title: Sine cosine optimization algorithm for feature selection

abstract: Nowadays, a dataset includes a huge number of features with irrelevant and redundant ones. Feature selection is required for a better machine-learning algorithms' performance. A system for feature selection is proposed in this work using a sine cosine algorithm (SCA). SCA is a new stochastic search algorithm for optimization problems. SCA optimization adaptively balances the exploration and exploitation to find the optimal solution quickly. The SCA can quickly explore the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The proposed fitness function used incorporates both classification accuracy and feature size reduction. The proposed system was tested on 18 datasets and shows an advance over other search methods as particle swarm optimization (PSO) and genetic algorithm (GA) optimizers commonly used in this context using different evaluation indicators.


Application of Katharine Kolkaba Comfort theory to nursing care of patient


title: The Gavagai Living Lexicon.

abstract: This paper presents the Gavagai Living Lexicon, which is an online distributional semantic model currently available in 14 different languages. We describe the underlying distributional semantic mo ...


title: Ultra high efficiency bidirectional dc-dc converter with multi-frequency pulse width modulation

abstract: An ultra high efficiency bidirectional dc-dc converter with multi-frequency pulse width modulation is proposed in this paper. This method selects a proper switching frequency instead of using detection of instantaneous current to achieve zero-voltage switching (ZVS). Since instantaneous current is not required, control loop instability and noise susceptibility issues can be avoided. The proposed approach allows ease of combining ZVS, multiphase interleaving, and coupled inductor techniques to achieve high efficiency over a wide load range. To verify the proposed approach, a 30 kW hardware prototype has been designed, fabricated and tested. Simulation and experimental results verify the effectiveness of the proposed approach in ZVS operation. Using precision meters and sensors, the efficiency was measured in a wide load range. From 10% to 100% load, the efficiency stays above 98% and peaks at 99.2%.


title: Normally-off high-voltage p-GaN gate GaN HFET with carbon-doped buffer

abstract: Normally-off GaN transistors for power applications in p-type GaN gate technology with a modified carbon-doped GaN buffer are presented. A combination of an AlGaN back-barrier with the carbon-doped buffer prevents early off-state punch-through. Simultaneously, the on-state resistance could be kept low and the threshold voltage with 1.1 V high enough for secure normally-off operation. 1000 V breakdown strength has been obtained for devices with 6 μm gate-drain spacing. The resulting breakdown scaling slope is 170 V/μm gate-drain distance. The on-state resistance is 7.4 Ωmm. The resulting V Br -to-R ON A ratio (1000 V, 0.62 mΩcm2) is beyond so far reported ratios for normally-off GaN transistors. Modifications of the p-type GaN layer have shown to additionally increase the threshold voltage by 0.4 V without paying a price in the on-state resistance of the device.


title: 3D intracranial artery segmentation using a convolutional autoencoder

abstract: Automated segmentation of intracranial arteries on magnetic resonance angiography (MRA) allows for quantification of cerebrovascular features, which provides tools for understanding aging and pathophysiological adaptations of the cerebrovascular system. Using a convolutional autoencoder (CAE) for segmentation is promising as it takes advantage of the autoencoder structure in effective noise reduction and feature extraction by representing high dimensional information with low dimensional latent variables. In this report, an optimized CAE model (Y-net) was trained to learn a 3D segmentation model of intracranial arteries from 49 cases of MRA data. The trained model was shown to perform better than the three traditional segmentation methods in both binary classification and visual evaluation.


title: Gamma band activity and the P3 reflect post-perceptual processes, not visual awareness

abstract: Abstract A primary goal in cognitive neuroscience is to identify neural correlates of conscious perception (NCC). By contrasting conditions in which subjects are aware versus unaware of identical visual stimuli, a number of candidate NCCs have emerged; among them are induced gamma band activity in the EEG and the P3 event-related potential. In most previous studies, however, the critical stimuli were always directly relevant to the subjects' task, such that aware versus unaware contrasts may well have included differences in post-perceptual processing in addition to differences in conscious perception per se. Here, in a series of EEG experiments, visual awareness and task relevance were manipulated independently. Induced gamma activity and the P3 were absent for task-irrelevant stimuli regardless of whether subjects were aware of such stimuli. For task-relevant stimuli, gamma and the P3 were robust and dissociable, indicating that each reflects distinct post-perceptual processes necessary for carrying-out the task but not for consciously perceiving the stimuli. Overall, this pattern of results challenges a number of previous proposals linking gamma band activity and the P3 to conscious perception.


Ers sar power satellite image interpretation and urban characteristics: The case of Athens


title: Cancer-related inflammation

abstract: The mediators and cellular effectors of inflammation are important constituents of the local environment of tumours. In some types of cancer, inflammatory conditions are present before a malignant change occurs. Conversely, in other types of cancer, an oncogenic change induces an inflammatory microenvironment that promotes the development of tumours. Regardless of its origin, 'smouldering' inflammation in the tumour microenvironment has many tumour-promoting effects. It aids in the proliferation and survival of malignant cells, promotes angiogenesis and metastasis, subverts adaptive immune responses, and alters responses to hormones and chemotherapeutic agents. The molecular pathways of this cancer-related inflammation are now being unravelled, resulting in the identification of new target molecules that could lead to improved diagnosis and treatment.


title: An Approach to Protect the Privacy of Cloud Data from Data Mining Based Attacks

abstract: Cloud computing has revolutionized the way computing and software services are delivered to the clients on demand. It offers users the ability to connect to computing resources and access IT managed services with a previously unknown level of ease. Due to this greater level of flexibility, the cloud has become the breeding ground of a new generation of products and services. However, the flexibility of cloud-based services comes with the risk of the security and privacy of users' data. Thus, security concerns among users of the cloud have become a major barrier to the widespread growth of cloud computing. One of the security concerns of cloud is data mining based privacy attacks that involve analyzing data over a long period to extract valuable information. In particular, in current cloud architecture a client entrusts a single cloud provider with his data. It gives the provider and outside attackers having unauthorized access to cloud, an opportunity of analyzing client data over a long period to extract sensitive information that causes privacy violation of clients. This is a big concern for many clients of cloud. In this paper, we first identify the data mining based privacy risks on cloud data and propose a distributed architecture to eliminate the risks.


title: On cervical zygapophysial joint pain after whiplash.

abstract: STUDY DESIGN ::: Narrative review. ::: ::: ::: OBJECTIVE ::: To summarize the evidence that implicates the cervical zygapophysial joints as the leading source of chronic neck pain after whiplash. ::: ::: ::: SUMMARY OF BACKGROUND DATA ::: Reputedly a patho-anatomic basis for neck pain after whiplash has been elusive. However, studies conducted in a variety of disparate disciplines indicate that this is not necessarily the case. ::: ::: ::: METHODS ::: Data were retrieved from studies that addressed the postmortem features and biomechanics of injury to the cervical zygapophysial joints, and from clinical studies of the diagnosis and treatment of zygapophysial joint pain, to illustrate convergent validity. ::: ::: ::: RESULTS ::: Postmortem studies show that a spectrum of injuries can befall the zygapophysial joints in motor vehicle accidents. Biomechanics studies of normal volunteers and of cadavers reveal the mechanisms by which such injuries can be sustained. Studies in cadavers and in laboratory animals have produced these injuries.Clinical studies have shown that zygapophysial joint pain is very common among patients with chronic neck pain after whiplash, and that this pain can be successfully eliminated by radiofrequency neurotomy. ::: ::: ::: CONCLUSION ::: The fact that multiple lines of evidence, using independent techniques, consistently implicate the cervical zygapophysial joints as a site of injury and source of pain, strongly implicates injury to these joints as a common basis for chronic neck pain after whiplash.


title: Retrographic sensing for the measurement of surface texture and shape

abstract: We describe a novel device that can be used as a 2.5D “scanner” for acquiring surface texture and shape. The device consists of a slab of clear elastomer covered with a reflective skin. When an object presses on the skin, the skin distorts to take on the shape of the object's surface. When viewed from behind (through the elastomer slab), the skin appears as a relief replica of the surface. A camera records an image of this relief, using illumination from red, green, and blue light sources at three different positions. A photometric stereo algorithm that is tailored to the device is then used to reconstruct the surface. There is no problem dealing with transparent or specular materials because the skin supplies its own BRDF. Complete information is recorded in a single frame; therefore we can record video of the changing deformation of the skin, and then generate an animation of the changing surface. Our sensor has no moving parts (other than the elastomer slab), uses inexpensive materials, and can be made into a portable device that can be used “in the field” to record surface shape and texture.


title: Quantum robot: structure, algorithms and applications

abstract: A brand-new paradigm of robots–quantum robots–is proposed through the fusion of quantum theory with robot technology. A quantum robot is essentially a complex quantum system which generally consists of three fundamental components: multi-quantum computing units (MQCU), quantum controller/actuator, and information acquisition units. Corresponding to the system structure, several learning control algorithms, including quantum searching algorithms and quantum reinforcement learning algorithms, are presented for quantum robots. The theoretical results show that quantum robots using quantum searching algorithms can reduce the complexity of the search problem from O($N^2)$ in classical robots to O($N\sqrt N)$. Simulation results demonstrate that quantum robots are also superior to classical robots in efficient learning under novel quantum reinforcement learning algorithms. Considering the advantages of quantum robots, some important potential applications are also analyzed and prospected.


