title: Two-way (true full-duplex) wireless

abstract: Current wireless systems are one-way (similar to walkie-talkies), meaning that disjoint time or frequency segments are used to transmit and to receive. Realization of two-way wireless has challenged the research community for many years. This article1 establishes the theory and presents practical realization of two-way (true full-duplex) wireless. In contrast to the widely accepted beliefs, it is shown that two-way wireless is not only feasible, but is fairly simple, with virtually no degradation in signal-to-noise-ratio2. The innovation is in the antenna design and multiple levels for cancelling self-interference. Methods are developed to support Multiple-Input Multiple-Output (MIMO) two-way transmission, and asynchronous two-way links (useful in networking applications). The developed hardware uses off-the-shelf components, antennas have a simple structure, are omnidirectional (can be directional, if needed), do not suffer from bandwidth limitations, have a small size/spacing, and the increase in overall complexity is minimal. It is shown that two-way wireless can do more than doubling the rate. In particular: 1) Facilitates wireless networking. In particular, the ability to handle asynchronous users enables superimposing a half-duplex, low bit rate, low power, easy to detect network for control signaling superimposed (physical overlay, rather than logical) on top of the network of primary full-duplex data links. The superimposed links are separated from the primary full-duplex data links in the code domain, and use time multiplexing plus Carrier Sense Multiple Access (CSMA) among themselves. However, the conventional problems of CSMA are avoided as control links operate in parallel with primary full-duplex data links. The physical layer of control links is designed such that full-duplex data links can detect and cancel the interference caused by the superimposed control links. 2) Enhances security through desirable jamming. 3) Provides the ground to realize unconditional security (beyond computational or information theoretical security), using a simple method introduced in this article. 4) Facilitates multi-node distributed & collaborative signaling, including realization of Network Information Theoretic setups, and cognitive wireless. 5) Exploiting feedback, it improves point-to-point throughput, and enables ultra low power transmission. 6) Doubles the point-to-point throughput.


Short Samples in Authorship Attribution: A New Approach


title: Planning chemical syntheses with deep neural networks and symbolic AI

abstract: To plan the syntheses of small organic molecules, chemists use retrosynthesis, a problem-solving technique in which target molecules are recursively transformed into increasingly simpler precursors. Computer-aided retrosynthesis would be a valuable tool but at present it is slow and provides results of unsatisfactory quality. Here we use Monte Carlo tree search and symbolic artificial intelligence (AI) to discover retrosynthetic routes. We combined Monte Carlo tree search with an expansion policy network that guides the search, and a filter network to pre-select the most promising retrosynthetic steps. These deep neural networks were trained on essentially all reactions ever published in organic chemistry. Our system solves for almost twice as many molecules, thirty times faster than the traditional computer-aided search method, which is based on extracted rules and hand-designed heuristics. In a double-blind AB test, chemists on average considered our computer-generated routes to be equivalent to reported literature routes.


title: ASTER: An Attentional Scene Text Recognizer with Flexible Rectification

abstract: A challenging aspect of scene text recognition is to handle text with distortions or irregular layout. In particular, perspective text and curved text are common in natural scenes and are difficult to recognize. In this work, we introduce ASTER, an end-to-end neural network model that comprises a rectification network and a recognition network. The rectification network adaptively transforms an input image into a new one, rectifying the text in it. It is powered by a flexible Thin-Plate Spline transformation which handles a variety of text irregularities and is trained without human annotations. The recognition network is an attentional sequence-to-sequence model that predicts a character sequence directly from the rectified image. The whole model is trained end to end, requiring only images and their groundtruth text. Through extensive experiments, we verify the effectiveness of the rectification and demonstrate the state-of-the-art recognition performance of ASTER. Furthermore, we demonstrate that ASTER is a powerful component in end-to-end recognition systems, for its ability to enhance the detector.


title: Design of Broadband Constant-Beamwidth Conical Corrugated-Horn Antennas [Antenna Designer's Notebook]

abstract: In this paper, a new design procedure is proposed for the design of wideband constant-beamwidth conical corrugated-horn antennas, with minimum design and construction complexity. The inputs to the procedure are the operating frequency band, the required minimum beamwidth in the entire frequency band, and the frequency in which the maximum gain is desired to occur. Based on these values, the procedure gives a relatively good design with a relative bandwidth of up to 2.5:1. Based on the proposed procedure, a corrugated-horn antenna with a constant beamwidth over the frequencies of 8 to 18 GHz was designed and simulated using commercial software. The designed antenna was also constructed, and its electromagnetic performance was measured. The measured results of the constructed prototype antenna confirmed the simulation results and satisfied the design requirements, validating the proposed design procedure.


title: User-centric ultra-dense networks for 5G: challenges, methodologies, and directions

abstract: Ultra-dense networking (UDN) is considered as a promising technology for 5G. In this article, we define user-centric UDN (UUDN) by introducing the philosophy of the network serving user and the "de-cellular" method. Based on the analysis of challenges and requirements of UUDN, a new architecture is presented that breaks through the traditional cellular architecture of the network controlling user. Dynamic AP grouping is proposed as the core function of UUDN, through which a user could enjoy satisfactory and secure service following her movement. Furthermore, we provide methods for mobility management, resource management, interference management, and security issues. We point out that these functions should be co-designed and jointly optimized in order to improve the system throughput with higher resource utilization, better user experience, and increased energy efficiency. Finally, future works in UUDN are discussed.


title: A generalized Blahut-Arimoto algorithm

abstract: Kavcic proposed in (A. Kavcic, 2001) an algorithm that apparently finds the mutual-information-rate-maximizing parameters of a Markov source at the input to an indecomposable finite-state channel. In this paper we prove that the stationary points of this algorithm indeed correspond one-to-one to the critical points of the information-rate curve. Kavcic's algorithm can be considered as a generalized Blahut-Arimoto algorithm, as it includes as special cases the classical Blahut-Arimoto algorithm for discrete memoryless channels (DMCs) and the solution to finding the capacity-achieving input distribution for finite-state channels with no noise (C.E. Shannon, 1948).


title: Partitioned Elias-Fano indexes

abstract: The Elias-Fano representation of monotone sequences has been recently applied to the compression of inverted indexes, showing excellent query performance thanks to its efficient random access and search operations. While its space occupancy is competitive with some state-of-the-art methods such as gamma-delta-Golomb codes and PForDelta, it fails to exploit the local clustering that inverted lists usually exhibit, namely the presence of long subsequences of close identifiers. In this paper we describe a new representation based on partitioning the list into chunks and encoding both the chunks and their endpoints with Elias-Fano, hence forming a two-level data structure. This partitioning enables the encoding to better adapt to the local statistics of the chunk, thus exploiting clustering and improving compression. We present two partition strategies, respectively with fixed and variable-length chunks. For the latter case we introduce a linear-time optimization algorithm which identifies the minimum-space partition up to an arbitrarily small approximation factor. We show that our partitioned Elias-Fano indexes offer significantly better compression than plain Elias-Fano, while preserving their query time efficiency. Furthermore, compared with other state-of-the-art compressed encodings, our indexes exhibit the best compression ratio/query time trade-off.


Precision-Recall-Gain Curves: PR Analysis Done Right


title: Tracking Dengue Epidemics using Twitter Content Classification and Topic Modelling

abstract: Detecting and preventing outbreaks of mosquito-borne diseases such as Dengue and Zika in Brasil and other tropical regions has long been a priority for governments in affected areas. Streaming social media content, such as Twitter, is increasingly being used for health vigilance applications such as flu detection. However, previous work has not addressed the complexity of drastic seasonal changes on Twitter content across multiple epidemic outbreaks. In order to address this gap, this paper contrasts two complementary approaches to detecting Twitter content that is relevant for Dengue outbreak detection, namely supervised classification and unsupervised clustering using topic modelling. Each approach has benefits and shortcomings. Our classifier achieves a prediction accuracy of about 80i¾?% based on a small training set of about 1,000 instances, but the need for manual annotation makes it hard to track seasonal changes in the nature of the epidemics, such as the emergence of new types of virus in certain geographical locations. In contrast, LDA-based topic modelling scales well, generating cohesive and well-separated clusters from larger samples. While clusters can be easily re-generated following changes in epidemics, however, this approach makes it hard to clearly segregate relevant tweets into well-defined clusters.


title: Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems

abstract: This article presents our initial results in deep learning for channel estimation and signal detection in orthogonal frequency-division multiplexing (OFDM). OFDM has been widely adopted in wireless broadband communications to combat frequency-selective fading in wireless channels. In this article, we take advantage of deep learning in handling wireless OFDM channels in an end-to-end approach. Different from existing OFDM receivers that first estimate CSI explicitly and then detect/recover the transmitted symbols with the estimated CSI, our deep learning based approach estimates CSI implicitly and recovers the transmitted symbols directly. To address channel distortion, a deep learning model is first trained offline using the data generated from the simulation based on the channel statistics and then used for recovering the online transmitted data directly. From our simulation results, the deep learning based approach has the ability to address channel distortions and detect the transmitted symbols with performance comparable to minimum mean-square error (MMSE) estimator. Furthermore, the deep learning based approach is more robust than conventional methods when fewer training pilots are used, the cyclic prefix (CP) is omitted, and nonlinear clipping noise is presented. In summary, deep learning is a promising tool for channel estimation and signal detection in wireless communications with complicated channel distortions and interferences.


