The demonstrated efficacy of the botanical neem (based on seed kernel extracts of Azadirachta indica), and its recent approval for use in the United States, has stimulated research and development of other botanical insecticides. However, the commercialization of new botanical insecticides can be hindered by a number of issues. The principal barriers to commercialization of new botanicals are (i) scarcity of the natural resource; (ii) standardization and quality control; and (iii) registration. These issues are no problem (i) or considerably less of a problem (ii, iii) with conventional insecticides. In this review I discuss these issues and suggest how the problems may be overcome in the future.


title: Mitosis Detection for Invasive Breast Cancer Grading in Histopathological Images

abstract: Histopathological grading of cancer not only offers an insight to the patients’ prognosis but also helps in making individual treatment plans. Mitosis counts in histopathological slides play a crucial role for invasive breast cancer grading using the Nottingham grading system. Pathologists perform this grading by manual examinations of a few thousand images for each patient. Hence, finding the mitotic figures from these images is a tedious job and also prone to observer variability due to variations in the appearances of the mitotic cells. We propose a fast and accurate approach for automatic mitosis detection from histopathological images. We employ area morphological scale space for cell segmentation. The scale space is constructed in a novel manner by restricting the scales with the maximization of relative-entropy between the cells and the background. This results in precise cell segmentation. The segmented cells are classified in mitotic and non-mitotic category using the random forest classifier. Experiments show at least 12% improvement in $F_{1}$ score on more than 450 histopathological images at $40\times $ magnification.


title: Adaptive Multi-Task Lasso: with application to eQTL detection

abstract: To understand the relationship between genomic variations among population and complex diseases, it is essential to detect eQTLs which are associated with phenotypic effects. However, detecting eQTLs remains a challenge due to complex underlying mechanisms and the very large number of genetic loci involved compared to the number of samples. Thus, to address the problem, it is desirable to take advantage of the structure of the data and prior information about genomic locations such as conservation scores and transcription factor binding sites. ::: ::: In this paper, we propose a novel regularized regression approach for detecting eQTLs which takes into account related traits simultaneously while incorporating many regulatory features. We first present a Bayesian network for a multi-task learning problem that includes priors on SNPs, making it possible to estimate the significance of each covariate adaptively. Then we find the maximum a posteriori (MAP) estimation of regression coefficients and estimate weights of covariates jointly. This optimization procedure is efficient since it can be achieved by using a projected gradient descent and a coordinate descent procedure iteratively. Experimental results on simulated and real yeast datasets confirm that our model outperforms previous methods for finding eQTLs.


title: DDM - A Cache-Only Memory Architecture

abstract: The Data Diffusion Machine (DDM), a cache-only memory architecture (COMA) that relies on a hierarchical network structure, is described. The key ideas behind DDM are introduced by describing a small machine, which could be a COMA on its own or a subsystem of a larger COMA, and its protocol. A large machine with hundreds of processors is also described. The DDM prototype project is discussed, and simulated performance results are presented. >


title: Cross-Modal Similarity Learning: A Low Rank Bilinear Formulation

abstract: The cross-media retrieval problem has received much attention in recent years due to the rapidly increasing multimedia data on the Internet. A new approach to the problem was raised which intends to match the different modal features directly. Thus, how to get rid of the heterogeneity between the different modalities and match the different modal features with different dimensions become the key points in this research. On the other hand, the metric learning shows great power to learn a distance metric to explore the relationship between data points. However, the traditional metric learning algorithms are only focusing on one modality features, which suffers difficulties to handle the heterogeneous features with different dimensions. Thus, in this paper, we proposed a heterogeneous similarity learning algorithm based on the metric learning for the cross-modal feature matching. With the nuclear penalization, an accelerated proximal gradient algorithm is successfully imported to find the optimal solution with the fast convergence rate of O(1/t^2). We applied it to the image-text cross-media retrieval problem, and compared it with several popular and the state-of-the-art algorithms. Experiments on two well known databases show that the proposed method achieves the best performance compared to the state-of-the-art algorithms.