title: Feature Selection Based on Class-Dependent Densities for High-Dimensional Binary Data

abstract: Data and knowledge management systems employ feature selection algorithms for removing irrelevant, redundant, and noisy information from the data. There are two well-known approaches to feature selection, feature ranking (FR) and feature subset selection (FSS). In this paper, we propose a new FR algorithm, termed as class-dependent density-based feature elimination (CDFE), for binary data sets. Our theoretical analysis shows that CDFE computes the weights, used for feature ranking, more efficiently as compared to the mutual information measure. Effectively, rankings obtained from both the two criteria approximate each other. CDFE uses a filtrapper approach to select a final subset. For data sets having hundreds of thousands of features, feature selection with FR algorithms is simple and computationally efficient but redundant information may not be removed. On the other hand, FSS algorithms analyze the data for redundancies but may become computationally impractical on high-dimensional data sets. We address these problems by combining FR and FSS methods in the form of a two-stage feature selection algorithm. When introduced as a preprocessing step to the FSS algorithms, CDFE not only presents them with a feature subset, good in terms of classification, but also relieves them from heavy computations. Two FSS algorithms are employed in the second stage to test the two-stage feature selection idea. We carry out experiments with two different classifiers (naive Bayes' and kernel ridge regression) on three different real-life data sets (NOVA, HIVA, and GINA) of the”Agnostic Learning versus Prior Knowledge” challenge. As a stand-alone method, CDFE shows up to about 92 percent reduction in the feature set size. When combined with the FSS algorithms in two-stages, CDFE significantly improves their classification accuracy and exhibits up to 97 percent reduction in the feature set size. We also compared CDFE against the winning entries of the challenge and found that it outperforms the best results on NOVA and HIVA while obtaining a third position in case of GINA.


title: Kinematic analysis of a dual-arm humanoid cooking robot

abstract: The dual-arm humanoid cooking robot is able to cook dishes like a master chef in ordinary home kitchens. The kinematic analysis is carried out for the arm of the robot. Coordinate frames are assigned to each link of the arm using the Denavit-Hartenberg convention. The working space of the arm is calculated. The kinematic equations are obtained by calculating the homogeneous transformation matrix. The inverse kinematic solution is obtained and verified by simulation. The Jacobian of the arm is obtained by the vector product method.


title: Classified security protection evaluation for vehicle information system

abstract: This paper firstly analyzes security vulnerabilities, threats and special security requirements of current vehicle information system. And further referring to the classified security protection evaluation standards of traditional information system, we establish classified security protection evaluation system for vehicle information system. This system summarizes typical information assets in car system, divides vehicle information system into two classes/levels: family and business, and defines target and requirement of security protection for two kinds of vehicle information system, respectively. Finally, a series of feasible evaluation methods and tools are presented for evaluation practice. A big contribution of this paper is to explore classified security protection evaluation for vehicle information system and fills up the gap of evaluating security state of automotive information system all over the world.


title: Implicit Discourse Relation Classification via Multi-Task Neural Networks

abstract: Without discourse connectives, classifying implicit discourse relations is a challenging task and a bottleneck for building a practical discourse parser. Previous research usually makes use of one kind of discourse framework such as PDTB or RST to improve the classification performance on discourse relations. Actually, under different discourse annotation frameworks, there exist multiple corpora which have internal connections. To exploit the combination of different discourse corpora, we design related discourse classification tasks specific to a corpus, and propose a novel Convolutional Neural Network embedded multi-task learning system to synthesize these tasks by learning both unique and shared representations for each task. The experimental results on the PDTB implicit discourse relation classification task demonstrate that our model achieves significant gains over baseline systems.


title: Nonreciprocal microwave band-gap structures.

abstract: An electrically controlled nonreciprocal electromagnetic band-gap material is proposed and studied. The new material is a periodic three-dimensional regular lattice of small magnetized ferrite spheres. In this paper, we consider plane electromagnetic waves in this medium and design an analytical model for the material parameters. An analytical solution for plane-wave reflection from a planar interface is also presented. In the proposed material, a new electrically controlled stop band appears for one of the two circularly polarized eigenwaves in a frequency band around the ferrimagnetic resonance frequency. This frequency can be well below the usual lattice band gap, which allows the realization of rather compact structures. The main properties of the material are outlined.


title: Organising Levels of Organisation for Biology Education: A Systematic Review of Literature

abstract: Many authors suggest an explicit reflection on the levels of organisation, based on known difficulties related to understanding biological systems. Yet, there is no scientific consensus on the characteristics of biological levels and the quality of their relationships. This review intends to present the state of the current discussion in order to establish an educational argumentation as a basis for the development of learning environments and teaching experiments in biology education. For this purpose, we conducted a systematic literature review. Three databases (BIOSIS, ERIC, Fachportal-Padagogik) were searched for literature on levels of organisation. The papers were analysed by means of a qualitative content analysis based on the following research questions: (1) Which levels of organisation do the authors name? (2) How do the authors describe the levels of organisation? (3) How do the authors describe the relationship between different levels of organisation? (4) How do the authors describe the challenges of these levels for biological education? (5) How do the authors describe the benefit of these levels for biological education? Based on the results, we propose a system of levels that features the concept of zooming-in and makes the relationships between the levels explicit.


title: Social implications of children’s smartphone addiction: The role of support networks and social engagement

abstract: Background and aims Most studies have regarded smartphone addiction as a condition stemming from individuals' psychological issues, so research has rarely examined it in relation to a lack of social resources and its social impacts. However, this study reinterprets smartphone addiction as a social problem stemming from a lack of offline social networks and resulting in a decline of social engagement. Methods This study drew on a survey of 2,000 children in Korea consisting of 991 males and 1,009 females with an average age of 12 years old. Using the STATA 14 structural equation modeling program, this study examined the relationships between children's lack of social networks, smartphone addiction, and social engagement. Results Social network variables, such as formal organizational membership, quality of relationship with parents, size of the peer group, and peer support, decrease smartphone addiction. Simply having good relationships and reciprocal feelings with peers do not have any influence on the smartphone addiction. The more the children become addicted to smartphones, the less they participate in social engagement. Discussion and conclusions This study provides a new understanding of smartphone addiction by focusing on its social aspects, augmenting prior studies that have addressed psychological factors. Findings suggest that children's lack of social networks may inhibit comfortable social interactions and feelings of support in the offline environment, which can heighten their desire to escape to smartphones. These children, unlike non-addicts, may not take advantage of the media to enrich their social lives and increase their level of social engagement.


title: Scalable Kernel TCP Design and Implementation for Short-Lived Connections

abstract: With the rapid growth of network bandwidth, increases in CPU cores on a single machine, and application API models demanding more short-lived connections, a scalable TCP stack is performance-critical. Although many clean-state designs have been proposed, production environments still call for a bottom-up parallel TCP stack design that is backward-compatible with existing applications. We present Fastsocket, a BSD Socket-compatible and scalable kernel socket design, which achieves table-level connection partition in TCP stack and guarantees connection locality for both passive and active connections. Fastsocket architecture is a ground up partition design, from NIC interrupts all the way up to applications, which naturally eliminates various lock contentions in the entire stack. Moreover, Fastsocket maintains the full functionality of the kernel TCP stack and BSD-socket-compatible API, and thus applications need no modifications. Our evaluations show that Fastsocket achieves a speedup of 20.4x on a 24-core machine under a workload of short-lived connections, outperforming the state-of-the-art Linux kernel TCP implementations. When scaling up to 24 CPU cores, Fastsocket increases the throughput of Nginx and HAProxy by 267% and 621% respectively compared with the base Linux kernel. We also demonstrate that Fastsocket can achieve scalability and preserve BSD socket API at the same time. Fastsocket is already deployed in the production environment of Sina WeiBo, serving 50 million daily active users and billions of requests per day.


title: Tracking the Trackers

abstract: Online tracking poses a serious privacy challenge that has drawn significant attention in both academia and industry. Existing approaches for preventing user tracking, based on curated blocklists, suffer from limited coverage and coarse-grained resolution for classification, rely on exceptions that impact sites' functionality and appearance, and require significant manual maintenance. In this paper we propose a novel approach, based on the concepts leveraged from $k$-Anonymity, in which users collectively identify unsafe data elements, which have the potential to identify uniquely an individual user, and remove them from requests. ::: ::: We deployed our system to 200,000 German users running the Cliqz Browser or the Cliqz Firefox extension to evaluate its efficiency and feasibility. Results indicate that our approach achieves better privacy protection than blocklists, as provided by Disconnect, while keeping the site breakage to a minimum, even lower than the community-optimized AdBlock Plus. We also provide evidence of the prevalence and reach of trackers to over 21 million pages of 350,000 unique sites, the largest scale empirical evaluation to date. 95% of the pages visited contain 3rd party requests to potential trackers and 78% attempt to transfer unsafe data. Tracker organizations are also ranked, showing that a single organization can reach up to 42% of all page visits in Germany.