title: Exploring QoE in Cellular Networks: How Much Bandwidth do you Need for Popular Smartphone Apps?

abstract: A quarter of the world population will be using smartphones to access the Internet in the near future. In this context, understanding the Quality of Experience (QoE) of popular services in such devices becomes paramount for cellular network operators, who need to offer high quality levels to reduce the risks of customers churning for quality dissatisfaction. In this paper we study the problem of QoE provisioning in smartphones, presenting the results obtained from subjective lab tests performed for five popular apps: YouTube, Facebook, Web browsing through Chrome, Google Maps, and WhatsApp. The analysis addresses the impact of the access downlink bandwidth on the QoE of these apps when accessed through smartphones. The results presented in this paper provide a sound basis for better understanding the QoE requirements of popular services and mobile apps, as well as for dimensioning the underlying provisioning network. To the best of our knowledge, this is the first paper providing such a comprehensive analysis of QoE in mobile devices.


title: Towards increased road safety: Real-time decision making for driverless city vehicles

abstract: This work elaborates on the topic of decision making for driverless city vehicles, particularly focusing on the aspects on how to develop a reliable approach which meets the requirements of safe city traffic. Decision making in this context refers to the problem of identifying the most appropriate driving maneuver to be performed in a given traffic situation. The overall decision making problem is decomposed into two consecutive stages. The first stage is safety-crucial, representing the decision regarding the set of feasible driving maneuvers. The second stage represents the decision regarding the most appropriate driving maneuver from the set of feasible ones. The developed decision making approach has been implemented in C++ and initially tested in a 3D simulation environment and, thereafter, in real-world experiments. The real-world experiments also included the integration of wireless communication between vehicles.


title: Antidepressive, anxiolytic, and antiaddictive effects of ayahuasca, psilocybin and lysergic acid diethylamide (LSD): a systematic review of clinical trials published in the last 25 years

abstract: To date, pharmacological treatments for mood and anxiety disorders and for drug dependence show limited efficacy, leaving a large number of patients suffering severe and persistent symptoms. Preliminary studies in animals and humans suggest that ayahuasca, psilocybin and lysergic acid diethylamide (LSD) may have antidepressive, anxiolytic, and antiaddictive properties. Thus, we conducted a systematic review of clinical trials published from 1990 until 2015, assessing these therapeutic properties. Electronic searches were performed using the PubMed, LILACS, and SciELO databases. Only clinical trials published in peer-reviewed journals were included. Of these, 151 studies were identified, of which six met the established criteria. Reviewed studies suggest beneficial effects for treatment-resistant depression, anxiety and depression associated with life-threatening diseases, and tobacco and alcohol dependence. All drugs were well tolerated. In conclusion, ayahuasca, psilocybin and LSD may be useful pharmacological tools for the treatment of drug dependence, and anxiety and mood disorders, especially in treatment-resistant patients. These drugs may also be useful pharmacological tools to understand psychiatric disorders and to develop new therapeutic agents. However, all studies reviewed had small sample sizes, and half of them were open-label, proof-of-concept studies. Randomized, double-blind, placebo-controlled studies with more patients are needed to replicate these preliminary findings.


title: WALNUT: Waging Doubt on the Integrity of MEMS Accelerometers with Acoustic Injection Attacks

abstract: Cyber-physical systems depend on sensors to make automated decisions. Resonant acoustic injection attacks are already known to cause malfunctions by disabling MEMS-based gyroscopes. However, an open question remains on how to move beyond denial of service attacks to achieve full adversarial control of sensor outputs. Our work investigates how analog acoustic injection attacks can damage the digital integrity of a popular type of sensor: the capacitive MEMS accelerometer. Spoofing such sensors with intentional acoustic interference enables an out-of-spec pathway for attackers to deliver chosen digital values to microprocessors and embedded systems that blindly trust the unvalidated integrity of sensor outputs. Our contributions include (1) modeling the physics of malicious acoustic interference on MEMS accelerometers, (2) discovering the circuit-level security flaws that cause the vulnerabilities by measuring acoustic injection attacks on MEMS accelerometers as well as systems that employ on these sensors, and (3) two software-only defenses that mitigate many of the risks to the integrity of MEMS accelerometer outputs. We characterize two classes of acoustic injection attacks with increasing levels of adversarial control: output biasing and output control. We test these attacks against 20 models of capacitive MEMS accelerometers from 5 different manufacturers. Our experiments find that 75% are vulnerable to output biasing, and 65% are vulnerable to output control. To illustrate end-to-end implications, we show how to inject fake steps into a Fitbit with a $5 speaker. In our self-stimulating attack, we play a malicious music file from a smartphone's speaker to control the on-board MEMS accelerometer trusted by a local app to pilot a toy RC car. In addition to offering hardware design suggestions to eliminate the root causes of insecure amplification and filtering, we introduce two low-cost software defenses that mitigate output biasing attacks: randomized sampling and 180 degree out-of-phase sampling. These software-only approaches mitigate attacks by exploiting the periodic and predictable nature of the malicious acoustic interference signal. Our results call into question the wisdom of allowing microprocessors and embedded systems to blindly trust that hardware abstractions alone will ensure the integrity of sensor outputs.


title: Direct Ray Tracing of Displacement Mapped Triangles

abstract: We present an algorithm for ray tracing displacement maps that requires no additional storage over the base model. Displacement maps are rarely used in ray tracing due to the cost associated with storing and intersecting the displaced geometry. This is unfortunate because displacement maps allow the addition of large amounts of geometric complexity into models. Our method works for models composed of triangles with normals at the vertices. In addition, we discuss a special purpose displacement that creates a smooth surface that interpolates the triangle vertices and normals of a mesh. The combination allows relatively coarse models to be displacement mapped and ray traced effectively.


title: Merged computation for Whirlpool hashing

abstract: This paper presents an improved hardware structure for the computation of the Whirlpool hash function. By merging the round key computation with the data compression and by using embedded memories to perform part of the Galois Field (28) multiplication, a core can be implemented in just 43% of the area of the best current related art while achieving a 12% higher throughput. The proposed core improves the Throughput per Slice compared to the state of the art by 160%, achieving a throughput of 5.47 Gbit/s with 2110 slices and 32 BRAMs on a VIRTEX II Pro FPGA. Results for a real application are also presented by considering a polymorphic computational approach.


title: GRAPE: Parallelizing Sequential Graph Computations

abstract: We demonstrate GRAPE, a parallel GRAP h query E ngine. GRAPE advocates a parallel model based on a simultaneous fixed point computation in terms of partial and incremental evaluation. It differs from prior systems in its ability to parallelize existing sequential graph algorithms as a whole, without the need for recasting the entire algorithms into a new model. One of its unique features is that under a monotonic condition, GRAPE parallelization guarantees to terminate with correct answers as long as the sequential algorithms "plugged in" are correct. We demonstrate its parallel computations, ease-of-use and performance compared with the start-of-the-art graph systems. We also demonstrate a use case of GRAPE in social media marketing.


title: Epitranscriptome sequencing technologies: decoding RNA modifications

abstract: This Review describes the latest methods for profiling common epitranscriptomic marks, their scale, resolution and ability to quantify. It also discusses remaining challenges.


title: Towards Urban 3D Reconstruction from Video

abstract: The paper introduces a data collection system and a processing pipeline for automatic geo-registered 3D reconstruction of urban scenes from video. The system collects multiple video streams, as well as GPS and INS measurements in order to place the reconstructed models in geo- registered coordinates. Besides high quality in terms of both geometry and appearance, we aim at real-time performance. Even though our processing pipeline is currently far from being real-time, we select techniques and we design processing modules that can achieve fast performance on multiple CPUs and GPUs aiming at real-time performance in the near future. We present the main considerations in designing the system and the steps of the processing pipeline. We show results on real video sequences captured by our system.


title: Video summarization using singular value decomposition

abstract: The authors propose a novel technique for video summarization based on singular value decomposition (SVD). For the input video sequence, we create a feature-frame matrix A, and perform the SVD on it. From this SVD, we are able, to not only derive the refined feature space to better cluster visually similar frames, but also define a metric to measure the amount of visual content contained in each frame cluster using its degree of visual changes. Then, in the refined feature space, we find the most static frame cluster, define it as the content unit, and use the context value computed from it as the threshold to cluster the rest of the frames. Based on this clustering result, either the optimal set of keyframes, or a summarized motion video with the user specified time length can be generated to support different user requirements for video browsing and content overview. Our approach ensures that the summarized video representation contains little redundancy, and gives equal attention to the same amount of contents.


title: Deconvolutional Paragraph Representation Learning

abstract: Learning latent representations from long text sequences is an important first step in many natural language processing applications. Recurrent Neural Networks (RNNs) have become a cornerstone for this challenging task. However, the quality of sentences during RNN-based decoding (reconstruction) decreases with the length of the text. We propose a sequence-to-sequence, purely convolutional and deconvolutional autoencoding framework that is free of the above issue, while also being computationally efficient. The proposed method is simple, easy to implement and can be leveraged as a building block for many applications. We show empirically that compared to RNNs, our framework is better at reconstructing and correcting long paragraphs. Quantitative evaluation on semi-supervised text classification and summarization tasks demonstrate the potential for better utilization of long unlabeled text data.