title: Dropout distillation

abstract: Dropout is a popular stochastic regularization technique for deep neural networks that works by randomly dropping (i.e. zeroing) units from the network during training. This randomization process allows to implicitly train an ensemble of exponentially many networks sharing the same parametrization, which should be averaged at test time to deliver the final prediction. A typical workaround for this intractable averaging operation consists in scaling the layers undergoing dropout randomization. This simple rule called "standard dropout" is efficient, but might degrade the accuracy of the prediction. In this work we introduce a novel approach, coined "dropout distillation", that allows us to train a predictor in a way to better approximate the intractable, but preferable, averaging process, while keeping under control its computational efficiency. We are thus able to construct models that are as efficient as standard dropout, or even more efficient, while being more accurate. Experiments on standard benchmark datasets demonstrate the validity of our method, yielding consistent improvements over conventional dropout.


DeSARM: A Decentralized Mechanism for Discovering Software Architecture Models at Runtime in Distributed Systems


title: Equipment Location in Hospitals Using RFID-Based Positioning System

abstract: Throughout various complex processes within hospitals, context-aware services and applications can help to improve the quality of care and reduce costs. For example, sensors and radio frequency identification (RFID) technologies for e-health have been deployed to improve the flow of material, equipment, personal, and patient. Bed tracking, patient monitoring, real-time logistic analysis, and critical equipment tracking are famous applications of real-time location systems (RTLS) in hospitals. In fact, existing case studies show that RTLS can improve service quality and safety, and optimize emergency management and time critical processes. In this paper, we propose a robust system for position and orientation determination of equipment. Our system utilizes passive (RFID) technology mounted on flooring plates and several peripherals for sensor data interpretation. The system is implemented and tested through extensive experiments. The results show that our system's average positioning and orientation measurement outperforms existing systems in terms of accuracy. The details of the system as well as the experimental results are presented in this paper.


title: Mobile robot positioning: Sensors and techniques

abstract: Abstract : Exact knowledge of the position of a vehicle is a fundamental problem in mobile robot applications. In the search for a solution, researchers and engineers have developed a variety of systems, sensors, and techniques for mobile robot positioning. This paper provides a review of relevant mobile robot positioning technologies. The paper defines seven categories for positioning systems: (1) Odometry, (2) Inertial Navigation, (3) Magnetic Compasses, (4) Active Beacons, (5) Global Positioning Systems, (6) Landmark Navigation, and (7) Model Matching. The characteristics of each category are discussed and examples of existing technologies are given for each category. The field of mobile robot navigation is active and vibrant, with more great systems and ideas being developed continuously. For this reason the examples presented in this paper serve only to represent their respective categories; they do not represent a judgment by the authors. Many ingenious approaches can be found in the literature, although, for reasons of brevity, not all could be cited in this paper. The appendix contains a tabular comparison of the positioning systems discussed in this review that includes system and description, features, accuracy (position), accuracy (orientation), effective range, and source of information. (47 refs.)


title: Corporate Social Responsibility: Strategic Implications

abstract: We describe a variety of perspectives on corporate social responsibility (CSR), which we use to develop a framework for consideration of the strategic implications of CSR. Based on this framework, we propose an agenda for additional theoretical and empirical research on CSR. We then review the papers in this special issue and relate them to the proposed agenda.


title: Continuous time mean variance asset allocation: a time consistent strategy. Working

abstract: We develop a numerical scheme for determining the optimal asset allocation strategy for time-consistent, continuous time, mean variance optimization. Any type of constraint can be applied to the investment policy. The optimal policies for time-consistent and pre-commitment strategies are compared. When realistic constraints are applied, the efficient frontiers for the pre-commitment and time-consistent strategies are similar, but the optimal investment strategies are quite different.


title: Computational design of actuated deformable characters

abstract: We present a method for fabrication-oriented design of actuated deformable characters that allows a user to automatically create physical replicas of digitally designed characters using rapid manufacturing technologies. Given a deformable character and a set of target poses as input, our method computes a small set of actuators along with their locations on the surface and optimizes the internal material distribution such that the resulting character exhibits the desired deformation behavior. We approach this problem with a dedicated algorithm that combines finite-element analysis, sparse regularization, and constrained optimization. We validate our pipeline on a set of two- and three-dimensional example characters and present results in simulation and physically-fabricated prototypes.