title: Solving Bilevel Multi-Objective Optimization Problems Using Evolutionary Algorithms

abstract: Bilevel optimization problems require every feasible upper-level solution to satisfy optimality of a lower-level optimization problem. These problems commonly appear in many practical problem solving tasks including optimal control, process optimization, game-playing strategy development, transportation problems, and others. In the context of a bilevel single objective problem, there exists a number of theoretical, numerical, and evolutionary optimization results. However, there does not exist too many studies in the context of having multiple objectives in each level of a bilevel optimization problem. In this paper, we address bilevel multi-objective optimization issues and propose a viable algorithm based on evolutionary multi-objective optimization (EMO) principles. Proof-of-principle simulation results bring out the challenges in solving such problems and demonstrate the viability of the proposed EMO technique for solving such problems. This paper scratches the surface of EMO-based solution methodologies for bilevel multi-objective optimization problems and should motivate other EMO researchers to engage more into this important optimization task of practical importance.


title: Teleoperation control of Baxter robot using body motion tracking

abstract: In this paper, we use Kinect Xbox 360 sensor to implement the motion control of Baxter robot, a semi humanoid robot with limbs of 7 DOF joints with collision avoidance capabilities. Two different methods using vector approach and inverse kinematics approach have been designed to achieve a given task. Primitive experiments have been carried out to verify the effectiveness of the developed methods. Human motions is captured by Kinect sensor and calculated with Processing Software using SimpleOpenNI wrapper for OpenNI and NITE. UDP protocol is adopted to send reference motion to Baxter robot. Python and RosPy script programming kit is used to calculate joint angles of Baxter robot based on vector approach and inverse kinematics approach. The experimental results demonstrate that both of our proposed approaches have achieved satisfactory performance.


title: Exploring mobile learning success factors

abstract: This paper is a comparative account and analysis of three mobile Web 2.0 projects instigated within a tertiary learning environment during 2008. Following the successful instigation of a mobile Web 2.0 project in the third year of a Bachelor of Product Design course during semester one, similar projects were initiated in semester two within the first-year and second-year Bachelor of Product Design courses. A common methodology for supporting and facilitating mobile Web 2.0 projects was used for all three projects. The projects were designed to explore the potential of mobile Web 2.0 tools to enhance both the formal and informal teaching and learning environments with a focus upon mobile blogging (moblogging). A comparison of student and teaching staff feedback from each of the three projects provided a basis for identifying and illustrating critical success factors within similar m-learning scenarios. Critical success factors identified include: the importance of the pedagogical integration of the technology into the course assessment, lecturer modelling of the pedagogical use of the tools, the need for regular formative feedback from lecturers to students, and the appropriate choice of mobile devices and software to support the pedagogical model underlying the course. Keywords: mobile learning; Web 2.0; moblogging; social constructivism; action research DOI: 10.1080/09687769.2010.494718


title: A social-network-aided efficient peer-to-peer live streaming system

abstract: In current peer-to-peer (P2P) live streaming systems, nodes in a channel form a P2P overlay for video sharing. To watch a new channel, a node depends on the centralized server to join in the overlay of the channel. In today's live streaming applications, the increase in the number of channels triggers users' desire of watching multiple channels successively or simultaneously. However, the support of such watching modes in current applications is no better than joining in different channel overlays successively or simultaneously through the centralized server, which if widely used, poses a heavy burden on the server. In order to achieve higher efficiency and scalability, we propose a Social-network-Aided efficient liVe strEaming system (SAVE). SAVE regards users' channel switching or multichannel watching as interactions between channels. By collecting the information of channel interactions, nodes' interests, and watching times, SAVE forms nodes in multiple channels with frequent interactions into an overlay, constructs bridges between overlays of channels with less frequent interactions, and enables nodes to identify friends sharing similar interests and watching times. Thus, a node can connect to a new channel while staying in its current overlay, using bridges or relying on its friends, reducing the need to contact the centralized server. We further propose the channel-closeness-based chunk-pushing strategy and capacity-based chunk provider selection strategy to enhance the system performance. Extensive experimental results from the PeerSim simulator and PlanetLab verify that SAVE outperforms other systems in system efficiency and server load reduction, as well as the effectiveness of the two proposed strategies.


title: Probabilistic fault detector for Wireless Sensor Network

abstract: This paper proposed a novel centralized hardware fault detection approach for a structured Wireless Sensor Network (WSN) based on Naive Bayes framework. For most WSNs, power supply is the main constraint of the network because most applications are in severe situation and the sensors are equipped with battery only. In other words, the battery's life is the network's life. To maximize the network's life, the proposed method, Centralized Naive Bayes Detector (CNBD) analyzes the end-to-end transmission time collected at the sink. Thus all the computation will not be performed in individual sensor node that poses no additional power burden to the battery of each sensor node. We have conducted thorough performance evaluation. The obtained results showed better performance can be obtained under a network size of 100-node WSN simulations at various network traffic conditions and different number of faulty nodes.


Creating a Traffic Merging Behavior Using NeuroEvolution of Augmenting Topologies


title: Neural Substrates of Spontaneous Musical Performance: An fMRI Study of Jazz Improvisation

abstract: To investigate the neural substrates that underlie spontaneous musical performance, we examined improvisation in professional jazz pianists using functional MRI. By employing two paradigms that differed widely in musical complexity, we found that improvisation (compared to production of over-learned musical sequences) was consistently characterized by a dissociated pattern of activity in the prefrontal cortex: extensive deactivation of dorsolateral prefrontal and lateral orbital regions with focal activation of the medial prefrontal (frontal polar) cortex. Such a pattern may reflect a combination of psychological processes required for spontaneous improvisation, in which internally motivated, stimulus-independent behaviors unfold in the absence of central processes that typically mediate self-monitoring and conscious volitional control of ongoing performance. Changes in prefrontal activity during improvisation were accompanied by widespread activation of neocortical sensorimotor areas (that mediate the organization and execution of musical performance) as well as deactivation of limbic structures (that regulate motivation and emotional tone). This distributed neural pattern may provide a cognitive context that enables the emergence of spontaneous creative activity.


title: Hyperspectral image analysis using deep learning — A review

abstract: Deep learning is a rather new approach to machine learning that has achieved remarkable results in a large number of different image processing applications. Lately, application of deep learning to detect and classify spectral and spatio-spectral signatures in hyperspectral images has emerged. The high dimensionality of hyperspectral images and the limited amount of labelled training data makes deep learning an appealing approach for analysing hyperspectral data. Auto-Encoder can be used to learn a hierarchical feature representation using solely unlabelled data, the learnt representation can be combined with a logistic regression classifier to achieve results in-line with existing state-of-the-art methods. In this paper, we compare results between a set of available publications and find that deep learning perform in line with state-of-the-art on many data sets but little evidence exists that deep learning outperform the reference methods.


title: A Direct Brain-to-Brain Interface in Humans

abstract: We describe the first direct brain-to-brain interface in humans and present results from experiments involving six different subjects. Our non-invasive interface, demonstrated originally in August 2013, combines electroencephalography (EEG) for recording brain signals with transcranial magnetic stimulation (TMS) for delivering information to the brain. We illustrate our method using a visuomotor task in which two humans must cooperate through direct brain-to-brain communication to achieve a desired goal in a computer game. The brain-to-brain interface detects motor imagery in EEG signals recorded from one subject (the "sender") and transmits this information over the internet to the motor cortex region of a second subject (the "receiver"). This allows the sender to cause a desired motor response in the receiver (a press on a touchpad) via TMS. We quantify the performance of the brain-to-brain interface in terms of the amount of information transmitted as well as the accuracies attained in (1) decoding the sender's signals, (2) generating a motor response from the receiver upon stimulation, and (3) achieving the overall goal in the cooperative visuomotor task. Our results provide evidence for a rudimentary form of direct information transmission from one human brain to another using non-invasive means.


title: Spatial Compressive Sensing for MIMO Radar

abstract: We study compressive sensing in the spatial domain to achieve target localization, specifically direction of arrival (DOA), using multiple-input multiple-output (MIMO) radar. A sparse localization framework is proposed for a MIMO array in which transmit and receive elements are placed at random. This allows for a dramatic reduction in the number of elements needed, while still attaining performance comparable to that of a filled (Nyquist) array. By leveraging properties of structured random matrices, we develop a bound on the coherence of the resulting measurement matrix, and obtain conditions under which the measurement matrix satisfies the so-called isotropy property. The coherence and isotropy concepts are used to establish uniform and non-uniform recovery guarantees within the proposed spatial compressive sensing framework. In particular, we show that non-uniform recovery is guaranteed if the product of the number of transmit and receive elements, MN (which is also the number of degrees of freedom), scales with K(log(G))^2, where K is the number of targets and G is proportional to the array aperture and determines the angle resolution. In contrast with a filled virtual MIMO array where the product MN scales linearly with G, the logarithmic dependence on G in the proposed framework supports the high-resolution provided by the virtual array aperture while using a small number of MIMO radar elements. In the numerical results we show that, in the proposed framework, compressive sensing recovery algorithms are capable of better performance than classical methods, such as beamforming and MUSIC.