From information design to experience design: smart artefacts and the disappearing computer


A Fast Elitist Multi-Objective Genetic Algorithm: NSGA-II


Organization and regulation of the postsynaptic density


title: A TOR-based anonymous communication approach to secure smart home appliances

abstract: Digital information has become a social infrastructure and with the expansion of the Internet, network infrastructure has become an indispensable part of social life and industrial activity for mankind. The idea of using existing electronics in smart home appliances and connecting them to the Internet is a new dimension along which technologies continue to grow, and in recent years mankind has witnessed an upsurge of usage of devices such as smart phone, smart television, home health-care device, smart LED light bulbs system, etc. Their build-in internet-controlled function has made them quite attractive to many segments of consumers and smart phone has become a common gadget for social networking. There are, however, serious challenges which need to be addressed as these tiny devices are designed for specific functions and lack processing capacity required for most security software. This research explores how these internet-enabled smart devices can be turned into very dangerous spots for distributed attacks purposes by cybercriminals for various ill intensions in a pinpointed manner. It then introduces a new approach to deal with such problems by taking advantage of the anonymous communication of the Onion Router (hereafter: TOR). It compares pros and cons of using anonymous communication scheme and justifies it be an efficient countermeasure to most attack scenarios.


title: Deep Big Multilayer Perceptrons for Digit Recognition

abstract: The competitive MNIST handwritten digit recognition benchmark has a long history of broken records since 1998. The most recent advancement by others dates back 8 years (error rate 0.4 old on-line back-propagation for plain multi-layer perceptrons yields a very low 0.35% error rate on the MNIST handwritten digits benchmark with a single MLP and 0.31% with a committee of seven MLP. All we need to achieve this until 2011 best result are many hidden layers, many neurons per layer, numerous deformed training images to avoid overfitting, and graphics cards to greatly speed up learning.


title: Deep Convolutional Neural Network Using Triplets of Faces, Deep Ensemble, and Score-Level Fusion for Face Recognition

abstract: This paper proposes a new face verification method that uses multiple deep convolutional neural networks (DCNNs) and a deep ensemble, that extracts two types of low dimensional but discriminative and high-level abstracted features from each DCNN, then combines them as a descriptor for face verification. Our DCNNs are built from stacked multi-scale convolutional layer blocks to present multi-scale abstraction. To train our DCNNs, we use different resolutions of triplets that consist of reference images, positive images, and negative images, and triplet-based loss function that maximize the ratio of distances between negative pairs and positive pairs and minimize the absolute distances between positive face images. A deep ensemble is generated from features extracted by each DCNN, and used as a descriptor to train the joint Bayesian learning and its transfer learning method. On the LFW, although we use only 198,018 images and only four different types of networks, the proposed method with the joint Bayesian learning and its transfer learning method achieved 98.33% accuracy. In addition to further increase the accuracy, we combine the proposed method and high dimensional LBP based joint Bayesian method, and achieved 99.08% accuracy on the LFW. Therefore, the proposed method helps to improve the accuracy of face verification when training data is insufficient to train DCNNs.


title: Accelerated simulation of spiking neural networks using GPUs

abstract: Spiking neural network simulators provide environments in which to implement and experiment with models of biological brain structures. Simulating large-scale models is computationally expensive, however, due to the number and interconnectedness of neurons in the brain. Furthermore, where such simulations are used in an embodied setting, the simulation must be real-time in order to be useful. In this paper we present a platform (nemo) for such simulations which achieves high performance on parallel commodity hardware in the form of graphics processing units (GPUs). This work makes use of the Izhikevich neuron model which provides a range of realistic spiking dynamics while being computationally efficient. Learning is facilitated through spike-timing dependent synaptic plasticity. Our GPU kernel can deliver up to 550 million spikes per second using a single device. This corresponds to a real-time simulation of around 55 000 neurons under biologically plausible conditions with 1000 synapses per neuron and a mean firing rate of 10 Hz.


GraphZip : Mining Graph Streams using Dictionary-based Compression


title: Multicast and unicast real-time video streaming over wireless LANs

abstract: We address the problem of real-time video streaming over wireless LANs for both unicast and multicast transmission. The wireless channel is modeled as a packet-erasure channel at the IP level. For the unicast scenario, we describe a novel hybrid Automatic Repeat reQuest (ARQ) algorithm that efficiently combines forward error control (FEC) coding with the ARQ protocol. For the multiple-users scenario, we formulate the problem of real-time video multicast as an optimization of a maximum regret cost function across the multicast user space. The proposed solution efficiently combines progressive source coding with FEC coding. We present a theoretical analysis of the unicast and multicast cases, as well as experimental results that demonstrate the performance advantages of the proposed algorithms over existing methods.


title: A compact wireless charging system development

abstract: In this paper, a high power density high efficiency wireless power transfer converter system via inductive coupling has been designed and developed. The detailed gate drive design, cooling system design and power stage development are presented.


Digital Forensic Identification, Collection, Examination and Decoding of Windows Registry Keys for Discovering User Activities Patterns


title: Diversity and disparity of sparassodonts (Metatheria) reveal non-analogue nature of ancient South American mammalian carnivore guilds

abstract: This study investigates whether terrestrial mammalian carnivore guilds of ancient South America, which developed in relative isolation, were similar to those of other continents. We do so through analyses of clade diversification, ecomorphology and guild structure in the Sparassodonta, metatherians that were the predominant mammalian carnivores of pre-Pleistocene South America. Body mass and 16 characters of the dentition are used to quantify morphological diversity (disparity) in sparassodonts and to compare them to extant marsupial and placental carnivores and extinct North American carnivoramorphans. We also compare trophic diversity of the Early Miocene terrestrial carnivore guild of Santa Cruz, Argentina to that of 14 modern and fossil guilds from other continents. We find that sparassodonts had comparatively low ecomorphological disparity throughout their history and that South American carnivore palaeoguilds, as represented by that of Santa Cruz, Argentina, were unlike modern or fossil carnivore guilds of other continents in their lack of mesocarnivores and hypocarnivores. Our results add to a growing body of evidence highlighting non-analogue aspects of extinct South American mammals and illustrate the dramatic effects that historical contingency can have on the evolution of mammalian palaeocommunities.


title: Video Game Addiction Test: Validity and Psychometric Characteristics

abstract: Abstract The study explores the reliability, validity, and measurement invariance of the Video game Addiction Test (VAT). Game-addiction problems are often linked to Internet enabled online games; the VAT has the unique benefit that it is theoretically and empirically linked to Internet addiction. The study used data (n=2,894) from a large-sample paper-and-pencil questionnaire study, conducted in 2009 on secondary schools in Netherlands. Thus, the main source of data was a large sample of schoolchildren (aged 13–16 years). Measurements included the proposed VAT, the Compulsive Internet Use Scale, weekly hours spent on various game types, and several psychosocial variables. The VAT demonstrated excellent reliability, excellent construct validity, a one-factor model fit, and a high degree of measurement invariance across gender, ethnicity, and learning year, indicating that the scale outcomes can be compared across different subgroups with little bias. In summary, the VAT can be helpful in the further study...


Neural Cryptography : From Symmetric Encryption to Adversarial Steganography


title: Model-free, Model-based, and General Intelligence

abstract: During the 60s and 70s, AI researchers explored intuitions about intelligence by writing programs that displayed intelligent behavior. Many good ideas came out from this work but programs written by hand were not robust or general. After the 80s, research increasingly shifted to the development of learners capable of inferring behavior and functions from experience and data, and solvers capable of tackling well-defined but intractable models like SAT, classical planning, Bayesian networks, and POMDPs. The learning approach has achieved considerable success but results in black boxes that do not have the flexibility, transparency, and generality of their model-based counterparts. Model-based approaches, on the other hand, require models and scalable algorithms. Model-free learners and model-based solvers have close parallels with Systems 1 and 2 in current theories of the human mind: the first, a fast, opaque, and inflexible intuitive mind; the second, a slow, transparent, and flexible analytical mind. In this paper, I review developments in AI and draw on these theories to discuss the gap between model-free learners and model-based solvers, a gap that needs to be bridged in order to have intelligent systems that are robust and general.


abstract: We propose a new learning method, "Generalized Learning Vector Quantization (GLVQ)," in which reference vectors are updated based on the steepest descent method in order to minimize the cost function. The cost function is determined so that the obtained learning rule satisfies the convergence condition. We prove that Kohonen's rule as used in LVQ does not satisfy the convergence condition and thus degrades recognition ability. Experimental results for printed Chinese character recognition reveal that GLVQ is superior to LVQ in recognition ability.


title: AC voltage regulator based on AC/AC buck converter

abstract: This paper introduces a high efficiency AC voltage regulator based on an AC/AC buck converter cascaded by a transformer in series with the input voltage. The AC/AC converter uses an overlap time in the gate signals to solve the commutation problem. Non-use of any snubber circuits and current sensors leads to lower cost, smaller size and simpler hardware. The converter generates only the compensation term which results in smaller switches and, thus, lower cost. Simulation and experimental results verify the performance of the proposed topology.


