title: Convolutional Neural Networks Based Pornographic Image Classification

abstract: Considering the fact that pornographic images are flooding on the web, we propose a pornographic image recognition method based on convolutional neural network. This method can be divided into two parts: coarse detection and fine detection. Because majority of images are normal, we use coarse detecting to quickly identify the normal images with no or fewer skin-color regions and facial images. For the images which contain much more skin-color regions, they need further identification through fine detecting. At first, we trained the CNN using the strategy of pre-training mid-level features non-fixed fine-tuning, then based on the trained model, we can classify whether the image is pornographic or not. Compared with exiting methods, performance of our method is better than the state-of-the-art.


From System Dynamics and Discrete Event to Practical Agent Based Modeling


title: Accelerated Active High-Temperature Cycling Test for Power MOSFETs

abstract: In this paper reliability test equipment is presented that allows accelerated failure tests of packaged power MOSFETs (e.g. TO220, TO263). The failure criterion used is an increase in thermal impedance which is observed in-situ during testing. The duration of the cycles depends on the shift of the junction temperature TJ: Testing with a temperature shift of TJ,min= 70 degC to TJ,max= 170 degC takes for 12 devices and 1 million cycles about 18 days. Thermo-mechanical finite element simulations and failure analysis accompany and support the experimental results. The paper gives an overview over the set-up and the measurement technique and discusses the outcome of simulation and test


title: Awareness of Social Influence for Service Recommendation

abstract: With increasing presence and adoption of Web Services on the World Wide Web, to recommend suitable services to users has become an important issue. However, existing personalization approaches, such as collaborative filtering or content based recommendations, are ignoring services' sociability because of the isolation of services without social relationships among them, and lacking of consideration of social influence. Therefore, there is a need for more accurate means to interlink them in a social-enhanced interest network, and to analyze and quantify the social influence. In this paper, we propose a methodology to connect distributed services into a global social service network for social influence-aware service recommendation, called recommend-as-you-go. First, we propose a novel platform to construct a global social service network by linking distributed services with social link using quality of social link, and then we propose a flexible model for effective awareness of social influence to provide a quantitative measure of the influential strength, Next, a novel social influence-aware service recommendation approach is presented based on global social service network, and finally, the experiment results show that our new approach can solve the quality of service recommendation problem well with quick query response, low usage threshold and high accuracy with user preferences by recommend-as-you-go.


title: Generative Adversarial Imitation Learning

abstract: Consider learning a policy from example expert behavior, without interaction with the expert or access to reinforcement signal. One approach is to recover the expert's cost function with inverse reinforcement learning, then extract a policy from that cost function with reinforcement learning. This approach is indirect and can be slow. We propose a new general framework for directly extracting a policy from data, as if it were obtained by reinforcement learning following inverse reinforcement learning. We show that a certain instantiation of our framework draws an analogy between imitation learning and generative adversarial networks, from which we derive a model-free imitation learning algorithm that obtains significant performance gains over existing model-free methods in imitating complex behaviors in large, high-dimensional environments.


title: Intellectual functioning in children with epilepsy: Frontal lobe epilepsy, childhood absence epilepsy and benign epilepsy with centro-temporal spikes

abstract: PURPOSE ::: The purpose of our study is to describe intellectual functioning in three common childhood epilepsy syndromes - frontal lobe epilepsy (FLE), childhood absence epilepsy (CAE) and benign epilepsy with centro-temporal spikes (BECTS). And also to determine the influence of epilepsy related variables, type of epilepsy, age at epilepsy onset, duration and frequency of epilepsy, and treatment on the scores. ::: ::: ::: METHODS ::: Intellectual functioning was examined in a group of 90 children with epilepsy (30 FLE, 30 CAE, 30 BECTS), aged 6-15 years, and compared with a control group (30). All subjects obtained a Full Scale IQ ≥ 70 and they were receiving no more than two antiepileptic medications. Participants completed the Wechsler Intelligence Scale for Children - Third Edition. The impact of epilepsy related variables (type of epilepsy, age at epilepsy onset, duration of epilepsy, seizure frequency and anti-epileptic drugs) on intellectual functioning was examined. ::: ::: ::: RESULTS ::: Children with FLE scored significantly worse than controls on WISC-III Verbal IQ, Full Scale IQ and Processing Speed Index. There was a trend for children with FLE to have lower intelligence scores than CAE and BECTS groups. Linear regression analysis showed no effect for age at onset, frequency of seizures and treatment. Type of epilepsy and duration of epilepsy were the best indicators of intellectual functioning. ::: ::: ::: CONCLUSION ::: It is crucial that children with FLE and those with a longer active duration of epilepsy are closely monitored to allow the early identification and evaluation of cognitive problems, in order to establish adequate and timely school intervention plans.


title: Design of an Ultra-Wideband Pulse Generator Based on Avalanche Transistor

abstract: Based on the avalanche effect of avalanche transistor, a kind of ultra-wideband nanosecond pulse circuit has been designed, whose frequency, pulse width and amplitude are tunable. In this paper, the principle, structure and selection of components' parameters in the circuit are analyzed in detail. The circuit generates periodic negative pulse, whose pulse full width is 890 ps and pulse amplitude is -11.2 V in simulation mode. By setting up circuit for experiment and changing parameters properly, a kind of ultra-wideband pulse with pulse width of 2.131 ns and pulse amplitude of -9.23 V is achieved. With the features such as simple structure, stable and reliable performance and low cost, this pulse generator is applicable to ultra-wideband wireless communication system.


title: A Beam Switching Quasi-Yagi Dipole Antenna

abstract: A high gain beam switching pattern reconfigurable quasi-Yagi dipole antenna is presented for wireless local area network (WLAN) systems at 5.2 GHz. The antenna consists of a microstrip-to-coplanar stripline (CPS) balun, the length of which can be controlled by using PIN diodes. The change of the length of the balun allows the currents on the two arms of the dipole to have different phase differences, thereby making the antenna operate at three states with the E-plane maximum beam direction towards 20°, -20°, and 0°, respectively. In order to validate the design method, a prototype of the proposed antenna with a practical biasing network was fabricated and measured. Measured results on the reflection coefficients, radiation patterns, and realized gains for three operating states are provided, which agree well with the numerical simulations.


title: The industrial internet of things (IIoT): An analysis framework

abstract: Abstract Historically, Industrial Automation and Control Systems (IACS) were largely isolated from conventional digital networks such as enterprise ICT environments. Where connectivity was required, a zoned architecture was adopted, with firewalls and/or demilitarized zones used to protect the core control system components. The adoption and deployment of ‘Internet of Things’ (IoT) technologies is leading to architectural changes to IACS, including greater connectivity to industrial systems. This paper reviews what is meant by Industrial IoT (IIoT) and relationships to concepts such as cyber-physical systems and Industry 4.0. The paper develops a definition of IIoT and analyses related partial IoT taxonomies. It develops an analysis framework for IIoT that can be used to enumerate and characterise IIoT devices when studying system architectures and analysing security threats and vulnerabilities. The paper concludes by identifying some gaps in the literature.


title: Moderating effects of governance on information infrastructure and e-government development

abstract: Drawing from the resource complementarity perspective of the resource-based view of a firm, this study examines the complementary role of governance dimensions—namely, voice and accountability, political stability, government effectiveness, regulatory quality, rule of law, and control of corruption—on the relationship between information infrastructure in a country and its e-government development. Based on publicly available archival data from 178 countries, our results provide support for the hypothesized model. Specifically, whereas political stability, government effectiveness, and rule of law moderated the relationship of information infrastructure with e-government development in a positive direction, voice and accountability and control of corruption moderated the relationship negatively. Further, the relationship between information infrastructure and e-government development was not contingent on regulatory quality. Our findings contribute to the theoretical discourse on e-government development by highlighting the complementary role of governance and provide suggestions for practice in managing e-government development by enhancing governance, thereby leveraging the effect of information infrastructure on e-government development. © 2012 Wiley Periodicals, Inc.


title: Performance comparison of vertical axis and horizontal axis wind turbines to get optimum power output

abstract: This paper presents the comparison of power captured by vertical and horizontal axis wind turbine (VAWT and HAWT). According to Betz, the limit of maximum coefficient power (CP) is 0.59. In this case CP is important parameter that determines the power extracted by a wind turbine we made. This paper investigates the impact of wind speed variation of wind turbine to extract the power. For VAWT we used H-darrieus type whose swept area is 3.14 m2 and so is HAWT. The wind turbines have 3 blades for each type. The air foil of both wind turbines are NACA 4412. We tested the model of wind turbine with various wind velocity which affects the performance. We have found that CP of HAWT is 0.54 with captured maximum power is 1363.6 Watt while the CP of VAWT is 0.34 with captured maximum power is 505.69 Watt. The power extracted of both wind turbines seems that HAWT power is much better than VAWT power.


title: KATAN and KTANTAN -- A Family of Small and Efficient Hardware-Oriented Block Ciphers

abstract: In this paper we propose a new family of very efficient hardware oriented block ciphers. The family contains six block ciphers divided into two flavors. All block ciphers share the 80-bit key size and security level. The first flavor, KATAN, is composed of three block ciphers, with 32, 48, or 64-bit block size. The second flavor, KTANTAN, contains the other three ciphers with the same block sizes, and is more compact in hardware, as the key is burnt into the device (and cannot be changed). ::: ::: The smallest cipher of the entire family, KTANTAN32, can be implemented in 462 GE while achieving encryption speed of 12.5 KBit/sec (at 100 KHz). KTANTAN48, which is the version we recommend for RFID tags uses 588 GE, whereas KATAN64, the largest and most flexible candidate of the family, uses 1054 GE and has a throughput of 25.1 Kbit/sec (at 100 KHz).