title: Overview of the NLPCC-ICCPOL 2016 Shared Task: Chinese Word Similarity Measurement

abstract: Word similarity computation is a fundamental task for natural language processing. We organize a semantic campaign of Chinese word similarity measurement at NLPCC-ICCPOL 2016. This task provides a benchmark dataset of Chinese word similarity (PKU-500 dataset), including 500 word pairs with their similarity scores. There are 21 teams submitting 24 systems in this campaign. In this paper, we describe clearly the data preparation and word similarity annotation, make an in-depth analysis on the evaluation results and give a brief introduction to participating systems.


title: Autonomously controlling flexible timelines: From domain-independent planning to robust execution

abstract: The introduction of more onboard autonomy in future single and multi-satellite missions is both a question of limited onboard resources and of how far can we actually thrust the autonomous functionalities deployed on board. In-flight experience with nasa's Deep Space 1 and Earth Observing 1 has shown how difficult it is to design, build and test reliable software for autonomy. The degree to which system-level onboard autonomy will be deployed in the single and multi satellite systems of tomorrow will depend, among other things, on the progress made in two key software technologies: autonomous onboard planning and robust execution. Parallel to the developments in these two areas, the actual integration of planning and execution engines is still nowadays a crucial issue in practical application. This paper presents an onboard autonomous model-based executive for execution of time-flexible plans. It describes its interface with an apsi-based timeline-based planner, its control approaches, architecture and its modelling language as an extension of apsl's ddl. In addition, it introduces a modified version of the classical blocks world toy planning problem which has been extended in scope and with a runtime environment for evaluation of integrated planning and executive engines.


title: Deep convolutional neural networks for human activity recognition with smartphone sensors

abstract: Human activity recognition (HAR) using smartphone sensors utilize time-series, multivariate data to detect activities. Time-series data have inherent local dependency characteristics. Moreover, activities tend to be hierarchical and translation invariant in nature. Consequently, convolutional neural networks (convnet) exploit these characteristics, which make it appropriate in dealing with time-series sensor data. In this paper, we propose an architecture of convnets with sensor data gathered from smartphone sensors to recognize activities. Experiments show that increasing the number of convolutional layers increases performance, but the complexity of the derived features decreases with every additional layer. Moreover, preserving the information passed from layer to layer is more important, as opposed to blindly increasing the hyperparameters to improve performance. The convnet structure can also benefit from a wider filter size and lower pooling size setting. Lastly, we show that convnet outperforms all the other state-of-the-art techniques in HAR, especially SVM, which achieved the previous best result for the data set.


The Strength of Weak Ties: A Network Theory Revisited


title: Neural Open Information Extraction

abstract: Conventional Open Information Extraction (Open IE) systems are usually built on hand-crafted patterns from other NLP tools such as syntactic parsing, yet they face problems of error propagation. In this paper, we propose a neural Open IE approach with an encoder-decoder framework. Distinct from existing methods, the neural Open IE approach learns highly confident arguments and relation tuples bootstrapped from a state-of-the-art Open IE system. An empirical study on a large benchmark dataset shows that the neural Open IE system significantly outperforms several baselines, while maintaining comparable computational efficiency.


title: Text-line examination for document forgery detection

abstract: In this paper, an approach for forgery detection using text-line information is presented. In questioned document examination, text-line rotation and alignment can be important clues for detecting tampered documents. Measuring and detecting such mis-rotations and mis-alignments are a cumbersome task. Therefore, an automated approach for verification of documents based on these two text-line features is proposed in this paper. An in-depth evaluation of the proposed methods shows its usefulness in the context of document security with an area under the ROC curve (AUC) score of AUC=0.89. The automatic nature of the approach allows the presented methods to be used in high-volume environments.


title: A simulation model for analysis of attacks on the Bitcoin peer-to-peer network

abstract: We present a simulation model of the Bitcoin peer-to-peer network, a widely deployed distributed electronic currency system. The model enables evaluations of the feasibility and cost of attacks on the Bitcoin network at full scale of 6,000 nodes. The simulation model is based on unmodified code from core segments of the Bitcoin reference implementation used by 99% of nodes. Parametrization of the model is performed based on large-scale measurements of the real-world network. We present preliminary validation results showing a reasonable correspondence of the propagation of messages in the Bitcoin network compared with simulation results. We apply the model to study the feasibility of a partitioning attack on the network and show that the attack is sensitive to the churn of the attacking nodes.


Mathematical Statistics with Mathematica, by Colin


title: Identifying ECUs Using Inimitable Characteristics of Signals in Controller Area Networks

abstract: In the last several decades, the automotive industry has come to incorporate the latest Information and Communications (ICT) technology, increasingly replacing mechanical components of vehicles with electronic components. These electronic control units (ECUs) communicate with each other in an in-vehicle network that makes the vehicle both safer and easier to drive. Controller Area Networks (CANs) are the current standard for such high quality in-vehicle communication. Unfortunately, however, CANs do not currently offer protection against security attacks. In particular, they do not allow for message authentication and hence are open to attacks that replay ECU messages for malicious purposes. Applying the classic cryptographic method of message authentication code (MAC) is not feasible since the CAN data frame is not long enough to include a sufficiently long MAC to provide effective authentication. In this paper, we propose a novel identification method, which works in the physical layer of an in-vehicle CAN network. Our method identifies ECUs using inimitable characteristics of signals enabling detection of a compromised or alien ECU being used in a replay attack. Unlike previous attempts to address security issues in the in-vehicle CAN network, our method works by simply adding a monitoring unit to the existing network, making it deployable in current systems and compliant with required CAN standards. Our experimental results show that the bit string and classification algorithm that we utilized yielded more accurate identification of compromised ECUs than any other method proposed to date. The false positive rate is more than 2 times lower than the method proposed by P.-S. Murvay et al. This paper is also the first to identify potential attack models that systems should be able to detect.


title: Document summarization based on word associations

abstract: In the age of big data, automatic methods for creating summaries of documents become increasingly important. In this paper we propose a novel, unsupervised method for (multi-)document summarization. In an unsupervised and language-independent fashion, this approach relies on the strength of word associations in the set of documents to be summarized. The summaries are generated by picking sentences which cover the most specific word associations of the document(s). We measure the performance on the DUC 2007 dataset. Our experiments indicate that the proposed method is the best-performing unsupervised summarization method in the state-of-the-art that makes no use of human-curated knowledge bases.


title: Real Time Apnoea Monitoring of Children Using the Microsoft Kinect Sensor: A Pilot Study

abstract: The objective of this study was to design a non-invasive system for the observation of respiratory rates and detection of apnoea using analysis of real time image sequences captured in any given sleep position and under any light conditions (even in dark environments). A Microsoft Kinect sensor was used to visualize the variations in the thorax and abdomen from the respiratory rhythm. These variations were magnified, analyzed and detected at a distance of 2.5 m from the subject. A modified motion magnification system and frame subtraction technique were used to identify breathing movements by detecting rapid motion areas in the magnified frame sequences. The experimental results on a set of video data from five subjects (3 h for each subject) showed that our monitoring system can accurately measure respiratory rate and therefore detect apnoea in infants and young children. The proposed system is feasible, accurate, safe and low computational complexity, making it an efficient alternative for non-contact home sleep monitoring systems and advancing health care applications.


title: Fostering Creativity in the Classroom for High Ability Students: Context Does Matter.

abstract: Researchers have argued for the importance of the classroom context in developing students’ creative potential. However, the emphasis on a performative learning culture in the classroom does not favour creativity. Thus, how creative potential can be realised as one of the educational goals in the classrooms remains a key question. This study measured creativity across three secondary schools using the Wallach-Kogan Creative Thinking Test (WKCT). A total of 283 students enrolled in the Express programme and 290 students enrolled in the Integrated Programme (IP) volunteered in the study. The same cohort of students took the 38-item WKCT twice; once at the beginning of Secondary One and then at the end of Secondary Three. Four aspects of creativity, namely fluency, flexibility, unusualness, and uniqueness, were investigated. Our analyses showed that (i) IP students showed a greater increase in scores over time when compared to Express students; (ii) when Programme and PSLE (Primary School Leaving Examination) were used to predict creativity scores in a multiple regression, the predictive power of Programme increased from Secondary 1 to Secondary 3 while that of PSLE decreased; and (iii) flexibility scores were more resistant to change than fluency scores. These findings suggest that the classroom context matters and that the removal of high-stakes examination can provide room for the development of creative potential.


title: A Spiking Network that Learns to Extract Spike Signatures from Speech Signals

abstract: Spiking neural networks (SNNs) with adaptive synapses reflect core properties of biological neural networks. Speech recognition, as an application involving audio coding and dynamic learning, provides a good test problem to study SNN functionality. We present a simple, novel, and efficient nonrecurrent SNN that learns to convert a speech signal into a spike train signature. The signature is distinguishable from signatures for other speech signals representing different words, thereby enabling digit recognition and discrimination in devices that use only spiking neurons. The method uses a small, nonrecurrent SNN consisting of Izhikevich neurons equipped with spike timing dependent plasticity (STDP) and biologically realistic synapses. This approach introduces an efficient and fast network without error-feedback training, although it does require supervised training. The new simulation results produce discriminative spike train patterns for spoken digits in which highly correlated spike trains belong to the same category and low correlated patterns belong to different categories. The proposed SNN is evaluated using a spoken digit recognition task where a subset of the Aurora speech dataset is used. The experimental results show that the network performs well in terms of accuracy rate and complexity.