title: Co-creation of value in a platform ecosystem: The case of enterprise software

abstract: It has been argued that platform technology owners cocreate business value with other firms in their platform ecosystems by encouraging complementary invention and exploiting indirect network effects. In this study, we examine whether participation in an ecosystem partnership improves the business performance of small independent software vendors (ISVs) in the enterprise software industry and how appropriability mechanisms influence the benefits of partnership. By analyzing the partnering activities and performance indicators of a sample of 1,210 small ISVs over the period 1996-2004, we find that joining a major platform owner's platform ecosystem is associated with an increase in sales and a greater likelihood of issuing an initial public offering (IPO). In addition, we show that these impacts are greater when ISVs have greater intellectual property rights or stronger downstream capabilities. This research highlights the value of interoperability between software products, and stresses that value cocreation and appropriation are not mutually exclusive strategies in interfirm collaboration.


title: A course on geometric group theory.

abstract: These notes are based on a series of lectures I gave at the Tokyo Institute of Technology from April to July 2005. They constituted a course entitled “An introduction to geometric group theory” totalling about 20 hours. The audience consisted of fourth year students, graduate students as well as several staff members. I therefore tried to present a logically coherent introduction to the subject, tailored to the background of the students, as well as including a number of diversions into more sophisticated applications of these ideas. There are many statements left as exercises. I believe that those essential to the logical developments will be fairly routine. Those related to examples or diversions may be more challenging. The notes assume a basic knowledge of group theory, and metric and topological spaces. We describe some of the fundamental notions of geometric group theory, such as quasi-isometries, and aim for a basic overview of hyperbolic groups. We describe group presentations from first principles. We give an outline description of fundamental groups and covering spaces, sufficient to allow us to illustrate various results with more explicit examples. We also give a crash course on hyperbolic geometry. Again the presentation is rather informal, and aimed at providing a source of examples of hyperbolic groups. This is not logically essential to most of what follows. In principle, the basic theory of hyperbolic groups can be developed with no reference to hyperbolic geometry, but interesting examples would be rather sparse. In order not to interupt the exposition, I have not given references in the main text. We give sources and background material as notes in the final section. I am very grateful for the generous support offered by the Tokyo Insititute of Technology, which allowed me to complete these notes, as well as giving me the freedom to pursue my own research interests. I am indebted to Sadayoshi Kojima for his invitation to spend six months there, and for many interesting conversations. I thank Toshiko Higashi for her constant help in making my stay a very comfortable and enjoyable one. My then PhD student Ken Shackleton accompanied me on my visit, and provided some tutorial assistance. Shigeru Mizushima and Hiroshi Ooyama helped with some matters of translatation etc.


title: Dictionary Learning for Robotic Grasp Recognition and Detection

abstract: The ability to grasp ordinary and potentially never-seen objects is an important feature in both domestic and industrial robotics. For a system to accomplish this, it must autonomously identify grasping locations by using information from various sensors, such as Microsoft Kinect 3D camera. Despite numerous progress, significant work still remains to be done in this field. To this effect, we propose a dictionary learning and sparse representation (DLSR) framework for representing RGBD images from 3D sensors in the context of determining such good grasping locations. In contrast to previously proposed approaches that relied on sophisticated regularization or very large datasets, the derived perception system has a fast training phase and can work with small datasets. It is also theoretically founded for dealing with masked-out entries, which are common with 3D sensors. We contribute by presenting a comparative study of several DLSR approach combinations for recognizing and detecting grasp candidates on the standard Cornell dataset. Importantly, experimental results show a performance improvement of 1.69% in detection and 3.16% in recognition over current state-of-the-art convolutional neural network (CNN). Even though nowadays most popular vision-based approach is CNN, this suggests that DLSR is also a viable alternative with interesting advantages that CNN has not.


title: Analyzing feature trajectories for event detection

abstract: We consider the problem of analyzing word trajectories in both time and frequency domains, with the specific goal of identifying important and less-reported, periodic and aperiodic words. A set of words with identical trends can be grouped together to reconstruct an event in a completely un-supervised manner. The document frequency of each word across time is treated like a time series, where each element is the document frequency - inverse document frequency (DFIDF) score at one time point. In this paper, we 1) first applied spectral analysis to categorize features for different event characteristics: important and less-reported, periodic and aperiodic; 2) modeled aperiodic features with Gaussian density and periodic features with Gaussian mixture densities, and subsequently detected each feature's burst by the truncated Gaussian approach; 3) proposed an unsupervised greedy event detection algorithm to detect both aperiodic and periodic events. All of the above methods can be applied to time series data in general. We extensively evaluated our methods on the 1-year Reuters News Corpus [3] and showed that they were able to uncover meaningful aperiodic and periodic events.