title: deepTools2: a next generation web server for deep-sequencing data analysis

abstract: We present an update to our Galaxy-based web server for processing and visualizing deeply sequenced data. Its core tool set, deepTools, allows users to perform complete bioinformatic workflows ranging from quality controls and normalizations of aligned reads to integrative analyses, including clustering and visualization approaches. Since we first described our deepTools Galaxy server in 2014, we have implemented new solutions for many requests from the community and our users. Here, we introduce significant enhancements and new tools to further improve data visualization and interpretation. deepTools continue to be open to all users and freely available as a web service at deeptools.ie-freiburg.mpg.de. The new deepTools2 suite can be easily deployed within any Galaxy framework via the toolshed repository, and we also provide source code for command line usage under Linux and Mac OS X. A public and documented API for access to deepTools functionality is also available.


title: A Combined π- and T-type immittance converter for constant current applications

abstract: This article evaluates the characteristics of a proposed π-CLCL immittance converter, which is a combination of the typical π-and T-type configurations, for constant current output applications. The input-output characteristics and efficiency characteristics are analyzed experimentally. The experimental results are compared to the recently evaluated simulation results of the proposed immittance converter. It is observed that the experimental results agree with those of the simulation ones, and confirm that the π-CLCL configuration is more efficient than the typical π-and T-type immittance converters while maintaining a nearly constant output current.


title: Dressing down: convergent reduction of the mental disc in Garra (Teleostei: Cyprinidae) in the Middle East

abstract: In the Middle East, species of Garra are believed to have invaded the area in two independent waves from the Indo-Malayan biogeographic region. This hypothesis is based on the structure of the mental disc, a unique specialization of the lower lip, which is believed to be an adaptation to fast-flowing waters. While several species have such a mental disc, others completely lack a mental disc, being adapted to slow-moving water or to subterranean life. In this study, the phylogenetic relationships of Middle Eastern Garra species, including 16 described and 4 undescribed species, were analysed using mitochondrial cytochrome c oxidase I sequences. The results are concordant with traditional hypotheses on two invasion events; however, these invasion events are independent from the presence, absence or shape of the mental disc. We postulate convergent reduction of the mental disc in 5–6 independent lineages of Garra in the Middle East.


title: Maximum Likelihood Reconstruction for Emission Tomography

abstract: Previous models for emission tomography (ET) do not distinguish the physics of ET from that of transmission tomography. We give a more accurate general mathematical model for ET where an unknown emission density lambda = lambda(x, y, z) generates, and is to be reconstructed from, the number of counts n(*)(d) in each of D detector units d. Within the model, we give an algorithm for determining an estimate lambdainsertion mark of lambda which maximizes the probability p(n(*)|lambda) of observing the actual detector count data n(*) over all possible densities lambda. Let independent Poisson variables n(b) with unknown means lambda(b), b = 1, ..., B represent the number of unobserved emissions in each of B boxes (pixels) partitioning an object containing an emitter. Suppose each emission in box b is detected in detector unit d with probability p(b, d), d = 1, ..., D with p(b,d) a one-step transition matrix, assumed known. We observe the total number n(*) = n(*)(d) of emissions in each detector unit d and want to estimate the unknown lambda = lambda(b), b = 1, ..., B. For each lambda, the observed data n(*) has probability or likelihood p(n(*)|lambda). The EM algorithm of mathematical statistics starts with an initial estimate lambda(0) and gives the following simple iterative procedure for obtaining a new estimate lambdainsertion mark(new), from an old estimate lambdainsertion mark(old), to obtain lambdainsertion mark(k), k = 1, 2, ..., lambdainsertion mark(new)(b)= lambdainsertion mark(old)(b)Sum of (n(*)p(b,d) from d=1 to D/Sum of lambdainsertion mark()old(b('))p(b('),d) from b(')=1 to B), b=1,...B.


title: $\nu$-net: Deep Learning for Generalized Biventricular Cardiac Mass and Function Parameters

abstract: Background: Cardiac MRI derived biventricular mass and function parameters, such as end-systolic volume (ESV), end-diastolic volume (EDV), ejection fraction (EF), stroke volume (SV), and ventricular mass (VM) are clinically well established. Image segmentation can be challenging and time-consuming, due to the complex anatomy of the human heart. ::: Objectives: This study introduces $\nu$-net (/nju:n$\varepsilon$t/) -- a deep learning approach allowing for fully-automated high quality segmentation of right (RV) and left ventricular (LV) endocardium and epicardium for extraction of cardiac function parameters. ::: Methods: A set consisting of 253 manually segmented cases has been used to train a deep neural network. Subsequently, the network has been evaluated on 4 different multicenter data sets with a total of over 1000 cases. ::: Results: For LV EF the intraclass correlation coefficient (ICC) is 98, 95, and 80 % (95 %), and for RV EF 96, and 87 % (80 %) on the respective data sets (human expert ICCs reported in parenthesis). The LV VM ICC is 95, and 94 % (84 %), and the RV VM ICC is 83, and 83 % (54 %). This study proposes a simple adjustment procedure, allowing for the adaptation to distinct segmentation philosophies. $\nu$-net exhibits state of-the-art performance in terms of dice coefficient. ::: Conclusions: Biventricular mass and function parameters can be determined reliably in high quality by applying a deep neural network for cardiac MRI segmentation, especially in the anatomically complex right ventricle. Adaption to individual segmentation styles by applying a simple adjustment procedure is viable, allowing for the processing of novel data without time-consuming additional training.


title: Success Factors of Agile Information Systems Development: A Qualitative Study

abstract: We conduct a qualitative, interview-based study with 12 participants in order to shed further light on the success factors of agile ISD because quantitative studies reported on contradictory results. First, we identify four success factors from previous literature that form the conceptual basis of our investigation. Those factors are the team, customer, organization, and communication. We extend previous findings by opening up the black box of each of those factors. This is achieved by identifying sub-factors that enable agile ISD success. We find that autonomy is the most important team characteristic, whereas agile values and management support are crucial for the organizational level. For the customer role, customer involvement is judged to be most important, and for communication, the meeting practices are the most frequently mentioned sub-factor. In contrast to the other categories, we note that sub-factors for the organizational level were mentioned by fewer participants.


title: The historical Coffin-Lowry syndrome family revisited: identification of two novel mutations of RPS6KA3 in three male patients.

abstract: Coffin-Lowry syndrome (CLS) is a rare X-linked dominant disorder characterized by intellectual disability, craniofacial abnormalities, short stature, tapering fingers, hypotonia, and skeletal malformations. CLS is caused by mutations in the Ribosomal Protein S6 Kinase, 90 kDa, Polypeptide 3 (RPS6KA3) gene located at Xp22.12, which encodes Ribosomal S6 Kinase 2 (RSK2). Here we analyzed RPS6KA3 in three unrelated CLS patients including one from the historical Coffin-Lowry syndrome family and found two novel mutations. To date, over 140 mutations in RPS6KA3 have been reported. However, the etiology of the very first familial case, which was described in 1971 by Lowry with detailed phenotype and coined the term CLS, has remained unknown. More than 40 years after the report, we succeeded in identifying deposited fibroblast cells from one patient of this historic family and found a novel heterozygous 216 bp in-frame deletion, encompassing exons 15 and 16 of RPS6KA3. Drop episodes in CLS patients were reported to be associated with truncating mutations deleting the C-terminal kinase domain (KD), and only one missense mutation and one single basepair duplication involving the C-terminal KD of RSK2 in the patients with drop episode have been reported thus far. Here we report the first in-frame deletion in C-terminal KD of RPS6KA3 in a CLS patient with drop episodes.


title: Toward Natural Turn-Taking in a Virtual Human Negotiation Agent

abstract: In this paper we assess our progress toward creating a virtual human negotiation agent with fluid turn-taking skills. To facilitate the design of this agent, we have collected a corpus of human-human negotiation roleplays as well as a corpus of Wizard-controlled human-agent negotiations in the same roleplay scenario. We compare the natural turn-taking behavior in our human-human corpus with that achieved in our Wizard-of-Oz corpus, and quantify our virtual human's turn-taking skills using a combination of subjective and objective metrics. We also discuss our design for a Wizard user interface to support real-time control of the virtual human's turn-taking and dialogue behavior, and analyze our wizard's usage of this interface.


title: BLDC motor controller for Regenerative Braking

abstract: Design of a controller for Regenerative Braking in a Brushless DC (BLDC) Motor is presented. The patterns of the switching pulses of the BLDC motor are described in terms of Boolean equations. The controller circuit is designed using the developed Boolean equations. This circuit plays a major role in motoring and regenerative action on BLDC motors. Necessary simulation is performed in PSIM. A cost analysis for the construction of controller circuit is also included here. This circuit can be useful for electric vehicles.


title: An End-to-End Approach to Natural Language Object Retrieval via Context-Aware Deep Reinforcement Learning

abstract: We propose an end-to-end approach to the natural language object retrieval task, which localizes an object within an image according to a natural language description, i.e., referring expression. Previous works divide this problem into two independent stages: first, compute region proposals from the image without the exploration of the language description; second, score the object proposals with regard to the referring expression and choose the top-ranked proposals. The object proposals are generated independently from the referring expression, which makes the proposal generation redundant and even irrelevant to the referred object. In this work, we train an agent with deep reinforcement learning, which learns to move and reshape a bounding box to localize the object according to the referring expression. We incorporate both the spatial and temporal context information into the training procedure. By simultaneously exploiting local visual information, the spatial and temporal context and the referring language a priori, the agent selects an appropriate action to take at each time. A special action is defined to indicate when the agent finds the referred object, and terminate the procedure. We evaluate our model on various datasets, and our algorithm significantly outperforms the compared algorithms. Notably, the accuracy improvement of our method over the recent method GroundeR and SCRC on the ReferItGame dataset are 7.67% and 18.25%, respectively.