title: A Survey on Utilization of Data Mining Approaches for Dermatological (Skin) Diseases Prediction

abstract:  Abstract— Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data. Index Terms— Erythemato-squamous diseases, Data mining, Dermatology, Medical data mining.


title: Comprehensive Topological Analysis of Conductive and Inductive Charging Solutions for Plug-In Electric Vehicles

abstract: The impending global energy crisis has opened up new opportunities for the automotive industry to meet the ever-increasing demand for cleaner and fuel-efficient vehicles. This has necessitated the development of drivetrains that are either fully or partially electrified in the form of electric and plug-in hybrid electric vehicles (EVs and HEVs), respectively, which are collectively addressed as plug-in EVs (PEVs). PEVs in general are equipped with larger on-board storage and power electronics for charging or discharging the battery, in comparison with HEVs. The extent to which PEVs are adopted significantly depends on the nature of the charging solution utilized. In this paper, a comprehensive topological survey of the currently available PEV charging solutions is presented. PEV chargers based on the nature of charging (conductive or inductive), stages of conversion (integrated single stage or two stages), power level (level 1, 2, or 3), and type of semiconductor devices utilized (silicon, silicon carbide, or gallium nitride) are thoroughly reviewed in this paper.


title: Boosting Collaborative Ontology Building with Key-Concept Extraction

abstract: We present a wiki-based collaborative environment for the semi-automatic incremental building of ontologies. The system relies on an existing platform, which has been extended with a component for terminology extraction from domain-specific textual corpora and with a further step aimed at matching the extracted concepts with pre-existing structured and semi-structured information. The system stands on the shoulders of a well-established user-friendly wiki architecture and it enables knowledge engineers and domain experts to collaborate in the ontology building process. We have performed a task-oriented evaluation of the tool in a real use case for incrementally constructing the missing part of an environmental ontology. The tool effectively supported the users in the task, thus showing its usefulness for knowledge extraction and ontology engineering.


Learning to Localize Using a LiDAR Intensity Map


title: Application of AI in Home Automation

abstract: Home automation is on horizon. It is an emerging technology and also a need of today. From the last decade a number of standards have been defined for home appliances. The main objectives of home automation are controlling, management and co-ordination of home appliances in a comfortable, effective and secure way. On the other hand, Artificial Intelligence is evolving as a technology for developing automatic systems that can perceive the environment, learn from environment, and can make decision using case based reasoning. Using Vision capability, knowledge based, learn ability, decision making and reasoning the AI provides a better solution for almost all automatic systems. In this paper we will see the types of home automation systems and then see how these system can utilize the AI tools so as to increase the effectiveness, powerfulness etc.


title: DR-NEGOTIATE - a system for automated agent negotiation with defeasible logic-based strategies

abstract: This paper reports on a system for automated agent negotiation, based on a formal and executable approach to capture the behavior of parties involved in a negotiation. It uses the JADE agent framework, and its major distinctive feature is the use of declarative negotiation strategies. The negotiation strategies are expressed in a declarative rules language, defeasible logic, and are applied using the implemented system DR-DEVICE. The key ideas and the overall system architecture are described, and a particular negotiation case is presented in detail.


title: On the relative value of cross-company and within-company data for defect prediction

abstract: We propose a practical defect prediction approach for companies that do not track defect related data. Specifically, we investigate the applicability of cross-company (CC) data for building localized defect predictors using static code features. Firstly, we analyze the conditions, where CC data can be used as is. These conditions turn out to be quite few. Then we apply principles of analogy-based learning (i.e. nearest neighbor (NN) filtering) to CC data, in order to fine tune these models for localization. We compare the performance of these models with that of defect predictors learned from within-company (WC) data. As expected, we observe that defect predictors learned from WC data outperform the ones learned from CC data. However, our analyses also yield defect predictors learned from NN-filtered CC data, with performance close to, but still not better than, WC data. Therefore, we perform a final analysis for determining the minimum number of local defect reports in order to learn WC defect predictors. We demonstrate in this paper that the minimum number of data samples required to build effective defect predictors can be quite small and can be collected quickly within a few months. Hence, for companies with no local defect data, we recommend a two-phase approach that allows them to employ the defect prediction process instantaneously. In phase one, companies should use NN-filtered CC data to initiate the defect prediction process and simultaneously start collecting WC (local) data. Once enough WC data is collected (i.e. after a few months), organizations should switch to phase two and use predictors learned from WC data.


title: Reinforcement Learning with Experience Replay for Model-Free Humanoid Walking Optimization

abstract: In this paper, a control system for humanoid robot walking is approximately optimized by means of reinforcement learning. Given is a 18 DOF humanoid whose gait is based on replaying a simple trajectory. This trajectory is translated into a reactive policy. A neural network whose input represents the robot state learns to produce appropriate output that additively modifies the initial control. The learning algorithm applied is actor–critic with experience replay. In 50 min of learning, the slow initial gait changes to a dexterous and fast walking. No model of the robot dynamics is engaged. The methodology in use is generic and can be applied to optimize control systems for diverse robots of comparable complexity.


title: Performance Analysis of Virtual Machine Introspection Tools in Cloud Environment

abstract: Virtual Machine (VM) security is one of the biggest issues with a cloud environment. This issue could be solved with the help of Virtual Machine Introspection (VMI) tools. VMI tools monitor the state of Virtual machine running in the cloud environment. VMI tools extract state of a virtual machine without the consent of the guest system. These VMI tools are built with the help of hardware virtualization functionality provided by Intel and AMD processors. These VMI tools facilitate the cloud providers to secure their virtual machines against attacks by observing the state of the VM. In this paper, we mainly discuss and analyze the five different VMI tools, their architecture, their key problems and performance analysis of these tools when adopted in the cloud environment.


title: Tradeoffs and Optimization in Analog CMOS Design

abstract: The selection of drain current, inversion coefficient, and channel length for each MOS device in an analog circuit results in significant tradeoffs in performance. The selection of inversion coefficient, which is a numerical measure of MOS inversion, enables design freely in weak, moderate, and strong inversion and facilitates optimum design. Here, channel width required for layout is easily found and implicitly considered in performance expressions. This paper gives hand expressions motivated by the EKV MOS model and measured data for MOS device performance, inclusive of velocity saturation and other small-geometry effects. A simple spreadsheet tool is then used to predict MOS device performance and map this into complete circuit performance. Tradeoffs and optimization of performance are illustrated by the design of three, 0.18-mum CMOS operational transconductance amplifiers optimized for DC, balanced, and AC performance. Measured performance shows significant tradeoffs in voltage gain, output resistance, transconductance bandwidth, input-referred flicker noise and offset voltage, and layout area.


title: The emergence, growth and decline of political priority for newborn survival in Bolivia

abstract: Bolivia is expected to achieve United Nations Millennium Development Goal Four reducing under-five child mortality by two-thirds between 2021 and 2025. However progress on child mortality reduction masks a disproportionately slow decline in newborn deaths during the 2000s. Bolivias neonatal mortality problem emerged on the policy agenda in the mid-1990s and grew through 2004 in relationship to political commitments to international development goals and the support of a strong policy network. Network status declined later in the decade. This study draws upon a framework for analysing determinants of political priority for global health initiatives to understand the trajectory of newborn survival policy in Bolivia from the early 1990s. A process-tracing case study methodology is used informed by interviews with 26 individuals with close knowledge of newborn survival policy in the country and extensive document analysis. The case of newborn survival in Bolivia highlights the significance of political commitments to international development goals health policy network characteristics (cohesion composition status and key actor support) and political transitions and instability in shaping agenda status especially decline-an understudied phenomenon considering the transitory nature of policy priorities. The study suggests that the sustainability of issue attention therefore become a focal point for health policy networks and analyses. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine (c) The Author 2013; all rights reserved.


title: Two-sided markets: An overview

abstract: The paper offers an introduction and a road map to the burgeoning literature on two-sided markets. In many industries, platforms court two (or more) sides that use the platform to interact with each other. The platforms’ usage or variable charges impact the two sides’ willingness to trade, and thereby their net surpluses from potential interactions; the platforms’ membership or fixed charges in turn determine the end-users’ presence on the platform. The platforms’ fine design of the structure of variable and fixed charges is relevant only if the two sides do not negotiate away the corresponding usage and membership externalities. The paper first focuses on usage charges and provides conditions for the allocation of the total usage charge (e.g., the price of a call or of a payment card transaction) between the two sides not to be neutral; the failure of the Coase theorem is necessary but not sufficient for two-sidedness. Second, the paper builds a canonical model integrating usage and membership externalities. This model allows us to unify and compare the results obtained in the two hitherto disparate strands of the literature emphasizing either form of externality; and to place existing membership (or indirect) externalities models on a stronger footing by identifying environments in which these models can accommodate usage pricing. We also obtain general results on usage pricing of independent interest.