title: Low-latency adaptive streaming over tcp

abstract: Media streaming over TCP has become increasingly popular because TCP's congestion control provides remarkable stability to the Internet. Streaming over TCP requires adapting to bandwidth availability, but unforunately, TCP can introduce significant latency at the application level, which causes unresponsive and poor adaptation. This article shows that this latency is not inherent in TCP but occurs as a result of throughput-optimized TCP implementations. We show that this latency can be minimized by dynamically tuning TCP's send buffer. Our evaluation shows that this approach leads to better application-level adaptation and it allows supporting interactive and other low-latency applications over TCP.


title: Datasets from Fifteen Years of Automated Requirements Traceability Research: Current State, Characteristics, and Quality

abstract: Software datasets play a crucial role in advancing automated software traceability research. They can be used by researchers in different ways to develop or validate new automated approaches. The diversity and quality of the datasets within a research community have a significant impact on the accuracy, generalizability, and reproducibility of the results and consequently on the usefulness and practicality of the techniques under study. Collecting and assessing the quality of such datasets are not trivial tasks and have been reported as an obstacle by many researchers in the domain of software engineering. This paper presents a first-of-its-kind study to review and assess the datasets that have been used in software traceability research over the last fifteen years. It presents and articulates the current status of these datasets, their characteristics, and their threats to validity. Furthermore, this paper introduces a Traceability-Dataset Quality Assessment (T-DQA) framework to categorize software traceability datasets and assist researchers to select appropriate datasets for their research based on different characteristics of the datasets and the context in which those datasets will be used.


title: A knowledge-based system for breast cancer classification using fuzzy logic method

abstract: A knowledge-based system is proposed for breast cancer disease classification.The knowledge-based system uses EM, PCA, CART and fuzzy rule-based methods.WDBC and Mammographic mass datasets are used for the method evaluation.The accuracies obtained by the method are respectively 0.932 and 0.941 for WDBC and Mammographic mass datasets. Breast cancer has become a common disease around the world. Expert systems are valuable tools that have been successful for the disease diagnosis. In this research, we accordingly develop a new knowledge-based system for classification of breast cancer disease using clustering, noise removal, and classification techniques. Expectation Maximization (EM) is used as a clustering method to cluster the data in similar groups. We then use Classification and Regression Trees (CART) to generate the fuzzy rules to be used for the classification of breast cancer disease in the knowledge-based system of fuzzy rule-based reasoning method. To overcome the multi-collinearity issue, we incorporate Principal Component Analysis (PCA) in the proposed knowledge-based system. Experimental results on Wisconsin Diagnostic Breast Cancer and Mammographic mass datasets show that proposed methods remarkably improves the prediction accuracy of breast cancer. The proposed knowledge-based system can be used as a clinical decision support system to assist medical practitioners in the healthcare practice.


title: Designing Sketches for Similarity Filtering

abstract: The amounts of currently produced data emphasize the importance of techniques for efficient data processing. Searching big data collections according to similarity of data well corresponds to human perception. This paper is focused on similarity search using the concept of sketches – a compact bit string representations of data objects compared by Hamming distance, which can be used for filtering big datasets. The object-to-sketch transformation is a form of the dimensionality reduction and thus there are two basic contradictory requirements: (1) The length of the sketches should be small for efficient manipulation, but (2) longer sketches retain more information about the data objects. First, we study various sketching methods for data modeled by metric space and we analyse their quality. Specifically, we study importance of several sketch properties for similarity search and we propose a high quality sketching technique. Further, we focus on the length of sketches by studying mutual influence of sketch properties such as correlation of their bits and the intrinsic dimensionality of a set of sketches. The outcome is an equation that allows us to estimate a suitable length of sketches for an arbitrary given dataset. Finally, we empirically verify proposed approach on two real-life datasets.