A Tetrahedral Microphone Processor for Ambisonic Recording Fons ADRIAENSEN


title: Birds of a "bad" feather flock together : the Dark Triad and mate choice

abstract: Previous research on the Dark Triad traits (i.e., Machiavellianism, psychopathy, and narcissism) has focused solely on the role the Dark Triad traits played in mate choice of actors. The current study ( N= 336) extends this by manipulating the apparent levels of Dark Triad traits in targets and correlating mate choice in these targets with individual differences in the Dark Triad traits in actors. As expected, both sexes preferred partners low in the Dark Triad traits for long-term mating, while those high in these traits were preferred for one-night stands. However, women high in psychopathy considered the Dark Triad traits in potential male partners more physically attractive and desirable for an one-night stand, as well as a potential husband. Men who were high on psychopathy were likewise attracted to psychopathy in potential mothers. Our findings are discussed from an evolutionary personality paradigm.


title: A collaborative methodology for tacit knowledge management: Application to scientific research

abstract: Tacit knowledge, which refers to the know-how, is critical to understand and reuse since it is located in the human heads. It represents the foremost element for human and team evaluation. Seeking for tacit knowledge is achieved only by communicating with the concerned persons, which makes losing it axiomatic if people leave their work without documenting their know-how. Thus, providing a collaborative environment based on a common conceptualization of the domain to formalize the experts' knowledge and to share their outcomes is required. However, some barriers pertaining to cultural and social factors such as personality traits impede?capturing the conceptual model. To cope with these issues, we have proposed a generic two-step methodology that copes with human barriers when capturing the domain experts' tacit knowledge, their skills, and seeds terms in order to converge to a common knowledge representation. Considering the scientific research management as a use case, we followed the proposed methodology to formalize our scientific research knowledge in the context of network and communication research field. Based on the generated ontology, we have developed a semantic web platform that allows collaboratively annotating experts' knowledge in a computer interpretable format that can be shared and reused by human and machines. Our evaluation is based on end users' quality of experience and feedbacks. Proposing a methodology for formalizing tacit knowledge to cover social barriers.Applying the proposed methodology to the scientific research management.Defining the Network and Communication Research Domain Ontology (NCRDO).Implementing and evaluating a collaborative semantic web platform based on NCRDO. Display Omitted


Automatic text structuring and retrieval-experiments in automatic encyclopedia searching


title: Experiential Avoidance and Technological Addictions in Adolescents

abstract: Background and aimsThis study focuses on the use of popular information and communication technologies (ICTs) by adolescents: the Internet, mobile phones, and video games. The relationship of ICT u...


4 . 2 : Design of an Improved Pixel for a Polysilicon Active Matrix Organic Light Emitting Diode Display


title: Quasi-developable B-spline surfaces in ship hull design

abstract: Abstract The use of developable surfaces in ship design is of engineering importance because they can be easily manufactured without stretching or tearing, or without the use of heat treatment. In some cases, a ship hull can be entirely designed with the use of developable surfaces. In this paper, a method to create a quasi-developable B -spline surface between two limit curves is presented. The centreline, chines and sheer lines of a vessel are modelled as B -spline curves. Between each pair of these boundary curves or directrix lines, the generator lines or rulings are created and a quasi-developable B -spline surface containing the rulings is defined. A procedure based on multiconic development is used to modify the directrix lines in case the rulings intersect inside the boundary curves, avoiding non-developable portions of the surface. B -spline curves and surfaces are widely used today in practically all the design and naval architecture computer programs. Some examples of ship hulls entirely created with developable surfaces are presented.


title: Differential cognitive effects of colloid cysts in the third ventricle that spare or compromise the fornix

abstract: A series of twelve cases, all of whom had received surgery for the removal of a colloid cyst in the third ventricle, was examined on a series of memory tests. The only consistent predictor of poor memory performance that could be detected from MRIs was the presence of bilateral interruption of the fornix, which occurred in three of the subjects. Although these three cases were poor on tests of learning and recall, there was evidence that recognition was less impaired. The subjects were also tested on a set of recognition and concurrent discriminations that closely matched tests given to non-human primates. Clear parallels were found between the apparent effects of fornix damage in these clinical cases and those observed following more selective surgery in non-human primates. These findings not only indicate that fornix damage is sufficient to induce anterograde amnesia but also support the validity of recent animal tests that are thought to capture aspects of episodic memory.


title: Conserved and Divergent Functions of the cAMP/PKA Signaling Pathway in Candida albicans and Candida tropicalis

abstract: Fungal species undergo many morphological transitions to adapt to changing environments, an important quality especially in fungal pathogens. For decades, Candida albicans has been one of the most prevalent human fungal pathogens, and recently, the prevalence of Candida tropicalis as a causative agent of candidiasis has increased. In C. albicans, the ability to switch between yeast and hyphal forms is thought to be a key virulence factor and is regulated by multiple signaling cascades—including the cyclic adenosine monophosphate/protein kinase A (cAMP/PKA), calcineurin, high-osmolarity glycerol (HOG), and mitogen-activated protein kinases (MAPK) signaling pathways—upon receiving environmental cues. The cAMP/PKA signaling pathway also triggers white-opaque switching in C. albicans. However, studies on C. tropicalis morphogenesis are limited. In this minireview, we discuss the regulation of the yeast-hypha transition, virulence, and white-opaque switching through the cAMP/PKA pathway in the closely related species C. albicans and C. tropicalis.


Categorical vs Dimensional Perception of Italian Emotional Speech


title: Leakage Current Mechanisms and Leakage Reduction Techniques in Deep-Submicrometer CMOS Circuits

abstract: High leakage current in deep-submicrometer regimes is becoming a significant contributor to power dissipation of CMOS circuits as threshold voltage, channel length, and gate oxide thickness are reduced. Consequently, the identification and modeling of different leakage components is very important for estimation and reduction of leakage power, especially for low-power applications. This paper reviews various transistor intrinsic leakage mechanisms, including weak inversion, drain-induced barrier lowering, gate-induced drain leakage, and gate oxide tunneling. Channel engineering techniques including retrograde well and halo doping are explained as means to manage short-channel effects for continuous scaling of CMOS devices. Finally, the paper explores different circuit techniques to reduce the leakage power consumption.


title: A 7GS/s, 1.2 V. pseudo logic encoder based flash ADC using TIQ technique

abstract: Ultra wide band communication systems extensively rely on low resolution and Giga samples per second data converters. Moreover, technology scaling beyond 90 nm drastically complicates the design of the communication systems. With the increasing complexity in communication systems, greater demand for higher performance and more conversion speed while maintaining power consumption at a reasonable level drives research towards new architectural and topological paradigms. In this paper, a novel architecture implementation for the 4-bit flash ADC is proposed. The design is suitable for low power high-speed applications. The architecture utilizes Threshold Quantization technique (TIQ), the gate sizes are carefully selected so that the input and output rise- and fall-times are about equal. The TIQ technique has been utilized here in order to meet low power requirements and for better implementation in SoC applications. The circuit is designed in CMOS 90 nm technology. A new digital encoder network called the Pseudo- dynamic NMOS encoder is proposed to be used in the proposed TIQ based architecture of flash ADC. The Pseudo-dynamic NMOS encoder outperforms all of the existing encoding networks. The new encoding network offers high data conversion rate with a low power consumption.


title: LIEGE:: link entities in web lists with knowledge base

abstract: A critical step in bridging the knowledge base with the huge corpus of semi-structured Web list data is to link the entity mentions that appear in the Web lists with the corresponding real world entities in the knowledge base, which we call list linking task. This task can facilitate many different tasks such as knowledge base population, entity search and table annotation. However, the list linking task is challenging because a Web list has almost no textual context, and the only input for this task is a list of entity mentions extracted from the Web pages. In this paper, we propose LIEGE, the first general framework to Link the entities in web lists with the knowledge base to the best of our knowledge. Our assumption is that entities mentioned in a Web list can be any collection of entities that have the same conceptual type that people have in mind. To annotate the list items in a Web list with entities that they likely mention, we leverage the prior probability of an entity being mentioned and the global coherence between the types of entities in the Web list. The interdependence between different entity assignments in a Web list makes the optimization of this list linking problem NP-hard. Accordingly, we propose a practical solution based on the iterative substitution to jointly optimize the identification of the mapping entities for the Web list items. We extensively evaluated the performance of our proposed framework over both manually annotated real Web lists extracted from the Web pages and two public data sets, and the experimental results show that our framework significantly outperforms the baseline method in terms of accuracy.