title: Towards High-Definition 3D Urban Mapping: Road Feature-Based Registration of Mobile Mapping Systems and Aerial Imagery

abstract: Various applications have utilized a mobile mapping system (MMS) as the main 3D urban remote sensing platform. However, the accuracy and precision of the three-dimensional data acquired by an MMS is highly dependent on the performance of the vehicle’s self-localization, which is generally performed by high-end global navigation satellite system (GNSS)/inertial measurement unit (IMU) integration. However, GNSS/IMU positioning quality degrades significantly in dense urban areas with high-rise buildings, which block and reflect the satellite signals. Traditional landmark updating methods, which improve MMS accuracy by measuring ground control points (GCPs) and manually identifying those points in the data, are both labor-intensive and time-consuming. In this paper, we propose a novel and comprehensive framework for automatically georeferencing MMS data by capitalizing on road features extracted from high-resolution aerial surveillance data. The proposed framework has three key steps: (1) extracting road features from the MMS and aerial data; (2) obtaining Gaussian mixture models from the extracted aerial road features; and (3) performing registration of the MMS data to the aerial map using a dynamic sliding window and the normal distribution transform (NDT). The accuracy of the proposed framework is verified using field data, demonstrating that it is a reliable solution for high-precision urban mapping.


title: Coasting control of EV motor considering cross coupling inductance

abstract: Coasting is a fuel-saving driving mode that is usually active when the vehicle slows down. In an electric vehicle (EV), coasting can be achieved by letting the q-axis current to be set to zero. However, braking action often occurs at high speeds when the gas pedal is tipped off. That is, negative torque is produced while the q-axis current is equal to zero. Such a braking torque reduces the coasting distance. To prevent such undesirable braking, a positive q-axis current command is injected in practice. In this paper, a cross-coupling inductance, L qd , is studied as a main cause for the braking torque. If it is present, torque can be developed by only the d-axis current. Further, it grows quickly as the speed increases. The problem can be solved by shifting the angle of the reference frame. The compensation angle is analytically derived using two different methods. The proposed compensation method was demonstrated effectively by simulation and experiments.


title: Joint segmentation of multivariate time series with hidden process regression for human activity recognition

abstract: The problem of human activity recognition is central for understanding and predicting the human behavior, in particular in a prospective of assistive services to humans, such as health monitoring, well being, security, etc. There is therefore a growing need to build accurate models which can take into account the variability of the human activities over time (dynamic models) rather than static ones which can have some limitations in such a dynamic context. In this paper, the problem of activity recognition is analyzed through the segmentation of the multidimensional time series of the acceleration data measured in the 3-d space using body-worn accelerometers. The proposed model for automatic temporal segmentation is a specific statistical latent process model which assumes that the observed acceleration sequence is governed by sequence of hidden (unobserved) activities. More specifically, the proposed approach is based on a specific multiple regression model incorporating a hidden discrete logistic process which governs the switching from one activity to another over time. The model is learned in an unsupervised context by maximizing the observed-data log-likelihood via a dedicated expectation–maximization (EM) algorithm. We applied it on a real-world automatic human activity recognition problem and its performance was assessed by performing comparisons with alternative approaches, including well-known supervised static classifiers and the standard hidden Markov model (HMM). The obtained results are very encouraging and show that the proposed approach is quite competitive even it works in an entirely unsupervised way and does not requires a feature extraction preprocessing step.


title: Comparison of Shunt and Series/Shunt nMOS Single-Pole Double-Throw Switches for X-Band Phased Array T/R Modules

abstract: This paper compares the performance of shunt and series/shunt single-pole double-throw nMOS switches designed in a 0.13 mum SiGe BiCMOS process for X-band phased array transmit/receive modules. From 8.5 to 10.5 GHz, the worst case return loss, insertion loss, and isolation are 14.5, 1.89, and 20.5 dB, respectively, for the reflective shunt switch, and 22.2, 2.33, and 22.5 dB, respectively, for the absorptive series/shunt switch. Both switches exhibit an IIP3 of about 28 dBm and dissipate no dc power. The performance of these switches are comparable to other CMOS switches found in triple well technologies, on non-standard substrates, using special device structures, or using extra dc biases


title: Weighted Semi-Global Matching and Center-Symmetric Census Transform for Robust Driver Assistance

abstract: Automotive applications based on stereo vision require robust and fast matching algorithms, which makes semi-global matching (SGM) a popular method in this field. Typically the Census transform is used as a cost function, since it is advantageous for outdoor scenes. We propose an extension based on center-symmetric local binary patterns, which allows better efficiency and higher matching quality. Our second contribution exploits knowledge about the three-dimensional structure of the scene to selectively enforce the smoothness constraints of SGM. It is shown that information about surface normals can be easily integrated by weighing the paths according to the gradient of the disparity. The different approaches are evaluated on the KITTI benchmark, which provides real imagery with LIDAR ground truth. The results indicate improved performance compared to state-of-the-art SGM based algorithms.


title: Magpie: Online Modelling and Performance-aware Systems

abstract: Understanding the performance of distributed systems requires correlation of thousands of interactions between numerous components -- a task best left to a computer. Today's systems provide voluminous traces from each component but do not synthesise the data into concise models of system performance. ::: ::: We argue that online performance modelling should be a ubiquitous operating system service and outline several uses including performance debugging, capacity planning, system tuning and anomaly detection. We describe the Magpie modelling service which collates detailed traces from multiple machines in an e-commerce site, extracts request-specific audit trails, and constructs probabilistic models of request behaviour. A feasibility study evaluates the approach using an offline demonstrator. Results show that the approach is promising, but that there are many challenges to building a truly ubiquitious, online modelling infrastructure.


title: Fog Computing: A Taxonomy, Survey and Future Directions

abstract: In recent years, the number of Internet of Things (IoT) devices/sensors has increased to a great extent. To support the computational demand of real-time latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named “Fog computing” has been introduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. In this chapter, we comprehensively analyse the challenges in Fogs acting as an intermediate layer between IoT devices/sensors and Cloud datacentres and review the current developments in this field. We present a taxonomy of Fog computing according to the identified challenges and its key features. We also map the existing works to the taxonomy in order to identify current research gaps in the area of Fog computing. Moreover, based on the observations, we propose future directions for research.


title: When Fashion Meets Big Data: Discriminative Mining of Best Selling Clothing Features

abstract: With the prevalence of e-commence websites and the ease of online shopping, consumers are embracing huge amounts of various options in products. Undeniably, shopping is one of the most essential activities in our society and studying consumer's shopping behavior is important for the industry as well as sociology and psychology. Indisputable, one of the most popular e-commerce categories is clothing business. There arises the needs for analysis of popular and attractive clothing features which could further boost many emerging applications, such as clothing recommendation and advertising. In this work, we design a novel system that consists of three major components: 1) exploring and organizing a large-scale clothing dataset from a online shopping website, 2) pruning and extracting images of best-selling products in clothing item data and user transaction history, and 3) utilizing a machine learning based approach to discovering fine-grained clothing attributes as the representative and discriminative characteristics of popular clothing style elements. Through the experiments over a large-scale online clothing shopping dataset, we demonstrate the effectiveness of our proposed system, and obtain useful insights on clothing consumption trends and profitable clothing features.


title: An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study

abstract: Smart meter roll-outs provide easy access to granular meter measurements, enabling advanced energy services, ranging from demand response measures, tailored energy feedback and smart home/building automation. To design such services, train and validate models, access to data that resembles what is expected of smart meters, collected in a real-world setting, is necessary. The REFIT electrical load measurements dataset described in this paper includes whole house aggregate loads and nine individual appliance measurements at 8-second intervals per house, collected continuously over a period of two years from 20 houses. During monitoring, the occupants were conducting their usual routines. At the time of publishing, the dataset has the largest number of houses monitored in the United Kingdom at less than 1-minute intervals over a period greater than one year. The dataset comprises 1,194,958,790 readings, that represent over 250,000 monitored appliance uses. The data is accessible in an easy-to-use comma-separated format, is time-stamped and cleaned to remove invalid measurements, correctly label appliance data and fill in small gaps of missing data.


title: A comparative analysis of methodologies for database schema integration

abstract: One of the fundamental principles of the database approach is that a database allows a nonredundant, unified representation of all data managed in an organization. This is achieved only when methodologies are available to support integration across organizational and application boundaries. Methodologies for database design usually perform the design activity by separately producing several schemas, representing parts of the application, which are subsequently merged. Database schema integration is the activity of integrating the schemas of existing or proposed databases into a global, unified schema. The aim of the paper is to provide first a unifying framework for the problem of schema integration, then a comparative review of the work done thus far in this area. Such a framework, with the associated analysis of the existing approaches, provides a basis for identifying strengths and weaknesses of individual methodologies, as well as general guidelines for future improvements and extensions.