title: BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model

abstract: We show that BERT (Devlin et al., 2018) is a Markov random field language model. This formulation gives way to a natural procedure to sample sentences from BERT. We generate from BERT and find that it can produce high-quality, fluent generations. Compared to the generations of a traditional left-to-right language model, BERT generates sentences that are more diverse but of slightly worse quality.


title: Convolutional Neural Networks for Text Categorization: Shallow Word-level vs. Deep Character-level

abstract: This paper reports the performances of shallow word-level convolutional neural networks (CNN), our earlier work (2015), on the eight datasets with relatively large training data that were used for testing the very deep character-level CNN in Conneau et al. (2016). Our findings are as follows. The shallow word-level CNNs achieve better error rates than the error rates reported in Conneau et al., though the results should be interpreted with some consideration due to the unique pre-processing of Conneau et al. The shallow word-level CNN uses more parameters and therefore requires more storage than the deep character-level CNN; however, the shallow word-level CNN computes much faster.


title: Anatomy and physiology of a color system in the primate visual cortex

abstract: Staining for the mitochondrial enzyme cytochrome oxidase reveals an array of dense regions (blobs) in the primate primary visual cortex. They are most obvious in the upper layers, 2 and 3, but can also be seen in layers 4B, 5, and 6, in register with the blobs in layers 2 and 3. We compared cells inside and outside blobs in macaque and squirrel monkeys, looking at their physiological responses and anatomical connections. Cells within blobs did not show orientation selectivity, whereas cells between blobs were highly orientation selective. Receptive fields of blob cells had circular symmetry and were of three main types, Broad-Band Center-Surround, Red-Green Double-Opponent, and Yellow-Blue Double-Opponent. Double-Opponent cells responded poorly or not at all to white light in any form, or to diffuse light at any wavelength. In contrast to blob cells, none of the cells recorded in layer 4C beta were Double-Opponent: like the majority of cells in the parvocellular geniculate layers, they were either Broad-Band or Color-Opponent Center-Surround, e.g., red-on-center green-off-surround. To our surprise cells in layer 4C alpha were orientation selective. In tangential penetrations throughout layers 2 and 3, optium orientation, when plotted against electrode position, formed long, regular, usually linear sequences, which were interrupted but not perturbed by the blobs. Staining area 18 for cytochrome oxidase reveals a series of alternating wide and narrow dense stripes, separated by paler interstripes. After small injections of horseradish peroxidase into area 18, we saw a precise set of connections from the blobs in area 17 to thin stripes in area 18, and from the interblob regions in area 17 to interstripes in area 18. Specific reciprocal connections also ran from thin stripes to blobs and from interstripes to interblobs. We have not yet determined the area 17 connections to thick stripes in area 18. In addition, within area 18 there are stripe-to-stripe and interstripe-to-interstripe intrinsic connections. These results suggest that a system involved in the processing of color information, especially color-spatial interactions, runs parallel to and separate from the orientation-specific system. Color, encoded in three coordinates by the major blob cell types, red-green, yellow-blue, and black-white, can be transformed into the three coordinates, red, green, and blue, of the Retinex algorithm of Land.


title: On the Visibility of Point Clouds

abstract: Is it possible to determine the visible subset of points directly from a given point cloud? Interestingly, in [7] it was shown that this is indeed the case - despite the fact that points cannot occlude each other, this task can be performed without surface reconstruction or normal estimation. The operator is very simple - it first transforms the points to a new domain and then constructs the convex hull in that domain. Points that lie on the convex hull of the transformed set of points are the images of the visible points. This operator found numerous applications in computer vision, including face reconstruction, keypoint detection, finding the best viewpoints, reduction of points, and many more. The current paper addresses a fundamental question: What properties should a transformation function satisfy, in order to be utilized in this operator? We show that three such properties are sufficient: the sign of the function, monotonicity, and a condition regarding the function's parameter. The correctness of an algorithm that satisfies these three properties is proved. Finally, we show an interesting application of the operator - assignment of visibility-confidence score. This feature is missing from previous approaches, where a binary yes/no visibility is determined. This score can be utilized in various applications, we illustrate its use in view-dependent curvature estimation.