Drum-Buffer-Rope: The Technique to Plan and Control the Production Using Theory of Constraints


Goos THE IMPACT OF ARTIFICIAL INTELLIGENCE ON EMPLOYMENT


title: A comparative evaluation of off-the-shelf distributed semantic representations for modelling behavioural data

abstract: In this paper we carry out an extensive comparison of many off-the-shelf distributed semantic vectors representations of words, for the purpose of making predictions about behavioural results or human annotations of data. In doing this comparison we also provide a guide for how vector similarity computations can be used to make such predictions, and introduce many resources available both in terms of datasets and of vector representations. Finally, we discuss the shortcomings of this approach and future research directions that might address them.


title: DC-Link Capacitor Voltage Regulation for Three-Phase Three-Level Inverter-Based Shunt Active Power Filter with Inverted Error Deviation Control

abstract: A new control technique known as inverted error deviation (IED) control is incorporated into the main DC-link capacitor voltage regulation algorithm of a three-level neutral-point diode clamped (NPC) inverter-based shunt active power filter (SAPF) to enhance its performance in overall DC-link voltage regulation so as to improve its harmonics mitigation performances. In the SAPF controller, DC-link capacitor voltage regulation algorithms with either the proportional-integral (PI) or fuzzy logic control (FLC) technique have played a significant role in maintaining a constant DC-link voltage across the DC-link capacitors. However, both techniques are mostly operated based on a direct voltage error manipulation approach which is insufficient to address the severe DC-link voltage deviation that occurs during dynamic-state conditions. As a result, the conventional algorithms perform poorly with large overshoot, undershoot, and slow response time. Therefore, the IED control technique is proposed to precisely address the DC-link voltage deviation. To validate effectiveness and feasibility of the proposed algorithm, simulation work in MATLAB-Simulink and experimental implementation utilizing a TMS320F28335 Digital Signal Processor (DSP) are performed. Moreover, conventional algorithms with PI and FLC techniques are tested too for comparison purposes. Both simulation and experimental results are presented, confirming the improvement achieved by the proposed algorithm in terms of accuracy and dynamic response in comparison to the conventional algorithms.


title: Locked-in syndrome: a challenge for embodied cognitive science

abstract: Embodied approaches in cognitive science hold that the body is crucial for cognition. What this claim amounts to, however, still remains unclear. This paper contributes to its clarification by confronting three ways of understanding embodiment—the sensorimotor approach, extended cognition and enactivism—with Locked-in syndrome (LIS). LIS is a case of severe global paralysis in which patients are unable to move and yet largely remain cognitively intact. We propose that LIS poses a challenge to embodied approaches to cognition requiring them to make explicit the notion of embodiment they defend and its role for cognition. We argue that the sensorimotor and the extended functionalist approaches either fall short of accounting for cognition in LIS from an embodied perspective or do it too broadly by relegating the body only to a historical role. Enactivism conceives of the body as autonomous system and of cognition as sense-making. From this perspective embodiment is not equated with bodily movement but with forms of agency that do not disappear with body paralysis. Enactivism offers a clarifying perspective on embodiment and thus currently appears to be the framework in embodied cognition best suited to address the challenge posed by LIS.


title: Algorithmic Transparency and Decision-Making Accountability: Thoughts for Buying Machine Learning Algorithms

abstract: There has been a great deal of research on how to achieve algorithmic accountability and transparency in automated decision-making systems - especially for those used in public governance. However, good accountability in the implementation and use of automated decision-making systems is far from simple. It involves multiple overlapping institutional, technical, and political considerations, and becomes all the more complex in the context of machine learning based, rather than rule based, decision systems. This chapter argues that relying on human oversight of automated systems, so called ‘human-in-the-loop’ approaches, is entirely deficient, and suggests addressing transparency and accountability during the procurement phase of machine learning systems - during their specification and parameterisation - is absolutely critical. In a machine learning based automated decision system, the accountability typically associated with a public official making a decision has already been displaced into the actions and decisions of those creating the system - the bureaucrats and engineers involved in building the relevant models, curating the datasets, and implementing a system institutionally. But what should those system designers be thinking about and asking for when specifying those systems? ::: ::: There are a lot of accountability mechanisms available for system designers to consider, including new computational transparency mechanisms, ‘fairness’ and non-discrimination, and ‘explainability’ of decisions. If an official specifies for a system to be transparent, fair, or explainable, however, it is important that they understand the limitations of such a specification in the context of machine learning. Each of these approaches is fraught with risks, limitations, and the challenging political economy of technology platforms in government. Without understand the complexities and limitations of those accountability and transparency ideas, they risk disempowering public officials in the face of private industry technology vendors, who use trade secrets and market power in deeply problematic ways, as well as producing deficient accountability outcomes. This chapter therefore outlines the risks associated with corporate cooption of those transparency and accountability mechanisms, and suggests that significant resources must be invested in developing the necessary skills in the public sector for deciding whether a machine learning system is useful and desirable, and how it might be made as accountable and transparent as possible.


title: The Biber Cognitive Estimation Test

abstract: Abstract Normative data from 113 participants, and cross-validation data from 49 additional participants, are presented for the Biber Cognitive Estimation Test (BCET), a 20-item test with five estimation questions in each of four categories: time/duration, quantity, weight, and distance. In Study 1, the range of normal answers is provided for each item, and a cut-off for impaired performance is suggested. Although very low IQ or education levels would be expected to invalidate this test as a measure of estimation skills, participants in the current sample made few errors. In Study 2, a cross-validation suggested a slightly more conservative cut-off score for abnormality. Study 3 examined cognitive estimation in demented (dementia of the Alzheimer’s type and dementia syndrome of Parkinson’s disease) versus intact elderly participants. Results indicated that the BCET was able to distinguish between demented and intact elderly participants.


title: Fast RGB-D people tracking for service robots

abstract: Service robots have to robustly follow and interact with humans. In this paper, we propose a very fast multi-people tracking algorithm designed to be applied on mobile service robots. Our approach exploits RGB-D data and can run in real-time at very high frame rate on a standard laptop without the need for a GPU implementation. It also features a novel depth-based sub-clustering method which allows to detect people within groups or even standing near walls. Moreover, for limiting drifts and track ID switches, an online learning appearance classifier is proposed featuring a three-term joint likelihood. We compared the performances of our system with a number of state-of-the-art tracking algorithms on two public datasets acquired with three static Kinects and a moving stereo pair, respectively. In order to validate the 3D accuracy of our system, we created a new dataset in which RGB-D data are acquired by a moving robot. We made publicly available this dataset which is not only annotated by hand, but the ground-truth position of people and robot are acquired with a motion capture system in order to evaluate tracking accuracy and precision in 3D coordinates. Results of experiments on these datasets are presented, showing that, even without the need for a GPU, our approach achieves state-of-the-art accuracy and superior speed.


title: Adaptive Seeding in Social Networks

abstract: The algorithmic challenge of maximizing information diffusion through word-of-mouth processes in social networks has been heavily studied in the past decade. While there has been immense progress and an impressive arsenal of techniques has been developed, the algorithmic frameworks make idealized assumptions regarding access to the network that can often result in poor performance of state-of-the-art techniques. In this paper we introduce a new framework which we call Adaptive Seeding. The framework is a two-stage stochastic optimization model designed to leverage the potential that typically lies in neighboring nodes of arbitrary samples of social networks. Our main result is an algorithm which provides a constant factor approximation to the optimal adaptive policy for any influence function in the Triggering model.


title: Measuring privacy leaks in Online Social Networks

abstract: Online Social Networking has gained huge popularity amongst the masses. It is common for the users of Online Social Networks (OSNs) to share information with digital friends but in the bargain they loose privacy. Users are unaware of the privacy risks involved when they share their sensitive information in the network. The users should be aware of their privacy quotient and should know where they stand in the privacy measuring scale. In this paper we have described and calculated the Privacy Quotient i.e a privacy metric to measure the privacy of the user's profile using the naive approach. In the starting of the paper we have given the detailed analysis of the survey that we have carried out to know how well do people understand privacy in online social networks. At last we have proposed a model that will ensure privacy in the unstructured data. It will make use of the Item Response Theory model to measure the privacy leaks in the messages and text that is being posted by the users of the online social networking sites.


title: Aliasing Detection and Reduction in Plenoptic Imaging

abstract: When using plenoptic camera for digital refocusing, angular undersampling can cause severe (angular) aliasing artifacts. Previous approaches have focused on avoiding aliasing by pre-processing the acquired light field via prefiltering, demosaicing, reparameterization, etc. In this paper, we present a different solution that first detects and then removes aliasing at the light field refocusing stage. Different from previous frequency domain aliasing analysis, we carry out a spatial domain analysis to reveal whether the aliasing would occur and uncover where in the image it would occur. The spatial analysis also facilitates easy separation of the aliasing vs. non-aliasing regions and aliasing removal. Experiments on both synthetic scene and real light field camera array data sets demonstrate that our approach has a number of advantages over the classical prefiltering and depth-dependent light field rendering techniques.


title: The 2016 WHO Classification of Tumours of the Urinary System and Male Genital Organs-Part A: Renal, Penile, and Testicular Tumours.

abstract: UNLABELLED ::: The fourth edition of the World Health Organization (WHO) classification of urogenital tumours (WHO "blue book"), published in 2016, contains significant revisions. These revisions were performed after consideration by a large international group of pathologists with special expertise in this area. A subgroup of these persons met at the WHO Consensus Conference in Zurich, Switzerland, in 2015 to finalize the revisions. This review summarizes the most significant differences between the newly published classification and the prior version for renal, penile, and testicular tumours. Newly recognized epithelial renal tumours are hereditary leiomyomatosis and renal cell carcinoma (RCC) syndrome-associated RCC, succinate dehydrogenase-deficient RCC, tubulocystic RCC, acquired cystic disease-associated RCC, and clear cell papillary RCC. The WHO/International Society of Urological Pathology renal tumour grading system was recommended, and the definition of renal papillary adenoma was modified. The new WHO classification of penile squamous cell carcinomas is based on the presence of human papillomavirus and defines histologic subtypes accordingly. Germ cell neoplasia in situ (GCNIS) of the testis is the WHO-recommended term for precursor lesions of invasive germ cell tumours, and testicular germ cell tumours are now separated into two fundamentally different groups: those derived from GCNIS and those unrelated to GCNIS. Spermatocytic seminoma has been designated as a spermatocytic tumour and placed within the group of non-GCNIS-related tumours in the 2016 WHO classification. ::: ::: ::: PATIENT SUMMARY ::: The 2016 World Health Organization (WHO) classification contains new renal tumour entities. The classification of penile squamous cell carcinomas is based on the presence of human papillomavirus. Germ cell neoplasia in situ of the testis is the WHO-recommended term for precursor lesions of invasive germ cell tumours.