Modeling Consciousness in Virtual Computational Machines


title: A software framework for integrated sensor network applications

abstract: Sensor networks have received wide attention in recent years for their revolutionary impact in numerous fields. To harness their full potential, researchers are beginning to build end-to-end solutions that integrate heterogeneous sensor deployments with traditional networks. While such efforts will bring true value to the use of sensor networks, there are several challenges that need to be kept in perspective. This paper highlights some of the key challenges, and presents a system software methodology to meet the difficult demands of development, deployment and long term management of sensor network systems.


title: Surfacing collaborated networks in dark web to find illicit and criminal content

abstract: The Tor Network, a hidden part of the Internet, is becoming an ideal hosting ground for illegal activities and services, including large drug markets, financial frauds, espionage, child sexual abuse. Researchers and law enforcement rely on manual investigations, which are both time-consuming and ultimately inefficient. The first part of this paper explores illicit and criminal content identified by prominent researchers in the dark web. We previously developed a web crawler that automatically searched websites on the internet based on pre-defined keywords and followed the hyperlinks in order to create a map of the network. This crawler has demonstrated previous success in locating and extracting data on child exploitation images, videos, keywords and linkages on the public internet. However, as Tor functions differently at the TCP level, and uses socket connections, further technical challenges are faced when crawling Tor. Some of the other inherent challenges for advanced Tor crawling include scalability, content selection tradeoffs, and social obligation. We discuss these challenges and the measures taken to meet them. Our modified web crawler for Tor, termed the “Dark Crawler” has been able to access Tor while simultaneously accessing the public internet. We present initial findings regarding what extremist and terrorist contents are present in Tor and how this content is connected to each other in a mapped network that facilitates dark web crimes. Our results so far indicate the most popular websites in the dark web are acting as catalysts for dark web expansion by providing necessary knowledgebase, support and services to build Tor hidden services and onion websites.


title: Self-resonant induction furnace with IGBT technology

abstract: In this paper the results of electrical induction furnace developed in Chile are presented. The development is based on a U.S. Invention Patent No. 6,466,467, whose objective is varying the inverter frequency according the natural resonant frequency of the load, cycle by cycle. In this scenario several equipments have been built on a power range of 250 kW. By using a melting crucible it was possible to successfully melt about 1000 kg of minerals.


title: Training Bit Fully Convolutional Network for Fast Semantic Segmentation

abstract: Fully convolutional neural networks give accurate, per-pixel prediction for input images and have applications like semantic segmentation. However, a typical FCN usually requires lots of floating point computation and large run-time memory, which effectively limits its usability. We propose a method to train Bit Fully Convolution Network (BFCN), a fully convolutional neural network that has low bit-width weights and activations. Because most of its computation-intensive convolutions are accomplished between low bit-width numbers, a BFCN can be accelerated by an efficient bit-convolution implementation. On CPU, the dot product operation between two bit vectors can be reduced to bitwise operations and popcounts, which can offer much higher throughput than 32-bit multiplications and additions. To validate the effectiveness of BFCN, we conduct experiments on the PASCAL VOC 2012 semantic segmentation task and Cityscapes. Our BFCN with 1-bit weights and 2-bit activations, which runs 7.8x faster on CPU or requires less than 1\% resources on FPGA, can achieve comparable performance as the 32-bit counterpart.


title: Design of a Open loop resonator based filter Antenna for WiFi applications

abstract: In this paper, an Open loop resonator based filter Antenna is presented. The proposed antenna is a combination of Square open loop resonator with a rectangular patch. The filter is designed to have a pass band at 2.45GHz. The influence of geometric parameters on filter characteristics is studied in detail. The antenna is placed as a second resonator, thus combining filtering and radiating functions. As the antenna resonant frequency encompasses with the filter band pass, the antenna characteristics are not altered. The proposed structure is of reduced size and cost effective as two components are combined together. The variation of filter and antenna characteristics with geometric parameters are analyzed and optimized results are obtained.


title: Probabilistic and Bottle-Neck Features for LVCSR of Meetings

abstract: In recent years, probabilistic features became an integral part of state-of-the-are LVCSR systems. In this work, we are exploring the possibility of obtaining the features directly from neural net without the necessity of converting output probabilities to features suitable for subsequent GMM-HMM system. We experimented with 5-layer MLP with bottle-neck in the middle layer. After training such a neural net, we used outputs of the bottle-neck as features for GMM-HMM recognition system. The benefits are twofold: first, improvement was gained when these features are used instead of the probabilistic features, second, the size of the system was reduced, as only part of the neural net is used. The experiments were performed on meetings recognition task defined in MST RT'05 evaluation.


title: Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

abstract: Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. Temporal data arise in these real-world applications often involves a mixture of long-term and short-term patterns, for which traditional approaches such as Autoregressive models and Gaussian Process may fail. In this paper, we proposed a novel deep learning framework, namely Long- and Short-term Time-series network (LSTNet), to address this open challenge. LSTNet uses the Convolution Neural Network (CNN) and the Recurrent Neural Network (RNN) to extract short-term local dependency patterns among variables and to discover long-term patterns for time series trends. Furthermore, we leverage traditional autoregressive model to tackle the scale insensitive problem of the neural network model. In our evaluation on real-world data with complex mixtures of repetitive patterns, LSTNet achieved significant performance improvements over that of several state-of-the-art baseline methods. All the data and experiment codes are available online.


title: DETECTION OF TRANSIENT IN RADIO FREQUENCY FINGERPRINTING USING SIGNAL PHASE

abstract: Radio Frequency Fingerprinting (RFF) is a technique, which has been used to identify wireless devices. It essentially involves the detection of the transient signal and the extraction of the fingerprint. The detection phase, in our opinion, is the most challenging yet crucial part of the RFF process. Current approaches, namely Threshold and Bayesian Step Change Detector, which use amplitude characteristics of signals for transient detection, perform poorly with certain types of signals. This paper presents a new algorithm that exploits the phase characteristics for detection purposes. Validation using Bluetooth signals has resulted in a success rate of approximately 85-90 percent. We anticipate that the higher detection rate will result in a higher classification rate and thus support various device authetication schemes in the wireless domain.


title: BALANCE: towards a usable pervasive wellness application with accurate activity inference

abstract: Technology offers the potential to objectively monitor people's eating and activity behaviors and encourage healthier lifestyles. BALANCE is a mobile phone-based system for long term wellness management. The BALANCE system automatically detects the user's caloric expenditure via sensor data from a Mobile Sensing Platform unit worn on the hip. Users manually enter information on foods eaten via an interface on an N95 mobile phone. Initial validation experiments measuring oxygen consumption during treadmill walking and jogging show that the system's estimate of caloric output is within 87% of the actual value. Future work will refine and continue to evaluate the system's efficacy and develop more robust data input and activity inference methods.


title: Chemical Constituents of the Leaves of Psidium guajava

abstract: Five constituents including one new pentacyclic triterpenoid guajanoic acid (1) and four known compounds beta-sitosterol (2), uvaol (3), oleanolic acid (4), and ursolic acid (5) have been isolated from the leaves of Psidium guajava. The new constituent 1 has been characterized as 3beta-p-E-coumaroyloxy-2alpha-methoxyurs-12-en-28-oic acid through 2D NMR techniques and chemical transformations. This is the first report of isolation of compound 3 from the genus Psidium.


title: Autonomous detection and tracking of a surface ship using onboard monocular vision

abstract: This study addresses autonomous detection and tracking of a surface ship using a monocular camera mounted on an unmanned surface vehicle (USV). Automatic feature extraction and tracking filter algorithms are used for vision-based detection and tracking in real-time. For target tracking, the bearing and range to the target ship with respect to the own ship are obtained by computer vision techniques. The pixel distance from the horizon to the target in camera images is used to extract the range information, and a probing maneuver procedure is designed to enhance observability of the target tracking filter. The feasibility and performance of the proposed tracking approach are validated through field experiments with two USVs.


title: A practical implementation of a continuous isotropic spherical omnidirectional drive

abstract: This paper presents a continuous isotropic spherical omnidirectional drive mechanism that is efficient in its mechanical simplicity and use of volume. Spherical omnidirectional mechanisms allow isotropic motion, although many are limited from achieving true isotropic motion by practical mechanical design considerations. The mechanism presented in this paper uses a single motor to drive a point on the great circle of the sphere parallel to the ground plane, and does not require a gearbox. Three mechanisms located 120° apart provide a stable drive platform for a mobile robot. Results show the omnidirectional ability of the robot and demonstrate the performance of the spherical mechanism compared to a popular commercial omnidirectional wheel over edges of varying heights and gaps of varying widths.


title: Learning the Shared Subspace for Multi-task Clustering and Transductive Transfer Classification

abstract: There are many clustering tasks which are closely related in the real world, e.g. clustering the web pages of different universities. However, existing clustering approaches neglect the underlying relation and treat these clustering tasks either individually or simply together. In this paper, we will study a novel clustering paradigm, namely multi-task clustering, which performs multiple related clustering tasks together and utilizes the relation of these tasks to enhance the clustering performance. We aim to learn a subspace shared by all the tasks, through which the knowledge of the tasks can be transferred to each other. The objective of our approach consists of two parts: (1) Within-task clustering: clustering the data of each task in its input space individually; and (2) Cross-task clustering: simultaneous learning the shared subspace and clustering the data of all the tasks together. We will show that it can be solved by alternating minimization, and its convergence is theoretically guaranteed. Furthermore, we will show that given the labels of one task, our multi-task clustering method can be extended to transductive transfer classification (a.k.a. cross-domain classification, domain adaption). Experiments on several cross-domain text data sets demonstrate that the proposed multi-task clustering outperforms traditional single-task clustering methods greatly. And the transductive transfer classification method is comparable to or even better than several existing transductive transfer classification approaches.


title: Eye-controlled wheelchair

abstract: This document outlines a method for implementing an eye tracking device as a method of electrical wheelchair control. Through the use of measured gaze points, it is possible to translate a desired movement into a physical one. This form of interface does not only provide a form of transportation for those with severe disability but also allow the user to get a sense of control back into their lives.