title: Luminous Ground: The Zero with a Thousand Faces

abstract: Whether or not the universe arises from ‘Nothing” depends on what one means by that word. A ‘quantum vacuum,’ can be distinguished from the philosopher’s, mystic’s or theologian’s idealized ‘nothing’. What we formerly took for nothing turns out, on closer examination, to have the makings of electrons, protons and neutrons. Ever since physics first penetrated the atom, a new kind of uncertainty has become a principle of science. The archetypes of cosmic history are those of raw nature. Nothing has ever existed. Aspect’s experiment showed electrons have extraordinarily mysterious aspects of being or moving we have dubbed “superpositions”. We are still left with interpreting the framed results of our experiments. Electrons emerge from the “black box” of science in a spectrum of possible directions. Analogously the philosophical pith of subquantal physics contains multiple avenues for continuing exploration.


title: Non-linear Convolution Filters for CNN-Based Learning

abstract: During the last years, Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in image classification. Their architectures have largely drawn inspiration by models of the primate visual system. However, while recent research results of neuroscience prove the existence of non-linear operations in the response of complex visual cells, little effort has been devoted to extend the convolution technique to non-linear forms. Typical convolutional layers are linear systems, hence their expressiveness is limited. To overcome this, various non-linearities have been used as activation functions inside CNNs, while also many pooling strategies have been applied. We address the issue of developing a convolution method in the context of a computational model of the visual cortex, exploring quadratic forms through the Volterra kernels. Such forms, constituting a more rich function space, are used as approximations of the response profile of visual cells. Our proposed second-order convolution is tested on CIFAR-10 and CIFAR-100. We show that a network which combines linear and non-linear filters in its convolutional layers, can outperform networks that use standard linear filters with the same architecture, yielding results competitive with the state-of-the-art on these datasets.


title: Delphi Survey on the Use of Robot Projector based Augmented Reality in Dramatic Activity for Young Children

abstract: The purpose of this study is to examine the appropriateness and effectiveness of the assistive use of robot projector based augmented reality (AR) to children’s dramatic activity. A system that employ a mobile robot mounted with a projector-camera is used to help manage children’s dramatic activity by projecting backdrops and creating a synthetic video imagery, where e.g. children’s faces is replaced with graphic characters. In this Delphi based study, a panel consist of 33 professionals include 11children education experts (college professors majoring in early childhood education), children field educators (kindergarten teachers and principals), and 11 AR and robot technology experts. The experts view the excerpts from the video taken from the actual usage situation. In the first stage of survey, we collect the panel's perspectives on applying the latest new technologies for instructing dramatic activity to children using an open ended questionnaire. Based on the results of the preliminary survey, the subsequent questionnaires (with 5 point Likert scales) are developed for the second and third in-depth surveys. In the second survey, 36 questions is categorized into 5 areas: (1) developmental and educational values, (2) impact on the teacher's role, (3) applicability and special considerations in the kindergarten, (4) external environment and required support, and (5) criteria for the selection of the story in the drama activity. The third survey mainly investigate how AR or robots can be of use in children’s dramatic activity in other ways (than as originally given) and to other educational domains. The surveys show that experts most appreciated the use of AR and robot for positive educational and developmental effects due to the children’s keen interests and in turn enhanced immersion into the dramatic activity. Consequently, the experts recommended that proper stories, scenes and technological realizations need to be selected carefully, in the light of children’s development, while lever aging on strengths of the technologies used.


title: Image Colorization with Generative Adversarial Networks

abstract: Over the last decade, the process of automatic image colorization has been of significant interest for several application areas including restoration of aged or degraded images. This problem is highly ill-posed due to the large degrees of freedom during the assignment of color information. Many of the recent developments in automatic colorization involve images that contain a common theme or require highly processed data such as semantic maps as input. In our approach, we attempt to fully generalize the colorization procedure using a conditional Deep Convolutional Generative Adversarial Network (DCGAN), extend current methods to high-resolution images and suggest training strategies that speed up the process and greatly stabilize it. The network is trained over datasets that are publicly available such as CIFAR-10 and Places365. The results of the generative model and traditional deep neural networks are compared.


title: DECAF: detecting and characterizing ad fraud in mobile apps

abstract: Ad networks for mobile apps require inspection of the visual layout of their ads to detect certain types of placement frauds. Doing this manually is error prone, and does not scale to the sizes of today's app stores. In this paper, we design a system called DECAF to automatically discover various placement frauds scalably and effectively. DECAF uses automated app navigation, together with optimizations to scan through a large number of visual elements within a limited time. It also includes a framework for efficiently detecting whether ads within an app violate an extensible set of rules that govern ad placement and display. We have implemented DECAF for Windows-based mobile platforms, and applied it to 1,150 tablet apps and 50,000 phone apps in order to characterize the prevalence of ad frauds. DECAF has been used by the ad fraud team in Microsoft and has helped find many instances of ad frauds.


title: QBF-Based Formal Verification: Experience and Perspectives

abstract: The language of Quantified Boolean Formulas (QBF) has a lot of potential applications to Formal Verification (FV) tasks, as it captures many of these tasks in a natural and compact way. Practical experience has been disappointing though. When compared with contending approaches such as SAT, QBF-based FV has invariably yielded unfavorable experimental results. This paper makes two contributions. We first provide an account of the status quo in QBF-based FV. We examine commonly adopted formalizations and the relative strengths of different decision procedures. In the second part of this paper, we investigate for the first time the relevance of some advanced QBF techniques to FV tasks. In particular, we describe the use and the benefits of restricted quantifiers, QBF certificates, alternative encodings for classical model checking problems, and encodings with free variables. These promising research perspectives seem to reverse the negative standing of QBF applied to FV, as confirmed by the experimental evidence we discuss. Experiments are conducted by extending the publicly available solver sKizzo in several ways, and they include the first case studies where QBF compares favorably to SAT, its traditional competitor. QBF turns out to be an order of magnitude faster than SAT in some tasks (e.g., automated design debugging of large circuits). Moreover, as the size of the problems grows, the SAT encodings result in excessive memory requirements leading to out-of-memory conditions, while the more compact QBF encodings continue to be manageable and solvable.


title: Efficient Classification of Multi-Labelled Text Streams by Clashing

abstract: We present a method for the classification of multi-labeled text documents explicitly designed for data stream applications that require to process a virtually infinite sequence of data using constant memory and constant processing time. ::: ::: Our method is composed of an online procedure used to efficiently map text into a low-dimensional feature space and a partition of this space into a set of regions for which the system extracts and keeps statistics used to predict multi-label text annotations. Documents are fed into the system as a sequence of words, mapped to a region of the partition, and annotated using the statistics computed from the labeled instances colliding in the same region. This approach is referred to as clashing. ::: ::: We illustrate the method in real-world text data, comparing the results with those obtained using other text classifiers. In addition, we provide an analysis about the effect of the representation space dimensionality on the predictive performance of the system. Our results show that the online embedding indeed approximates the geometry of the full corpus-wise TF and TF-IDF space. The model obtains competitive F measures with respect to the most accurate methods, using significantly fewer computational resources. In addition, the method achieves a higher macro-averaged F measure than methods with similar running time. Furthermore, the system is able to learn faster than the other methods from partially labeled streams.


title: In-Vehicle Network Attacks and Countermeasures: Challenges and Future Directions

abstract: The emergence of in-vehicle networks, which are composed of controller area network (CAN) buses and a great number of ECUs, significantly reduces the difficulty of vehicle designing, repairing and refitting. While owing to the intrinsic vulnerabilities of in-vehicle networks and the increasingly rich interfaces to connect in-vehicle networks to the outside, adversarial attacks can be easily implemented on in-vehicle networks. Such attacks cause serious threats to automobile security and drivers� privacy. In light of this, we provide in this article a detailed guidance, explain the basic concepts, introduce the vulnerabilities of in-vehicle networks, and summarize the attacking methodologies. We also provide countermeasures for in-vehicle networks and point out challenges and future directions.


title: TechMiner: Extracting Technologies from Academic Publications

abstract: In recent years we have seen the emergence of a variety of scholarly datasets. Typically these capture 'standard' scholarly entities and their connections, such as authors, affiliations, venues, publications, citations, and others. However, as the repositories grow and the technology improves, researchers are adding new entities to these repositories to develop a richer model of the scholarly domain. In this paper, we introduce TechMiner, a new approach, which combines NLP, machine learning and semantic technologies, for mining technologies from research publications and generating an OWL ontology describing their relationships with other research entities. The resulting knowledge base can support a number of tasks, such as: richer semantic search, which can exploit the technology dimension to support better retrieval of publications; richer expert search; monitoring the emergence and impact of new technologies, both within and across scientific fields; studying the scholarly dynamics associated with the emergence of new technologies; and others. TechMiner was evaluated on a manually annotated gold standard and the results indicate that it significantly outperforms alternative NLP approaches and that its semantic features improve performance significantly with respect to both recall and precision.


title: Multi-objective evolutionary algorithm using problem-specific genetic operators for community detection in networks

abstract: Automatic network clustering is an important method for mining the meaningful communities of complex networks. Uncovered communities help to understand the potential system structure and functionality. Many algorithms that use multiple optimization criteria and optimize a population of solutions are difficult to apply to real systems because they suffer a long optimization process. In this paper, in order to accelerate the optimization process and to uncover multiple significant community structures more effectively, a multi-objective evolutionary algorithm is proposed and evaluated using problem-specific genetic mutation and group crossover, and problem-specific initialization. Since crossover operators mainly contribute to performance of genetic algorithms, more problem-specific group crossover operators are introduced and evaluated for intelligent evolution of population. The experiments on both artificial and real-world networks demonstrate that the proposed evolutionary algorithm with problem-specific genetic operations has effective performance on discovering the community structure of networks.