title: Green operations strategy of a luxury car manufacturer

abstract: This paper investigates the strategic environmental decisions of a luxury car manufacturer. Through case study research, the investigation sheds light on why and how the company is adopting green technologies. Being pressured by different stakeholders to become greener, luxury car manufacturers carry significant opportunities for environmental improvement given the nature of their manufacturing processes and products. Because of their low-volume production, manufacturers may be able to increase output and still reduce overall emissions when compared to high-volume manufacturers. In the case study company this was found to be possible only because of new ideas brought by a change in ownership. Luxury manufacturers may also be a test-bed for the development and experimentation of green technologies as part of a strategic approach to environmental initiatives. This paper contributes to the fields of green technology adoption and operations strategy in automotive manufacturing groups.


title: Routing in vehicular ad hoc networks: A survey

abstract: Vehicular ad hoc network (VANET) is an emerging new technology integrating ad hoc network, wireless LAN (WLAN) and cellular technology to achieve intelligent inter-vehicle communications and improve road traffic safety and efficiency. VANETs are distinguished from other kinds of ad hoc networks by their hybrid network architectures, node movement characteristics, and new application scenarios. Therefore, VANETs pose many unique networking research challenges, and the design of an efficient routing protocol for VANETs is very crucial. In this article, we discuss the research challenge of routing in VANETs and survey recent routing protocols and related mobility models for VANETs.


title: SQCK: A Declarative File System Checker

abstract: The lowly state of the art for file system checking and repair does not match what is needed to keep important data available for users. Current file system checkers, such as e2fsck, are complex pieces of imperfect code written in low-level languages. We introduce SQCK, a file system checker based on a declarative query language; declarative queries are a natural match for the cross-checking that must be performed across the many structures of a file system image. We show that SQCK is able to perform the same functionality as e2fsck with surprisingly elegant and compact queries. We also show that SQCK can easily perform more useful repairs than e2fsck by combining information available across the file system. Finally, our prototype implementation of SQCK achieves this improved functionality with comparable performance to e2fsck.


Learning Conditional Generative Models for Temporal Point Processes


title: Threat Image Projection (TIP) into X-ray images of cargo containers for training humans and machines

abstract: We propose a framework for Threat Image Projection (TIP) in cargo transmission X-ray imagery. The method exploits the approximately multiplicative nature of X-ray imagery to extract a library of threat items. These items can then be projected into real cargo. We show using experimental data that there is no significant qualitative or quantitative difference between real threat images and TIP images. We also describe methods for adding realistic variation to TIP images in order to robustify Machine Learning (ML) based algorithms trained on TIP. These variations are derived from cargo X-ray image formation, and include: (i) translations; (ii) magnification; (iii) rotations; (iv) noise; (v) illumination; (vi) volume and density; and (vii) obscuration. These methods are particularly relevant for representation learning, since it allows the system to learn features that are invariant to these variations. The framework also allows efficient addition of new or emerging threats to a detection system, which is important if time is critical. We have applied the framework to training ML-based cargo algorithms for (i) detection of loads (empty verification), (ii) detection of concealed cars (ii) detection of Small Metallic Threats (SMTs). TIP also enables algorithm testing under controlled conditions, allowing one to gain a deeper understanding of performance. Whilst we have focused on robustifying ML-based threat detectors, our TIP method can also be used to train and robustify human threat detectors as is done in cabin baggage screening.


title: Attribute Value Weighting in K-Modes Clustering

abstract: In this paper, we generalize the k-modes clustering algorithm by weighting attribute value in the dissimilarity computation. Such a generalization generates clusters with stronger intra-similarities, leading to better clustering performance. Experimental results on real life data show that the new k-modes algorithm is superior to the standard k-modes algorithm with respect to clustering accuracy.


title: Computed tomography-based training model for otoplasty

abstract: Otoplasty for the correction of protruding ears is characterized by various techniques and a common and popular cosmetic procedure. For the surgeon, whether beginner or advanced, it is essential to understand the principles and master techniques for standard auricular deformities before applying further sophisticated methods, because a lot of complications and failures are caused by wrong indication and incorrect surgical techniques. The different surgical steps are best learned from teaching models. Therefore, we developed two different silicone models of protruding ears with moderate auricular deformities: one with conchal hyperplasia for the training of conchal resection, and one without antihelix for creating an antihelical fold by suturing technique, based on computed tomography scans of patients. The silicone ear models were evaluated during four standardized surgery courses for residents in otorhinolaryngology by 91 participants using specially designed questionnaires. Nearly all participants rated the training on the auricular models as very helpful (n = 51) or good (n = 31); the scores for the different techniques and properties of the models ranged from 2.0 to 2.6 in a range from 1 (very good) to 4 (inadequate). The good results demonstrate the possibility for learning different surgical otoplasty techniques with this newly designed teaching tool.


title: Joint Network Coding and Superposition Coding for Multi-User Information Exchange in Wireless Relaying Networks

abstract: A joint network coding and superposition coding (JNSC) scheme is proposed for information exchange between more than two users in a wireless relaying network. In this paper we consider two scenarios in a relaying network with four nodes: single and multiple information exchange loops between three source nodes, and two alternative transmission schemes, i.e. pure time division (PTD) and pure network coding (PNC), are also considered in order to compare with JNSC. The achievable rate regions of the PTD, PNC and JNSC schemes are all characterized, indicating the JNSC scheme is not always superior to the other two schemes. The sum rate optimization problem with a certain traffic pattern is also solved. We showed that the maximum coding gains of the JNSC and PNC schemes compared with the PTD scheme is achieved as the transmission rate of each node is one third of the sum rate in the network. Simulation results also reveal this phenomenon.


title: Drone Relays for Battery-Free Networks

abstract: Battery-free sensors, such as RFIDs, are annually attached to billions of items including pharmaceutical drugs, clothes, and manufacturing parts. The fundamental challenge with battery-free sensors is that they are only reliable at short distances of tens of centimeters to few meters. As a result, today's systems for communicating with and localizing battery-free sensors are crippled by the limited range. To overcome this challenge, this paper presents RFly, a system that leverages drones as relays for battery-free networks. RFly delivers two key innovations. It introduces the first full-duplex relay for battery-free networks. The relay can seamlessly integrate with a deployed RFID infrastructure, and it preserves phase and timing characteristics of the forwarded packets. RFly also develops the first RF-localization algorithm that can operate through a mobile relay. We built a hardware prototype of RFly's relay into a custom PCB circuit and mounted it on a Parrot Bebop drone. Our experimental evaluation demonstrates that RFly enables communication with commercial RFIDs at over 50 m. Moreover, its through-relay localization algorithm has a median accuracy of 19 centimeters. These results demonstrate that RFly provides powerful primitives for communication and localization in battery-free networks.


title: EEG-Based Brain–Computer Interfaces for Communication and Rehabilitation of People with Motor Impairment: A Novel Approach of the 21st Century

abstract: People with severe motor impairment face many challenges in communication and control of the environment, whilst survivors from neurological disorders have increased demand for advanced, adaptive and personalized rehabilitation. The last decades many studies have underlined the importance of brain-computer interfaces (BCIs) with great contributions ranging from communication restoration to motor rehabilitation. In this work we review BCI research that focuses on noninvasive, electroencephalography (EEG)-based BCI systems for people with motor impairment as far as communication and rehabilitation aspects are concerned. More specifically we overview milestone approaches that are primarily intended to help severely paralyzed and/or locked-in state patients by using three different BCI modalities, i.e., slow cortical potentials, sensorimotor rhythms and P300 potentials as operational mechanisms. In addition, we review BCI systems with special emphasis on restoration of motor function for patients with spinal cord injury and chronic stroke. Finally, we summarize how EEG-based BCI systems have contributed to communication and rehabilitation of motor impaired people, stress out advantages and limitations and discuss the challenges that these systems should address in the future.


title: Reinforcement learning algorithms for solving classification problems

abstract: We describe a new framework for applying reinforcement learning (RL) algorithms to solve classification tasks by letting an agent act on the inputs and learn value functions. This paper describes how classification problems can be modeled using classification Markov decision processes and introduces the Max-Min ACLA algorithm, an extension of the novel RL algorithm called actor-critic learning automaton (ACLA). Experiments are performed using 8 datasets from the UCI repository, where our RL method is combined with multi-layer perceptrons that serve as function approximators. The RL method is compared to conventional multi-layer perceptrons and support vector machines and the results show that our method slightly outperforms the multi-layer perceptron and performs equally well as the support vector machine. Finally, many possible extensions are described to our basic method, so that much future research can be done to make the proposed method even better.


title: Diffuse Mirrors: 3D Reconstruction from Diffuse Indirect Illumination Using Inexpensive Time-of-Flight Sensors

abstract: The functional difference between a diffuse wall and a mirror is well understood: one scatters back into all directions, and the other one preserves the directionality of reflected light. The temporal structure of the light, however, is left intact by both: assuming simple surface reflection, photons that arrive first are reflected first. In this paper, we exploit this insight to recover objects outside the line of sight from second-order diffuse reflections, effectively turning walls into mirrors. We formulate the reconstruction task as a linear inverse problem on the transient response of a scene, which we acquire using an affordable setup consisting of a modulated light source and a time-of-flight image sensor. By exploiting sparsity in the reconstruction domain, we achieve resolutions in the order of a few centimeters for object shape (depth and laterally) and albedo. Our method is robust to ambient light and works for large room-sized scenes. It is drastically faster and less expensive than previous approaches using femtosecond lasers and streak cameras, and does not require any moving parts.