title: Link Prediction on a Network of Co-occurring MeSH Terms: Towards Literature-based Discovery

abstract: Objectives: Literature-based discovery (LBD) is a text mining methodology for automatically generating research hypotheses from existing knowledge. We mimic the process of LBD as a classification problem on a graph of MeSH terms. We employ unsupervised and supervised link prediction methods for predicting previously unknown connections between biomedical concepts. Methods: We evaluate the effectiveness of link prediction through a series of experiments using a MeSH network that contains the history of link formation between biomedical concepts. We performed link prediction using proximity measures, such as common neighbor (CN), Jaccard coefficient (JC), Adamic / Adar index (AA) and preferential attachment (PA). Our approach relies on the assumption that similar nodes are more likely to establish a link in the future. Results: Applying an unsupervised approach, the AA measure achieved the best performance in terms of area under the ROC curve (AUC = 0.76), followed by CN, JC, and PA. In a supervised approach, we evaluate whether proximity measures can be combined to define a model of link formation across all four predictors. We applied various classifiers, including decision trees, k-nearest neighbors, logistic regression, multilayer perceptron, naive Bayes, and random forests. Random forest classifier accomplishes the best performance (AUC = 0.87). Conclusions: The link prediction approach proved to be effective for LBD processing. Supervised statistical learning approaches clearly outperform an unsupervised approach to link prediction.


title: Network Anomaly Detection Using Co-clustering

abstract: Early Internet architecture design goals did not put security as a high priority. However, today Internet security is a quickly growing concern. The prevalence of Internet attacks has increased significantly, but still the challenge of detecting such attacks generally falls on the end hosts and service providers, requiring system administrators to detect and block attacks on their own. In particular, as social networks have become central hubs of information and communication, they are increasingly the target of attention and attacks. This creates a challenge of carefully distinguishing malicious connections from normal ones. Previous work has shown that for a variety of Internet attacks, there is a small subset of connection measurements that are good indicators of whether a connection is part of an attack or not. In this paper we look at the effectiveness of using two different co-clustering algorithms to both cluster connections as well as mark which connection measurements are strong indicators of what makes any given cluster anomalous relative to the total data set. We run experiments with these co-clustering algorithms on the KDD 1999 Cup data set. In our experiments we find that soft co-clustering, running on samples of data, finds consistent parameters that are strong indicators of anomalous detections and creates clusters, that are highly pure. When running hard co-clustering on the full data set (over 100 runs), we on average have one cluster with 92.44% attack connections and the other with 75.84% normal connections. These results are on par with the KDD 1999 Cup winning entry, showing that co-clustering is a strong, unsupervised method for separating normal connections from anomalous ones. Finally, we believe that the ideas presented in this work may inspire research for anomaly detection in social networks, such as identifying spammers and fraudsters.


title: Quantitative detection of soybean rust using image processing techniques

abstract: Rust caused by Phakopsora pachyrhizi Syd. is a major constraint to soybean product in Asia. Early detection and possibilities of controlling plant diseases by the integration of several image processing methods has been the subject of extensive research. The main contribution of this paper is to present different methodologies for quantitatively detecting soybean rust at each stage of disease development, identify disease even before specific symptoms become visible and grade based on percentage of disease severity. Severity of rust infection levels at each stage of disease development was observed for 25 days on soybean leaf. Then color distribution and pixel relationship in rust infected leaf image was calculated based on global and local features for quantifying rust severity. Further, rust disease was categorized into grades based on infection severity levels and percentage disease index (PDI) was calculated. The maximum PDI of 95.5 was observed at 25 th day and minimum PDI of 0.2 was observed at 6 th day.


title: ShadowCuts: Photometric Stereo with Shadows

abstract: We present an algorithm for performing Lambertian photometric stereo in the presence of shadows. The algorithm has three novel features. First, a fast graph cuts based method is used to estimate per pixel light source visibility. Second, it allows images to be acquired with multiple illuminants, and there can be fewer images than light sources. This leads to better surface coverage and improves the reconstruction accuracy by enhancing the signal to noise ratio and the condition number of the light source matrix. The ability to use fewer images than light sources means that the imaging effort grows sublinearly with the number of light sources. Finally, the recovered shadow maps are combined with shading information to perform constrained surface normal integration. This reduces the low frequency bias inherent to the normal integration process and ensures that the recovered surface is consistent with the shadowing configuration The algorithm works with as few as four light sources and four images. We report results for light source visibility detection and high quality surface reconstructions for synthetic and real datasets.


Levels of B 2 B e-commerce adoption and competitive advantage in SMEs : A 1 comparison study between Egypt and USA


title: Scalable and Adaptive Resource Scheduling in IEEE 802.16 WiMAX Networks

abstract: This paper proposes the so-called adaptive resource scheduling (ARS) schemes to cope with both uplink and downlink traffic transmissions in WiMAX, in order to fully utilize the radio resource, and meet the various QoS requirements as well. Specifically, for the uplink transmission, each SS control and adjust its local sessions in a distributed manner; while for downlink the BS performs the resource scheduling in a centralized manner. The corresponding rate control mechanism is given, by adopting the control theory method as well. The simulation results also validate the efficiency and practice of our proposed scheme.


title: A New Variational Framework for Multiview Surface Reconstruction

abstract: The creation of surfaces from overlapping images taken from different vantages is a hard and important problem in computer vision. Recent developments fall primarily into two categories: the use of dense matching to produce point clouds from which surfaces are built, and the construction of surfaces from images directly. This paper presents a new method for surface reconstruction falling in the second category. First, a strongly motivated variational framework is built from the ground up based on a limiting case of photo-consistency. The framework includes a powerful new edge preserving smoothness term and exploits the input images exhaustively, directly yielding high quality surfaces instead of dealing with issues (such as noise or misalignment) after the fact. Numeric solution is accomplished with a combination of Gauss-Newton descent and the finite element method, yielding deep convergence in few iterates. The method is fast, robust, very insensitive to view/scene configurations, and produces state-of-the-art results in the Middlebury evaluation.


title: Latent Dirichlet Markov Allocation for Sentiment Analysis

abstract: In recent years probabilistic topic models have gained tremendous attention in data mining and natural language processing research areas. In the field of information retrieval for text mining, a variety of probabilistic topic models have been used to analyse content of documents. A topic model is a generative model for documents, it specifies a probabilistic procedure by which documents can be generated. All topic models share the idea that documents are mixture of topics, where a topic is a probability distribution over words. In this paper we describe Latent Dirichlet Markov Allocation Model (LDMA), a new generative probabilistic topic model, based on Latent Dirichlet Allocation (LDA) and Hidden Markov Model (HMM), which emphasizes on extracting multi-word topics from text data. LDMA is a four-level hierarchical Bayesian model where topics are associated with documents, words are associated with topics and topics in the model can be presented with single- or multi-word terms. To evaluate performance of LDMA, we report results in the field of aspect detection in sentiment analysis, comparing to the basic LDA model.


title: Smoothing methods for mathematical programs with equilibrium constraints

abstract: In the recent optimization world, mathematical programs with equilibrium constraints (MPECs) have been receiving much attention and there have been proposed a number of methods for solving MPECs. We provide a brief review of the recent achievements in the MPEC field and, as further applications of MPECs, we also mention the developments of the stochastic mathematical programs with equilibrium constraints (SMPECs).


title: Effective transfer learning of affordances for household robots

abstract: Learning how to use functional objects is essential for robots that are to carry out household tasks. However, learning every object from scratch would be a very naive and time-consuming approach. In this paper, we propose transfer learning of affordances to reduce the number of exploratory actions needed to learn how to use a new object. Through embodied interaction with the object, the robot discovers the object's similarity to previously learned objects by comparing their shape features and spatial relations between object parts. The robot actively selects object parts along with parameterized actions and evaluates the effects on-line. We demonstrate through real-world experiments with the humanoid robot NAO that our method is able to speed up the use of a new type of garbage can by transferring the affordances learned previously for similar garbage cans.


title: A Statistical Test For Host-Parasite Coevolution

abstract: A new method, ParaFit, has been developed to test the significance of a global hypothesis of coevolution between parasites and their hosts. Individual host-parasite association links can also be tested. The test statistics are functions of the host and parasite phylogenetic trees and of the set of host-parasite association links. Numerical simulations are used to show that the method has correct rate of type I error and good power except under extreme error conditions. An application to real data (pocket gophers and chewing lice) is presented.


title: CDM ESD protection in CMOS integrated circuits

abstract: The impacts of charged-device-model (CDM) electrostatic discharge (ESD) events on integrated circuit (IC) products are presented in this paper. The mechanism of chip-level CDM ESD event is introduced with some case studies on CDM ESD damages. Besides the chip-level CDM ESD event, the board-level CDM ESD event, which had been reported to cause damages in many customer-returned ICs, is also investigated in this work. The chip-level and board-level CDM ESD levels of several test devices and test circuits fabricated in CMOS processes are characterized and compared. The experimental results have shown that the board-level CDM ESD level of the test circuit is much lower than the chip-level CDM ESD level, which indicates that the board-level CDM ESD test is more critical than the chip-level CDM ESD test in the field applications. In addition, failure analysis reveals that the failure on the test circuit under board-level CDM ESD test is much severer than that under chip-level CDM ESD test.


title: The asymmetrical 'relationship': parallels between prostitution and the development of sex robots

abstract: In this paper I examine the model of asymmetrical 'relationship' that is imported from prostitution-client sex work to human-robot sex. Specifically, I address the arguments proposed by David Levy who identifies prostitution/sex work as a model that can be imported into human-robot sex relations. I draw on literature in anthropology that deals with the anthropomorphism of nonhuman things and the way that things reflect back to us gendered notions of sexuality. In the final part of the paper I propose that prostitution is no ordinary activity and relies on the ability to use a person as a thing and this is why parallels between sex robots and prostitution are so frequently found by their advocates.