title: An Analysis of Causality between Events and its Relation to Temporal Information

abstract: In this work we present an annotation framework to capture causality between events, inspired by TimeML, and a language resource covering both temporal and causal relations. This data set is then used to build an automatic extraction system for causal signals and causal links between given event pairs. The evaluation and analysis of the system’s performance provides an insight into explicit causality in text and the connection between temporal and causal relations.


title: Object-centric Sampling for Fine-grained Image Classification

abstract: This paper proposes to go beyond the state-of-the-art deep convolutional neural network (CNN) by incorporating the information from object detection, focusing on dealing with fine-grained image classification. Unfortunately, CNN suffers from over-fiting when it is trained on existing fine-grained image classification benchmarks, which typically only consist of less than a few tens of thousands training images. Therefore, we first construct a large-scale fine-grained car recognition dataset that consists of 333 car classes with more than 150 thousand training images. With this large-scale dataset, we are able to build a strong baseline for CNN with top-1 classification accuracy of 81.6%. One major challenge in fine-grained image classification is that many classes are very similar to each other while having large within-class variation. One contributing factor to the within-class variation is cluttered image background. However, the existing CNN training takes uniform window sampling over the image, acting as blind on the location of the object of interest. In contrast, this paper proposes an \emph{object-centric sampling} (OCS) scheme that samples image windows based on the object location information. The challenge in using the location information lies in how to design powerful object detector and how to handle the imperfectness of detection results. To that end, we design a saliency-aware object detection approach specific for the setting of fine-grained image classification, and the uncertainty of detection results are naturally handled in our OCS scheme. Our framework is demonstrated to be very effective, improving top-1 accuracy to 89.3% (from 81.6%) on the large-scale fine-grained car classification dataset.


title: A Projection‐Based Approach to General‐Form Tikhonov Regularization

abstract: We present a projection-based iterative algorithm for computing general-form Tikhonov regularized solutions to the problem $\min_x\{\| Ax-b \|_2^2+\lambda^2\| Lx \|_2^2\}$, where the regularization matrix $L$ is not the identity. Our algorithm is designed for the common case where $\lambda$ is not known a priori. It is based on a joint bidiagonalization algorithm and is appropriate for large-scale problems when it is computationally infeasible to transform the regularized problem to standard form. By considering the projected problem, we show how estimates of the corresponding optimal regularization parameter can be efficiently obtained. Numerical results illustrate the promise of our projection-based approach.


title: Rewarded Outcomes Enhance Reactivation of Experience in the Hippocampus

abstract: Summary Remembering experiences that lead to reward is essential for survival. The hippocampus is required for forming and storing memories of events and places, but the mechanisms that associate specific experiences with rewarding outcomes are not understood. Event memory storage is thought to depend on the reactivation of previous experiences during hippocampal sharp wave ripples (SWRs). We used a sequence switching task that allowed us to examine the interaction between SWRs and reward. We compared SWR activity after animals traversed spatial trajectories and either received or did not receive a reward. Here, we show that rat hippocampal CA3 principal cells are significantly more active during SWRs following receipt of reward. This SWR activity was further enhanced during learning and reactivated coherent elements of the paths associated with the reward location. This enhanced reactivation in response to reward could be a mechanism to bind rewarding outcomes to the experiences that precede them.


title: Traceability in Agile Projects

abstract: Agile methods are becoming an increasingly mainstream approach to software development. They are characterized by short iterations with frequent deliverables, test-driven development, lightweight documentation, and frequent interactions with the customer. Perhaps unsurprisingly, traceability is often seen as unnecessary and therefore unwanted in agile projects. This is due to the perceived overhead of creating and maintaining traceability links and the assumption that agile developers have sufficient understanding of a project to implement a change without the support of previously defined traceability links. This chapter explores the challenges, benefits, techniques, and processes of tracing across a broad spectrum of agile projects.


title: Building Program Vector Representations for Deep Learning

abstract: Deep learning has made significant breakthroughs in various fields of artificial intelligence. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the "coding criterion" to build program vector representations, which are the premise of deep learning for program analysis. We evaluate the learned vector representations both qualitatively and quantitatively. We conclude, based on the experiments, the coding criterion is successful in building program representations. To evaluate whether deep learning is beneficial for program analysis, we feed the representations to deep neural networks, and achieve higher accuracy in the program classification task than "shallow" methods. This result confirms the feasibility of deep learning to analyze programs.


title: DEM: A variational treatment of dynamic systems

abstract: This paper presents a variational treatment of dynamic models that furnishes time-dependent conditional densities on the path or trajectory of a system's states and the time-independent densities of its parameters. These are obtained by maximising a variational action with respect to conditional densities, under a fixed-form assumption about their form. The action or path-integral of free-energy represents a lower bound on the model's log-evidence or marginal likelihood required for model selection and averaging. This approach rests on formulating the optimisation dynamically, in generalised coordinates of motion. The resulting scheme can be used for online Bayesian inversion of nonlinear dynamic causal models and is shown to outperform existing approaches, such as Kalman and particle filtering. Furthermore, it provides for dual and triple inferences on a system's states, parameters and hyperparameters using exactly the same principles. We refer to this approach as dynamic expectation maximisation (DEM).


title: ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech

abstract: In this work, we propose an alternative solution for parallel wave generation by WaveNet. In contrast to parallel WaveNet (Oord et al., 2018), we distill a Gaussian inverse autoregressive flow from the autoregressive WaveNet by minimizing a novel regularized KL divergence between their highly-peaked output distributions. Our method computes the KL divergence in closed-form, which simplifies the training algorithm and provides very efficient distillation. In addition, we propose the first text-to-wave neural architecture for speech synthesis, which is fully convolutional and enables fast end-to-end training from scratch. It significantly outperforms the previous pipeline that connects a text-to-spectrogram model to a separately trained WaveNet (Ping et al., 2018). We also successfully distill a parallel waveform synthesizer conditioned on the hidden representation in this end-to-end model.


title: A.: The interspeech 2009 emotion challenge

abstract: The last decade has seen a substantial body of literature on the recognition of emotion from speech. However, in comparison to related speech processing tasks such as Automatic Speech and Speaker Recognition, practically no standardised corpora and test-conditions exist to compare performances under exactly the same conditions. Instead a multiplicity of evaluation strategies employed – such as cross-validation or percentage splits without proper instance definition – prevents exact reproducibility. Further, in order to face more realistic scenarios, the community is in desperate need of more spontaneous and less prototypical data. This INTERSPEECH 2009 Emotion Challenge aims at bridging such gaps between excellent research on human emotion recognition from speech and low compatibility of results. The FAU Aibo Emotion Corpus [1] serves as basis with clearly defined test and training partitions incorporating speaker independence and different room acoustics as needed in most reallife settings. This paper introduces the challenge, the corpus, the features, and benchmark results of two popular approaches towards emotion recognition from speech. Index Terms: emotion, challenge, feature types, classification


title: Rethinking the Faster R-CNN Architecture for Temporal Action Localization

abstract: We propose TAL-Net, an improved approach to temporal action localization in video that is inspired by the Faster RCNN object detection framework. TAL-Net addresses three key shortcomings of existing approaches: (1) we improve receptive field alignment using a multi-scale architecture that can accommodate extreme variation in action durations; (2) we better exploit the temporal context of actions for both proposal generation and action classification by appropriately extending receptive fields; and (3) we explicitly consider multi-stream feature fusion and demonstrate that fusing motion late is important. We achieve state-of-the-art performance for both action proposal and localization on THUMOS'14 detection benchmark and competitive performance on ActivityNet challenge.


title: SDN for network security

abstract: Software Defined Networking, SDN, is the programmable separation of control and forwarding elements of networking that enables software control of network forwarding that can be logically and/or physically separated from physical switches and routers. The following important question is considered in this paper: to what extent can SDN-based networks address network security management problems? The new opportunities for enhancing network security brought by this separation are considered in the paper


title: Too Much Knowledge? Security Beliefs and Protective Behaviors Among United States Internet Users

abstract: Home computers are frequently the target of malicious attackers because they are usually administered by non-experts. Prior work has found that users who make security decisions about their home computers often possess different mental models of information security threats, and use those mental models to make decisions about security. Using a survey, we asked a large representative sample of United States Internet users about different causal beliefs related to computer security, and about the actions they regularly undertake to protect their computers. We found demographic differences in both beliefs about security and security behaviors that pose challenges for helping users become more informed about security. Many participants reported weakly held beliefs about viruses and hackers, and these were the least likely to say they take protective actions. These results suggest that all security knowledge is not the same, educating users about security is not simply a more-is-better issue, and not all users should receive the same messages.


title: Towards random field modeling of wavelet statistics

abstract: The paper investigates the statistical characterization of signals and images in the wavelet domain. In particular, in contrast to common decorrelated-coefficient models, we find that the correlation between wavelet scales can be surprisingly substantial, even across several scales. We investigate possible choices of statistical-interaction models. One efficient and fast strategy which describes the wavelet-based statistical correlations is illustrated. Finally, the effectiveness of the proposed tool towards an efficient hierarchical MRF (Markov random field) modeling of within-scale neighborhoods and across-scale dependencies is demonstrated.


title: Improving the performance of Naive Bayes multinomial in e-mail foldering by introducing distribution-based balance of datasets

abstract: E-mail foldering or e-mail classification into user predefined folders can be viewed as a text classification/categorization problem. However, it has some intrinsic properties that make it more difficult to deal with, mainly the large cardinality of the class variable (i.e. the number of folders), the different number of e-mails per class state and the fact that this is a dynamic problem, in the sense that e-mails arrive in our mail-folders following a time-line. Perhaps because of these problems, standard text-oriented classifiers such as Naive Bayes Multinomial do no obtain a good accuracy when applied to e-mail corpora. In this paper, we identify the imbalance among classes/folders as the main problem, and propose a new method based on learning and sampling probability distributions. Our experiments over a standard corpus (ENRON) with seven datasets (e-mail users) show that the results obtained by Naive Bayes Multinomial significantly improve when applying the balancing algorithm first. For the sake of completeness in our experimental study we also compare this with another standard balancing method (SMOTE) and classifiers.