title: Non-functional Requirements to Architectural Concerns: ML and NLP at Crossroads

abstract: There has been no commonsense on how to identify problem domain concerns in architectural modeling of software systems. Even, there is no commonly accepted method for modeling the non-functional requirements (NFRs) effectively associated with the architectural aspects in the solution domain. This paper introduces the use of a machine learning (ML) method based on support vector machines to relate NFRs to classified "architectural concerns" in an automated way. This method uses natural language processing techniques to fragment the plain NFR texts under the supervision of domain experts. The contribution of this approach lies in continuously applying ML techniques against previously discovered ldquoNFR - architectural concernsrdquo associations to improve the intelligence of repositories for requirements engineering. The paper illustrates a charted roadmap and demonstrates the automated requirements engineering toolset for this roadmap. It also validates the approach and effectiveness of the toolset on the snapshot of a real-life project.


title: Simultaneous Optical Flow and Intensity Estimation from an Event Camera

abstract: Event cameras are bio-inspired vision sensors which mimic retinas to measure per-pixel intensity change rather than outputting an actual intensity image. This proposed paradigm shift away from traditional frame cameras offers significant potential advantages: namely avoiding high data rates, dynamic range limitations and motion blur. Unfortunately, however, established computer vision algorithms may not at all be applied directly to event cameras. Methods proposed so far to reconstruct images, estimate optical flow, track a camera and reconstruct a scene come with severe restrictions on the environment or on the motion of the camera, e.g. allowing only rotation. Here, we propose, to the best of our knowledge, the first algorithm to simultaneously recover the motion field and brightness image, while the camera undergoes a generic motion through any scene. Our approach employs minimisation of a cost function that contains the asynchronous event data as well as spatial and temporal regularisation within a sliding window time interval. Our implementation relies on GPU optimisation and runs in near real-time. In a series of examples, we demonstrate the successful operation of our framework, including in situations where conventional cameras suffer from dynamic range limitations and motion blur.


title: Preventing Capability Leaks in Secure JavaScript Subsets

abstract: Publishers wish to sandbox third-party advertisements to protect themselves from malicious advertisements. One promising approach, used by ADsafe, Dojo Secure, and Jacaranda, sandboxes advertisements by statically verifying that their JavaScript conforms to a safe subset of the language. These systems blacklist known dangerous properties that would let advertisements escape the sandbox. Unfortunately, this approach does not prevent advertisements from accessing new methods added to the built-in prototype objects by the hosting page. In this paper, we show that onethird of the Alexa US Top 100 web sites would be exploitable by an ADsafe-verified advertisement. We propose an improved statically verified JavaScript subset that whitelists known-safe properties using namespaces. Our approach maintains the expressiveness and performance of static verification while improving security.


Towards a Quality Framework for Enterprise Architecture Models


title: A Neural Model for User Geolocation and Lexical Dialectology

abstract: We propose a simple yet effective text- based user geolocation model based on a neural network with one hidden layer, which achieves state of the art performance over three Twitter benchmark geolocation datasets, in addition to producing word and phrase embeddings in the hidden layer that we show to be useful for detecting dialectal terms. As part of our analysis of dialectal terms, we release DAREDS, a dataset for evaluating dialect term detection methods.


efining urban resilience : A review


title: How Useful Are Natural Language Interfaces to the Semantic Web for Casual End-Users?

abstract: Natural language interfaces offer end-users a familiar and convenient option for querying ontology-based knowledge bases. Several studies have shown that they can achieve high retrieval performance as well as domain independence. This paper focuses on usability and investigates if NLIs are useful from an end-user's point of view. To that end, we introduce four interfaces each allowing a different query language and present a usability study benchmarking these interfaces. The results of the study reveal a clear preference for full sentences as query language and confirm that NLIs are useful for querying Semantic Web data.


Multi-Modal Fusion for Moment in Time Video Classification


title: Deformable Generator Network: Unsupervised Disentanglement of Appearance and Geometry

abstract: We present a deformable generator model to disentangle the appearance and geometric information for both image and video data in a purely unsupervised manner. The appearance generator network models the information related to appearance, including color, illumination, identity or category, while the geometric generator performs geometric warping, such as rotation and stretching, through generating deformation field which is used to warp the generated appearance to obtain the final image or video sequences. Two generators take independent latent vectors as input to disentangle the appearance and geometric information from image or video sequences. For video data, a nonlinear transition model is introduced to both the appearance and geometric generators to capture the dynamics over time. The proposed scheme is general and can be easily integrated into different generative models. An extensive set of qualitative and quantitative experiments shows that the appearance and geometric information can be well disentangled, and the learned geometric generator can be conveniently transferred to other image datasets to facilitate knowledge transfer tasks.


title: Macro-FF: Improving AI Planning with Automatically Learned Macro-Operators

abstract: Despite recent progress in AI planning, many benchmarks remain challenging for current planners. In many domains, the performance of a planner can greatly be improved by discovering and exploiting information about the domain structure that is not explicitly encoded in the initial PDDL formulation. In this paper we present and compare two automated methods that learn relevant information from previous experience in a domain and use it to solve new problem instances. Our methods share a common four-step strategy. First, a domain is analyzed and structural information is extracted, then macro-operators are generated based on the previously discovered structure. A filtering and ranking procedure selects the most useful macro-operators. Finally, the selected macros are used to speed up future searches. ::: ::: We have successfully used such an approach in the fourth international planning competition IPC-4. Our system, Macro-FF, extends Hoffmann's state-of-the-art planner FF 2.3 with support for two kinds of macro-operators, and with engineering enhancements. We demonstrate the effectiveness of our ideas on benchmarks from international planning competitions. Our results indicate a large reduction in search effort in those complex domains where structural information can be inferred.


title: Multichannel Audio Source Separation With Deep Neural Networks

abstract: This article addresses the problem of multichannel audio source separation. We propose a framework where deep neural networks (DNNs) are used to model the source spectra and combined with the classical multichannel Gaussian model to exploit the spatial information. The parameters are estimated in an iterative expectation-maximization (EM) fashion and used to derive a multichannel Wiener filter. We present an extensive experimental study to show the impact of different design choices on the performance of the proposed technique. We consider different cost functions for the training of DNNs, namely the probabilistically motivated Itakura–Saito divergence, and also Kullback–Leibler, Cauchy, mean squared error, and phase-sensitive cost functions. We also study the number of EM iterations and the use of multiple DNNs, where each DNN aims to improve the spectra estimated by the preceding EM iteration. Finally, we present its application to a speech enhancement problem. The experimental results show the benefit of the proposed multichannel approach over a single-channel DNN-based approach and the conventional multichannel nonnegative matrix factorization-based iterative EM algorithm.


title: Knowledge Entropy and Feature Selection in Incomplete Decision Systems

abstract: In this paper, concepts of knowledge entropy and knowledge entropy-based uncertainty measures are given in incomplete information systems and decision systems, and some important properties of them are investigated. From these properties, it can be shown that these measures provide important approaches to measure the uncertainty ability of different knowledge in incomplete decision systems. Then the relationships among these knowledge entropies proposed are discussed as well. A new definition of reduct is proposed and a heuristic algorithm with low computational complexity is constructed to improve computational efficiency of feature selection in incomplete decision systems. Experimental results demonstrate that our algorithm can provide an efficient solution to find a minimal subset of the features from incomplete data sets.


title: Dialogue Act Tagging with Transformation-Based Learning

abstract: For the task of recognizing dialogue acts, we are applying the Transformation-Based Learning (TBL) machine learning algorithm. To circumvent a sparse data problem, we extract values of well-motivated features of utterances, such as speaker direction, punctuation marks, and a new feature, called dialogue act cues, which we find to be more effective than cue phrases and word n-grams in practice. We present strategies for constructing a set of dialogue act cues automatically by minimizing the entropy of the distribution of dialogue acts in a training corpus, filtering out irrelevant dialogue act cues, and clustering semantically-related words. In addition, to address limitations of TBL, we introduce a Monte Carlo strategy for training efficiently and a committee method for computing confidence measures. These ideas are combined in our working implementation, which labels held-out data as accurately as any other reported system for the dialogue act tagging task.


title: A Survey on Image-Based Rendering-Representation , Sampling and Compression

abstract: Image-based rendering (IBR) has attracted a lot of research interest recently. In this paper, we survey the various techniques developed for IBR, including representation, sampling and compression. The goal is to provide an overview of research for IBR in a complete and systematic manner. We observe that essentially all the IBR representations are derived from the plenoptic function, which is seven dimensional and difficult to handle. We classify various IBR representations into two categories based on how the plenoptic function is simplified, namely restraining the viewing space and introducing source descriptions. In the former category, we summarize six common assumptions that were often made in various approaches and discuss how the dimension of the plenoptic function can be reduced based on these assumptions. In the latter category, we further categorize the methods based on what kind of source description was introduced, such as scene geometry, texture map or reflection model. Sampling and compression are also discussed respectively for both categories.


title: Learning from Labeled and Unlabeled Data using Graph Mincuts

abstract: Many application domains suffer from not having enough labeled training data for learning. However, large amounts of unlabeled examples can often be gathered cheaply. As a result, there has been a great deal of work in recent years on how unlabeled data can be used to aid classification. We consider an algorithm based on finding minimum cuts in graphs, that uses pairwise relationships among the examples in order to learn from both labeled and unlabeled data.