title: Conditional WaveGAN

abstract: Generative models are successfully used for image synthesis in the recent years. But when it comes to other modalities like audio, text etc little progress has been made. Recent works focus on generating audio from a generative model in an unsupervised setting. We explore the possibility of using generative models conditioned on class labels. Concatenation based conditioning and conditional scaling were explored in this work with various hyper-parameter tuning methods. In this paper we introduce Conditional WaveGANs (cWaveGAN). Find our implementation at https://github.com/acheketa/cwavegan


title: Bodypart Recognition Using Multi-stage Deep Learning

abstract: Automatic medical image analysis systems often start from identifying the human body part contained in the image. Specifically, given a transversal slice, it is important to know which body part it comes from, namely “slice-based bodypart recognition”. This problem has its unique characteristic - the body part of a slice is usually identified by local discriminative regions instead of global image context, e.g., a cardiac slice is differentiated from an aorta arch slice by the mediastinum region. To leverage this characteristic, we design a multi-stage deep learning framework that aims at: (1) discover the local regions that are discriminative to the bodypart recognition, and (2) learn a bodypart identifier based on these local regions. These two tasks are achieved by the two stages of our learning scheme, respectively. In the pre-train stage, a convolutional neural network (CNN) is learned in a multi-instance learning fashion to extract the most discriminative local patches from the training slices. In the boosting stage, the learned CNN is further boosted by these local patches for bodypart recognition. By exploiting the discriminative local appearances, the learned CNN becomes more accurate than global image context-based approaches. As a key hallmark, our method does not require manual annotations of the discriminative local patches. Instead, it automatically discovers them through multi-instance deep learning. We validate our method on a synthetic dataset and a large scale CT dataset (7000+ slices from wholebody CT scans). Our method achieves better performances than state-of-the-art approaches, including the standard CNN.


title: Page segmentation using minimum homogeneity algorithm and adaptive mathematical morphology

abstract: Document layout analysis or page segmentation is the task of decomposing document images into many different regions such as texts, images, separators, and tables. It is still a challenging problem due to the variety of document layouts. In this paper, we propose a novel hybrid method, which includes three main stages to deal with this problem. In the first stage, the text and non-text elements are classified by using minimum homogeneity algorithm. This method is the combination of connected component analysis and multilevel homogeneity structure. Then, in the second stage, a new homogeneity structure is combined with an adaptive mathematical morphology in the text document to get a set of text regions. Besides, on the non-text document, further classification of non-text elements is applied to get separator regions, table regions, image regions, etc. The final stage, in refinement region and noise detection process, all regions both in the text document and non-text document are refined to eliminate noises and get the geometric layout of each region. The proposed method has been tested with the dataset of ICDAR2009 page segmentation competition and many other databases with different languages. The results of these tests showed that our proposed method achieves a higher accuracy compared to other methods. This proves the effectiveness and superiority of our method.


title: A longitudinal study of the association between violent video game play and aggression among adolescents.

abstract: In the past 2 decades, correlational and experimental studies have found a positive association between violent video game play and aggression. There is less evidence, however, to support a long-term relation between these behaviors. This study examined sustained violent video game play and adolescent aggressive behavior across the high school years and directly assessed the socialization (violent video game play predicts aggression over time) versus selection hypotheses (aggression predicts violent video game play over time). Adolescents (N 1,492, 50.8% female) were surveyed annually from Grade 9 to Grade 12 about their video game play and aggressive behaviors. Nonviolent video game play, frequency of overall video game play, and a comprehensive set of potential 3rd variables were included as covariates in each analysis. Sustained violent video game play was significantly related to steeper increases in adolescents’ trajectory of aggressive behavior over time. Moreover, greater violent video game play predicted higher levels of aggression over time, after controlling for previous levels of aggression, supporting the socialization hypothesis. In contrast, no support was found for the selection hypothesis. Nonviolent video game play also did not predict higher levels of aggressive behavior over time. Our findings, and the fact that many adolescents play video games for several hours every day, underscore the need for a greater understanding of the long-term relation between violent video games and aggression, as well as the specific game characteristics (e.g., violent content, competition, pace of action) that may be responsible for this association.


title: Hyperspectral Image Classification Using Band Selection and Morphological Profiles

abstract: In this paper, we propose a simple unsupervised framework to effectively select and combine spectral information and spatial features for Support Vector Machine (SVM)-based classification when spatial features are the widely used morphological profiles (MPs). To overcome the difficulty of high dimensionality of resulting features, it is a common practice that MPs are extracted from principal components (PCs). In this paper, we investigate another technique on spectral feature selection, which is unsupervised band selection (BS). We find out that using selected bands as spectral features can improve classification performance because they contain more critical characteristics for classification; in particular, using the selected bands, combined with the MPs extracted from PCs, can yield the highest accuracy, due to the fact that major PCs contain less noise for extracting more reliable MPs. The overall unsupervised nature of feature selection provides the flexibility of implementation. We believe that such finding is instructive to feature selection and extraction for spectral/spatial-based hyperspectral image classification.


title: MEC—A Near-Optimal Online Reinforcement Learning Algorithm for Continuous Deterministic Systems

abstract: In this paper, the first probably approximately correct (PAC) algorithm for continuous deterministic systems without relying on any system dynamics is proposed. It combines the state aggregation technique and the efficient exploration principle, and makes high utilization of online observed samples. We use a grid to partition the continuous state space into different cells to save samples. A near-upper Q operator is defined to produce a near-upper Q function using samples in each cell. The corresponding greedy policy effectively balances between exploration and exploitation. With the rigorous analysis, we prove that there is a polynomial time bound of executing nonoptimal actions in our algorithm. After finite steps, the final policy reaches near optimal in the framework of PAC. The implementation requires no knowledge of systems and has less computation complexity. Simulation studies confirm that it is a better performance than other similar PAC algorithms.


The Private Sector's Perspective, Priorities and Role in Regional Integration and Implications for Regional Trade Arrangements


title: Separation from protein kinase C--a calcium-independent TPA-activated phosphorylating system.

abstract: A calcium-independent but 12-O-tetradecanoylphorbol-13-acetate (TPA)- or diacylglycerol-activated phospholipid-dependent phosphorylating activity has been separated from protein kinase C. This has been made possible by employing calcium-dependent hydrophobic interaction chromatography. The material bound to phenyl-Sepharose in the presence of calcium at low ionic strength was eluted with EGTA and was protein kinase C. While the unbound material passing through the phenyl-Sepharose column showed no appreciable protein kinase C activity, instead it had a high phosphorylating activity manifested in the absence of calcium and in the presence of TPA plus phospholipid. The identification of this phosphorylating activity, distinct from protein kinase C, leads to important clues to cellular responses monitored by TPA in the absence of calcium.


title: Developing an Ontology of the Cyber Security Domain

abstract: This paper reports on a trade study we performed to support the development of a Cyber ontology from an initial malware ontology. The goals of the Cyber ontology effort are first described, followed by a discussion of the ontology development methodology used. The main body of the paper then follows, which is a description of the potential ontologies and standards that could be utilized to extend the Cyber ontology from its initially constrained malware focus. These resources include, in particular, Cyber and malware standards, schemas, and terminologies that directly contributed to the initial malware ontology effort. Other resources are upper (sometimes called 'foundational') ontologies. Core concepts that any Cyber ontology will extend have already been identified and rigorously defined in these foundational ontologies. However, for lack of space, this section is profoundly reduced. In addition, utility ontologies that are focused on time, geospatial, person, events, and network operations are briefly described. These utility ontologies can be viewed as specialized super-domain or even mid-level ontologies, since they span many, if not most, ontologies -including any Cyber ontology. An overall view of the ontological architecture used by the trade study is also given. The report on the trade study concludes with some proposed next steps in the iterative evolution of the


title: Graphene-based composite materials

abstract: The remarkable mechanical properties of carbon nanotubes arise from the exceptional strength and stiffness of the atomically thin carbon sheets (graphene) from which they are formed. In contrast, bulk graphite, a polycrystalline material, has low fracture strength and tends to suffer failure either by delamination of graphene sheets or at grain boundaries between the crystals. Now Stankovich et al. have produced an inexpensive polymer-matrix composite by separating graphene sheets from graphite and chemically tuning them. The material contains dispersed graphene sheets and offers access to a broad range of useful thermal, electrical and mechanical properties. Individual sheets of graphene can be readily incorporated into a polymer matrix, giving rise to composite materials having potentially useful electronic properties. Graphene sheets—one-atom-thick two-dimensional layers of sp2-bonded carbon—are predicted to have a range of unusual properties. Their thermal conductivity and mechanical stiffness may rival the remarkable in-plane values for graphite (∼3,000 W m-1 K-1 and 1,060 GPa, respectively); their fracture strength should be comparable to that of carbon nanotubes for similar types of defects1,2,3; and recent studies have shown that individual graphene sheets have extraordinary electronic transport properties4,5,6,7,8. One possible route to harnessing these properties for applications would be to incorporate graphene sheets in a composite material. The manufacturing of such composites requires not only that graphene sheets be produced on a sufficient scale but that they also be incorporated, and homogeneously distributed, into various matrices. Graphite, inexpensive and available in large quantity, unfortunately does not readily exfoliate to yield individual graphene sheets. Here we present a general approach for the preparation of graphene-polymer composites via complete exfoliation of graphite9 and molecular-level dispersion of individual, chemically modified graphene sheets within polymer hosts. A polystyrene–graphene composite formed by this route exhibits a percolation threshold10 of ∼0.1 volume per cent for room-temperature electrical conductivity, the lowest reported value for any carbon-based composite except for those involving carbon nanotubes11; at only 1 volume per cent, this composite has a conductivity of ∼0.1 S m-1, sufficient for many electrical applications12. Our bottom-up chemical approach of tuning the graphene sheet properties provides a path to a broad new class of graphene-based materials and their use in a variety of applications.