An American Courtship: psychologists and Advertising Theory in the progressive era


title: An Overview of Entity Relation Extraction Techniques

abstract: Natural language processing requires deep understanding of semantic relationships between entities. This paper presents comprehensive review of various aspects of the Entity Relation Extraction task. Here, an attempt is made to cover in detail some of the important supervised and Semi-supervised classification approaches to the relation extraction task along with critical analyses. One of the most important aspects of Entity relation is higher-order relations. So there is a need of inventing approaches capable of handling higher order relations efficiently. Finally, in this paper some relation extraction applications like Question answering and Bio-text mining are also discuss.


title: Evolutionary-based feature selection approaches with new criteria for data mining: A case study of credit approval data

abstract: In this paper, the feature selection problem was formulated as a multi-objective optimization problem, and new criteria were proposed to fulfill the goal. Foremost, data were pre-processed with missing value replacement scheme, re-sampling procedure, data type transformation procedure, and min-max normalization procedure. After that a wide variety of classifiers and feature selection methods were conducted and evaluated. Finally, the paper presented comprehensive experiments to show the relative performance of the classification tasks. The experimental results revealed the success of proposed methods in credit approval data. In addition, the numeric results also provide guides in selection of feature selection methods and classifiers in the knowledge discovery process.


title: Cognitive-Radio-Based Internet of Things: Applications, Architectures, Spectrum Related Functionalities, and Future Research Directions

abstract: Recent research and technology trends are shifting toward IoT and CRNs. However, we think that the things-oriented, Internet-oriented, and semantic-oriented versions of IoT are meaningless if IoT objects are not equipped with cognitive radio capability. Equipping IoT objects with CR capability has lead to a new research dimension of CR-based IoT. In this article, we present an overview of CR-based IoT systems. We highlight potential applications of CR-based IoT systems. We survey architectures and frameworks of CR-based IoT systems. We furthermore discuss spectrum-related functionalities for CR-based IoT systems. Finally, we present open issues, research challenges, and future direction for these CR-based IoT networks.


title: Role of Macrophage Inflammatory Protein-3α and Its Ligand CCR6 in Rheumatoid Arthritis

abstract: We examined the expression and participation of CCR6 and its ligand MIP-3alpha in rheumatoid arthritis (RA) by ELISA, RT-PCR, real-time PCR (TaqMan) analysis, monocyte chemotaxis, and two- and four-color flow cytometry. We found that RA synovial fluid (SF) contained significantly more MIP-3alpha than osteoarthritis (OA), indicating a potential role for MIP-3alpha in RA. IL-1beta, IL-18, and TNF-alpha stimulated RA fibroblast MIP-3alpha production at 48 hours of incubation in vitro. By TaqMan analysis, there were more CCR6 mRNA transcripts in RA synovial tissue (ST) than in OA or normal (NL) ST, and elevated MIP-3alpha mRNA expression in RA compared with NL ST. By FACS analysis, there were significantly elevated percentages of CD3+/CD4+/CD45RO+/CCR6+ memory lymphocytes found in RA peripheral blood (PB) compared with NL PB or RA SF. We also found that MIP-3alpha induced monocyte chemotactic activity at 1.25 pM, consistent with concentrations of MIP-3alpha found in RA SF. Furthermore, MIP-3alpha accounted for 40% of RA SF chemotactic activity for monocytes in modified Boyden chamber assays. We confirmed that MIP-3alpha may be binding a G-coupled protein receptor because in vitro monocyte chemotaxis was inhibited by preincubation of monocytes with pertussis toxin. RA patient clinical data revealed that CD3+ lymphocyte/CCR6 expression inversely correlated with the age of the patient, indicating that CCR6 expression may be important in the development of RA at a younger age. Overall, these studies indicate that MIP-3alpha and CCR6 may function to recruit monocytes and memory lymphocytes from the RA PB to the RA joint. These results further indicate that expression of CCR6, the receptor for MIP-3alpha, can be correlated with RA development.


title: DIP: the Database of Interacting Proteins

abstract: The Database of Interacting Proteins (DIP; http://dip.doe-mbi.ucla.edu) is a database that documents experimentally determined protein-protein interactions. This database is intended to provide the scientific community with a comprehensive and integrated tool for browsing and efficiently extracting information about protein interactions and interaction networks in biological processes. Beyond cataloging details of protein-protein interactions, the DIP is useful for understanding protein function and protein-protein relationships, studying the properties of networks of interacting proteins, benchmarking predictions of protein-protein interactions, and studying the evolution of protein-protein interactions.


title: Accurate Language Identification of Twitter Messages

abstract: We present an evaluation of “off-theshelf” language identification systems as applied to microblog messages from Twitter. A key challenge is the lack of an adequate corpus of messages annotated for language that reflects the linguistic diversity present on Twitter. We overcome this through a “mostly-automated” approach to gathering language-labeled Twitter messages for evaluating language identification. We present the method to construct this dataset, as well as empirical results over existing datasets and off-theshelf language identifiers. We also test techniques that have been proposed in the literature to boost language identification performance over Twitter messages. We find that simple voting over three specific systems consistently outperforms any specific system, and achieves state-of-the-art accuracy on the task.


Experiences from Developing the Domain-Specific Entity Search Engine GeneView


Efficient BackProp


title: The Utility Problem of Web Content Popularity Prediction

abstract: The ability to generate and share content on social media platforms has changed the Internet. With the growing rate of content generation, efforts have been directed at making sense of such data. One of the most researched problem concerns predicting web content popularity. We argue that the evolution of state-of-the-art approaches has been optimized towards improving the predictability of average behaviour of data: items with low levels of popularity. We demonstrate this effect using a utility-based framework for evaluating numerical web content popularity prediction tasks, focusing on highly popular items. Additionally, it is demonstrated that gains in predictive and ranking ability of such type of cases can be obtained via naive approaches, based on strategies to tackle imbalanced domains learning tasks.


title: Neuroticism, Trait Fear of Missing Out, and Phubbing: The Mediating Role of State Fear of Missing Out and Problematic Instagram Use

abstract: One of the relatively new negative consequences of smartphone use is “phubbing” (snubbing someone while an individual checks their smartphone in the middle of a real-life conversation). The purpose of the present study was to investigate the direct and indirect associations of neuroticism, trait anxiety, and trait fear of missing out with phubbing via state fear of missing out and problematic Instagram use. A total of 423 adolescents and emerging adults aged between 14 and 21 years (53% female) participated in the study. Findings indicated that females had significantly higher scores of phubbing, fear of missing out, problematic Instagram use, trait anxiety, and neuroticism. Path analysis showed that trait fear of missing out and neuroticism were indirectly associated with phubbing via state fear of missing out and problematic Instagram use. State fear of missing out was directly and indirectly associated with phubbing via problematic Instagram use. The present study is the first to demonstrate empirical evidence for the relationship between different dimensions of fear of missing out, problematic Instagram use, and phubbing.


Analyzing the Impact of Spelling Errors on POS-Tagging and Chunking in Learner English


title: Methodologies, tools and languages for building ontologies. Where is their meeting point

abstract: In this paper we review and compare the main methodologies, tools and languages for building ontologies that have been reported in the literature, as well as the main relationships among them. Ontology technology is nowadays mature enough: many methodologies, tools and languages are already available. The future work in this field should be driven towards the creation of a common integrated workbench for ontology developers to facilitate ontology development, exchange, evaluation, evolution and management, to provide methodological support for these tasks, and translations to and from different ontology languages. This workbench should not be created from scratch, but instead integrating the technology components that are currently available.


title: Kernel principal component analysis and support vector machines for stock price prediction

abstract: Financial time series are complex, non-stationary and deterministically chaotic. Technical indicators are used with principal component analysis (PCA) in order to identify the most influential inputs in the context of the forecasting model. Neural networks (NN) and support vector regression (SVR) are used with different inputs. Our assumption is that the future value of a stock price depends on the financial indicators although there is no parametric model to explain this relationship. This relationship comes from technical analysis. Comparison shows that SVR and MLP networks require different inputs. The MLP networks outperform the SVR technique.