Sensor fusion for multi-player activity recognition in game sports


title: A Post-Rectification Approach of Depth Images of Kinect v2 for 3D Reconstruction of Indoor Scenes

abstract: 3D reconstruction of indoor scenes is a hot research topic in computer vision. Reconstructing fast, low-cost, and accurate dense 3D maps of indoor scenes have applications in indoor robot positioning, navigation, and semantic mapping. In other studies, the Microsoft Kinect for Windows v2 (Kinect v2) is utilized to complete this task, however, the accuracy and precision of depth information and the accuracy of correspondence between the RGB and depth (RGB-D) images still remain to be improved. In this paper, we propose a post-rectification approach of the depth images to improve the accuracy and precision of depth information. Firstly, we calibrate the Kinect v2 with a planar checkerboard pattern. Secondly, we propose a post-rectification approach of the depth images according to the reflectivity-related depth error. Finally, we conduct tests to evaluate this post-rectification approach from the perspectives of accuracy and precision. In order to validate the effect of our post-rectification approach, we apply it to RGB-D simultaneous localization and mapping (SLAM) in an indoor environment. Experimental results show that once our post-rectification approach is employed, the RGB-D SLAM system can perform a more accurate and better visual effect 3D reconstruction of indoor scenes than other state-of-the-art methods.


title: Convex Relaxations of Optimal Power Flow Problems: An Illustrative Example

abstract: Recently, there has been significant interest in convex relaxations of the optimal power flow (OPF) problem. A semidefinite programming (SDP) relaxation globally solves many OPF problems. However, there exist practical problems for which the SDP relaxation fails to yield the global solution. Conditions for the success or failure of the SDP relaxation are valuable for determining whether the relaxation is appropriate for a given OPF problem. To move beyond existing conditions, which only apply to a limited class of problems, a typical conjecture is that failure of the SDP relaxation can be related to physical characteristics of the system. By presenting an example OPF problem with two equivalent formulations, this paper demonstrates that physically based conditions cannot universally explain algorithm behavior. The SDP relaxation fails for one formulation but succeeds in finding the global solution to the other formulation. Since these formulations represent the same system, success (or otherwise) of the SDP relaxation must involve factors beyond just the network physics. The lack of universal physical conditions for success of the SDP relaxation motivates the development of tighter convex relaxations capable of solving a broader class of problems. Tools from polynomial optimization theory provide a means of developing tighter relaxations. We use the example OPF problem to illustrate relaxations from the Lasserre hierarchy for polynomial optimization and a related"mixed semidefinite/second-order cone programming"hierarchy.


title: Measuring referential integrity in distributed databases

abstract: Distributed relational databases are used by different organizations located at multiple sites that work together on common projects. In this article, we focus on distributed relational databases with incomplete and inconsistent content. We propose to measure referential integrity errors in them for integration and interoperability purposes. We propose local and global referential integrity metrics at three levels: column, table and database. We assume each table can be asynchronously updated at any site and new records are periodically broadcasted to all sites. We explain several distributed query optimization issues. Our proposal is useful in database integration, multiple database interoperability and data quality assurance. We discuss applications of our proposal in distributed scientific databases.


title: Application of vaccines and dietary supplements in aquaculture: possibilities and challenges

abstract: The development of vaccines has proven essential for the development of a successful finfish aquaculture industry by preventing the occurrence of diseases like furunculosis and vibriosis in industrialised finfish farming. Further developments, like DNA vaccines, will aid in controlling even more diseases in the future. There are however many diseases where it is difficult to produce effective vaccines. Furthermore, many disease outbreaks may occur due to impaired animal welfare. Identifying factors associated with disease and optimizing health and welfare through biotechnological developments is likely to be an important research area in the future. The fact that dietary manipulation can affect fish gut microbiota thus improving disease resistance is well known from mammalian science, and is slowly gaining ground in finfish research. Both prebiotic and probiotic approaches have been used in fish, with particular focus on lactic acid bacteria. Positive effects include enhanced growth and feed efficiency, improved immunity and disease resistance. The synbiotic concept (using a combination of probiotics and prebiotics) is particularly promising and is gaining increased interest within the research community. Immunostimulants may also improve disease resistence via increase humoral and cellular immune responses. The most promising immunostimulants at present are β-glucans, alginate and Ergosan. Additionally, medical plant extracts and their products are receiving increased attention as immune modulators, but further studies are needed. There are also great expectations or the future usage of microalgae to control microbiota and optimize fish health.


title: The definition, classification, and prognosis of chronic kidney disease: a KDIGO Controversies Conference report

abstract: The definition and classification for chronic kidney disease was proposed by the National Kidney Foundation Kidney Disease Outcomes Quality Initiative (NKF-KDOQI) in 2002 and endorsed by the Kidney Disease: Improving Global Outcomes (KDIGO) in 2004. This framework promoted increased attention to chronic kidney disease in clinical practice, research and public health, but has also generated debate. It was the position of KDIGO and KDOQI that the definition and classification should reflect patient prognosis and that an analysis of outcomes would answer key questions underlying the debate. KDIGO initiated a collaborative meta-analysis and sponsored a Controversies Conference in October 2009 to examine the relationship of estimated glomerular filtration rate (GFR) and albuminuria to mortality and kidney outcomes. On the basis of analyses in 45 cohorts that included 1,555,332 participants from general, high-risk, and kidney disease populations, conference attendees agreed to retain the current definition for chronic kidney disease of a GFR <60 ml/min per 1.73 m(2) or a urinary albumin-to-creatinine ratio >30 mg/g, and to modify the classification by adding albuminuria stage, subdivision of stage 3, and emphasizing clinical diagnosis. Prognosis could then be assigned based on the clinical diagnosis, stage, and other key factors relevant to specific outcomes. KDIGO has now convened a workgroup to develop a global clinical practice guideline for the definition, classification, and prognosis of chronic kidney disease.


title: Monte-Carlo Tree Search for Simulation-based Play Strategy Analysis

abstract: Games are often designed to shape player behavior in a desired way; however, it can be unclear how design decisions affect the space of behaviors in a game. Designers usually explore this space through human playtesting, which can be time-consuming and of limited effectiveness in exhausting the space of possible behaviors. In this paper, we propose the use of automated planning agents to simulate humans of varying skill levels to generate game playthroughs. Metrics can then be gathered from these playthroughs to evaluate the current game design and identify its potential flaws. We demonstrate this technique in two games: the popular word game Scrabble and a collectible card game of our own design named Cardonomicon. Using these case studies, we show how using simulated agents to model humans of varying skill levels allows us to extract metrics to describe game balance (in the case of Scrabble) and highlight potential design flaws (in the case of Cardonomicon).


title: Automatically Detecting SSL Error-Handling Vulnerabilities in Hybrid Mobile Web Apps

abstract: Today, there are many hybrid apps in which both native Android app UI and WebView UI are used. To protect the security and privacy of the communications, these hybrid apps all use HTTPS by WebView, a key component in modern web browser. In this paper, we show there is another type of SSL vulnerability that stems from the error-handling code in the hybrid mobile web apps. At a high level, this error-handling code should have stopped the communication but it still proceeds regardless of certificate errors, thereby leading to the MITM attacks. To automatically identify these vulnerable apps, we present a hybrid approach that combines both static analysis and dynamic analysis. We have implemented our approach and evaluated with 13,820 real world mobile web apps from a third party market, of which 645 are confirmed truly vulnerable, with an average overhead of 60.8 seconds per app.


title: Effects of Pros and Cons of Applying Big Data Analytics to Consumers’ Responses in an E-Commerce Context

abstract: The era of Big Data analytics has begun in most industries within developed countries. This new analytics tool has raised motivation for experts and researchers to study its impacts to business values and challenges. However, studies which help to understand customers’ views and their behavior towards the applications of Big Data analytics are lacking. This research aims to explore and determine the pros and cons of applying Big Data analytics that affects customers’ responses in an e-commerce environment. Data analyses were conducted in a sample of 273 respondents from Vietnam. The findings found that information search, recommendation system, dynamic pricing, and customer services had significant positive effects on customers’ responses. Privacy and security, shopping addiction, and group influences were found to have significant negative effects on customers’ responses. Customers’ responses were measured at intention and behavior stages. Moreover, positive and negative effects simultaneously presented significant effect on customers’ responses. Each dimension of positive and negative factors had different significant impacts on customers’ intention and behavior. Specifically, information search had a significant influence on customers’ intention and improved customers’ behavior. Shopping addiction had a drastic change from intention to behavior compared to group influences and privacy and security. This study contributes to improve understanding of customers’ responses under big data era. This could play an important role to develop sustainable consumers market. E-vendors can rely on Big Data analytics but over usage may have some negative applications.


title: A robust-coded pattern projection for dynamic 3D scene measurement

abstract: Abstract This paper presents a new coded structured light pattern which permits to solve the correspondence problem by a single shot and without using geometrical constraints. The pattern is composed by the projection of a grid made by coloured slits in such a way that each slit with its two neighbours appears only once in the pattern. The technique proposed permits a rapid and robust 3D scene measurement, even with moving objects.


title: Decomposition of Big Tensors With Low Multilinear Rank

abstract: Tensor decompositions are promising tools for big data analytics as they bring multiple modes and aspects of data to a unified framework, which allows us to discover complex internal structures and correlations of data. Unfortunately most existing approaches are not designed to meet the major challenges posed by big data analytics. This paper attempts to improve the scalability of tensor decompositions and provides two contributions: A flexible and fast algorithm for the CP decomposition (FFCP) of tensors based on their Tucker compression; A distributed randomized Tucker decomposition approach for arbitrarily big tensors but with relatively low multilinear rank. These two algorithms can deal with huge tensors, even if they are dense. Extensive simulations provide empirical evidence of the validity and efficiency of the proposed algorithms.