title: The relation of the distal femoral physis and the medial patellofemoral ligament

abstract: Purpose ::: The purpose of this study was to analyse true lateral radiographs of children and adolescents to determine the relation of the origin of the MPFL and the distal femoral physis considering the complex anatomy of the physis. The hypothesis was that the femoral insertion of the MPFL is distal to the growth plate.


title: Thermal Inkjet Application in the Preparation of Oral Dosage Forms: Dispensing of Prednisolone Solutions and Polymorphic Characterization by Solid-State Spectroscopic Techniques

abstract: The utility of thermal inkjet (TIJ) technology for preparing solid dosage forms of drugs was examined. Solutions of prednisolone in a solvent mixture of ethanol, water, and glycerol (80/17/3 by volume) were dispensed onto poly(tetrafluoroethylene)-coated fiberglass films using TIJ cartridges and a personal printer and using a micropipette for comparison. The post-dried, TIJ-dispensed samples were shown to contain a mixture of prednisolone Forms I and III based on PXRD analyses that were confirmed by Raman analyses. The starting commercial material was determined to be Form I. Samples prepared by dispensing the solution from a micropipette initially showed only Form I; subsequent Raman mapping of these samples revealed the presence of two polymorphs. Raman mapping of the TIJ-dispensed samples also showed both polymorphs. The results indicate that the solvent mixture used in the dispensing solution combined with the thermal treatment of the samples after dispensing were likely the primary reason for the generation of the two polymorphs. The advantages of using a multidisciplinary approach to characterize drug delivery systems are demonstrated using solid state mapping techniques. Both PXRD and Raman spectroscopy were needed to fully characterize the samples. Finally, this report clarifies prednisolone's polymorphic nomenclature existent in the scientific literature. © 2007 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 97:2619–2636, 2008


title: Levenshtein distance for graph spectral features

abstract: Graph structures play a critical role in computer vision, but they are inconvenient to use in pattern recognition tasks because of their combinatorial nature and the consequent difficulty in constructing feature vectors. Spectral representations have been used for this task which are based on the eigensystem of the graph Laplacian matrix. However, graphs of different sizes produce eigensystems of different sizes where not all eigenmodes are present in both graphs. We use the Levenshtein distance to compare spectral representations under graph edit operations which add or delete vertices. The spectral representations are therefore of different sizes. We use the concept of the string-edit distance to allow for the missing eigenmodes and compare the correct modes to each other. We evaluate the method by first using generated graphs to compare the effect of vertex deletion operations. We then examine the performance of the method on graphs from a shape database.


title: Where the Truth Lies: Explaining the Credibility of Emerging Claims on the Web and Social Media

abstract: The web is a huge source of valuable information. However, in recent times, there is an increasing trend towards false claims in social media, other web-sources, and even in news. Thus, factchecking websites have become increasingly popular to identify such misinformation based on manual analysis. Recent research proposed methods to assess the credibility of claims automatically. However, there are major limitations: most works assume claims to be in a structured form, and a few deal with textual claims but require that sources of evidence or counter-evidence are easily retrieved from the web. None of these works can cope with newly emerging claims, and no prior method can give user-interpretable explanations for its verdict on the claim's credibility. ::: ::: This paper overcomes these limitations by automatically assessing the credibility of emerging claims, with sparse presence in web-sources, and generating suitable explanations from judiciously selected sources. To this end, we retrieve diverse articles about the claim, and model the mutual interaction between: the stance (i.e., support or refute) of the sources, the language style of the articles, the reliability of the sources, and the claim's temporal footprint on the web. Extensive experiments demonstrate the viability of our method and its superiority over prior works. We show that our methods work well for early detection of emerging claims, as well as for claims with limited presence on the web and social media.


title: An onboard monocular vision system for autonomous takeoff, hovering and landing of a micro aerial vehicle

abstract: In this paper, we present an onboard monocular vision system for autonomous takeoff, hovering and landing of a Micro Aerial Vehicle (MAV). Since pose information with metric scale is critical for autonomous flight of a MAV, we present a novel solution to six degrees of freedom (DOF) pose estimation. It is based on a single image of a typical landing pad which consists of the letter “H” surrounded by a circle. A vision algorithm for robust and real-time landing pad recognition is implemented. Then the 5 DOF pose is estimated from the elliptic projection of the circle by using projective geometry. The remaining geometric ambiguity is resolved by incorporating the gravity vector estimated by the inertial measurement unit (IMU). The last degree of freedom pose, yaw angle of the MAV, is estimated from the ellipse fitted from the letter “H”. The efficiency of the presented vision system is demonstrated comprehensively by comparing it to ground truth data provided by a tracking system and by using its pose estimates as control inputs to autonomous flights of a quadrotor.


title: Real-time tracking of low-resolution vehicles for wide-area persistent surveillance

abstract: Live wide-area persistent surveillance (WAPS) systems must provide effective multi-target tracking on downlinked video streams in real-time. This paper presents the first published aerial tracking system that is documented to process over 100 megapixels per second. The implementation addresses the challenges with the mosaicked, low-resolution, grayscale NITF imagery provided by most currently fielded WAPS platforms and the flexible computation architecture required to provide real-time performance. This paper also provides ground-truth for repeatable evaluation of wide-area persistent surveillance on a 2009 dataset collected by AFRL [1] that is available to the public as well as a quantitative analysis of this real-time implementation. To our knowledge, this is the only publication that (1) provides details of a real-time implementation for detection and tracking in (2) mosaicked, composed imagery from a fielded WAPS sensor, and (3) provides annotation data and quantitative analysis for repeatable WAPS tracking experimentation in the computer vision community.


Analysis of mutually decoupled primary coils for IPT systems for EV charging


title: Modeling human performance of pen stroke gestures

abstract: This paper presents a quantitative human performance model of making single-stroke pen gestures within certain error constraints in terms of production time. Computed from the properties of Curves, Line segments, and Corners (CLC) in a gesture stroke, the model may serve as a foundation for the design and evaluation of existing and future gesture-based user interfaces at the basic motor control efficiency level, similar to the role of previous "laws of action" played to pointing, crossing or steering-based user interfaces. We report and discuss our experimental results on establishing and validating the CLC model, together with other basic empirical findings in stroke gesture production.


title: Content-adaptive inverse tone mapping

abstract: Tone mapping is an important technique used for displaying high dynamic range (HDR) content on low dynamic range (LDR) devices. On the other hand, inverse tone mapping enables LDR content to appear with an HDR effect on HDR displays. The existing inverse tone mapping algorithms usually focus on enhancing the luminance in over-exposed regions with less (or even no) effort on the process of the wellexposed regions. In this paper, we propose an algorithm with not only enhancement in the over-exposed regions but also in the remaining well-exposed regions. This paper provides an ”histogram-based” method for inverse tone mapping. The proposed algorithm contains a content-adaptive inverse tone mapping operator, which has different responses with different scene characteristics. Scene classification is included in this algorithm to select the environment parameters. Lastly, enhancement of the over-exposed regions, which reconstructs the truncated information, is performed.


title: Context-free Attacks Using Keyboard Acoustic Emanations

abstract: The emanations of electronic and mechanical devices have raised serious privacy concerns. It proves possible for an attacker to recover the keystrokes by acoustic signal emanations. Most existing malicious applications adopt context-based approaches, which assume that the typed texts are potentially correlated. Those approaches often incur a high cost during the context learning stage, and can be limited by randomly typed contents (e.g., passwords). Also, context correlations can increase the risk of successive false recognition. We present a context-free and geometry-based approach to recover keystrokes. Using off-the-shelf smartphones to record acoustic emanations from keystrokes, this design estimates keystrokes' physical positions based on the Time Difference of Arrival (TDoA) method. We conduct extensive experiments and the results show that more than 72.2\% of keystrokes can be successfully recovered.


title: “ Why would anybody do this ? ” : Older Adults ’ Understanding of and Experiences with Crowd Work

abstract: Diversifying participation in crowd work can benefit the worker and requester. Increasing numbers of older adults are online, but little is known about their awareness of or how they engage in mainstream crowd work. Through an online survey with 505 seniors, we found that most have never heard of crowd work but would be motivated to complete tasks by earning money or working on interesting or stimulating tasks. We follow up results from the survey with interviews and observations of 14 older adults completing crowd work tasks. While our survey data suggests that financial incentives are encouraging, in-depth interviews reveal that a combination of personal and social incentives may be stronger drivers of participation, but only if older adults can overcome accessibility issues and understand the purpose of crowd work. This paper contributes insights into how crowdsourcing sites could better engage seniors and other users.