Overview of the FIRE 2016 Microblog track: Information Extraction from Microblogs Posted during Disasters


title: Joint Cascade Face Detection and Alignment

abstract: We present a new state-of-the-art approach for face detection. The key idea is to combine face alignment with detection, observing that aligned face shapes provide better features for face classification. To make this combination more effective, our approach learns the two tasks jointly in the same cascade framework, by exploiting recent advances in face alignment. Such joint learning greatly enhances the capability of cascade detection and still retains its realtime performance. Extensive experiments show that our approach achieves the best accuracy on challenging datasets, where all existing solutions are either inaccurate or too slow.


title: The amygdala: vigilance and emotion

abstract: Here we provide a review of the animal and human literature concerning the role of the amygdala in fear conditioning, considering its potential influence over autonomic and hormonal changes, motor behavior and attentional processes. A stimulus that predicts an aversive outcome will change neural transmission in the amygdala to produce the somatic, autonomic and endocrine signs of fear, as well as increased attention to that stimulus. It is now clear that the amygdala is also involved in learning about positively valenced stimuli as well as spatial and motor learning and this review strives to integrate this additional information. A review of available studies examining the human amygdala covers both lesion and electrical stimulation studies as well as the most recent functional neuroimaging studies. Where appropriate, we attempt to integrate basic information on normal amygdala function with our current understanding of psychiatric disorders, including pathological anxiety.


title: The cumulative cost of additional wakefulness: Dose-response effects on neurobehavioral functions and sleep physiology from Chronic sleep restriction and total sleep deprivation

abstract: OBJECTIVES: To inform the debate over whether human sleep can be chronically reduced without consequences, we conducted a dose-response chronic sleep restriction experiment in which waking neurobehavioral and sleep physiological functions were monitored and compared to those for total sleep deprivation. DESIGN: The chronic sleep restriction experiment involved randomization to one of three sleep doses (4 h, 6 h, or 8 h time in bed per night), which were maintained for 14 consecutive days. The total sleep deprivation experiment involved 3 nights without sleep (0 h time in bed). Each study also involved 3 baseline (pre-deprivation) days and 3 recovery days. SETTING: Both experiments were conducted under standardized laboratory conditions with continuous behavioral, physiological and medical monitoring. PARTICIPANTS: A total of n = 48 healthy adults (ages 21-38) participated in the experiments. INTERVENTIONS: Noctumal sleep periods were restricted to 8 h, 6 h or 4 h per day for 14 days, or to 0 h for 3 days. All other sleep was prohibited. RESULTS: Chronic restriction of sleep periods to 4 h or 6 h per night over 14 consecutive days resulted in significant cumulative, dose-dependent deficits in cognitive performance on all tasks. Subjective sleepiness ratings showed an acute response to sleep restriction but only small further increases on subsequent days, and did not significantly differentiate the 6 h and 4 h conditions. Polysomnographic variables and delta power in the non-REM sleep EEG-a putative marker of sleep homeostasis--displayed an acute response to sleep restriction with negligible further changes across the 14 restricted nights. Comparison of chronic sleep restriction to total sleep deprivation showed that the latter resulted in disproportionately large waking neurobehavioral and sleep delta power responses relative to how much sleep was lost. A statistical model revealed that, regardless of the mode of sleep deprivation, lapses in behavioral alertness were near-linearly related to the cumulative duration of wakefulness in excess of 15.84 h (s.e. 0.73 h). CONCLUSIONS: Since chronic restriction of sleep to 6 h or less per night produced cognitive performance deficits equivalent to up to 2 nights of total sleep deprivation, it appears that even relatively moderate sleep restriction can seriously impair waking neurobehavioral functions in healthy adults. Sleepiness ratings suggest that subjects were largely unaware of these increasing cognitive deficits, which may explain why the impact of chronic sleep restriction on waking cognitive functions is often assumed to be benign. Physiological sleep responses to chronic restriction did not mirror waking neurobehavioral responses, but cumulative wakefulness in excess of a 15.84 h predicted performance lapses across all four experimental conditions. This suggests that sleep debt is perhaps best understood as resulting in additional wakefulness that has a neurobiological"cost"which accumulates over time. Language: en


title: INXS: Bridging the throughput and energy gap for spiking neural networks

abstract: In recent years, multiple neuromorphic architectures have been designed to execute cognitive applications that deal with image and speech analysis. These architectures have followed one of two approaches. One class of architectures is based on machine learning with artificial neural networks. A second class is focused on emulating biology with spiking neuron models, in an attempt to eventually approach the brain's accuracy and energy efficiency. A prominent example of the second class is IBM's TrueNorth processor that can execute large spiking networks on a low-power tiled architecture, and achieve high accuracy on a variety of tasks. However, as we show in this work, there are many inefficiencies in the TrueNorth design. We propose a new architecture, INXS, for spiking neural networks that improves upon the computational efficiency and energy efficiency of the TrueNorth design by 3,129× and 10× respectively. The architecture uses memristor crossbars to compute the effects of input spikes on several neurons in parallel. Digital units are then used to update neuron state. We show that the parallelism offered by crossbars is critical in achieving high throughput and energy efficiency.


title: Reconstruction of surfaces behind occlusions in range images

abstract: Analysis and reconstruction of range images usually focuses on complex objects completely contained in the field of view; little attention has been devoted so far to the reconstruction of partially occluded simple-shaped wide areas like parts of a wall hidden behind furniture pieces in an indoor range image. The work in this paper is aimed at such reconstruction. First of all the range image is partitioned and surfaces are fitted to these partitions. A further step locates possibly, occluded areas, while a final step determines which areas are actually occluded. The reconstruction of data occurs in this last step.


title: Camera model identification with the use of deep convolutional neural networks

abstract: In this paper, we propose a camera model identification method based on deep convolutional neural networks (CNNs). Unlike traditional methods, CNNs can automatically and simultaneously extract features and learn to classify during the learning process. A layer of preprocessing is added to the CNN model, and consists of a high pass filter which is applied to the input image. Before feeding the CNN, we examined the CNN model with two types of residuals. The convolution and classification are then processed inside the network. The CNN outputs an identification score for each camera model. Experimental comparison with a classical two steps machine learning approach shows that the proposed method can achieve significant detection performance. The well known object recognition CNN models, AlexNet and GoogleNet, are also examined.


title: Active Exploration in Dynamic Environments

abstract: Whenever an agent learns to control an unknown environment, two opposing principles have to be combined, namely: exploration (long-term optimization) and exploitation (short-term optimization). Many real-valued connectionist approaches to learning control realize exploration by randomness in action selection. This might be disadvantageous when costs are assigned to "negative experiences". The basic idea presented in this paper is to make an agent explore unknown regions in a more directed manner. This is achieved by a so-called competence map, which is trained to predict the controller's accuracy, and is used for guiding exploration. Based on this, a bistable system enables smoothly switching attention between two behaviors - exploration and exploitation - depending on expected costs and knowledge gain. ::: ::: The appropriateness of this method is demonstrated by a simple robot navigation task.


title: Clinical practice The diagnosis of imported malaria in children

abstract: The present paper reviews the diagnosis of imported malaria in children. Malaria is caused by a parasite called Plasmodium and occurs in over 100 countries worldwide. Children account for 10–15% of all patients with imported malaria and are at risk to develop severe and life-threatening complications especially when infected with Plasmodium falciparum. Case–fatality ratios vary between 0.2% and 0.4%. Children visiting friends and relatives in malaria endemic areas and immigrants and refugees account for the vast majority of cases. Symptoms are non-specific and delayed infections (more than 3 months after return from an endemic country) may occur. Microscopic analysis of the thick blood film is the cornerstone of laboratory diagnosis. For pragmatic reasons, EDTA-anticoagulated blood is accepted, provided that slides are prepared within 1 h after collection. Information about the Plasmodium species (in particular P. falciparum versus the non-falciparum species) and the parasite density is essential for patient management. Molecular methods in reference settings are an adjunct for species differentiation. Signals generated by automated hematology analyzers may trigger the diagnosis of malaria in non-suspected cases. Malaria rapid diagnostic tests are reliable in the diagnosis of P. falciparum but not for the detection of the non-falciparum species. They do not provide information about parasite density and should be used as an adjunct (and not a substitute) to microscopy. In case of persistent suspicion and negative microscopy results, repeat testing every 8–12 h for at least three consecutive samplings is recommended. A high index of suspicion and a close interaction with the laboratory may assure timely diagnosis of imported malaria.


title: Quaternion Convolutional Neural Networks for Heterogeneous Image Processing

abstract: Convolutional neural networks (CNN) have recently achieved state-of-the-art results in various applications. In the case of image recognition, an ideal model has to learn independently of the training data, both local dependencies between the three components (R,G,B) of a pixel, and the global relations describing edges or shapes, making it efficient with small or heterogeneous datasets. Quaternion-valued convolutional neural networks (QCNN) solved this problematic by introducing multidimensional algebra to CNN. This paper proposes to explore the fundamental reason of the success of QCNN over CNN, by investigating the impact of the Hamilton product on a color image reconstruction task performed from a gray-scale only training. By learning independently both internal and external relations and with less parameters than real valued convolutional encoder-decoder (CAE), quaternion convolutional encoder-decoders (QCAE) perfectly reconstructed unseen color images while CAE produced worst and gray-scale versions.